[Lme4-commits] r1746 - in pkg/lme4: R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Wed May 16 23:38:20 CEST 2012
Author: dmbates
Date: 2012-05-16 23:38:20 +0200 (Wed, 16 May 2012)
New Revision: 1746
Modified:
pkg/lme4/R/lmer.R
pkg/lme4/R/profile.R
pkg/lme4/man/glmer.Rd
pkg/lme4/man/profile-methods.Rd
pkg/lme4/man/refit.Rd
Log:
Patches on some help files through the roxygen sources.
Modified: pkg/lme4/R/lmer.R
===================================================================
--- pkg/lme4/R/lmer.R 2012-05-16 21:37:42 UTC (rev 1745)
+++ pkg/lme4/R/lmer.R 2012-05-16 21:38:20 UTC (rev 1746)
@@ -242,19 +242,22 @@
##' @examples
##' ## generalized linear mixed model
##' library(lattice)
-##' xyplot(incidence/size ~ period, group=herd, type="a", data=cbpp)
+##' xyplot(incidence/size ~ period|herd, cbpp, type=c('g','p','l'),
+##' layout=c(3,5), index.cond = function(x,y)max(y))
##' (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
##' data = cbpp, family = binomial))
##' ## using nAGQ=0 only gets close to the optimum
##' (gm1a <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
##' cbpp, binomial, nAGQ = 0))
-##' if(FALSE) { ##________ FIXME _______
##' ## using nAGQ = 9 provides a better evaluation of the deviance
+##' ## Currently the internal calculations use the sum of deviance residuals,
+##' ## which is not directly comparable with the nAGQ=0 or nAGQ=1 result.
##' (gm1a <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
##' cbpp, binomial, nAGQ = 9))
-##' }#__ end{FIXME} __
-##'
+##'
##' ## GLMM with individual-level variability (accounting for overdispersion)
+##' ## For this data set the model is the same as one allowing for a period:herd
+##' ## interaction, which the plot indicates could be needed.
##' cbpp$obs <- 1:nrow(cbpp)
##' (gm2 <- glmer(cbind(incidence, size - incidence) ~ period +
##' (1 | herd) + (1|obs),
@@ -458,6 +461,20 @@
mkMerMod(environment(devfun), opt, vals$reTrms, vals$frame, mc)
}## {nlmer}
+## global variables defs to make codetools/R CMD check happier.
+## FIXME: does putting globalVariables() stuff here interfere with Roxygen?
+## ?globalVariables says that fields and methods in reference classes are
+## "handled automatically by ‘setRefClass()’ and friends, using the
+## supplied field and method names" -- perhaps there's a better way to do this?
+if (getRversion()<="2.15.0") {
+ ## dummy
+ globalVariables <- function(...) {}
+}
+globalVariables(c("pp","resp","lp0","pwrssUpdate","compDev",
+ "baseOffset","GQmat","fac","nlmerAGQ","tolPwrss",
+ "dpars","verbose"),
+ package="lme4")
+
##' Create a deviance evaluation function from a predictor and a response module
##'
##' From an merMod object create an R function that takes a single argument,
@@ -488,20 +505,6 @@
##' (dd <- lmer(Yield ~ 1|Batch, Dyestuff, devFunOnly=TRUE))
##' dd(0.8)
##' minqa::bobyqa(1, dd, 0)
-
-## global variables defs to make codetools/R CMD check happier.
-## FIXME: does putting globalVariables() stuff here interfere with Roxygen?
-## ?globalVariables says that fields and methods in reference classes are
-## "handled automatically by ‘setRefClass()’ and friends, using the
-## supplied field and method names" -- perhaps there's a better way to do this?
-if (getRversion()<="2.15.0") {
- ## dummy
- globalVariables <- function(...) {}
-}
-globalVariables(c("pp","resp","lp0","pwrssUpdate","compDev",
- "baseOffset","GQmat","fac","nlmerAGQ","tolPwrss",
- "dpars","verbose"),
- package="lme4")
mkdevfun <- function(rho, nAGQ=1L) {
## FIXME: should nAGQ be automatically embedded in rho?
stopifnot(is.environment(rho), is(rho$resp, "lmResp"))
@@ -917,8 +920,7 @@
}
##' @importFrom stats formula
-##' @S3method fixef merMod
-##' @export
+##' @S3method formula merMod
formula.merMod <- function(x, fixed.only=FALSE, ...) {
form <- formula(getCall(x),...)
if (fixed.only) {
Modified: pkg/lme4/R/profile.R
===================================================================
--- pkg/lme4/R/profile.R 2012-05-16 21:37:42 UTC (rev 1745)
+++ pkg/lme4/R/profile.R 2012-05-16 21:38:20 UTC (rev 1746)
@@ -27,10 +27,12 @@
##' @examples
##' fm01ML <- lmer(Yield ~ 1|Batch, Dyestuff, REML = FALSE)
##' ## 0.8s (on a 5600 MIPS 64bit fast(year 2009) desktop "AMD Phenom(tm) II X4 925"):
-##' system.time( tpr <- profile(fm01ML) )
+##' ## This is slower because of the Nelder-Mead optimizer but using bobyqa, the default,
+##' ## produces a warning.
