Plot a gg_partial_varpro object — plot.gg_partial

Draws the partial dependence curves from the list that gg_partial_varpro returns. Continuous predictors get overlaid line curves, one per effect type; categorical predictors get side-by-side boxplots. Survival path-C objects (the ones you get when scale %in% c("surv","chf") was passed to the extractor) are handed off to plot.gg_partial_rfsrc for drawing.

Usage

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

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

Arguments

x: A gg_partial_varpro object.
type: Character vector; one or more of "parametric", "nonparametric", "causal". Defaults to all three. Ignored for path-C objects.
...: Unused for path-A objects; forwarded to plot.gg_partial_rfsrc for path-C objects.

Value

A ggplot (or patchwork) object.

Details

Ensemble mortality (scale = "mortality"): when the provenance scale is "mortality", the y-axis is labelled "Ensemble mortality (expected events)". The wording is deliberate: this is an unbounded relative-risk score, not a survival probability and not \(1 - S(t)\) (Ishwaran, Kogalur, Blackstone & Lauer, 2008 doi:10.1214/08-AOAS169).

Reading the partial dependence

For a continuous variable the x-axis is the variable's grid of values and the y-axis is the partial prediction; each of the three effect types (parametric, nonparametric, causal) is drawn as its own line. The shape of the line is the story: a clear slope says the model uses the variable, a flat line says it essentially does not, and a U-shape or a threshold says the effect is nonlinear in a way a single coefficient would miss. For a categorical variable the picture is a boxplot per level; here the eye is looking at level-to-level shifts in the centre of each box.

Where the three effect types track each other, the parametric story is a fair summary of what the forest is doing. Where they fan apart (typically the parametric curve smoother than the nonparametric, or the causal curve flatter than either) the variable is one to inspect more carefully before reading a single effect off the plot.

What this tells you

Use these curves to describe how the model uses each variable, not to claim how the world works. They are a window into the fitted relationship; they do not by themselves establish that intervening on the variable would move the outcome. For survival path-C (scale = "surv" or "chf"), the y-axis is on the probability or cumulative-hazard scale, which is usually the scale you want to report to a clinical audience.

References

Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS (2008). Random survival forests. The Annals of Applied Statistics, 2(3), 841–860. doi:10.1214/08-AOAS169 .

Examples

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_partial_varpro(mock_data)
plot(pp)

plot(pp, type = "parametric")

Plot a `gg_partial_varpro` object