see

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### Setup R environment for 'effectsize' and 'see' packages

Source: https://easystats.github.io/see/reference/articles/effectsize

Loads the necessary 'effectsize' and 'see' R packages and loads example datasets 'mtcars' and 'iris' for demonstration purposes.

```R
library(effectsize)
library(see)
data(mtcars)
data(iris)
```

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

### R Example: Plotting a Described Distribution

Source: https://easystats.github.io/see/reference/reference/plot.see_parameters_distribution

This R code example demonstrates how to use the `plot()` method with an object generated by `parameters::describe_distribution()`. It sets up a reproducible example, generates sample data, describes its distribution, and then plots the result, showing the basic usage of the `plot.see_parameters_distribution` method.

```R
library(parameters)
set.seed(333)
x <- sample(1:100, 1000, replace = TRUE)
result <- describe_distribution(x)
result
#>
#>  Mean |    SD |   IQR |          Range | Skewness | Kurtosis |    n | n_Missing
#> -------------------------------------------------------------------------------
#> 50.18 | 28.66 | 48.75 | [1.00, 100.00] |     0.02 |    -1.16 | 1000 |         0
plot(result)
```

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

### Setup R Environment and Fit Bayesian Models

Source: https://easystats.github.io/see/reference/articles/bayestestR

Loads necessary R packages (`bayestestR`, `insight`, `see`, `rstanarm`, `ggplot2`) and sets a modern theme. It then fits two example Bayesian models: a fixed-effects model using `stan_glm` and downloads a pre-existing `brms` model with random effects and zero-inflation.

```R
library(bayestestR)
library(insight)
library(see)
library(rstanarm)
library(ggplot2)

theme_set(theme_modern())

set.seed(123)
# model with fixed effects only
model <<- rstanarm::stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0)

# model with fixed and random effects as well as zero-inflation component
model2 <<- insight::download_model("brms_zi_3")
```

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

### Install the 'see' R package

Source: https://easystats.github.io/see/reference/index

Instructions for installing the 'see' package from various sources: CRAN for the stable release, r-universe for the latest development version, or GitHub for direct development access. Choose the command based on your desired version and source.

```R
install.packages("see")
```

```R
install.packages("see", repos = "https://easystats.r-universe.dev")
```

```R
remotes::install_github("easystats/see")
```

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

### R Example: Plotting Support Intervals

Source: https://easystats.github.io/see/reference/reference/plot.see_si

This R example demonstrates how to generate and plot support intervals using `bayestestR::si()` and the `plot` method for `see_si` objects. It includes setting up a `stan_glm` model and visualizing its support intervals.

```R
library(rstanarm)
library(bayestestR)
set.seed(123)
m <<- suppressWarnings(stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0))
result <- si(m, verbose = FALSE)
result
#> Support Interval
#>
#> Parameter                     |     BF = 1 SI | Effects |   Component
#> ---------------------------------------------------------------------
#> (Intercept)                   | [ 4.39, 5.18] |   fixed | conditional
#> Petal.Width                   | [-0.16, 2.00] |   fixed | conditional
#> Speciesversicolor             | [-1.68, 0.21] |   fixed | conditional
#> Speciesvirginica              | [-0.59, 1.54] |   fixed | conditional
#> Petal.Width:Speciesversicolor | [-0.61, 1.73] |   fixed | conditional
#> Petal.Width:Speciesvirginica  | [-1.27, 0.77] |   fixed | conditional
plot(result)
```

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

### R Example: Plotting Estimated Contrasts with modelbased

Source: https://easystats.github.io/see/reference/reference/plot.see_estimate_contrasts

Illustrates a practical example of using the `plot` method with `estimate_contrasts` and `estimate_means` from the `modelbased` package. It demonstrates the workflow of fitting a linear model, estimating contrasts, and then visualizing them.

```R
# \donttest{
library(modelbased)

model <- lm(Sepal.Width ~ Species, data = iris)
contrasts <- estimate_contrasts(model)
#> We selected `contrast=c("Species")`.
means <- estimate_means(model)
#> We selected `by=c("Species")`.
plot(contrasts, means)
# }
```

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

### R Example: Plotting Posterior Predictive Checks with `performance`

Source: https://easystats.github.io/see/reference/reference/print.see_performance_pp_check

Provides R code examples demonstrating how to perform posterior predictive checks using `check_predictions` from the `performance` package and visualize the results with the `plot` method, including a specific example for discrete count models.

```R
library(performance)

model <- lm(Sepal.Length ~ Species * Petal.Width + Petal.Length, data = iris)
check_predictions(model)

# dot-plot style for count-models
d <- iris
d$poisson_var <- rpois(150, 1)
model <- glm(
  poisson_var ~ Species + Petal.Length + Petal.Width,
  data = d,
  family = poisson()
)
out <- check_predictions(model)
plot(out, type = "discrete_dots")
```

