ctPredictTIP: ctPredictTIP

In ctsem: Continuous Time Structural Equation Modelling

ctPredictTIP

Description

Outputs the estimated effect of time independent predictors (covariate moderators) on the expected observations.

Usage

ctPredictTIP(
  sf,
  tipreds = "all",
  subject = 1,
  timestep = "auto",
  doDynamics = TRUE,
  plot = TRUE,
  quantiles = c(0.16, 0.5, 0.84),
  discreteTimeQuantiles = c(0.025, 0.5, 0.975),
  showUncertainty = TRUE,
  TIPvalues = NA
)

Arguments

sf	A fitted ctStanFit object from the ctsem package.
tipreds	A character vector specifying which time independent predictors to use. Default is 'all', which uses all time independent predictors in the model.
subject	An integer value specifying the internal ctsem subject ID (mapping visible under myfit$setup$idmap) for which predictions are made. This is relevant only when time dependent predictors are also included in the model.
timestep	A numeric value specifying the time step for predictions. Default is 'auto', which tries to automatically determine an appropriate time step.
doDynamics	A logical value indicating whether to plot the effects of time independent predictors on the dynamics of the system. Default is TRUE. Can be problematic for systems with many dimensions.
plot	A logical value indicating whether to ggplot the results instead of returning a data.frame of predictions. Default is TRUE.
quantiles	A numeric vector specifying the quantiles of the time independent predictors to plot. Default is 1SD either side and the median, c(.32,.5,.68).
discreteTimeQuantiles	a numeric vector of length 3 specifying the quantiles of the discrete time points to plot, when showUncertainty is TRUE.
showUncertainty	A logical value indicating whether to plot the uncertainty of the predictions. Default is TRUE.
TIPvalues	An nvalue * nTIpred numeric matrix specifying the fixed values for each time independent predictor effect to plot. Default is NA, which instead relies on the quantiles specified in the quantiles argument.

Details

This function estimates the effects of covariate moderators on the expected process and observations for a specified subject in a dynamic system. The covariate moderators are defined at the specified quantiles, and their effects on the trajectory are plotted or returned as a data frame.

Value

If plot is TRUE, a list of ggplot objects showing the estimated effects of covariate moderators. Otherwise, a data frame with the predictions.

Examples

# Example usage:
ctPredictTIP(ctstantestfit, tipreds='all', doDynamics=FALSE, plot=TRUE)

ctsem documentation built on Sept. 11, 2024, 9:06 p.m.