the missing value in xgbTree training with caret

[jianqin123]

I try to build a binary classifier modeling with xgbTree and tuning parames with caret.If I put the data having missing value into train funciton,I get the error information.

Error in na.fail.default(list(label = c(1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,  : 
 missing values in object 

If I put the data pre-processed by na.omit() or set na.action=na.pass in train function ,there is no error.In this page https://github.com/topepo/caret/pull/512/commits/b68df5794c2742ba2b1767850a356c6e81b044fa ,It tells it is the source code makes this error.So how can I use this to solve my problem .na.omit and set na.action=na.pass is not I intended.
Thanks in advance !

[topepo]

As of version 6.0-71:

A significant bug was fixed where the internals of how R creates a model matrix was ignoring na.action when the default was set to na.fail (issue #461). This means that train will now immediately fail if there are any missing data. To use imputation, use na.action = na.pass and the imputation method of your choice in the preProcess argument. Also, a warning is issued if the user asks for imputation but uses the formula method and excludes missing data in na.action

So you should use use na.action = na.pass if xgboost can deal with the missing data

[jianqin123]

If I use na.action =na.pass,the information shows the models is trained by the same number samples as pre-rocessed by na.omit.

I set na.action=na.pass in trainXgbModel ,and 2 rows in train1[1:100,] have missing values

> xgb1<-trainXgbModel(train1[1:100,],3,0.05,0.1,3:10,0.7,0.8,2.5)
> xgb1
eXtreme Gradient Boosting 

 **98 samples**
233 predictors
  2 classes: 'n', 'p'

remove na.action=na.pass

> xgb1<-trainXgbModel(na.omit(train1[1:100,]),3,0.05,0.1,3:10,0.7,0.8,2.5)
> xgb1
eXtreme Gradient Boosting 

 **98 samples**
233 predictors
  2 classes: 'n', 'p' 

then the sample number both equals to 98,So I wonder if set na.action=na.pass is equal to na.omit(train1[1:100,]).

[topepo]

That's helpful but I'll need a reproducible example to figure out what is going on.

[strakaps]

This thread was very useful, thanks! @topepo has there been any movement with this issue?