---
title: "ggcoefstats"
output:
  rmarkdown::html_vignette:
    toc: true
vignette: >
  %\VignetteIndexEntry{ggcoefstats}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r}
#| label = "setup",
#| include = FALSE

source("../setup.R")
```

```{r}
#| label = "suggested_pkgs",
#| include = FALSE

pkgs <- c(
  "BayesFactor",
  "lme4",
  "metaplus",
  "survival"
)

successfully_loaded <- purrr::map_lgl(pkgs, requireNamespace, quietly = TRUE)
can_evaluate <- all(successfully_loaded)

if (can_evaluate) {
  purrr::walk(pkgs, library, character.only = TRUE)
} else {
  knitr::opts_chunk$set(eval = FALSE)
}
```

---

You can cite this package/vignette as:

```{r}
#| label = "citation",
#| echo = FALSE,
#| comment = ""
citation("ggstatsplot")
```

---

Lifecycle:
[![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://lifecycle.r-lib.org/articles/stages.html)

The function `ggcoefstats` generates **dot-and-whisker plots** for regression
models saved in a tidy data frame. The tidy data frames are prepared using
`parameters::model_parameters`. Additionally, if available, the model summary
indices are also extracted from `performance::model_performance`.

In this vignette, we will see examples of how to use this function. We will try
to cover as many classes of objects as possible. Unfortunately, there is no
single dataset that will be helpful for carrying out all types of regression
analyses and, therefore, we will use various datasets to explore data-specific
hypotheses using regression models.

## General structure of the plots

Although the statistical models displayed in the plot may differ based on the
class of models being investigated, there are few aspects of the plot that will
be invariant across models:

  - The dot-whisker plot contains a dot representing the **estimate** and their
    **confidence intervals** (`95%` is the default). The estimate can either be
    effect sizes (for tests that depend on the `F`-statistic) or regression
    coefficients (for tests with `t`-, $\chi^{2}$-, and `z`-statistic), etc. The
    confidence intervals can sometimes be asymmetric if bootstrapping was used.

  - The label attached to dot will provide more details from the statistical
    test carried out and it will typically contain estimate, statistic, and
    *p*-value.

  - The caption will contain diagnostic information, if available, about models
    that can be useful for model selection: The smaller the Akaike's Information
    Criterion (**AIC**) and the Bayesian Information Criterion (**BIC**) values,
    the "better" the model is.

  - The output of this function will be a `{ggplot2}` object and, thus, it can be
    further modified (e.g., change themes, etc.) with `{ggplot2}` functions.

## Supported models

Most of the regression models that are supported in the underlying packages are
also supported by `ggcoefstats()`. 

```{r}
#| label = "supported"
insight::supported_models()
```

## Examples of supported models

The following examples are organized by statistics type.

There used to be a much longer vignette with examples of a wide collection of
regression models, but for the sake of maintainability, I have removed it. The
old version can be found [here](https://github.com/IndrajeetPatil/ggstatsplot/blob/master/old/effsize_interpretation.Rmd).

### *t*-statistic

linear model (`lm`) and linear mixed-effects model (`lmer`/`lmerMod`)

```{r}
#| label = "lmer1",
#| fig.height = 8,
#| fig.width = 6

library(lme4)

# lm model
mod1 <- stats::lm(formula = Reaction ~ Days, data = sleepstudy)

# merMod model
mod2 <- lme4::lmer(Reaction ~ Days + (Days | Subject), sleepstudy)

# combining the two different plots
combine_plots(
  plotlist = list(
    ggcoefstats(mod1) +
      ggplot2::labs(x = parse(text = "'regression coefficient' ~italic(beta)")),
    ggcoefstats(mod2) +
      ggplot2::labs(
        x = parse(text = "'regression coefficient' ~italic(beta)"),
        y = "fixed effects"
      )
  ),
  plotgrid.args = list(nrow = 2L),
  annotation.args = list(title = "Relationship between movie budget and its IMDB rating")
)
```

Note that for mixed-effects models, only the *fixed* effects are shown because
there are no confidence intervals for *random* effects terms. In case, you would
like to see these terms, you can use `parameters::model_parameters()`.

### *z*-statistic

Aalen's additive regression model for censored data (`aareg`)

