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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