[Lme4-commits] r1599 - in pkg/lme4Eigen: . R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Mon Feb 13 18:47:32 CET 2012
Author: dmbates
Date: 2012-02-13 18:47:32 +0100 (Mon, 13 Feb 2012)
New Revision: 1599
Removed:
pkg/lme4Eigen/R/lme4Eigen-package.R
Modified:
pkg/lme4Eigen/DESCRIPTION
pkg/lme4Eigen/R/AllClass.R
pkg/lme4Eigen/man/merMod-class.Rd
Log:
Leave data set documentation in the man directory only. Change aliases in documentation to S3 methods where appropriate.
Modified: pkg/lme4Eigen/DESCRIPTION
===================================================================
--- pkg/lme4Eigen/DESCRIPTION 2012-02-10 23:41:09 UTC (rev 1598)
+++ pkg/lme4Eigen/DESCRIPTION 2012-02-13 17:47:32 UTC (rev 1599)
@@ -40,7 +40,6 @@
'AllGeneric.R'
'GHrule.R'
'jml.R'
- 'lme4Eigen-package.R'
'lmer.R'
'lmList.R'
'mlirt.R'
Modified: pkg/lme4Eigen/R/AllClass.R
===================================================================
--- pkg/lme4Eigen/R/AllClass.R 2012-02-10 23:41:09 UTC (rev 1598)
+++ pkg/lme4Eigen/R/AllClass.R 2012-02-13 17:47:32 UTC (rev 1599)
@@ -849,13 +849,13 @@
##'
##' @name merMod-class
##' @aliases merMod-class lmerMod-class glmerMod-class nlmerMod-class
-##' anova,merMod-method coef,merMod-method deviance,merMod-method
-##' fitted,merMod-method formula,merMod-method logLik,merMod-method
-##' model.frame,merMod-method model.matrix,merMod-method print,merMod-method
-##' show,merMod-method simulate,merMod-method summary,merMod-method
-##' terms,merMod-method update,merMod-method VarCorr,merMod-method
-##' vcov,merMod-method print,summary.mer-method show,summary.mer-method
-##' summary,summary.mer-method vcov,summary.mer-method
+##' anova.merMod coef.merMod deviance.merMod
+##' fitted.merMod formula.merMod logLik.merMod
+##' model.frame.merMod model.matrix.merMod print.merMod
+##' show.merMod simulate.merMod summary.merMod
+##' terms.merMod update.merMod VarCorr.merMod
+##' vcov.merMod print.summary.mer show.summary.mer
+##' summary.summary.mer vcov.summary.mer
##' @docType class
##' @section Objects from the Class: Objects are created by calls to
##' \code{\link{lmer}}, \code{\link{glmer}} or \code{\link{nlmer}}.
Deleted: pkg/lme4Eigen/R/lme4Eigen-package.R
===================================================================
--- pkg/lme4Eigen/R/lme4Eigen-package.R 2012-02-10 23:41:09 UTC (rev 1598)
+++ pkg/lme4Eigen/R/lme4Eigen-package.R 2012-02-13 17:47:32 UTC (rev 1599)
@@ -1,478 +0,0 @@
-### roxygen2 documentation for data sets in the package
-
-##' Breakage angle of chocolate cakes
-##'
-##' Data on the breakage angle of chocolate cakes made with three different
-##' recipes and baked at six different temperatures. This is a split-plot
-##' design with the recipes being whole-units and the different temperatures
-##' being applied to sub-units (within replicates). The experimental notes
-##' suggest that the replicate numbering represents temporal ordering.
-##'
-##' The \code{replicate} factor is nested within the \code{recipe} factor, and
-##' \code{temperature} is nested within \code{replicate}.
-##'
-##' @name cake
-##' @docType data
-##' @format A data frame with 270 observations on the following 5 variables.
-##' \describe{
-##' \item{\code{replicate}}{a factor with levels \code{1} to \code{15}}
-##' \item{\code{recipe}}{a factor with levels \code{A}, \code{B} and \code{C}}
-##' \item{\code{temperature}}{an ordered factor with levels \code{175}
-##' < \code{185} < \code{195} < \code{205} < \code{215} < \code{225}}
-##' \item{\code{angle}}{a numeric vector giving the angle at which the
-##' cake broke.}
-##' \item{\code{temp}}{numeric value of the baking temperature (degrees F).}
-##' }
-##' @references Cook, F. E. (1938) \emph{Chocolate cake, I. Optimum baking
-##' temperature}. Master's Thesis, Iowa State College.
-##'
-##' Cochran, W. G., and Cox, G. M. (1957) \emph{Experimental designs}, 2nd Ed.
-##' New York, John Wiley \& Sons.
