SurvGPR_Predict: Predict log-survival time with Gaussian process regression...
In ajmolstad/SurvGPR: Survival time prediction using Gaussian process regression

A function for prediction based on an object of class "SurvGRP"; models fit by SurvGPR. See github.com/ajmolstad/SurvGPR for examples.

1	SurvGPR_Predict(results, barT, Z.full, K.full, train.inds, test.inds, times=NULL)

`results`	An object of class "SurvGPR" obtained from fitting a model using `SurvGPR.`
`barT`	A vector of length n_{\rm train} to be used for prediction. This should contain the log-survival times of the training (and/or their imputed values). If the training data is that which we used to fit the “result” object, this should be the `Tout` object.
`Z.full`	A matrix of dimension (n_{\rm train} + n_{\rm test}) \times q with the same covariates (arranged in the same order) as Z used to obtain “results”. Note that n_{\rm train} must be the dimension of `barT`.
`K.full`	Kernel matrices corresponding to the first M variance components of `results$sigma2` – an array of dimension (n_{\rm train} + n_{\rm test}) \times (n_{\rm train} + n_{\rm test}) \times M.
`train.inds`	The indices of `Z.full` and `K.full` which correspond to the training data. Note that `barT` must be ordered so that its elements correspond to the train.inds.
`test.inds`	The indices of `Z.full` and `K.full` which correspond to the test data, i.e., the data for which we want to make predictions.
`times`	A vector of times (on the original scale) at which we want to evaluate a survival function for the testing data.

`test.inds`	The indices of `Z.full` and K.full which correspond to the test data, i.e., the data for which we made predictions.
`log.pred`	The predicted log-survival times for the testing subjects.
`survFunc`	An n_{\rm test} \times {\rm length}(\texttt{times}) matrix containing the estimated survival curves. The kth row is the estimated survival curve for the kth subject in the testing data evaluated at the inputed times.