predict.INLAjoint: Computes predictions for a given model fitted with INLAjoint

In DenisRustand/INLAjoint: Multivariate Joint Modeling for Longitudinal and Time-to-Event Outcomes with 'INLA'

Computes predictions for a given model fitted with INLAjoint

Description

This function allows to compute predictions for a given model fitted with INLAjoint, the default behavior (without arguments) returns fitted values for each component of the model. It is also possible to supply a dataset for which predictions are required, this dataset must have the same structure as the dataset used for the model fitting (i.e., same columns). The default returned predictions corresponds to the linear predictors for each outcomes.

Usage

## S3 method for class 'INLAjoint'
predict(
  object,
  newData = NULL,
  newDataSurv = NULL,
  timePoints = NULL,
  NtimePoints = 50,
  NsampleHY = 20,
  NsampleFE = 20,
  NsampleRE = 50,
  id = NULL,
  Csurv = NULL,
  startTime = NULL,
  horizon = NULL,
  baselineHaz = "interpolation",
  return.samples = FALSE,
  FEonly = FALSE,
  survival = FALSE,
  CIF = FALSE,
  inv.link = FALSE,
  NidLoop = "auto",
  resErrLong = FALSE,
  set.samples = NULL,
  silentMode = FALSE,
  ...
)

Arguments

object	an object that contains a model fitted with INLAjoint.
newData	a dataset with the same columns as those used to fit the model. When using a longitudinal marker to predict longitudinal and subsequent survival outcomes, only the longitudinal information (i.e., structure of the longitudinal data) is required. It is also possible to predict the average trajectories conditional on covariates by setting the value of the longitudinal outcomes included in the model to NA.
newDataSurv	a dataset for survival information (only useful when both longitudinal and survival data are provided for the predictions, otherwise using the argument newData is working too).
timePoints	a vector of the time points at which predictions are computed (for both longitudinal and survival outcomes), this also control the precision of the integration for time-dependent shared terms and the computation of cumulative risks (e.g., for survival or CIF curves), thus many time points will increase the accuracy of predictions. Default is NULL as these time points are automatically computed when not defined manually.
NtimePoints	number of time points at which the predictions are computed (for both longitudinal and survival outcomes), these time points are equidistant between time 0 and horizon time. This also control the precision of the integration for time-dependent shared terms and the computation of cumulative risks (e.g., for survival or CIF curves), thus many time points will increase the accuracy of predictions.
NsampleHY	number of samples for hyperparameters used to assess uncertainty when computing predictions. Default is 20.
NsampleFE	number of samples of fixed effects for each hyperparameters samples used to assess uncertainty when computing predictions. Default is 30 (i.e., 30 x NsampleHY).
NsampleRE	number of random effects realizations for each sample specified in 'NsampleHY' and 'NsampleFE'. Default is 50 (i.e., 50 x NsampleFE x NsampleHY, resulting in 20000 random effects samples per new individual with default values). These random effects realizations are conditional on observed longitudinal outcomes values provided in 'newData' and survival time provided in 'newDataSurv' when a survival model is included. If 'newDataSurv' is NULL, they are conditional on survival up to latest longitudinal recorded measurement. When outcomes are set to NA, the realizations are sampled from the marginal distribution of random effects.
id	name of the individual id variable, default is NULL as it is automatically grabbed from the fitted model but when fitting simple survival models, providing id when fitting the model is not mandatory and thus this can be useful (an explicit message is printed in this specific case).
Csurv	conditional survival, gives the starting value of the at-risk period (i.e., starting value at which risk predictions for survival models are computed). Default is the last longitudinal observation time provided in 'newData' but this is replaced by the value of 'Csurv' when provided.
horizon	horizon of the prediction.
baselineHaz	method used to evaluate the baseline hazard value, default is 'interpolation' which is currently recommended. Experimental alternatives are being developed, including 'splines' for an interpolation with splines but has not been properly validated with simulations yet.
return.samples	boolean, when set to TRUE the samples are returned instead of summary statistics over the samples. Default is FALSE.
survival	boolean, when set to TRUE the summary statistics over survival functions are computed in addition to the summary statistics over the risk functions.
CIF	boolean, when set to TRUE the summary statistics over cumulative incidence functions are computed in addition to the summary statistics over the risk functions. Only applies to competing risks.
inv.link	boolean, when set to TRUE the summary statistics are computed over the predictions of longitudinal components after applying the inverse link function for each samples in addition to the summary statistics over the linear predictors.
NidLoop	Gives the number of individuals for which we compute predictions at once. For large number of individuals, this will loop over groups of 'NidLoop' individuals and could make predictions computations faster.
resErrLong	boolean, when set to TRUE the residual error for Gaussian or lognormal longitudinal outcomes is added to the uncertainty of predictions (default is FALSE which predicts the true underlying value of the longitudinal marker, i.e., error-free).
set.samples	replace random effects with pre-sampled values. #' @param silentMode a boolean that will stop printing messages during computations if turned to TRUE.
...	Extra arguments.

DenisRustand/INLAjoint documentation built on April 13, 2025, 1:50 p.m.