PEjmcs {FastJM} | R Documentation |
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(
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,
...
)
Arguments
seed |
a numeric value of seed to be specified for cross validation. |
object |
object of class 'jmcs'. |
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 |
quadpoint |
the number of pseudo-adaptive Gauss-Hermite quadrature points if |
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. |
initial.para |
Initial guess of parameters for cross validation. Default is FALSE. |
LOCF |
a logical value to indicate whether the last-observation-carried-forward approach applies to prediction.
If |
LOCFcovariate |
a vector of string with time-dependent survival covariates if |
clongdata |
a long format data frame where time-dependent survival covariates are incorporated. Default is NULL. |
... |
Further arguments passed to or from other methods. |
Value
a list of matrices with conditional probabilities for subjects.
Author(s)
Shanpeng Li lishanpeng0913@ucla.edu