CODEWITHSUNDEEP
rnar-carettraining-data
DP78
DP78

Reputation: 9

NA impact in Caret Train

Following the example reported in the link below I have the following error:

Using nnet for prediction, am i doing it right?

Error in na.fail.default(list(y = c(0, 0.0998334166468282, 0.198669330795061, : missing values in object

To solve this error I use the condition na.action = na.omit

#Fit model
model <- train(y ~ x1 + x2, te, method='nnet', linout=TRUE, trace = FALSE,
               #Grid of tuning parameters to try:
               tuneGrid=expand.grid(.size=c(1,5,10),.decay=c(0,0.001,0.1)),
               na.action = na.omit) 
ps <- predict(model, te)

is.na(te)
nrow(te)
nrow(ps)

Is this condition the only way to proceed?

In fact the consequence is that the number of rows of the ps is different to the number of ps data.

Upvotes: 0

Views: 705

Answers (1)

topepo
topepo

Reputation: 14316

Given that you are lagging the data, this is probably the best approach. Note that: 

> sum(!complete.cases(te))
[1] 2

The model can't predict these which is why 

> nrow(ps)
[1] 199
> nrow(te)
[1] 201

and this is because:

> formals(predict.train)$na.action
na.omit

(Note that this will probably be changed to na.fail in the next version of the package)

Upvotes: 1

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