seefeel-analysis.qmd

---
title: "Supplementary material"
subtitle: "Analysis of affective valence (`affval`) and perceived exertion (`perexe`) outcomes"
date: "`r format(Sys.time(), '%d %b %Y')`"
format: 
  pdf:
    toc: true
    highlight-style: atom-one
editor: source
---

```{R}
#| include: false

knitr::read_chunk('seefeel-analysis.R')
```


\newpage

# Set up

## Packages

```{R, libs}
#| echo: true
```

\newpage

## Constants

```{R, consts}
#| echo: true
```

## Functions

The below functions make the calculation of Cohen's $f^2$ effect size statistic on `lme4::lmer()` (`merMod` class objects) possible. These functions are used later after models are fitted.

```{R, funcs1}
#| echo: true
```

Convenience function for tidy printing of data.

```{R, funcs2}
#| echo: true
```

\newpage

# Data

## Import

```{R, read}
#| include: false
```

```{R, read}
#| eval: false
```



## Wrangling

```{R, wrangle}
#| echo: true
```


## Model predictions dataset

This step creates a dataset for predictions from the models used later on.

```{R, predictors_dataset}
#| echo: true
```


\newpage

# Modelling

## Outcome: `affval`

### Stepwise selection and final model


```{R, affval_mod_stepwise_sel}
#| echo: true

```


### Effect size calculations

```{R, affval_effect_sizes}
#| echo: true

```

### Model predicted `affval`

```{R, affval_mod_preds}
#| echo: true
#| fig-width: 10
#| fig-height: 6

```





\newpage


## Outcome: `perexe`

### Stepwise selection and final model


```{R, perexe_mod_stepwise_sel}
#| echo: true

```


### Effect size calculations

```{R, perexe_effect_sizes}
#| echo: true

```

### Model predicted `perexe`

```{R, perexe_mod_preds}
#| echo: true
#| fig-width: 10
#| fig-height: 6

```


\newpage


# R session information

```{R, sess_info}
#| echo: true

```