### Example Usage

Source: https://docs.r4photobiology.info/ggpmisc/reference/scale_y_Pvalue.html

Demonstrates how to use `scale_x_logFC` and `scale_y_Pvalue` for plotting, and `scale_y_FDR` with custom limits.

```APIDOC
## Examples

```R
set.seed(12346)
my.df <- data.frame(x = rnorm(50, sd = 4),
                    y = 10^-runif(50, min = 0, max = 20))

# Example 1: Using scale_x_logFC and scale_y_Pvalue
ggplot(my.df, aes(x, y)) + 
  geom_point() + 
  scale_x_logFC() + 
  scale_y_Pvalue()

# Example 2: Using scale_y_FDR with custom limits
ggplot(my.df, aes(x, y)) + 
  geom_point() + 
  scale_x_logFC() + 
  scale_y_FDR(limits = c(NA, 1e-20))
```
```

--------------------------------

### Example 1: Default label construction

Source: https://docs.r4photobiology.info/ggpmisc/reference/use_label.html

Demonstrates the default label construction using `use_label()` with `stat_poly_eq`.

```APIDOC
## Example 1: Default label construction

# generate artificial data
set.seed(4321)
x <- 1:100
y <- (x + x^2 + x^3) + rnorm(length(x), mean = 0, sd = mean(x^3) / 4)
my.data <- data.frame(x = x,
                      y = y * 1e-5,
                      group = c("A", "B"),
                      y2 = y * 1e-5 + c(2, 0))

# give a name to a formula
formula <- y ~ poly(x, 3, raw = TRUE)

# default label constructed by use_label()
ggplot(data = my.data,
       mapping = aes(x = x, y = y2, colour = group)) +
  geom_point() +
  stat_poly_line(formula = formula) +
  stat_poly_eq(mapping = use_label(),
               formula = formula)
```

--------------------------------

### Install ggpmisc from GitHub

Source: https://docs.r4photobiology.info/ggpmisc/index.html

Install the current unstable version of ggpmisc directly from GitHub sources. Ensure the 'remotes' package is installed first.

```r
# install.packages("remotes") # nolint: commented_code_linter.
remotes::install_github("aphalo/ggpmisc")
```

--------------------------------

### Example 2: User specified label components

Source: https://docs.r4photobiology.info/ggpmisc/reference/use_label.html

Shows how to specify custom label components using `use_label()`.

```APIDOC
## Example 2: User specified label components

# user specified label components
ggplot(data = my.data,
       mapping = aes(x = x, y = y2, colour = group)) +
  geom_point() +
  stat_poly_line(formula = formula) +
  stat_poly_eq(mapping = use_label("eq", "F"),
              formula = formula)
```

--------------------------------

### Numeric Output for stat_quant_eq

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_quant_eq.html

This example demonstrates obtaining the numeric coefficients of the regression equations from stat_quant_eq.

```R
if (gginnards.installed)
  ggplot(my.data, aes(x, y)) +
    geom_point() +
    stat_quant_eq(formula = formula, geom = "debug_group", output.type = "numeric")
```

--------------------------------

### Load refitME Package

Source: https://docs.r4photobiology.info/ggpmisc/articles/poly-advanced.html

Loads the 'refitME' package into the R session. Ensure this package is installed before running.

```R
library(refitME)
```

--------------------------------

### Example 5: Combine other mappings with default labels

Source: https://docs.r4photobiology.info/ggpmisc/reference/use_label.html

Shows how to combine other aesthetic mappings with default labels generated by `use_label()`.

```APIDOC
## Example 5: Combine other mappings with default labels

# combine other mappings with default labels
ggplot(data = my.data,
       mapping = aes(x = x, y = y2)) +
  geom_point(mapping = aes(colour = group)) +
  stat_poly_line(mapping = aes(colour = group), formula = formula) +
  stat_poly_eq(mapping = use_label(aes(colour = group)),
              formula = formula)
```

