[R] glmnet error: no-comfortable arguments

[Hu Xinghai]

I come across the following error training Logistic Regression model using
cv.glmnet:

> Error in drop(y %*% rep(1, nc)) : error in evaluating the argument 'x' in
> selecting a method for function 'drop': Error in y %*% rep(1, nc) :
> non-conformable arguments
> error in evaluating the argument 'x' in selecting a method for function
> 'drop': Error in y %*% rep(1, nc) : non-conformable arguments


The error appears occasionally. However, since I need to run over a
parameter grid to optimize a parameter, the logistic regression needs to
run for multiple time; and therefore, almost certainly this error would be
hit.

Below is my code:

>     cellDF = df[(df$cell_id == cellid), ]
>     X = cellDF[, c(5:(ncol(cellDF)-2) )]
>     X$median_age = as.numeric(X$median_age)
>     X = data.matrix(X)
>     Y = cellDF$signup
>     impWeights = as.double(cellDF$trW)
>     has_NA = union(apply(is.na(X), 1, any), sapply(Y, is.na) )
>     has_NA = union(has_NA, sapply(impWeights, is.na))
>     X = X[!has_NA,]
>     Y = Y[!has_NA]
>     impWeights = impWeights[!has_NA]
>     nfolds = 8
>     YPosIdx = which(Y == 1)
>     YNegIdx = which(Y == 0)
>     LYPos = length(YPosIdx)
>     LYNeg = length(YNegIdx)
>     samplePos = sample(c(1:nfolds), LYPos, replace = TRUE)
>     sampleNeg = sample(c(1:nfolds), LYNeg, replace = TRUE)
>     order = match(c(1: length(Y)), c(YPosIdx, YNegIdx))
>     foldid = c(samplePos, sampleNeg)[order]
>     model = cv.glmnet(x = X, y = Y, weights = impWeights,
> family="binomial", type.measure="auc", lambda = lambdaGrid, nfolds =
> nfolds, foldid = foldid)
>     fit = predict(model, censusX, s = "lambda.1se", type = "response")


I read some posts online about the issue, suggesting that there might be
NA, and I should use data.matrix instead of as.matrix, and also I need to
fix foldid to make sure both positive and negative samples exists. I tried
all these tricks, but none helps.

Is there any thought about it?

Thanks

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