Predict new data using a model fit
# S3 method for tdmorefit predict( object, newdata = NULL, regimen = NULL, parameters = NULL, covariates = NULL, se.fit = FALSE, level = 0.95, mc.maxpts = 100, ..., .progress = interactive() )
object | A tdmorefit object |
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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. |
A data.frame