Exploratory Graphics for Pharmacometrics
Edit a Rmd Template from xgx
Determine the name of a Rmd template
get_rmd_str returns a Rmd template string, based on the desired PKPD...
Pipe operator
predict.nls
Prediction data frame for nls
Prediction data frame for polr
Prediction data frame from ggplot2 Get predictions with standard error...
Objects exported from other packages
Stat object for producing smooths through ordinal data
Stat ggproto object for binning by quantile for xgx_stat_ci
Stat ggproto object for creating ggplot layers of binned confidence in...
Calls the standard theme for xGx graphics
Append filenames to bottom of the plot
Create a status (e.g. DRAFT) annotation layer
Annotate a png file or directory of png files
Produce an xgx-styled report the given dataset using xgx R markdown te...
Sets the default breaks for log10
Sets the default breaks for a time axis
Check data for various issues
xgx_conf_int returns a dataframe with mean +/- confidence intervals
Append filenames to bottom of the plot
Plot data with mean and confidence intervals
Plot data with median and percent intervals
Nice labels for log10.
Sets the default minor_breaks for log10 scales
Create a new xgx plot
Saving plot, automatically annotating the status and denoting the file...
Saving table as an image, also labeling the program that created the t...
log10 scales the x axis with a "pretty" set of breaks
Reverse-log transform for the x scale.
Convert time units for plotting
log10 scales the y axis with a "pretty" set of breaks
percentchangelog10 transform for the y scale.
Reverselog transform for the y scale.
Plot data with mean and confidence intervals
Plot data with median and percent intervals
Wrapper for stat_smooth
Summarize Covariate information in a dataset
Check data for various issues
Calls the standard theme for xGx graphics
Sets the standard theme for xGx graphics
Supports a structured approach for exploring PKPD data <https://opensource.nibr.com/xgx/>. It also contains helper functions for enabling the modeler to follow best R practices (by appending the program name, figure name location, and draft status to each plot). In addition, it enables the modeler to follow best graphical practices (by providing a theme that reduces chart ink, and by providing time-scale, log-scale, and reverse-log-transform-scale functions for more readable axes). Finally, it provides some data checking and summarizing functions for rapidly exploring pharmacokinetics and pharmacodynamics (PKPD) datasets.