cv_loglik_p: Cross-validated observed-data log-likelihood for B-spline...
In sarahlotspeich/sieveSurv: Nonparametric Survival Estimator with B-spline Sieves

Description Usage Arguments Value

View source: R/cv_loglik_p.R

Cross-validated observed-data log-likelihood for B-spline coefficients

cv_loglik_p(
  nfold = 5,
  interp = FALSE,
  seed = NULL,
  time,
  event,
  covar = NULL,
  bspline,
  data,
  tol = 1e-04,
  max_iter = 1000,
  assume_last = FALSE
)

`interp`	Indicator of whether the B-spline coefficients in the testing data should be linearly interpolated from the trained model. Defaults to `FALSE`.
`seed`	(Optional) For reproducibility in assigning the folds, an integer to specify the random number generator. Default is `NULL`.
`time`	Column name for follow-up time
`event`	Column name for event indicators
`covar`	(Optional) column name(s) for additional fully-observed covariates. Default is `NULL`, which estimates unconditional survival.
`bspline`	Column names for B-spline basis.
`data`	Dataframe or matrix containing (at least) named columns `time`, `event`, `covar`, and `bspline`.
`tol`	Tolerance to define convergence. Default is `1E-4`.
`max_iter`	Maximum number of iterations allowed for the EM algorithm. Default is `1000`.
`assume_last`	Assume last observed `time` is an event (for use when the integration of the survival function is desired). Default is `FALSE`.
`nfolds`	Specifies the number of cross-validation folds. The default value is `5`. Although `nfolds` can be as large as the sample size (leave-one-out cross-validation), it is not recommended for large datasets. The smallest value allowable is `3`.

`avg_pred_loglik`	Stores the average predicted log likelihood.
`pred_loglik`	Stores the predicted log likelihoood in each fold.
`converged`	Stores the convergence status of the EM algorithm in each run.