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invivoPKfit (version 2.0.1)

predict.pk: Get predictions

Description

Extract predictions from a fitted `pk` object.

Usage

# S3 method for pk
predict(
  object,
  newdata = NULL,
  model = NULL,
  method = NULL,
  type = "conc",
  exclude = TRUE,
  use_scale_conc = FALSE,
  suppress.messages = NULL,
  include_NAs = FALSE,
  ...
)

Value

A data.frame with one row for each `data_group`, `model` and `method`. Includes variable `Conc_est` that contains the predicted concentration or AUC at that timepoint given the TK parameters for that `model` and `method` specified in [coefs()]. If `use_scale_conc

un-transformed concentrations in the same units as `object$data$Conc.Units`. If `use_scale_conc

concentrations in the same units as `object$data$Conc_trans.Units`.

Arguments

object

A [pk] object.

newdata

Optional: A `data.frame` with new data for which to make predictions. If NULL (the default), then predictions will be made for the data in `object$data`. `newdata` is required to contain at least the following variables: `Time`, `Time.Units`, `Dose`, `Route`, and `Media`.

model

Optional: Specify one or more of the fitted models for which to make predictions. If NULL (the default), predictions will be returned for all of the models in `object$stat_model`.

method

Optional: Specify one or more of the [optimx::optimx()] methods for which to make predictions. If NULL (the default), predictions will be returned for all of the models in `object$settings_optimx$method`.

type

Either `"conc"` (the default) or `"auc"`. `type = "conc"` predicts concentrations; `type = "auc"` predicts area under the concentration-time curve (AUC).

exclude

Logical: `TRUE` to return `NA_real_` for any observations in the data marked for exclusion (if there is a variable `exclude` in the data, an observation is marked for exclusion when `exclude `FALSE` to return the prediction for each observation, regardless of exclusion. Default `TRUE`.

use_scale_conc

Possible values: `TRUE`, `FALSE`, or a named list with elements `dose_norm` and `log10_trans` which themselves should be either `TRUE` or `FALSE`. If `use_scale_conc = TRUE`, then the concentration scaling/transformations in `object` will be applied to both predicted and observed concentrations before the log-likelihood is computed. If `use_scale_conc = FALSE` (the default for this function), then no concentration scaling or transformation will be applied before the log-likelihood is computed. If `use_scale_conc = list(dose_norm = ..., log10_trans = ...)`, then the specified dose normalization and/or log10-transformation will be applied.

suppress.messages

Logical: whether to suppress message printing. If NULL (default), uses the setting in `object$settings_preprocess$suppress.messages`

include_NAs

Logical: `FALSE` by default. Determines whether to include aborted fits which have NAs as coefficients.

...

Additional arguments.

Author

Caroline Ring, Gilberto Padilla Mercado

See Also

Other methods for fitted pk objects: AAFE.pk(), AFE.pk(), AIC.pk(), BIC.pk(), coef.pk(), coef_sd.pk(), eval_tkstats.pk(), get_fit.pk(), get_hessian.pk(), get_tkstats.pk(), logLik.pk(), residuals.pk(), rmse.pk(), rsq.pk()