predict.tdmorefit: Predict new data using a model fit

In tdmore-dev/tdmore: In silico evaluation of precision dosing

Description

Predict new data using a model fit

Usage

## S3 method for class 'tdmorefit'
predict(
  object,
  newdata = NULL,
  regimen = NULL,
  parameters = NULL,
  covariates = NULL,
  se.fit = FALSE,
  level = 0.95,
  mc.maxpts = 100,
  ...,
  .progress = interactive()
)

Arguments

object	A tdmorefit object
newdata	A data.frame with new data and the columns to predict, or a numeric vector to specify times, and predict all model output or NULL to interpolate between 0 and the maximum known times
regimen	Treatment regimen
parameters	named numeric vector of fixed parameters
covariates	the model covariates, named vector, or data.frame with column 'TIME', and at least TIME 0
se.fit	TRUE to provide a confidence interval on the prediction, adding columns xxx.median, xxx.upper and xxx.lower FALSE to show the model prediction (IPRED)
level	The confidence interval, or NA to return all mc.maxpts results
mc.maxpts	Maximum number of points to sample in Monte Carlo simulation
...	ignored
.progress	Allows to specify a plyr-like progress object A plyr progress object is a list with 3 function definitions: 'init(N)', 'step()' and 'term()'. This can also be specified as a boolean. TRUE uses the default dplyr progress_estimated.

Value

A data.frame

tdmore-dev/tdmore documentation built on Jan. 1, 2022, 3:21 a.m.