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plot_residuals.R
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plot_residuals.R
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#' Residuals plotted against population predictions
#'
#' @description Model residuals plotted against population predictions (PRED).
#'
#' The residuals can be one of:
#' \itemize{
#' \item RES: model residuals
#' \item WRES: weighted model residuals
#' \item CWRES: conditional weighted model residuals
#' \item EWRES/ECWRES: Monte Carlo based model residuals
#' \item NPDE: Normalized prediction distribution error
#' }
#'
#' @inheritParams dv_vs_pred
#' @param res Type of residual to be used. Default is "CWRES".
#' @inheritSection xplot_scatter Layers mapping
#' @inheritSection xplot_scatter Faceting
#' @inheritSection xplot_scatter Template titles
#' @seealso \code{\link{xplot_scatter}}
#' @examples
#' # Standard residual
#' res_vs_pred(xpdb_ex_pk, res = c('IWRES', 'CWRES'))
#'
#' # Absolute value of the residuals
#' absval_res_vs_pred(xpdb_ex_pk, res = 'CWRES')
#'
#' @export
res_vs_pred <- function(xpdb,
mapping = NULL,
res = 'CWRES',
group = 'ID',
type = 'pls',
title = '@y vs. @x | @run',
subtitle = 'Ofv: @ofv',
caption = '@dir',
tag = NULL,
log = NULL,
guide = TRUE,
facets,
.problem,
quiet,
...) {
# Check input
check_xpdb(xpdb, check = 'data')
if (missing(.problem)) .problem <- default_plot_problem(xpdb)
check_problem(.problem, .subprob = NULL, .method = NULL)
if (missing(quiet)) quiet <- xpdb$options$quiet
if (length(res) > 1) {
if (missing(facets)) facets <- add_facet_var(facets = xpdb$xp_theme$facets,
variable = 'variable')
opt <- data_opt(.problem = .problem,
filter = only_obs(xpdb, .problem, quiet),
tidy = TRUE, value_col = res)
vars <- aes_c(aes(
x = .data[[xp_var(xpdb, .problem, type = 'pred')$col]],
y = .data[["value"]]), mapping)
} else {
if (missing(facets)) facets <- xpdb$xp_theme$facets
opt <- data_opt(.problem = .problem,
filter = only_obs(xpdb, .problem, quiet))
vars <- aes_c(aes(
x = .data[[xp_var(xpdb, .problem, type = 'pred')$col]],
y = .data[[toupper(res)]]), mapping)
}
xplot_scatter(xpdb = xpdb, group = group, quiet = quiet,
opt = opt, mapping = vars,
type = type, guide = guide, facets = facets,
xscale = check_scales('x', log),
yscale = check_scales('y', log),
title = title, subtitle = subtitle, caption = caption,
tag = tag, plot_name = stringr::str_remove(deparse(match.call()[[1]]), "(\\w+\\.*)+::"),
guide_slope = 0, ...)
}
#' @rdname res_vs_pred
#' @export
absval_res_vs_pred <- function(xpdb,
mapping = NULL,
res = 'CWRES',
group = 'ID',
type = 'pls',
title = '@y vs. @x | @run',
subtitle = 'Ofv: @ofv',
caption = '@dir',
tag = NULL,
log = NULL,
guide = FALSE,
facets,
.problem,
quiet,
...) {
# Check input
check_xpdb(xpdb, check = 'data')
if (missing(.problem)) .problem <- default_plot_problem(xpdb)
check_problem(.problem, .subprob = NULL, .method = NULL)
if (missing(quiet)) quiet <- xpdb$options$quiet
if (length(res) > 1) {
if (missing(facets)) facets <- add_facet_var(facets = xpdb$xp_theme$facets,
variable = 'variable')
opt <- data_opt(.problem = .problem,
filter = only_obs(xpdb, .problem, quiet),
tidy = TRUE, value_col = res)
vars <- aes_c(aes(x = .data[[xp_var(xpdb, .problem, type = 'pred')$col]],
y = abs(.data[["value"]])), mapping)
} else {
if (missing(facets)) facets <- xpdb$xp_theme$facets
opt <- data_opt(.problem = .problem,
filter = only_obs(xpdb, .problem, quiet))
vars <- aes_c(aes(x = .data[[xp_var(xpdb, .problem, type = 'pred')$col]],
y = abs(.data[[toupper(res)]])), mapping)
}
xplot_scatter(xpdb = xpdb, group = group, quiet = quiet,
opt = opt, mapping = vars,
type = type, guide = guide, facets = facets,
xscale = check_scales('x', log),
yscale = check_scales('y', log),
title = title, subtitle = subtitle, caption = caption,
tag = tag, plot_name = stringr::str_remove(deparse(match.call()[[1]]), "(\\w+\\.*)+::"),
guide_slope = 0, ...)