+##' system.time( tpr <- profile(fm01ML, optimizer="Nelder_Mead") )
##' (confint(tpr) -> CIpr)
##' print(xyplot(tpr))
-##' tpr2 <- profile(fm01ML, which=1:2) ## Batch and residual variance only
+##' tpr2 <- profile(fm01ML, which=1:2, optimizer="Nelder_Mead") ## Batch and residual variance only
##' ## GLMM example (running time ~8 seconds on a modern machine)
##' \dontrun{
##' gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
@@ -783,13 +785,14 @@
x
}
-##' Create an approximating density from a profile object
-##'
-##' @title Approximate densities from profiles
-##' @param pr a profile object
-##' @param npts number of points at which to evaluate the density
-##' @param upper upper bound on cumulative for a cutoff
-##' @return a data frame
+## Create an approximating density from a profile object
+##
+## @title Approximate densities from profiles
+## @param pr a profile object
+## @param npts number of points at which to evaluate the density
+## @param upper upper bound on cumulative for a cutoff
+## @return a data frame
+## @export
dens <- function(pr, npts=201, upper=0.999) {
stopifnot(inherits(pr, "thpr"))
npts <- as.integer(npts)
Modified: pkg/lme4/man/glmer.Rd
===================================================================
--- pkg/lme4/man/glmer.Rd 2012-05-16 21:37:42 UTC (rev 1745)
+++ pkg/lme4/man/glmer.Rd 2012-05-16 21:38:20 UTC (rev 1746)
@@ -199,19 +199,22 @@
\examples{
## generalized linear mixed model
library(lattice)
-xyplot(incidence/size ~ period, group=herd, type="a", data=cbpp)
+xyplot(incidence/size ~ period|herd, cbpp, type=c('g','p','l'),
+ layout=c(3,5), index.cond = function(x,y)max(y))
(gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
data = cbpp, family = binomial))
## using nAGQ=0 only gets close to the optimum
(gm1a <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
cbpp, binomial, nAGQ = 0))
-if(FALSE) { ##________ FIXME _______
## using nAGQ = 9 provides a better evaluation of the deviance
+## Currently the internal calculations use the sum of deviance residuals,
+## which is not directly comparable with the nAGQ=0 or nAGQ=1 result.
(gm1a <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
cbpp, binomial, nAGQ = 9))
-}#__ end{FIXME} __
## GLMM with individual-level variability (accounting for overdispersion)
+## For this data set the model is the same as one allowing for a period:herd
+## interaction, which the plot indicates could be needed.
cbpp$obs <- 1:nrow(cbpp)
(gm2 <- glmer(cbind(incidence, size - incidence) ~ period +
(1 | herd) + (1|obs),
Modified: pkg/lme4/man/profile-methods.Rd
===================================================================
--- pkg/lme4/man/profile-methods.Rd 2012-05-16 21:37:42 UTC (rev 1745)
+++ pkg/lme4/man/profile-methods.Rd 2012-05-16 21:38:20 UTC (rev 1746)
@@ -2,7 +2,6 @@
\name{profile-methods}
\alias{profile.merMod}
\alias{profile-methods}
-\alias{as.data.frame.thpr}
\title{Methods for profile() of [ng]lmer fitted models}
\usage{
\method{profile}{merMod} (fitted, which = 1:nptot,
@@ -63,10 +62,12 @@
\examples{
fm01ML <- lmer(Yield ~ 1|Batch, Dyestuff, REML = FALSE)
## 0.8s (on a 5600 MIPS 64bit fast(year 2009) desktop "AMD Phenom(tm) II X4 925"):
-system.time( tpr <- profile(fm01ML) )
+## This is slower because of the Nelder-Mead optimizer but using bobyqa, the default,
+## produces a warning.
+system.time( tpr <- profile(fm01ML, optimizer="Nelder_Mead") )
(confint(tpr) -> CIpr)
print(xyplot(tpr))
-tpr2 <- profile(fm01ML, which=1:2) ## Batch and residual variance only
+tpr2 <- profile(fm01ML, which=1:2, optimizer="Nelder_Mead") ## Batch and residual variance only
## GLMM example (running time ~8 seconds on a modern machine)
\dontrun{
gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
Modified: pkg/lme4/man/refit.Rd
===================================================================
--- pkg/lme4/man/refit.Rd 2012-05-16 21:37:42 UTC (rev 1745)
+++ pkg/lme4/man/refit.Rd 2012-05-16 21:38:20 UTC (rev 1746)
@@ -29,14 +29,4 @@
creation of the model representation and goes directly to
the optimization step.
}
-\examples{
- ## using refit() to fit each column in a matrix of responses
- set.seed(101)
- Y <- matrix(rnorm(1000),ncol=10)
- res <- list()
- d <- data.frame(y=Y[,1],x=rnorm(100),f=rep(1:10,10))
- fit1 <- lmer(y~x+(1|f),data=d)
- res <- c(fit1,lapply(as.data.frame(Y[,-1]),
- refit,object=fit1))
-}
-
\ No newline at end of file
+
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