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plot.manyglm is ignoring small predicted values, rather than truncating them to keep them on the plot. In default.plot.manyglm, lines 410-4:
yhtmp <- c(yh)
yh.is.zero <- yhtmp < (-6)
yh0 <- yhtmp[!yh.is.zero]
should probably do something like yh0=pmax(-6,yh) instead
bootstrap P-values in summary.manyglm and summary.manylm are computed as (# resampled stats >= observed stat) / nBoot rather than (# resampled stats >= observed stat + 1) / ( nBoot +1). The latter is preferred, but is currently only implemented in summary for perm.resid, please fix.
If object$coef has been tinkered with, this should be respected in subsequent predictions
plot coefficients with confidence intervals for each species
mvabund uses what was once a "pre-release" of the Rcpp::Nullable<>
class in src/mvabund_types.h
.
Rcpp itself now has a slightly newer version, and including both now leads to errors.
In order to sort this out, I would suggest to simply rely on Rcpp alone. I have verified that mvabund then build fine.
We can either just remove src/mvabund_types.h
, or keep it around (if we every need it again) but keep it empty. I would be happy to prepare a corresponding pull request for either option.
With best wishes for 2017, Dirk
data(spider)
boxplot(spider$abund)
abund=mvabund(spider$abund)
boxplot(abund) #exact opposite labels
The Travis CI support via file .travis.yml
could be updated to using Ubuntu 20.04 and slightlt nicer internal logic.
However, a bigger / more important change may be to also switch to / enable GitHub Actions. I am doing that for my repos now one-by-one because I have run out of so-called "free" credits at Travis. This repo runs under Alice's handle and allocation so we get by with the 10,000 credits from Travis.
More details (and examples) are at https://eddelbuettel.github.io/r-ci/ -- if it helps I would be happy to simplify and improve the setup. We can simply turn both Travis CI and GitHub Actions on (with a small update to Travis).
Hello,
I'm getting the error ‘variable lengths differ’ when using the a $ in the manyglm formula, when composition =T
Herbivores <- read.csv(file = "Herbivore_specialisation.csv", header = TRUE)
Herb_spp <- mvabund(Herbivores[,5:11])
mod3 <- manyglm(Herb_spp ~ Herbivores$Habitat*Herbivores$DayNight, composition = T, family="negative_binomial")
Error in model.frame.default(formula = formLong, data = dat, subset = subset, :
variable lengths differ (found for 'Herbivores$Habitat')
I am able to get the model to work using:
Can this be corrected?
Thank you
This fails in two cases / two reasons (with the mvabund version here in github):
data(spider)
spiddat <- mvabund(spider$abund)
X <- spider$x
glm.spid <- manyglm(spiddat[,1:3]~X, family="negative.binomial")
summary(glm.spid, nBoot = 100, p.uni = 'none')
summary(glm.spid, nBoot = 100, p.uni = 'unadjusted')
# Error in `colnames<-`(`*tmp*`, value = c("(Intercept)", "Xsoil.dry", "Xbare.sand", :
# length of 'dimnames' [2] not equal to array extent
summary(glm.spid, nBoot = 100, p.uni = 'adjusted')
# Error in `colnames<-`(`*tmp*`, value = c("(Intercept)", "Xsoil.dry", "Xbare.sand", :
# length of 'dimnames' [2] not equal to array extent
This fails in line 255 because there are not enough columns in unit_signic
.
require(mvabund)
require(vegan)
# data
data(pyrifos)
take <- c('binitent', 'olchaeta', 'caenhora', 'cloedipt',
'chaoobsc', 'gammpule', 'libellae', 'agdasphr')
abu <- pyrifos[ , names(pyrifos) %in% take]
abu <- round((exp(abu) - 1)/10)
env <- data.frame(time = rep(c(-4, -1, 0.1, 1, 2, 4, 8, 12, 15, 19, 24), each = 12),
treatment = rep(c(0.1, 0, 0, 0.9, 0, 44, 6, 0.1, 44, 0.9, 0, 6), 11),
replicate = gl(12, 1, length=132))
abu <- mvabund(abu)
# subset to only one time point
take_abu <- abu[env$time == '-4', ]
take_env <- env[env$time == '-4', ]
# model + summary
mod <- manyglm(take_abu ~ factor(treatment), data = take_env)
summary(mod, nBoot = 100, p.uni = 'adjusted')
This fails because unit_signic
is empty!
In both cases I suspect the error coming from RtoGlmSmry()
in Line 199.
However, I haven't tracked it further down (I not a C++ Ninja and not experienced in debugging C++), though I expect the problem in Rinterface.cpp
or further down in glmtest.cpp
.
Currently manyglm is struggling to handle majority of gamma distributed data that I have been attempting to model.
The RtoGlm function easily results in NA's for the z$var.estimator when the gamma family is used (below code from manyglm)....
z <- RtoGlm(modelParam, Y, X, O)
if (any(z$var.est == 0)) {
z$var.estimator = pmax(z$var.est, 1e-06)
z$residuals = (Y - z$fit)/sqrt(z$var.est)
Which stops the model estimation at the above step because any(z$var.est == 0) returns an NA. Not sure why RtoGlm is returning NA's here quite often for gamma data which glm has no issues fitting. Perhaps best to replace NA's with 0 and then run the model replacing the var.estimator appropriately for example below (using tidyverse replace_na).
z <- RtoGlm(modelParam, Y, X, O)
z$var.est[is.na(z$var.est)]<-0
if (any(replace_na(z$var.est,0) == 0)) {
z$var.estimator = pmax(z$var.est, 1e-06)
z$residuals = (Y - z$fit)/sqrt(z$var.est)
Happy to provide data and further discussion if need be. This issue makes it really hard for gamma function to be taken up and applied widely!
cheers
e.g. fit a Poisson regression by specifying family=poisson
, and use the link, variance functions provided by R.
