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mjskay denisashah

tidytreatment's Issues

Using survival (time-to-event) outcomes

Thanks for continuing development! Would you perhaps know whether there is any way to use survival (time-to-event) outcomes with any of the BART engines that {tidytreatment} is supporting? We are currently evaluating the {grf}, {personalized}, and {StratifiedMedicine} packages for the use on our clinical data, and having support for survival endpoints in {tidytreatment} would be great for the comparison. Thanks for any hints you may have!

is_01_integer_vector(newdata[, treatment]) is not TRUE

Hi!
Thanks for this package - it looks really handy. I know it's still in early stages, so perhaps that is the reason why I'm getting this error.

I'm trying to get the treatment effects from a classification bartMachine model (but this error also occurs with a regression model as well).

I've created a data frame called dat and have followed your vignette, calculating the propensity score:
(Note: This doesn't format nicely on Github, but if you copy and paste it into R, you will get the data frame with the propensity score included).

tibble::tribble(
                 ~x1,                ~x2, ~c1, ~c2, ~c3, ~c4, ~c5, ~c6, ~z, ~y,       ~prop_score,
  -0.619983928438955, -0.704383293245453,  2L,  1L,  5L,  2L,  4L,  4L,  1, 1L, 0.542908575444251,
  -0.626634394521585,  -1.29479197186313,  2L,  1L,  3L,  3L,  4L,  2L,  0, 1L, 0.472973074828456,
  -0.211822807502002,  0.509778990213741,  1L,  1L,  3L,  2L,  1L,  2L,  0, 1L, 0.534461492086001,
  -0.901743029895167,  -1.23626678676882,  1L,  1L,  3L,  3L,  2L,  1L,  0, 1L, 0.469981801468016,
   0.108343378149696,  0.500197708935189,  2L,  0L,  5L,  4L,  1L,  4L,  0, 1L,  0.46428639428549,
   0.711996308261449, -0.601781192572445,  2L,  1L,  4L,  3L,  1L,  1L,  1, 1L, 0.527992738829824,
   -1.38204158657383,  -1.23812835972309,  1L,  0L,  5L,  2L,  2L,  1L,  1, 1L, 0.500471458430563,
   0.721034867054809,  0.509705714053607,  2L,  1L,  4L,  2L,  2L,  1L,  0, 1L, 0.542532509472265,
   0.707471997522852,  0.565327806243676,  2L,  1L,  3L,  3L,  1L,  1L,  1, 0L, 0.523616539070879,
    0.70342326385171,  0.498471660056034,  1L,  1L,  1L,  3L,  1L,  4L,  1, 0L, 0.390898999903596,
   0.709016830182189,  0.405428236852683,  1L,  1L,  3L,  3L,  1L,  4L,  1, 1L, 0.407917511977301,
   0.719187348357036,  0.569524495448596,  1L,  0L,  5L,  4L,  1L,  3L,  0, 1L, 0.484834964845595,
   0.719743462605666,  0.503915016980274,  1L,  1L,  5L,  4L,  2L,  3L,  1, 1L, 0.476156586501844,
   0.708793635755663, -0.446566834486588,  1L,  1L,  5L,  4L,  1L,  4L,  0, 1L, 0.524972723679686,
    0.72673036391176,  0.508745711467959,  1L,  1L,  1L,  3L,  1L,  4L,  0, 1L,   0.6294890530542,
   0.717929677790862, -0.502116337087818,  2L,  1L,  3L,  4L,  1L,  3L,  0, 0L, 0.496876401125496,
   0.723212745676559,  0.510802082592574,  1L,  1L,  1L,  3L,  1L,  3L,  1, 1L, 0.600318725870474,
   0.121425965803968,  0.296052357185999,  1L,  1L,  2L,  4L,  1L,  4L,  0, 0L, 0.545459684037481,
   0.722287187034774,  0.558292991175618,  1L,  1L,  2L,  3L,  1L,  3L,  0, 1L, 0.537113083141673,
   0.716249243063561, -0.662962588231552,  2L,  1L,  3L,  1L,  2L,  3L,  1, 0L, 0.502049711413861,
   0.717828654765672,  -1.22899310053262,  2L,  1L,  3L,  1L,  1L,  2L,  0, 1L, 0.483631686895801,
   0.706389311802865,  0.565629138649073,  1L,  1L,  3L,  4L,  1L,  2L,  1, 0L, 0.536503059865542,
  -0.203417031998133, -0.770222158480375,  2L,  1L,  3L,  2L,  2L,  3L,  0, 1L, 0.484053590181911,
   0.711436137741298,   -1.2301795104596,  2L,  0L,  4L,  3L,  1L,  2L,  1, 0L, 0.524062945928105,
   0.724140796755138,  0.505182463040808,  1L,  1L,  4L,  4L,  1L,  4L,  0, 1L, 0.502258493897457,
