JMH (version 1.0.3)

MAEQJMMLSM: 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,
  ...
)

Value

a list of matrices with conditional probabilities for subjects.

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.

Author

Shanpeng Li

See Also

JMMLSM, survfitJMMLSM