elaborator_plot_quant_trends: Generates boxplots for each treatment group and laboratory...
In elaborator: A 'shiny' Application for Exploring Laboratory Data

View source: R/elaborator_plot_quant_trends.R

elaborator_plot_quant_trends

R Documentation

Generates boxplots for each treatment group and laboratory parameter

Description

Generate boxplots showing the distribution of laboratory values at each visits of a study separately by treatment group and laboratory parameter according to the quantitative trend analysis.

Usage

elaborator_plot_quant_trends(
  elab_data,
  signtest = TRUE,
  Visit1,
  Visit2,
  sameaxes,
  cols = NULL,
  labelvis = NULL,
  labcolumn,
  sortpoints = FALSE,
  pcutoff = 0.01,
  infotest = NULL,
  sortinput,
  bordercol = NULL,
  add_points = TRUE,
  connect_lines,
  lin_data = NULL,
  outliers,
  tolerated_percentage = 100,
  color_lines_options,
  custom_visits = NULL
)

Arguments

`elab_data`	elaborator data
`signtest`	logical, if true sign test, otherwise t-test for comparing Visit1 and Visit2 is performed
`Visit1`	name of visit time point one
`Visit2`	name of visit time point two, or vector of visit names for pairwise comparison to Visit1
`sameaxes`	logical indicating if boxplot should have the same y-axis range for all treatment groups within a laboratory parameter
`cols`	vector with colors of boxplots
`labelvis`	vector of visit names used for labeling
`labcolumn`	column name of dat1 which includes the laboratory parameter names
`sortpoints`	logical indicating if dots in boxplots should be sorted from smallest to largest
`pcutoff`	numeric for p-value threshold; if p-value of specified test is below this threshold, the background of the respective plot is colorized.
`infotest`	list object from the perform test function
`sortinput`	vector of laboratory parameter names in the order in which laboratory parameters are presented in the output
`bordercol`	color of boxes which are not selected for hypothesis testing
`add_points`	logical indicating if points showing patients' values should be added to the boxplots
`connect_lines`	logical indicating if subject values should be connected between visits via lines
`lin_data`	data set with connection line data
`tolerated_percentage`	tolerated percentage value
`color_lines_options`	option how the lines between visits should be colorized. Possible options are "first_last","each_visit", "custom_visits" or "all_grey". In the first three options decrease is colorized blue and an increase orange.
`custom_visits`	if col_lines_options is "custom_visits" then two visits need to be selected. Otherwise all lines are grey.