Fails:
[from the demo code]
glm.spid.bin <- manyglm(pres.abs~soil.dry+bare.sand+moss, data=X, family="binomial")
summary(glm.spid.bin)
Error in summary.manyglm(glm.spid.bin) :
'family' not defined. Choose one of 'poisson', 'negative.binomial', 'binomial' for an manyglm object
The family is:
glm.spid.bin$family
[1] "binomial(link=logit)"
But summary.manyglm looks for an exact string match on the family (object$family == "binomial"), so it fails. Probably this test should be more like the one used in anova.manyglm (substr(object$family, 1, 1) == "b")
sessionInfo()
R version 3.2.2 (2015-08-14)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] mvabund_3.11.5
Hello - I am getting many lines of output printed to the console when running anova.manyglm on a model fit as follows:
mv.full <- manyglm(mvDat ~ Treatment*Genotype, offset=effort, family="negative_binomial")
Where effort = log(rowSums(mvDat))
When I run a test with:
anova(mv.full, nBoot=199, cor.type="R", test="wald")
The expected test results are returned, but only after printing many lines to the console that look like this:
l=nan, theta=1000000.0000, yi=44.0000, mu=-nan
l=nan, theta=1000000.0000, yi=24.0000, mu=-nan
l=nan, theta=1000000.0000, yi=43.0000, mu=-nan
l=nan, theta=1000000.0000, yi=41.0000, mu=-nan
l=nan, theta=1000000.0000, yi=45.0000, mu=-nan
l=nan, theta=1000000.0000, yi=8.0000, mu=-nan
There is one line for every sample*nBoot and occasionally yi=inf
. This output does not appear if I remove the offset or use to a different resampling technique. Plotting the model diagnostics does not reveal any obvious problems and I get nearly identical statistics setting resamp = "perm.resid"
. However, this behavior does not occur when I add a similar offset to any of the example data provided with the package, so I am not sure how to provide a simple reproducible example. I am currently using the newest mvabund v4.1.1. Could this be some type of minor bug? Or perhaps my model is set up incorrectly in some way that is not obvious to me?
Hi,
Thanks so much for developing this great package.
I seem to have an issue with passing user-defined permutation to summary.manyglm by bootID. The version of mvabund is v3.11.9.
I only have 6 rows in a multivariate abundance data, so I was trying to perform an exact permutation by 'bootID = allPerm(6)'. Then, I get the error message below.
'Error in max(bootID) : invalid 'type' (symbol) of argument'. Please see the example.
require(mvabund)
require(permute)
data(spider)
spiddat <- mvabund(spider$abund)
X <- spider$x
spiddat2 <- spiddat[1:6, ]
X2 <- X[1:6, ]
glm.spid <- manyglm(spiddat2[,1:3]~X2, family="negative.binomial")
summary(glm.spid, bootID = allPerms(6))
Error in max(bootID) : invalid 'type' (symbol) of argument
I am wondering if I'm not defining it right. Any advice would be extremely helpful.
Regards,
Shun
for say poisson data:
library(mvabund)
Y <- mvabund(sapply(1:4, function(x) rpois(30, 5)))
m <- manyglm(Y ~ 1 , family = 'poisson')
summary(m)
Causes a segmentation fault
For manyglm objects fitted using binomial("cloglog") model, anova.manyglm currently ignores the cloglog bit and tests significance using a logit link. Alice is working on this one as we speak...
e.g. (using code at pull request #15 which fixes a previous bug with family calls)
example(manyglm)
glm2.ft=manyglm(pres.abssoil.dry,data=X,family=binomial("cloglog"))1,data=X,family=binomial("cloglog"))
glm1.ft=manyglm(pres.abs
sum(deviance(glm1.ft)-deviance(glm2.ft)) # returns [1] 129.3183
anova(glm2.ft,nBoot=9)$table[2,3] #returns the wrong answer, [1] 131.0454
glm1.ftb=manyglm(pres.abs1,data=X,family=binomial())soil.dry,data=X,family=binomial())
glm2.ftb=manyglm(pres.abs
sum(deviance(glm1.ftb)-deviance(glm2.ftb)) #returns [1] 131.0454
anova(glm2.ftb,nBoot=9)$table[2,3] #returns [1] 131.0454
there is a set seed somewhere in anova.manylm, such that it always return identical P-values. e.g.
example(manylm)
anova(fit, fit0, fit1, fit2, nBoot=5000, test="F", p.uni="adjust")
anova(fit, fit0, fit1, fit2, nBoot=5000, test="F", p.uni="adjust")
We should have different P-values due to MC error but don't.
This does not seem to be an issue in summary.manylm nor in the manyglm functions.
Eve's block bootstrap should be added as an inference method
Hi,
I am trying to run a manyglm with a gamma distribution but am getting a strange error which I don't understand.
My error can be replicated with (shortened version):
mydata <- read.csv("https://raw.githubusercontent.com/HaydenSchilling/MGLMs-Otoliths/master/Data/Otolith_data_mmol_mol_Ca.csv")
E_data <- mydata[,c(2:8,10:14)]
elements <- mvabund(E_data) # select only element data
gamma_glm <- manyglm(elements ~ 1, family="gamma")
The error says:
Error in if (any(z$var.est == 0)) { :
missing value where TRUE/FALSE needed
It is possible to run a univariate glm with gamma distribution on all of variables I'm trying to use in the manyglm
glm1 <- glm(elements[,1] ~ 1, family =Gamma(link="log"))
Any ideas would be very much appreciated,
Thanks!
if calling anova.manyany on two objects, where one has comp=TRUE and the other has COMP=FALSE, nonsensical answers are returned (because only the first variable from COMP=FALSE is used)
fitted values are typically truncated to a max of 10^6 or a min of 10^-6. Can control of this value be given to the user, but maybe defaulting to maxFit=10^6? In case they have a dataset with some huge counts?
Hello!
I am wondering if this is an issue under development or an error. I have used the binstruc
function to build a dataframe for a binomial manyglm
to calculate the proportional rather than discrete composition. The new binomial structured matrix builds successfully and manyglm
runs successfully with family = "negative.binomial"
but with family = "binomial"
I get this fatal error:
gsl: ../../gsl-2.1/matrix/copy_source.c:31: ERROR: matrix sizes are different
Default GSL error handler invoked.
