uacv: Estimation of the Universal Approximate Cross Validation...

In VivianePhilipps/multLPM: Joint model for multivariate latent processes and clinical endpoints using an exact likelihood inference method

Description

Estimation of the Universal Approximate Cross Validation (UACV) criterion for a joint model

Usage

uacv(model, Y, D, data, var.time, RE = "block-diag", BM = "diag", B, posfix,
  breaks = NULL, delayed = TRUE)

Arguments

model	a JointMult model
Y	a list of multlcmm objects
D	a list of two-sided formula defining the event part of the model
data	data.frame containing the observations and variables
var.time	a character vector indicating the name of the time variables
RE	an indicator of the random effect structure between dimensions
BM	an indicator of the correlation of the Brownian motions
B	vector cntaining initial values for the parameters
posfix	optional vector specifying the indices in vector B of the parameters that are not estimated
breaks	optional vector specifying the break points in the case where the event time is discretized
delayed	logical vector indicating if delayed entry should be accounted for

Value

the value of the Universal Approximate Cross Validation criterion

Author(s)

Cecile Proust-Lima and Viviane Philipps

References

Commenges D, Proust-Lima C, Samieri C, Liquet B. A universal approximate cross-validation criterion for regular risk functions. The International Journal of Biostatistics 2015; 11(1): 51–67. doi: 10.1515/ijb-2015-0004

VivianePhilipps/multLPM documentation built on March 13, 2021, 6:35 a.m.