--------------------------------

### Install ggpmisc from R-Universe

Source: https://docs.r4photobiology.info/ggpmisc/index.html

Install the current unstable version of ggpmisc from the R-Universe repository, which provides binaries for multiple platforms.

```r
install.packages("ggpmisc",
                 repos = c("https://aphalo.r-universe.dev",
                           "https://cloud.r-project.org"))
```

--------------------------------

### Example 4: Combine mapping to label aesthetic with other mappings

Source: https://docs.r4photobiology.info/ggpmisc/reference/use_label.html

Illustrates combining the `label` aesthetic mapping with other mappings using `use_label()` and `aes()`.

```APIDOC
## Example 4: Combine mapping to label aesthetic with other mappings

# combine the mapping to the label aesthetic with other mappings
ggplot(data = my.data,
       mapping = aes(x = x, y = y2)) +
  geom_point(mapping = aes(colour = group)) +
  stat_poly_line(mapping = aes(colour = group), formula = formula) +
  stat_poly_eq(mapping = use_label("grp", "eq", "F",
                                   aes(grp.label = group)),
              formula = formula)
```

--------------------------------

### Example 3: User specified label components and separator

Source: https://docs.r4photobiology.info/ggpmisc/reference/use_label.html

Demonstrates specifying custom label components and a custom separator with `use_label()`.

```APIDOC
## Example 3: User specified label components and separator

# user specified label components and separator
ggplot(data = my.data,
       mapping = aes(x = x, y = y2, colour = group)) +
  geom_point() +
  stat_poly_line(formula = formula) +
  stat_poly_eq(mapping = use_label("R2", "F", sep = "*\" with \"*"),
               formula = formula)
```

--------------------------------

### Example: FC_format with different log bases

Source: https://docs.r4photobiology.info/ggpmisc/reference/FC_format.html

Demonstrates using FC_format with various log bases for data and labels to format a sequence of numbers.

```R
FC_format(2, 10)(1:5)
```

```R
FC_format(0, 2)(c(1/4, 1/2, 1,2,4,8))
```

```R
FC_format(10, 0)(-1:3)
```

```R
FC_format(0, 10)(c(0.1, 1, 10, 100, 100))
```

--------------------------------

### Multiple Quantiles with Text Output

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_quant_eq.html

This example shows how to specify multiple quantiles (0.25, 0.5, 0.75) and display their corresponding regression equations as text.

```R
if (gginnards.installed)
  ggplot(my.data, aes(x, y)) +
    geom_point() +
    stat_quant_eq(formula = formula, quantiles = c(0.25, 0.5, 0.75),
                  geom = "debug_group", output.type = "text")
```

--------------------------------

### Markdown Output for stat_quant_eq

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_quant_eq.html

This example shows how to configure stat_quant_eq to output the regression equations in markdown format, suitable for reports.

```R
if (gginnards.installed)
  ggplot(my.data, aes(x, y)) +
    geom_point() +
    stat_quant_eq(mapping = aes(label = after_stat(eq.label)),
                  formula = formula, geom = "debug_group",
                  output.type = "markdown")
```

--------------------------------

### Inspect Residuals with Robust Linear Model (rlm)

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_fit_residuals.html

This example shows how to inspect residuals using stat_fit_residuals with the 'rlm' method for robust linear modeling. The 'gginnards' package must be installed and loaded.

```R
if (gginnards.installed)
  ggplot(my.data, aes(x, y)) +
    stat_fit_residuals(formula = my.formula, method = "rlm",
                       geom = "debug_group")
```

--------------------------------

### Load 'segmented' Package

Source: https://docs.r4photobiology.info/ggpmisc/articles/poly-advanced.html

Loads the 'segmented' package into the R session. Ensure the package is installed before running.

```r
library(segmented)
```