```{r}
#| label = "aareg",
#| fig.height = 5,
#| fig.width = 5

library(survival)

# model
afit <- survival::aareg(
  formula = Surv(time, status) ~ age + sex + ph.ecog,
  data = lung,
  dfbeta = TRUE
)

ggcoefstats(
  x = afit,
  title = "Aalen's additive regression model",
  subtitle = "(for censored data)",
  digits = 3
)
```

### $\chi^2$-statistic 

Cox proportional hazards regression model (`coxph`)

```{r}
#| label = "coxph",
#| fig.height = 4,
#| fig.width = 4

library(survival)

# create the simplest-test data set
test1 <- list(
  time = c(4, 3, 1, 1, 2, 2, 3),
  status = c(1, 1, 1, 0, 1, 1, 0),
  x = c(0, 2, 1, 1, 1, 0, 0),
  sex = c(0, 0, 0, 0, 1, 1, 1)
)

# fit a stratified model
mod_coxph <-
  survival::coxph(
    formula = Surv(time, status) ~ x + strata(sex),
    data = test1
  )


ggcoefstats(
  x = mod_coxph,
  title = "Cox proportional hazards regression model"
)
```

Another example with `frailty` term.

```{r}
#| label = "coxph.penal",
#| fig.height = 4,
#| fig.width = 4

library(survival)

# model
mod_coxph <- survival::coxph(
  formula = Surv(time, status) ~ age + sex + frailty(inst),
  data = lung
)


ggcoefstats(
  x = mod_coxph,
  title = "Proportional Hazards Regression Model\nwith Frailty penalty function"
)
```

### *F*-statistic

omnibus ANOVA (`aov`)

```{r}
#| label = "aov1",
#| fig.height = 6,
#| fig.width = 8
library(ggplot2)

# model
mod_aov <- stats::aov(formula = rating ~ mpaa * genre, data = movies_long)

ggcoefstats(
  x = mod_aov,
  effectsize.type = "omega", # changing the effect size estimate being displayed
  point.args = list(color = "red", size = 4, shape = 15), # changing the point geom
  package = "dutchmasters", # package from which color palette is to be taken
  palette = "milkmaid", # color palette for labels
  title = "omnibus ANOVA", # title for the plot
  exclude.intercept = TRUE
) +
  # further modification with the ggplot2 commands
  # note the order in which the labels are entered
  ggplot2::scale_y_discrete(labels = c("MPAA", "Genre", "Interaction term")) +
  ggplot2::labs(x = "effect size estimate (eta-squared)", y = NULL)
```

Note that we can also use this function for model selection. You can try out
different models with the code below and see how the AIC and BIC values change.

```{r}
#| label = "aov2",
#| fig.height = 10,
#| fig.width = 10

combine_plots(
  plotlist = list(
    # model 1
    ggcoefstats(
      x = stats::aov(formula = rating ~ mpaa, data = movies_long),
      title = "1. Only MPAA ratings"
    ),
    # model 2
    ggcoefstats(
      x = stats::aov(formula = rating ~ genre, data = movies_long),
      title = "2. Only genre"
    ),
    # model 3
    ggcoefstats(
      x = stats::aov(formula = rating ~ mpaa + genre, data = movies_long),
      title = "3. Additive effect of MPAA and genre"
    ),
    # model 4
    ggcoefstats(
      x = stats::aov(formula = rating ~ mpaa * genre, data = movies_long),
      title = "4. Multiplicative effect of MPAA and genre"
    )
  ),
  annotation.args = list(title = "Model selection using ggcoefstats")
)
```

### Bayesian models - no statistic

