How to eliminate "NA/NaN/Inf in foreign function call (arg 7)" running predict with randomForest

Question

I have researched this extensively without finding a solution. I have cleaned my data set as follows:

library("raster")
impute.mean <- function(x) replace(x, is.na(x) | is.nan(x) | is.infinite(x) , 
mean(x, na.rm = TRUE))
losses <- apply(losses, 2, impute.mean)
colSums(is.na(losses))
isinf <- function(x) (NA <- is.infinite(x))
infout <- apply(losses, 2, is.infinite)
colSums(infout)
isnan <- function(x) (NA <- is.nan(x))
nanout <- apply(losses, 2, is.nan)
colSums(nanout)

The problem arises running the predict algorithm:

options(warn=2)
p  <-   predict(default.rf, losses, type="prob", inf.rm = TRUE, na.rm=TRUE, nan.rm=TRUE)

All the research says it should be NA's or Inf's or NaN's in the data but I don't find any. I am making the data and the randomForest summary available for sleuthing at [deleted] Traceback doesn't reveal much (to me anyway):

4: .C("classForest", mdim = as.integer(mdim), ntest = as.integer(ntest), 
       nclass = as.integer(object$forest$nclass), maxcat = as.integer(maxcat), 
       nrnodes = as.integer(nrnodes), jbt = as.integer(ntree), xts = as.double(x), 
       xbestsplit = as.double(object$forest$xbestsplit), pid = object$forest$pid, 
       cutoff = as.double(cutoff), countts = as.double(countts), 
       treemap = as.integer(aperm(object$forest$treemap, c(2, 1, 
           3))), nodestatus = as.integer(object$forest$nodestatus), 
       cat = as.integer(object$forest$ncat), nodepred = as.integer(object$forest$nodepred), 
       treepred = as.integer(treepred), jet = as.integer(numeric(ntest)), 
       bestvar = as.integer(object$forest$bestvar), nodexts = as.integer(nodexts), 
       ndbigtree = as.integer(object$forest$ndbigtree), predict.all = as.integer(predict.all), 
       prox = as.integer(proximity), proxmatrix = as.double(proxmatrix), 
       nodes = as.integer(nodes), DUP = FALSE, PACKAGE = "randomForest")
3: predict.randomForest(default.rf, losses, type = "prob", inf.rm = TRUE, 
       na.rm = TRUE, nan.rm = TRUE)
2: predict(default.rf, losses, type = "prob", inf.rm = TRUE, na.rm = TRUE, 
       nan.rm = TRUE)
1: predict(default.rf, losses, type = "prob", inf.rm = TRUE, na.rm = TRUE, 
       nan.rm = TRUE)

Answer 1

Your code is not entirely reproducible (there's no running of the actual randomForest algorithm) but you are not replacing Inf values with the means of column vectors. This is because the na.rm = TRUE argument in the call to mean() within your impute.mean function does exactly what it says -- removes NA values (and not Inf ones).

You can see this, for example, by:

impute.mean <- function(x) replace(x, is.na(x) | is.nan(x) | is.infinite(x), mean(x, na.rm = TRUE))
losses <- apply(losses, 2, impute.mean)
sum( apply( losses, 2, function(.) sum(is.infinite(.))) )
# [1] 696

To get rid of infinite values, use:

impute.mean <- function(x) replace(x, is.na(x) | is.nan(x) | is.infinite(x), mean(x[!is.na(x) & !is.nan(x) & !is.infinite(x)]))
losses <- apply(losses, 2, impute.mean)
sum(apply( losses, 2, function(.) sum(is.infinite(.)) ))
# [1] 0