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

### Example of applying theme_azurelight to a ggplot in R

Source: https://easystats.github.io/see/reference/reference/theme_azurelight

This example demonstrates how to use `theme_azurelight` with `ggplot2` to create a scatter plot with the custom theme applied. It loads necessary libraries and uses the `iris` dataset.

```R
library(ggplot2)
library(see)

data(iris)

ggplot(iris, aes(Sepal.Length, Sepal.Width, colour = Species)) +
  geom_point2(size = 2.5) +
  scale_color_social() +
  theme_azurelight()
```

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

### R Example for Plotting Principal Components Analysis Results

Source: https://easystats.github.io/see/reference/reference/plot.see_parameters_pca

This R code example demonstrates the practical application of the `plot()` method for visualizing principal component analysis. It shows how to load the `parameters` library, prepare data from `mtcars`, perform PCA using `principal_components()`, and then generate a plot of the results, including the output of the PCA object.

```R
library(parameters)
data(mtcars)
result <- principal_components(mtcars[, 1:7], n = "all", threshold = 0.2)
result
#> # Loadings from Principal Component Analysis (no rotation)
#>
#> Variable |   PC1 |   PC2 |  PC3 |   PC4 |  PC5 |   PC6 | Complexity
#> -------------------------------------------------------------------
#> mpg      | -0.93 |       |      | -0.30 |      |       |       1.30
#> cyl      |  0.96 |       |      |       |      | -0.21 |       1.18
#> disp     |  0.95 |       |      | -0.23 |      |       |       1.16
#> hp       |  0.87 |  0.36 |      |       | 0.30 |       |       1.64
#> drat     | -0.75 |  0.48 | 0.44 |       |      |       |       2.47
#> wt       |  0.88 | -0.35 | 0.26 |       |      |       |       1.54
#> qsec     | -0.54 | -0.81 |      |       |      |       |       1.96
#>
#> The 6 principal components accounted for 99.30% of the total variance of the original data (PC1 = 72.66%, PC2 = 16.52%, PC3 = 4.93%, PC4 = 2.26%, PC5 = 1.85%, PC6 = 1.08%).
#>
plot(result)
```

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

### General Updates for see 0.2.1

Source: https://easystats.github.io/see/reference/news/index

Version 0.2.1 of the 'see' package introduces more comprehensive examples on the package website and adds new color palettes for plotting.

```APIDOC
see package 0.2.1 General:
  - More comprehensive examples available on the package website.
  - Added new color-palettes.
```

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

### R Example: Plotting Compared Model Parameters

Source: https://easystats.github.io/see/reference/reference/plot.see_compare_parameters

An R code example demonstrating the usage of `plot()` with `parameters::compare_parameters()` output. It sets up three linear models, compares their parameters, and then generates a plot of the comparison.

```R
data(iris)
lm1 <- lm(Sepal.Length ~ Species, data = iris)
lm2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
lm3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
result <- parameters::compare_parameters(lm1, lm2, lm3)
plot(result)
```

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

### Comparing Multiple Linear Model Performances in R

Source: https://easystats.github.io/see/reference/articles/performance

This example demonstrates how to compare the performance of several linear models using `compare_performance()`. It shows how to fit multiple models, compute their performance metrics, and then visualize the results.

```R
data(iris)
lm1 <- lm(Sepal.Length ~ Species, data = iris)
lm2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
lm3 <- lm(Sepal.Length ~ Species * Sepal.Width, data = iris)
lm4 <- lm(Sepal.Length ~ Species * Sepal.Width + Petal.Length + Petal.Width, data = iris)
result <- compare_performance(lm1, lm2, lm3, lm4)

result
#> # Comparison of Model Performance Indices
#>
#> Name | Model | AIC (weights) | AICc (weights) | BIC (weights) |    R2
#> ---------------------------------------------------------------------
#> lm1  |    lm | 231.5 (<.001) |  231.7 (<.001) | 243.5 (<.001) | 0.619
#> lm2  |    lm | 106.2 (<.001) |  106.6 (<.001) | 121.3 (<.001) | 0.837
#> lm3  |    lm | 187.1 (<.001) |  187.9 (<.001) | 208.2 (<.001) | 0.727
#> lm4  |  lm |  78.8 (>.999) |   80.1 (>.999) | 105.9 (>.999) | 0.871
#>
#> Name | R2 (adj.) |  RMSE | Sigma
#> --------------------------------
#> lm1  |     0.614 | 0.510 | 0.515
#> lm2  |     0.833 | 0.333 | 0.338
#> lm3  |     0.718 | 0.431 | 0.440
#> lm4  |     0.865 | 0.296 | 0.305

plot(result)
```

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

### R example for plotting simulated model parameters

Source: https://easystats.github.io/see/reference/reference/plot.see_parameters_simulate