```{r}
#| label = "BFBayesFactor",
#| fig.width = 10,
#| fig.height = 10

library(BayesFactor)

# one sample t-test
mod1 <- ttestBF(mtcars$wt, mu = 3)

# independent t-test
mod2 <- ttestBF(formula = wt ~ am, data = mtcars)

# paired t-test
mod3 <- ttestBF(x = sleep$extra[1:10], y = sleep$extra[11:20], paired = TRUE)

# correlation
mod4 <- correlationBF(y = iris$Sepal.Length, x = iris$Sepal.Width)

# contingency tabs (not supported)
data("raceDolls")
mod5 <- contingencyTableBF(
  raceDolls,
  sampleType = "indepMulti",
  fixedMargin = "cols"
)

# anova
data("puzzles")
mod6 <- anovaBF(
  formula = RT ~ shape * color + ID,
  data = puzzles,
  whichRandom = "ID",
  whichModels = "top",
  progress = FALSE
)

# regression-1
mod7 <- regressionBF(rating ~ ., data = attitude, progress = FALSE)

# meta-analysis
t <- c(-0.15, 2.39, 2.42, 2.43, -0.15, 2.39, 2.42, 2.43)
N <- c(100, 150, 97, 99, 99, 97, 100, 150)
mod8 <- meta.ttestBF(t, N, rscale = 1, nullInterval = c(0, Inf))

# proportion test
mod9 <- proportionBF(y = 15, N = 25, p = 0.5)

# list of plots
combine_plots(
  plotlist = list(
    ggcoefstats(mod1, title = "one sample t-test"),
    ggcoefstats(mod2, title = "independent t-test"),
    ggcoefstats(mod3, title = "paired t-test"),
    ggcoefstats(mod4, title = "correlation"),
    ggcoefstats(mod5, title = "contingency table", effectsize.type = "cramers_v"),
    ggcoefstats(mod6, title = "anova"),
    ggcoefstats(mod7, title = "regression-1"),
    ggcoefstats(mod8, title = "meta-analysis"),
    ggcoefstats(mod9, title = "proportion test")
  ),
  annotation.args = list(title = "Example from `{BayesFactor}` package")
)
```

### Regression models with `list` outputs

Note that a number of regression models will return an object of class `list`,
in which case this function will fail. But often you can extract the object of
interest from this list and use it to plot the regression coefficients.

```r
library(gamm4)

# data
dat <- gamSim(1, n = 400, scale = 2)

# now add 20 level random effect `fac'...
dat$fac <- fac <- as.factor(sample(1:20, 400, replace = TRUE))
dat$y <- dat$y + model.matrix(~ fac - 1) %*% rnorm(20) * .5

# model object
br <- gamm4::gamm4(
    formula = y ~ s(x0) + x1 + s(x2),
    data = dat,
    random = ~ (1 | fac)
  )

# looking at the classes of the objects contained in the list
purrr::map(br, class)

combine_plots(
  plotlist = list(
    # first object plot (only parametric terms are shown)
    ggcoefstats(
      x = br$gam,
      title = "generalized additive model (parametric terms)",
      digits = 3
    ),
    # second object plot
    ggcoefstats(
      x = br$mer,
      title = "linear mixed-effects model",
      digits = 3
    )
  ),
  plotgrid.args = list(nrow = 1)
)
```

## Meta-analysis

In case the estimates you are displaying come from multiple studies, you can
also use this function to carry out random-effects meta-analysis. The data frame
you enter **must** contain at the minimum the following three columns-

  - `term`: a column with names/identifiers to annotate each study/effect
  - `estimate`: a column with the observed effect sizes or outcomes
  - `std.error`: a column the corresponding standard errors

### parametric

```{r}
#| label = "meta1",
#| fig.height = 7,
#| fig.width = 8

library(metaplus)