--------------------------------

### Load robustbase package

Source: https://docs.r4photobiology.info/ggpmisc/articles/poly-advanced.html

Loads the 'robustbase' package into the current R session. Ensure this package is installed before loading.

```r
library(robustbase)
```

--------------------------------

### stat_distrmix_eq with components = "members"

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_distrmix_eq.html

Example of using stat_distrmix_eq when components are specified as 'members'.

```R
ggplot(faithful, aes(x = waiting)) +
  stat_distrmix_line(components = "members") +
  stat_distrmix_eq(components = "members")
```

--------------------------------

### Overlaying histogram with stat_distrmix_line()

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_distrmix_line.html

This example overlays a histogram of the 'waiting' variable with a density fit from stat_distrmix_line(). The components are colored and filled based on the 'component' aesthetic.

```R
ggplot(faithful, aes(x = waiting)) +
  geom_histogram(aes(y = after_stat(density)), bins = 20) +
  stat_distrmix_line(aes(colour = after_stat(component),
                        fill = after_stat(component)),
                     geom = "area", linewidth = 1, alpha = 0.25, se = FALSE)
```

--------------------------------

### Install ggpmisc from CRAN

Source: https://docs.r4photobiology.info/ggpmisc/index.html

Use this command to install the most recent stable version of the ggpmisc package from CRAN.

```r
install.packages("ggpmisc")
```

--------------------------------

### stat_distrmix_line() with components = "sum"

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_distrmix_line.html

This example shows how to use stat_distrmix_line() when fitting a mixture of Normal distributions, specifying that the components should be summed.

```R
ggplot(faithful, aes(x = waiting)) +
  stat_distrmix_line(components = "sum")
```

--------------------------------

### Check output type for a specified type

Source: https://docs.r4photobiology.info/ggpmisc/reference/check_output_type.html

Passes through the specified output type if it is not NULL. This example shows 'text' as the specified type.

```R
check_output_type("text")
#> [1] "text"
```

--------------------------------

### Linear Regression Fit Summary with Defaults

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_fit_tb.html

Applies a linear regression model and displays a summary of the fit using default settings. Ensure the 'broom' package is installed.

```R
if (broom.installed)
  library(broom)

# data for examples
  x <- c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
  covariate <- sqrt(x) + rnorm(9)
  group <- factor(c(rep("A", 4), rep("B", 5)))
  my.df <- data.frame(x, group, covariate)

gginnards.installed  <- requireNamespace("gginnards", quietly = TRUE)

if (gginnards.installed)
  library(gginnards)

## covariate is a numeric or continuous variable
# Linear regression fit summary, all defaults
if (broom.installed)
  ggplot(my.df, aes(covariate, x)) +
    geom_point() +
    stat_fit_tb() +
    expand_limits(y = 70)
```

--------------------------------

### Debug panel for stat_multcomp

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_multcomp.html

This example demonstrates how to use stat_multcomp with debug_panel to inspect the intermediate data generated for multiple comparisons. It shows the structure and content of the data before it's used for plotting.

```R
stat_multcomp(label.type = "bars",
                output.type = "numeric",
                geom = "debug_panel")
```

--------------------------------

### Basic Polynomial Regression Equation in LaTeX

Source: https://docs.r4photobiology.info/ggpmisc/articles/latex-labels.html

Displays a polynomial regression equation using LaTeX formatting. Ensure the 'latex2exp' package is installed for rendering.

```R
formula <- y ~ poly(x, 3, raw = TRUE)
ggplot(my.data, aes(x, y)) +
  geom_point() +
  stat_poly_line(formula = formula) +
  stat_poly_eq(formula = formula,
               geom = "latex", hjust = 0, vjust = 1)
```

--------------------------------

### stat_distrmix_line() with components = "members"

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_distrmix_line.html

This example demonstrates using stat_distrmix_line() to fit a mixture of Normal distributions, with the 'components' argument set to 'members' to consider individual components.

```R
ggplot(faithful, aes(x = waiting)) +
  stat_distrmix_line(components = "members")
```

