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rpredictglmnet

How to predict using only weights obtained from glmnet in R?

One can make predictions using predict(fit,newx) method. But how to predict if I don't have the fit object itself but only the weights of the predictors as vector file and new observations for predictors as a matrix file? The predictors and the outcome both are continuous variables.

Solution

  • You can just use matrix multiplication, which is what glmnet does. In the predict function, it is: as.matrix(cbind2(1, x) %*% coefs)

    Example:

    library(glmnet)

x=matrix(rnorm(100*20),100,20)
y=rnorm(100)
fit1=glmnet(x,y)
coefs <- coef(fit1,s=0.01) # extract coefficients at a single value of lambda

manaul_pred <- as.matrix(cbind2(1, x) %*% coefs)
pred <- predict(fit1,newx=x,s=0.01)

manual_pred - pred # there is a negligible difference due to numeric precision