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Plot a fitted PMRM.

Source: R/plot.R
plot.pmrm_fit.Rd

Plot a fitted progression model for repeated measures (PMRM) against the data.

Usage

# S3 method for class 'pmrm_fit'
plot(
  x,
  y = NULL,
  ...,
  confidence = 0.95,
  show_data = TRUE,
  show_marginals = TRUE,
  show_predictions = FALSE,
  facet = TRUE,
  alpha = 0.25
)

Arguments

x

A fitted model object of class "pmrm_fit" returned by a pmrm model-fitting function.

y

Not used.

...

Not used.

confidence

Numeric between 0 and 1, the confidence level to use in the 2-sided confidence intervals.

show_data

TRUE to plot data-based visit-specific data means and confidence intervals as boxes. FALSE to omit.

show_marginals

TRUE to plot model-based confidence intervals and estimates of marginal means as boxes and horizontal lines within those boxes, respectively. Uses pmrm_marginals() with the given level of confidence. FALSE to omit.

show_predictions

TRUE to plot expected outcomes and confidence bands with lines and shaded regions, respectively. Uses predict.pmrm_fit() with adjust = FALSE and the given level of confidence on the original dataset used to fit the model. Predictions on a full dataset are generally slow, so the default is FALSE.

facet

TRUE to facet the plot by study arm, FALSE to overlay everything in a single panel.

alpha

Numeric between 0 and 1, opacity level of the model-based confidence bands.

Value

A ggplot object with the plot.

Details

The plot shows the following elements:

  • Raw estimates and confidence intervals on the data, as boxes (if show_data is TRUE).

  • Model-based estimates and confidence intervals as points and error bars, respectively (if show_marginals is TRUE).

  • Continuous model-based estimates and confidence bands as lines and shaded regions, respectively (if show_predictions is TRUE).

See also

Other visualization: print.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
  )
  plot(fit)