--------------------------------

### stat_quant_band with rqss Method and qss Formula

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_quant_band.html

Applies the 'rqss' method with a 'qss(x)' formula to fit an additive model. Note: This example may produce warnings if 'qss' is not available.

```R
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_band(method = "rqss",
                  formula = y ~ qss(x))
```

--------------------------------

### Linear Regression Fit Summary with Default Formatting

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_fit_tb.html

Applies a linear regression model and displays a summary of the fit using default table formatting. Ensure the 'broom' package is installed.

```R
if (broom.installed)
  ggplot(my.df, aes(covariate, x)) +
    geom_point() +
    stat_fit_tb(tb.type = "fit.summary") +
    expand_limits(y = 70)
```

--------------------------------

### Example data generation for logFC scales

Source: https://docs.r4photobiology.info/ggpmisc/reference/scale_x_logFC.html

Generates a data frame with random normal data for demonstrating the usage of log fold change scales.

```R
set.seed(12346)
my.df <- data.frame(x = rnorm(50, sd = 4), y = rnorm(50, sd = 4))
```

--------------------------------

### stat_quant_band with rqss Method and qss(y, constraint = "D") Formula

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_quant_band.html

Uses the 'rqss' method with a constrained 'qss(y)' formula. Note: This example may produce warnings if 'qss' is not available.

```R
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_band(method = "rqss",
                  formula = x ~ qss(y, constraint = "D"))
```

--------------------------------

### Debug Correlation Output in LaTeX

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_correlation.html

Use this snippet to get a debug output of the correlation calculation in LaTeX format. Ensure gginnards is installed for this functionality.

```r
if (gginnards.installed)
  ggplot(my.data, aes(x, y)) +
    geom_point() +
    stat_correlation(geom = "debug_group", output.type = "LaTeX")
```

--------------------------------

### Plot with Ternary Shape Scale and Hidden Guide

Source: https://docs.r4photobiology.info/ggpmisc/reference/scale_shape_outcome.html

Shows how to create a plot with `scale_shape_outcome` while disabling the legend guide. Note: `guide = FALSE` is deprecated; use `guide = "none"`.

```R
ggplot(my.df, aes(x, y, shape = outcome3)) +
  geom_point() +
  scale_shape_outcome(guide = FALSE) +
  theme_bw()
#> Warning: The `guide` argument in `scale_*()` cannot be `FALSE`. This was deprecated in
#> ggplot2 3.3.4.
#> ℹ Please use "none" instead.
```

--------------------------------

### Generate artificial data for plotting examples

Source: https://docs.r4photobiology.info/ggpmisc/articles/output-types.html

Generates a set of artificial data using a fixed seed for reproducibility. This data is suitable for use in subsequent plotting examples.

```R
set.seed(4321)
```

--------------------------------

### Basic stat_distrmix_eq Usage

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_distrmix_eq.html

Demonstrates the default usage of stat_distrmix_eq with stat_distrmix_line.

```R
ggplot(faithful, aes(x = waiting)) +
  stat_distrmix_line(components = "sum") +
  stat_distrmix_eq()
```

--------------------------------

### Citation for refitME Package

Source: https://docs.r4photobiology.info/ggpmisc/articles/poly-advanced.html

Prints the citation details for the 'refitME' package. Use this to properly attribute the package in academic work.

```R
print(citation(package = "refitME", auto = TRUE), bibtex = FALSE)
```

--------------------------------

### Get Citation for lspline Package

Source: https://docs.r4photobiology.info/ggpmisc/articles/poly-advanced.html

Prints the citation information for the 'lspline' package. Use this when citing the package in publications.

```r
print(citation(package = "lspline", auto = TRUE), bibtex = FALSE)
```

