# A tibble: 3,912 × 61
   data_point       x        y geodetic_datum plot123 plot1 plot2 plot3 bec_zone
        <dbl>   <dbl>    <dbl> <chr>            <dbl> <dbl> <dbl> <dbl>    <dbl>
 1          1 524355. 5446972. wgs84utm11n          3     0     0     1        2
 2          2 524350. 5446986. wgs84utm11n          3     0     0     1        2
 3          3 524350. 5446981. wgs84utm11n          3     0     0     1        2
 4          4 524350. 5446954. wgs84utm11n          3     0     0     1        2
 5          5 524202. 5447057. wgs84utm11n          3     0     0     1        2
 6          6 524185. 5447071. wgs84utm11n          3     0     0     1        2
 7          7 524215. 5447056. wgs84utm11n          3     0     0     1        2
 8          8 524197. 5447070. wgs84utm11n          3     0     0     1        2
 9          9 524197. 5447065. wgs84utm11n          3     0     0     1        2
10         10 524225. 5447060. wgs84utm11n          3     0     0     1        2
# ℹ 3,902 more rows
# ℹ 52 more variables: bec_ichdw1 <dbl>, bec_ichmw4 <dbl>, bec_essfmw4 <dbl>,
#   density <dbl>, intensity <dbl>, elevation_asl <dbl>, slope <dbl>,
#   rix <dbl>, aspect <dbl>, precip <dbl>, twi <dbl>, wind <dbl>,
#   species <dbl>, larix_spp <dbl>, pseudo_m <dbl>, picea_eng <dbl>,
#   pinus_con <dbl>, pinus_pond <dbl>, pinus_albi <dbl>, thuja_plic <dbl>,
#   tsuga_hete <dbl>, height_cm <dbl>, distance_m <dbl>, mpb_2005 <dbl>, …

# A tibble: 3,866 × 3
         x        y distance_m
     <dbl>    <dbl>      <dbl>
 1 524355. 5446972.       16.6
 2 524350. 5446986.       14.2
 3 524350. 5446981.       15.5
 4 524350. 5446954.       38.3
 5 524202. 5447057.       29.2
 6 524185. 5447071.       31.7
 7 524215. 5447056.       26.8
 8 524197. 5447070.       29.0
 9 524197. 5447065.       29.7
10 524225. 5447060.       24.3
# ℹ 3,856 more rows

    Kruskal-Wallis rank sum test

data:  ann_seedlings_allplots by plot123
Kruskal-Wallis chi-squared = 440.03, df = 2, p-value < 2.2e-16

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1  3912  2.24  2.63   1.83    1.86  2.16     0  42.5  42.5  4.75     49.6
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  1.82    NA   1.82    1.82     0  1.82  1.82     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  2.85    NA   2.85    2.85     0  2.85  2.85     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  2.03    NA   2.03    2.03     0  2.03  2.03     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  1.82    NA   1.82    1.82     0  1.82  1.82     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  2.85    NA   2.85    2.85     0  2.85  2.85     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  2.03    NA   2.03    2.03     0  2.03  2.03     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  1.82    NA   1.82    1.82     0  1.82  1.82     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  2.85    NA   2.85    2.85     0  2.85  2.85     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  2.03    NA   2.03    2.03     0  2.03  2.03     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  1.82    NA   1.82    1.82     0  1.82  1.82     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  2.85    NA   2.85    2.85     0  2.85  2.85     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  2.03    NA   2.03    2.03     0  2.03  2.03     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  1.82    NA   1.82    1.82     0  1.82  1.82     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  2.85    NA   2.85    2.85     0  2.85  2.85     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  2.03    NA   2.03    2.03     0  2.03  2.03     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  1.82    NA   1.82    1.82     0  1.82  1.82     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  2.85    NA   2.85    2.85     0  2.85  2.85     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  2.03    NA   2.03    2.03     0  2.03  2.03     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  1.82    NA   1.82    1.82     0  1.82  1.82     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  2.85    NA   2.85    2.85     0  2.85  2.85     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  2.03    NA   2.03    2.03     0  2.03  2.03     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  1.82    NA   1.82    1.82     0  1.82  1.82     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  2.85    NA   2.85    2.85     0  2.85  2.85     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1     1  2.03    NA   2.03    2.03     0  2.03  2.03     0    NA       NA
# ℹ 1 more variable: se <dbl>

# A tibble: 8 × 4
  species count  mean    sd
    <dbl> <int> <dbl> <dbl>
1       0   319 1512. 227. 
2       1   209 1510. 222. 
3       2   290 1514. 221. 
4       3  2877 1517. 225. 
5       4    85 1407. 208. 
6       5    52 1709.  91.1
7       6    46 1551. 219. 
8       7    34 1520. 218. 

# A tibble: 3 × 4
  plot123 count  mean    sd
    <dbl> <int> <dbl> <dbl>
1       1  1037 1736.  14.1
2       2  1268 1672.  12.7
3       3  1607 1251.  37.8

# A tibble: 8 × 4
  species count  mean    sd
    <dbl> <int> <dbl> <dbl>
1       0   319  13.5  8.21
2       1   209  14.2  7.70
3       2   290  13.8  8.20
4       3  2877  13.7  7.97
5       4    85  16.9  7.46
6       5    52  10.1  5.48
7       6    46  14.2  7.19
8       7    34  14.0  7.79

# A tibble: 3 × 4
  plot123 count  mean    sd
    <dbl> <int> <dbl> <dbl>
1       1  1037  7.82  4.99
2       2  1268  7.56  1.47
3       3  1607 22.5   2.72

# A tibble: 8 × 4
  species count  mean    sd
    <dbl> <int> <dbl> <dbl>
1       0   319  5.08 0.599
2       1   209  5.05 0.565
3       2   290  5.09 0.623
4       3  2877  5.10 0.576
5       4    85  4.91 0.434
6       5    52  5.39 0.598
7       6    46  5.10 0.593
8       7    34  5.19 0.465

# A tibble: 3 × 4
  plot123 count  mean    sd
    <dbl> <int> <dbl> <dbl>
1       1  1037  5.52 0.742
2       2  1268  4.82 0.513
3       3  1607  5.03 0.277

# A tibble: 8 × 4
  species count  mean    sd
    <dbl> <int> <dbl> <dbl>
1       0   319  93.9  64.3
2       1   209  86.3  48.4
3       2   290  90.9  56.8
4       3  2877  91.4  57.6
5       4    85  68.6  26.7
6       5    52 157.  101. 
7       6    46  84.9  43.0
8       7    34  79.4  28.0

# A tibble: 3 × 4
  plot123 count  mean    sd
    <dbl> <int> <dbl> <dbl>
1       1  1037 144.   79.2
2       2  1268  99.2  29.9
3       3  1607  51.7  10.5

# A tibble: 8 × 4
  species count  mean    sd
    <dbl> <int> <dbl> <dbl>
1       0   319  4.94 1.29 
2       1   209  4.89 1.37 
3       2   290  5.10 1.28 
4       3  2877  5.04 1.28 
5       4    85  5.00 1.34 
6       5    52  5.12 0.773
7       6    46  4.91 0.875
8       7    34  5.18 0.991

# A tibble: 3 × 4
  plot123 count  mean    sd
    <dbl> <int> <dbl> <dbl>
1       1  1037  5.19 0.616
2       2  1268  4.78 1.28 
3       3  1607  5.13 1.53 

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1  3912  35.4  29.8   27.7    30.2  22.9  1.57  169.  168.  1.92     4.44
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1  1037  20.7  17.6   14.7    17.0  8.61  4.05  88.3  84.3  1.90     2.84
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range   skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl>
1     1  1268  29.0  13.7   29.7    28.8  16.9  3.60  61.5  57.9 0.0452    -1.03
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1  1607  50.0  37.8   41.5    44.5  30.2  1.57  169.  168.  1.21     1.04
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1  2877  35.3  29.6   27.7    30.1  22.9  1.57  169.  168.  1.93     4.51
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1   319  33.3  28.0   25.6    28.7  22.3  3.60  169.  166.  1.95     5.26
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1   290  35.9  30.7   28.0    30.3  22.7  4.27  165.  160.  2.00     4.66
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1   209  36.2  29.7   27.7    31.2  20.5  4.72  164.  159.  1.88     4.15
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1    85  44.8  40.9   33.8    37.7  27.4  2.02  160.  158.  1.47     1.26
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1    52  26.8  20.5   19.5    24.4  12.8  4.27  76.9  72.7  1.01   -0.495
# ℹ 1 more variable: se <dbl>

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1    34  42.3  35.5   31.0    37.4  20.1  5.62  131.  125.  1.25    0.266
# ℹ 1 more variable: se <dbl>

    Kruskal-Wallis rank sum test

data:  distance_m by plot123
Kruskal-Wallis chi-squared = 683.68, df = 2, p-value < 2.2e-16

             2          3          4
  0 0.51754386 0.33684211 0.14561404
  1 0.21138211 0.74796748 0.04065041
  2 0.27034884 0.50000000 0.22965116

             2          3          4
  0 0.71256039 0.42105263 0.49700599
  1 0.06280193 0.20175439 0.02994012
  2 0.22463768 0.37719298 0.47305389

        2         3         4 
0.3992285 0.4397300 0.1610415 

        0         1         2 
0.5496625 0.1186114 0.3317261 

1 2 3 
0 0 1 

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range   skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl>
1     1  1037  5.75 0.123   5.80    5.76 0.113  5.45  5.96 0.511 -0.800   -0.256
# ℹ 1 more variable: se <dbl>

              1           2           3           4
  0 0.286634461 0.164251208 0.499194847 0.049919485
  1 0.000000000 0.769230769 0.230769231 0.000000000
  2 0.009661836 0.199677939 0.626409018 0.164251208

        1         2         3         4 
0.1451104 0.1940063 0.5559937 0.1048896 

         0          1          2 
0.48974763 0.02050473 0.48974763 

1 2 3 
0 0 1 

# A tibble: 1 × 13
   vars     n  mean     sd median trimmed    mad   min   max range   skew
  <dbl> <dbl> <dbl>  <dbl>  <dbl>   <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl>
1     1  1268  5.32 0.0923   5.34    5.33 0.0932  5.14  5.46 0.316 -0.515
# ℹ 2 more variables: kurtosis <dbl>, se <dbl>

             1          2          3          4
  0 0.02053516 0.50902302 0.33789670 0.13254512

         1          2          3          4 
0.02053516 0.50902302 0.33789670 0.13254512 

0 
1 

        1         2         3 
0.1468575 0.8531425 0.0000000 

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range   skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl>
1     1  1607  3.97 0.126   3.99    3.99 0.137  3.60  4.11 0.511 -0.866   -0.177
# ℹ 1 more variable: se <dbl>

         1          2          3          4 
0.05547035 0.37781186 0.43558282 0.13113497 

         0          1          2 
0.71523517 0.03808793 0.24667689 

        1         2         3 
0.0603272 0.3504601 0.5892127 

         0          1          2 
0.71523517 0.03808793 0.24667689 

    Kruskal-Wallis rank sum test

data:  seedlings_allplots_sp$fire_class by seedlings_allplots_sp$plot123
Kruskal-Wallis chi-squared = 41.131, df = 2, p-value = 1.171e-09

    Kruskal-Wallis rank sum test

data:  seedlings_allplots_sp$fire_high by seedlings_allplots_sp$plot123
Kruskal-Wallis chi-squared = 15.83, df = 2, p-value = 0.0003652

    Kruskal-Wallis rank sum test

data:  seedlings_allplots_sp$fire_med by seedlings_allplots_sp$plot123
Kruskal-Wallis chi-squared = 137.19, df = 2, p-value < 2.2e-16

    Kruskal-Wallis rank sum test

data:  seedlings_allplots_sp$fire_low by seedlings_allplots_sp$plot123
Kruskal-Wallis chi-squared = 301.88, df = 2, p-value < 2.2e-16

    Kruskal-Wallis rank sum test

data:  seedlings_allplots_sp$mpb_grey by seedlings_allplots_sp$plot123
Kruskal-Wallis chi-squared = 202, df = 2, p-value < 2.2e-16

    Kruskal-Wallis rank sum test

data:  seedlings_allplots_sp$mpb_red by seedlings_allplots_sp$plot123
Kruskal-Wallis chi-squared = 969.49, df = 2, p-value < 2.2e-16

         0          1          2 
0.71845673 0.03788669 0.24365659 

         1          2          3          4 
0.06117483 0.37122002 0.43343761 0.13416754 

         1          2          3 
0.06047967 0.34897463 0.59054571 

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range   skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl>
1     1  2877  4.88 0.787   5.27    4.90 0.803  3.60  5.96  2.36 -0.237    -1.68
# ℹ 1 more variable: se <dbl>

         0          1          2 
0.68652038 0.05329154 0.26018809 

         1          2          3          4 
0.05329154 0.36363636 0.42946708 0.15360502 

         1          2          3 
0.07210031 0.35109718 0.57680251 

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range   skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl>
1     1   319  4.86 0.798   5.25    4.87 0.873  3.64  5.96  2.32 -0.184    -1.72
# ℹ 1 more variable: se <dbl>

         0          1          2 
0.66206897 0.03793103 0.30000000 

         1          2          3          4 
0.04482759 0.39655172 0.44827586 0.11034483 

         1          2          3 
0.04137931 0.37586207 0.58275862 

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range   skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl>
1     1   290  4.87 0.779   5.22    4.87 0.885  3.65  5.91  2.26 -0.180    -1.70
# ℹ 1 more variable: se <dbl>

         0          1          2 
0.73684211 0.02870813 0.23444976 

         1          2          3          4 
0.02392344 0.42105263 0.46411483 0.09090909 

         1          2          3 
0.07655502 0.34449761 0.57894737 

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range   skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl>
1     1   209  4.85 0.774   5.21    4.86 0.883  3.68  5.89  2.21 -0.184    -1.71
# ℹ 1 more variable: se <dbl>

         0          1          2 
0.78823529 0.02352941 0.18823529 

         1          2          3          4 
0.03529412 0.42352941 0.47058824 0.07058824 

        1         2         3 
0.1294118 0.4941176 0.3764706 

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1    85  4.47 0.680   4.08    4.43 0.454  3.64  5.89  2.25 0.513    -1.43
# ℹ 1 more variable: se <dbl>

         0          1          2 
0.63461538 0.03846154 0.32692308 

         1          2          3          4 
0.01923077 0.42307692 0.36538462 0.19230769 

         1          2          3 
0.00000000 0.03846154 0.96153846 

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range  skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl>
1     1    52  5.59 0.337   5.66    5.65 0.148  4.05  5.89  1.84 -3.41     12.7
# ℹ 1 more variable: se <dbl>

        0         2 
0.5384615 0.1153846 

         1          2          3          4 
0.03846154 0.26923077 0.30769231 0.03846154 

        1         2         3 
0.0000000 0.2692308 0.3846154 

# A tibble: 1 × 13
   vars     n  mean    sd median trimmed   mad   min   max range   skew kurtosis
  <dbl> <dbl> <dbl> <dbl>  <dbl>   <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>    <dbl>
1     1    34  4.89 0.767   5.24    4.90 0.857  3.84  5.89  2.05 -0.186    -1.79
# ℹ 1 more variable: se <dbl>

Generating 999 simulations of CSR  ...
1, 2, 3, ......10.........20.........30.........40.........50.........60..
.......70.........80.........90.........100.........110.........120.........130
.........140.........150.........160.........170.........180.........190........
.200.........210.........220.........230.........240.........250.........260......
...270.........280.........290.........300.........310.........320.........330....
.....340.........350.........360.........370.........380.........390.........400..
.......410.........420.........430.........440.........450.........460.........470
.........480.........490.........500.........510.........520.........530........
.540.........550.........560.........570.........580.........590.........600......
...610.........620.........630.........640.........650.........660.........670....
.....680.........690.........700.........710.........720.........730.........740..
.......750.........760.........770.........780.........790.........800.........810
.........820.........830.........840.........850.........860.........870........
.880.........890.........900.........910.........920.........930.........940......
...950.........960.........970.........980.........990........
999.

Done.

Generating 999 simulations of CSR  ...
1, 2, 3, ......10 [2:30 remaining] .........20 [2:22 remaining] ...
......30 [2:21 remaining] .........40 [2:19 remaining] .........50 [2:26 remaining] ..
.......60 [2:23 remaining] .........70 [2:25 remaining] .........80 [2:21 remaining] .
........90 [2:19 remaining] .........100 [2:16 remaining] .........110
 [2:13 remaining] .........120 [2:11 remaining] .........130 [2:10 remaining] .........
140 [2:12 remaining] .........150 [2:11 remaining] .........160 [2:09 remaining] ........
.170 [2:07 remaining] .........180 [2:05 remaining] .........190 [2:05 remaining] .......
..200 [2:03 remaining] .........210 [2:01 remaining] .........220 [1:59 remaining] ......
...230 [1:57 remaining] .........240 [1:55 remaining] .........250 [1:53 remaining] .....
....260 [1:51 remaining] .........270 [1:51 remaining] .........280 [1:49 remaining] ....
.....290 [1:47 remaining] .........300 [1:45 remaining] .........310 [1:44 remaining] ...
......320 [1:42 remaining] .........330 [1:41 remaining] .........340 [1:39 remaining] ..
.......350 [1:38 remaining] .........360 [1:36 remaining] .........370 [1:34 remaining] .
........380 [1:33 remaining] .........390 [1:31 remaining] .........400
 [1:30 remaining] .........410 [1:29 remaining] .........420 [1:28 remaining] .........
430 [1:26 remaining] .........440 [1:25 remaining] .........450 [1:23 remaining] ........
.460 [1:21 remaining] .........470 [1:20 remaining] .........480 [1:19 remaining] .......
..490 [1:17 remaining] .........500 [1:16 remaining] .........510 [1:14 remaining] ......
...520 [1:13 remaining] .........530 [1:11 remaining] .........540 [1:10 remaining] .....
....550 [1:08 remaining] .........560 [1:07 remaining] .........570 [1:05 remaining] ....
.....580 [1:04 remaining] .........590 [1:02 remaining] .........600 [1:01 remaining] ...
......610 [59 sec remaining] .........620 [58 sec remaining] .........630 [56 sec remaining] ..
.......640 [55 sec remaining] .........650 [53 sec remaining] .........660 [51 sec remaining] .
........670 [50 sec remaining] .........680 [49 sec remaining] .........690
 [47 sec remaining] .........700 [46 sec remaining] .........710 [44 sec remaining] .........
720 [42 sec remaining] .........730 [41 sec remaining] .........740 [39 sec remaining] ........
.750 [38 sec remaining] .........760 [36 sec remaining] .........770 [35 sec remaining] .......
..780 [33 sec remaining] .........790 [32 sec remaining] .........800 [30 sec remaining] ......
...810 [29 sec remaining] .........820 [27 sec remaining] .........830 [26 sec remaining] .....
....840 [24 sec remaining] .........850 [23 sec remaining] .........860 [21 sec remaining] ....
.....870 [20 sec remaining] .........880 [18 sec remaining] .........890 [17 sec remaining] ...
......900 [15 sec remaining] .........910 [14 sec remaining] .........920 [12 sec remaining] ..
.......930 [11 sec remaining] .........940 [9 sec remaining] .........950 [8 sec remaining] .
........960 [6 sec remaining] .........970 [4 sec remaining] .........980
 [3 sec remaining] .........990 [1 sec remaining] ........
999.