I have produced a (hopefully) reproducible example using the spider dataset:
library(mvabund)
data("spider")
abund <- spider$abund
abund$Total <- rowSums(abund) #calculate row totals for total number of individuals
abund_fails <- abund[,13] - abund[1:12] #calculate failures as difference between row totals and individual species abundance
abund <- abund[1:12] #cut out total column
X <- as.data.frame(spider$x) #store environmental vars
##build binstruc function (copied from https://github.com/cran/mvabund/blob/master/R/binstruc.R)
binstruc <- function(y1, y2, order =1){
if(any(y1<0) | any(y2<0)) stop("binomial data must consist of integers only")
y1 <- as.matrix(y1)
if(nrow(y1)!=NROW(y2)) stop("'y1' must have the same number of rows as 'y2'")
if(order==1){
nvars <- ncol(y1)
y2 <- as.matrix(y2)
if(is.null(colnames(y2))) { colnames(y2) <- colnames(y1)
} else if(is.null(colnames(y1))) colnames(y1) <- colnames(y2)
if(nvars!=ncol(y2)) stop("'y1' must have the same number of columns as 'y2'")
y <- cbind(y1,y2)
colnam <- colnames(y)
if(is.null(colnam)) {
colnames(y) <- c(paste("succ",1:nvars, sep=""), paste("fail",1:nvars, sep=""))
struc <- c( rep("succ", nvars), rep("fail", nvars) )
} else {
struc <- c( rep("succ",times = nvars), rep("fail", times=nvars) )
colnames(y) <- paste(struc , ".", colnam, sep = "")
}
n <- y[ , struc=="succ", drop=FALSE ] + y[, struc=="fail", drop=FALSE]
if(any(n - n[,1] != 0))
stop("the number of trials must be the same for each variable")
} else if(order==2){
nvars <- ncol(y1)
if(NCOL(y2)==1){
y2 <- c(y2)
} else if(nvars!=ncol(y2)) {
stop("'y2' must either have the same number of columns as 'y1' or be a vector")
} else if(any(y2- y2[,1]!=0))
stop("the number of trials must be the same for each variable")
if(any(y2-y1<0)) stop("the number of trials must be larger than the number of successses")
y <- cbind(y1,y2-y1)
colnam <- colnames(y)
if(is.null(colnam)) {
colnames(y) <- c(paste("succ",1:nvars, sep=""), paste("fail",1:nvars, sep=""))
} else {
struc <- c( rep("succ",times = nvars), rep("fail", times=nvars) )
colnames(y) <- paste(struc , ".", colnam, sep = "")
}
} else stop("'order' must be in either '1' or '2'")
return(y)
}
abund_binstruc <- binstruc(abund, abund_fails, 1) #build successes-failures matrix
abund_mvabund <- mvabund(abund_binstruc) #convert to mvabund object
abund_manyglm <- manyglm(abund_mvabund ~ soil.dry, data = X, family = "binomial") #run manyglm
This functionality looks incredibly promising! But if this is still in development could you please suggest another way of computing proportional compositions?
Error in dimnames<-.data.frame
(*tmp*
, value = list(tl, c("Res.Df", :
invalid 'dimnames' given for data frame
I get this error message when I use summary() following model fitting with manyglm. When p.uni="unadjusted" is not included, summary() works fine, but whenever p.uni= is included, error message appears. I am using R version 3.2.4 and just installed mvabund via install.packages function. If you could find out what I am doing wrong, that would be very helpful. Below are my data, code, and the error message. Thank you very much.
Sincerely,
Kiyoshi
mydata <- structure(list(Area = structure(1:12, .Label = c("CTV", "Falconbridge",
"Franco", "Hwy144", "Kukagami", "Landfill", "Laurentian", "Nepewassi Lake",
"ONeil", "Sand Bay Rd", "Tilton Lake", "Trout Lake"), class = "factor"),
Rank = structure(c(4L, 4L, 2L, 3L, 2L, 4L, 3L, 1L, 3L, 1L,
2L, 1L), .Label = c("Reference", "Moderate", "Semibarren",
"Barren"), class = "factor"), ANAM = c(1L, 4L, 0L, 1L, 19L,
1L, 4L, 88L, 9L, 41L, 6L, 21L), HYVE = c(10L, 7L, 12L, 2L,
3L, 7L, 6L, 26L, 4L, 9L, 5L, 11L), LICA = c(0L, 2L, 2L, 37L,
24L, 16L, 25L, 19L, 0L, 21L, 49L, 85L), LICL = c(71L, 32L,
52L, 33L, 77L, 45L, 40L, 110L, 91L, 80L, 56L, 93L), LIPI = c(16L,
61L, 69L, 14L, 4L, 34L, 51L, 96L, 52L, 38L, 11L, 20L), LISE = c(28L,
56L, 32L, 5L, 13L, 1L, 0L, 41L, 72L, 24L, 1L, 46L), LISY = c(0L,
4L, 13L, 2L, 9L, 11L, 1L, 72L, 0L, 14L, 5L, 6L), PSCR = c(7L,
13L, 18L, 9L, 16L, 13L, 9L, 36L, 9L, 17L, 13L, 29L)), .Names = c("Area",
"Rank", "ANAM", "HYVE", "LICA", "LICL", "LIPI", "LISE", "LISY",
"PSCR"), class = "data.frame", row.names = c(NA, -12L))
mv <- mvabund(mydata[,3:10])
fit <- manyglm(mv ~ Rank, data=mydata, family="negative.binomial")
summary(fit, resamp="monte.carlo", p.uni="unadjusted", test="LR")
Error in rownames<-
(*tmp*
, value = c("ANAM", "HYVE", "LICA", "LICL", :
length of 'dimnames' [1] not equal to array extent
Gordana to incorporate her copula for manyglm
a good thing to add to manyglm would be observation weights (like the weights argument on glm).
