# MAEQJMMLSM: A metric of prediction accuracy of joint model by comparing... In JMH: Joint Model of Heterogeneous Repeated Measures and Survival Data

 MAEQJMMLSM R Documentation

## 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"),
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 lishanpeng0913@ucla.edu

`JMMLSM, survfitJMMLSM`