   0.716268318989523, -0.512667654828064,  1L,  1L,  4L,  1L,  2L,  3L,  0, 0L, 0.589050987302026,
  -0.210806733608954,  -1.45495789372891,  2L,  1L,  4L,  3L,  2L,  1L,  1, 1L, 0.496656909399398,
   0.719827976889018,  0.562633419916495,  1L,  1L,  1L,  2L,  1L,  4L,  0, 0L, 0.587927508222399,
   0.715648299615523,  0.238069956615491,  2L,  1L,  2L,  3L,  2L,  4L,  1, 0L, 0.518915001679177,
   0.735343066119825,  0.559735033474916,  1L,  1L,  2L,  4L,  1L,  3L,  1, 1L,  0.53282840822453,
   0.723612845213415,  -1.23197380567581,  1L,  1L,  5L,  4L,  1L,  2L,  0, 0L, 0.516384808056013,
   0.111768030844326, -0.471829269894991,  2L,  1L,  3L,  2L,  2L,  1L,  0, 1L, 0.490294929493957,
   -1.37244386883096,  -1.23542008622328,  1L,  1L,  5L,  2L,  2L,  1L,  0, 1L, 0.519759047557842,
   0.725708390097975,   -1.4187950549084,  1L,  1L,  4L,  3L,  1L,  3L,  0, 1L, 0.506215512190674,
  -0.203895625066123, -0.707663908453269,  2L,  1L,  4L,  3L,  1L,  2L,  1, 1L, 0.531299969705177,
   0.720558921143588, -0.507896987328083,  2L,  1L,  5L,  3L,  2L,  1L,  1, 1L,  0.57566280672683,
    0.71560806106389,  0.505113232185129,  2L,  1L,  2L,  4L,  1L,  4L,  0, 0L, 0.515829756245115,
   0.112391830248744,  0.507387374908298,  1L,  1L,  3L,  4L,  1L,  1L,  1, 1L, 0.500827148021631,
   0.730676501651419,  0.561127755107954,  2L,  0L,  2L,  4L,  1L,  4L,  1, 0L, 0.546148981837634,
   0.103926715042258,  0.408184629561685,  2L,  1L,  1L,  4L,  1L,  3L,  0, 0L, 0.381803787913373,
   0.707640791779242,  0.564613846564169,  2L,  1L,  4L,  4L,  1L,  3L,  1, 1L,   0.4466918335167,
  -0.619983733513069,  -1.34729960729906,  2L,  1L,  5L,  3L,  1L,  2L,  1, 1L, 0.507999212628759,
   0.731751828434166,  0.570879206514913,  2L,  0L,  4L,  4L,  1L,  2L,  1, 1L,  0.49503576582592,
  -0.906234718467424, -0.464784881069768,  2L,  1L,  4L,  3L,  2L,  2L,  0, 1L, 0.543727828195565,
   0.724195955009647,  0.519071824873624,  2L,  1L,  4L,  2L,  2L,  4L,  0, 0L, 0.511732005235294,
   -1.36261767399284, -0.597298931815627,  1L,  1L,  3L,  3L,  2L,  2L,  1, 1L,  0.48168744557023,
   0.718465384803859,  0.561570383543805,  1L,  1L,  4L,  3L,  2L,  2L,  1, 1L, 0.444191762248526,
   0.722609922246391,  -1.24064443844372,  1L,  1L,  3L,  4L,  1L,  3L,  0, 1L, 0.453540383935195,
  -0.626390809541331,  0.200706298006921,  2L,  1L,  3L,  2L,  2L,  3L,  0, 1L, 0.496724791477404,
    0.72080545663214,  0.509088972581472,  2L,  1L,  2L,  3L,  1L,  4L,  0, 0L, 0.602933394597977,
   0.725824895374323,  0.570465404411146,  1L,  1L,  4L,  4L,  1L,  3L,  1, 0L,  0.49442028949831,
  -0.622466825029192,  0.457245654530033,  2L,  1L,  1L,  3L,  4L,  2L,  1, 0L, 0.362605952613939,
   0.116647951498765,  0.299363240289861,  1L,  1L,  4L,  4L,  2L,  2L,  1, 0L, 0.549513210484241,
   0.722665069475875,  0.506555312675104,  1L,  1L,  3L,  4L,  1L,  4L,  1, 0L, 0.481206097532479,
   0.718578588309193, -0.600093475766179,  1L,  1L,  5L,  3L,  2L,  3L,  0, 1L, 0.537925251015914,
   0.725199649049049, -0.436045678081683,  2L,  1L,  4L,  4L,  1L,  2L,  1, 1L,  0.53854007572209,
   0.109924118084875, -0.515552543971096,  2L,  1L,  2L,  2L,  3L,  2L,  1, 0L, 0.540454680707154,
  -0.629026458623944, -0.693398805838134,  1L,  1L,  5L,  3L,  1L,  3L,  0, 1L, 0.575609798586739,
   0.720875462565464,  -1.23199800070166,  2L,  1L,  1L,  4L,  1L,  3L,  1, 1L,  0.35477001964168,
   0.720589423513613,  0.507715514920805,  2L,  1L,  2L,  4L,  1L,  4L,  0, 0L, 0.572874158281384,
  -0.619909290626461,  -1.23575360710521,  1L,  1L,  4L,  4L,  2L,  2L,  0, 1L, 0.516137793254588,
   0.733118514241284, -0.401101963210555,  1L,  0L,  5L,  3L,  1L,  4L,  0, 1L,  0.52694365070584,