Done.

Generating 999 simulations of CSR  ...
1, 2, 3, ......10 [3:35 remaining] .........20 [3:32 remaining] ...
......30 [3:44 remaining] .........40 [3:38 remaining] .........50 [3:34 remaining] ..
.......60 [3:35 remaining] .........70 [3:31 remaining] .........80 [3:26 remaining] .
........90 [3:26 remaining] .........100 [3:25 remaining] .........110
 [3:23 remaining] .........120 [3:20 remaining] .........130 [3:18 remaining] .........
140 [3:15 remaining] .........150 [3:14 remaining] .........160 [3:10 remaining] ........
.170 [3:09 remaining] .........180 [3:07 remaining] .........190 [3:04 remaining] .......
..200 [3:01 remaining] .........210 [3:00 remaining] .........220 [2:57 remaining] ......
...230 [2:54 remaining] .........240 [2:51 remaining] .........250 [2:48 remaining] .....
....260 [2:48 remaining] .........270 [2:45 remaining] .........280 [2:42 remaining] ....
.....290 [2:40 remaining] .........300 [2:37 remaining] .........310 [2:35 remaining] ...
......320 [2:34 remaining] .........330 [2:31 remaining] .........340 [2:29 remaining] ..
.......350 [2:27 remaining] .........360 [2:25 remaining] .........370 [2:22 remaining] .
........380 [2:20 remaining] .........390 [2:17 remaining] .........400
 [2:16 remaining] .........410 [2:13 remaining] .........420 [2:11 remaining] .........
430 [2:09 remaining] .........440 [2:07 remaining] .........450 [2:05 remaining] ........
.460 [2:02 remaining] .........470 [2:00 remaining] .........480 [1:57 remaining] .......
..490 [1:55 remaining] .........500 [1:53 remaining] .........510 [1:51 remaining] ......
...520 [1:49 remaining] .........530 [1:46 remaining] .........540 [1:44 remaining] .....
....550 [1:41 remaining] .........560 [1:39 remaining] .........570 [1:37 remaining] ....
.....580 [1:34 remaining] .........590 [1:32 remaining] .........600 [1:30 remaining] ...
......610 [1:28 remaining] .........620 [1:25 remaining] .........630 [1:23 remaining] ..
.......640 [1:21 remaining] .........650 [1:18 remaining] .........660 [1:16 remaining] .
........670 [1:14 remaining] .........680 [1:12 remaining] .........690
 [1:09 remaining] .........700 [1:07 remaining] .........710 [1:05 remaining] .........
720 [1:03 remaining] .........730 [1:00 remaining] .........740 [58 sec remaining] ........
.750 [56 sec remaining] .........760 [54 sec remaining] .........770 [52 sec remaining] .......
..780 [49 sec remaining] .........790 [47 sec remaining] .........800 [45 sec remaining] ......
...810 [43 sec remaining] .........820 [40 sec remaining] .........830 [38 sec remaining] .....
....840 [36 sec remaining] .........850 [34 sec remaining] .........860 [31 sec remaining] ....
.....870 [29 sec remaining] .........880 [27 sec remaining] .........890 [25 sec remaining] ...
......900 [22 sec remaining] .........910 [20 sec remaining] .........920 [18 sec remaining] ..
.......930 [16 sec remaining] .........940 [13 sec remaining] .........950 [11 sec remaining] .
........960 [9 sec remaining] .........970 [7 sec remaining] .........980
 [4 sec remaining] .........990 [2 sec remaining] ........
999.

Done.

Generating 999 simulations of CSR  ...
1, 2, 3, ......10.........20.........30.........40.........50.........60..
.......70.........80.........90.........100.........110.........120.........130
.........140.........150.........160.........170.........180.........190........
.200.........210.........220.........230.........240.........250.........260......
...270.........280.........290.........300.........310.........320.........330....
.....340.........350.........360.........370.........380.........390.........400..
.......410.........420.........430.........440.........450.........460.........470
.........480.........490.........500.........510.........520.........530........
.540.........550.........560.........570.........580.........590.........600......
...610.........620.........630.........640.........650.........660.........670....
.....680.........690.........700.........710.........720.........730.........740..
.......750.........760.........770.........780.........790.........800.........810
.........820.........830.........840.........850.........860.........870........
.880.........890.........900.........910.........920.........930.........940......
...950.........960.........970.........980.........990........
999.

Done.

Generating 999 simulations of CSR  ...
1, 2, 3, ......10.........20.........30.........40.........50.........60..
.......70.........80.........90.........100.........110.........120.........130
.........140.........150.........160.........170.........180.........190........
.200.........210.........220.........230.........240.........250.........260......
...270.........280.........290.........300.........310.........320.........330....
.....340.........350.........360.........370.........380.........390.........400..
.......410.........420.........430.........440.........450.........460.........470
.........480.........490.........500.........510.........520.........530........
.540.........550.........560.........570.........580.........590.........600......
...610.........620.........630.........640.........650.........660.........670....
.....680.........690.........700.........710.........720.........730.........740..
.......750.........760.........770.........780.........790.........800.........810
.........820.........830.........840.........850.........860.........870........
.880.........890.........900.........910.........920.........930.........940......
...950.........960.........970.........980.........990........
999.

Done.

Generating 999 simulations of CSR  ...
1, 2, 3, ......10.........20.........30.........40.........50.........60..
.......70.........80.........90.........100.........110.........120.........130
.........140.........150.........160.........170.........180.........190........
.200.........210.........220.........230.........240.........250.........260......
...270.........280.........290.........300.........310.........320.........330....
.....340.........350.........360.........370.........380.........390.........400..
.......410.........420.........430.........440.........450.........460.........470
.........480.........490.........500.........510.........520.........530........
.540.........550.........560.........570.........580.........590.........600......
...610.........620.........630.........640.........650.........660.........670....
.....680.........690.........700.........710.........720.........730.........740..
.......750.........760.........770.........780.........790.........800.........810
.........820.........830.........840.........850.........860.........870........
.880.........890.........900.........910.........920.........930.........940......
...950.........960.........970.........980.........990........
999.

Done.

Generating 999 simulations of CSR  ...
1, 2, 3, ......10.........20.........30.........40.........50.........60..
.......70.........80.........90.........100.........110.........120.........130
.........140.........150.........160.........170.........180.........190........
.200.........210.........220.........230.........240.........250.........260......
...270.........280.........290.........300.........310.........320.........330....
.....340.........350.........360.........370.........380.........390.........400..
.......410.........420.........430.........440.........450.........460.........470
.........480.........490.........500.........510.........520.........530........
.540.........550.........560.........570.........580.........590.........600......
...610.........620.........630.........640.........650.........660.........670....
.....680.........690.........700.........710.........720.........730.........740..
.......750.........760.........770.........780.........790.........800.........810
.........820.........830.........840.........850.........860.........870........
.880.........890.........900.........910.........920.........930.........940......
...950.........960.........970.........980.........990........
999.

Done.

Generating 999 simulations of CSR  ...
1, 2, 3, ......10.........20.........30.........40.........50.........60..
.......70.........80.........90.........100.........110.........120.........130
.........140.........150.........160.........170.........180.........190........
.200.........210.........220.........230.........240.........250.........260......
...270.........280.........290.........300.........310.........320.........330....
.....340.........350.........360.........370.........380.........390.........400..
.......410.........420.........430.........440.........450.........460.........470
.........480.........490.........500.........510.........520.........530........
.540.........550.........560.........570.........580.........590.........600......
...610.........620.........630.........640.........650.........660.........670....
.....680.........690.........700.........710.........720.........730.........740..
.......750.........760.........770.........780.........790.........800.........810
.........820.........830.........840.........850.........860.........870........
.880.........890.........900.........910.........920.........930.........940......
...950.........960.........970.........980.........990........
999.

Done.

Generating 999 simulations of CSR  ...
1, 2, 3, ......10.........20.........30.........40.........50.........60..
.......70.........80.........90.........100.........110.........120.........130
.........140.........150.........160.........170.........180.........190........
.200.........210.........220.........230.........240.........250.........260......
...270.........280.........290.........300.........310.........320.........330....
.....340.........350.........360.........370.........380.........390.........400..
.......410.........420.........430.........440.........450.........460.........470
.........480.........490.........500.........510.........520.........530........
.540.........550.........560.........570.........580.........590.........600......
...610.........620.........630.........640.........650.........660.........670....
.....680.........690.........700.........710.........720.........730.........740..
.......750.........760.........770.........780.........790.........800.........810
.........820.........830.........840.........850.........860.........870........
.880.........890.........900.........910.........920.........930.........940......
...950.........960.........970.........980.........990........
999.

Done.

Generating 999 simulations of CSR  ...
1, 2, 3, ......10.........20.........30.........40.........50.........60..
.......70.........80.........90.........100.........110.........120.........130
.........140.........150.........160.........170.........180.........190........
.200.........210.........220.........230.........240.........250.........260......
...270.........280.........290.........300.........310.........320.........330....
.....340.........350.........360.........370.........380.........390.........400..
.......410.........420.........430.........440.........450.........460.........470
.........480.........490.........500.........510.........520.........530........
.540.........550.........560.........570.........580.........590.........600......
...610.........620.........630.........640.........650.........660.........670....
.....680.........690.........700.........710.........720.........730.........740..
.......750.........760.........770.........780.........790.........800.........810
.........820.........830.........840.........850.........860.........870........
.880.........890.........900.........910.........920.........930.........940......
...950.........960.........970.........980.........990........
999.

Done.

# A tibble: 2 × 4
   Npar    Df Deviance `Pr(>Chi)`
  <dbl> <dbl>    <dbl>      <dbl>
1     2    NA      NA          NA
2     3     1    3339.          0

# A tibble: 2 × 4
   Npar    Df Deviance `Pr(>Chi)`
  <dbl> <dbl>    <dbl>      <dbl>
1     1    NA     NA    NA       
2     2     1     49.6   1.88e-12

# A tibble: 2 × 4
   Npar    Df Deviance `Pr(>Chi)`
  <dbl> <dbl>    <dbl>      <dbl>
1     1    NA    NA       NA     
2     2     1     2.75     0.0970

# A tibble: 2 × 4
   Npar    Df Deviance `Pr(>Chi)`
  <dbl> <dbl>    <dbl>      <dbl>
1     1    NA     NA    NA       
2     2     1     72.3   1.84e-17

# A tibble: 2 × 4
   Npar    Df Deviance `Pr(>Chi)`
  <dbl> <dbl>    <dbl>      <dbl>
1     1    NA     NA   NA        
2     2     1     17.6  0.0000279

# A tibble: 2 × 4
   Npar    Df Deviance `Pr(>Chi)`
  <dbl> <dbl>    <dbl>      <dbl>
1     1    NA     NA     NA      
2     2     1     34.0    5.64e-9

# A tibble: 2 × 4
   Npar    Df Deviance  `Pr(>Chi)`
  <dbl> <dbl>    <dbl>       <dbl>
1     1    NA     NA   NA         
2     2     1     22.3  0.00000232

Inhomogeneous cluster point process model
Fitted to point pattern dataset 'seedlings_pooled'
Fitted by minimum contrast
    Summary statistic: inhomogeneous K-function
Minimum contrast fit (object of class "minconfit")
Model: Thomas process
Fitted by matching theoretical K function to seedlings_pooled

Internal parameters fitted by minimum contrast ($par):
       kappa       sigma2 
3.412206e-06 2.610398e+03 

Fitted cluster parameters:
       kappa        scale 
3.412206e-06 5.109205e+01 
Mean cluster size:  [pixel image]

Converged successfully after 155 function evaluations

Starting values of parameters:
       kappa       sigma2 
 0.001863476 20.059649144 
Domain of integration: [ 0 , 342.5 ]
Exponents: p= 2, q= 0.25

----------- TREND  -----
Point process model
Fitted to data: X
Fitting method: maximum likelihood (Berman-Turner approximation)
Model was fitted using glm()
Algorithm converged
Call:
ppm.ppp(Q = X, trend = trend, rename.intercept = FALSE, covariates = covariates, 
    covfunargs = covfunargs, use.gam = use.gam, forcefit = TRUE, 
    improve.type = ppm.improve.type, improve.args = ppm.improve.args, 
    nd = nd, eps = eps)
Edge correction: "border"
    [border correction distance r = 0 ]
--------------------------------------------------------------------------------
Quadrature scheme (Berman-Turner) = data + dummy + weights

Data pattern:
Planar point pattern:  3912 points
Average intensity 0.00186 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units

Dummy quadrature points:
     130 x 130 grid of dummy points, plus 4 corner points
     dummy spacing: 10.53800 x 11.78773 units

Original dummy parameters: =
Planar point pattern:  16904 points
Average intensity 0.00805 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units
Quadrature weights:
     (counting weights based on 130 x 130 array of rectangular tiles)
All weights:
    range: [3.45, 124]  total: 2100000
Weights on data points:
    range: [3.45, 62.1] total: 65500
Weights on dummy points:
    range: [3.45, 124]  total: 2030000
--------------------------------------------------------------------------------
FITTED :

Nonstationary Poisson process

---- Intensity: ----

Log intensity: ~distance_tree_im + aspect_im + rix_im + wind_im + mpb_grey_im + 
mpb_red_im + fire_high_im + fire_low_im
Model depends on external covariates 'distance_tree_im', 'aspect_im', 'rix_im', 
'wind_im', 'mpb_grey_im', 'mpb_red_im', 'fire_high_im' and 'fire_low_im'
Covariates provided:
    distance_tree_im: im
    aspect_im: im
    rix_im: im
    wind_im: im
    mpb_grey_im: im
    mpb_red_im: im
    fire_high_im: im
    fire_low_im: im

Fitted trend coefficients:
     (Intercept) distance_tree_im        aspect_im           rix_im 
     -4.55392239       0.20382037       0.20206115      -4.07766302 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
     -0.19616851      -1.32681744       0.27083362       0.66306646 
     fire_low_im 
      0.06398365 

                    Estimate        S.E.      CI95.lo    CI95.hi Ztest
(Intercept)      -4.55392239 0.224024694 -4.993002723 -4.1148421   ***
distance_tree_im  0.20382037 0.001332111  0.201209478  0.2064313   ***
aspect_im         0.20206115 0.022639906  0.157687753  0.2464346   ***
rix_im           -4.07766302 0.302333192 -4.670225191 -3.4851009   ***
wind_im          -0.19616851 0.027383589 -0.249839362 -0.1424977   ***
mpb_grey_im      -1.32681744 0.181360910 -1.682278297 -0.9713566   ***
mpb_red_im        0.27083362 0.049594050  0.173631073  0.3680362   ***
fire_high_im      0.66306646 0.051853589  0.561435298  0.7646976   ***
fire_low_im       0.06398365 0.035903465 -0.006385845  0.1343532      
                       Zval
(Intercept)      -20.327770
distance_tree_im 153.005519
aspect_im          8.925000
rix_im           -13.487315
wind_im           -7.163725
mpb_grey_im       -7.315895
mpb_red_im         5.461010
fire_high_im      12.787282
fire_low_im        1.782102

----------- gory details -----

Fitted regular parameters (theta):
     (Intercept) distance_tree_im        aspect_im           rix_im 
     -4.55392239       0.20382037       0.20206115      -4.07766302 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
     -0.19616851      -1.32681744       0.27083362       0.66306646 
     fire_low_im 
      0.06398365 

Fitted exp(theta):
     (Intercept) distance_tree_im        aspect_im           rix_im 
      0.01052584       1.22607789       1.22392285       0.01694702 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
      0.82187373       0.26532032       1.31105692       1.94073441 
     fire_low_im 
      1.06607497 

----------- CLUSTER  -----------
Model: Thomas process

Fitted cluster parameters:
       kappa        scale 
3.412206e-06 5.109205e+01 
Mean cluster size:  [pixel image]

