reporting_func_example.Rmd

---
title: "lmerTest Reporting Test"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

This document tests the reporting functions created by Alex Danvers for printing APA-style results when using lmerTest.

# Loading Libraries and Custom Functions

```{r load functions}
# load the required libraries
library(lmerTest)
library(piecewiseSEM)

# load the custom functions I wrote
source("~/Dropbox/R Resources/lmerT_reporting_functions.R")
source("~/Dropbox/R Resources/sim.ml1_func.R")
```

# Creating Test Data

This generates data with a single continuous predictor and single continuous outcome.

```{r create test data}
# the first number is measures per cluster, second is number of clusters
test.dat <- sim.ml1(10,30)
```

# Estimating Models

```{r estimate models}
int.only <- lmer(y ~ 1 + (1|id), data=test.dat)
fixed.x <- lmer(y ~ x + (1|id), data=test.dat)
random.x <- lmer(y ~ x + (x|id), data=test.dat)

# model comparisons
comp1 <- anova(int.only, fixed.x)
comp2 <- anova(fixed.x, random.x)
```

# Reporting

We found that a model using x as a predictor fit the data significantly better than an intercept-only model (`r chi_inline(comp1)`). We then tested whether treating the predictor x as a random effect significantly improved model fit. We found that it did (`r chi_inline(comp2)`).

In the fixed effects model, x was a statistically significant predictor (`r lmerT.coef(fixed.x, 2)`). It was also significant in the random effects model (`r lmerT.coef(random.x, 2)`).