I'm having a problem converting rxGlm models to normal glm models. Every time I try and covert my models I get the same error:
Error in qr.lm(object) : lm object does not have a proper 'qr' component. Rank zero or should not have used lm(.., qr=FALSE).
Here's a simple example:
cols <- colnames(iris) vars <- cols[!cols %in% "Sepal.Length"] form1 <- as.formula(paste("Sepal.Length ~", paste(vars, collapse = "+"))) rx_version <- rxGlm(formula = form1, data = iris, family = gaussian(link = 'log'), computeAIC = TRUE) # here is the equivalent model with base R R_version <- glm(formula = form1, data = iris, family = gaussian(link = 'log')) summary(as.glm(rx_version)) #this always gives the above error
I cant seem to find this "qr" component (I'm assuming this is related to matrix decomposition) to specify in rxGlm formula.
Anyone else dealt with this?Answer1:
rxGlm objects don't have a
qr component, and converting to a
glm object won't create one. This is intentional, as computing the QR decomposition of the model matrix requires the full dataset to be in memory which would defeat the purpose of using the rx* functions.
as.glm is really meant more for supporting model import/export via PMML. Most of the things that you'd want to do can be done with the
rxGlm object, without converting. Eg
rxGlm computes the coefficient std errors as part of the fit, without requiring a
qr component afterwards.