-##'
-##' Lee, Y., Nelder, J. A., and Pawitan, Y. (2006) \emph{Generalized linear
-##' models with random effects. Unified analysis via H-likelihood}. Boca Raton,
-##' Chapman and Hall/CRC.
-##' @source Original data were presented in Cook (1938), and reported in Cochran
-##' and Cox (1957, p. 300). Also cited in Lee, Nelder and Pawitan (2006).
-##' @keywords datasets
-##' @examples
-##' str(cake)
-##' ## 'temp' is continuous, 'temperature' an ordered factor with 6 levels
-##'
-##' fm1 <- lmer(angle ~ recipe * temperature + (1|recipe:replicate), cake, REML= FALSE)
-##' print(fm1, corr=FALSE)
-##' fm2 <- lmer(angle ~ recipe + temperature + (1|recipe:replicate), cake, REML= FALSE)
-##' print(fm2, corr=FALSE)
-##' fm3 <- lmer(angle ~ recipe + temp + (1|recipe:replicate), cake, REML= FALSE)
-##' fm3
-##'
-##' ## and now "choose" :
-##' anova(fm3, fm2, fm1)
-##'
-NULL
-
-##' Contagious bovine pleuropneumonia
-##'
-##' Contagious bovine pleuropneumonia (CBPP) is a major disease of cattle in
-##' Africa, caused by a mycoplasma. This dataset describes the serological
-##' incidence of CBPP in zebu cattle during a follow-up survey implemented in 15
-##' commercial herds located in the Boji district of Ethiopia. The goal of the
-##' survey was to study the within-herd spread of CBPP in newly infected herds.
-##' Blood samples were quarterly collected from all animals of these herds to
-##' determine their CBPP status. These data were used to compute the
-##' serological incidence of CBPP (new cases occurring during a given time
-##' period). Some data are missing (lost to follow-up).
-##'
-##' Serological status was determined using a competitive enzyme-linked
-##' immuno-sorbent assay (cELISA).
-##'
-##' @name cbpp
-##' @docType data
-##' @format A data frame with 56 observations on the following 4 variables.
-##' \describe{
-##' \item{\code{herd}}{A factor identifying the herd (1 to 15).}
-##' \item{\code{incidence}}{The number of new serological cases for a
-##' given herd and time period.}
-##' \item{\code{size}}{A numeric vector describing herd size at the
-##' beginning of a given time period.}
-##' \item{\code{period}}{A factor with levels \code{1} to \code{4}.}
-##' }
-##' @source Lesnoff, M., Laval, G., Bonnet, P., Abdicho, S., Workalemahu, A.,
-##' Kifle, D., Peyraud, A., Lancelot, R., Thiaucourt, F. (2004) Within-herd
-##' spread of contagious bovine pleuropneumonia in Ethiopian highlands.
-##' \emph{Preventive Veterinary Medicine} \bold{64}, 27--40.
-##' @keywords datasets
-##' @examples
-##'
-##' ## response as a matrix
-##' (m1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
-##' cbpp, binomial, nAGQ=25L))
-##' ## response as a vector of probabilities and usage of argument "weights"
-##' m1p <- glmer(incidence / size ~ period + (1 | herd), weights = size,
-##' cbpp, binomial, nAGQ=25L)
-##' ## Confirm that these are equivalent:
-##' stopifnot(all.equal(fixef(m1), fixef(m1p), tol = 1e-5),
-##' all.equal(ranef(m1), ranef(m1p), tol = 1e-5),
-##' TRUE)
-##' ## Can this section be moved to a test file? I don't think it belongs in an example. DB
-##' for(m in c(m1, m1p)) {
-##' cat("-------\n\nCall: ",
-##' paste(format(getCall(m)), collapse="\n"), "\n")
-##' print(logLik(m)); cat("AIC:", AIC(m), "\n") ; cat("BIC:", BIC(m),"\n")
-##' }
-##' stopifnot(all.equal(logLik(m1), logLik(m1p), tol = 1e-5),
-##' all.equal(AIC(m1), AIC(m1p), tol = 1e-5),
-##' all.equal(BIC(m1), BIC(m1p), tol = 1e-5))
-##'
-##' ## GLMM with individual-level variability (accounting for overdispersion)
-##' cbpp$obs <- 1:nrow(cbpp)
-##' (m2 <- glmer(cbind(incidence, size - incidence) ~ period +
-##' (1 | herd) + (1|obs),
-##' family = binomial, data = cbpp))
-##'
-##'
-NULL
-
-##' Yield of dyestuff by batch
-##'
-##' The \code{Dyestuff} data frame provides the yield of dyestuff (Naphthalene
-##' Black 12B) from 5 different preparations from each of 6 different batchs of
-##' an intermediate product (H-acid). The \code{Dyestuff2} data were generated
-##' data in the same structure but with a large residual variance relative to
-##' the batch variance.
