[Lme4-commits] r1664 - in pkg/lme4: R man tests

[noreply at r-forge.r-project.org]

Author: bbolker
Date: 2012-03-16 14:48:48 +0100 (Fri, 16 Mar 2012)
New Revision: 1664

Added:
   pkg/lme4/tests/resids.R
Modified:
   pkg/lme4/R/lmer.R
   pkg/lme4/R/profile.R
   pkg/lme4/man/mcmcsamp.Rd
   pkg/lme4/man/profile-methods.Rd
Log:

  do-roxy based updates (missing arguments, although still incomplete)
  fix/test residuals, especially Pearson resids from GLMMs
 


Modified: pkg/lme4/R/lmer.R
===================================================================
--- pkg/lme4/R/lmer.R	2012-03-15 21:23:34 UTC (rev 1663)
+++ pkg/lme4/R/lmer.R	2012-03-16 13:48:48 UTC (rev 1664)
@@ -1131,6 +1131,7 @@
            partial = .NotYetImplemented())
 }
 
+## FIXME: document somewhere that residuals(glmerfit) returns deviance residuals by default
 ##' @S3method residuals glmResp
 residuals.glmResp <- function(object, type = c("deviance", "pearson",
                                       "working", "response", "partial"),
@@ -1144,7 +1145,7 @@
                d.res <- sqrt(object$devResid())
                ifelse(y > mu, d.res, -d.res)
            },
-           pearson = object$wtres(),
+           pearson = object$wtres,
            working = object$wrkResids(),
            response = y - mu,
            partial = .NotYetImplemented())

Modified: pkg/lme4/R/profile.R
===================================================================
--- pkg/lme4/R/profile.R	2012-03-15 21:23:34 UTC (rev 1663)
+++ pkg/lme4/R/profile.R	2012-03-16 13:48:48 UTC (rev 1664)
@@ -13,6 +13,8 @@
 ##' @param delta stepping scale for deciding on next point to profile
 ##' @param verbose level of output from internal calculations
 ##' @param devtol tolerance for fitted deviances less than baseline (supposedly minimum) deviance
+##' @param startval method for picking starting conditions for optimization (STUB)
+##' @param optimizer (character or function) optimizer to use (see \code{\link{lmer}} for details)
 ##' @param \dots potential further arguments for \code{profile} methods.
 ##' @section Methods: FIXME: These (signatures) will change soon --- document
 ##' \bold{after} change!

Modified: pkg/lme4/man/mcmcsamp.Rd
===================================================================
--- pkg/lme4/man/mcmcsamp.Rd	2012-03-15 21:23:34 UTC (rev 1663)
+++ pkg/lme4/man/mcmcsamp.Rd	2012-03-16 13:48:48 UTC (rev 1664)
@@ -1,18 +1,18 @@
 \name{mcmcsamp}
 \alias{mcmcsamp}
-\title{Create an McMC sample}
+\title{Create an MCMC sample}
 \usage{
   mcmcsamp(object, n = 1L, verbose = FALSE, ...)
 }
 \arguments{
   \item{object}{a fitted model object}
 
-  \item{n}{number of samples to generate.  Defaults to 1
-  but that value doesn't make sense.}
+  \item{n}{number of samples to generate.  Defaults to 1;
+  for real use values of 200-1000 are more typical}
 
   \item{verbose}{should verbose output be given?}
 
-  \item{...}{}
+  \item{...}{additional, optional arguments (not used)}
 }
 \value{
   a Markov chain Monte Carlo sample as a matrix

Modified: pkg/lme4/man/profile-methods.Rd
===================================================================
--- pkg/lme4/man/profile-methods.Rd	2012-03-15 21:23:34 UTC (rev 1663)
+++ pkg/lme4/man/profile-methods.Rd	2012-03-16 13:48:48 UTC (rev 1664)
@@ -30,6 +30,12 @@
   \item{devtol}{tolerance for fitted deviances less than
   baseline (supposedly minimum) deviance}
 
+  \item{startval}{method for picking starting conditions
+  for optimization (STUB)}
+
+  \item{optimizer}{(character or function) optimizer to use
+  (see \code{\link{lmer}} for details)}
+
   \item{\dots}{potential further arguments for
   \code{profile} methods.}
 }

Added: pkg/lme4/tests/resids.R
===================================================================
--- pkg/lme4/tests/resids.R	                        (rev 0)
+++ pkg/lme4/tests/resids.R	2012-03-16 13:48:48 UTC (rev 1664)
@@ -0,0 +1,18 @@
+library(lme4)
+
+## raw residuals for LMMs
+fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
+stopifnot(all.equal(residuals(fm1),sleepstudy$Reaction-fitted(fm1)))
+
+r1 <- residuals(fm1,type="pearson")
+
+## deviance/Pearson residuals for GLMMs
+gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
+             family = binomial, data = cbpp)
+p <- fitted(gm1)
+n <- cbpp$size
+v <- n*p*(1-p)
+obs_p <- cbpp$incidence/cbpp$size
+stopifnot(all.equal(residuals(gm1,"pearson"),(obs_p-p)/sqrt(p*(1-p))*n))
+
+r2 <- residuals(gm1,type="deviance")