MAEQJMMLSM: A metric of prediction accuracy of joint model by comparing...

In JMH: Joint Model of Heterogeneous Repeated Measures and Survival Data

A metric of prediction accuracy of joint model by comparing the predicted risk with the empirical risks stratified on different predicted risk group.

Description

A metric of prediction accuracy of joint model by comparing the predicted risk with the empirical risks stratified on different predicted risk group.

Usage

MAEQJMMLSM(
  seed = 100,
  object,
  landmark.time = NULL,
  horizon.time = NULL,
  obs.time = NULL,
  method = c("Laplace", "GH"),
  quadpoint = NULL,
  maxiter = 1000,
  survinitial = TRUE,
  n.cv = 3,
  quantile.width = 0.25,
  opt = "nlminb",
  initial.para = FALSE,
  ...
)

Arguments

seed	a numeric value of seed to be specified for cross validation.
object	object of class 'JMMLSM'.
landmark.time	a numeric value of time for which dynamic prediction starts..
horizon.time	a numeric vector of future times for which predicted probabilities are to be computed.
obs.time	a character string of specifying a longitudinal time variable.
method	estimation method for predicted probabilities. If Laplace, then the empirical empirical estimates of random effects is used. If GH, then the standard Gauss-Hermite quadrature is used.
quadpoint	the number of standard Gauss-Hermite quadrature points if method = "GH".
maxiter	the maximum number of iterations of the EM algorithm that the function will perform. Default is 10000.
survinitial	Fit a Cox model to obtain initial values of the parameter estimates. Default is TRUE.
n.cv	number of folds for cross validation. Default is 3.
quantile.width	a numeric value of width of quantile to be specified. Default is 0.25.
opt	Optimization method to fit a linear mixed effects model, either nlminb (default) or optim.
initial.para	Initial guess of parameters for cross validation. Default is FALSE.
...	Further arguments passed to or from other methods.

Value

a list of matrices with conditional probabilities for subjects.

Author(s)

Shanpeng Li [e-mail]

See Also

JMMLSM, survfitJMMLSM

JMH documentation built on June 22, 2024, 7:08 p.m.