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#' Visualizing observed vs. predicted values for a regression model
#'
#' This function plots the predicted values against the observed values based on
#' tidymodels results for a regression model.
#' @param dat The predictions data frame in the [organize_data()] result. Following
#' variables are required: `.outcome`, `.pred`, `.color`, and `.hover`.
#' @param y_name The y/response variable for the model.
#' @param alpha The opacity for the geom points.
#' @param size The size for the geom points.
#' @param source A character string of length 1 that matches the source argument
#' in event_data().
#' @keywords internal
#' @export
#' @return
#' A [plotly::ggplotly()] object.
plot_numeric_obs_pred <- function(dat, y_name, alpha = 1, size = 1, source = NULL) {
p <- ggplot2::ggplot(dat, ggplot2::aes(x = .outcome, y = .pred)) +
ggplot2::geom_abline(lty = 2, col = "#0080a4", alpha = 1/2) +
ggplot2::geom_point(
ggplot2::aes(
customdata = .row,
color = .color,
text = .hover
),
alpha = alpha,
size = size
) +
ggplot2::scale_color_identity() +
tune::coord_obs_pred() +
ggplot2::labs(x = y_name, y = "Predicted") +
ggplot2::theme(legend.position = "none")
ggplotly2(p, tooltip = "text", source = source) %>%
plotly::layout(dragmode = "select") %>%
plotly::toWebGL()
}
#' Visualizing residuals vs. predicted values for a regression model
#'
#' This function plots the predicted values against the residuals based on
#' tidymodels results for a regression model.
#' @inheritParams plot_numeric_obs_pred
#' @keywords internal
#' @export
#' @return
#' A [plotly::ggplotly()] object.
plot_numeric_res_pred <- function(dat, y_name, size = 1, source = NULL) {
p <- ggplot2::ggplot(dat, ggplot2::aes(x = .pred, y = .residual)) +
ggplot2::geom_hline(yintercept = 0, lty = 2, col = "#0080a4", alpha = 1/2) +
ggplot2::geom_point(
ggplot2::aes(
customdata = .row,
color = .color,
text = .hover,
alpha = .alpha
),
size = size
) +
ggplot2::scale_color_identity() +
ggplot2::labs(x = "Predicted", y = "Residual") +
ggplot2::theme(legend.position = "none")
ggplotly2(p, tooltip = "text", source = source) %>%
plotly::layout(dragmode = "select") %>%
plotly::toWebGL()
}
#' Visualizing residuals vs. a numeric column for a regression model
#'
#' This function plots the residuals against a numeric column based on
#' tidymodels results for a regression model.
#' @inheritParams plot_numeric_obs_pred
#' @param numcol The numerical column to plot against the residuals.
#' @keywords internal
#' @export
#' @return
#' A [plotly::ggplotly()] object.
plot_numeric_res_numcol <-
function(dat, y_name, numcol, alpha = 1, size = 1, source = NULL) {
p <- ggplot2::ggplot(dat, ggplot2::aes(x = !!rlang::sym(numcol), y = .residual)) +
ggplot2::geom_hline(yintercept = 0, lty = 2, col = "#0080a4", alpha = 1/2) +
ggplot2::geom_point(ggplot2::aes(
customdata = .row,
color = .color,
text = .hover
),
alpha = alpha,
size = size
) +
ggplot2::labs(y = "Residual") +
ggplot2::scale_color_identity() +
ggplot2::theme(legend.position = "none")
ggplotly2(p, tooltip = "text", source = source) %>%
plotly::layout(dragmode = "select") %>%
plotly::toWebGL()
}
#' Visualizing residuals vs. a factor column for a regression model
#'
#' This function plots the residuals against a factor column based on
#' tidymodels results for a regression model.
#' @inheritParams plot_numeric_obs_pred
#' @param factorcol The factor column to plot against the residuals.
#' @keywords internal
#' @export
#' @return
#' A [plotly::ggplotly()] object.
plot_numeric_res_factorcol <-
function(dat, y_name, factorcol, alpha = 1, size = 1, source = NULL) {
p <-
ggplot2::ggplot(dat, ggplot2::aes(y = stats::reorder(
!!rlang::sym(factorcol),
.residual
), x = .residual)) +
ggplot2::geom_point(alpha = .3) +
ggplot2::geom_abline(lty = 2, col = "#0080a4", alpha = 1/2) +
ggplot2::geom_point(ggplot2::aes(
customdata = .row,
color = .color,
text = .hover
),
alpha = alpha,
size = size
) +
ggplot2::labs(y = NULL, x = "Residual") +
ggplot2::scale_color_identity() +
ggplot2::theme(legend.position = "none")
ggplotly2(p, tooltip = "text", source = source) %>%
plotly::layout(dragmode = "select") %>%
plotly::toWebGL()
}
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