-##'
-##' The \code{Dyestuff} data are described in Davies and Goldsmith (1972) as
-##' coming from \dQuote{an investigation to find out how much the variation from
-##' batch to batch in the quality of an intermediate product (H-acid)
-##' contributes to the variation in the yield of the dyestuff (Naphthalene Black
-##' 12B) made from it. In the experiment six samples of the intermediate,
-##' representing different batches of works manufacture, were obtained, and five
-##' preparations of the dyestuff were made in the laboratory from each sample.
-##' The equivalent yield of each preparation as grams of standard colour was
-##' determined by dye-trial.}
-##'
-##' The \code{Dyestuff2} data are described in Box and Tiao (1973) as
-##' illustrating \dQuote{ the case where between-batches mean square is less
-##' than the within-batches mean square. These data had to be constructed for
-##' although examples of this sort undoubtably occur in practice, they seem to
-##' be rarely published.}
-##'
-##' @name Dyestuff
-##' @aliases Dyestuff Dyestuff2
-##' @docType data
-##' @format Data frames, each with 30 observations on the following 2 variables.
-##' \describe{
-##' \item{\code{Batch}}{a factor indicating the batch of the
-##' intermediate product from which the preparation was created.}
-##' \item{\code{Yield}}{the yield of dyestuff from the preparation
-##' (grams of standard color).}
-##' }
-##' @source O.L. Davies and P.L. Goldsmith (eds), \emph{Statistical Methods in
-##' Research and Production, 4th ed.}, Oliver and Boyd, (1972), section 6.4
-##'
-##' G.E.P. Box and G.C. Tiao, \emph{Bayesian Inference in Statistical Analysis},
-##' Addison-Wesley, (1973), section 5.1.2
-##' @keywords datasets
-##' @examples
-##'
-##' \dontshow{ # useful for the lme4-authors --- development, debugging, etc:
-##' commandArgs()[-1]
-##' if(FALSE) ## R environment variables:
-##' local({ ne <- names(e <- Sys.getenv())
-##' list(R = e[grep("^R", ne)],
-##' "_R" = e[grep("^_R",ne)]) })
-##' Sys.getenv("R_ENVIRON")
-##' Sys.getenv("R_PROFILE")
-##' cat("R_LIBS:\n"); (RL <- strsplit(Sys.getenv("R_LIBS"), ":")[[1]])
-##' nRL <- normalizePath(RL)
-##' cat("and extra .libPaths():\n")
-##' .libPaths()[is.na(match(.libPaths(), nRL))]
-##'
-##' sessionInfo()
-##' pkgI <- function(pkgname) {
-##' pd <- packageDescription(pkgname)
-##' cat(sprintf("%s -- built: %s\n%*s -- dir : %s\n",
-##' pkgname, pd$Built, nchar(pkgname), "",
-##' dirname(dirname(attr(pd, "file")))))
-##' }
-##' pkgI("Matrix")
-##' pkgI("Rcpp")
-##' pkgI("RcppEigen")
-##' pkgI("minqa")
-##' pkgI("lme4Eigen")
-##' }
-##' str(Dyestuff)
-##' dotplot(reorder(Batch, Yield) ~ Yield, Dyestuff,
-##' ylab = "Batch", jitter.y = TRUE, aspect = 0.3,
-##' type = c("p", "a"))
-##' dotplot(reorder(Batch, Yield) ~ Yield, Dyestuff2,
-##' ylab = "Batch", jitter.y = TRUE, aspect = 0.3,
-##' type = c("p", "a"))
-##' (fm1 <- lmer(Yield ~ 1|Batch, Dyestuff))
-##' (fm2 <- lmer(Yield ~ 1|Batch, Dyestuff2))
-##'
-NULL
-
-##' Sparse Gauss-Hermite quadrature grids
-##'
-##' \code{GQN} contains the non-redundant quadrature nodes and weights for
-##' integration of a scalar function of a \code{d}-dimensional argument with
-##' respect to the density function of the \code{d}-dimensional Gaussian
-##' density function. These are stored in a list of lists. The outer list
-##' is indexed by the dimension, \code{d}, in the range of 1 to 20. The inner
-##' list is indexed by \code{k}, the order of the quadrature.
-##'
-##' @note These are only the non-redundant nodes. To regenerate the whole
-##' array of nodes, all possible permutations of axes and all possible
-##' combinations of \eqn{\pm 1}{+/- 1} must be applied to the axes.
-##' The function \code{\link{GQdk}} reproduces the entire array of nodes.