# renaming to what the function expects
df <- dplyr::rename(mag, estimate = yi, std.error = sei, term = study)

ggcoefstats(
  x = df,
  meta.analytic.effect = TRUE,
  bf.message = TRUE,
  meta.type = "parametric",
  title = "parametric random-effects meta-analysis"
)
```

### robust

```{r}
#| label = "meta2",
#| fig.height = 7,
#| fig.width = 8

library(metaplus)

# renaming to what the function expects
df <- dplyr::rename(mag, estimate = yi, std.error = sei, term = study)

ggcoefstats(
  x = df,
  meta.analytic.effect = TRUE,
  meta.type = "robust",
  title = "robust random-effects meta-analysis"
)
```

### Bayesian

```{r}
#| label = "meta3",
#| fig.height = 7,
#| fig.width = 8

library(metaplus)

# renaming to what the function expects
df <- dplyr::rename(mag, estimate = yi, std.error = sei, term = study)

ggcoefstats(
  x = df,
  meta.analytic.effect = TRUE,
  meta.type = "bayes",
  title = "Bayesian random-effects meta-analysis"
)
```

## Data frames

Sometimes you don't have a model object but a custom data frame that you want
display using this function. If a data frame is to be plotted, it **must**
contain columns named `term` (names of predictors), and `estimate`
(corresponding estimates of coefficients or other quantities of interest). Other
optional columns are `conf.low` and `conf.high` (for confidence intervals), and
`p.value`. You will also have to specify the type of statistic relevant for
regression models (`"t"`, `"z"`, `"f"`, `"chi"`) in case you want to display
statistical labels.

You can also provide a data frame containing all the other relevant information
for additionally displaying labels with statistical information.

```{r}
#| label = "dataframe",
#| fig.height = 7,
#| fig.width = 7

# let's create a data frame
df_full <- tibble::tribble(
  ~term, ~statistic, ~estimate, ~std.error, ~p.value, ~df.error,
  "study1", 0.158, 0.0665, 0.778, 0.875, 5L,
  "study2", 1.33, 0.542, 0.280, 0.191, 10L,
  "study3", 1.24, 0.045, 0.030, 0.001, 12L,
  "study4", 0.156, 0.500, 0.708, 0.885, 8L,
  "study5", 0.33, 0.032, 0.280, 0.101, 2L,
  "study6", 1.04, 0.085, 0.030, 0.001, 3L
)

ggcoefstats(
  x = df_full,
  meta.analytic.effect = TRUE,
  statistic = "t",
  package = "LaCroixColoR",
  palette = "paired"
)
```

## Non-plot outputs

This function can also be used to extract outputs other than a plot, although it
is much more preferable to use the underlying functions instead
(`parameters::model_parameters`).

```{r}
#| label = "other_output",
#| error = TRUE

# data
DNase1 <- subset(DNase, Run == 1)

# using a selfStart model
nlmod <- stats::nls(density ~ SSlogis(log(conc), Asym, xmid, scal), DNase1)

# data frames
ggcoefstats(nlmod) %>% extract_stats()
```

## Summary of graphics and tests

Details about underlying functions used to create graphics and statistical tests carried out can be found in the function documentation:
<https://indrajeetpatil.github.io/ggstatsplot/reference/gghistostats.html>

## Not supported

This vignette was supposed to give a comprehensive account of regression models
supported by `ggcoefstats`. The list of supported models will keep expanding as
additional tidiers are added to the `parameters` and `performance` packages.

Note that not **all** models supported in these packages will be supported by
`ggcoefstats()`. In particular, classes of objects for which there is no column
for `estimate` (e.g., `kmeans`, `optim`, `muhaz`, `survdiff`, `zoo`, etc.) are
not supported.

## Suggestions

If you find any bugs or have any suggestions/remarks, please file an issue on
`GitHub`: <https://github.com/IndrajeetPatil/ggstatsplot/issues>
