Trouble with predicting a fitted model in R's GLMNET package

glmnet, machine-learning, r, regression, statistics

Solution

prediction <- predict(cv.fit, testData$mpg, s="lambda.1se")

It seems that testData$mpg is a vector, model should use the whole testdata set to predict instead of the single mpg values.

In your case, it should be something like

testdata <- as.matrix(data.frame(cylinderDummy[,2:ncol(cylinderDummy)], testData$displacement,
        testData$horsepower, testData$weight, testData$acceleration,
        originDummy[,2:ncol(originDummy)]))
prediction <- predict(cv.fit, testData, s="lambda.1se")

Problem

I am trying to predict a car's `mpg` based on a number of variables by using ridge regression in R's `glmnet` package. I have already separated the data into training and test data and dummy coded the categorical variables. I fit a cross-validation model as follows: ``` require("glmnet") x <- as.matrix(data.frame(cylinderDummy[,2:ncol(cylinderDummy)], trainData$displacement, trainData$horsepower, trainData$weight, trainData$acceleration, originDummy[,2:ncol(originDummy)])) y <- trainData$mpg cv.fit <- cv.glmnet(x, y, alpha = 1, nfolds=5,type.measure="mse") ``` That's all well and good, however, the problem occurs when I attempt to use the `predict()` function on the test data from the fitted model: ``` prediction <- predict(cv.fit, testData$mpg, s="lambda.1se") ``` I get the following error: ``` Error in as.matrix(cbind2(1, newx) %*% nbeta) : error in evaluating the argument 'x' in selecting a method for function 'as.matrix': Error in t(.Call(Csparse_dense_crossprod, y, t(x))) : error in evaluating the argument 'x' in selecting a method for function 't': Error: Cholmod error 'X and/or Y have wrong dimensions' at file ../MatrixOps/cholmod_sdmult.c, line 90 ``` Can anyone tell me what I'm doing wrong?? Thank you!

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