   0.109681139709487,  0.253943880615392,  2L,  1L,  2L,  4L,  1L,  2L,  1, 1L, 0.523559866215833,
   0.712225907332477, -0.656323340422975,  1L,  0L,  4L,  4L,  1L,  3L,  0, 1L, 0.545722198624145,
   -0.61889936012751,  -1.26243642978487,  2L,  1L,  5L,  3L,  1L,  1L,  0, 1L, 0.507690724444577,
   0.713970410991397,  0.411081182749688,  2L,  1L,  4L,  3L,  1L,  2L,  0, 1L, 0.468726934695203,
   0.111487497649392,  0.408112049324159,  2L,  1L,  4L,  3L,  2L,  2L,  1, 1L, 0.461339321470336,
   -1.36675566300839,  0.558908094710503,  2L,  1L,  3L,  3L,  4L,  4L,  1, 0L, 0.482849394107573,
   -0.61442067164354,  0.209336574406927,  1L,  1L,  3L,  2L,  1L,  3L,  0, 1L, 0.496461374043943,
   0.722188924504669,  0.502200183110036,  1L,  1L,  5L,  3L,  1L,  3L,  0, 1L, 0.481887715622555,
   0.725123520192068,  0.554979283191126,  1L,  1L,  5L,  3L,  1L,  3L,  0, 1L, 0.472118605257476,
  -0.216783501151248, -0.764927126370616,  1L,  1L,  3L,  1L,  3L,  3L,  1, 0L, 0.476628185701138,
   0.718328936832771,  0.565338885010626,  1L,  1L,  5L,  4L,  1L,  2L,  1, 0L, 0.435179355921364,
   -0.21117063974132,  0.500494141597518,  2L,  1L,  5L,  4L,  1L,  2L,  0, 1L, 0.468645476521097,
  -0.206724659814808,  0.574497114796512,  2L,  1L,  4L,  3L,  3L,  1L,  0, 1L, 0.501432198543294,
   -1.36932037985284,  0.567997232201149,  2L,  1L,  1L,  3L,  4L,  1L,  1, 1L, 0.599436746922836,
   0.103966399059827,  0.504659922136247,  1L,  0L,  4L,  4L,  1L,  3L,  1, 1L, 0.483589668724281,
   0.727686345407945,  0.567986494849604,  1L,  0L,  3L,  3L,  1L,  3L,  0, 1L, 0.557420216204061,
   0.716519648044397,  0.562063890488908,  1L,  1L,  5L,  4L,  1L,  2L,  0, 1L, 0.428216720236038,
   0.726464317138348, -0.403663594344434,  1L,  1L,  4L,  4L,  1L,  4L,  0, 1L,  0.54682416705555,
   0.724022157955659,  0.400649301941948,  2L,  1L,  3L,  2L,  2L,  1L,  0, 1L, 0.477434214942826,
  -0.215075694683002, -0.514529209915271,  2L,  1L,  3L,  3L,  5L,  1L,  0, 0L, 0.529332088250662,
   0.725958151563208,   0.28972809288225,  2L,  0L,  1L,  4L,  1L,  2L,  0, 1L,  0.44612359356437,
   0.710721498938538,  0.506107609222488,  1L,  1L,  5L,  3L,  1L,  2L,  1, 0L, 0.510874236024449,
   -1.37732485889494,  0.453978511184122,  2L,  1L,  3L,  2L,  2L,  1L,  1, 1L, 0.372521351932443,
   0.715464511195532,  0.564518579400541,  1L,  1L,  4L,  3L,  1L,  3L,  1, 1L,   0.4466918335167,
   0.728048482425467, -0.510827085283348,  2L,  0L,  4L,  3L,  1L,  2L,  0, 1L, 0.585580676804591,
   0.717609720508345,  0.505631545659016,  1L,  1L,  3L,  3L,  1L,  2L,  1, 1L, 0.466635347329185,
  -0.618705658028534,  -1.27862760188277,  1L,  1L,  4L,  2L,  3L,  2L,  1, 1L, 0.512945806663173,
  -0.625618829322605,  0.561396621884238,  2L,  1L,  4L,  3L,  1L,  2L,  1, 1L, 0.441711034943067,
   0.708812343450583,  0.572875444566286,  1L,  0L,  1L,  2L,  1L,  3L,  1, 0L, 0.595732714429437,
    0.72424286539536,  0.415448738622642,  1L,  1L,  4L,  4L,  1L,  3L,  0, 0L, 0.473707014647853,
   0.707444790047399,   0.55620764090138,  2L,  1L,  3L,  4L,  1L,  2L,  1, 1L, 0.499117321588344,
   0.728219716757736,  0.401105645225583,  1L,  1L,  3L,  4L,  1L,  3L,  0, 1L, 0.463864060774488,
   -1.36383562682315,  -1.53411062215581,  2L,  1L,  5L,  3L,  1L,  1L,  0, 1L, 0.510263169175049,
   0.717364967554017,  0.501866846558604,  1L,  1L,  3L,  3L,  1L,  4L,  0, 0L, 0.429866002810877,
     0.1149139984673,  0.448119671702872,  2L,  1L,  4L,  3L,  1L,  3L,  1, 1L, 0.457492818734489,
   0.118315042985935,  0.456710336927965,  2L,  1L,  2L,  3L,  1L,  2L,  1, 1L, 0.423369972581007,
  -0.912383449456444, -0.705311104570593,  2L,  1L,  1L,  2L,  3L,  2L,  1, 1L, 0.371203388587355,
    0.70840841613744, -0.723831830767927,  2L,  1L,  4L,  3L,  3L,  3L,  1, 1L, 0.524183552730355
  ) %>% as.data.frame()

