PEjmcs {FastJM}

A metric of prediction accuracy of joint model by comparing the predicted risk with the counting process.

Description

A metric of prediction accuracy of joint model by comparing the predicted risk with the counting process.

Usage

PEjmcs(
  object,
  seed = 100,
  landmark.time = NULL,
  horizon.time = NULL,
  obs.time = NULL,
  method = c("Laplace", "GH"),
  quadpoint = NULL,
  maxiter = NULL,
  n.cv = 3,
  survinitial = TRUE,
  ...
)

Arguments

object

object of class 'jmcs'.

seed

a numeric value of seed to be specified for cross validation.

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 pseudo-adaptive Gauss-Hermite quadrature is used.

quadpoint

the number of pseudo-adaptive 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.

n.cv

number of folds for cross validation. Default is 3.

survinitial

Fit a Cox model to obtain initial values of the parameter estimates. Default is TRUE.

...

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

jmcs, survfitjmcs