fitted.pmrm_fit: Fitted values

In pmrm: Progression Models for Repeated Measures

Fitted values

Description

Compute the fitted values of a fitted progression model for repeated measures.

Usage

## S3 method for class 'pmrm_fit'
fitted(object, data = object$data, adjust = TRUE, ...)

Arguments

object	A fitted model object of class "pmrm_fit".
data	A tibble or data frame with one row per patient visit. This is the new data for making predictions. It must have all the same columns as the original you fit with the model, except that the outcome column can be entirely absent. object$data is an example dataset that will work. It is just like the original data, except that rows with missing responses are removed, and the remaining rows are sorted by patient ID and categorical scheduled visit.
adjust	TRUE or FALSE. adjust = TRUE returns estimates and inference for covariate-adjusted mu_ij values (defined in vignette("models", package = "pmrm")) for new data. adjust = FALSE instead returns inference on mu_ij - W %*% gamma, the non-covariate-adjusted predictions useful in plotting a continuous disease progression trajectory in plot.pmrm_fit().
...	Not used.

Details

For pmrm, fitted() is much faster than predict() for large datasets, but the output only includes the estimates (no measures of uncertainty).

Value

A numeric vector of fitted values corresponding to the rows of the data supplied in the data argument.

See Also

Other predictions: predict.pmrm_fit(), residuals.pmrm_fit()

Examples

  set.seed(0L)
  simulation <- pmrm_simulate_decline_proportional(
    visit_times = seq_len(5L) - 1,
    gamma = c(1, 2)
  )
  fit <- pmrm_model_decline_proportional(
    data = simulation,
    outcome = "y",
    time = "t",
    patient = "patient",
    visit = "visit",
    arm = "arm",
    covariates = ~ w_1 + w_2
  )
  str(fitted(fit))

pmrm documentation built on March 12, 2026, 5:07 p.m.