[R] How to deal with missing values when using Random Forrest

[Weidong Gu]

Hi,

You can set na.action=na.roughfix which fills NAs with the mean or
mode of the missing variable.

Other option is to impute missing values using rfImpute, then run
randomForest on the complete data set.

Weidong Gu

On Sat, Feb 25, 2012 at 6:24 PM, kevin123 <kevincorry123 at gmail.com> wrote:
> I am using the package Random Forrest to test and train a model,
> I aim to predict (LengthOfStay.days),:
>
>> library(randomForest)
>> model <- randomForest( LengthOfStay.days~.,data = training,
> + importance=TRUE,
> + keep.forest=TRUE
> + )
>
>
> *This is a small portion of the data frame:   *
>
> *data(training)*
>
> LengthOfStay.days CharlsonIndex.numeric DSFS.months
> 1                  0                   0.0         8.5
> 6                  0                   0.0         3.5
> 7                  0                   0.0         0.5
> 8                  0                   0.0         0.5
> 9                  0                   0.0         1.5
> 11                 0                   1.5         NaN
>
>
>
> *Error message*
>
> Error in na.fail.default(list(LengthOfStay.days = c(0, 0, 0, 0, 0, 0,  :
>  missing values in object,
>
> I would greatly appreciate any help
>
> Thanks
>
> Kevin
>
>
> --
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