Diagnostic Plots for Cox Proportional Hazards Model with ggplot2

Source: R/ggcoxdiagnostics.R

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

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'