Notes from David:
Thank you for creating and maintaining mvabund
. I want to extract the Univariate section, which comes after the Multivariate ANOVA table as one table, but the current attributes have the p-value and the test table separated. We could extract the 2 tables and merge them, but I think being able to extract the original format would be great.
https://stackoverflow.com/questions/65316449/extract-mvabundanova-manyglms-p-values-and-tests-as-one-table
I noticed that the .travis.yml
file is a little stale, and would like to suggest two changes:
[ Now, I don't mean to impose "my" style here. I am happy with this older / existing / maintained (by me) setup but I am of course aware that most of the world moved to another format (see the official Travis docs). I don't use that, but you are of course free to switch. I just can't help. ]
With the disclaimer out of the way, happy to support/update the existing one. Or not if you feel what is there now is good enough.
example(anova.manylm)
bootid = matrix(NA,4,28)
for(i in 1:4)
{
bootid[i,1] = i
bootid[i,2:28] = (1:28)[-i]
}
anova(fit,bootID=bootid)
anova(fit,bootID=as.numeric(bootid))
The tweedie family is a very flexible family of distributions, We are only interested in tweedie distributions with power between 1 and 2, this means that they have mass at 0 but otherwise are similar to a gamma distribution,
Useful Links
There is an R package tweedie that is mostly fortran
a paper that describes ML estimation of various parameters: Dunn and Smyth 2005
a paper describes the unit deviance function and the link functions Application of the Tweedie distribution to zero-catch data in CPUE analysis
This would be really great for plotting them later on.
Thanks,
Gordana
Add support for the gamma family using the inverse link
update manyglm.R
update resampTest.h
update glm.cpp
update glmtest.cpp
update Rinterface.cpp
update manyglm.Rd
write some tests for manyglm
can users specify a vector of families as input, so that different variance/link functions are used on different columns of data?
Thanks Ben for catching this issue
pairwise.comp wasn't looking for the factor in the manyglm fit environment, however when it is put the in the global environment it works.
library(mvabund)
#import data
tb_dat <- read.csv("tb_dat.csv",header=T)
fishspp <- mvabund(tb_dat[,8:13])
#Round off fishspp to integer values
fishspp <- round(fishspp)
#fit a manyglm object
pref.mod <- manyglm(fishspp ~ region + spp.zone + spp + offset(log(time_viewed)), data = tb_dat)
#pairwise comp doesn't work as it can't find spp within the model
pref.aov <- anova(pref.mod, pairwise.comp = spp,nBoot = 199)
pref.aov
#now if we put species in the global environment it works!
spp <- as.factor(tb_dat$spp)
pref.aov <- anova(pref.mod, pairwise.comp = spp, nBoot = 199)
pref.aov
a solution is maybe to deparse the name or to make the documentation and usage examples more clear
## Visualise the effect of treatment on copepod abundance
tasm.cop <- mvabund(Tasmania$copepods)
#treatment <- Tasmania$treatment
# create >2 treatment variables variables
treatment = c(rep(c("Undis", "Dist"), 4),rep(c("Undis2", "Dist2"), 4))
block <- Tasmania$block
#plot(tasm.cop ~ treatment, col=as.numeric(block))
## Fitting predictive models using a negative binomial model for counts:
tasm.cop.nb <- manyglm(tasm.cop ~ treatment, family="negative.binomial")
# works fine...
anova_tasm.cop.nb = anova.manyglm(tasm.cop.nb, pairwise.comp = treatment, nBoot = 3, test="wald")
## Fitting predictive models using a negative binomial model for counts, and shrink:
tasm.cop.nb_shrink <- manyglm(tasm.cop ~ treatment, family="negative.binomial", cor.type = "shrink", test="wald")
# doesn't work
anova_pairwise_tasm.cop.nb = anova.manyglm(tasm.cop.nb_shrink, pairwise.comp = treatment, nBoot = 3, test="wald")
Time elapsed: 0 hr 0 min 0 sec
Error in dimnames(x) <- dn :
length of 'dimnames' [2] not equal to array extent
In addition: Warning messages:
1: In anova.manyglm(m, show.time = "none", keep.boot = TRUE, nBoot = anova_obj$nBoot, :
The likelihood ratio test can only be used if correlation matrix of the abundances is is assumed to be the Identity matrix. The Wald Test will be used.
2: In anova.manyglm(m, show.time = "none", keep.boot = TRUE, nBoot = anova_obj$nBoot, :
The likelihood ratio test can only be used if correlation matrix of the abundances is is assumed to be the Identity matrix. The Wald Test will be used.
3: In anova.manyglm(m, show.time = "none", keep.boot = TRUE, nBoot = anova_obj$nBoot, :
The likelihood ratio test can only be used if correlation matrix of the abundances is is assumed to be the Identity matrix. The Wald Test will be used.
4: In anova.manyglm(m, show.time = "none", keep.boot = TRUE, nBoot = anova_obj$nBoot, :
The likelihood ratio test can only be used if correlation matrix of the abundances is is assumed to be the Identity matrix. The Wald Test will be used.
5: In anova.manyglm(m, show.time = "none", keep.boot = TRUE, nBoot = anova_obj$nBoot, :
The likelihood ratio test can only be used if correlation matrix of the abundances is is assumed to be the Identity matrix. The Wald Test will be used.
6: In anova.manyglm(m, show.time = "none", keep.boot = TRUE, nBoot = anova_obj$nBoot, :
The likelihood ratio test can only be used if correlation matrix of the abundances is is assumed to be the Identity matrix. The Wald Test will be used.
I've done my best to find where it's going wrong and as far as I can tell it's in do_pairwise_comp ()
am <- anova.manyglm(m,
show.time = 'none',
keep.boot = TRUE,
nBoot = anova_obj$nBoot,resamp = resamp, cor.type = cor.type)
which gives a table like
Analysis of Variance Table
Model: subY ~ subWhat
Multivariate test:
Res.Df Df.diff wald Pr(>wald)
(Intercept) 5
subWhat 4 2 0.25
Arguments:
Test statistics calculated assuming correlated response via ridge regularization
P-value calculated using 3 iterations via PIT-trap resampling.
and am$table[2, 3]
is NaN
whereas running it without cor.type = 'shrink'
gives am$table[2, 3]
a numerical value (165.5)
Analysis of Deviance Table
Model: subY ~ subWhat
Multivariate test:
Res.Df Df.diff Dev Pr(>Dev)
(Intercept) 5
subWhat 4 2 165.5 0.25
Arguments:
Test statistics calculated assuming uncorrelated response (for faster computation)
P-value calculated using 3 iterations via PIT-trap resampling.
