---
title: "Getting Started with mlstats"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting Started with mlstats}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup, message = FALSE}
library(mlstats)
library(dplyr)
```

The **mlstats** package provides tools for multilevel descriptive statistics
and data preparation. It is designed for data where observations are nested
within groups — for example, repeated daily measurements per person, students
within classrooms, or employees within teams.

## Example Data

To demonstrate, we use `media_diary`, a simulated daily diary dataset included
with **mlstats**. It mimics a study in which 100 participants completed brief
daily surveys for 14 consecutive days (*N* = 100 persons, *T* = 1,400 daily
observations). The variables are:

- **`person`**: person identifier
- **`self_control`**: trait self-control, measured once at study entry (stable,
  between-person characteristic; ICC ≈ 1)
- **`wellbeing`**: daily positive wellbeing (1–7)
- **`screen_time`**: minutes of entertainment media consumed that day
- **`stress`**: daily perceived stress (1–7)
- **`enjoyment`**: enjoyment of the media watched that day (1–7)

```{r data}
data("media_diary")
media_diary
```

The data are in long format: each row is one diary entry (one person on one
day). The `person` column identifies which person a row belongs to.

## Multilevel Descriptive Statistics

`mldesc()` produces a publication-ready descriptive statistics table that
combines means, standard deviations, ranges, ICCs, and a within-/between-group
correlation matrix in a single object:

```{r mldesc, warning = FALSE}
vars <- c("self_control", "wellbeing", "screen_time", "stress")

result <- mldesc(
  data  = media_diary,
  group = "person",
  vars  = vars
)

result
```

### Estimation Method

Three estimation methods are available via the `method` argument:

- **`method = "decomposition"`** (default): Uses the variance-decomposition
  approach to estimate within- and between-group correlations. Between-group
  correlations and descriptive statistics are weighted by group size when `weight = TRUE`
  (the default). Set `weight = FALSE` to give every group equal influence.
- **`method = "sem"`**: Fits a two-level structural equation model via `lavaan`
  using robust maximum likelihood. This handles very unequal group sizes more
  rigorously.
- **`method = "bayes"`**: Fits Bayesian multilevel models via `brms`,
  reporting credible intervals instead of p-values. Requires the additional
  `ci` and `folder` arguments; see `vignette("multilevel-descriptives")`.
- See `vignette("correlation-methods")` for a detailed comparison.

### Customising the Output

Several options control the appearance of the output:

- **`significance = "detailed"`**: Adds stars for *p* < .05, *p* < .01, and *p* < .001.
  The default (`"basic"`) marks only *p* < .05.
- **`flip = TRUE`**: Swaps the correlation matrix (between above, within below).
- **`remove_leading_zero = FALSE`**: Keeps the leading zero in decimal numbers.
  The default removes it for APA formatting (`.45` instead of `0.45`).

### Pretty Printing

The result can be formatted for publication via `print()`. All print methods
accept optional arguments `table_title`, `correlation_note`,
`significance_note`, and `note_text`.

**tinytable** is included with **mlstats** (no extra installation needed):

```{r tt-output, warning = FALSE}
result |>
  print(format = "tt")
```

If more customization is needed, **gt** produces richly formatted HTML tables.
It must be installed separately:

```{r gt-install, eval = FALSE}
install.packages("gt")
```

```{r gt-basic}
result |>
  print(format = "gt")
```

Both `tt` and `gt` smoothly render to HTML, PDF, or Word via R Markdown or Quarto.

For details on customising printed tables — including custom titles, notes,
variable labels, and column selection — see `vignette("tables")`.

For detailed coverage of all `mldesc()` options and `within_between_correlations()`
(the underlying function), including ICC and correlation matrix interpretation,
see `vignette("multilevel-descriptives")`.

## Decomposing Variables into Within- and Between-Person Components

Before fitting multilevel models, time-varying predictors are typically
decomposed into their within-group and between-group components.
`decompose_within_between()` makes this easy by adding three new columns per variable:

- **`_grand_mean_centered`**: grand-mean-centered value
- **`_between_{group}`**: group mean (stable between-group component)
- **`_within_{group}`**: deviation from the group mean (within-group fluctuation)

```{r decompose}
media_diary |>
  decompose_within_between(
    group = "person",
    vars  = c("stress", "screen_time")
  ) |>
  select(starts_with("stress"))
```

The within and between components serve as separate predictors in Random
Effects Within-Between (REWB) models, which estimate distinct within-group
and between-group effects. See `vignette("rewb-models")` for a full guide to
data preparation and REWB model fitting with mlstats, including all options
of `decompose_within_between()`.

## References

Bell, A., Fairbrother, M., & Jones, K. (2019). Fixed and random effects models:
Making an informed choice. *Quality & Quantity, 53*(2), 1051–1074.
https://doi.org/10.1007/s11135-018-0802-x

Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in
cross-sectional multilevel models: A new look at an old issue.
*Psychological Methods, 12*(2), 121–138.
https://doi.org/10.1037/1082-989X.12.2.121

Pedhazur, E. J. (1997). *Multiple regression in behavioral research: Explanation
and prediction* (3rd ed.). Harcourt Brace.
