Looking at the 'contrasts' function, it seems to be imported from
'rms' and to take only 'rms'-type fits (which are different from base-R
[g]lm() fits) as input.
I'm not sure exactly what contrasts you're trying to generate, but I
would probably use the emmeans package for the task, since it handles a
wide range of model types.
Alternately, you could use one of the modeling functions from rms,
which should work with rms::contrast(), although I ran into trouble for
some reason:
fit1 <- ols(y ~ x + I(x^2), data = mydata)
> Error in if (!length(fname) || !any(fname == zname)) { :
missing value where TRUE/FALSE needed
I guess rms handles I()-protected terms differently? (The model with
just y~x didn't throw an error ...)
On 2025-02-18 12:00 p.m., Sorkin, John wrote:
I am using the contrast package to produce contrasts from a glm model that
contains a quadratic term:
fit0 <- glm(y ~ x + I(x*x),data=mydata)
I would like to know how to specify the quadratic term, I(x*x), in the contrast
function.
When I try to use
contrast(fit0,list(x=4))
I receive an error message;
#Error in generateData(fit = list(coefficients = c(`(Intercept)` =
0.5262645536229, :not enough factors
When I try to use
contrast(fit0,list(x=4,I(x*x)=16))
I receive an error message:
#Error: unexpected '=' in "contrast(fit0,list(x=4,I(x*x)="
Other variants of the call to contrast including
contrast(fit0,list(x=4,"x*x"=16)) and
contrast(fit0,list(x=4,"I(x*x}"=16))
produce similar error messsages.
Please see my code below.
if (!require(contrast)) install.packages("contrast")
library(contrast)
help(contrast)
x <- 1:100
y <- x + x^2 + rnorm(1)
mydata <- data.frame(x=x,y=y)
fit0 <- glm(y ~ x + I(x*x),data=mydata)
summary(fit0)
contrast(fit0,list(x=4))
#Error in generateData(fit = list(coefficients = c(`(Intercept)` =
#0.5262645536229, : not enough factors
contrast(fit0,list(x=4,I(x*x)=16))
#Error: unexpected '=' in "contrast(fit0,list(x=4,I(x*x)="
Thank you,
John
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