R: Create Deviance Evaluation Function from Predictor and...

mkdevfun {lme4}

R Documentation

Create Deviance Evaluation Function from Predictor and Response Module

Description

From a merMod object create an R function that takes a single argument, which is the new parameter value, and returns the deviance.

Usage

mkdevfun(rho, nAGQ = 1L, maxit = 100, verbose = 0, control = list())

Arguments

`rho`	an environment containing `pp`, a prediction module, typically of class `merPredD` and `resp`, a response module, e.g., of class `lmerResp`.
`nAGQ`	scalar integer - the number of adaptive Gauss-Hermite quadrature points. A value of 0 indicates that both the fixed-effects parameters and the random effects are optimized by the iteratively reweighted least squares algorithm.
`maxit`	scalar integer, currently only for GLMMs: the maximal number of Pwrss update iterations.
`verbose`	scalar logical: print verbose output?
`control`	list of control parameters, a subset of those specified by `lmerControl` (`tolPwrss` and `compDev` for GLMMs, `tolPwrss` for NLMMs)

Details

The function returned by mkdevfun evaluates the deviance of the model represented by the predictor module, pp, and the response module, resp.

For lmer model objects the argument of the resulting function is the variance component parameter, theta, with lower bound. For glmer or nlmer model objects with nAGQ = 0 the argument is also theta. However, when nAGQ > 0, the argument is c(theta, beta).

Value

A function of one numeric argument.

Examples

(dd <- lmer(Yield ~ 1|Batch, Dyestuff, devFunOnly=TRUE))
dd(0.8)
minqa::bobyqa(1, dd, 0)

[Package lme4 version 1.1-13 Index]