Final standard error and CI
(allowing for correlation of cluster process):
                    Estimate       S.E.      CI95.lo      CI95.hi Ztest
(Intercept)      -4.55392239 5.93712790 -16.19047925 7.0826344695      
distance_tree_im  0.20382037 0.05985977   0.08649737 0.3211433660   ***
aspect_im         0.20206115 0.50870443  -0.79498120 1.1991035047      
rix_im           -4.07766302 6.86434684 -17.53153560 9.3762095555      
wind_im          -0.19616851 1.06575706  -2.28501396 1.8926769328      
mpb_grey_im      -1.32681744 0.67724286  -2.65418905 0.0005541651      
mpb_red_im        0.27083362 1.20191523  -2.08487693 2.6265441793      
fire_high_im      0.66306646 0.53221516  -0.38005608 1.7061890125      
fire_low_im       0.06398365 0.36401712  -0.64947678 0.7774440909      
                       Zval
(Intercept)      -0.7670245
distance_tree_im  3.4049640
aspect_im         0.3972074
rix_im           -0.5940351
wind_im          -0.1840649
mpb_grey_im      -1.9591457
mpb_red_im        0.2253350
fire_high_im      1.2458617
fire_low_im       0.1757710

----------- cluster strength indices ----------
Mean sibling probability 0.8993361
Count overdispersion index (on original window): 1186.113
Cluster strength: 8.934048

Spatial persistence index (over window): 0

Bound on distance from Poisson process (over window): 1
     = min (1, 6921.312, 108667300, 107077400, 10343.86)

Inhomogeneous cluster point process model
Fitted to point pattern dataset 'pinus_con_pooled'
Fitted by minimum contrast
    Summary statistic: inhomogeneous K-function
Minimum contrast fit (object of class "minconfit")
Model: Thomas process
Fitted by matching theoretical K function to pinus_con_pooled

Internal parameters fitted by minimum contrast ($par):
       kappa       sigma2 
2.872829e-06 2.789562e+03 

Fitted cluster parameters:
       kappa        scale 
2.872829e-06 5.281631e+01 
Mean cluster size:  [pixel image]

Converged successfully after 183 function evaluations

Starting values of parameters:
       kappa       sigma2 
 0.001370455 27.330821189 
Domain of integration: [ 0 , 342.5 ]
Exponents: p= 2, q= 0.25

----------- TREND  -----
Point process model
Fitted to data: X
Fitting method: maximum likelihood (Berman-Turner approximation)
Model was fitted using glm()
Algorithm converged
Call:
ppm.ppp(Q = X, trend = trend, rename.intercept = FALSE, covariates = covariates, 
    covfunargs = covfunargs, use.gam = use.gam, forcefit = TRUE, 
    improve.type = ppm.improve.type, improve.args = ppm.improve.args, 
    nd = nd, eps = eps)
Edge correction: "border"
    [border correction distance r = 0 ]
--------------------------------------------------------------------------------
Quadrature scheme (Berman-Turner) = data + dummy + weights

Data pattern:
Planar point pattern:  2877 points
Average intensity 0.00137 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units

Dummy quadrature points:
     110 x 110 grid of dummy points, plus 4 corner points
     dummy spacing: 12.45400 x 13.93095 units

Original dummy parameters: =
Planar point pattern:  12104 points
Average intensity 0.00577 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units
Quadrature weights:
     (counting weights based on 110 x 110 array of rectangular tiles)
All weights:
    range: [4.34, 173]  total: 2100000
Weights on data points:
    range: [4.34, 86.7] total: 67600
Weights on dummy points:
    range: [4.34, 173]  total: 2030000
--------------------------------------------------------------------------------
FITTED :

Nonstationary Poisson process

---- Intensity: ----

Log intensity: ~distance_tree_im + aspect_im + rix_im + wind_im + mpb_grey_im + 
mpb_red_im + fire_high_im + fire_low_im
Model depends on external covariates 'distance_tree_im', 'aspect_im', 'rix_im', 
'wind_im', 'mpb_grey_im', 'mpb_red_im', 'fire_high_im' and 'fire_low_im'
Covariates provided:
    distance_tree_im: im
    aspect_im: im
    rix_im: im
    wind_im: im
    mpb_grey_im: im
    mpb_red_im: im
    fire_high_im: im
    fire_low_im: im

Fitted trend coefficients:
     (Intercept) distance_tree_im        aspect_im           rix_im 
     -4.93131716       0.20364793       0.22203450      -3.48540224 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
     -0.26184906      -1.44215819       0.23728590       0.78198395 
     fire_low_im 
      0.07242254 

                    Estimate        S.E.      CI95.lo    CI95.hi Ztest
(Intercept)      -4.93131716 0.265043152 -5.450792192 -4.4118421   ***
distance_tree_im  0.20364793 0.001570362  0.200570077  0.2067258   ***
aspect_im         0.22203450 0.026389586  0.170311861  0.2737571   ***
rix_im           -3.48540224 0.360054570 -4.191096234 -2.7797083   ***
wind_im          -0.26184906 0.032035215 -0.324636925 -0.1990612   ***
mpb_grey_im      -1.44215819 0.225553989 -1.884235883 -1.0000805   ***
mpb_red_im        0.23728590 0.057702805  0.124190482  0.3503813   ***
fire_high_im      0.78198395 0.059642816  0.665086179  0.8988817   ***
fire_low_im       0.07242254 0.041820647 -0.009544423  0.1543895      
                       Zval
(Intercept)      -18.605714
distance_tree_im 129.682166
aspect_im          8.413717
rix_im            -9.680206
wind_im           -8.173788
mpb_grey_im       -6.393849
mpb_red_im         4.112207
fire_high_im      13.111117
fire_low_im        1.731741

----------- gory details -----

Fitted regular parameters (theta):
     (Intercept) distance_tree_im        aspect_im           rix_im 
     -4.93131716       0.20364793       0.22203450      -3.48540224 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
     -0.26184906      -1.44215819       0.23728590       0.78198395 
     fire_low_im 
      0.07242254 

Fitted exp(theta):
     (Intercept) distance_tree_im        aspect_im           rix_im 
     0.007216991      1.225866486      1.248614454      0.030641431 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
     0.769627185      0.236416976      1.267803532      2.185804493 
     fire_low_im 
     1.075109524 

----------- CLUSTER  -----------
Model: Thomas process

Fitted cluster parameters:
       kappa        scale 
2.872829e-06 5.281631e+01 
Mean cluster size:  [pixel image]

Final standard error and CI
(allowing for correlation of cluster process):
                    Estimate       S.E.      CI95.lo     CI95.hi Ztest
(Intercept)      -4.93131716 6.17115088 -17.02655064  7.16391632      
distance_tree_im  0.20364793 0.06422929   0.07776083  0.32953503    **
aspect_im         0.22203450 0.54535943  -0.84685035  1.29091935      
rix_im           -3.48540224 7.05888106 -17.32055489 10.34975040      
wind_im          -0.26184906 1.13593934  -2.48824925  1.96455113      
mpb_grey_im      -1.44215819 0.72069973  -2.85470370 -0.02961267     *
mpb_red_im        0.23728590 1.24492341  -2.20271915  2.67729095      
fire_high_im      0.78198395 0.59408275  -0.38239685  1.94636475      
fire_low_im       0.07242254 0.37875378  -0.66992123  0.81476631      
                       Zval
(Intercept)      -0.7990920
distance_tree_im  3.1706394
aspect_im         0.4071342
rix_im           -0.4937613
wind_im          -0.2305132
mpb_grey_im      -2.0010528
mpb_red_im        0.1906028
fire_high_im      1.3162879
fire_low_im       0.1912127

----------- cluster strength indices ----------
Mean sibling probability 0.9085078
Count overdispersion index (on original window): 958.0997
Cluster strength: 9.929888

Spatial persistence index (over window): 0

Bound on distance from Poisson process (over window): 1
     = min (1, 4977.151, 62522750, 61539960, 7841.93)

Inhomogeneous cluster point process model
Fitted to point pattern dataset 'larix_spp_pooled'
Fitted by minimum contrast
    Summary statistic: inhomogeneous K-function
Minimum contrast fit (object of class "minconfit")
Model: Thomas process
Fitted by matching theoretical K function to larix_spp_pooled

Internal parameters fitted by minimum contrast ($par):
       kappa       sigma2 
3.461944e-06 2.846486e+03 

Fitted cluster parameters:
       kappa        scale 
3.461944e-06 5.335247e+01 
Mean cluster size:  [pixel image]

Converged successfully after 107 function evaluations

Starting values of parameters:
       kappa       sigma2 
1.519552e-04 2.726308e+02 
Domain of integration: [ 0 , 342.5 ]
Exponents: p= 2, q= 0.25

----------- TREND  -----
Point process model
Fitted to data: X
Fitting method: maximum likelihood (Berman-Turner approximation)
Model was fitted using glm()
Algorithm converged
Call:
ppm.ppp(Q = X, trend = trend, rename.intercept = FALSE, covariates = covariates, 
    covfunargs = covfunargs, use.gam = use.gam, forcefit = TRUE, 
    improve.type = ppm.improve.type, improve.args = ppm.improve.args, 
    nd = nd, eps = eps)
Edge correction: "border"
    [border correction distance r = 0 ]
--------------------------------------------------------------------------------
Quadrature scheme (Berman-Turner) = data + dummy + weights

Data pattern:
Planar point pattern:  319 points
Average intensity 0.000152 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units

Dummy quadrature points:
     40 x 40 grid of dummy points, plus 4 corner points
     dummy spacing: 34.24850 x 38.31012 units

Original dummy parameters: =
Planar point pattern:  1604 points
Average intensity 0.000764 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units
Quadrature weights:
     (counting weights based on 40 x 40 array of rectangular tiles)
All weights:
    range: [54.7, 1310] total: 2100000
Weights on data points:
    range: [54.7, 656]  total: 71300
Weights on dummy points:
    range: [54.7, 1310] total: 2030000
--------------------------------------------------------------------------------
FITTED :

Nonstationary Poisson process

---- Intensity: ----

Log intensity: ~distance_tree_im + aspect_im + rix_im + wind_im + mpb_grey_im + 
mpb_red_im + fire_high_im + fire_low_im
Model depends on external covariates 'distance_tree_im', 'aspect_im', 'rix_im', 
'wind_im', 'mpb_grey_im', 'mpb_red_im', 'fire_high_im' and 'fire_low_im'
Covariates provided:
    distance_tree_im: im
    aspect_im: im
    rix_im: im
    wind_im: im
    mpb_grey_im: im
    mpb_red_im: im
    fire_high_im: im
    fire_low_im: im

Fitted trend coefficients:
     (Intercept) distance_tree_im        aspect_im           rix_im 
     -7.27189370       0.19224538       0.27989784      -4.36098774 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
     -0.18685673      -0.86084434       0.20096611       0.67353037 
     fire_low_im 
      0.05477299 

                    Estimate        S.E.    CI95.lo       CI95.hi Ztest
(Intercept)      -7.27189370 0.777692853 -8.7961437 -5.7476437206   ***
distance_tree_im  0.19224538 0.004471982  0.1834805  0.2010103075   ***
aspect_im         0.27989784 0.080816087  0.1215012  0.4382944563   ***
rix_im           -4.36098774 1.082744212 -6.4831274 -2.2388480859   ***
wind_im          -0.18685673 0.095269496 -0.3735815 -0.0001319453     *
mpb_grey_im      -0.86084434 0.508128164 -1.8567572  0.1350685618      
mpb_red_im        0.20096611 0.175099087 -0.1422218  0.5441540110      
fire_high_im      0.67353037 0.181205281  0.3183745  1.0286861914   ***
fire_low_im       0.05477299 0.131251636 -0.2024755  0.3120214700      
                       Zval
(Intercept)      -9.3505986
distance_tree_im 42.9888541
aspect_im         3.4633926
rix_im           -4.0277174
wind_im          -1.9613490
mpb_grey_im      -1.6941481
mpb_red_im        1.1477279
fire_high_im      3.7169467
fire_low_im       0.4173128

----------- gory details -----

Fitted regular parameters (theta):
     (Intercept) distance_tree_im        aspect_im           rix_im 
     -7.27189370       0.19224538       0.27989784      -4.36098774 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
     -0.18685673      -0.86084434       0.20096611       0.67353037 
     fire_low_im 
      0.05477299 

Fitted exp(theta):
     (Intercept) distance_tree_im        aspect_im           rix_im 
     0.000694795      1.211967878      1.322994643      0.012765772 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
     0.829562582      0.422804941      1.222583334      1.961148687 
     fire_low_im 
     1.056300798 

----------- CLUSTER  -----------
Model: Thomas process

Fitted cluster parameters:
       kappa        scale 
3.461944e-06 5.335247e+01 
Mean cluster size:  [pixel image]

Final standard error and CI
(allowing for correlation of cluster process):
                    Estimate       S.E.      CI95.lo   CI95.hi Ztest       Zval
(Intercept)      -7.27189370 6.15780355 -19.34096688 4.7971795       -1.1809233
distance_tree_im  0.19224538 0.05749162   0.07956389 0.3049269   ***  3.3438855
aspect_im         0.27989784 0.51978934  -0.73887055 1.2986662        0.5384832
rix_im           -4.36098774 7.06177847 -18.20181922 9.4798437       -0.6175481
wind_im          -0.18685673 1.08864398  -2.32055971 1.9468463       -0.1716417
mpb_grey_im      -0.86084434 0.82415242  -2.47615339 0.7544647       -1.0445208
mpb_red_im        0.20096611 1.32859967  -2.40304139 2.8049736        0.1512616
fire_high_im      0.67353037 0.60029369  -0.50302364 1.8500844        1.1220014
fire_low_im       0.05477299 0.41901992  -0.76649096 0.8760369        0.1307169

----------- cluster strength indices ----------
Mean sibling probability 0.8898113
Count overdispersion index (on original window): 58.45387
Cluster strength: 8.075342

Spatial persistence index (over window): 0

Bound on distance from Poisson process (over window): 1
     = min (1, 468.1832, 450059.7, 442830.5, 665.2215)

Inhomogeneous cluster point process model
Fitted to point pattern dataset 'picea_eng_pooled'
Fitted by minimum contrast
    Summary statistic: inhomogeneous K-function
Minimum contrast fit (object of class "minconfit")
Model: Thomas process
Fitted by matching theoretical K function to picea_eng_pooled

Internal parameters fitted by minimum contrast ($par):
       kappa       sigma2 
3.544082e-06 2.182906e+03 

Fitted cluster parameters:
       kappa        scale 
3.544082e-06 4.672157e+01 
Mean cluster size:  [pixel image]

Converged successfully after 109 function evaluations

Starting values of parameters:
       kappa       sigma2 
1.381411e-04 2.915626e+02 
Domain of integration: [ 0 , 342.5 ]
Exponents: p= 2, q= 0.25

----------- TREND  -----
Point process model
Fitted to data: X
Fitting method: maximum likelihood (Berman-Turner approximation)
Model was fitted using glm()
Algorithm converged
Call:
ppm.ppp(Q = X, trend = trend, rename.intercept = FALSE, covariates = covariates, 
    covfunargs = covfunargs, use.gam = use.gam, forcefit = TRUE, 
    improve.type = ppm.improve.type, improve.args = ppm.improve.args, 
    nd = nd, eps = eps)
Edge correction: "border"
    [border correction distance r = 0 ]
--------------------------------------------------------------------------------
Quadrature scheme (Berman-Turner) = data + dummy + weights

Data pattern:
Planar point pattern:  290 points
Average intensity 0.000138 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units

Dummy quadrature points:
     40 x 40 grid of dummy points, plus 4 corner points
     dummy spacing: 34.24850 x 38.31012 units

Original dummy parameters: =
Planar point pattern:  1604 points
Average intensity 0.000764 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units
Quadrature weights:
     (counting weights based on 40 x 40 array of rectangular tiles)
All weights:
    range: [69.1, 1310] total: 2100000
Weights on data points:
    range: [69.1, 656]  total: 68100
Weights on dummy points:
    range: [69.1, 1310] total: 2030000
--------------------------------------------------------------------------------
FITTED :

Nonstationary Poisson process

---- Intensity: ----

Log intensity: ~distance_tree_im + aspect_im + rix_im + wind_im + mpb_grey_im + 
mpb_red_im + fire_high_im + fire_low_im
Model depends on external covariates 'distance_tree_im', 'aspect_im', 'rix_im', 
'wind_im', 'mpb_grey_im', 'mpb_red_im', 'fire_high_im' and 'fire_low_im'
Covariates provided:
    distance_tree_im: im
    aspect_im: im
    rix_im: im
    wind_im: im
    mpb_grey_im: im
    mpb_red_im: im
    fire_high_im: im
    fire_low_im: im

Fitted trend coefficients:
     (Intercept) distance_tree_im        aspect_im           rix_im 
     -7.67826858       0.18729223       0.15773278      -2.86388669 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
     -0.18388693      -0.78136144       0.46231971       0.63801339 
     fire_low_im 
      0.01262337 

                    Estimate        S.E.       CI95.lo     CI95.hi Ztest
(Intercept)      -7.67826858 0.797977751 -9.2422762349 -6.11426093   ***
distance_tree_im  0.18729223 0.005034229  0.1774253246  0.19715914   ***
aspect_im         0.15773278 0.080955825 -0.0009377183  0.31640328      
rix_im           -2.86388669 1.079565804 -4.9797967868 -0.74797660    **
wind_im          -0.18388693 0.102699266 -0.3851737933  0.01739993      
mpb_grey_im      -0.78136144 0.507978245 -1.7769805010  0.21425763      
mpb_red_im        0.46231971 0.173155111  0.1229419244  0.80169749    **
fire_high_im      0.63801339 0.200948480  0.2441616037  1.03186517    **
fire_low_im       0.01262337 0.131904723 -0.2459051379  0.27115188      
                        Zval
(Intercept)      -9.62215872
distance_tree_im 37.20376012
aspect_im         1.94838090
rix_im           -2.65281346
wind_im          -1.79053793
mpb_grey_im      -1.53817893
mpb_red_im        2.66997435
fire_high_im      3.17500977
fire_low_im       0.09570066

