How to deal with missing values when using Random Forrest

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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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,
What part of that error message is unclear? Have you looked at the  
randomForest page? It tells you what the default behavior is na.fail.
I would greatly appreciate any help
I would seem that the way forward is to remove the cases with missing  
values or to impute values.
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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
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

--
View this message in context: http://r.789695.n4.nabble.com/How-to-deal-with-missing-values-when-using-Random-Forrest-tp4421254p4421254.html
Sent from the R help mailing list archive at Nabble.com.

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