Is there a fix for this? I'm using the latest version of mvabund from github
Dear,
I was wondering if there is a way to combine the pairwise comparison option with the p.uni="adjusted" option in anova.manyglm to obtain adjusted p-values for every response variable for every possible pairwise combination of factor levels.
While p.uni="adjusted" or pairwise comparisons work fine when specified separately, I get the following error message when I use both arguments together:
Error in dimnames(x) <- dn :
length of 'dimnames' [2] not equal to array extent
Many thanks in advance for your time and consideration.
Hi Alice,
predict.manyglm is not working when the formula contains polynomial terms. This isn't urgent for me as I can work around.
Cheers,
Eve
#Minimal example below
#Simulate toy data
Y=rpois(100, 2)
X=data.frame(x1=rnorm(100), x2=rnorm(100))
#fit model and predict response from original data
mod= manyglm(Y~poly(x1,2)+poly(x2,2), data=X)
predict(mod)
predict(mod, newdata=X)
predict(mod, newdata=mod$x)
#This error is received for all 3 attempts
#Error in poly(x1, 2) : object 'x1' not found
check this out and try not to get your mind blown...
library(mvabund) data(spider) spider$x=data.frame(spider$x) spidMV=mvabund(spider$abund) ft_spid=manyglm(spidMV~soil.dry, data=spider$x, composition=TRUE) #error: object 'soil.dry' not found ft_spidmoss=manyglm(spidMV~moss, data=spider$x, composition=TRUE) # works fine
The nCores function on manyany returns an error if used in combination with composition=TRUE
It would be super if there was an nCores argument for anova/summary.many(g)lm!
Hi, I have two questions. First, when I use the pairwise.comp option with the manyglm function I am receiving an error that I can not seem to figure out. All the rest of the functions work with my data up to this point. Attached is a simplified data file and R-script that reproduced the error, and below is the script again for easy viewing.
####script
Data<-read.csv("ComMat.csv")
Community<-(Data[,3:5]+0.1) #Creating matrix, and adding tiny constant
Factor<-Data$Factor
Mv_Matrix<-mvabund::mvabund(Community) #works
Mod_Mv_Matrix<-mvabund::manyglm(Mv_Matrix ~ Factor, family="gamma") #works
mvabund::anova.manyglm(Mod_Mv_Matrix, nBoot = 1000, test="LR") #works
mvabund::anova.manyglm(Mod_Mv_Matrix,nBoot = 1000, test="LR", p.uni="adjusted") #works
mvabund::anova.manyglm(Mod_Mv_Matrix,nBoot = 1000, pairwise.comp = ~ Factor, test="LR", p.uni="adjusted")
Everything works until the last line with the pairwise comparisons, when I get this error:
Error in apply(resampled_stats, 1, per_row) :
dim(X) must have a positive length
I would be grateful for any help
Thanks
Jamie
Running RD CMD check --as-cran
where RD
is an alias for a very recent R-devel build leads to a lot of line noise. I can fix that -- as I have fixed it for other packages:
* using log directory ‘/home/edd/git/mvabund.Rcheck’
* using R Under development (unstable) (2015-08-31 r69241)
* using platform: x86_64-pc-linux-gnu (64-bit)
* using session charset: UTF-8
* using option ‘--as-cran’
* checking for file ‘mvabund/DESCRIPTION’ ... OK
* this is package ‘mvabund’ version ‘3.11.3.2’
* checking CRAN incoming feasibility ... NOTE
Possibly mis-spelled words in DESCRIPTION:
Rcpp (12:48)
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for executable files ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘mvabund’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
RcppVersion: no visible global function definition for
‘installed.packages’
absspp: no visible global function definition for ‘var’
anova.manyany: no visible global function definition for ‘logLik’
anova.manyglm : <anonymous>: no visible global function definition for
‘formula’
anova.manylm : <anonymous>: no visible global function definition for
‘formula’
as.mvformula: no visible global function definition for ‘as.formula’
as.mvformula: no visible global function definition for ‘terms’
as.mvformula: no visible global function definition for ‘model.frame’
best.r.sq: no visible global function definition for ‘terms’
best.r.sq: no visible global function definition for ‘reformulate’
best.r.sq: no visible global function definition for ‘update’
betaest: Error while checking: invalid 'envir' argument
bootAnova: no visible global function definition for ‘logLik’
case.names.manyglm: no visible global function definition for ‘weights’
case.names.manyglm: no visible global function definition for
‘residuals’
case.names.manylm: no visible global function definition for ‘weights’
case.names.manylm: no visible global function definition for
‘residuals’
cooks.distance.manylm: no visible global function definition for
‘residuals’
cooks.distance.manylm: no visible global function definition for
‘deviance’
cooks.distance.manylm: no visible global function definition for
‘df.residual’
covratio.manylm: no visible global function definition for
‘weighted.residuals’
cv.glm1path: no visible global function definition for ‘flush.console’
cv.glm1path: no visible binding for global variable ‘sd’
default.boxplot.mvformula: no visible global function definition for
‘terms’
default.meanvar.plot.mvabund: no visible global function definition for
‘dev.list’
default.meanvar.plot.mvabund: no visible global function definition for
‘postscript’
default.meanvar.plot.mvabund: no visible global function definition for
‘pdf’
default.meanvar.plot.mvabund: no visible global function definition for
‘jpeg’
default.meanvar.plot.mvabund: no visible global function definition for
‘bmp’
default.meanvar.plot.mvabund: no visible global function definition for
‘png’
default.meanvar.plot.mvabund: no visible global function definition for
‘dev.off’
default.meanvar.plot.mvabund: no visible global function definition for
‘var’
default.meanvar.plot.mvabund: no visible global function definition for
‘lm’
default.meanvar.plot.mvformula: no visible global function definition
for ‘dev.list’
default.meanvar.plot.mvformula: no visible global function definition
for ‘terms’
default.meanvar.plot.mvformula: no visible global function definition
for ‘model.response’
default.meanvar.plot.mvformula: no visible global function definition
for ‘model.frame’
default.meanvar.plot.mvformula: no visible global function definition
for ‘var’
default.meanvar.plot.mvformula: no visible global function definition
for ‘palette’
default.meanvar.plot.mvformula: no visible global function definition
for ‘rainbow’
default.meanvar.plot.mvformula: no visible global function definition
for ‘postscript’
default.meanvar.plot.mvformula: no visible global function definition
for ‘pdf’
default.meanvar.plot.mvformula: no visible global function definition
for ‘jpeg’
