momentLVMr: Moments for a reduced lvm model

In bozenne/lavaReduce: Latent Variable Models with linear predictor

Description

Compute the likelihood, score and hessian of a reduced lvm model

Usage

gaussianLP_method.lvm

gaussianLP_objective.lvm(x, p, data, ...)

gaussianLP_logLik.lvm(object, p, data, ...)

gaussianLP_gradient.lvm(x, p, data, ...)

gaussianLP_score.lvm(x, p, data, indiv = FALSE, implementation = "R", ...)

gaussianLP_hessian.lvm(x, p, n, type, ...)

gaussian1LP_method.lvm

gaussian1LP_logLik.lvm(object, p, data, ...)

gaussian1LP_objective.lvm(x, p, data, ...)

gaussian1LP_score.lvm(x, p, data, indiv = FALSE, implementation = "R", ...)

gaussian1LP_gradient.lvm(x, p, data, ...)

gaussian1LP_hessian.lvm(x, type, ...)

gaussian2LP_method.lvm

gaussian2LP_logLik.lvm(object, p, data, ...)

gaussian2LP_objective.lvm(x, p, data, ...)

gaussian2LP_score.lvm(x, p, data, indiv = FALSE, implementation = "R", ...)

gaussian2LP_gradient.lvm(x, p, data, ...)

gaussian2LP_hessian.lvm(x, type, ...)

Arguments

x, object	lvm.reduced-object
p	current parameter estimate
data	dataset
...	additional arguments
indiv	should the individual contribution be returned? Else average over the observations
implementation	default is R. If set to cpp all the computation of the gradient is made in a C++ routine.
n	number of observations
type	method used to compute the hessian

Format

An object of class character of length 1.

Details

this function assumes that the external parameters in p are at the end of the vector

bozenne/lavaReduce documentation built on May 24, 2019, 3:05 a.m.