GLMs2pdf.Rmd

---
title: "Linear, Generalized, and Mixed/Multilevel models in R"
author: "Francisco Rodríguez-Sánchez"
date: "https://frodriguezsanchez.net"
aspectratio: 43  # use 169 for wide format
fontsize: 10pt
output: 
  binb::metropolis:
    keep_tex: no
    incremental: yes
    fig_caption: no
    pandoc_args: ["--lua-filter=hideslide.lua"]
urlcolor: blue
linkcolor: blue
header-includes:
  - \definecolor{shadecolor}{RGB}{230,230,230}
  - \setbeamercolor{frametitle}{bg=gray}
---



```{r knitr_setup, include=FALSE, cache=FALSE}

options(knitr.duplicate.label = "allow")

library("knitr")

### Chunk options ###

## Text results
opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE, size = 'tiny')

## Code decoration
opts_chunk$set(tidy = FALSE, comment = NA, highlight = TRUE, prompt = FALSE, crop = TRUE)

# ## Cache
# opts_chunk$set(cache = TRUE, cache.path = "knitr_output/cache/")

# ## Plots
# opts_chunk$set(fig.path = "knitr_output/figures/")
opts_chunk$set(fig.align = 'center', out.width = '90%')

### Hooks ###
## Crop plot margins
knit_hooks$set(crop = hook_pdfcrop)

## Reduce font size
## use tinycode = TRUE as chunk option to reduce code font size
# see http://stackoverflow.com/a/39961605
knit_hooks$set(tinycode = function(before, options, envir) {
  if (before) return(paste0("\n \\", options$size, "\n\n"))
  else return("\n\n \\normalsize \n")
  })

```


# GLM as unified framework for data analysis

```{r child = 'framework.Rmd'}
```

# Introduction to linear models

```{r child = 'lm_intro.Rmd'}
```


# Linear models

```{r child = 'lm.Rmd'}
```


# Variable and model selection

```{r child = 'model_selection.Rmd'}
```

# Model comparison

```{r child = 'model_comparison_trees.Rmd'}
```


# Regression to the mean

```{r child = 'regression-to-the-mean.Rmd'}
```

# Causal inference

```{r child = 'causal-inference.Rmd'}
```


# Generalised Linear Models

# Binomial GLM (logistic regression)

```{r child = 'glm_binomial.Rmd'}
```


# GLM for count data: Poisson regression

```{r child = 'glm_count.Rmd'}
```


# Modelling zero-inflated count data

```{r child = 'glm_count_zeroinfl.Rmd'}
```


# Mixed / Multilevel models

```{r child = 'mixed_models.Rmd'}
```

-----

```{r echo=FALSE, out.width="100%"}
knitr::include_graphics("images/flowchart.png")
```


## END

![](images/CClogo.png)
     
Source code and materials: https://github.com/Pakillo/LM-GLM-GLMM-intro