Dependent variables vs Predictions plot

Dependent variables vs Predictions plot

plot_dv_vs_predictions(run = NULL, compartment = NULL, dv = "DV",
  predictions = "PRED", log_dv = FALSE, facetted = TRUE,
  smoothing_method = NULL, smoothing_se = TRUE, identity_line = TRUE,
  x_scale = "linear", y_scale = "linear", logticks_annotation = TRUE,
  facet_scales = "free", transparency = FALSE,
  keep_time_zero = FALSE, auto_legend = TRUE)

Arguments

run	pmxploit NONMEM run object.
compartment	integer. Number of the compartment of the dependent variable.
dv	character. Name of the dependent variable column in the output tables. Default is "DV".
predictions	character vector. Name of the predictions column(s) in the dataset. Default is "PRED" for both population predictions and individual predictions.#'
log_dv	logical. Set it to TRUE if the source dependent variable are in log_scale.
facetted	logical. If TRUE (default), plots each "DV vs predictions" subset inside a separate facet.
smoothing_method	character. ggplot2 smoothing method.
smoothing_se	logical. Display confidence interval around smooth?
identity_line	logical. Draws an identity line. Default is TRUE.
x_scale	character. X-axis scale, one of "linear" or "log". Default is "linear".
y_scale	character. Y-axis scale, one of "linear" or "log". Default is "linear".
logticks_annotation	logical. If x_scale and/or y_scale are "log", adds ticks annotation on the axis.
facet_scales	character. ggplot2 facet scales. Default is "free".
transparency	logical. Plot scatterplot dots with transparency, useful to avoid overplotting with large datasets. Default is FALSE.#'
keep_time_zero	logical. If TRUE, does not ignore values at initiation (when TIME = 0). Default is FALSE.
auto_legend	logical. When TRUE (default), default pmxploit plotting options are applied (title, labels, axis...).
split_by	character vector. Name of categorical covariates used to split the data.

Value

A ggplot2 object.

Examples

EXAMPLERUN %>%
  plot_dv_vs_predictions(compartment = 2, predictions = "PRED")

EXAMPLERUN %>%
  group_by(CMT) %>%
  plot_dv_vs_predictions(compartment = 2:3, predictions = "PRED")

EXAMPLERUN %>%
  plot_dv_vs_predictions(compartment = 2, predictions = "PRED", x_scale = "log", y_scale = "log")

EXAMPLERUN %>%
  plot_dv_vs_predictions(compartment = 2, predictions = "PRED", smoothing_method = "lm")
#> Warning: Removed 1 rows containing non-finite values (stat_smooth).
#> Warning: Removed 1 rows containing missing values (geom_point).
EXAMPLERUN %>%
  plot_dv_vs_predictions(compartment = 2, predictions = "PRED", smoothing_method = "loess")
#> Warning: Removed 1 rows containing non-finite values (stat_smooth).
#> Warning: Removed 1 rows containing missing values (geom_point).
#> Warning: Removed 1 rows containing missing values (geom_smooth).
EXAMPLERUN %>%
  plot_dv_vs_predictions(compartment = 2, predictions = "PRED", identity_line = FALSE)
EXAMPLERUN %>%
  plot_dv_vs_predictions(compartment = 2, predictions = "PRED", transparency = TRUE)

EXAMPLERUN %>%
  group_by(STUD) %>%
  plot_dv_vs_predictions(compartment = 2, predictions = "PRED")