pastaPlot allows you to plot fixed and random effects of a linear mixel model of class ‘lme4’ or ‘glmmTMB’ in a single spaghetti plot. While the pastaPlot() returns a ggplot object, most basic customizations can be done within the function itself. So feel free to check the documentation before further modifying the plot yourself!
Predicted values and confidence intervals of fixed effects are calculated using ggpredict() function from ggeffects package (Lüdecke, 2018). At this stage, random effects can only be plotted for one variable at a time. Here, the cookPasta function automatically checks if you specified random vs. fixed intercept and slope in your model (e.g., random intercept, random slope “(1 + time | id)”) and plots them accordingly.
As the CRAN submission is pending, at this stage, you can install pastaPlot only from GitHub:
# install.packages("devtools")
# devtools::install_github("JanPalnau/pastaPlot")
install.packages("pastaPlot", repos = c('https://janpalnau.r-universe.dev', 'https://cloud.r-project.org'))
Here is an example passing a lme4 model object based on longitudinal data:
library(pastaPlot)
data("ecovia_data")
lme4_model <- lme4::lmer(CO2 ~ 1 + time*condition + (1 + time | id), data=ecovia_data, REML = FALSE, control = lme4::lmerControl(optimizer = "bobyqa"))
pastaPlot(lme4_model, "time", "id", group = "condition", legend.title = "Condition",
group.labels = c("Control", "Intervention"), ci.int = TRUE, xlab = "Time (days)",
ylab = "CO2")
Plotting of glmmTMB models works in the same fashion:
data("jsp_data")
glmmTMB_model <- glmmTMB::glmmTMB(math_score_y3 ~ 1 + math_score_y1*gender +
(1 + math_score_y1 | school), data=jsp_data, REML = FALSE)
pastaPlot(glmmTMB_model, "math_score_y1", "school", group = "gender",
legend.title = "Gender", group.labels = c("Male", "Female"), ci.int = FALSE,
xlab = "Math score (year 1)", ylab = "Math score (year 3)")
If you have any questions, encounter errors, or miss some features, feel free to contact me: jan.palnau@mailbox.org