> On Dec 9, 2016, at 2:45 PM, Hu Xinghai <huxinghai1...@gmail.com> wrote: > > 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?
This duplicates a posting on StackOverflow. If you read the Posting Guide you will find advice cautioning you against cross-posting. If you do not get a satisfactory answer in a reasonable interval which I would say would be 24 hours at a minimum you can justify cross-posting but it should be accompanied by a reference to the original posting> http://stackoverflow.com/questions/41070103/glmnet-error-non-conformable-arguments The posting guide (as well as the StackOverflow help pages) ask the you post sufficient sample data to support demonstration and testing. You already have two close votes on the basis of failing to do that. On StackOverflow you can search on `[MCVE]` and "great reproducible example" or you can read the relevant sections of the Posting Guide. Using dput() to post data objects delivers the most specificity in data structure. -- david. > > Thanks > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. David Winsemius Alameda, CA, USA ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