--------------------------------

### Manually Assembling Labels with sprintf()

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_poly_eq.html

Shows how to manually create the label string using sprintf() within aes(). This method offers flexible formatting for R-squared, F-value, and p-value labels.

```R
ggplot(my.data, aes(x, y)) +
  geom_point() +
  stat_poly_line(formula = formula) +
  stat_poly_eq(aes(label =  sprintf("%s*\" with \"*%s*\" and \"*%s",
                                    after_stat(rr.label),
                                    after_stat(f.value.label),
                                    after_stat(p.value.label))),
               formula = formula)
```

--------------------------------

### stat_distrmix_eq with components = "members" and se = TRUE

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_distrmix_eq.html

Demonstrates stat_distrmix_eq with 'members' components and enabling standard error display.

```R
ggplot(faithful, aes(x = waiting)) +
  stat_distrmix_line(components = "members") +
  stat_distrmix_eq(components = "members", se = TRUE)
```

--------------------------------

### stat_distrmix_line() with k = 1 (single Normal distribution)

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_distrmix_line.html

This example demonstrates the special case where k = 1, fitting a single Normal distribution to a subset of the faithful dataset. Parameters irrelevant to a single distribution fit are ignored.

```R
ggplot(subset(faithful, waiting > 66), aes(x = waiting)) +
  stat_distrmix_line(k = 1)
```

--------------------------------

### Get Citation for 'splines' Package

Source: https://docs.r4photobiology.info/ggpmisc/articles/poly-advanced.html

Prints the citation information for the 'splines' package in R. Use this when citing the package in publications.

```r
print(citation(package = "splines", auto = TRUE), bibtex = FALSE)
```

--------------------------------

### Combine label mapping with other mappings using use_label

Source: https://docs.r4photobiology.info/ggpmisc/reference/use_label.html

Demonstrates combining the label aesthetic mapping with other aesthetic mappings (e.g., color) using `use_label()` and `aes()`. Requires artificial data and a formula definition.

```R
set.seed(4321)
x <- 1:100
y <- (x + x^2 + x^3) + rnorm(length(x), mean = 0, sd = mean(x^3) / 4)
my.data <- data.frame(x = x,
                      y = y * 1e-5,
                      group = c("A", "B"),
                      y2 = y * 1e-5 + c(2, 0))

# give a name to a formula
formula <- y ~ poly(x, 3, raw = TRUE)

# combine the mapping to the label aesthetic with other mappings
ggplot(data = my.data,
       mapping = aes(x = x, y = y2)) +
  geom_point(mapping = aes(colour = group)) +
  stat_poly_line(mapping = aes(colour = group), formula = formula) +
  stat_poly_eq(mapping = use_label("grp", "eq", "F",
                                   aes(grp.label = group)),
              formula = formula)
```

--------------------------------

### Get Citation for MASS Package

Source: https://docs.r4photobiology.info/ggpmisc/articles/poly-advanced.html

Prints the citation information for the MASS package. Use this when publishing research that utilizes the package.

```r
print(citation(package = "MASS", auto = TRUE), bibtex = FALSE)
```

--------------------------------

### Overlaying histogram with stat_distrmix_line() and components = "members"

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_distrmix_line.html

This example overlays a histogram with a density fit from stat_distrmix_line(), using 'members' for components and coloring/filling by component. `se = FALSE` is used to disable standard error shading.

```R
ggplot(faithful, aes(x = waiting)) +
  stat_distrmix_line(aes(colour = after_stat(component),
                        fill = after_stat(component)),
                     geom = "area", linewidth = 1, alpha = 0.25,
                     components = "members", se = FALSE)
```

--------------------------------

### Inspecting stat_quant_band Data with geom_debug_group

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_quant_band.html

Uses geom_debug_group from the gginnards package to inspect the data generated by stat_quant_band. Requires gginnards to be installed.

```R
gginnards.installed <- requireNamespace("gginnards", quietly = TRUE)

if (gginnards.installed)
  library(gginnards)

if (gginnards.installed)
  ggplot(mpg, aes(displ, hwy)) +
    stat_quant_band(geom = "debug_group")
```