----------- gory details -----

Fitted regular parameters (theta):
     (Intercept) distance_tree_im        aspect_im           rix_im 
     -7.67826858       0.18729223       0.15773278      -2.86388669 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
     -0.18388693      -0.78136144       0.46231971       0.63801339 
     fire_low_im 
      0.01262337 

Fitted exp(theta):
     (Intercept) distance_tree_im        aspect_im           rix_im 
    0.0004627755     1.2059796585     1.1708532811     0.0570466062 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
    0.8320298752     0.4577823459     1.5877528367     1.8927170442 
     fire_low_im 
    1.0127033803 

----------- CLUSTER  -----------
Model: Thomas process

Fitted cluster parameters:
       kappa        scale 
3.544082e-06 4.672157e+01 
Mean cluster size:  [pixel image]

Final standard error and CI
(allowing for correlation of cluster process):
                    Estimate       S.E.      CI95.lo    CI95.hi Ztest
(Intercept)      -7.67826858 6.28253661 -19.99181408  4.6352769      
distance_tree_im  0.18729223 0.06276401   0.06427704  0.3103074    **
aspect_im         0.15773278 0.54988539  -0.92002278  1.2354883      
rix_im           -2.86388669 7.89657666 -18.34089255 12.6131192      
wind_im          -0.18388693 1.04041414  -2.22306118  1.8552873      
mpb_grey_im      -0.78136144 0.83618413  -2.42025221  0.8575293      
mpb_red_im        0.46231971 1.22588195  -1.94036477  2.8650042      
fire_high_im      0.63801339 0.72464813  -0.78227085  2.0582976      
fire_low_im       0.01262337 0.39909173  -0.76958205  0.7948288      
                        Zval
(Intercept)      -1.22216058
distance_tree_im  2.98407062
aspect_im         0.28684665
rix_im           -0.36267446
wind_im          -0.17674398
mpb_grey_im      -0.93443706
mpb_red_im        0.37713232
fire_high_im      0.88044578
fire_low_im       0.03163024

----------- cluster strength indices ----------
Mean sibling probability 0.9113956
Count overdispersion index (on original window): 63.12209
Cluster strength: 10.28612

Spatial persistence index (over window): 0

Bound on distance from Poisson process (over window): 1
     = min (1, 469.5974, 574487.9, 567597.6, 753.0458)

Inhomogeneous cluster point process model
Fitted to point pattern dataset 'pseudo_m_pooled'
Fitted by minimum contrast
    Summary statistic: inhomogeneous K-function
Minimum contrast fit (object of class "minconfit")
Model: Thomas process
Fitted by matching theoretical K function to pseudo_m_pooled

Internal parameters fitted by minimum contrast ($par):
       kappa       sigma2 
2.800326e-06 2.533211e+03 

Fitted cluster parameters:
       kappa        scale 
2.800326e-06 5.033101e+01 
Mean cluster size:  [pixel image]

Converged successfully after 125 function evaluations

Starting values of parameters:
       kappa       sigma2 
9.955688e-05 4.147339e+02 
Domain of integration: [ 0 , 342.5 ]
Exponents: p= 2, q= 0.25

----------- TREND  -----
Point process model
Fitted to data: X
Fitting method: maximum likelihood (Berman-Turner approximation)
Model was fitted using glm()
Algorithm converged
Call:
ppm.ppp(Q = X, trend = trend, rename.intercept = FALSE, covariates = covariates, 
    covfunargs = covfunargs, use.gam = use.gam, forcefit = TRUE, 
    improve.type = ppm.improve.type, improve.args = ppm.improve.args, 
    nd = nd, eps = eps)
Edge correction: "border"
    [border correction distance r = 0 ]
--------------------------------------------------------------------------------
Quadrature scheme (Berman-Turner) = data + dummy + weights

Data pattern:
Planar point pattern:  209 points
Average intensity 9.96e-05 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units

Dummy quadrature points:
     32 x 32 grid of dummy points, plus 4 corner points
     dummy spacing: 42.81062 x 47.88764 units

Original dummy parameters: =
Planar point pattern:  1028 points
Average intensity 0.00049 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units
Quadrature weights:
     (counting weights based on 32 x 32 array of rectangular tiles)
All weights:
    range: [121, 2050]  total: 2100000
Weights on data points:
    range: [121, 1030]  total: 79200
Weights on dummy points:
    range: [121, 2050]  total: 2020000
--------------------------------------------------------------------------------
FITTED :

Nonstationary Poisson process

---- Intensity: ----

Log intensity: ~distance_tree_im + aspect_im + rix_im + wind_im + mpb_grey_im + 
mpb_red_im + fire_high_im + fire_low_im
Model depends on external covariates 'distance_tree_im', 'aspect_im', 'rix_im', 
'wind_im', 'mpb_grey_im', 'mpb_red_im', 'fire_high_im' and 'fire_low_im'
Covariates provided:
    distance_tree_im: im
    aspect_im: im
    rix_im: im
    wind_im: im
    mpb_grey_im: im
    mpb_red_im: im
    fire_high_im: im
    fire_low_im: im

Fitted trend coefficients:
     (Intercept) distance_tree_im        aspect_im           rix_im 
     -6.64190062       0.17735010       0.21032067      -4.98184229 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
     -0.29070679      -0.76784970       0.05135115       0.51798464 
     fire_low_im 
      0.24566420 

                    Estimate        S.E.      CI95.lo     CI95.hi Ztest
(Intercept)      -6.64190062 1.013423579 -8.628174335 -4.65562690   ***
distance_tree_im  0.17735010 0.006266996  0.165067011  0.18963318   ***
aspect_im         0.21032067 0.095290326  0.023555059  0.39708627     *
rix_im           -4.98184229 1.337283066 -7.602868934 -2.36081564   ***
wind_im          -0.29070679 0.123639634 -0.533036018 -0.04837756     *
mpb_grey_im      -0.76784970 0.588329484 -1.920954300  0.38525490      
mpb_red_im        0.05135115 0.225292579 -0.390214187  0.49291650      
fire_high_im      0.51798464 0.259882118  0.008625046  1.02734423     *
fire_low_im       0.24566420 0.151187720 -0.050658289  0.54198668      
                      Zval
(Intercept)      -6.553924
distance_tree_im 28.299060
aspect_im         2.207157
rix_im           -3.725346
wind_im          -2.351243
mpb_grey_im      -1.305136
mpb_red_im        0.227931
fire_high_im      1.993152
fire_low_im       1.624895

----------- gory details -----

Fitted regular parameters (theta):
     (Intercept) distance_tree_im        aspect_im           rix_im 
     -6.64190062       0.17735010       0.21032067      -4.98184229 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
     -0.29070679      -0.76784970       0.05135115       0.51798464 
     fire_low_im 
      0.24566420 

Fitted exp(theta):
     (Intercept) distance_tree_im        aspect_im           rix_im 
     0.001304545      1.194049053      1.234073722      0.006861410 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
     0.747734890      0.464009756      1.052692485      1.678641167 
     fire_low_im 
     1.278470187 

----------- CLUSTER  -----------
Model: Thomas process

Fitted cluster parameters:
       kappa        scale 
2.800326e-06 5.033101e+01 
Mean cluster size:  [pixel image]

Final standard error and CI
(allowing for correlation of cluster process):
                    Estimate       S.E.     CI95.lo  CI95.hi Ztest        Zval
(Intercept)      -6.64190062 5.89475421 -18.1954066 4.911605       -1.12674768
distance_tree_im  0.17735010 0.07345742   0.0333762 0.321324     *  2.41432509
aspect_im         0.21032067 0.60073935  -0.9671068 1.387748        0.35010303
rix_im           -4.98184229 6.80260515 -18.3147034 8.351019       -0.73234330
wind_im          -0.29070679 1.15647024  -2.5573468 1.975933       -0.25137421
mpb_grey_im      -0.76784970 1.04828643  -2.8224534 1.286754       -0.73248081
mpb_red_im        0.05135115 1.50162151  -2.8917729 2.994475        0.03419714
fire_high_im      0.51798464 0.81309003  -1.0756425 2.111612        0.63705693
fire_low_im       0.24566420 0.45701840  -0.6500754 1.141404        0.53753678

----------- cluster strength indices ----------
Mean sibling probability 0.9181527
Count overdispersion index (on original window): 38.6918
Cluster strength: 11.21787

Spatial persistence index (over window): 0

Bound on distance from Poisson process (over window): 1
     = min (1, 308.4498, 270866.6, 267031.2, 516.5468)

Inhomogeneous cluster point process model
Fitted to point pattern dataset 'pinus_pond_pooled'
Fitted by minimum contrast
    Summary statistic: inhomogeneous K-function
Minimum contrast fit (object of class "minconfit")
Model: Thomas process
Fitted by matching theoretical K function to pinus_pond_pooled

Internal parameters fitted by minimum contrast ($par):
       kappa       sigma2 
2.185480e-06 1.820436e+03 

Fitted cluster parameters:
       kappa        scale 
2.185480e-06 4.266656e+01 
Mean cluster size:  [pixel image]

Converged successfully after 85 function evaluations

Starting values of parameters:
       kappa       sigma2 
4.048964e-05 8.449762e+02 
Domain of integration: [ 0 , 342.5 ]
Exponents: p= 2, q= 0.25

----------- TREND  -----
Point process model
Fitted to data: X
Fitting method: maximum likelihood (Berman-Turner approximation)
Model was fitted using glm()
Algorithm converged
Call:
ppm.ppp(Q = X, trend = trend, rename.intercept = FALSE, covariates = covariates, 
    covfunargs = covfunargs, use.gam = use.gam, forcefit = TRUE, 
    improve.type = ppm.improve.type, improve.args = ppm.improve.args, 
    nd = nd, eps = eps)
Edge correction: "border"
    [border correction distance r = 0 ]
--------------------------------------------------------------------------------
Quadrature scheme (Berman-Turner) = data + dummy + weights

Data pattern:
Planar point pattern:  85 points
Average intensity 4.05e-05 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units

Dummy quadrature points:
     32 x 32 grid of dummy points, plus 4 corner points
     dummy spacing: 42.81062 x 47.88764 units

Original dummy parameters: =
Planar point pattern:  1028 points
Average intensity 0.00049 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units
Quadrature weights:
     (counting weights based on 32 x 32 array of rectangular tiles)
All weights:
    range: [256, 2050]  total: 2100000
Weights on data points:
    range: [256, 1030]  total: 43300
Weights on dummy points:
    range: [256, 2050]  total: 2060000
--------------------------------------------------------------------------------
FITTED :

Nonstationary Poisson process

---- Intensity: ----

Log intensity: ~distance_tree_im + aspect_im + rix_im + wind_im + mpb_grey_im + 
mpb_red_im + fire_high_im + fire_low_im
Model depends on external covariates 'distance_tree_im', 'aspect_im', 'rix_im', 
'wind_im', 'mpb_grey_im', 'mpb_red_im', 'fire_high_im' and 'fire_low_im'
Covariates provided:
    distance_tree_im: im
    aspect_im: im
    rix_im: im
    wind_im: im
    mpb_grey_im: im
    mpb_red_im: im
    fire_high_im: im
    fire_low_im: im

Fitted trend coefficients:
     (Intercept) distance_tree_im        aspect_im           rix_im 
      -2.9381024        0.2118847        0.4219905       -8.0187825 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
      -1.0385356      -14.3955315        0.7411791       -0.3247937 
     fire_low_im 
      -0.7200928 

                    Estimate         S.E.       CI95.lo      CI95.hi Ztest
(Intercept)       -2.9381024   1.59653157 -6.067247e+00    0.1910420      
distance_tree_im   0.2118847   0.01073776  1.908391e-01    0.2329303   ***
aspect_im          0.4219905   0.16212534  1.042307e-01    0.7397503    **
rix_im            -8.0187825   2.34985716 -1.262442e+01   -3.4131471   ***
wind_im           -1.0385356   0.19984911 -1.430233e+00   -0.6468385   ***
mpb_grey_im      -14.3955315 951.31982314 -1.878948e+03 1850.1570596      
mpb_red_im         0.7411791   0.34523720  6.452659e-02    1.4178316     *
fire_high_im      -0.3247937   0.44375260 -1.194533e+00    0.5449454      
fire_low_im       -0.7200928   0.27902271 -1.266967e+00   -0.1732183    **
                        Zval
(Intercept)      -1.84030334
distance_tree_im 19.73267051
aspect_im         2.60286570
rix_im           -3.41245531
wind_im          -5.19659840
mpb_grey_im      -0.01513217
mpb_red_im        2.14686908
fire_high_im     -0.73192529
fire_low_im      -2.58076764

----------- gory details -----

Fitted regular parameters (theta):
     (Intercept) distance_tree_im        aspect_im           rix_im 
      -2.9381024        0.2118847        0.4219905       -8.0187825 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
      -1.0385356      -14.3955315        0.7411791       -0.3247937 
     fire_low_im 
      -0.7200928 

Fitted exp(theta):
     (Intercept) distance_tree_im        aspect_im           rix_im 
    5.296614e-02     1.236005e+00     1.524994e+00     3.292206e-04 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
    3.539727e-01     5.598866e-07     2.098408e+00     7.226764e-01 
     fire_low_im 
    4.867071e-01 

----------- CLUSTER  -----------
Model: Thomas process

Fitted cluster parameters:
       kappa        scale 
2.185480e-06 4.266656e+01 
Mean cluster size:  [pixel image]

Final standard error and CI
(allowing for correlation of cluster process):
                    Estimate        S.E.       CI95.lo      CI95.hi Ztest
(Intercept)       -2.9381024   8.9021500 -2.038600e+01   14.5097910      
distance_tree_im   0.2118847   0.1127885 -9.176654e-03    0.4329460      
aspect_im          0.4219905   0.7174507 -9.841871e-01    1.8281681      
rix_im            -8.0187825  10.9489299 -2.947829e+01   13.4407257      
wind_im           -1.0385356   1.3155398 -3.616946e+00    1.5398750      
mpb_grey_im      -14.3955315 951.3203830 -1.878949e+03 1850.1581570      
mpb_red_im         0.7411791   1.5137328 -2.225683e+00    3.7080409      
fire_high_im      -0.3247937   1.3015994 -2.875882e+00    2.2262942      
fire_low_im       -0.7200928   0.8439248 -2.374155e+00    0.9339693      
                        Zval
(Intercept)      -0.33004413
distance_tree_im  1.87860235
aspect_im         0.58818042
rix_im           -0.73238048
wind_im          -0.78943684
mpb_grey_im      -0.01513216
mpb_red_im        0.48963665
fire_high_im     -0.24953435
fire_low_im      -0.85326658

----------- cluster strength indices ----------
Mean sibling probability 0.9523849
Count overdispersion index (on original window): 61.53161
Cluster strength: 20.00175

Spatial persistence index (over window): 0

Bound on distance from Poisson process (over window): 1
     = min (1, 159.3448, 128348.5, 127104.4, 356.3214)

Inhomogeneous cluster point process model
Fitted to point pattern dataset 'pinus_monti_pooled'
Fitted by minimum contrast
    Summary statistic: inhomogeneous K-function
Minimum contrast fit (object of class "minconfit")
Model: Thomas process
Fitted by matching theoretical K function to pinus_monti_pooled

Internal parameters fitted by minimum contrast ($par):
       kappa       sigma2 
1.484750e-05 2.526558e+02 

Fitted cluster parameters:
       kappa        scale 
1.484750e-05 1.589515e+01 
Mean cluster size:  [pixel image]

Converged successfully after 81 function evaluations

Starting values of parameters:
       kappa       sigma2 
4.620583e-05 6.149680e+01 
Domain of integration: [ 0 , 342.5 ]
Exponents: p= 2, q= 0.25

----------- TREND  -----
Point process model
Fitted to data: X
Fitting method: maximum likelihood (Berman-Turner approximation)
Model was fitted using glm()
Algorithm converged
Call:
ppm.ppp(Q = X, trend = trend, rename.intercept = FALSE, covariates = covariates, 
    covfunargs = covfunargs, use.gam = use.gam, forcefit = TRUE, 
    improve.type = ppm.improve.type, improve.args = ppm.improve.args, 
    nd = nd, eps = eps)
Edge correction: "border"
    [border correction distance r = 0 ]
--------------------------------------------------------------------------------
Quadrature scheme (Berman-Turner) = data + dummy + weights

Data pattern:
Planar point pattern:  97 points
Average intensity 4.62e-05 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units

Dummy quadrature points:
     32 x 32 grid of dummy points, plus 4 corner points
     dummy spacing: 42.81062 x 47.88764 units

Original dummy parameters: =
Planar point pattern:  1028 points
Average intensity 0.00049 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units
Quadrature weights:
     (counting weights based on 32 x 32 array of rectangular tiles)
All weights:
    range: [97.6, 2050] total: 2100000
Weights on data points:
    range: [97.6, 1030] total: 24700
Weights on dummy points:
    range: [97.6, 2050] total: 2070000
--------------------------------------------------------------------------------
FITTED :

Nonstationary Poisson process

---- Intensity: ----

Log intensity: ~distance_tree_im + aspect_im + rix_im + wind_im + mpb_grey_im + 
mpb_red_im + fire_high_im + fire_low_im
Model depends on external covariates 'distance_tree_im', 'aspect_im', 'rix_im', 
'wind_im', 'mpb_grey_im', 'mpb_red_im', 'fire_high_im' and 'fire_low_im'
Covariates provided:
    distance_tree_im: im
    aspect_im: im
    rix_im: im
    wind_im: im
    mpb_grey_im: im
    mpb_red_im: im
    fire_high_im: im
    fire_low_im: im

Fitted trend coefficients:
     (Intercept) distance_tree_im        aspect_im           rix_im 
     -29.8028259        0.1351359       -0.3582562       -1.6312003 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
       4.0273722      -15.3667919        0.1234843        1.0638541 
     fire_low_im 
      -0.5984416 

