In R, using package lme4, I have used the following 2 mixed models to determine I have a signifacnt interaction between a covariate (continous, normally distributed) and a factor (three levels: herbivores, plants, predators):
test1 <- lmer( mode ~ sr * func.group + (1|community), data=nr.test, REML="FALSE") test2 < -lmer( mode ~ sr + func.group + (1|community), data=nr.test, REML="FALSE") anova(test1, test2) Data: nr.test Models: test2: mode ~ sr + func.group + (1 | community) test1: mode ~ sr * func.group + (1 | community) Df AIC BIC logLik Chisq Chi Df Pr(>Chisq) nr.test2 6 77.458 82.093 -32.729 nr.test1 8 62.570 68.751 -23.285 18.887 2 7.919e-05 ***
I have obviously plotted the interaction for each level of factor, and all three levels are positive relationships which cross over each other (i.e. different levels of slope steepness).
However, I would like to be able to identify which level of the factor are significantly different from one another, and which are also significantly different from a slope value of zero (i.e. which slopes are significant).
I have installed the R package phia, and used the command
testInteractions(test1, pairwise="func.group", slope="sr")
and recieved the following output:
Adjusted slope for sr Chisq Test: P-value adjustment method: holm Value Df Chisq Pr(> Chisq) herbivore-plant 0.11878 1 4.3061 0.03798 * herbivore-predator 0.40567 1 65.3283 1.902e-15 *** plant-predator 0.28689 1 30.3474 7.224e-08 ***
It would appear for each level of factor the slopes are significantly different from one another, but how can I know which ones are significantly different from zero, and how would I report this?