I have a significant interaction from the following linear mixed effects model:
library(lmer4)
library(ggplot2)
library(lmerTest)
library(effects)
library(extrafont)
mod_father_son <- lmer(AIP_s_child.z ~ AIP_s_parent.z*Q_mean.z +
(1 + AIP_s_parent.z:Q_mean.z || Family_number),
data = data_father_son, REML = FALSE)
My data looks similar to this (Q_mean and AIP_s are z-scores):
Id_parent Family_number AIP_s_parent.z Q_mean.z Child_id AIP_s_child.z
A1 1 -.008 -0.5 B1 .005
A1 1 -.008 -0.5 B2 .04
C1 2 .06 -.006 D1 -.007
E1 3 -.1 0.02 F1 -.06
I want to visualise it in the best way and I was thinking a line graph showing the interaction at +1, 0, -1 of Q_mean.z. My code for my current graph is:
fs <- as.data.frame(Effect(c("AIP_s_parent.z","Q_mean.z"),mod = mod_father_son))
fs <- mutate(fs, Q_mean.z = as.character(Q_mean.z))
plot_fs <- ggplot(fs, aes(AIP_s_parent.z,fit, group=Q_mean.z, linetype = Q_mean.z))+
geom_line() +
labs(x = "Gender stereotypes father", y = "Gender stereotypes son", linetype = "Questionnaire score")
plot_fs + theme_classic() +
scale_color_gradient(low = "black", high = "gray90") +
theme(text=element_text(size=12, family="Times New Roman"))
And the graph looks like this:
Is this correct as the interpretation of the graph makes no sense with the output of the model? I've been told by someone that i should use predict () with ggplot however, I don't know how to do that. Additionally, I'd like only three lines on my plot (+1, 0, -1 of Q_mean.z) as five is confusing and difficult to read. If a line graph is not the best way to visualise the interaction, I am open to suggestions.