[R] predict missing values with svm

[James W. MacDonald]

Hi Andreas,

Andreas Wittmann wrote:
> Dear R-Users,
> 
> i want to use the function svm of the e1071 package to predict missing data
> 
> ############################################################
> 
> data(iris)
> 
> ## create missing completely at random data
> for (i in 1:5)
> {
>  mcar <- rbinom(dim(iris)[1], size=1, prob=0.1)
>  iris[mcar == 1, i] <- NA
> }
> 
> ok <- complete.cases(iris)
> 
> model <- svm(Species ~ ., data=iris[ok,])
> 
> ## try to predict the missing values for Species
> ## neither
> pred <- predict(model, iris[5])
> ## nor
> pred <- predict(model, iris[!ok, -5])
> ## seems to work....

ind <- is.na(iris[,5]) & !apply(iris[,-5], 1, function(x) any(is.na(x))
predict(model, iris[ind,-5])

Best,

Jim


> 
> Many thanks if anyone could tell me what i do wrong and what is the 
> problem here.
> 
> best regards
> 
> Andreas
> 
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-- 
James W. MacDonald, M.S.
Biostatistician
Douglas Lab
University of Michigan
Department of Human Genetics
5912 Buhl
1241 E. Catherine St.
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734-615-7826