# Simulate! Simulate! - Part 2: A linear mixed model

In my second simulation example I show how to simulate data from a basic two-level hierarchical design. I go on to explore how well the random effects variance component is estimated for different sample sizes.

# Unstandardizing coefficients from a GLMM

Unstandardizing coefficients in order to interpret them on the original scale can be needed when explanatory variables were standardized to help with model convergence when fitting generalized linear mixed models. Here I show one approach to unstandardizing for a generalized linear mixed model fit with lme4.

# Making many added variable plots with purrr and ggplot2

In this post I show one approach for making added variable plots from a model with many continuous explanatory variables. Since this is done for every variable in the model, I show how to automate the process via functions from package purrr.

# Reversing the order of a ggplot2 legend

A quick example of reversing the legend order in a ggplot2 plot, which is done via guide_legend() instead of by changing the order of the levels in the dataset.

# Simulate! Simulate! - Part 1: A linear model

Where I discuss simulations, why I love them, and get started on a simulation series with a simple two-group linear model simulation.

# Fiesta 2017 gas mileage

Since January is the month for analyzing last year's data, I take a quick look at my 2017 gas mileage in my commuter car (Fiesta! đźŽ†). I use package googlesheets for reading data, skimr for a quick summary of the dataset, and ggplot2 for plotting.

# Combining many datasets in R

I talk about why I'm seeing more students who have many datasets to read in and combine and then go through an example to show one way to approach the problem.

# Using DHARMa for residual checks of unsupported models

Checking for model fit from generalized linear mixed models (GLMM) can be challenging. The DHARMa package helps with this by giving simulated residuals but doesn't work with all model types. I show how to use tools in DHARMa to extend it for use with unsupported models fit with glmmTMB() and zeroinfl().

#### Ariel Muldoon

I currently work as a consulting statistician, advising natural and social science researchers on statistics, statistical programming, and study design. I create and teach R workshops for applied science graduate students who are just getting started in R, where my goal is to make their transition to a programming language as smooth as possible. See my workshop materials at my website.