This R code demonstrates how to simulate parameters from a linear model using `parameters::simulate_parameters()` and then visualize the results using the `plot()` method from the `see` package. It includes model fitting, parameter simulation, and the plotting call, along with the output of the simulated parameters.

```R
library(parameters)
m <<- lm(mpg ~ wt + cyl + gear, data = mtcars)
result <- simulate_parameters(m)
result
#> # Fixed Effects
#> 
#> Parameter   | Coefficient |         95% CI |      p
#> ---------------------------------------------------
#> (Intercept) |       42.19 | [33.75, 51.65] | < .001
#> wt          |       -3.39 | [-4.99, -1.75] | < .001
#> cyl         |       -1.54 | [-2.40, -0.70] | < .001
#> gear        |       -0.47 | [-2.11,  1.07] | 0.530
#> 
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed)
#>   computed using a simulated multivariate normal distribution
#>   approximation.
plot(result)
```

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

### R Example: Plotting Bayes Factors for Model Comparison

Source: https://easystats.github.io/see/reference/reference/plot.see_bayesfactor_models

Demonstrates how to use the `plot` method with `bayestestR::bayesfactor_models` to visualize model comparison results. It shows examples with single and multiple pies, and different value displays (probability vs. Bayes Factor), along with custom fill scales.

```R
library(bayestestR)
library(see)

lm0 <- lm(qsec ~ 1, data = mtcars)
lm1 <- lm(qsec ~ drat, data = mtcars)
lm2 <- lm(qsec ~ wt, data = mtcars)
lm3 <- lm(qsec ~ drat + wt, data = mtcars)

result <- bayesfactor_models(lm1, lm2, lm3, denominator = lm0)

plot(result, n_pies = "one", value = "probability", sort = TRUE) +
  scale_fill_pizza(reverse = TRUE)

plot(result, n_pies = "many", value = "BF", log = TRUE) +
  scale_fill_pizza(reverse = FALSE)
```

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

### Setup R Environment for 'parameters' Package Analysis

Source: https://easystats.github.io/see/reference/articles/parameters

This R code block initializes the environment by loading essential packages such as 'parameters', 'effectsize', 'insight', 'see', 'glmmTMB', 'lme4', 'lavaan', 'metafor', and 'ggplot2'. It also loads sample datasets, sets a random seed for reproducibility, and applies a modern ggplot2 theme for consistent plotting aesthetics.

```R
library(parameters)
library(effectsize)
library(insight)
library(see)
library(glmmTMB)
library(lme4)
library(lavaan)
library(metafor)
library(ggplot2)
data("Salamanders")
data("iris")
data("sleepstudy")
data("qol_cancer")

set.seed(12345)
sleepstudy$grp <- sample.int(5, size = 180, replace = TRUE)

theme_set(theme_modern())
```

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

### Prepare Data for 'see' Color Scale Examples in R

Source: https://easystats.github.io/see/reference/articles/seecolorscales

Loads `ggplot2` and `see` libraries, then prepares the `iris` dataset by adding new group variables and creates three custom data frames (`d1`, `d2`, `d3`) with varying numbers of groups for demonstrating line geoms. Finally, it sets a modern theme for plots.

```R
library(ggplot2)
library(see)

data(iris)
iris$group4 <- as.factor(sample(1:4, size = nrow(iris), replace = TRUE))
iris$group5 <- as.factor(sample(1:5, size = nrow(iris), replace = TRUE))

d1 <- data.frame(
  x = rep(1:20, 3),
  y = c(
    seq(2, 4, length.out = 20),
    seq(3, 6, length.out = 20),
    seq(5, 3, length.out = 20)
  ),
  group = rep(factor(1:3), each = 20)
)

d2 <- data.frame(
  x = rep(1:20, 4),
  y = c(
    seq(2, 4, length.out = 20),
    seq(3, 6, length.out = 20),
    seq(5, 3, length.out = 20),
    seq(4, 2.5, length.out = 20)
  ),
  group = rep(factor(1:4), each = 20)
)

d3 <- data.frame(
  x = rep(1:20, 5),
  y = c(
    seq(2, 4, length.out = 20),
    seq(3, 6, length.out = 20),
    seq(5, 3, length.out = 20),
    seq(4, 2.5, length.out = 20),
    seq(3.5, 4.5, length.out = 20)
  ),
  group = rep(factor(1:5), each = 20)
)

theme_set(theme_modern(legend.position = "bottom"))
```

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

### R Example: Plotting Model Performance Comparison with performance and see

Source: https://easystats.github.io/see/reference/reference/plot.see_compare_performance

