Frequently asked questions about mixed-effects models, covering the necessity of random slopes, appropriate p-value calculation methods, parallelization limitations, convergence issues, and optimizer selection.
Whereas the direction of main effects can be interpreted from the sign of the estimate, the interpretation of interaction effects often requires plots. This task is facilitated by the R package sjPlot. For instance, using the plot_model function, I plotted the interaction between two continuous variables.
library(lme4)
#> Loading required package: Matrix
library(sjPlot)
#> Learn more about sjPlot with 'browseVignettes("sjPlot")'.
library(ggplot2)
theme_set(theme_sjplot())
# Create data partially based on code by Ben Bolker # from https://stackoverflow.
Whereas the direction of main effects can be interpreted from the sign of the estimate, the interpretation of interaction effects often requires plots. This task is facilitated by the R package sjPlot. For instance, using the plot_model function, I plotted the interaction between a continuous variable and a categorical variable. The categorical variable was passed to the fill argument of plot_model.
library(lme4)
#> Loading required package: Matrix
library(sjPlot)
#> Install package "strengejacke" from GitHub (`devtools::install_github("strengejacke/strengejacke")`) to load all sj-packages at once!
A custom R function to create ggplot2 visualizations of fixed effects from models refitted with multiple optimizers using lme4's allFit function, enabling visual assessment of convergence validity in mixed-effects models.
When a model has struggled to find enough information in the data to account for every predictor---especially for every random effect---, convergence warnings appear (Brauer & Curtin, 2018; Singmann & Kellen, 2019). In this article, I review the issue of convergence before presenting a new plotting function in R that facilitates the visualisation of the fixed effects fitted by different optimization algorithms (also dubbed optimizers).
I would like to ask for advice regarding some plots that were created using brms::mcmc_plot(), and cannot be opened in R now. The plots were created last year using brms 2.17.0, and were saved in RDS objects. The problem I have is that I cannot open the plots in R now because I get an error related to a missing function. I would be very grateful if someone could please advise me if they can think of a possible reason or solution.
Whereas the direction of main effects can be interpreted from the sign of the estimate, the interpretation of interaction effects often requires plots. This task is facilitated by the R package sjPlot (Lüdecke, 2022). In Bernabeu (2022), the sjPlot function called plot_model served as the basis for the creation of some custom functions. One of these functions is alias_interaction_plot, which allows the plotting of interactions between a continuous variable and a categorical variable.
Whereas the direction of main effects can be interpreted from the sign of the estimate, the interpretation of interaction effects often requires plots. This task is facilitated by the R package sjPlot (Lüdecke, 2022). In Bernabeu (2022), the sjPlot function called plot_model served as the basis for the creation of some custom functions. Two of these functions are deciles_interaction_plot and sextiles_interaction_plot. These functions allow the plotting of interactions between two continuous variables.
Frequentist and Bayesian statistics are sometimes regarded as fundamentally different philosophies. Indeed, can both qualify as philosophies or is one of them just a pointless ritual? Is frequentist statistics only about $p$ values? Are frequentist estimates diametrically opposed to Bayesian posterior distributions? Are confidence intervals and credible intervals irreconcilable? Will R crash if lmerTest and brms are simultaneously loaded?
This post presents a run-through of a Bayesian workflow in R. The content is *closely* based on Bernabeu (2022), which was in turn based on lots of other references, also cited here.