Plot a gg_partialpro object

Produces ggplot2 partial dependence curves from the named list returned by gg_partialpro, which wraps varpro::partialpro output.

Usage

# S3 method for class 'gg_partialpro'
plot(x, type = c("parametric", "nonparametric", "causal"), ...)

Arguments

x

A gg_partialpro object.

type

Character vector; one or more of "parametric", "nonparametric", "causal". Defaults to all three.

...

Not currently used.

Value

A single ggplot or a named list with continuous and categorical elements when both types of predictors are present.

Details

Each variable produces up to three effect curves: parametric, nonparametric, and causal. The type argument controls which are shown.

See also

gg_partialpro

Examples

## ggRandomForests does not depend on the varpro package; we construct a
## minimal mock of the partialpro() output so the example runs everywhere.
set.seed(42)
n_obs <- 30
n_pts <- 15
mock_data <- list(
  age = list(
    xvirtual    = seq(30, 80, length.out = n_pts),
    xorg        = sample(seq(30, 80, by = 5), n_obs, replace = TRUE),
    yhat.par    = matrix(rnorm(n_obs * n_pts), nrow = n_obs),
    yhat.nonpar = matrix(rnorm(n_obs * n_pts), nrow = n_obs),
    yhat.causal = matrix(rnorm(n_obs * n_pts), nrow = n_obs)
  ),
  sex = list(
    xvirtual    = c(0, 1),
    xorg        = sample(c(0, 1), n_obs, replace = TRUE),
    yhat.par    = matrix(rnorm(n_obs * 2), nrow = n_obs),
    yhat.nonpar = matrix(rnorm(n_obs * 2), nrow = n_obs),
    yhat.causal = matrix(rnorm(n_obs * 2), nrow = n_obs)
  )
)

pp <- gg_partialpro(mock_data)
result <- plot(pp)

# Continuous predictors get one panel per variable; categorical get
# box-plots over the parametric / nonparametric / causal effect types.
result$continuous

result$categorical


# Restrict to one or two effect types
plot(pp, type = "nonparametric")
#> $continuous

#> 
#> $categorical

#> 
plot(pp, type = c("parametric", "causal"))
#> $continuous

#> 
#> $categorical

#>