Show nomogram for regression models

Creates a nomogram (graphical calculator) for regression models, particularly useful for Cox proportional hazards models. A nomogram allows visual calculation of predicted outcomes by assigning points to variable values and summing them to get total points that correspond to predicted probabilities.

Usage

br_show_nomogram(
  breg,
  idx = NULL,
  time_points = c(12, 24, 36),
  fun_at = NULL,
  point_range = c(0, 100),
  title = NULL,
  subtitle = NULL
)

Arguments

breg

A breg object with fitted regression models.

idx

Index or name of the model to use for the nomogram. If NULL, uses the first model.

time_points

For Cox models, time points at which to show survival probabilities. Default is c(12, 24, 36) representing months.

fun_at

For non-survival models, the function values at which to show predictions.

point_range

Range of points to use in the nomogram scale. Default is c(0, 100).

title

Plot title. If NULL, generates automatic title.

subtitle

Plot subtitle.

Value

A ggplot2 object showing the nomogram.

See also

Other br_show: br_show_coxph_diagnostics(), br_show_fitted_line(), br_show_fitted_line_2d(), br_show_forest(), br_show_forest_ggstats(), br_show_forest_ggstatsplot(), br_show_residuals(), br_show_risk_network(), br_show_survival_curves(), br_show_table(), br_show_table_gt()

Examples

# \donttest{
# Cox regression nomogram

lung <- survival::lung |> dplyr::filter(ph.ecog != 3)
lung$ph.ecog <- factor(lung$ph.ecog)
mds <- br_pipeline(
  lung,
  y = c("time", "status"),
  x = c("age", "ph.ecog"),
  x2 = "sex",
  method = "coxph"
)
#> exponentiate estimates of model(s) constructed from coxph method at default
p <- br_show_nomogram(mds)
#> `idx` not set, use the first model
#> Cox model: intercept term present but no intercept coefficient (as expected for
#> semi-parametric models)
p



# Linear regression nomogram
mds_lm <- br_pipeline(
  mtcars,
  y = "mpg",
  x = c("hp", "wt"),
  x2 = "vs",
  method = "gaussian"
)
p2 <- br_show_nomogram(mds_lm, fun_at = c(15, 20, 25, 30))
#> `idx` not set, use the first model
p2

# }