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IndrajeetPatilIndrajeetPatil
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run styler
1 parent f8baf90 commit 13617a3

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‎R/data_plot.R

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@@ -85,7 +85,6 @@ data_plot <- function(x, ...) {
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}
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#' Complete figure with its attributes
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#'
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#' The `data_plot()` function usually stores information (such as title, axes
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}
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#' @keywords internal
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.retrieve_data <- function(x) {
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# retrieve model

‎R/geom_binomdensity.R

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@@ -82,8 +82,6 @@ geom_binomdensity <- function(data,
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}
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# Utilities ---------------------------------------------------------------
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.geom_binomdensity_scale <- function(data, x, y, scale = "auto") {

‎R/geom_violindot.R

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}
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list(
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geom_violinhalf(
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mapping = mapping,

‎R/plot.check_distribution.R

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}
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#' @export
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plot.see_check_distribution_numeric <- function(x,
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size_point = 2,

‎R/plot.check_predictions.R

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}
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.plot_pp_check <- function(x,
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linewidth,
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size_point,

‎R/plot.compare_parameters.R

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}
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#' @export
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data_plot.see_compare_parameters <- function(x, ...) {
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col_coefficient <- grep("^(Coefficient|Log-Odds|Log-Mean|Odds Ratio|Risk Ratio|IRR)\\.", colnames(x))

‎R/plot.compare_performance.R

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@@ -51,8 +51,6 @@ data_plot.compare_performance <- function(x, data = NULL, ...) {
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}
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# Plot --------------------------------------------------------------------
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#' Plot method for comparing model performances
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#'

‎R/plot.describe_distribution.R

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@@ -24,8 +24,6 @@ data_plot.parameters_distribution <- function(x, data = NULL, ...) {
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}
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# Plot --------------------------------------------------------------------
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#' Plot method for describing distributions of vectors
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#'
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}
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.plot_see_parameters_distribution <- function(x,
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alpha_dispersion,
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color_dispersion,

‎R/plot.equivalence_test.R

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@@ -201,8 +201,6 @@ plot.see_equivalence_test <- function(x,
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}
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# data frame method --------------------------------
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#' @export
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}
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# freq models method --------------------------------
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#' @rdname plot.see_equivalence_test

‎R/plot.estimate_contrasts.R

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}
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#' @keywords internal
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.data_contrasts_and_means <- function(contrasts, means, x_name, y_name) {
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polygons <- contrasts
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}
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# Plot --------------------------------------------------------------------
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#' Plot method for estimating contrasts

‎R/plot.estimate_density.R

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}
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# Density df --------------------------------------------------------------------
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#' @export

‎R/plot.hdi.R

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data_plot.bayestestR_eti <- data_plot.hdi
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#' @keywords internal
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.data_plot_hdi <- function(x,
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data = NULL,
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}
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#' @keywords internal
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.compute_densities_hdi <- function(x, hdi, name = "Y") {
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hdi <- hdi[order(hdi$CI, decreasing = TRUE), ]
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}
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#' @keywords internal
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.classify_hdi <- function(x, breakpoints, labels, if_lower = TRUE) {
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limits <- list(
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}
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# Plot --------------------------------------------------------------------
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#' Plot method for uncertainty or credible intervals

‎R/plot.n_factors.R

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data_plot.n_clusters <- data_plot.n_factors
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# Plot --------------------------------------------------------------------
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#' Plot method for numbers of clusters to extract or factors to retain

‎R/plot.p_direction.R

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}
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#' @keywords internal
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.compute_densities_pd <- function(x, name = "Y", null = 0) {
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out <- .as.data.frame_density(
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# Plot --------------------------------------------------------------------
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#' Plot method for probability of direction

‎R/plot.p_significance.R

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}
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#' @keywords internal
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.compute_densities_ps <- function(x, name = "Y", threshold = 0) {
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out <- .as.data.frame_density(stats::density(x))
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}
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# Plot --------------------------------------------------------------------
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#' Plot method for practical significance

‎R/plot.parameters_brms_meta.R

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}
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# Plot --------------------------------------------------------------------
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#' Plot method for Model Parameters from Bayesian Meta-Analysis

‎R/plot.parameters_model.R

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.meta_measure <- function(meta_measure) {
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switch(meta_measure,
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MD = "Raw Mean Difference",

‎R/plot.parameters_pca.R

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data_plot.parameters_efa <- data_plot.parameters_pca
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# Plot --------------------------------------------------------------------
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#' Plot method for principal component analysis

‎R/plot.parameters_sem.R

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# Separate labels
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edges$Label_Regression <- ifelse(edges$Component == "Regression", edges$Label, "")
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edges$Label_Correlation <- ifelse(edges$Component == "Correlation", edges$Label, "")
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# Plot --------------------------------------------------------------------
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#' @param threshold_coefficient Numeric, threshold at which value coefficients will be displayed.
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#' @param threshold_p Numeric, threshold at which value p-values will be displayed.

‎R/plot.point_estimates.R

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data_plot.map_estimate <- data_plot.point_estimate
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# Plot --------------------------------------------------------------------
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#' Plot method for point estimates of posterior samples

‎R/plot.rope.R

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# Plot --------------------------------------------------------------------
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#' Plot method for Region of Practical Equivalence

‎R/scale_color_bluebrown.R

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# Aliases -----------------------------------------------------------------
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scale_colour_bluebrown_d <- scale_color_bluebrown_d
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# Fill --------------------------------------------------------------------
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#' @rdname scale_color_bluebrown
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#' @export
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scale_fill_bluebrown <- function(palette = "contrast",
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# Palette --------------------------------------------------------------------
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bluebrown_palettes <- list(
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`full` = bluebrown_colors(),
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`contrast` = bluebrown_colors("lightblue", "blue", "darkblue", "grey", "darkbrown", "brown", "lightbrown"),
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`rainbow` = bluebrown_colors("darkblue", "blue", "lightblue", "grey", "lightbrown", "brown", "darkbrown")
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#' Blue-brown design color palette
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#' The palette based on blue-brown colors.

‎R/scale_color_colorhex.R

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scale_colour_colorhex_d <- scale_color_colorhex_d
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# Fill --------------------------------------------------------------------
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#' @rdname scale_color_colorhex
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#' @export
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scale_fill_colorhex <- function(palette = 1014416,
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# Palette --------------------------------------------------------------------
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#' Color palettes from <https://www.color-hex.com/>

‎R/scale_color_flat.R

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# Aliases -----------------------------------------------------------------
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scale_colour_flat_d <- scale_color_flat_d
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# Fill --------------------------------------------------------------------
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#' @rdname scale_color_flat
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scale_fill_flat <- function(palette = "contrast",
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# Palette --------------------------------------------------------------------
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# The palette based on flat design colors: https://www.materialui.co/flatuicolors
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flat_colors_list <- c(
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`red` = "#e74c3c",
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flat_palettes <- list(
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`ice` = flat_colors("purple", "deep purple", "blue", "light blue"),
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#' Flat UI color palette
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#' The palette based on [Flat UI](https://materialui.co/flatuicolors).

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