Show Cox proportional hazards model diagnostic plots

Creates diagnostic plots specifically for Cox regression models. Focuses on Schoenfeld residuals plots to assess proportional hazards assumption and other Cox-specific diagnostics. Inspired by survminer::ggcoxzph with enhanced visualization and computation optimizations to work in bregr.

Usage

br_show_coxph_diagnostics(
  breg,
  idx = 1,
  type = "schoenfeld",
  resid = TRUE,
  se = TRUE,
  point_col = "red",
  point_size = 1,
  point_alpha = 0.6,
  ...
)

Arguments

breg

A regression object with results (must pass assert_breg_obj_with_results()).

idx

Index or name (focal variable) of the Cox model to plot. Must be a single model.

type

Type of Cox diagnostic plot. Options: "schoenfeld" (default for Schoenfeld residuals), "martingale" (martingale residuals), "deviance" (deviance residuals).

resid

Logical, if TRUE the residuals are included on the plot along with smooth fit.

se

Logical, if TRUE confidence bands at two standard errors will be added.

point_col

Color for residual points.

point_size

Size for residual points.

point_alpha

Alpha (transparency) for residual points.

...

Additional arguments passed to survival::cox.zph.

Value

A ggplot2 object or list of plots.

See also

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

Examples

# Create Cox models
mds <- br_pipeline(
  survival::lung,
  y = c("time", "status"),
  x = colnames(survival::lung)[6:10],
  x2 = c("age", "sex"),
  method = "coxph"
)
#> exponentiate estimates of model(s) constructed from coxph method at default

# Show Cox diagnostic plots
p1 <- br_show_coxph_diagnostics(mds, idx = 1)
p1
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'

p2 <- br_show_coxph_diagnostics(mds, type = "martingale")
p2
#> `geom_smooth()` using formula = 'y ~ x'