A metric of prediction accuracy of joint model by comparing the predicted risk with the counting process.
PEjmcs(
seed = 100,
object,
landmark.time = NULL,
horizon.time = NULL,
obs.time = NULL,
method = c("Laplace", "GH"),
quadpoint = NULL,
maxiter = NULL,
n.cv = 3,
survinitial = TRUE,
initial.para = FALSE,
LOCF = FALSE,
LOCFcovariate = NULL,
clongdata = NULL,
...
)a list of matrices with conditional probabilities for subjects.
a numeric value of seed to be specified for cross validation.
object of class 'jmcs'.
a numeric value of time for which dynamic prediction starts..
a numeric vector of future times for which predicted probabilities are to be computed.
a character string of specifying a longitudinal time variable.
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.
the number of pseudo-adaptive Gauss-Hermite quadrature points if method = "GH".
the maximum number of iterations of the EM algorithm that the function will perform. Default is 10000.
number of folds for cross validation. Default is 3.
Fit a Cox model to obtain initial values of the parameter estimates. Default is TRUE.
Initial guess of parameters for cross validation. Default is FALSE.
a logical value to indicate whether the last-observation-carried-forward approach applies to prediction.
If TRUE, then LOCFcovariate and clongdata must be specified to indicate
which time-dependent survival covariates are included for dynamic prediction. Default is FALSE.
a vector of string with time-dependent survival covariates if LOCF = TRUE. Default is NULL.
a long format data frame where time-dependent survival covariates are incorporated. Default is NULL.
Further arguments passed to or from other methods.
Shanpeng Li lishanpeng0913@ucla.edu
jmcs, survfitjmcs