I have run a GLM binomial model
fit <- glm(highlow ~ V1 + V2 + V3 + V4 + V5 + V6 + V7 + V8 + V9 + V10,
family="binomial")
To test the null hypothesis of V1 = V2 I have used the following code.
glht.mod <- glht(fit, linfct = c("V1 - V2 = 0"))
summary(glht.mod)
My question is can I test whether V1 = V2 = V3 (null hypothesis of all three coefficients being equal - note this is not the same as testing whether V1 = V2 and V2 = V3 in separate iterations)?
If it is any help, I am able to achieve this in SAS using the following code
proc logistic;
model highlow = V1 V2 V3 V4 V5 V6 V7 V8 V9 V10;
test1: V1 = V2;
test2: V1 = V2 = V3;
run;
A possibile solution for your problem:
set.seed(1)
n <- 1000
highlow <- factor(runif(n)>0.5)
X <- matrix(rnorm(n*10),nrow=n)
df <- data.frame(highlow, X)
names(df) <- c("highlow", paste("V",1:10,sep=""))
fit <- glm(highlow ~ V1 + V2 + V3 + V4 + V5 + V6 + V7 + V8 + V9 + V10,
family="binomial", data=df)
library(car)
linearHypothesis(fit, c("V1-V2", "V2-V3"), c(0,0))
################
Linear hypothesis test
Hypothesis:
V1 - V2 = 0
V2 - V3 = 0
Model 1: restricted model
Model 2: highlow ~ V1 + V2 + V3 + V4 + V5 + V6 + V7 + V8 + V9 + V10
Res.Df Df Chisq Pr(>Chisq)
1 991
2 989 2 0.2761 0.871