This R code demonstrates how to use `performance::compare_performance` to compare multiple linear models and then visualize the comparison using the `plot` method from the `see` package. It shows model creation, performance comparison, and the final plotting step.

```R
library(performance)
data(iris)
lm1 <- lm(Sepal.Length ~ Species, data = iris)
lm2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
lm3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
result <- compare_performance(lm1, lm2, lm3)
result
# # Comparison of Model Performance Indices
# 
# Name | Model | AIC (weights) | AICc (weights) | BIC (weights) |    R2
# ---------------------------------------------------------------------
# lm1  |    lm | 231.5 (<.001) |  231.7 (<.001) | 243.5 (<.001) | 0.619
# lm2  |    lm | 106.2 (0.566) |  106.6 (0.611) | 121.3 (0.964) | 0.837
# lm3  |    lm | 106.8 (0.434) |  107.6 (0.389) | 127.8 (0.036) | 0.840
# 
# Name | R2 (adj.) |  RMSE | Sigma
# --------------------------------
# lm1  |     0.614 | 0.510 | 0.515
# lm2  |     0.833 | 0.333 | 0.338
# lm3  |     0.835 | 0.330 | 0.336
plot(result)
```

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

### R: `ggplot2` Examples with `scale_color_pizza`

Source: https://easystats.github.io/see/reference/reference/scale_color_pizza

These examples demonstrate the practical application of `scale_fill_pizza_d()` for discrete fill and `scale_color_pizza_c()` for continuous color in `ggplot2` visualizations, using the `iris` dataset and `theme_modern()` from the `see` package.

```R
library(ggplot2)
library(see)

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_boxplot() +
  theme_modern() +
  scale_fill_pizza_d()

ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
  geom_point() +
  theme_modern() +
  scale_color_pizza_c()
```

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

### R Example: Plotting bayestestR Point Estimates

Source: https://easystats.github.io/see/reference/reference/plot.see_point_estimate

Demonstrates how to use the `plot` method for `see_point_estimate` objects. This example sets up a `stan_glm` model, calculates point estimates using `bayestestR::point_estimate()`, and then visualizes the results using the `plot` method.

```R
library(rstanarm)
library(bayestestR)
set.seed(123)
m <<- suppressWarnings(stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0))
result <- point_estimate(m, centrality = "median")
result
plot(result)
```

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

### Update: R `see` Package Compatibility with `performance` API

Source: https://easystats.github.io/see/reference/news/index

Code and examples within the `see` package were updated to align with new API decisions made for the *performance* package, ensuring seamless integration.

```R
## Internal API adjustments for `performance` package compatibility.
```

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

### R Example: Plotting Bayesian Meta-Analysis Model Parameters

Source: https://easystats.github.io/see/reference/reference/plot.see_parameters_brms_meta

This R code example demonstrates the full workflow for plotting parameters from a Bayesian meta-analysis model. It includes loading necessary libraries, preparing data, fitting a `brms` model, extracting parameters with `model_parameters()`, and finally visualizing them using the specialized `plot()` method.

```R
# \donttest{
library(parameters)
library(brms)
library(metafor)
data(dat.bcg)

dat <- escalc(
  measure = "RR",
  ai = tpos,
  bi = tneg,
  ci = cpos,
  di = cneg,
  data = dat.bcg
)
dat$author <- make.unique(dat$author)

# model
set.seed(123)
priors <- c(
  prior(normal(0, 1), class = Intercept),
  prior(cauchy(0, 0.5), class = sd)
)
model <- suppressWarnings(
  brm(yi | se(vi) ~ 1 + (1 | author), data = dat, refresh = 0, silent = 2)
)

# result
mp <- model_parameters(model)
plot(mp)
# }
```

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

### Prepare data and set ggplot2 theme for 'see' package examples

Source: https://easystats.github.io/see/reference/articles/seecolorscales_dark

Loads necessary R packages (`ggplot2`, `see`), prepares sample dataframes (`d1`, `d2`, `d3`) with varying numbers of groups (three, four, and five) for demonstration. It also modifies the `iris` dataset and sets a global `ggplot2` theme (`theme_abyss`) suitable for dark backgrounds.

```R
library(ggplot2)
library(see)

data(iris)
iris$group4 <- as.factor(sample(1:4, size = nrow(iris), replace = TRUE))
iris$group5 <- as.factor(sample(1:5, size = nrow(iris), replace = TRUE))

d1 <- data.frame(
  x = rep(1:20, 3),
  y = c(
    seq(2, 4, length.out = 20),
    seq(3, 6, length.out = 20),
    seq(5, 3, length.out = 20)
  ),
  group = rep(factor(1:3), each = 20)
)

d2 <- data.frame(
  x = rep(1:20, 4),
  y = c(
    seq(2, 4, length.out = 20),
    seq(3, 6, length.out = 20),
    seq(5, 3, length.out = 20),
    seq(4, 2.5, length.out = 20)
  ),
  group = rep(factor(1:4), each = 20)
)

d3 <- data.frame(
  x = rep(1:20, 5),
  y = c(
    seq(2, 4, length.out = 20),
    seq(3, 6, length.out = 20),
    seq(5, 3, length.out = 20),
    seq(4, 2.5, length.out = 20),
    seq(3.5, 4.5, length.out = 20)
  ),
  group = rep(factor(1:5), each = 20)
)

theme_set(theme_abyss(legend.position = "bottom"))
```