                    Estimate         S.E.       CI95.lo       CI95.hi Ztest
(Intercept)      -29.8028259 2.263641e+00   -34.2394813  -25.36617047   ***
distance_tree_im   0.1351359 1.255967e-02     0.1105194    0.15975236   ***
aspect_im         -0.3582562 1.501453e-01    -0.6525355   -0.06397679     *
rix_im            -1.6312003 1.796335e+00    -5.1519517    1.88955107      
wind_im            4.0273722 4.456050e-01     3.1540024    4.90074209   ***
mpb_grey_im      -15.3667919 1.447433e+03 -2852.2824490 2821.54886512      
mpb_red_im         0.1234843 2.714039e-01    -0.4084575    0.65542612      
fire_high_im       1.0638541 3.077903e-01     0.4605963    1.66711188   ***
fire_low_im       -0.5984416 3.032126e-01    -1.1927274   -0.00415585     *
                         Zval
(Intercept)      -13.16587833
distance_tree_im  10.75950726
aspect_im         -2.38606315
rix_im            -0.90807146
wind_im            9.03798611
mpb_grey_im       -0.01061659
mpb_red_im         0.45498359
fire_high_im       3.45642548
fire_low_im       -1.97367004

----------- gory details -----

Fitted regular parameters (theta):
     (Intercept) distance_tree_im        aspect_im           rix_im 
     -29.8028259        0.1351359       -0.3582562       -1.6312003 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
       4.0273722      -15.3667919        0.1234843        1.0638541 
     fire_low_im 
      -0.5984416 

Fitted exp(theta):
     (Intercept) distance_tree_im        aspect_im           rix_im 
    1.139717e-13     1.144692e+00     6.988940e-01     1.956945e-01 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
    5.611327e+01     2.119762e-07     1.131432e+00     2.897517e+00 
     fire_low_im 
    5.496676e-01 

----------- CLUSTER  -----------
Model: Thomas process

Fitted cluster parameters:
       kappa        scale 
1.484750e-05 1.589515e+01 
Mean cluster size:  [pixel image]

Final standard error and CI
(allowing for correlation of cluster process):
                    Estimate         S.E.       CI95.lo      CI95.hi Ztest
(Intercept)      -29.8028259    8.4721112 -4.640786e+01  -13.1977931   ***
distance_tree_im   0.1351359    0.1126779 -8.570872e-02    0.3559804      
aspect_im         -0.3582562    0.7493148 -1.826886e+00    1.1103739      
rix_im            -1.6312003    9.9651263 -2.116249e+01   17.9000883      
wind_im            4.0273722    1.6738473  7.466917e-01    7.3080527     *
mpb_grey_im      -15.3667919 1447.4337833 -2.852285e+03 2821.5512934      
mpb_red_im         0.1234843    2.5012855 -4.778945e+00    5.0259139      
fire_high_im       1.0638541    1.2259981 -1.339058e+00    3.4667662      
fire_low_im       -0.5984416    1.8752206 -4.273806e+00    3.0769231      
                        Zval
(Intercept)      -3.51775671
distance_tree_im  1.19931137
aspect_im        -0.47811168
rix_im           -0.16369088
wind_im           2.40605708
mpb_grey_im      -0.01061658
mpb_red_im        0.04936834
fire_high_im      0.86774530
fire_low_im      -0.31913132

----------- cluster strength indices ----------
Mean sibling probability 0.9549818
Count overdispersion index (on original window): 52.1872
Cluster strength: 21.21326

Spatial persistence index (over window): 0

Bound on distance from Poisson process (over window): 1
     = min (1, 181.8133, 175571.9, 176694.5, 418.6965)

Inhomogeneous cluster point process model
Fitted to point pattern dataset 'tsuga_hete_pooled'
Fitted by minimum contrast
    Summary statistic: inhomogeneous K-function
Minimum contrast fit (object of class "minconfit")
Model: Thomas process
Fitted by matching theoretical K function to tsuga_hete_pooled

Internal parameters fitted by minimum contrast ($par):
       kappa       sigma2 
2.934178e-06 2.001022e+03 

Fitted cluster parameters:
       kappa        scale 
2.934178e-06 4.473278e+01 
Mean cluster size:  [pixel image]

Converged successfully after 89 function evaluations

Starting values of parameters:
       kappa       sigma2 
1.619586e-05 1.885789e+03 
Domain of integration: [ 0 , 342.5 ]
Exponents: p= 2, q= 0.25

----------- TREND  -----
Point process model
Fitted to data: X
Fitting method: maximum likelihood (Berman-Turner approximation)
Model was fitted using glm()
Algorithm converged
Call:
ppm.ppp(Q = X, trend = trend, rename.intercept = FALSE, covariates = covariates, 
    covfunargs = covfunargs, use.gam = use.gam, forcefit = TRUE, 
    improve.type = ppm.improve.type, improve.args = ppm.improve.args, 
    nd = nd, eps = eps)
Edge correction: "border"
    [border correction distance r = 0 ]
--------------------------------------------------------------------------------
Quadrature scheme (Berman-Turner) = data + dummy + weights

Data pattern:
Planar point pattern:  34 points
Average intensity 1.62e-05 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units

Dummy quadrature points:
     32 x 32 grid of dummy points, plus 4 corner points
     dummy spacing: 42.81062 x 47.88764 units

Original dummy parameters: =
Planar point pattern:  1028 points
Average intensity 0.00049 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units
Quadrature weights:
     (counting weights based on 32 x 32 array of rectangular tiles)
All weights:
    range: [410, 2050]  total: 2100000
Weights on data points:
    range: [410, 1030]  total: 24200
Weights on dummy points:
    range: [410, 2050]  total: 2080000
--------------------------------------------------------------------------------
FITTED :

Nonstationary Poisson process

---- Intensity: ----

Log intensity: ~distance_tree_im + aspect_im + rix_im + wind_im + mpb_grey_im + 
mpb_red_im + fire_high_im + fire_low_im
Model depends on external covariates 'distance_tree_im', 'aspect_im', 'rix_im', 
'wind_im', 'mpb_grey_im', 'mpb_red_im', 'fire_high_im' and 'fire_low_im'
Covariates provided:
    distance_tree_im: im
    aspect_im: im
    rix_im: im
    wind_im: im
    mpb_grey_im: im
    mpb_red_im: im
    fire_high_im: im
    fire_low_im: im

Fitted trend coefficients:
     (Intercept) distance_tree_im        aspect_im           rix_im 
     -13.8562554        0.2182580        0.1705663        4.0896098 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
       0.0106779      -14.9145558       -0.8547068       -0.1267294 
     fire_low_im 
      -0.4010784 

                    Estimate         S.E.       CI95.lo      CI95.hi Ztest
(Intercept)      -13.8562554    2.3749709   -18.5111128   -9.2013980   ***
distance_tree_im   0.2182580    0.0196139     0.1798155    0.2567006   ***
aspect_im          0.1705663    0.2440049    -0.3076744    0.6488071      
rix_im             4.0896098    3.8044444    -3.3669641   11.5461837      
wind_im            0.0106779    0.2987726    -0.5749055    0.5962614      
mpb_grey_im      -14.9145558 1577.3381707 -3106.4405618 3076.6114502      
mpb_red_im        -0.8547068    0.6467687    -2.1223501    0.4129365      
fire_high_im      -0.1267294    0.7606515    -1.6175788    1.3641201      
fire_low_im       -0.4010784    0.3932683    -1.1718700    0.3697132      
                         Zval
(Intercept)      -5.834284307
distance_tree_im 11.127719415
aspect_im         0.699028439
rix_im            1.074955875
wind_im           0.035739242
mpb_grey_im      -0.009455522
mpb_red_im       -1.321503147
fire_high_im     -0.166606360
fire_low_im      -1.019859689

----------- gory details -----

Fitted regular parameters (theta):
     (Intercept) distance_tree_im        aspect_im           rix_im 
     -13.8562554        0.2182580        0.1705663        4.0896098 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
       0.0106779      -14.9145558       -0.8547068       -0.1267294 
     fire_low_im 
      -0.4010784 

Fitted exp(theta):
     (Intercept) distance_tree_im        aspect_im           rix_im 
    9.600740e-07     1.243908e+00     1.185976e+00     5.971659e+01 
         wind_im      mpb_grey_im       mpb_red_im     fire_high_im 
    1.010735e+00     3.331890e-07     4.254079e-01     8.809721e-01 
     fire_low_im 
    6.695975e-01 

----------- CLUSTER  -----------
Model: Thomas process

Fitted cluster parameters:
       kappa        scale 
2.934178e-06 4.473278e+01 
Mean cluster size:  [pixel image]

Final standard error and CI
(allowing for correlation of cluster process):
                    Estimate         S.E.       CI95.lo      CI95.hi Ztest
(Intercept)      -13.8562554 6.422691e+00 -2.644450e+01   -1.2680128     *
distance_tree_im   0.2182580 9.293931e-02  3.610031e-02    0.4004157     *
aspect_im          0.1705663 6.091137e-01 -1.023274e+00    1.3644072      
rix_im             4.0896098 6.588385e+00 -8.823388e+00   17.0026079      
wind_im            0.0106779 1.161029e+00 -2.264898e+00    2.2862536      
mpb_grey_im      -14.9145558 1.577338e+03 -3.106441e+03 3076.6118527      
mpb_red_im        -0.8547068 1.381814e+00 -3.563013e+00    1.8535991      
fire_high_im      -0.1267294 1.170332e+00 -2.420539e+00    2.1670801      
fire_low_im       -0.4010784 7.647762e-01 -1.900012e+00    1.0978554      
                         Zval
(Intercept)      -2.157391017
distance_tree_im  2.348392885
aspect_im         0.280023830
rix_im            0.620730207
wind_im           0.009196929
mpb_grey_im      -0.009455521
mpb_red_im       -0.618539645
fire_high_im     -0.108284933
fire_low_im      -0.524438939

----------- cluster strength indices ----------
Mean sibling probability 0.9312881
Count overdispersion index (on original window): 35.22245
Cluster strength: 13.55351

Spatial persistence index (over window): 0

Bound on distance from Poisson process (over window): 1
     = min (1, 81.98862, 23049.96, 22799.9, 150.9209)

Inhomogeneous cluster point process model
Fitted to point pattern dataset 'seedlings_pooled'
Fitted by minimum contrast
    Summary statistic: inhomogeneous K-function
Minimum contrast fit (object of class "minconfit")
Model: Thomas process
Fitted by matching theoretical K function to seedlings_pooled

Internal parameters fitted by minimum contrast ($par):
       kappa       sigma2 
3.341176e-06 2.667702e+03 

Fitted cluster parameters:
       kappa        scale 
3.341176e-06 5.164981e+01 
Mean cluster size:  [pixel image]

Converged successfully after 143 function evaluations

Starting values of parameters:
       kappa       sigma2 
 0.001863476 20.059649144 
Domain of integration: [ 0 , 342.5 ]
Exponents: p= 2, q= 0.25

----------- TREND  -----
Point process model
Fitted to data: X
Fitting method: maximum likelihood (Berman-Turner approximation)
Model was fitted using glm()
Algorithm converged
Call:
ppm.ppp(Q = X, trend = trend, rename.intercept = FALSE, covariates = covariates, 
    covfunargs = covfunargs, use.gam = use.gam, forcefit = TRUE, 
    improve.type = ppm.improve.type, improve.args = ppm.improve.args, 
    nd = nd, eps = eps)
Edge correction: "border"
    [border correction distance r = 0 ]
--------------------------------------------------------------------------------
Quadrature scheme (Berman-Turner) = data + dummy + weights

Data pattern:
Planar point pattern:  3912 points
Average intensity 0.00186 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units

Dummy quadrature points:
     130 x 130 grid of dummy points, plus 4 corner points
     dummy spacing: 10.53800 x 11.78773 units

Original dummy parameters: =
Planar point pattern:  16904 points
Average intensity 0.00805 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units
Quadrature weights:
     (counting weights based on 130 x 130 array of rectangular tiles)
All weights:
    range: [3.45, 124]  total: 2100000
Weights on data points:
    range: [3.45, 62.1] total: 65500
Weights on dummy points:
    range: [3.45, 124]  total: 2030000
--------------------------------------------------------------------------------
FITTED :

Nonstationary Poisson process

---- Intensity: ----

Log intensity: ~distance_tree_im + aspect_im + rix_im + wind_im + mpb_grey_im + 
mpb_red_im + fire_high_im + fire_low_im + mpb_red_im * fire_high_im + 
mpb_red_im * fire_high_im + mpb_red_im * fire_low_im + mpb_red_im * fire_low_im
Model depends on external covariates 'distance_tree_im', 'aspect_im', 'rix_im', 
'wind_im', 'mpb_grey_im', 'mpb_red_im', 'fire_high_im' and 'fire_low_im'
Covariates provided:
    distance_tree_im: im
    aspect_im: im
    rix_im: im
    wind_im: im
    mpb_grey_im: im
    mpb_red_im: im
    fire_high_im: im
    fire_low_im: im

Fitted trend coefficients:
            (Intercept)        distance_tree_im               aspect_im 
             -4.8192084               0.2045116               0.1615850 
                 rix_im                 wind_im             mpb_grey_im 
             -3.6876434              -0.1750796              -1.3027875 
             mpb_red_im            fire_high_im             fire_low_im 
              0.5715231               0.8274246               0.2216878 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
             -0.5941272              -0.8373945 

                          Estimate       S.E.    CI95.lo    CI95.hi Ztest
(Intercept)             -4.8192084 0.22568815 -5.2615491 -4.3768678   ***
distance_tree_im         0.2045116 0.00133018  0.2019045  0.2071187   ***
aspect_im                0.1615850 0.02309085  0.1163278  0.2068423   ***
rix_im                  -3.6876434 0.30308771 -4.2816844 -3.0936024   ***
wind_im                 -0.1750796 0.02753363 -0.2290445 -0.1211147   ***
mpb_grey_im             -1.3027875 0.18139236 -1.6583100 -0.9472650   ***
mpb_red_im               0.5715231 0.05747955  0.4588652  0.6841809   ***
fire_high_im             0.8274246 0.06295795  0.7040293  0.9508199   ***
fire_low_im              0.2216878 0.04000529  0.1432789  0.3000968   ***
mpb_red_im:fire_high_im -0.5941272 0.10887056 -0.8075095 -0.3807448   ***
mpb_red_im:fire_low_im  -0.8373945 0.09673608 -1.0269938 -0.6477953   ***
                              Zval
(Intercept)             -21.353396
distance_tree_im        153.747321
aspect_im                 6.997794
rix_im                  -12.166919
wind_im                  -6.358755
mpb_grey_im              -7.182152
mpb_red_im                9.943068
fire_high_im             13.142497
fire_low_im               5.541463
mpb_red_im:fire_high_im  -5.457189
mpb_red_im:fire_low_im   -8.656486

----------- gory details -----

Fitted regular parameters (theta):
            (Intercept)        distance_tree_im               aspect_im 
             -4.8192084               0.2045116               0.1615850 
                 rix_im                 wind_im             mpb_grey_im 
             -3.6876434              -0.1750796              -1.3027875 
             mpb_red_im            fire_high_im             fire_low_im 
              0.5715231               0.8274246               0.2216878 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
             -0.5941272              -0.8373945 

Fitted exp(theta):
            (Intercept)        distance_tree_im               aspect_im 
            0.008073175             1.226925650             1.175372409 
                 rix_im                 wind_im             mpb_grey_im 
            0.025030920             0.839390190             0.271773162 
             mpb_red_im            fire_high_im             fire_low_im 
            1.770962342             2.287420157             1.248181682 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
            0.552044197             0.432836794 

----------- CLUSTER  -----------
Model: Thomas process

Fitted cluster parameters:
       kappa        scale 
3.341176e-06 5.164981e+01 
Mean cluster size:  [pixel image]

Final standard error and CI
(allowing for correlation of cluster process):
                          Estimate       S.E.      CI95.lo    CI95.hi Ztest
(Intercept)             -4.8192084 6.08143088 -16.73859394 7.10017708      
distance_tree_im         0.2045116 0.05931047   0.08826519 0.32075794   ***
aspect_im                0.1615850 0.48712855  -0.79316936 1.11633945      
rix_im                  -3.6876434 6.87125201 -17.15504989 9.77976302      
wind_im                 -0.1750796 1.08162400  -2.29502370 1.94486447      
mpb_grey_im             -1.3027875 0.68551527  -2.64637276 0.04079771      
mpb_red_im               0.5715231 1.39323083  -2.15915915 3.30220534      
fire_high_im             0.8274246 0.74801333  -0.63865458 2.29350381      
fire_low_im              0.2216878 0.39175910  -0.54614588 0.98952156      
mpb_red_im:fire_high_im -0.5941272 0.90397135  -2.36587846 1.17762412      
mpb_red_im:fire_low_im  -0.8373945 1.05329692  -2.90181856 1.22702948      
                              Zval
(Intercept)             -0.7924465
distance_tree_im         3.4481532
aspect_im                0.3317092
rix_im                  -0.5366771
wind_im                 -0.1618674
mpb_grey_im             -1.9004500
mpb_red_im               0.4102142
fire_high_im             1.1061629
fire_low_im              0.5658780
mpb_red_im:fire_high_im -0.6572411
mpb_red_im:fire_low_im  -0.7950223

----------- cluster strength indices ----------
Mean sibling probability 0.8992746
Count overdispersion index (on original window): 1154.045
Cluster strength: 8.927985

Spatial persistence index (over window): 0

Bound on distance from Poisson process (over window): 1
     = min (1, 6855.946, 106588100, 104991400, 10242.69)

