ANOVA problems with revoScaleR::rxGlm() in R - r

Question

I build lots of GLMs. Usually on large data sets with many model parameters. This means that base R's glm() function isn't really useful because it won't cope with the size/complexity, so I usually use revoScaleR::rxGlm() instead.
However I'd like to be able to do ANOVA tests on pairs of nested models, and I haven't found a way to do this with the model objects that rxGlm() creates, because R's anova() function won't work with them. revoScaleR provides an as.glm() function which converts an rxGlm() object to a glm() object - sort of - but it doesn't work here.
For example:
library(dplyr)
data(mtcars)
# don't like having named rows
mtcars <- mtcars %>%
mutate(veh_name = rownames(.)) %>%
select(veh_name, everything())
# fit a GLM: mpg ~ everything else
glm_a1 <- glm(mpg ~ cyl + disp + hp + drat + wt + qsec + vs + am + gear + carb,
data = mtcars,
family = gaussian(link = "identity"),
trace = TRUE)
summary(glm_a1)
# fit another GLM where gear is removed
glm_a2 <- glm(mpg ~ cyl + disp + hp + drat + wt + qsec + vs + am + carb,
data = mtcars,
family = gaussian(link = "identity"),
trace = TRUE)
summary(glm_a2)
# F test on difference
anova(glm_a1, glm_a2, test = "F")
works fine, but if instead I do:
library(dplyr)
data(mtcars)
# don't like having named rows
mtcars <- mtcars %>%
mutate(veh_name = rownames(.)) %>%
select(veh_name, everything())
glm_b1 <- rxGlm(mpg ~ cyl + disp + hp + drat + wt + qsec + vs + am + gear + carb,
data = mtcars,
family = gaussian(link = "identity"),
verbose = 1)
summary(glm_b1)
# fit another GLM where gear is removed
glm_b2 <- rxGlm(mpg ~ cyl + disp + hp + drat + wt + qsec + vs + am + carb,
data = mtcars,
family = gaussian(link = "identity"),
verbose = 1)
summary(glm_b2)
# F test on difference
anova(as.glm(glm_b1), as.glm(glm_b2), test = "F")
I see the error message:
Error in qr.lm(object) : lm object does not have a proper 'qr'
component. Rank zero or should not have used lm(.., qr=FALSE)
The same problem cropped up on a previous SO posting: Error converting rxGlm to GLM but doesn't seem to have been solved.
Can anyone help please? if as.glm() isn't going to help here, is there some other way? Could I write a custom function to do this (stretching my coding abilities to their limit I suspect!)?
Also, is SO the best forum, or would one of the other StackExchange forums be a better place to look for guidance?
Thank you.

Answer 1

Partial solution...
my_anova <- function (model_1, model_2, test_type)
{
# only applies for nested GLMs. How do I test for this?
cat("\n")
if(test_type != "F")
{
cat("Invalid function call")
}
else
{
# display model formulae
cat("Model 1:", format(glm_b1$formula), "\n")
cat("Model 2:", format(glm_b2$formula), "\n")
if(test_type == "F")
{
if (model_1$df[2] < model_2$df[2]) # model 1 is big, model 2 is small
{
dev_s <- model_2$deviance
df_s <- model_2$df[2]
dev_b <- model_1$deviance
df_b <- model_1$df[2]
}
else # model 2 is big, model 1 is small
{
dev_s <- model_1$deviance
df_s <- model_1$df[2]
dev_b <- model_2$deviance
df_b <- model_2$df[2]
}
F <- (dev_s - dev_b) / ((df_s - df_b) * dev_b / df_b)
}
# still need to calculate the F tail probability however
# df of F: numerator: df_s - df_b
# df of F: denominator: df_b
F_test <- pf(F, df_s - df_b, df_b, lower.tail = FALSE)
cat("\n")
cat("F: ", round(F, 4), "\n")
cat("Pr(>F):", round(F_test, 4))
}
}