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

### Setup R Libraries for Data Analysis

Source: https://easystats.github.io/see/reference/articles/datawizard

Loads essential R packages 'datawizard', 'see', and 'ggplot2' for data manipulation, visualization, and statistical modeling. It also sets a modern theme for ggplot2 plots.

```R
library(datawizard)
library(see)
library(ggplot2)
theme_set(theme_modern())
```

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

### Example of plotting density estimation with rstanarm and bayestestR

Source: https://easystats.github.io/see/reference/reference/plot.see_estimate_density

Demonstrates how to use the `plot()` method on an `estimate_density` object derived from a `stan_glm` model. It initializes a model, calculates density estimation, and then plots the result, illustrating a typical workflow for visualizing posterior distributions.

```R
library(rstanarm)
library(bayestestR)
set.seed(123)
m <<- suppressWarnings(stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0))
result <- estimate_density(m)
plot(result)
```

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

### R `see` Package `geom_point_borderless` Example with Fill Aesthetic

Source: https://easystats.github.io/see/reference/reference/geom_point2

Illustrates the use of `geom_point_borderless` from the `see` package, highlighting that its color aesthetic is 'fill' instead of 'color'. This example applies the function to the Iris dataset, coloring points by species.

```R
ggplot(iris, aes(x = Petal.Width, y = Sepal.Length, fill = Species)) +
  geom_point_borderless(size = 4) +
  theme_modern()
```

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

### R: Example of plotting ROPE results

Source: https://easystats.github.io/see/reference/reference/plot.see_rope

This R example demonstrates how to use the `plot` method on a `see_rope` object. It involves loading `rstanarm` and `bayestestR`, fitting a `stan_glm` model, calculating ROPE, and then plotting the `result` object, showing the output of the `rope` function.

```R
library(rstanarm)
library(bayestestR)
set.seed(123)
m <<- suppressWarnings(stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0))
result <- rope(m)
#> Possible multicollinearity between Petal.Width:Speciesversicolor and
#>   Petal.Width (r = 0.87), Petal.Width:Speciesvirginica and
#>   Petal.Width:Speciesversicolor (r = 0.79). This might lead to
#>   inappropriate results. See 'Details' in '?rope'.
result
#> # Proportion of samples inside the ROPE [-0.08, 0.08]:
#> 
#> Parameter                     | inside ROPE
#> -------------------------------------------
#> (Intercept)                   |      0.00 %
#> Petal.Width                   |      3.89 %
#> Speciesversicolor             |      4.63 %
#> Speciesvirginica              |      8.50 %
#> Petal.Width:Speciesversicolor |      6.79 %
#> Petal.Width:Speciesvirginica  |     10.74 %
#> 
plot(result)
```

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

### Example Usage of plot.see_p_direction in R

Source: https://easystats.github.io/see/reference/reference/plot.see_p_direction

This R code example demonstrates how to plot the probability of direction results. It initializes a `stan_glm` model, calculates the probability of direction using `p_direction`, and then visualizes the `result` object using the `plot` method.

```R
library(rstanarm)
library(bayestestR)
set.seed(123)
m <<- suppressWarnings(stan_glm(Sepal.Length ~ Petal.Width * Species, data = iris, refresh = 0))
result <- p_direction(m)
plot(result)
```

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

### R Examples: Applying theme_modern to ggplot

Source: https://easystats.github.io/see/reference/reference/theme_modern

Demonstrates how to apply the `theme_modern` to `ggplot2` visualizations. The examples show basic usage and how to enable inner tick marks using the `show.ticks` argument, enhancing plot readability. Requires `ggplot2` and `see` packages.

```R
library(ggplot2)
library(see)

ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_point() +
  scale_color_see() +
  theme_modern()

# for a slightly better orientation, tick marks can be added
ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length, color = Species)) +
  geom_point() +
  scale_color_see() +
  theme_modern(show.ticks = TRUE)
```

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

### R Examples for Okabe-Ito Color Palette Retrieval

Source: https://easystats.github.io/see/reference/reference/okabeito_colors

Illustrates practical usage of `okabeito_colors()` in R, showing how to retrieve the full palette, select specific colors, and apply options like `original_names` and `black_first`.

```R
okabeito_colors()
#>     orange light blue      green      amber       blue        red     purple
#>  "#E69F00"  "#56B4E9"  "#009E73"  "#F5C710"  "#0072B2"  "#D55E00"  "#CC79A7"
#>       grey      black
#>  "#999999"  "#000000"

okabeito_colors(c("red", "light blue", "orange"))
#>        red light blue     orange
#>  "#D55E00"  "#56B4E9"  "#E69F00"

okabeito_colors(original_names = TRUE)
#>         orange       sky blue   bluish green          amber           blue
#>      "#E69F00"      "#56B4E9"      "#009E73"      "#F5C710"      "#0072B2"
#>     vermillion reddish purple           grey          black
#>      "#D55E00"      "#CC79A7"      "#999999"      "#000000"

okabeito_colors(black_first = TRUE)
#>      black     orange light blue      green      amber       blue        red
#>  "#000000"  "#E69F00"  "#56B4E9"  "#009E73"  "#F5C710"  "#0072B2"  "#D55E00"
#>     purple       grey
#>  "#CC79A7"  "#999999"
```