Inhomogeneous cluster point process model
Fitted to point pattern dataset 'pinus_con_pooled'
Fitted by minimum contrast
    Summary statistic: inhomogeneous K-function
Minimum contrast fit (object of class "minconfit")
Model: Thomas process
Fitted by matching theoretical K function to pinus_con_pooled

Internal parameters fitted by minimum contrast ($par):
       kappa       sigma2 
2.831666e-06 2.787664e+03 

Fitted cluster parameters:
       kappa        scale 
2.831666e-06 5.279833e+01 
Mean cluster size:  [pixel image]

Converged successfully after 247 function evaluations

Starting values of parameters:
       kappa       sigma2 
 0.001370455 27.330821189 
Domain of integration: [ 0 , 342.5 ]
Exponents: p= 2, q= 0.25

----------- TREND  -----
Point process model
Fitted to data: X
Fitting method: maximum likelihood (Berman-Turner approximation)
Model was fitted using glm()
Algorithm converged
Call:
ppm.ppp(Q = X, trend = trend, rename.intercept = FALSE, covariates = covariates, 
    covfunargs = covfunargs, use.gam = use.gam, forcefit = TRUE, 
    improve.type = ppm.improve.type, improve.args = ppm.improve.args, 
    nd = nd, eps = eps)
Edge correction: "border"
    [border correction distance r = 0 ]
--------------------------------------------------------------------------------
Quadrature scheme (Berman-Turner) = data + dummy + weights

Data pattern:
Planar point pattern:  2877 points
Average intensity 0.00137 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units

Dummy quadrature points:
     110 x 110 grid of dummy points, plus 4 corner points
     dummy spacing: 12.45400 x 13.93095 units

Original dummy parameters: =
Planar point pattern:  12104 points
Average intensity 0.00577 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units
Quadrature weights:
     (counting weights based on 110 x 110 array of rectangular tiles)
All weights:
    range: [4.34, 173]  total: 2100000
Weights on data points:
    range: [4.34, 86.7] total: 67600
Weights on dummy points:
    range: [4.34, 173]  total: 2030000
--------------------------------------------------------------------------------
FITTED :

Nonstationary Poisson process

---- Intensity: ----

Log intensity: ~distance_tree_im + aspect_im + rix_im + wind_im + mpb_grey_im + 
mpb_red_im + fire_high_im + fire_low_im + mpb_red_im * fire_high_im + 
mpb_red_im * fire_high_im + mpb_red_im * fire_low_im + mpb_red_im * fire_low_im
Model depends on external covariates 'distance_tree_im', 'aspect_im', 'rix_im', 
'wind_im', 'mpb_grey_im', 'mpb_red_im', 'fire_high_im' and 'fire_low_im'
Covariates provided:
    distance_tree_im: im
    aspect_im: im
    rix_im: im
    wind_im: im
    mpb_grey_im: im
    mpb_red_im: im
    fire_high_im: im
    fire_low_im: im

Fitted trend coefficients:
            (Intercept)        distance_tree_im               aspect_im 
             -5.0307548               0.2042264               0.1736705 
                 rix_im                 wind_im             mpb_grey_im 
             -3.2714499              -0.2506922              -1.4199048 
             mpb_red_im            fire_high_im             fire_low_im 
              0.5195014               0.8277711               0.2408036 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
             -0.2462394              -0.8783495 

                          Estimate        S.E.    CI95.lo      CI95.hi Ztest
(Intercept)             -5.0307548 0.264949235 -5.5500458 -4.511463874   ***
distance_tree_im         0.2042264 0.001561856  0.2011652  0.207287542   ***
aspect_im                0.1736705 0.027054869  0.1206439  0.226697065   ***
rix_im                  -3.2714499 0.357895233 -3.9729117 -2.569988138   ***
wind_im                 -0.2506922 0.032182881 -0.3137695 -0.187614934   ***
mpb_grey_im             -1.4199048 0.225582645 -1.8620387 -0.977770950   ***
mpb_red_im               0.5195014 0.068588740  0.3850699  0.653932870   ***
fire_high_im             0.8277711 0.073421775  0.6838671  0.971675173   ***
fire_low_im              0.2408036 0.046740787  0.1491934  0.332413900   ***
mpb_red_im:fire_high_im -0.2462394 0.124825119 -0.4908922 -0.001586702     *
mpb_red_im:fire_low_im  -0.8783495 0.113283051 -1.1003802 -0.656318830   ***
                              Zval
(Intercept)             -18.987618
distance_tree_im        130.758777
aspect_im                 6.419196
rix_im                   -9.140803
wind_im                  -7.789614
mpb_grey_im              -6.294389
mpb_red_im                7.574150
fire_high_im             11.274191
fire_low_im               5.151895
mpb_red_im:fire_high_im  -1.972675
mpb_red_im:fire_low_im   -7.753583

----------- gory details -----

Fitted regular parameters (theta):
            (Intercept)        distance_tree_im               aspect_im 
             -5.0307548               0.2042264               0.1736705 
                 rix_im                 wind_im             mpb_grey_im 
             -3.2714499              -0.2506922              -1.4199048 
             mpb_red_im            fire_high_im             fire_low_im 
              0.5195014               0.8277711               0.2408036 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
             -0.2462394              -0.8783495 

Fitted exp(theta):
            (Intercept)        distance_tree_im               aspect_im 
            0.006533877             1.226575770             1.189663503 
                 rix_im                 wind_im             mpb_grey_im 
            0.037951361             0.778261866             0.241737027 
             mpb_red_im            fire_high_im             fire_low_im 
            1.681189214             2.288212943             1.272271188 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
            0.781735024             0.415468064 

----------- CLUSTER  -----------
Model: Thomas process

Fitted cluster parameters:
       kappa        scale 
2.831666e-06 5.279833e+01 
Mean cluster size:  [pixel image]

Final standard error and CI
(allowing for correlation of cluster process):
                          Estimate       S.E.      CI95.lo     CI95.hi Ztest
(Intercept)             -5.0307548 6.49710881 -17.76485409  7.70334443      
distance_tree_im         0.2042264 0.06392245   0.07894066  0.32951206    **
aspect_im                0.1736705 0.52223681  -0.84989484  1.19723583      
rix_im                  -3.2714499 7.38053142 -17.73702567 11.19412586      
wind_im                 -0.2506922 1.16363524  -2.53137539  2.02999095      
mpb_grey_im             -1.4199048 0.73026171  -2.85119147  0.01138185      
mpb_red_im               0.5195014 1.48412549  -2.38933111  3.42833393      
fire_high_im             0.8277711 0.79806852  -0.73641442  2.39195669      
fire_low_im              0.2408036 0.41833221  -0.57911242  1.06071970      
mpb_red_im:fire_high_im -0.2462394 0.98452048  -2.17586413  1.68338525      
mpb_red_im:fire_low_im  -0.8783495 1.15628304  -3.14462265  1.38792359      
                              Zval
(Intercept)             -0.7743067
distance_tree_im         3.1949082
aspect_im                0.3325512
rix_im                  -0.4432540
wind_im                 -0.2154388
mpb_grey_im             -1.9443780
mpb_red_im               0.3500387
fire_high_im             1.0372181
fire_low_im              0.5756278
mpb_red_im:fire_high_im -0.2501110
mpb_red_im:fire_low_im  -0.7596319

----------- cluster strength indices ----------
Mean sibling probability 0.9097562
Count overdispersion index (on original window): 925.7861
Cluster strength: 10.0811

Spatial persistence index (over window): 0

Bound on distance from Poisson process (over window): 1
     = min (1, 4918.627, 61990170, 61016470, 7808.503)

Inhomogeneous cluster point process model
Fitted to point pattern dataset 'larix_spp_pooled'
Fitted by minimum contrast
    Summary statistic: inhomogeneous K-function
Minimum contrast fit (object of class "minconfit")
Model: Thomas process
Fitted by matching theoretical K function to larix_spp_pooled

Internal parameters fitted by minimum contrast ($par):
       kappa       sigma2 
3.395855e-06 2.928236e+03 

Fitted cluster parameters:
       kappa        scale 
3.395855e-06 5.411318e+01 
Mean cluster size:  [pixel image]

Converged successfully after 109 function evaluations

Starting values of parameters:
       kappa       sigma2 
1.519552e-04 2.726308e+02 
Domain of integration: [ 0 , 342.5 ]
Exponents: p= 2, q= 0.25

----------- TREND  -----
Point process model
Fitted to data: X
Fitting method: maximum likelihood (Berman-Turner approximation)
Model was fitted using glm()
Algorithm converged
Call:
ppm.ppp(Q = X, trend = trend, rename.intercept = FALSE, covariates = covariates, 
    covfunargs = covfunargs, use.gam = use.gam, forcefit = TRUE, 
    improve.type = ppm.improve.type, improve.args = ppm.improve.args, 
    nd = nd, eps = eps)
Edge correction: "border"
    [border correction distance r = 0 ]
--------------------------------------------------------------------------------
Quadrature scheme (Berman-Turner) = data + dummy + weights

Data pattern:
Planar point pattern:  319 points
Average intensity 0.000152 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units

Dummy quadrature points:
     40 x 40 grid of dummy points, plus 4 corner points
     dummy spacing: 34.24850 x 38.31012 units

Original dummy parameters: =
Planar point pattern:  1604 points
Average intensity 0.000764 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units
Quadrature weights:
     (counting weights based on 40 x 40 array of rectangular tiles)
All weights:
    range: [54.7, 1310] total: 2100000
Weights on data points:
    range: [54.7, 656]  total: 71300
Weights on dummy points:
    range: [54.7, 1310] total: 2030000
--------------------------------------------------------------------------------
FITTED :

Nonstationary Poisson process

---- Intensity: ----

Log intensity: ~distance_tree_im + aspect_im + rix_im + wind_im + mpb_grey_im + 
mpb_red_im + fire_high_im + fire_low_im + mpb_red_im * fire_high_im + 
mpb_red_im * fire_high_im + mpb_red_im * fire_low_im + mpb_red_im * fire_low_im
Model depends on external covariates 'distance_tree_im', 'aspect_im', 'rix_im', 
'wind_im', 'mpb_grey_im', 'mpb_red_im', 'fire_high_im' and 'fire_low_im'
Covariates provided:
    distance_tree_im: im
    aspect_im: im
    rix_im: im
    wind_im: im
    mpb_grey_im: im
    mpb_red_im: im
    fire_high_im: im
    fire_low_im: im

Fitted trend coefficients:
            (Intercept)        distance_tree_im               aspect_im 
             -7.9322068               0.1942318               0.2598378 
                 rix_im                 wind_im             mpb_grey_im 
             -3.5221913              -0.1446052              -0.8195224 
             mpb_red_im            fire_high_im             fire_low_im 
              0.4586193               1.1433927               0.1470851 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
             -1.2506713              -0.4305350 

                          Estimate        S.E.     CI95.lo     CI95.hi Ztest
(Intercept)             -7.9322068 0.804981128 -9.50994084 -6.35447280   ***
distance_tree_im         0.1942318 0.004530169  0.18535283  0.20311076   ***
aspect_im                0.2598378 0.081895001  0.09932652  0.42034902    **
rix_im                  -3.5221913 1.128199898 -5.73342250 -1.31096017    **
wind_im                 -0.1446052 0.096396272 -0.33353840  0.04432805      
mpb_grey_im             -0.8195224 0.507951345 -1.81508879  0.17604390      
mpb_red_im               0.4586193 0.195507042  0.07543256  0.84180608     *
fire_high_im             1.1433927 0.213053220  0.72581611  1.56096938   ***
fire_low_im              0.1470851 0.146827267 -0.14069105  0.43486126      
mpb_red_im:fire_high_im -1.2506713 0.369925203 -1.97571140 -0.52563125   ***
mpb_red_im:fire_low_im  -0.4305350 0.351792945 -1.12003649  0.25896651      
                             Zval
(Intercept)             -9.853904
distance_tree_im        42.875178
aspect_im                3.172816
rix_im                  -3.121957
wind_im                 -1.500112
mpb_grey_im             -1.613388
mpb_red_im               2.345794
fire_high_im             5.366700
fire_low_im              1.001756
mpb_red_im:fire_high_im -3.380876
mpb_red_im:fire_low_im  -1.223831

----------- gory details -----

Fitted regular parameters (theta):
            (Intercept)        distance_tree_im               aspect_im 
             -7.9322068               0.1942318               0.2598378 
                 rix_im                 wind_im             mpb_grey_im 
             -3.5221913              -0.1446052              -0.8195224 
             mpb_red_im            fire_high_im             fire_low_im 
              0.4586193               1.1433927               0.1470851 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
             -1.2506713              -0.4305350 

Fitted exp(theta):
            (Intercept)        distance_tree_im               aspect_im 
           0.0003589933            1.2143777384            1.2967197021 
                 rix_im                 wind_im             mpb_grey_im 
           0.0295346438            0.8653638923            0.4406420349 
             mpb_red_im            fire_high_im             fire_low_im 
           1.5818883945            3.1373947065            1.1584525460 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
           0.2863125245            0.6501611701 

----------- CLUSTER  -----------
Model: Thomas process

Fitted cluster parameters:
       kappa        scale 
3.395855e-06 5.411318e+01 
Mean cluster size:  [pixel image]

Final standard error and CI
(allowing for correlation of cluster process):
                          Estimate      S.E.      CI95.lo   CI95.hi Ztest
(Intercept)             -7.9322068 5.9657464 -19.62485496 3.7604413      
distance_tree_im         0.1942318 0.0569044   0.08270122 0.3057624   ***
aspect_im                0.2598378 0.5052683  -0.73046991 1.2501454      
rix_im                  -3.5221913 6.5139199 -16.28923971 9.2448570      
wind_im                 -0.1446052 1.0850258  -2.27121670 1.9820064      
mpb_grey_im             -0.8195224 0.8311922  -2.44862913 0.8095842      
mpb_red_im               0.4586193 1.5331636  -2.54632610 3.4635647      
fire_high_im             1.1433927 0.8329625  -0.48918366 2.7759692      
fire_low_im              0.1470851 0.4563484  -0.74734126 1.0415115      
mpb_red_im:fire_high_im -1.2506713 1.1480098  -3.50072926 0.9993866      
mpb_red_im:fire_low_im  -0.4305350 1.2727021  -2.92498526 2.0639153      
                              Zval
(Intercept)             -1.3296252
distance_tree_im         3.4133000
aspect_im                0.5142570
rix_im                  -0.5407176
wind_im                 -0.1332735
mpb_grey_im             -0.9859603
mpb_red_im               0.2991327
fire_high_im             1.3726822
fire_low_im              0.3223088
mpb_red_im:fire_high_im -1.0894256
mpb_red_im:fire_low_im  -0.3382842

----------- cluster strength indices ----------
Mean sibling probability 0.8889218
Count overdispersion index (on original window): 62.14689
Cluster strength: 8.00267

Spatial persistence index (over window): 0

Bound on distance from Poisson process (over window): 1
     = min (1, 473.4086, 456240.4, 448687.3, 669.6126)

Inhomogeneous cluster point process model
Fitted to point pattern dataset 'picea_eng_pooled'
Fitted by minimum contrast
    Summary statistic: inhomogeneous K-function
Minimum contrast fit (object of class "minconfit")
Model: Thomas process
Fitted by matching theoretical K function to picea_eng_pooled

Internal parameters fitted by minimum contrast ($par):
       kappa       sigma2 
3.439472e-06 2.292981e+03 

Fitted cluster parameters:
       kappa        scale 
3.439472e-06 4.788508e+01 
Mean cluster size:  [pixel image]

Converged successfully after 105 function evaluations

Starting values of parameters:
       kappa       sigma2 
1.381411e-04 2.915626e+02 
Domain of integration: [ 0 , 342.5 ]
Exponents: p= 2, q= 0.25

----------- TREND  -----
Point process model
Fitted to data: X
Fitting method: maximum likelihood (Berman-Turner approximation)
Model was fitted using glm()
Algorithm converged
Call:
ppm.ppp(Q = X, trend = trend, rename.intercept = FALSE, covariates = covariates, 
    covfunargs = covfunargs, use.gam = use.gam, forcefit = TRUE, 
    improve.type = ppm.improve.type, improve.args = ppm.improve.args, 
    nd = nd, eps = eps)
Edge correction: "border"
    [border correction distance r = 0 ]
--------------------------------------------------------------------------------
Quadrature scheme (Berman-Turner) = data + dummy + weights

Data pattern:
Planar point pattern:  290 points
Average intensity 0.000138 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units

Dummy quadrature points:
     40 x 40 grid of dummy points, plus 4 corner points
     dummy spacing: 34.24850 x 38.31012 units

Original dummy parameters: =
Planar point pattern:  1604 points
Average intensity 0.000764 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units
Quadrature weights:
     (counting weights based on 40 x 40 array of rectangular tiles)
All weights:
    range: [69.1, 1310] total: 2100000
Weights on data points:
    range: [69.1, 656]  total: 68100
Weights on dummy points:
    range: [69.1, 1310] total: 2030000
--------------------------------------------------------------------------------
FITTED :

Nonstationary Poisson process

---- Intensity: ----

Log intensity: ~distance_tree_im + aspect_im + rix_im + wind_im + mpb_grey_im + 
mpb_red_im + fire_high_im + fire_low_im + mpb_red_im * fire_high_im + 
mpb_red_im * fire_high_im + mpb_red_im * fire_low_im + mpb_red_im * fire_low_im
Model depends on external covariates 'distance_tree_im', 'aspect_im', 'rix_im', 
'wind_im', 'mpb_grey_im', 'mpb_red_im', 'fire_high_im' and 'fire_low_im'
Covariates provided:
    distance_tree_im: im
    aspect_im: im
    rix_im: im
    wind_im: im
    mpb_grey_im: im
    mpb_red_im: im
    fire_high_im: im
    fire_low_im: im

