xgx_stat_ci: Plot data with mean and confidence intervals

In xgxr: Exploratory Graphics for Pharmacometrics

Plot data with mean and confidence intervals

Description

xgx_stat_ci returns a ggplot layer plotting mean +/- confidence intervals

Usage

xgx_stat_ci(
  mapping = NULL,
  data = NULL,
  conf_level = 0.95,
  distribution = "normal",
  bins = NULL,
  breaks = NULL,
  geom = list("point", "line", "errorbar"),
  position = "identity",
  fun.args = list(),
  fun.data = NULL,
  na.rm = FALSE,
  orientation = "x",
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Arguments

mapping	Set of aesthetic mappings created by 'aes' or 'aes_'. If specified and 'inherit.aes = TRUE' (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.
data	The data to be displayed in this layer. There are three options: If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot. A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify for which variables will be created. A function will be called with a single argument, the plot data. The return value must be a data.frame., and will be used as the layer data.
conf_level	The percentile for the confidence interval (should fall between 0 and 1). The default is 0.95, which corresponds to a 95 percent confidence interval.
distribution	The distribution which the data follow, used for calculating confidence intervals. The options are "normal", "lognormal", and "binomial". The "normal" option will use the Student t Distribution to calculate confidence intervals, the "lognormal" option will transform data to the log space first. The "binomial" option will use the binom.exact function to calculate the confidence intervals. Note: binomial data must be numeric and contain only 1's and 0's.
bins	number of bins to cut up the x data, cuts data into quantiles.
breaks	breaks to cut up the x data, if this option is used, bins is ignored
geom	Use to override the default geom. Can be a list of multiple geoms, e.g. list("point","line","errorbar"), which is the default.
position	Position adjustment, either as a string, or the result of a call to a position adjustment function.
fun.args	Optional additional arguments passed on to the functions.
fun.data	A function that is given the complete data and should return a data frame with variables ymin, y, and ymax.
na.rm	If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.
orientation	The orientation of the layer, passed on to ggplot2::stat_summary. Only implemented for ggplot2 v.3.3.0 and later. The default ("x") summarizes y values over x values (same behavior as ggplot2 v.3.2.1 or earlier). Setting orientation = "y" will summarize x values over y values, which may be useful in some situations where you want to flip the axes, e.g. to create forest plots. Setting orientation = NA will try to automatically determine the orientation from the aesthetic mapping (this is more stable for ggplot2 v.3.3.2 compared to v.3.3.0). See stat_summary (v.3.3.0 or greater) for more information.
show.legend	logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes.
inherit.aes	If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders.
...	other arguments passed on to layer. These are often aesthetics, used to set an aesthetic to a fixed value, like color = "red" or size = 3. They may also be parameters to the paired geom/stat.

Details

This function can be used to generate mean +/- confidence interval plots for different distributions, and multiple geoms with a single function call.

Value

ggplot2 plot layer

Examples

# default settings for normally distributed data, 95% confidence interval,  
data <- data.frame(x = rep(c(1, 2, 3), each = 20),
                   y = rep(c(1, 2, 3), each = 20) + stats::rnorm(60),
                   group = rep(1:3, 20))
xgx_plot(data, ggplot2::aes(x = x, y = y)) + 
  xgx_stat_ci(conf_level = 0.95)

# try different geom 
xgx_plot(data, ggplot2::aes(x = x, y = y)) + 
  xgx_stat_ci(conf_level = 0.95, geom = list("ribbon", "point", "line"))
 
# plotting lognormally distributed data
data <- data.frame(x = rep(c(1, 2, 3), each = 20),
                   y = 10^(rep(c(1, 2, 3), each = 20) + stats::rnorm(60)),
                   group = rep(1:3, 20))
xgx_plot(data, ggplot2::aes(x = x, y = y)) + 
  xgx_stat_ci(conf_level = 0.95, distribution = "lognormal")
  
# note: you DO NOT need to use both distribution = "lognormal"
# and scale_y_log10()
xgx_plot(data, ggplot2::aes(x = x, y = y)) + 
  xgx_stat_ci(conf_level = 0.95) + xgx_scale_y_log10()
 
# plotting binomial data
data <- data.frame(x = rep(c(1, 2, 3), each = 20),
                   y = stats::rbinom(60, 1, rep(c(0.2, 0.6, 0.8),
                   each = 20)),
                   group = rep(1:3, 20))
xgx_plot(data, ggplot2::aes(x = x, y = y)) + 
  xgx_stat_ci(conf_level = 0.95, distribution = "binomial")
 
# including multiple groups in same plot
xgx_plot(data, ggplot2::aes(x = x, y = y)) + 
  xgx_stat_ci(conf_level = 0.95, distribution = "binomial", 
              ggplot2::aes(color = factor(group)),
              position = ggplot2::position_dodge(width = 0.5))
              
# plotting ordinal or multinomial data
set.seed(12345) 
data = data.frame(x = 120*exp(stats::rnorm(100,0,1)), 
              response = sample(c("Mild","Moderate","Severe"), 100, replace = TRUE),
              covariate = sample(c("Male","Female"), 100, replace = TRUE))
  
xgx_plot(data = data) + 
  xgx_stat_ci(mapping = ggplot2::aes(x = x, response = response, colour = covariate), 
              distribution = "ordinal", bins = 4) + 
  ggplot2::scale_y_continuous(labels = scales::percent_format()) + ggplot2::facet_wrap(~response)

xgx_plot(data = data) + 
  xgx_stat_ci(mapping = ggplot2::aes(x = x, response = response, colour = response), 
              distribution = "ordinal", bins = 4) + 
  ggplot2::scale_y_continuous(labels = scales::percent_format()) + ggplot2::facet_wrap(~covariate)

# Example plotting categorical vs categorical data
set.seed(12345)
data = data.frame(x = 120*exp(stats::rnorm(100,0,1)),
                  response = sample(c("Trt1", "Trt2", "Trt3"), 100, replace = TRUE),
                  covariate = factor(
                    sample(c("White","Black","Asian","Other"), 100, replace = TRUE),
                                     levels = c("White", "Black", "Asian", "Other")))

xgx_plot(data = data) +
  xgx_stat_ci(mapping = ggplot2::aes(x = response, response = covariate),
              distribution = "ordinal") +
  xgx_stat_ci(mapping = ggplot2::aes(x = 1, response = covariate), geom = "hline",
              distribution = "ordinal") +
  ggplot2::scale_y_continuous(labels = scales::percent_format()) + 
  ggplot2::facet_wrap(~covariate) + 
  ggplot2::xlab("Treatment group") + 
  ggplot2::ylab("Percent of subjects by category")

# Same example with orientation flipped (only works for ggplot2 v.3.3.0 or later)
# only run if ggplot2 v.3.3.0 or later
ggplot2_geq_v3.3.0 <- utils::compareVersion(
  as.character(utils::packageVersion("ggplot2")), '3.3.0') >= 0

if(ggplot2_geq_v3.3.0){

xgx_plot(data = data) +
xgx_stat_ci(mapping = ggplot2::aes(y = response, response = covariate), orientation = "y",
            distribution = "ordinal") +
  xgx_stat_ci(mapping = ggplot2::aes(y = 1, response = covariate), orientation = "y", 
              geom = "vline", distribution = "ordinal") +
  ggplot2::scale_x_continuous(labels = scales::percent_format()) +
  ggplot2::facet_wrap(~covariate) +
  ggplot2::ylab("Treatment group") +
  ggplot2::xlab("Percent of subjects by category")
  
}

 

xgxr documentation built on March 31, 2023, 11:46 p.m.