devfun2 | R Documentation |
The deviance is profiled with respect to the fixed-effects parameters but not with respect to sigma; that is, the function takes parameters for the variance-covariance parameters and for the residual standard deviation. The random-effects variance-covariance parameters are on the standard deviation/correlation scale, not the theta (Cholesky factor) scale.
devfun2(fm, useSc = if(isLMM(fm)) TRUE else NA,
transfuns = list(from.chol = Cv_to_Sv,
to.chol = Sv_to_Cv,
to.sd = identity), ...)
fm |
a fitted model inheriting from class |
useSc |
( |
transfuns |
a |
... |
arguments passed to the internal |
Returns a function that takes a vector of standard deviations and correlations and returns the deviance (or REML criterion). The function has additional attributes
a named vector giving the parameter values at the optimum
the deviance at the optimum, (i.e., not the REML criterion).
the optimal variance-covariance parameters on the “theta” (Cholesky factor) scale
standard errors of fixed effect parameters
Even if the original model was fitted using REML=TRUE
as by default
with lmer()
, this returns the deviance, i.e., the objective
function for maximum (log) likelihood (ML).
For the REML objective function, use getME(fm, "devfun")
instead.
m1 <- lmer(Reaction~Days+(Days|Subject),sleepstudy)
dd <- devfun2(m1, useSc=TRUE)
pp <- attr(dd,"optimum")
## extract variance-covariance and residual std dev parameters
sigpars <- pp[grepl("^\\.sig",names(pp))]
all.equal(unname(dd(sigpars)),deviance(refitML(m1)))
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