The code is as follows:

set_bart_machine_num_cores(2)

  var_select_bart <- bartMachine(
    X = select(dat,-y,-z),
    y = select(dat, y)[[1]],
    num_burn_in = 2000,
    num_iterations_after_burn_in = 5000,
    serialize = T,
    verbose = F
    )

  var_select <- bartMachine::var_selection_by_permute_cv(var_select_bart, k_folds = 5)

  prop_bart <- bartMachine(
    X = select(dat,var_select$important_vars_cv), 
    y = as.factor(select(dat, z)[[1]]), 
    num_burn_in = 2000,
    num_iterations_after_burn_in = 5000,
    serialize = T,
    verbose = F
    ) 

  dat$prop_score <-  prop_bart$p_hat_train

  prior_incl_prob <- setNames(rep(1, times = ncol(dat) - 1), colnames(dat)[colnames(dat) != 'y'])
  prior_incl_prob['z'] <- 2

bartM <- build_bart_machine(
    X = select(dat,-y),
    y = select(dat, y)[[1]],
    num_burn_in = 2000, 
    num_iterations_after_burn_in = 5000,
    serialize = T,
    verbose = F,
    cov_prior_vec = prior_incl_prob
    )

But when I run the following code to extract the treatment effects, I get this error:

posterior_treat_eff <- treatment_effects(bartM, treatment = "z")
Error in fitted_with_counter_factual_draws(model, newdata, treatment, : is_01_integer_vector(newdata[, treatment]) | is.logical(newdata[, .... is not TRUE

Any reasons why? I'm honestly stumped - your example with the simulate_su_hill_data() works just fine. AFAIK, my set up is identical - including using the integer class for categorical variables.

