README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE, eval = TRUE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

library(tidyverse)
library(dplyr)
library(VarDecomp)
```

# VarDecomp

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VarDecomp can be used for variance decomposition, model fit checks and output visualizations of brms models.

## Installation

You can install the development version of VarDecomp like so:

```{r eval = FALSE}
devtools::install_github("gabewinter/VarDecomp")
```

## Documentation

Full documentation website on: https://gabewinter.github.io/VarDecomp

## Example

```{r example, eval = TRUE}
library(tidyverse)

md = tibble(
  sex = sample(rep(c(-0.5, 0.5), each = 500)),
  species = sample(rep(c("species1","species2","species3","species4","species5"), each = 200))) %>% 

## Create a variables 
  dplyr::mutate(height = rnorm(1000, mean = 170, sd = 10),
                mass = 5 + 0.5 * height + rnorm(1000, mean = 0, sd = 5)) %>% 
  dplyr::mutate(height = height - mean(height),
                mass = log(mass))


mod = brms_model(Chainset = 2,
                 Response = "mass", 
                 FixedEffect = c("sex","height"), 
                 Family = "gaussian", 
                 Data = md)

model_fit(mod, Group = "sex")

model_summary(mod)

plot_intervals(mod)

PS = var_decomp(mod)

plot_R2(PS)

```