[R-meta] Interaction between a continuous variable and a 3-level categorical variable
Martineau, Roger (AAFC/AAC)
roger@m@rt|ne@u @end|ng |rom AGR@GC@CA
Thu Nov 24 22:22:07 CET 2022
Dear list-members,
I have a large data set and I want to report the interaction term between a continuous variable and a 3-level categorical variable. My data are somewhat similar to the cake data set but the interactions are significant.
With the example on cake, the lmer model 1 and the anova(m1) indicate that both the intercepts and the slopes are not different (P = 0.98 and 0.96, respectively) between levels of recipe (i.e. A, B and C). I use the rma.mv function and the robust function in my meta-analysis. The corresponding model to model 1 with rma.mv would be model m2 which gives the same coefficients and P-values as model m1. When I apply the robust function on model 2, I get the same coefficients and somewhat different P-values as expected.
My question is: how can I get the interaction terms (P = 0.98 and 0.96; or others similar) from model 2 before and after using the robust function ?
Best regards,
Dr Roger Martineau, mv PhD
Qu�bec, Canada
str(cake)
cake<- within(cake, {
ID <- 1:nrow(cake)
})
m1 <- lmer(angle~-1+temp*recipe + (1|replicate), data=cake)
summary(m1)
anova(m1)
m2 <- rma.mv(angle ~ -1 + temp*recipe, V=0, random = list(~ 1|replicate, ~ 1|ID), data=cake)
summary(m2)
robust(m2, cluster=cake$replicate)
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