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Plot marginal predictions from sim_adrf()

Source: R/plot.clarify_adrf.R
plot.clarify_adrf.Rd

plot.clarify_adrf() plots the output of sim_adrf(). For the average dose-response function (ADRF, requested with contrast = "adrf" in sim_adrf()), this is a plot of the average marginal mean of the outcome against the requested values of the focal predictor; for the average marginal effects function (AMEF, requested with contrast = "amef" in sim_adrf()), this is a plot of the instantaneous average marginal effect of the focal predictor on the outcome against the requested values of the focal predictor.

Usage

# S3 method for class 'clarify_adrf'
plot(
  x,
  ci = TRUE,
  level = 0.95,
  method = "quantile",
  baseline = NULL,
  color = "black",
  simultaneous = FALSE,
  ...
)

Arguments

x

a clarify_adrf object resulting from a call to sim_adrf().

ci

logical; whether to display confidence bands for the estimates. Default is TRUE.

level

the confidence level desired. Default is .95 for 95% confidence intervals.

method

the method used to compute confidence bands. Can be "wald" to use a Normal approximation or "quantile" to use the simulated sampling distribution (default). See summary.clarify_est() for details. Abbreviations allowed.

baseline

logical; whether to include a horizontal line at y = 0 on the plot. Default is FALSE for the ADRF (since 0 might not be in the range of the outcome) and TRUE for the AMEF.

color

the color of the line and confidence band in the plot.

simultaneous

logical; whether confidence bands should be simultaneous or not (i.e., for nominal coverage of the whole effect curve); default is FALSE, but TRUE is recommended. See Details at summary.clarify_est() for details.

...

for plot(), further arguments passed to ggplot2::geom_density().

Value

A ggplot object.

Details

These plots are produced using ggplot2::geom_line() and ggplot2::geom_ribbon(). The confidence bands should be interpreted pointwise (i.e., they do not account for simultaneous inference) unless simultaneous = TRUE.

See also

summary.clarify_est() for computing p-values and confidence intervals for the estimated quantities.

Examples

## See help("sim_adrf") for examples