Diagnostic Plots for Cox Proportional Hazards Model with ggplot2

Displays diagnostics graphs presenting goodness of Cox Proportional Hazards Model fit, that can be calculated with coxph function.

Usage

ggcoxdiagnostics(
  fit,
  type = c("martingale", "deviance", "score", "schoenfeld", "dfbeta", "dfbetas",
    "scaledsch", "partial"),
  ...,
  linear.predictions = type %in% c("martingale", "deviance"),
  ox.scale = ifelse(linear.predictions, "linear.predictions", "observation.id"),
  hline = TRUE,
  sline = TRUE,
  sline.se = TRUE,
  hline.col = "red",
  hline.size = 1,
  hline.alpha = 1,
  hline.yintercept = 0,
  hline.lty = "dashed",
  sline.col = "blue",
  sline.size = 1,
  sline.alpha = 0.3,
  sline.lty = "dashed",
  point.col = "black",
  point.size = 1,
  point.shape = 19,
  point.alpha = 1,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  ggtheme = ggplot2::theme_bw()
)

Arguments

fit: an object of class coxph.object - created with coxph function.
type: the type of residuals to present on Y axis of a diagnostic plot. The same as in residuals.coxph: character string indicating the type of residual desired. Possible values are "martingale", "deviance", "score", "schoenfeld", "dfbeta", "dfbetas" and "scaledsch". Only enough of the string to determine a unique match is required.
...: further arguments passed to residuals.coxph or to the function ggpar for customizing the plot.
linear.predictions: (deprecated, see ox.scale) a logical value indicating whether to show linear predictions for observations (TRUE) or just indexed of observations (FALSE) on X axis.
ox.scale: one value from c("linear.predictions", "observation.id", "time"). It defines what will be presented on OX scale. Possible values: y hat for "linear.predictions", Id of an observation for "observation.id" or Time for "time".
hline: a logical - should the horizontal line be added to highlight the Y=0 level.
sline, sline.se: a logical - should the smooth line be added to highlight the local average for residuals.
hline.col, hline.size, hline.lty, hline.alpha, hline.yintercept: color, size, linetype, visibility and Y-axis coordinate to be used for geom_hline. Used only when hline = TRUE.
sline.col, sline.size, sline.lty, sline.alpha: color, size, linetype and visibility to be used for geom_smooth. Used only when sline = TRUE.
point.col, point.size, point.shape, point.alpha: color, size, shape and visibility to be used for points.
title, subtitle, caption: main title, subtitle and caption.
ggtheme: function, ggplot2 theme name. Default value is ggplot2::theme_bw(). Allowed values include ggplot2 official themes: see theme.

Value

Returns an object of class ggplot.

Functions

ggcoxdiagnostics(): Diagnostic Plots for Cox Proportional Hazards Model with ggplot2

Author

Marcin Kosinski , m.p.kosinski@gmail.com

Examples


library(survival)
coxph.fit2 <- coxph(Surv(futime, fustat) ~ age + ecog.ps, data=ovarian)
ggcoxdiagnostics(coxph.fit2, type = "deviance")
#> `geom_smooth()` using formula = 'y ~ x'


ggcoxdiagnostics(coxph.fit2, type = "schoenfeld", title = "Diagnostic plot")
#> `geom_smooth()` using formula = 'y ~ x'

ggcoxdiagnostics(coxph.fit2, type = "deviance", ox.scale = "time")
#> Warning: ox.scale='time' works only with type=schoenfeld/scaledsch
#> `geom_smooth()` using formula = 'y ~ x'

ggcoxdiagnostics(coxph.fit2, type = "schoenfeld", ox.scale = "time",
                 title = "Diagnostic plot", subtitle = "Data comes from survey XYZ",
                 font.subtitle = 9)
#> `geom_smooth()` using formula = 'y ~ x'

ggcoxdiagnostics(coxph.fit2, type = "deviance", ox.scale = "linear.predictions",
                 caption = "Code is available here - link", font.caption = 10)
#> `geom_smooth()` using formula = 'y ~ x'

ggcoxdiagnostics(coxph.fit2, type = "schoenfeld", ox.scale = "observation.id")
#> `geom_smooth()` using formula = 'y ~ x'

ggcoxdiagnostics(coxph.fit2, type = "scaledsch", ox.scale = "time")
#> `geom_smooth()` using formula = 'y ~ x'