}
#' Residuals plotted against the independent variable
#'
#' @description Model residuals plotted against the independent variable (IDV).
#'
#' The residuals can be one of:
#' \itemize{
#' \item RES: model residuals
#' \item WRES: weighted model residuals
#' \item CWRES: conditional weighted model residuals
#' \item EWRES/ECWRES: Monte Carlo based model residuals
#' \item NPDE: Normalized prediction distribution error
#' }
#'
#' @inheritParams dv_vs_pred
#' @param res Type of residual to be used. Default is "CWRES".
#' @inheritSection xplot_scatter Layers mapping
#' @inheritSection xplot_scatter Template titles
#' @seealso \code{\link{xplot_scatter}}
#' @examples
#' # Standard residual
#' res_vs_idv(xpdb_ex_pk, res = c('IWRES', 'CWRES'))
#'
#' # Absolute value of the residuals
#' absval_res_vs_idv(xpdb_ex_pk, res = 'CWRES')
#'
#' @export
res_vs_idv <- function(xpdb,
mapping = NULL,
res = 'CWRES',
group = 'ID',
type = 'pls',
title = '@y vs. @x | @run',
subtitle = 'Ofv: @ofv',
caption = '@dir',
tag = NULL,
log = NULL,
guide = TRUE,
facets,
.problem,
quiet,
...) {
# Check input
check_xpdb(xpdb, check = 'data')
if (missing(.problem)) .problem <- default_plot_problem(xpdb)
check_problem(.problem, .subprob = NULL, .method = NULL)
if (missing(quiet)) quiet <- xpdb$options$quiet
if (length(res) > 1) {
if (missing(facets)) facets <- add_facet_var(facets = xpdb$xp_theme$facets,
variable = 'variable')
opt <- data_opt(.problem = .problem,
filter = only_obs(xpdb, .problem, quiet),
tidy = TRUE, value_col = res)
vars <- aes_c(aes(x = .data[[xp_var(xpdb, .problem, type = 'idv')$col]],
y = .data[["value"]]), mapping)
} else {
if (missing(facets)) facets <- xpdb$xp_theme$facets
opt <- data_opt(.problem = .problem,
filter = only_obs(xpdb, .problem, quiet))
vars <- aes_c(aes(x = .data[[xp_var(xpdb, .problem, type = 'idv')$col]],
y = .data[[toupper(res)]]), mapping)
}
xplot_scatter(xpdb = xpdb, group = group, quiet = quiet,
opt = opt, mapping = vars,
type = type, guide = guide, facets = facets,
xscale = check_scales('x', log),
yscale = check_scales('y', log),
title = title, subtitle = subtitle, caption = caption,
tag = tag, plot_name = stringr::str_remove(deparse(match.call()[[1]]), "(\\w+\\.*)+::"),
guide_slope = 0, ...)
}
#' @rdname res_vs_idv
#' @export
absval_res_vs_idv <- function(xpdb,
mapping = NULL,
res = 'CWRES',
group = 'ID',
type = 'pls',
title = '@y vs. @x | @run',
subtitle = 'Ofv: @ofv',
caption = '@dir',
tag = NULL,
log = NULL,
guide = FALSE,
facets,
.problem,
quiet,
...) {
# Check input
check_xpdb(xpdb, check = 'data')
if (missing(.problem)) .problem <- default_plot_problem(xpdb)
check_problem(.problem, .subprob = NULL, .method = NULL)
if (missing(quiet)) quiet <- xpdb$options$quiet
if (length(res) > 1) {
if (missing(facets)) facets <- add_facet_var(facets = xpdb$xp_theme$facets,
variable = 'variable')
opt <- data_opt(.problem = .problem,
filter = only_obs(xpdb, .problem, quiet),
tidy = TRUE, value_col = res)
vars <- aes_c(aes(x = .data[[xp_var(xpdb, .problem, type = 'idv')$col]],
y = abs(.data[["value"]])), mapping)
} else {
if (missing(facets)) facets <- xpdb$xp_theme$facets
opt <- data_opt(.problem = .problem,
filter = only_obs(xpdb, .problem, quiet))
vars <- aes_c(aes(x = .data[[xp_var(xpdb, .problem, type = 'idv')$col]],
y = abs(.data[[toupper(res)]])), mapping)
}
xplot_scatter(xpdb = xpdb, group = group, quiet = quiet,
opt = opt, mapping = vars,
type = type, guide = guide, facets = facets,
xscale = check_scales('x', log),
yscale = check_scales('y', log),
title = title, subtitle = subtitle, caption = caption,
tag = tag, plot_name = stringr::str_remove(deparse(match.call()[[1]]), "(\\w+\\.*)+::"),
guide_slope = 0, ...)
}