Predicted survival as a tidy data frame

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

Usage

# S3 method for class '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,
  ...
)

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.

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.

Examples

data(veteran, package = "survival")
#> Warning: data set ‘veteran’ not found
fit <- bpph(Surv(time, status) ~ karno, data = veteran, approach = "mle", init = 0)
pr <- predict(fit, times = seq(0, 400, by = 2))
#> Warning: Bernstein-polynomial (gamma) information matrix is ill-conditioned (kappa = 1.04e+12, degree = 12); delta-method survival standard errors and confidence bands may be unreliable. Try refitting with a lower Bernstein degree (argument `degree` to bpph(), bppo(), bpaft(), or spbp()).
predict(fit, veteran[1:2, ], type = "survival", eval_time = c(100, 200))
#> Warning: Bernstein-polynomial (gamma) information matrix is ill-conditioned (kappa = 1.04e+12, degree = 12); delta-method survival standard errors and confidence bands may be unreliable. Try refitting with a lower Bernstein degree (argument `degree` to bpph(), bppo(), bpaft(), or spbp()).
#> # A tibble: 2 × 1
#>   .pred       
#>   <I<list>>   
#> 1 <df [2 × 2]>
#> 2 <df [2 × 2]>
predict(fit, veteran[1:2, ], type = "time")
#> Warning: Bernstein-polynomial (gamma) information matrix is ill-conditioned (kappa = 1.04e+12, degree = 12); delta-method survival standard errors and confidence bands may be unreliable. Try refitting with a lower Bernstein degree (argument `degree` to bpph(), bppo(), bpaft(), or spbp()).
#> # A tibble: 2 × 1
#>   .pred_time
#>        <dbl>
#> 1         72
#> 2        411
if (FALSE) { # \dontrun{
  ggplot2::ggplot(pr, ggplot2::aes(time, surv)) + ggplot2::geom_line()
} # }

Usage

Arguments

Value

See also

Examples