default.meanvar.plot.mvformula: no visible global function definition
for ‘bmp’
default.meanvar.plot.mvformula: no visible global function definition
for ‘png’
default.meanvar.plot.mvformula: no visible global function definition
for ‘dev.off’
default.meanvar.plot.mvformula: no visible global function definition
for ‘lm’
default.plot.manyglm: no visible global function definition for
‘dev.list’
default.plot.manyglm: no visible global function definition for
‘postscript’
default.plot.manyglm: no visible global function definition for ‘pdf’
default.plot.manyglm: no visible global function definition for ‘jpeg’
default.plot.manyglm: no visible global function definition for ‘bmp’
default.plot.manyglm: no visible global function definition for ‘png’
default.plot.manyglm: no visible global function definition for
‘dev.off’
default.plot.manyglm: no visible global function definition for
‘residuals’
default.plot.manyglm: no visible global function definition for
‘rainbow’
default.plot.manyglm: no visible global function definition for
‘df.residual’
default.plot.manyglm: no visible global function definition for
‘dev.interactive’
default.plot.manylm: no visible global function definition for
‘residuals’
default.plot.manylm: no visible global function definition for
‘dev.list’
default.plot.manylm: no visible global function definition for
‘postscript’
default.plot.manylm: no visible global function definition for ‘pdf’
default.plot.manylm: no visible global function definition for ‘jpeg’
default.plot.manylm: no visible global function definition for ‘bmp’
default.plot.manylm: no visible global function definition for ‘png’
default.plot.manylm: no visible global function definition for
‘dev.off’
default.plot.manylm: no visible global function definition for
‘predict’
default.plot.manylm: no visible global function definition for
‘weights’
default.plot.manylm: no visible global function definition for
‘rainbow’
default.plot.manylm: no visible global function definition for
‘df.residual’
default.plot.manylm: no visible global function definition for
‘na.omit’
default.plot.manylm: no visible global function definition for
‘weighted.residuals’
default.plot.manylm: no visible global function definition for
‘deviance’
default.plot.manylm: no visible global function definition for
‘dev.interactive’
default.plot.mvabund: no visible global function definition for
‘dev.list’
default.plot.mvabund: no visible global function definition for
‘as.formula’
default.plot.mvabund: no visible global function definition for
‘update’
default.plot.mvabund: no visible global function definition for
‘postscript’
default.plot.mvabund: no visible global function definition for ‘pdf’
default.plot.mvabund: no visible global function definition for ‘jpeg’
default.plot.mvabund: no visible global function definition for ‘bmp’
default.plot.mvabund: no visible global function definition for ‘png’
default.plot.mvabund: no visible global function definition for
‘dev.cur’
default.plot.mvabund: no visible global function definition for
‘dev.off’
default.plot.mvabund: no visible global function definition for
‘dev.set’
default.plot.mvformula: no visible global function definition for
‘dev.list’
default.plot.mvformula: no visible global function definition for
‘as.formula’
default.plot.mvformula: no visible global function definition for
‘terms’
default.plot.mvformula: no visible global function definition for
‘model.response’
default.plot.mvformula: no visible global function definition for
‘model.frame’
default.plot.mvformula: no visible global function definition for
‘postscript’
default.plot.mvformula: no visible global function definition for ‘pdf’
default.plot.mvformula: no visible global function definition for
‘jpeg’
default.plot.mvformula: no visible global function definition for ‘bmp’
default.plot.mvformula: no visible global function definition for
‘dev.cur’
default.plot.mvformula: no visible global function definition for
‘dev.off’
default.plot.mvformula: no visible global function definition for
‘dev.set’
default.plotMvaFactor: no visible global function definition for
‘dev.list’
default.plotMvaFactor: no visible global function definition for
‘postscript’
default.plotMvaFactor: no visible global function definition for ‘pdf’
default.plotMvaFactor: no visible global function definition for ‘jpeg’
default.plotMvaFactor: no visible global function definition for ‘bmp’
default.plotMvaFactor: no visible global function definition for ‘png’
default.plotMvaFactor: no visible global function definition for
‘dev.cur’
default.plotMvaFactor: no visible global function definition for
‘dev.off’
default.plotMvaFactor: no visible global function definition for
‘dev.set’
default.plotMvaFactor: no visible global function definition for
‘rainbow’
default.print.anova.manyglm: no visible global function definition for
‘printCoefmat’
default.print.summary.manyglm: no visible global function definition
for ‘symnum’
default.print.summary.manyglm: no visible binding for global variable
‘quantile’
default.print.summary.manyglm: no visible global function definition
for ‘quantile’
deviance.manylm: no visible global function definition for ‘na.fail’
deviance.manylm: no visible global function definition for ‘na.omit’
deviance.manylm: no visible global function definition for ‘na.exclude’
dfbeta.manyglm: no visible global function definition for
‘variable.names’
dfbeta.manylm: no visible global function definition for
‘variable.names’
dfbetas.manylm: no visible global function definition for ‘dfbeta’
dffits.manylm: no visible global function definition for
‘weighted.residuals’
effic.make.link : mu.eta.fct: no visible global function definition for
‘dnorm’
effic.make.link : linkinv.fct: no visible global function definition
for ‘qnorm’
effic.make.link : linkinv.fct: no visible global function definition
for ‘pnorm’
effic.make.link : mu.eta.fct: no visible global function definition for
‘dcauchy’
effic.make.link : linkinv.fct: no visible global function definition
for ‘qcauchy’
effic.make.link : linkinv.fct: no visible global function definition
for ‘pcauchy’
effic.make.link: no visible global function definition for ‘make.link’
extend.x.formula: no visible global function definition for ‘terms’
extend.x.formula: no visible global function definition for
‘reformulate’
extend.x.formula: no visible global function definition for ‘update’
extractAIC.manyglm: no visible global function definition for ‘logLik’
formulaMva: no visible global function definition for ‘terms’
formulaMva: no visible global function definition for ‘model.frame’
formulaUnimva: no visible global function definition for ‘update’
formulaUnimva: no visible global function definition for ‘terms’
