LGOCV caret train - validation

Question

Why does initial part of below code run, but when I try to run later part of code I get an error? I am learning data mining from the page and trying to understand how to perform cross validation using LGOCV option
library(mlbench)
data(Sonar)
str(Sonar)
library(caret)
set.seed(998)
inTraining <- createDataPartition(Sonar$Class, p = 0.75, list = FALSE)
training <- Sonar[inTraining, ]
testing <- Sonar[-inTraining, ]
fitControl <- trainControl(## 10-fold CV
method = "repeatedcv",
number = 10,
## repeated ten times
repeats = 10)
gbmGrid <- expand.grid(.interaction.depth = c(1, 5, 9),
.n.trees = (1:15)*100,
.shrinkage = 0.1)
fitControl <- trainControl(method = "repeatedcv",
number = 10,
repeats = 10,
## Estimate class probabilities
classProbs = TRUE,
## Evaluate performance using
## the following function
summaryFunction = twoClassSummary)
set.seed(825)
gbmFit3 <- train(Class ~ ., data = training,
method = "gbm",
trControl = fitControl,
verbose = FALSE,
tuneGrid = gbmGrid,
## Specify which metric to optimize
metric = "ROC")
gbmFit3
Get error below: (
datarow <- 1:nrow(training)
fitControl <- trainControl(method = "LGOCV",
summaryFunction = twoClassSummary,
classProbs = TRUE,
index = list(TrainSet = datarow ),
savePredictions = TRUE)
gbmFit4 <- train(Class ~ ., data = training,
method = "gbm",
trControl = fitControl,
verbose = FALSE,
tuneGrid = gbmGrid,
## Specify which metric to optimize
metric = "ROC")
My error is as below
Error in { :
task 1 failed - "arguments imply differing number of rows: 0, 1"
In addition: Warning messages:
1: In eval(expr, envir, enclos) :
predictions failed for TrainSet: interaction.depth=1, shrinkage=0.1, n.trees=1500 Error in 1:ncol(tmp) : argument of length 0
2: In eval(expr, envir, enclos) :
predictions failed for TrainSet: interaction.depth=5, shrinkage=0.1, n.trees=1500 Error in 1:ncol(tmp) : argument of length 0
3: In eval(expr, envir, enclos) :
predictions failed for TrainSet: interaction.depth=9, shrinkage=0.1, n.trees=150
session info:
sessionInfo()
R version 3.0.1 (2013-05-16)
Platform: x86_64-w64-mingw32/x64 (64-bit)
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] parallel splines stats graphics grDevices utils datasets methods base
other attached packages:
[1] gbm_2.1 survival_2.37-4 mlbench_2.1-1 pROC_1.5.4 caret_5.17-7 reshape2_1.2.2
[7] plyr_1.8 lattice_0.20-15 foreach_1.4.1 cluster_1.14.4
loaded via a namespace (and not attached):
[1] codetools_0.2-8 compiler_3.0.1 grid_3.0.1 iterators_1.0.6 stringr_0.6.2 tools_3.0.1

Answer 1

You also posted the same question on CrossValidated. We normally say to make very sure that you are not in error before look for help and then contact the package author.
The problem is your use of datarow <- 1:nrow(training). You are tuning model on all of the instances and leaving nothing to compute the hold-out estimates.
I'm not really sure what you are try to do.
Max