--------------------------------

### Add ANOVA Table with Flipped Coordinates

Source: https://docs.r4photobiology.info/ggpmisc/articles/user-guide.html

Displays an ANOVA table after flipping the coordinate system. This example also adjusts x-axis expansion.

```R
ggplot(chickwts, aes(factor(feed), weight)) +
  stat_summary(fun.data = "mean_se") +
  stat_fit_tb(tb.type = "fit.anova", label.x = "left", size = 3) +
  scale_x_discrete(expand = expansion(mult = c(0.2, 0.5))) +
  coord_flip()
```

--------------------------------

### Load lspline Package

Source: https://docs.r4photobiology.info/ggpmisc/articles/poly-advanced.html

Loads the 'lspline' package into the R session. Ensure this is run before using any functions from the package.

```r
library(lspline)
```

--------------------------------

### Manually Assembling Labels with paste()

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_poly_eq.html

Demonstrates how to manually construct the label string for the regression equation using paste() and aes(). This allows for custom formatting of R-squared and n labels.

```R
ggplot(my.data, aes(x, y)) +
  geom_point() +
  stat_poly_line(formula = formula) +
  stat_poly_eq(aes(label =  paste(after_stat(rr.label),
                                  after_stat(n.label), sep = "*\", \"*")),
               formula = formula)
```

--------------------------------

### Manually Assemble Label with sprintf()

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_quant_eq.html

Illustrates manual assembly and mapping of a specific label using sprintf() and aes() for stat_quant_eq. This is an alternative to paste() for custom label formatting.

```R
ggplot(my.data, aes(x, y2, color = group, grp.label = group)) +
  geom_point() +
  stat_quant_band(method = "rq", formula = formula,
                  quantiles = c(0.05, 0.5, 0.95)) +
  stat_quant_eq(mapping = aes(label = sprintf("%s*\": \"*%s",
                                              after_stat(grp.label),
                                              after_stat(eq.label))),
                quantiles = c(0.05, 0.5, 0.95),
                formula = formula, size = 3)
```

--------------------------------

### Get Citation for 'segmented' Package

Source: https://docs.r4photobiology.info/ggpmisc/articles/poly-advanced.html

Use this to cite the 'segmented' package in publications. It provides the package name, author, year, title, and DOI.

```r
print(citation(package = "segmented", auto = TRUE), bibtex = FALSE)
```

--------------------------------

### Augmenting Fits with nls

Source: https://docs.r4photobiology.info/ggpmisc/articles/user-guide.html

Fit a non-linear model using `stat_fit_augment` with the 'nls' method. Provide model formula and starting parameters in `method.args`.

```R
args <- list(formula = y ~ k * e ^ x,
             start = list(k = 1, e = 2))
ggplot(mtcars, aes(wt, mpg)) +
  geom_point() +
  stat_fit_augment(method = "nls",
                   method.args = args)
```

--------------------------------

### Linear Regression with Table Theme and Non-Default Position

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_fit_tb.html

Demonstrates linear regression using stat_fit_tb with a specified table theme (ttheme_gtlight) and non-default positioning for the table. Requires the 'broom' package.

```r
if (broom.installed)
  ggplot(my.df, aes(covariate, x)) +
    geom_point() +
    stat_fit_tb(table.theme = ttheme_gtlight,
                npcx = "left", npcy = "bottom") +
    expand_limits(y = 35)
```

--------------------------------

### Inspecting stat_quant_band Data with fm.values = TRUE

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_quant_band.html

Uses geom_debug_group with fm.values = TRUE to inspect detailed fitting information from stat_quant_band. Requires gginnards to be installed.

```R
if (gginnards.installed)
  ggplot(mpg, aes(displ, hwy)) +
    stat_quant_band(geom = "debug_group", fm.values = TRUE)
```