formulaUnimva: no visible global function definition for ‘as.formula’
get.design: no visible global function definition for ‘as.formula’
get.design: no visible global function definition for ‘model.matrix’
get.design: no visible global function definition for ‘contrasts’
get.design: no visible global function definition for ‘terms’
get.polys: no visible global function definition for ‘contrasts’
get.polys: no visible global function definition for ‘contrasts<-’
get.polys: no visible global function definition for ‘poly’
make.vslink: no visible global function definition for ‘make.link’
manyany: Error while checking: invalid 'envir' argument
manyglm: no visible global function definition for ‘model.offset’
manyglm: no visible global function definition for ‘model.response’
manyglm: no visible global function definition for ‘model.matrix’
manyglm: no visible global function definition for ‘.getXlevels’
manylm: no visible global function definition for ‘model.response’
manylm: no visible global function definition for ‘model.weights’
manylm: no visible global function definition for ‘model.offset’
manylm: no visible global function definition for ‘is.empty.model’
manylm: no visible global function definition for ‘model.matrix’
manylm: no visible global function definition for ‘.getXlevels’
manylm.fit: no visible binding for global variable ‘data’
manylm.influence: no visible global function definition for
‘weighted.residuals’
manylm.influence.measures : is.influential: no visible global function
definition for ‘pf’
manylm.influence.measures: no visible global function definition for
‘weighted.residuals’
manylm.influence.measures: no visible global function definition for
‘df.residual’
manylm.influence.measures: no visible global function definition for
‘variable.names’
mu.update: Error while checking: invalid 'envir' argument
mvformula: no visible global function definition for ‘formula’
mvformula: no visible global function definition for ‘terms’
plot.glm1path: no visible global function definition for ‘loess.smooth’
plot.manyany: no visible global function definition for ‘qnorm’
plot.manyany: no visible global function definition for ‘rainbow’
plotFormulafeature: no visible global function definition for
‘model.matrix’
plotFormulafeature: no visible global function definition for
‘model.frame’
plotFormulafeature: no visible global function definition for ‘terms’
predict.manyglm: no visible binding for global variable ‘na.pass’
predict.manyglm: no visible global function definition for ‘formula’
predict.manyglm: Error while checking: invalid 'envir' argument
predict.manylm: no visible binding for global variable ‘na.pass’
predict.manylm: no visible global function definition for ‘formula’
predict.manylm: no visible global function definition for ‘as.formula’
predict.manylm: no visible global function definition for ‘model.frame’
predict.manylm: no visible global function definition for ‘lm’
predict.manylm: no visible global function definition for ‘predict.lm’
predict.traitglm: no visible global function definition for ‘coef’
print.anova.manyany: no visible global function definition for
‘printCoefmat’
print.anova.manylm: no visible global function definition for
‘printCoefmat’
print.manyany: no visible global function definition for ‘qnorm’
print.manyglm: no visible global function definition for ‘coef’
print.manyglm: no visible global function definition for ‘naprint’
print.manylm: no visible global function definition for ‘coef’
print.manylm: no visible global function definition for ‘naprint’
print.summary.manylm: no visible global function definition for
‘symnum’
print.summary.manylm: no visible binding for global variable ‘quantile’
print.summary.manylm: no visible global function definition for
‘quantile’
quasibinomialMany: no visible global function definition for
‘make.link’
quasipoisson: no visible global function definition for ‘make.link’
rcalc: no visible global function definition for ‘var’
residuals.glm1path: no visible global function definition for ‘runif’
residuals.glm1path: no visible global function definition for ‘qnorm’
residuals.manyany: no visible global function definition for ‘runif’
residuals.manyglm: no visible global function definition for ‘coef’
residuals.manyglm: no visible global function definition for ‘runif’
residuals.manyglm: no visible global function definition for ‘qnorm’
ridgeParamEst: no visible global function definition for ‘runif’
ridgeParamEst: no visible global function definition for ‘cov’
rstandard.manylm: no visible global function definition for ‘deviance’
rstandard.manylm: no visible global function definition for
‘df.residual’
rstandard.manylm: no visible global function definition for
‘weighted.residuals’
shiftpoints: no visible global function definition for ‘na.omit’
traitglm: no visible global function definition for ‘coef’
transAxis: no visible global function definition for ‘terms’
transAxis: no visible global function definition for ‘dev.off’
weighted.residuals.manylm: no visible global function definition for
‘weights’
weighted.residuals.manylm: no visible global function definition for
‘residuals’
weights.manyglm: no visible global function definition for ‘naresid’
Undefined global functions or variables:
.getXlevels as.formula bmp coef contrasts contrasts<- cov data
dcauchy dev.cur dev.interactive dev.list dev.off dev.set deviance
df.residual dfbeta dnorm flush.console formula installed.packages
is.empty.model jpeg lm loess.smooth logLik make.link model.frame
model.matrix model.offset model.response model.weights na.exclude
na.fail na.omit na.pass naprint naresid palette pcauchy pdf pf png
pnorm poly postscript predict predict.lm printCoefmat qcauchy qnorm
quantile rainbow reformulate residuals runif sd symnum terms update
var variable.names weighted.residuals weights
Consider adding
importFrom("grDevices", "bmp", "dev.cur", "dev.interactive",
"dev.list", "dev.off", "dev.set", "jpeg", "palette", "pdf",
"png", "postscript", "rainbow")
importFrom("stats", ".getXlevels", "as.formula", "coef", "contrasts",
"contrasts<-", "cov", "dcauchy", "deviance", "df.residual",
"dfbeta", "dnorm", "formula", "is.empty.model", "lm",
"loess.smooth", "logLik", "make.link", "model.frame",
"model.matrix", "model.offset", "model.response",
"model.weights", "na.exclude", "na.fail", "na.omit",
"na.pass", "naprint", "naresid", "pcauchy", "pf", "pnorm",
"poly", "predict", "predict.lm", "printCoefmat", "qcauchy",
"qnorm", "quantile", "reformulate", "residuals", "runif",
"sd", "symnum", "terms", "update", "var", "variable.names",
"weighted.residuals", "weights")
importFrom("utils", "data", "flush.console", "installed.packages")
to your NAMESPACE.
