spsurv (version 1.1.0)

predict.spbp: Predicted survival as a tidy data frame

Description

Survival (and optional CI) on a time grid, as a data.frame for ggplot2::geom_line.

Usage

# S3 method for spbp
predict(
  object,
  newdata = NULL,
  times = NULL,
  eval_time = NULL,
  type = NULL,
  conf_type = NULL,
  interval = 0.95,
  interval.type = c("hpd", "quantile"),
  monotone = NULL,
  ...
)

Value

For curve predictions, same structure as as.data.frame.survfitbp. For type = "survival", a tibble with list-column .pred (elements contain .eval_time and .pred_survival). For type = "time", a tibble with .pred_time. For type = "linear_pred", a tibble with

.pred_linear_pred.

Arguments

object

Fitted "spbp" from spbp / bpph / etc.

newdata

Optional data.frame of covariate profiles (same convention as survfit.spbp).

times

Time grid. Default: a dense sequence from 0 to the maximum observed time (suitable for smooth ggplot2::geom_line / geom_ribbon plots). Pass a long seq(...) to override resolution; use observed event times only if stepwise curves are intended.

eval_time

Evaluation times for type = "survival" (required).

type

Prediction type. For tidymodels/censored compatibility use "survival", "time", or "linear_pred". For survival curves (default), use NULL, "curve", or a confidence transformation ("log", "log-log", "plain").

conf_type

Confidence interval transformation for curve predictions ("log", "log-log", "plain"). Used when type is NULL, "curve", or a confidence transformation name.

interval

Confidence level for curve predictions; passed to survfit.spbp.

interval.type, monotone

Passed to survfit.spbp (Bayesian fits).

...

Passed to survfit.spbp for curve predictions.

See Also

survfit.spbp, augment.spbp

Examples

data(veteran, package = "survival")
fit <- bpph(Surv(time, status) ~ karno, data = veteran, approach = "mle", init = 0)
pr <- predict(fit, times = seq(0, 400, by = 2))
predict(fit, veteran[1:2, ], type = "survival", eval_time = c(100, 200))
predict(fit, veteran[1:2, ], type = "time")
if (FALSE) {
  ggplot2::ggplot(pr, ggplot2::aes(time, surv)) + ggplot2::geom_line()
}