Fitted trend coefficients:
            (Intercept)        distance_tree_im               aspect_im 
             -7.9412342               0.1877239               0.1190340 
                 rix_im                 wind_im             mpb_grey_im 
             -2.4884514              -0.1629429              -0.7632578 
             mpb_red_im            fire_high_im             fire_low_im 
              0.6999958               0.7672865               0.1842831 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
             -0.3833109              -0.7228712 

                          Estimate       S.E.     CI95.lo     CI95.hi Ztest
(Intercept)             -7.9412342 0.80287693 -9.51484402 -6.36762429   ***
distance_tree_im         0.1877239 0.00499824  0.17792752  0.19752026   ***
aspect_im                0.1190340 0.08297591 -0.04359581  0.28166379      
rix_im                  -2.4884514 1.09515135 -4.63490863 -0.34199422     *
wind_im                 -0.1629429 0.10357872 -0.36595346  0.04006768      
mpb_grey_im             -0.7632578 0.50799052 -1.75890090  0.23238534      
mpb_red_im               0.6999958 0.20204818  0.30398865  1.09600298   ***
fire_high_im             0.7672865 0.24684776  0.28347380  1.25109926    **
fire_low_im              0.1842831 0.15138525 -0.11242651  0.48099276      
mpb_red_im:fire_high_im -0.3833109 0.41475245 -1.19621077  0.42958897      
mpb_red_im:fire_low_im  -0.7228712 0.32224291 -1.35445573 -0.09128673     *
                             Zval
(Intercept)             -9.890973
distance_tree_im        37.558001
aspect_im                1.434561
rix_im                  -2.272244
wind_im                 -1.573131
mpb_grey_im             -1.502504
mpb_red_im               3.464499
fire_high_im             3.108339
fire_low_im              1.217312
mpb_red_im:fire_high_im -0.924192
mpb_red_im:fire_low_im  -2.243249

----------- gory details -----

Fitted regular parameters (theta):
            (Intercept)        distance_tree_im               aspect_im 
             -7.9412342               0.1877239               0.1190340 
                 rix_im                 wind_im             mpb_grey_im 
             -2.4884514              -0.1629429              -0.7632578 
             mpb_red_im            fire_high_im             fire_low_im 
              0.6999958               0.7672865               0.1842831 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
             -0.3833109              -0.7228712 

Fitted exp(theta):
            (Intercept)        distance_tree_im               aspect_im 
           0.0003557671            1.2065003478            1.1264082060 
                 rix_im                 wind_im             mpb_grey_im 
           0.0830384584            0.8496397074            0.4661453512 
             mpb_red_im            fire_high_im             fire_low_im 
           2.0137442764            2.1539137295            1.2023561948 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
           0.6816009559            0.4853566822 

----------- CLUSTER  -----------
Model: Thomas process

Fitted cluster parameters:
       kappa        scale 
3.439472e-06 4.788508e+01 
Mean cluster size:  [pixel image]

Final standard error and CI
(allowing for correlation of cluster process):
                          Estimate       S.E.      CI95.lo    CI95.hi Ztest
(Intercept)             -7.9412342 6.29696359 -20.28305600  4.4005877      
distance_tree_im         0.1877239 0.06225315   0.06570997  0.3097378    **
aspect_im                0.1190340 0.51438495  -0.88914198  1.1272100      
rix_im                  -2.4884514 7.62279685 -17.42885871 12.4519559      
wind_im                 -0.1629429 1.05226077  -2.22533610  1.8994503      
mpb_grey_im             -0.7632578 0.83931821  -2.40829124  0.8817757      
mpb_red_im               0.6999958 1.34762409  -1.94129887  3.3412905      
fire_high_im             0.7672865 0.88007481  -0.95762840  2.4922015      
fire_low_im              0.1842831 0.44985448  -0.69741545  1.0659817      
mpb_red_im:fire_high_im -0.3833109 1.39421936  -3.11593064  2.3493088      
mpb_red_im:fire_low_im  -0.7228712 1.06038773  -2.80119300  1.3554505      
                              Zval
(Intercept)             -1.2611212
distance_tree_im         3.0154924
aspect_im                0.2314103
rix_im                  -0.3264486
wind_im                 -0.1548503
mpb_grey_im             -0.9093783
mpb_red_im               0.5194296
fire_high_im             0.8718424
fire_low_im              0.4096505
mpb_red_im:fire_high_im -0.2749287
mpb_red_im:fire_low_im  -0.6817046

----------- cluster strength indices ----------
Mean sibling probability 0.90983
Count overdispersion index (on original window): 62.0095
Cluster strength: 10.09016

Spatial persistence index (over window): 0

Bound on distance from Poisson process (over window): 1
     = min (1, 464.6307, 552044.8, 545045.1, 737.95)

Inhomogeneous cluster point process model
Fitted to point pattern dataset 'pseudo_m_pooled'
Fitted by minimum contrast
    Summary statistic: inhomogeneous K-function
Minimum contrast fit (object of class "minconfit")
Model: Thomas process
Fitted by matching theoretical K function to pseudo_m_pooled

Internal parameters fitted by minimum contrast ($par):
       kappa       sigma2 
2.801052e-06 2.727889e+03 

Fitted cluster parameters:
       kappa        scale 
2.801052e-06 5.222920e+01 
Mean cluster size:  [pixel image]

Converged successfully after 101 function evaluations

Starting values of parameters:
       kappa       sigma2 
9.955688e-05 4.147339e+02 
Domain of integration: [ 0 , 342.5 ]
Exponents: p= 2, q= 0.25

----------- TREND  -----
Point process model
Fitted to data: X
Fitting method: maximum likelihood (Berman-Turner approximation)
Model was fitted using glm()
Algorithm converged
Call:
ppm.ppp(Q = X, trend = trend, rename.intercept = FALSE, covariates = covariates, 
    covfunargs = covfunargs, use.gam = use.gam, forcefit = TRUE, 
    improve.type = ppm.improve.type, improve.args = ppm.improve.args, 
    nd = nd, eps = eps)
Edge correction: "border"
    [border correction distance r = 0 ]
--------------------------------------------------------------------------------
Quadrature scheme (Berman-Turner) = data + dummy + weights

Data pattern:
Planar point pattern:  209 points
Average intensity 9.96e-05 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units

Dummy quadrature points:
     32 x 32 grid of dummy points, plus 4 corner points
     dummy spacing: 42.81062 x 47.88764 units

Original dummy parameters: =
Planar point pattern:  1028 points
Average intensity 0.00049 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units
Quadrature weights:
     (counting weights based on 32 x 32 array of rectangular tiles)
All weights:
    range: [121, 2050]  total: 2100000
Weights on data points:
    range: [121, 1030]  total: 79200
Weights on dummy points:
    range: [121, 2050]  total: 2020000
--------------------------------------------------------------------------------
FITTED :

Nonstationary Poisson process

---- Intensity: ----

Log intensity: ~distance_tree_im + aspect_im + rix_im + wind_im + mpb_grey_im + 
mpb_red_im + fire_high_im + fire_low_im + mpb_red_im * fire_high_im + 
mpb_red_im * fire_high_im + mpb_red_im * fire_low_im + mpb_red_im * fire_low_im
Model depends on external covariates 'distance_tree_im', 'aspect_im', 'rix_im', 
'wind_im', 'mpb_grey_im', 'mpb_red_im', 'fire_high_im' and 'fire_low_im'
Covariates provided:
    distance_tree_im: im
    aspect_im: im
    rix_im: im
    wind_im: im
    mpb_grey_im: im
    mpb_red_im: im
    fire_high_im: im
    fire_low_im: im

Fitted trend coefficients:
            (Intercept)        distance_tree_im               aspect_im 
            -7.06235920              0.18110851              0.12082526 
                 rix_im                 wind_im             mpb_grey_im 
            -4.45487060             -0.24458948             -0.76610769 
             mpb_red_im            fire_high_im             fire_low_im 
             0.52317264              0.57434612              0.61935407 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
             0.07460503             -1.87672945 

                           Estimate        S.E.     CI95.lo       CI95.hi Ztest
(Intercept)             -7.06235920 1.015279479 -9.05227042 -5.0724479889   ***
distance_tree_im         0.18110851 0.006262802  0.16883365  0.1933833788   ***
aspect_im                0.12082526 0.097373412 -0.07002312  0.3116736406      
rix_im                  -4.45487060 1.320502800 -7.04300853 -1.8667326649   ***
wind_im                 -0.24458948 0.124594240 -0.48878971 -0.0003892579     *
mpb_grey_im             -0.76610769 0.588196524 -1.91895169  0.3867363122      
mpb_red_im               0.52317264 0.256908297  0.01964163  1.0267036504     *
fire_high_im             0.57434612 0.313634010 -0.04036524  1.1890574849      
fire_low_im              0.61935407 0.174611343  0.27712213  0.9615860115   ***
mpb_red_im:fire_high_im  0.07460503 0.548341739 -1.00012503  1.1493350953      
mpb_red_im:fire_low_im  -1.87672945 0.446773064 -2.75238857 -1.0010703388   ***
                              Zval
(Intercept)             -6.9560740
distance_tree_im        28.9181301
aspect_im                1.2408445
rix_im                  -3.3736169
wind_im                 -1.9630882
mpb_grey_im             -1.3024689
mpb_red_im               2.0364178
fire_high_im             1.8312622
fire_low_im              3.5470437
mpb_red_im:fire_high_im  0.1360557
mpb_red_im:fire_low_im  -4.2006325

----------- gory details -----

Fitted regular parameters (theta):
            (Intercept)        distance_tree_im               aspect_im 
            -7.06235920              0.18110851              0.12082526 
                 rix_im                 wind_im             mpb_grey_im 
            -4.45487060             -0.24458948             -0.76610769 
             mpb_red_im            fire_high_im             fire_low_im 
             0.52317264              0.57434612              0.61935407 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
             0.07460503             -1.87672945 

Fitted exp(theta):
            (Intercept)        distance_tree_im               aspect_im 
           0.0008567545            1.1985452302            1.1284277145 
                 rix_im                 wind_im             mpb_grey_im 
           0.0116218237            0.7830259188            0.4648187703 
             mpb_red_im            fire_high_im             fire_low_im 
           1.6873725929            1.7759688784            1.8577276892 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
           1.0774585084            0.1530899756 

----------- CLUSTER  -----------
Model: Thomas process

Fitted cluster parameters:
       kappa        scale 
2.801052e-06 5.222920e+01 
Mean cluster size:  [pixel image]

Final standard error and CI
(allowing for correlation of cluster process):
                           Estimate       S.E.      CI95.lo   CI95.hi Ztest
(Intercept)             -7.06235920 5.94045640 -18.70543981 4.5807214      
distance_tree_im         0.18110851 0.07030606   0.04331116 0.3189059    **
aspect_im                0.12082526 0.57442055  -1.00501833 1.2466688      
rix_im                  -4.45487060 6.88220386 -17.94374229 9.0340011      
wind_im                 -0.24458948 1.15120426  -2.50090837 2.0117294      
mpb_grey_im             -0.76610769 1.06882670  -2.86096952 1.3287541      
mpb_red_im               0.52317264 1.73729038  -2.88185394 3.9281992      
fire_high_im             0.57434612 0.88036429  -1.15113619 2.2998284      
fire_low_im              0.61935407 0.47750888  -0.31654615 1.5552543      
mpb_red_im:fire_high_im  0.07460503 1.89637662  -3.64222483 3.7914349      
mpb_red_im:fire_low_im  -1.87672945 1.16770188  -4.16538309 0.4119242      
                               Zval
(Intercept)             -1.18885801
distance_tree_im         2.57600139
aspect_im                0.21034286
rix_im                  -0.64730291
wind_im                 -0.21246402
mpb_grey_im             -0.71677447
mpb_red_im               0.30114289
fire_high_im             0.65239597
fire_low_im              1.29705245
mpb_red_im:fire_high_im  0.03934083
mpb_red_im:fire_low_im  -1.60719913

----------- cluster strength indices ----------
Mean sibling probability 0.9123928
Count overdispersion index (on original window): 38.68251
Cluster strength: 10.41459

Spatial persistence index (over window): 0

Bound on distance from Poisson process (over window): 1
     = min (1, 305.3009, 246645.2, 242860.4, 492.6281)

Inhomogeneous cluster point process model
Fitted to point pattern dataset 'pinus_pond_pooled'
Fitted by minimum contrast
    Summary statistic: inhomogeneous K-function
Minimum contrast fit (object of class "minconfit")
Model: Thomas process
Fitted by matching theoretical K function to pinus_pond_pooled

Internal parameters fitted by minimum contrast ($par):
       kappa       sigma2 
2.139194e-06 1.488569e+03 

Fitted cluster parameters:
       kappa        scale 
2.139194e-06 3.858197e+01 
Mean cluster size:  [pixel image]

Converged successfully after 89 function evaluations

Starting values of parameters:
       kappa       sigma2 
4.048964e-05 8.449762e+02 
Domain of integration: [ 0 , 342.5 ]
Exponents: p= 2, q= 0.25

----------- TREND  -----
Point process model
Fitted to data: X
Fitting method: maximum likelihood (Berman-Turner approximation)
Model was fitted using glm()
Algorithm converged
Call:
ppm.ppp(Q = X, trend = trend, rename.intercept = FALSE, covariates = covariates, 
    covfunargs = covfunargs, use.gam = use.gam, forcefit = TRUE, 
    improve.type = ppm.improve.type, improve.args = ppm.improve.args, 
    nd = nd, eps = eps)
Edge correction: "border"
    [border correction distance r = 0 ]
--------------------------------------------------------------------------------
Quadrature scheme (Berman-Turner) = data + dummy + weights

Data pattern:
Planar point pattern:  85 points
Average intensity 4.05e-05 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units

Dummy quadrature points:
     32 x 32 grid of dummy points, plus 4 corner points
     dummy spacing: 42.81062 x 47.88764 units

Original dummy parameters: =
Planar point pattern:  1028 points
Average intensity 0.00049 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units
Quadrature weights:
     (counting weights based on 32 x 32 array of rectangular tiles)
All weights:
    range: [256, 2050]  total: 2100000
Weights on data points:
    range: [256, 1030]  total: 43300
Weights on dummy points:
    range: [256, 2050]  total: 2060000
--------------------------------------------------------------------------------
FITTED :

Nonstationary Poisson process

---- Intensity: ----

Log intensity: ~distance_tree_im + aspect_im + rix_im + wind_im + mpb_grey_im + 
mpb_red_im + fire_high_im + fire_low_im + mpb_red_im * fire_high_im + 
mpb_red_im * fire_high_im + mpb_red_im * fire_low_im + mpb_red_im * fire_low_im
Model depends on external covariates 'distance_tree_im', 'aspect_im', 'rix_im', 
'wind_im', 'mpb_grey_im', 'mpb_red_im', 'fire_high_im' and 'fire_low_im'
Covariates provided:
    distance_tree_im: im
    aspect_im: im
    rix_im: im
    wind_im: im
    mpb_grey_im: im
    mpb_red_im: im
    fire_high_im: im
    fire_low_im: im

Fitted trend coefficients:
            (Intercept)        distance_tree_im               aspect_im 
             -4.8178398               0.2138951               0.3159806 
                 rix_im                 wind_im             mpb_grey_im 
             -5.1307932              -0.8975168             -15.3688865 
             mpb_red_im            fire_high_im             fire_low_im 
              1.3150813               0.1299337              -0.4722468 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
            -17.1900061              -2.1497025 

                           Estimate         S.E.       CI95.lo      CI95.hi
(Intercept)              -4.8178398 1.839615e+00 -8.423418e+00   -1.2122616
distance_tree_im          0.2138951 1.024773e-02  1.938100e-01    0.2339803
aspect_im                 0.3159806 1.670707e-01 -1.147201e-02    0.6434332
rix_im                   -5.1307932 2.756542e+00 -1.053352e+01    0.2719292
wind_im                  -0.8975168 2.085505e-01 -1.306268e+00   -0.4887654
mpb_grey_im             -15.3688865 1.582411e+03 -3.116838e+03 3086.1005020
mpb_red_im                1.3150813 3.595368e-01  6.104020e-01    2.0197605
fire_high_im              0.1299337 4.547677e-01 -7.613946e-01    1.0212619
fire_low_im              -0.4722468 2.935897e-01 -1.047672e+00    0.1031784
mpb_red_im:fire_high_im -17.1900061 2.972640e+03 -5.843457e+03 5809.0769104
mpb_red_im:fire_low_im   -2.1497025 8.814769e-01 -3.877366e+00   -0.4220395
                        Ztest         Zval
(Intercept)                ** -2.618939860
distance_tree_im          *** 20.872448911
aspect_im                      1.891298417
rix_im                        -1.861315317
wind_im                   *** -4.303595207
mpb_grey_im                   -0.009712320
mpb_red_im                ***  3.657709395
fire_high_im                   0.285714450
fire_low_im                   -1.608526609
mpb_red_im:fire_high_im       -0.005782741
mpb_red_im:fire_low_im      * -2.438750725

----------- gory details -----

Fitted regular parameters (theta):
            (Intercept)        distance_tree_im               aspect_im 
             -4.8178398               0.2138951               0.3159806 
                 rix_im                 wind_im             mpb_grey_im 
             -5.1307932              -0.8975168             -15.3688865 
             mpb_red_im            fire_high_im             fire_low_im 
              1.3150813               0.1299337              -0.4722468 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
            -17.1900061              -2.1497025 

Fitted exp(theta):
            (Intercept)        distance_tree_im               aspect_im 
           8.084232e-03            1.238493e+00            1.371604e+00 
                 rix_im                 wind_im             mpb_grey_im 
           5.911869e-03            4.075805e-01            2.115326e-07 
             mpb_red_im            fire_high_im             fire_low_im 
           3.725054e+00            1.138753e+00            6.235996e-01 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
           3.423538e-08            1.165188e-01 