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd line widths ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking compilation flags in Makevars ... OK
* checking for GNU extensions in Makefiles ... OK
* checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
* checking compiled code ... OK
* checking examples ... [53s/54s] OK
Examples with CPU or elapsed time > 5s
user system elapsed
anova.manyany 7.868 0.056 7.939
traitglm 6.812 0.000 6.821
summary.manyglm 4.996 0.004 5.016
* checking PDF version of manual ... OK
* DONE
Status: 2 NOTEs
It looks worse than it is. The suggested change to NAMESPACE
will do most of this. But it helps to have a version of R-devel nearby ...
Jakub to provide a version of manyglm that uses MARGE
when fitting a Poisson model to a count dataset with outliers, outlying residuals come back as Inf which gives an error when you try to plot. Maybe truncate Inf to some big value and return a warning?
From a forwarded email of Davids to Julian:
"
There is a block function in manyglm. I would like to:
says residual is default, when it is not, says you can use none, but returns an error if you try this
When doing an anova using a randomly generated (independent and identically distributed) BootID matrix, with resamp= "case", the p-values are all around 0.5. If no BootID is used, p-values on the same data are 0.001. This suggests that when BootID argument is supplied, the offset is not being implemented correctly.
CODE TO REPLICATE BELOW:
rm(list=ls())
set.seed(42)
x = runif(500)
y = runif(500)
library(RandomFields)
temperature = RandomFields::RFsimulate(model = RandomFields::RMexp(var=1, scale=0.1),
x=x, y=y,grid=F,n=1)$variable1
#20 species
nspec = 20
responseMultiSpecies = matrix(nrow=500, ncol=nspec)
Beta_multi = rnorm(nspec,10,1)
for (j in 1:nspec){
spatial_residuals4 = RandomFields::RFsimulate(model = RandomFields::RMexp(var=1, scale=0.05),
x=x, y=y,grid=F, n=1)$variable1
responseMultiSpecies[,j] = temperature*Beta_multi[j] + spatial_residuals4 + rnorm(500)
responseMultiSpecies[,j]=rbinom(n=500, size=1, prob=exp( responseMultiSpecies[,j])/c(1+ exp(responseMultiSpecies[,j])))
}
multispecies_dat=data.frame(responseMultiSpecies, temperature)
library(mvabund)
responseMultiSpecies=mvabund(multispecies_dat[,1:20]) #20 species multivariate reponse
mod.1 = manyglm(responseMultiSpecies1, data = multispecies_dat,family="binomial")temperature, data = multispecies_dat,family="binomial")
mod.2 = manyglm(responseMultiSpecies
bootID_IID = matrix(sample(1:500,500*1000,replace=T),nrow=1000,ncol=500)
mvabund.case.bootID.0 = anova(mod.1, mod.2, resamp="case",bootID=bootID_IID)
mvabund.case.bootID.none = anova(mod.1, mod.2, resamp="case")
mvabund.case.bootID.0$table
mvabund.case.bootID.none$table
mvabund.case.bootID.0_ = c()
mvabund.case.bootID.none_ = c()
for (i in 1:50){
bootID_IID = matrix(sample(1:500,500*1000,replace=T),nrow=1000,ncol=500)
mvabund.case.bootID.0_[i] = anova(mod.1, mod.2, resamp="case",bootID=bootID_IID)$table[2,4]
mvabund.case.bootID.none_[i] = anova(mod.1, mod.2, resamp="case")$table[2,4]
}
Originally in an email from David to Julian, will close if fixed
This is a weird one in the C code.
For the below design, there are 3 reps in 2 treatments, so when using resampling the smallest P-value you should be able to get is 1/10 (10 possible permutations of 6 obs in 2 treatments). But the univariate P-value for the second response is 0.002.
My guess is that there is some floating point error in the code – we should be using statements like
t*>t-eps
rather than t*>=t
but perhaps in the code for univariate P-values this isn’t happening?
library(mvabund)
load("kos_bug.RData")
kos2
kosFac
# fitting a linear model to log-transformed proportions:
prop.mva = kos2.mva/kosFac$sampleTotals
kos2.lm = manylm(log(prop.mva) ~ condition, data = kosFac)
anova(kos2.lm,p.uni="unadjusted",nBoot=999)
Analysis of Variance Table
Model: manylm(formula = log(prop.mva) ~ condition, data = kosFac)
Overall test for all response variables
Test statistics:
Res.Df Df.diff val(F) Pr(>F)
(Intercept) 5
condition 4 1 282 0.094 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Univariate Tests
Test statistics:
K02014 K00059 K02033 K02034 K07316
F value Pr(>F) F value Pr(>F) F value Pr(>F) F value Pr(>F) F value Pr(>F)
(Intercept)
condition 51.331 0.094 148.638 0.002 44.148 0.094 37.196 0.094 0.643 0.591
Arguments: with 999 resampling iterations using residual (without replacement) resampling and response assumed to be uncorrelated
plot custom contrasts for factor variable coeficients in manyglm models.
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