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

### Plotting Model Parameters for SEM Models

Source: https://easystats.github.io/see/reference/articles/parameters

This example demonstrates how to extract and plot model parameters from Structural Equation Models (SEM) created with the `lavaan` package. It shows the typical workflow of defining a model structure, fitting the model, and then visualizing its parameters.

```R
structure <- " visual  =~ x1 + x2 + x3
               textual =~ x4 + x5 + x6
               speed   =~ x7 + x8 + x9 "

model <- lavaan::cfa(structure, data = HolzingerSwineford1939)
result <- model_parameters(model)
plot(result)
```

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

### Load R Packages for Correlation Plotting

Source: https://easystats.github.io/see/reference/articles/correlation

Loads the necessary R packages: 'correlation' for statistical analysis, 'see' for visualization, and 'ggplot2' for advanced plotting capabilities. These packages are essential for running the subsequent examples.

```R
library(correlation)
library(see)
library(ggplot2)
```

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

### Calculate Practical Significance (R)

Source: https://easystats.github.io/see/reference/articles/parameters

Demonstrates the calculation of practical significance for a linear model using the p_significance() function. The snippet includes an example of plotting the resulting practical significance values.

```R
data(qol_cancer)
model <- lm(QoL ~ time + age + education, data = qol_cancer)
result <- p_significance(model)

plot(result)
```

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

### R: Display all available Material Design colors

Source: https://easystats.github.io/see/reference/reference/material_colors

This example demonstrates how to use `material_colors()` without any arguments to retrieve and display a named character vector of all available Material Design colors and their corresponding hex codes.

```R
material_colors()
#>         red        pink      purple deep purple      indigo        blue
#>   "#f44336"   "#E91E63"   "#9C27B0"   "#673AB7"   "#3F51B5"   "#2196F3"
#>  light blue        cyan        teal       green light green        lime
#>   "#03A9F4"   "#00BCD4"   "#009688"   "#4CAF50"   "#8BC34A"   "#CDDC39"
#>      yellow       amber      orange deep orange       brown        grey
#>   "#FFEB3B"   "#FFC107"   "#FF9800"   "#FF5722"   "#795548"   "#9E9E9E"
#>   blue grey
#>   "#607D8B"
```

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

### Example Usage of geom_violindot in R

Source: https://easystats.github.io/see/reference/reference/geom_violindot

Illustrates how to use `geom_violindot` with `ggplot2` to create a violin-dot plot, demonstrating basic setup and theme application.

```R
library(ggplot2)
library(see)

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_violindot() +
  theme_modern()
```

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

### R: `golden_ratio` function examples

Source: https://easystats.github.io/see/reference/reference/golden_ratio

Illustrates how to call the `golden_ratio` function without arguments to retrieve the golden ratio constant, and with an argument to multiply a given number by the golden ratio.

```R
golden_ratio()
#> [1] 1.618034
golden_ratio(10)
#> [1] 16.18034
```

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

### R Examples: Applying See Color Palettes in ggplot2

Source: https://easystats.github.io/see/reference/reference/scale_color_see

Demonstrates how to use `scale_fill_see`, `scale_colour_see`, and `scale_color_see` functions from the `see` package to customize `ggplot2` plots with various color palettes for discrete and continuous data.

```R
library(ggplot2)
library(see)

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_boxplot() +
  theme_modern() +
  scale_fill_see()
```

```R
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, colour = Species)) +
  geom_point() +
  theme_abyss() +
  scale_colour_see(palette = "light")
```

```R
ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
  geom_point() +
  theme_modern() +
  scale_color_see(discrete = FALSE)
```

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

### R: Get specific Flat UI color hex codes

Source: https://easystats.github.io/see/reference/reference/flat_colors

This R example illustrates how to use the `flat_colors()` function by passing specific color names as arguments. It demonstrates retrieving the hex codes for 'dark red' and 'teal' from the Flat UI palette.

```R
flat_colors("dark red", "teal")
#>  dark red      teal
#> "#c0392b" "#16a085"
```

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

### Calculate p-value Function and Plot Consonance (R)

Source: https://easystats.github.io/see/reference/articles/parameters

Demonstrates the use of the p_function() to calculate and visualize the p-value function and consonance/compatibility plot for a linear model. It includes examples for default plotting and customizing confidence interval levels and line sizes.

```R
data(iris)
model <- lm(Sepal.Length ~ Species, data = iris)

result <- p_function(model)
plot(result)
```

```R
result <- p_function(model, ci_levels = c(0.5, 0.7, emph = 0.9))
plot(result, size_line = c(0.6, 1.2))
```

