r - Cannot specify probability function for extraTrees model in caret package -

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everyone,

recently, have been using extratrees model in caret package. however, noticed probability function extratrees model set null using following scripts:

extratrees_para <- getmodelinfo('extratrees', regex = f)[[1]]  extratrees_para$prob

i noticed in original package of extratress, can used generate probability prediction classification problems. i'd specify prob function extratrees_para. 

extratrees_para$prob <- function(modelfit, newdata, submodels = null){ as.data.frame(predict(modelfit, newdata, probability = true)) } extratrees_para$type <- 'classification'

then construct train function build model

extratreesgrid <- expand.grid(.mtry=1:2,                       .numrandomcuts=1)    modelfit_extratrees <- train(outcome~., data=training_scaled_sel,                          method = extratrees_para,                          metric = "roc",                          trcontrol = traincontrol(method = 'repeatedcv',                                                   repeats=1,                                                   classprob = t,                                                   summaryfunction = twoclasssummary),                          ntree = 3000,                           tunegrid = extratreesgrid)

however, keep getting error message not informative

"error in train.default(x, y, weights = w, ...) : final tuning parameters not determined in addition: warning messages: 1: in nominaltrainworkflow(x = x, y = y, wts = weights, info = traininfo, : there missing values in resampled performance measures. 2: in train.default(x, y, weights = w, ...) : missing values found in aggregated results"

below session information. appreciated if can me this. thanks!

sessioninfo()  r version 3.1.2 (2014-10-31) platform: x86_64-w64-mingw32/x64 (64-bit)  locale: [1] lc_collate=english_united states.1252  [2] lc_ctype=english_united states.1252    [3] lc_monetary=english_united states.1252 [4] lc_numeric=c                           [5] lc_time=english_united states.1252      attached base packages: [1] grid      stats     graphics  grdevices utils     datasets  methods   [8] base       other attached packages: [1] dmwr_0.4.1           biocinstaller_1.16.5 caret_6.0-41         [4] ggplot2_1.0.0        lattice_0.20-29      extratrees_1.0.5     [7] rjava_0.9-6           loaded via namespace (and not attached): [1] abind_1.4-3         bitops_1.0-6        bradleyterry2_1.0-5 [4] brglm_0.5-9         car_2.0-24          catools_1.17.1      [7] class_7.3-11        codetools_0.2-9     colorspace_1.2-4    [10] compiler_3.1.2      digest_0.6.8        e1071_1.6-4           [13] foreach_1.4.2       gdata_2.16.1        gplots_2.17.0       [16] gtable_0.1.2        gtools_3.4.1        iterators_1.0.7     [19] kernsmooth_2.23-13  lme4_1.1-7          mass_7.3-35         [22] matrix_1.1-4        mgcv_1.8-3          minqa_1.2.4         [25] munsell_0.4.2       nlme_3.1-118        nloptr_1.0.4        [28] nnet_7.3-8          parallel_3.1.2      pbkrtest_0.4-2      [31] plyr_1.8.1          proc_1.8            proto_0.3-10        [34] quantmod_0.4-4      quantreg_5.11       rcpp_0.11.4         [37] reshape2_1.4.1      rocr_1.0-7          rpart_4.1-8         [40] scales_0.2.4        sparsem_1.6         splines_3.1.2       [43] stringr_0.6.2       tools_3.1.2         ttr_0.22-0          [46] xts_0.9-7           zoo_1.7-12

i don't think generate class probabilities when first added model. i'm not sure why version didn't work here i'm adding package:

modelinfo <- list(label = "random forest randomization",                   library = c("extratrees"),                   loop = null,                   type = c('regression', 'classification'),                   parameters = data.frame(parameter = c('mtry', 'numrandomcuts'),                                           class = c('numeric', 'numeric'),                                           label = c('# randomly selected predictors', '# random cuts')),                   grid = function(x, y, len = null){                     expand.grid(mtry = var_seq(p = ncol(x),                                                 classification = is.factor(y),                                                 len = len),                                  numrandomcuts = 1:len)                   },                   fit = function(x, y, wts, param, lev, last, classprobs, ...)                      extratrees(x, y, mtry = param$mtry, numrandomcuts = param$numrandomcuts, ...),                   predict = function(modelfit, newdata, submodels = null)                     predict(modelfit, newdata),                   prob = function(modelfit, newdata, submodels = null)                     predict(modelfit, newdata, probability = true),                   levels = function(x) x$obslevels,                   tags = c("random forest", "ensemble model", "bagging", "implicit feature selection"),                   sort = function(x) x[order(x[,1]),])

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