Returns a data.frame with all combos of predictors

This function synthesizes predictors for all combinations of predictor values. It is used internally in plot.mcpfit() and may be useful if you want to build your own custom plot.

Usage

interpolate_newdata(
  fit,
  by = NULL,
  x_values = get_x_values(fit, by),
  at = NULL
)

Arguments

fit

An mcpfit object.

by

Character vector of categorical or varying-effect columns to evaluate separately. Categorical model predictors are always included.

x_values

Numeric vector of x-values to evaluate at.

at

Named list setting additional continuous predictors to fixed values. They default to their observed means. For example, at = list(age = 40).

Value

tibble with

• Cols for par_x

• unique levels combos of factorial vars

• fixed values for additional continuous predictors

Details

The par_x variable will be interpolated with higher resolution around the change points where the values can change abruptly, but lower resolution in between to speed up the computation.

Categorical variables and requested varying-effect groups are combined factorially (all level combinations). Additional continuous predictors are held at their observed means, or at values supplied through at.

Author

Jonas Kristoffer Lindeløv jonas@lindeloev.dk

Examples

if (FALSE) { # \dontrun{
# Get predictors for a fit
fit = mcp_example("multiple")
newdata = interpolate_newdata(fit)

# Fit summary
fitted(fit, newdata)

# Predictions for each sample
prediction = predict(fit, newdata, summary = FALSE)
prediction[, c(".chain", ".iteration", ".draw", "x", "group", "z", "predict")]

# Custom plot
library(ggplot2)
newdata = interpolate_newdata(fit)
plotdata = fitted(fit, newdata)
ggplot(plotdata, aes(x = x, y = fitted, color = group)) +
  geom_ribbon(aes(ymin = `Q2.5`, ymax = `Q97.5`, fill = group), alpha = 0.3) +
  geom_line(lwd = 2) +
  geom_point(aes(y = y), data = fit$data)
} # }