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

### R Example: Generating and Plotting an ROC Curve

Source: https://easystats.github.io/see/reference/reference/plot.see_performance_roc

Illustrates a complete R workflow for creating a ROC curve. It includes loading necessary libraries, preparing sample data, fitting a binomial generalized linear model, generating ROC data using `performance::performance_roc`, and finally plotting the result using the `see` package's `plot` method.

```R
library(performance)
data(iris)
set.seed(123)
iris$y <- rbinom(nrow(iris), size = 1, .3)

folds <- sample(nrow(iris), size = nrow(iris) / 8, replace = FALSE)
test_data <- iris[folds, ]
train_data <- iris[-folds, ]

model <- glm(y ~ Sepal.Length + Sepal.Width, data = train_data, family = "binomial")
result <- performance_roc(model, new_data = test_data)
result
#> AUC: 37.66%
plot(result)
```

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

### R: Examples of Applying Social Color Palettes in ggplot2

Source: https://easystats.github.io/see/reference/reference/scale_color_social

These R code examples demonstrate how to integrate `scale_fill_social` and `scale_color_social` from the `see` package with `ggplot2`. The first two examples apply discrete fill scales to boxplots and violin plots of the `iris` dataset, showcasing default and 'ice' palettes. The third example illustrates a continuous color scale for a scatter plot, mapping `Sepal.Length` to color.

```R
library(ggplot2)
library(see)

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_boxplot() +
  theme_modern() +
  scale_fill_social()

ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_violin() +
  theme_modern() +
  scale_fill_social(palette = "ice")

ggplot(iris, aes(x = Petal.Length, y = Petal.Width, color = Sepal.Length)) +
  geom_point() +
  theme_modern() +
  scale_color_social(discrete = FALSE)
```

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

### Modeling Count Data with Poisson GLM and Model Checks in R

Source: https://easystats.github.io/see/reference/articles/performance

This example shows how to fit a Generalized Linear Model (GLM) with a Poisson family for count data in R and then perform a general model check using `check_model`.

```R
model <- glm(
  count ~ spp + mined + cover,
  family = poisson(),
  data = Salamanders
)
check_model(model)
```

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

### Citing the 'see' R Package

Source: https://easystats.github.io/see/reference/articles/performance

Demonstrates how to obtain citation information for the 'see' package in R, including BibTeX entry for publications.

```R
citation("see")
```

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

### R Example: Retrieve all blue-brown hex codes

Source: https://easystats.github.io/see/reference/reference/bluebrown_colors

Demonstrates how to retrieve all available blue-brown color hex codes by calling the `bluebrown_colors` function without any arguments. The output shows the color names and their corresponding hex values.

```R
bluebrown_colors()
#>  lightblue       blue   darkblue       grey lightbrown      brown  darkbrown
#>  "#6DC0E0"  "#5B93AE"  "#1F4454"  "#dbdbdb"  "#92673C"  "#61381A"  "#391D07"
```

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

### R Example: Apply Abyss Theme to ggplot

Source: https://easystats.github.io/see/reference/reference/theme_abyss

Demonstrates how to apply the `theme_abyss` to a ggplot visualization using the `iris` dataset. This example creates a scatter plot with white points and then applies the custom theme.

```R
library(ggplot2)
library(see)

ggplot(iris, aes(x = Sepal.Width, y = Sepal.Length)) +
  geom_point(color = "white") +
  theme_abyss()
```

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

### Perform Practical Equivalence Test with Multiple CIs

Source: https://easystats.github.io/see/reference/articles/bayestestR

This example extends the practical equivalence test by specifying multiple confidence intervals (89% and 95%) for the HDI calculation. It uses the `equivalence_test` function on a `model` object, providing a more comprehensive view of parameter equivalence against the ROPE.

```R
result <- equivalence_test(model, ci = c(0.89, 0.95))

result
```

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

### Cite 'see' R package

Source: https://easystats.github.io/see/reference/articles/seecolorscales_dark

Demonstrates how to cite the 'see' R package in publications using the `citation()` function, providing both a plain text and BibTeX entry for academic referencing.

```R
citation("see")
#> To cite package 'see' in publications use:
#>
#>   Lüdecke et al., (2021). see: An R Package for Visualizing Statistical
#>   Models. Journal of Open Source Software, 6(64), 3393.
#>   https://doi.org/10.21105/joss.03393
#>
#> A BibTeX entry for LaTeX users is
#>
#>   @Article{,
#>     title = {{see}: An {R} Package for Visualizing Statistical Models},
#>     author = {Daniel Lüdecke and Indrajeet Patil and Mattan S. Ben-Shachar and Brenton M. Wiernik and Philip Waggoner and Dominique Makowski},
#>     journal = {Journal of Open Source Software},
#>     year = {2021},
#>     volume = {6},
#>     number = {64},
#>     pages = {3393},
#>     doi = {10.21105/joss.03393},
#>   }
```

