Plotting interactions while controlling for other predictors.
Hi David, For plotting interactions between two continuous variables, maybe a gamm model might be more adapted? However, by defaut, in the visreg package, you can facet one of your continuous variables (which is subsetted relative to its distribution) and plot the other one accordingly. This would be coded the following way: visreg(m1, X2, by="X3") #overlay=TRUE to have them all combined in one plot, partial=TRUE to have the partial residuals (which do not include RE by default), by default X1 will be fixed to its median This will produce a conditional plot in which all random effects (or X1 in this case) are fixed at a certain level (you can also fix this at a particular value with cond=list(X1=XXXX) ). Nevertheless, this simply calls for predict.lmer(). It is still pretty convenient IMHO. Hope this helps. Sincerely, GA2 Le mer. 12 d?c. 2018 ? 17:51, David Sidhu <dsidhu at ucalgary.ca> a ?crit :
I am interested in creating a plot of an interaction that accounts for other variables present in a model. I am attaching a reproducible example of my try so far, but I don?t believe this is quite getting at what I?d like. Essentially, I want to show the interaction between X2 and X3, while controlling for X1. Thank you very much. Dave set.seed(182) require(data.table) require(lme4) require(ggplot2) X1 = rnorm(1000) X2 = rnorm(1000) X3 = sample(1:3, size = 1000, replace = TRUE) X3 <- as.factor(X3) DV = rnorm(1000) Item <- rep(1:10, times = 100) Subject <- rep(1:100, each = 10) d <- as.data.frame(cbind(X1, X2, X3, DV, Item, Subject)) m1 <- lmer(DV ~ X1 + X2*X3 + (1|Subject) + (1|Item), data = d) d$Pred <- predict(m1) library(ggplot2) ggplot(d, aes(x = X2, y = Pred, col = factor(X3), group = X3)) + geom_jitter(alpha = .2) + geom_smooth(method = "lm") --- David M. Sidhu, MSc PhD Candidate Department of Psychology University of Calgary
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