--------------------------------

### Pearson Correlation Test Statistics

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_fit_glance.html

Displays Pearson correlation coefficient and p-value. The 'broom' package must be installed. The formula is automatically extracted from the arguments.

```R
if (broom.installed)
  ggplot(mtcars, aes(x = disp, y = mpg)) +
    geom_point() +
    stat_fit_glance(method = "cor.test",
                    label.y = "bottom",
                    method.args = list(formula = ~ x + y),
                    mapping = aes(label = sprintf('r[Pearson]~"="~%.3f~~italic(P)~"="~%.2g',
                                  after_stat(estimate), after_stat(p.value))),
                    parse = TRUE)
```

--------------------------------

### Load Required R Packages

Source: https://docs.r4photobiology.info/ggpmisc/articles/major-axis-regression.html

Loads the necessary R packages for performing major axis regression and plotting. Ensure these packages are installed before running.

```R
library(ggpmisc)
library(smatr)
library(lmodel2)
```

--------------------------------

### Manually Assemble Label with paste()

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_quant_eq.html

Demonstrates manual assembly and mapping of a specific label using paste() and aes() for stat_quant_eq. This allows for custom formatting of the equation label.

```R
ggplot(my.data, aes(x, y2, color = group, grp.label = group)) +
  geom_point() +
  stat_quant_line(method = "rq", formula = formula,
                  quantiles = c(0.05, 0.5, 0.95),
                  linewidth = 0.5) +
  stat_quant_eq(mapping = aes(label = paste(after_stat(grp.label), "*\": \"*",
                                            after_stat(eq.label), sep = "")),
                quantiles = c(0.05, 0.5, 0.95),
                formula = formula, size = 3)
```

--------------------------------

### Multiple Quantiles with Numeric Output

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_quant_eq.html

This snippet demonstrates specifying multiple quantiles and retrieving the numeric coefficients for each.

```R
if (gginnards.installed)
  ggplot(my.data, aes(x, y)) +
    geom_point() +
    stat_quant_eq(formula = formula, quantiles = c(0.25, 0.5, 0.75),
                  geom = "debug_group", output.type = "numeric")
```

--------------------------------

### Fitting Higher-Order Polynomials with stat_poly_eq

Source: https://docs.r4photobiology.info/ggpmisc/articles/user-guide.html

Fit and display equations for higher-order polynomials using the poly() function in the formula. This example uses a 5th-degree polynomial.

```R
formula <- y ~ poly(x, 5, raw = TRUE)
ggplot(my.data, aes(x, y)) +
  geom_point() +
  stat_poly_line(formula = formula) +
  stat_poly_eq(mapping = use_label("eq"), formula = formula, size = 2.7)
```

--------------------------------

### Polynomials using poly() function

Source: https://docs.r4photobiology.info/ggpmisc/reference/check_poly_formula.html

Demonstrates checking formulas that use the `poly()` function. It distinguishes between raw and orthogonal polynomials, with only raw polynomials yielding a TRUE result for label checking.

```r
check_poly_formula(y ~ poly(x, 2, raw = TRUE)) # label o.k.
#> [1] TRUE
```

```r
check_poly_formula(y ~ poly(x, 2)) # orthogonal polynomial -> bad label
#> Warning: 'poly()' in model formula has to be passed 'raw = TRUE': 'eq.label' set to NA!
#> [1] FALSE
```

--------------------------------

### Combine other mappings with default labels using use_label

Source: https://docs.r4photobiology.info/ggpmisc/reference/use_label.html

Illustrates combining other aesthetic mappings (e.g., color) with default labels generated by `use_label()`. Requires artificial data and a formula definition.

```R
set.seed(4321)
x <- 1:100
y <- (x + x^2 + x^3) + rnorm(length(x), mean = 0, sd = mean(x^3) / 4)
my.data <- data.frame(x = x,
                      y = y * 1e-5,
                      group = c("A", "B"),
                      y2 = y * 1e-5 + c(2, 0))

# give a name to a formula
formula <- y ~ poly(x, 3, raw = TRUE)

# combine other mappings with default labels
ggplot(data = my.data,
       mapping = aes(x = x, y = y2)) +
  geom_point(mapping = aes(colour = group)) +
  stat_poly_line(mapping = aes(colour = group), formula = formula) +
  stat_poly_eq(mapping = use_label(aes(colour = group)),
              formula = formula)
```