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

### R Example: Plotting Iris Data with `geom_poolpoint`

Source: https://easystats.github.io/see/reference/reference/geom_poolpoint

Demonstrates how to use `geom_poolpoint` with the `iris` dataset to label points with row names, applying `scale_color_flat_d()` and `theme_modern()` for styling. This example visualizes Petal.Width against Sepal.Length, colored by Species.

```R
library(ggplot2)
library(see)

ggplot(iris, aes(x = Petal.Width, y = Sepal.Length, color = Species)) +
  geom_poolpoint(label = rownames(iris)) +
  scale_color_flat_d() +
  theme_modern()
```

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

### R Example: Visualizing n_factors Results with plot.see_n_factors

Source: https://easystats.github.io/see/reference/reference/plot.see_n_factors

Demonstrates practical usage of the `plot.see_n_factors` method in R. It shows how to apply `parameters::n_factors` to the `mtcars` dataset and then visualize the results using the `plot()` function with different `type` arguments ('bar', 'line', 'area') to illustrate the method agreement procedure.

```R
data(mtcars)
result <- parameters::n_factors(mtcars, type = "PCA")
result
plot(result) # type = "bar" by default
plot(result, type = "line")
plot(result, type = "area")
```

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

### Example of plotting model assumptions for linear models in R

Source: https://easystats.github.io/see/reference/reference/plot.see_check_model

This example demonstrates how to use the `plot` method with the `check_model` function from the `performance` package to visualize diagnostic plots for a simple linear regression model in R.

```R
library(performance)

model <- lm(qsec ~ drat + wt, data = mtcars)
plot(check_model(model))
```

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

### Citing the 'see' Package in R

Source: https://easystats.github.io/see/reference/articles/parameters

This R code snippet demonstrates how to obtain the citation information for the 'see' package, which is essential for proper attribution in publications. It outputs both a standard citation format and a BibTeX entry for LaTeX users.

```R
citation("see")
#> To cite package 'see' in publications use:
#>
#>   Lüdecke et al., (2021). see: An R Package for Visualizing Statistical
#>   Models. Journal of Open Source Software, 6(64), 3393.
#>   https://doi.org/10.21105/joss.03393
#>
#> A BibTeX entry for LaTeX users is
#>
#>   @Article{,
#>     title = {{see}: An {R} Package for Visualizing Statistical Models},
#>     author = {Daniel Lüdecke and Indrajeet Patil and Mattan S. Ben-Shachar and Brenton M. Wiernik and Philip Waggoner and Dominique Makowski},
#>     journal = {Journal of Open Source Software},
#>     year = {2021},
#>     volume = {6},
#>     number = {64},
#>     pages = {3393},
#>     doi = {10.21105/joss.03393},
#>   }
```

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

### R Example: Plotting Iris Data with `geom_pooljitter`

Source: https://easystats.github.io/see/reference/reference/geom_poolpoint

Illustrates the application of `geom_pooljitter` to the `iris` dataset, showing how to add jittered, labeled points while using `scale_color_flat_d()` and `theme_modern()`. This example also visualizes Petal.Width against Sepal.Length, colored by Species.

```R
ggplot(iris, aes(x = Petal.Width, y = Sepal.Length, color = Species)) +
  geom_pooljitter(label = rownames(iris)) +
  scale_color_flat_d() +
  theme_modern()
```

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

### Load the 'see' R package

Source: https://easystats.github.io/see/reference/index

Loads the 'see' package into the current R session, making its functions and plotting methods available for use. For a more integrated experience with the entire 'easystats' ecosystem, it is recommended to load the 'easystats' package instead.

```R
library("see")
```

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

### Cite 'see' R Package

Source: https://easystats.github.io/see/reference/articles/datawizard

Demonstrates how to cite the 'see' R package in publications, providing both the output from the `citation()` function and a BibTeX entry for LaTeX users.

```R
citation("see")
#> To cite package 'see' in publications use:
#>
#>   Lüdecke et al., (2021). see: An R Package for Visualizing Statistical
#>   Models. Journal of Open Source Software, 6(64), 3393.
#>   https://doi.org/10.21105/joss.03393
#>
#> A BibTeX entry for LaTeX users is
#>
#>   @Article{,
#>     title = {{see}: An {R} Package for Visualizing Statistical Models},
#>     author = {Daniel Lüdecke and Indrajeet Patil and Mattan S. Ben-Shachar and Brenton M. Wiernik and Philip Waggoner and Dominique Makowski},
#>     journal = {Journal of Open Source Software},
#>     year = {2021},
#>     volume = {6},
#>     number = {64},
#>     pages = {3393},
#>     doi = {10.21105/joss.03393},
#>   }
```

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

### Project License Details

Source: https://easystats.github.io/see/reference/LICENSE-text

This snippet specifies the copyright year and the entity holding the copyright for the 'see' project, as part of its licensing information.

```Text
YEAR: 2023
COPYRIGHT HOLDER: see authors

```
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