----------- CLUSTER  -----------
Model: Thomas process

Fitted cluster parameters:
       kappa        scale 
2.139194e-06 3.858197e+01 
Mean cluster size:  [pixel image]

Final standard error and CI
(allowing for correlation of cluster process):
                           Estimate         S.E.       CI95.lo      CI95.hi
(Intercept)              -4.8178398    6.8933812 -1.832862e+01    8.6929390
distance_tree_im          0.2138951    0.1252220 -3.153556e-02    0.4593258
aspect_im                 0.3159806    0.6641796 -9.857876e-01    1.6177488
rix_im                   -5.1307932    6.4573109 -1.778689e+01    7.5253035
wind_im                  -0.8975168    1.3196669 -3.484016e+00    1.6889829
mpb_grey_im             -15.3688865 1582.4118115 -3.116839e+03 3086.1012728
mpb_red_im                1.3150813    1.8252930 -2.262427e+00    4.8925898
fire_high_im              0.1299337    1.3428969 -2.502096e+00    2.7619633
fire_low_im              -0.4722468    1.0575910 -2.545087e+00    1.6005935
mpb_red_im:fire_high_im -17.1900061 2972.6410102 -5.843459e+03 5809.0793128
mpb_red_im:fire_low_im   -2.1497025    5.2985110 -1.253459e+01    8.2351881
                        Ztest         Zval
(Intercept)                   -0.698908087
distance_tree_im               1.708126827
aspect_im                      0.475745714
rix_im                        -0.794571198
wind_im                       -0.680108555
mpb_grey_im                   -0.009712318
mpb_red_im                     0.720476791
fire_high_im                   0.096756268
fire_low_im                   -0.446530634
mpb_red_im:fire_high_im       -0.005782739
mpb_red_im:fire_low_im        -0.405718239

----------- cluster strength indices ----------
Mean sibling probability 0.9615241
Count overdispersion index (on original window): 64.12825
Cluster strength: 24.99028

Spatial persistence index (over window): 0

Bound on distance from Poisson process (over window): 1
     = min (1, 163.0558, 167585.5, 166328.6, 407.5603)

Inhomogeneous cluster point process model
Fitted to point pattern dataset 'pinus_monti_pooled'
Fitted by minimum contrast
    Summary statistic: inhomogeneous K-function
Minimum contrast fit (object of class "minconfit")
Model: Thomas process
Fitted by matching theoretical K function to pinus_monti_pooled

Internal parameters fitted by minimum contrast ($par):
       kappa       sigma2 
2.190736e-05 1.841946e+02 

Fitted cluster parameters:
       kappa        scale 
2.190736e-05 1.357183e+01 
Mean cluster size:  [pixel image]

Converged successfully after 71 function evaluations

Starting values of parameters:
       kappa       sigma2 
4.620583e-05 6.149680e+01 
Domain of integration: [ 0 , 342.5 ]
Exponents: p= 2, q= 0.25

----------- TREND  -----
Point process model
Fitted to data: X
Fitting method: maximum likelihood (Berman-Turner approximation)
Model was fitted using glm()
Algorithm converged
Call:
ppm.ppp(Q = X, trend = trend, rename.intercept = FALSE, covariates = covariates, 
    covfunargs = covfunargs, use.gam = use.gam, forcefit = TRUE, 
    improve.type = ppm.improve.type, improve.args = ppm.improve.args, 
    nd = nd, eps = eps)
Edge correction: "border"
    [border correction distance r = 0 ]
--------------------------------------------------------------------------------
Quadrature scheme (Berman-Turner) = data + dummy + weights

Data pattern:
Planar point pattern:  97 points
Average intensity 4.62e-05 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units

Dummy quadrature points:
     32 x 32 grid of dummy points, plus 4 corner points
     dummy spacing: 42.81062 x 47.88764 units

Original dummy parameters: =
Planar point pattern:  1028 points
Average intensity 0.00049 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units
Quadrature weights:
     (counting weights based on 32 x 32 array of rectangular tiles)
All weights:
    range: [97.6, 2050] total: 2100000
Weights on data points:
    range: [97.6, 1030] total: 24700
Weights on dummy points:
    range: [97.6, 2050] total: 2070000
--------------------------------------------------------------------------------
FITTED :

Nonstationary Poisson process

---- Intensity: ----

Log intensity: ~distance_tree_im + aspect_im + rix_im + wind_im + mpb_grey_im + 
mpb_red_im + fire_high_im + fire_low_im + mpb_red_im * fire_high_im + 
mpb_red_im * fire_high_im + mpb_red_im * fire_low_im + mpb_red_im * fire_low_im
Model depends on external covariates 'distance_tree_im', 'aspect_im', 'rix_im', 
'wind_im', 'mpb_grey_im', 'mpb_red_im', 'fire_high_im' and 'fire_low_im'
Covariates provided:
    distance_tree_im: im
    aspect_im: im
    rix_im: im
    wind_im: im
    mpb_grey_im: im
    mpb_red_im: im
    fire_high_im: im
    fire_low_im: im

Fitted trend coefficients:
            (Intercept)        distance_tree_im               aspect_im 
            -29.3219142               0.1190285              -0.6037328 
                 rix_im                 wind_im             mpb_grey_im 
             -1.8570270               3.9904517             -15.1966795 
             mpb_red_im            fire_high_im             fire_low_im 
              1.5201704               1.7104478               0.2777261 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
             -2.0481166              -2.3233291 

                           Estimate         S.E.       CI95.lo      CI95.hi
(Intercept)             -29.3219142 2.303908e+00 -3.383749e+01  -24.8063374
distance_tree_im          0.1190285 1.172968e-02  9.603874e-02    0.1420182
aspect_im                -0.6037328 1.664888e-01 -9.300449e-01   -0.2774207
rix_im                   -1.8570270 1.723606e+00 -5.235233e+00    1.5211789
wind_im                   3.9904517 4.529810e-01  3.102625e+00    4.8782782
mpb_grey_im             -15.1966795 1.451209e+03 -2.859514e+03 2829.1210117
mpb_red_im                1.5201704 4.244028e-01  6.883562e-01    2.3519846
fire_high_im              1.7104478 3.814416e-01  9.628360e-01    2.4580597
fire_low_im               0.2777261 3.772679e-01 -4.617054e-01    1.0171577
mpb_red_im:fire_high_im  -2.0481166 6.757957e-01 -3.372652e+00   -0.7235813
mpb_red_im:fire_low_im   -2.3233291 6.263161e-01 -3.550886e+00   -1.0957721
                        Ztest         Zval
(Intercept)               *** -12.72703322
distance_tree_im          ***  10.14763497
aspect_im                 ***  -3.62626667
rix_im                         -1.07740800
wind_im                   ***   8.80931332
mpb_grey_im                    -0.01047174
mpb_red_im                ***   3.58190457
fire_high_im              ***   4.48416659
fire_low_im                     0.73615097
mpb_red_im:fire_high_im    **  -3.03067398
mpb_red_im:fire_low_im    ***  -3.70951519

----------- gory details -----

Fitted regular parameters (theta):
            (Intercept)        distance_tree_im               aspect_im 
            -29.3219142               0.1190285              -0.6037328 
                 rix_im                 wind_im             mpb_grey_im 
             -1.8570270               3.9904517             -15.1966795 
             mpb_red_im            fire_high_im             fire_low_im 
              1.5201704               1.7104478               0.2777261 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
             -2.0481166              -2.3233291 

Fitted exp(theta):
            (Intercept)        distance_tree_im               aspect_im 
           1.843548e-13            1.126402e+00            5.467668e-01 
                 rix_im                 wind_im             mpb_grey_im 
           1.561361e-01            5.407931e+01            2.512846e-07 
             mpb_red_im            fire_high_im             fire_low_im 
           4.573004e+00            5.531438e+00            1.320125e+00 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
           1.289776e-01            9.794697e-02 

----------- CLUSTER  -----------
Model: Thomas process

Fitted cluster parameters:
       kappa        scale 
2.190736e-05 1.357183e+01 
Mean cluster size:  [pixel image]

Final standard error and CI
(allowing for correlation of cluster process):
                           Estimate         S.E.       CI95.lo      CI95.hi
(Intercept)             -29.3219142 7.400948e+00 -4.382751e+01  -14.8163230
distance_tree_im          0.1190285 9.814768e-02 -7.333743e-02    0.3113944
aspect_im                -0.6037328 6.710662e-01 -1.918998e+00    0.7115327
rix_im                   -1.8570270 1.106375e+01 -2.354158e+01   19.8275235
wind_im                   3.9904517 1.653906e+00  7.488557e-01    7.2320478
mpb_grey_im             -15.1966795 1.451210e+03 -2.859517e+03 2829.1232899
mpb_red_im                1.5201704 1.520792e+00 -1.460528e+00    4.5008685
fire_high_im              1.7104478 1.285025e+00 -8.081545e-01    4.2290501
fire_low_im               0.2777261 1.600661e+00 -2.859511e+00    3.4149635
mpb_red_im:fire_high_im  -2.0481166 2.325766e+00 -6.606534e+00    2.5103007
mpb_red_im:fire_low_im   -2.3233291 3.828423e+00 -9.826900e+00    5.1802415
                        Ztest        Zval
(Intercept)               *** -3.96191337
distance_tree_im               1.21274885
aspect_im                     -0.89966212
rix_im                        -0.16784789
wind_im                     *  2.41274406
mpb_grey_im                   -0.01047173
mpb_red_im                     0.99959105
fire_high_im                   1.33106214
fire_low_im                    0.17350719
mpb_red_im:fire_high_im       -0.88062029
mpb_red_im:fire_low_im        -0.60686327

----------- cluster strength indices ----------
Mean sibling probability 0.9517392
Count overdispersion index (on original window): 23.56747
Cluster strength: 19.72074

Spatial persistence index (over window): 0

Bound on distance from Poisson process (over window): 1
     = min (1, 157.2645, 122068.4, 123257.9, 349.1904)

Inhomogeneous cluster point process model
Fitted to point pattern dataset 'tsuga_hete_pooled'
Fitted by minimum contrast
    Summary statistic: inhomogeneous K-function
Minimum contrast fit (object of class "minconfit")
Model: Thomas process
Fitted by matching theoretical K function to tsuga_hete_pooled

Internal parameters fitted by minimum contrast ($par):
       kappa       sigma2 
3.394556e-06 1.513896e+03 

Fitted cluster parameters:
       kappa        scale 
3.394556e-06 3.890881e+01 
Mean cluster size:  [pixel image]

Converged successfully after 83 function evaluations

Starting values of parameters:
       kappa       sigma2 
1.619586e-05 1.885789e+03 
Domain of integration: [ 0 , 342.5 ]
Exponents: p= 2, q= 0.25

----------- TREND  -----
Point process model
Fitted to data: X
Fitting method: maximum likelihood (Berman-Turner approximation)
Model was fitted using glm()
Algorithm converged
Call:
ppm.ppp(Q = X, trend = trend, rename.intercept = FALSE, covariates = covariates, 
    covfunargs = covfunargs, use.gam = use.gam, forcefit = TRUE, 
    improve.type = ppm.improve.type, improve.args = ppm.improve.args, 
    nd = nd, eps = eps)
Edge correction: "border"
    [border correction distance r = 0 ]
--------------------------------------------------------------------------------
Quadrature scheme (Berman-Turner) = data + dummy + weights

Data pattern:
Planar point pattern:  34 points
Average intensity 1.62e-05 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units

Dummy quadrature points:
     32 x 32 grid of dummy points, plus 4 corner points
     dummy spacing: 42.81062 x 47.88764 units

Original dummy parameters: =
Planar point pattern:  1028 points
Average intensity 0.00049 points per square unit
Window: rectangle = [522992.6, 524362.6] x [5445752, 5447285] units
                    (1370 x 1532 units)
Window area = 2099300 square units
Quadrature weights:
     (counting weights based on 32 x 32 array of rectangular tiles)
All weights:
    range: [410, 2050]  total: 2100000
Weights on data points:
    range: [410, 1030]  total: 24200
Weights on dummy points:
    range: [410, 2050]  total: 2080000
--------------------------------------------------------------------------------
FITTED :

Nonstationary Poisson process

---- Intensity: ----

Log intensity: ~distance_tree_im + aspect_im + rix_im + wind_im + mpb_grey_im + 
mpb_red_im + fire_high_im + fire_low_im + mpb_red_im * fire_high_im + 
mpb_red_im * fire_high_im + mpb_red_im * fire_low_im + mpb_red_im * fire_low_im
Model depends on external covariates 'distance_tree_im', 'aspect_im', 'rix_im', 
'wind_im', 'mpb_grey_im', 'mpb_red_im', 'fire_high_im' and 'fire_low_im'
Covariates provided:
    distance_tree_im: im
    aspect_im: im
    rix_im: im
    wind_im: im
    mpb_grey_im: im
    mpb_red_im: im
    fire_high_im: im
    fire_low_im: im

Fitted trend coefficients:
            (Intercept)        distance_tree_im               aspect_im 
           -13.86448141              0.23820461              0.01315783 
                 rix_im                 wind_im             mpb_grey_im 
             4.17725806              0.04289329            -14.77944218 
             mpb_red_im            fire_high_im             fire_low_im 
             0.59115313             -0.47987389             -0.06280497 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
             0.67176213             -3.05733610 

                            Estimate         S.E.       CI95.lo      CI95.hi
(Intercept)             -13.86448141 2.384364e+00   -18.5377485   -9.1912144
distance_tree_im          0.23820461 1.963091e-02     0.1997287    0.2766805
aspect_im                 0.01315783 2.574103e-01    -0.4913572    0.5176728
rix_im                    4.17725806 3.838459e+00    -3.3459836   11.7004997
wind_im                   0.04289329 2.969476e-01    -0.5391134    0.6249000
mpb_grey_im             -14.77944218 1.580428e+03 -3112.3606036 3082.8017193
mpb_red_im                0.59115313 7.884131e-01    -0.9541082    2.1364145
fire_high_im             -0.47987389 1.046157e+00    -2.5303038    1.5705561
fire_low_im              -0.06280497 4.001136e-01    -0.8470131    0.7214032
mpb_red_im:fire_high_im   0.67176213 1.617983e+00    -2.4994257    3.8429499
mpb_red_im:fire_low_im   -3.05733610 1.279468e+00    -5.5650482   -0.5496240
                        Ztest         Zval
(Intercept)               *** -5.814751009
distance_tree_im          *** 12.134158035
aspect_im                      0.051116173
rix_im                         1.088264305
wind_im                        0.144447322
mpb_grey_im                   -0.009351547
mpb_red_im                     0.749801222
fire_high_im                  -0.458701625
fire_low_im                   -0.156967854
mpb_red_im:fire_high_im        0.415184994
mpb_red_im:fire_low_im      * -2.389536142

----------- gory details -----

Fitted regular parameters (theta):
            (Intercept)        distance_tree_im               aspect_im 
           -13.86448141              0.23820461              0.01315783 
                 rix_im                 wind_im             mpb_grey_im 
             4.17725806              0.04289329            -14.77944218 
             mpb_red_im            fire_high_im             fire_low_im 
             0.59115313             -0.47987389             -0.06280497 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
             0.67176213             -3.05733610 

Fitted exp(theta):
            (Intercept)        distance_tree_im               aspect_im 
           9.522089e-07            1.268969e+00            1.013245e+00 
                 rix_im                 wind_im             mpb_grey_im 
           6.518687e+01            1.043827e+00            3.813904e-07 
             mpb_red_im            fire_high_im             fire_low_im 
           1.806070e+00            6.188614e-01            9.391266e-01 
mpb_red_im:fire_high_im  mpb_red_im:fire_low_im 
           1.957684e+00            4.701277e-02 

----------- CLUSTER  -----------
Model: Thomas process

Fitted cluster parameters:
       kappa        scale 
3.394556e-06 3.890881e+01 
Mean cluster size:  [pixel image]

Final standard error and CI
(allowing for correlation of cluster process):
                            Estimate         S.E.       CI95.lo      CI95.hi
(Intercept)             -13.86448141    7.3272179 -2.822556e+01    0.4966018
distance_tree_im          0.23820461    0.1000093  4.219008e-02    0.4342191
aspect_im                 0.01315783    0.6237628 -1.209395e+00    1.2357104
rix_im                    4.17725806    7.6317739 -1.078074e+01   19.1352600
wind_im                   0.04289329    1.2945926 -2.494462e+00    2.5802481
mpb_grey_im             -14.77944218 1580.4278205 -3.112361e+03 3082.8021662
mpb_red_im                0.59115313    1.3962517 -2.145450e+00    3.3277562
fire_high_im             -0.47987389    1.4612107 -3.343794e+00    2.3840464
fire_low_im              -0.06280497    1.1130460 -2.244335e+00    2.1187252
mpb_red_im:fire_high_im   0.67176213    2.6298535 -4.482656e+00    5.8261803
mpb_red_im:fire_low_im   -3.05733610    2.9884960 -8.914681e+00    2.8000085
                        Ztest         Zval
(Intercept)                   -1.892189035
distance_tree_im            *  2.381825736
aspect_im                      0.021094287
rix_im                         0.547350868
wind_im                        0.033132657
mpb_grey_im                   -0.009351545
mpb_red_im                     0.423385791
fire_high_im                  -0.328408423
fire_low_im                   -0.056426206
mpb_red_im:fire_high_im        0.255437089
mpb_red_im:fire_low_im        -1.023035018

----------- cluster strength indices ----------
Mean sibling probability 0.9393388
Count overdispersion index (on original window): 69.54263
Cluster strength: 15.485

Spatial persistence index (over window): 0

Bound on distance from Poisson process (over window): 1
     = min (1, 108.5779, 46052.37, 45699.08, 213.6324)