--------------------------------

### Fit Segmented Linear Model with Two Breakpoints

Source: https://docs.r4photobiology.info/ggpmisc/articles/poly-advanced.html

Use stat_poly_line with lm2segmented.lm to fit a segmented linear model with specified starting breakpoint values.

```r
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_poly_line(method = lm2segmented.lm,
                 method.args = list(psi = c(3, 5)))

```

--------------------------------

### No Weights, Quantile at Upper Boundary

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_quant_eq.html

Shows how to set the quantile to the upper boundary (0.95) when weights are not used. Includes a warning about non-positive fis.

```R
ggplot(my.data, aes(x, y)) +
  geom_point() +
  stat_quant_line(formula = formula, quantiles = 0.95) +
  stat_quant_eq(formula = formula, quantiles = 0.95)
```

--------------------------------

### Fit Quantile Line using rqss (x ~ y)

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_quant_line.html

This example demonstrates fitting a quantile smoothing spline using the 'rqss' method with x as the dependent variable. The formula 'x ~ qss(y, constraint = "D")' is used, and confidence intervals are automatically included by default when se is not specified.

```R
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_line(method = "rqss",
                  formula = x ~ qss(y, constraint = "D"),
                  quantiles = 0.5)
```

--------------------------------

### Weighted Regression with R-squared and P-value

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_fit_glance.html

Displays R-squared and p-value for a weighted linear regression model. Ensure the 'broom' package is installed and the 'weight' aesthetic is mapped.

```R
if (broom.installed)
  ggplot(mtcars, aes(x = disp, y = mpg, weight = cyl)) +
    stat_smooth(method = "lm") +
    geom_point(aes(colour = factor(cyl))) +
    stat_fit_glance(method = "lm",
                    label.y = "bottom",
                    method.args = list(formula = y ~ x, weights = quote(weight)),
                    mapping = aes(label = sprintf('r^2~"="~%.3f~~italic(P)~"="~%.2g',
                                  after_stat(r.squared), after_stat(p.value))),
                    parse = TRUE)
```

--------------------------------

### Display Quartile Band with ggpmisc

Source: https://docs.r4photobiology.info/ggpmisc/articles/user-guide.html

Use stat_quant_band to visualize the spread of observations between quartiles, similar to a box plot. This example fits a second-degree polynomial model.

```r
ggplot(my.data, aes(x, y)) +
  geom_point() +
  stat_quant_band(formula = y ~ poly(x, 2))
```

--------------------------------

### Load necessary R packages and set xdvir engine

Source: https://docs.r4photobiology.info/ggpmisc/articles/output-types.html

Loads essential R packages for plotting and typesetting, including grid, ggpmisc, xdvir, ggtext, and marquee. It also explicitly sets the 'xdvir' engine to 'xetex' to avoid potential 'luatex' issues.

```R
library(grid)
library(ggpmisc)
library(xdvir)
library(ggtext)
library(marquee)

options(xdvir.engine="xetex") # luatex can fail
```

--------------------------------

### Controlling R-squared Digits

Source: https://docs.r4photobiology.info/ggpmisc/reference/stat_poly_eq.html

Illustrates how to set the number of digits for the R-squared value in the displayed equation. Use rr.digits to control precision, for example, to 4 digits.

```R
ggplot(my.data, aes(x, y)) +
  geom_point() +
  stat_poly_line(formula = formula) +
  stat_poly_eq(formula = formula, rr.digits = 4)
```