Session info below:

> sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin19.5.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /usr/local/Cellar/openblas/0.3.10_1/lib/libopenblasp-r0.3.10.dylib

locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] tidybayes_2.3.1     forcats_0.5.0       stringr_1.4.0       dplyr_1.0.2         purrr_0.3.4         readr_1.3.1         tidyr_1.1.2        
 [8] tibble_3.0.4        ggplot2_3.3.2       tidyverse_1.3.0     tidytreatment_0.1.0 bartMachine_1.2.5.1 missForest_1.4      itertools_0.1-3    
[15] iterators_1.0.12    foreach_1.5.0       randomForest_4.6-14 bartMachineJARs_1.1 rJava_0.9-13       

loaded via a namespace (and not attached):
  [1] readxl_1.3.1         backports_1.1.10     plyr_1.8.6           igraph_1.2.5         splines_4.0.2        svUnit_1.0.3        
  [7] crosstalk_1.1.0.1    listenv_0.8.0        TH.data_1.0-10       rstantools_2.1.1     inline_0.3.15        digest_0.6.27       
 [13] htmltools_0.5.0      rsconnect_0.8.16     fansi_0.4.1          magrittr_1.5         globals_0.12.5       brms_2.13.5         
 [19] modelr_0.1.8         RcppParallel_5.0.2   matrixStats_0.56.0   MCMCpack_1.4-9       xts_0.12.1           sandwich_2.5-1      
 [25] prettyunits_1.1.1    colorspace_2.0-0     rvest_0.3.6          blob_1.2.1           ggdist_2.3.0         haven_2.3.1         
 [31] xfun_0.16            callr_3.5.1          crayon_1.3.4         jsonlite_1.7.1       survival_3.1-12      zoo_1.8-8           
 [37] glue_1.4.2           gtable_0.3.0         emmeans_1.5.1        MatrixModels_0.4-1   V8_3.2.0             distributional_0.2.1
 [43] pkgbuild_1.1.0       rstan_2.21.2         datapasta_3.1.0      future.apply_1.6.0   abind_1.4-5          SparseM_1.78        
 [49] scales_1.1.1         mvtnorm_1.1-1        DBI_1.1.0            miniUI_0.1.1.1       Rcpp_1.0.5           xtable_1.8-4        
 [55] tmvnsim_1.0-2        stats4_4.0.2         StanHeaders_2.21.0-6 DT_0.15              httr_1.4.2           htmlwidgets_1.5.1   
 [61] threejs_0.3.3        arrayhelpers_1.1-0   lavaan_0.6-7         ellipsis_0.3.1       farver_2.0.3         pkgconfig_2.0.3     
 [67] loo_2.3.1            dbplyr_1.4.4         utf8_1.1.4           here_0.1             tidyselect_1.1.0     rlang_0.4.8         
 [73] reshape2_1.4.4       later_1.1.0.1        cellranger_1.1.0     munsell_0.5.0        tools_4.0.2          cli_2.1.0           
 [79] generics_0.1.0       broom_0.7.0          ggridges_0.5.2       evaluate_0.14        fastmap_1.0.1        yaml_2.2.1          
 [85] blavaan_0.3-10       mcmc_0.9-7           fs_1.5.0             processx_3.4.4       knitr_1.29           bcf_1.3             
 [91] future_1.18.0        nlme_3.1-148         mime_0.9             quantreg_5.70        xml2_1.3.2           nonnest2_0.5-5      
 [97] compiler_4.0.2       bayesplot_1.7.2      shinythemes_1.1.2    rstudioapi_0.13      curl_4.3             reprex_0.3.0        
[103] pbivnorm_0.6.0       stringi_1.4.6        ps_1.4.0             Brobdingnag_1.2-6    lattice_0.20-41      Matrix_1.2-18       
[109] markdown_1.1         shinyjs_2.0.0        vctrs_0.3.4          CompQuadForm_1.4.3   pillar_1.4.6         lifecycle_0.2.0     
[115] bridgesampling_1.0-0 estimability_1.3     conquer_1.0.2        httpuv_1.5.4         R6_2.5.0             promises_1.1.1      
[121] gridExtra_2.3        codetools_0.2-16     colourpicker_1.1.0   MASS_7.3-51.6        gtools_3.8.2         assertthat_0.2.1    
[127] rprojroot_2.0.2      withr_2.3.0          shinystan_2.5.0      mnormt_2.0.2         multcomp_1.4-14      hms_0.5.3           
[133] parallel_4.0.2       grid_4.0.2           coda_0.19-4          rmarkdown_2.5        shiny_1.5.0          lubridate_1.7.9     
[139] base64enc_0.1-3      dygraphs_1.1.1.6

Add unit testing

We need testing of the functionality of the package functions to ensure that breaking changes/updates in the future will be caught. This is for our own sanity but also so that others can contribute with more confidence.

Unit testing is needed for

  • treatment_effects(...)
  • avg_treatment_effects(...), tidy_ate(...), tidy_att(...)
  • common support functions
  • covariance importance

Great package!

Hey, very nice package! Are you continuing the development? Please do so, it looks super useful already! Perhaps adding bartCause would be an option as well?

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