-##' @seealso \code{\link{GQdk}}
-##' @name GQN
-##' @docType data
-##' @format A list of lists.
-##' @examples
-##' GQN[[3]][[5]]
-##'
-NULL
-
-##' University Lecture/Instructor Evaluations by Students at ETH
-##'
-##' University lecture evaluations by students at ETH Zurich, anonymized for
-##' privacy protection. This is an interesting \dQuote{medium} sized example of
-##' a \emph{partially} nested mixed effect model.
-##'
-##' The main goal of the survey is to find \dQuote{the best liked prof},
-##' according to the lectures given. Statistical analysis of such data has been
-##' the basis for a (student) jury selecting the final winners.
-##'
-##' The present data set has been anonymized and slightly simplified on purpose.
-##'
-##' @name InstEval
-##' @docType data
-##' @format A data frame with 73421 observations on the following 7 variables.
-##' \describe{
-##' \item{\code{s}}{a factor with levels \code{1:2972} denoting
-##' individual students.}
-##' \item{\code{d}}{a factor with 1128 levels from \code{1:2160}, denoting
-##' individual professors or lecturers.}% ("d": \dQuote{Dozierende} in German)
-##' \item{\code{studage}}{an ordered factor with levels \code{2} <
-##' \code{4} < \code{6} < \code{8}, denoting student's \dQuote{age}
-##' measured in the \emph{semester} number the student has been enrolled.}
-##' \item{\code{lectage}}{an ordered factor with 6 levels, \code{1} <
-##' \code{2} < ... < \code{6}, measuring how many semesters back the
-##' lecture rated had taken place.}
-##' \item{\code{service}}{a binary factor with levels \code{0} and
-##' \code{1}; a lecture is a \dQuote{service}, if held for a
-##' different department than the lecturer's main one.}
-##' \item{\code{dept}}{a factor with 14 levels from \code{1:15}, using a
-##' random code for the department of the lecture.}
-##'
-##' \item{\code{y}}{a numeric vector of \emph{ratings} of lectures by
-##' the students, using the discrete scale \code{1:5}, with meanings
-##' of \sQuote{poor} to \sQuote{very good}.}
-##' }
-##' Each observation is one student's rating for a specific lecture
-##' (of one lecturer, during one semester in the past).
-##' @keywords datasets
-##' @examples
-##'
-##' str(InstEval)
-##'
-##' head(InstEval, 16)
-##' xtabs(~ service + dept, InstEval)
-##'
-NULL
-
-##' Paste strength by batch and cask
-##'
-##' Strength of a chemical paste product; its quality depending on the delivery
-##' batch, and the cask within the delivery.
-##'
-##' The data are described in Davies and Goldsmith (1972) as coming from
-##' \dQuote{ deliveries of a chemical paste product contained in casks where, in
-##' addition to sampling and testing errors, there are variations in quality
-##' between deliveries \dots{} As a routine, three casks selected at random from
-##' each delivery were sampled and the samples were kept for reference. \dots{}
-##' Ten of the delivery batches were sampled at random and two analytical tests
-##' carried out on each of the 30 samples}.
-##'
-##' @name Pastes
-##' @docType data
-##' @format A data frame with 60 observations on the following 4 variables.
-##' \describe{
-##' \item{\code{strength}}{paste strength.}
-##' \item{\code{batch}}{delivery batch from which the sample was
-##' sample. A factor with 10 levels: \sQuote{A} to \sQuote{J}.}
-##' \item{\code{cask}}{cask within the delivery batch from which the
-##' sample was chosen. A factor with 3 levels: \sQuote{a} to
-##' \sQuote{c}.}
-##' \item{\code{sample}}{the sample of paste whose strength was assayed,
-##' two assays per sample. A factor with 30 levels: \sQuote{A:a} to
-##' \sQuote{J:c}.}
-##' }
-##' @source O.L. Davies and P.L. Goldsmith (eds), \emph{Statistical Methods in
-##' Research and Production, 4th ed.}, Oliver and Boyd, (1972), section 6.5
-##' @keywords datasets
-##' @examples
-##' str(Pastes)
-##' dotplot(cask ~ strength | reorder(batch, strength), Pastes,
-##' strip = FALSE, strip.left = TRUE, layout = c(1, 10),
-##' ylab = "Cask within batch",
-##' xlab = "Paste strength", jitter.y = TRUE)
-##' ## Modifying the factors to enhance the plot
-##' Pastes <- within(Pastes, batch <- reorder(batch, strength))
-##' Pastes <- within(Pastes, sample <- reorder(reorder(sample, strength),
-##' as.numeric(batch)))
-##' dotplot(sample ~ strength | batch, Pastes,
-##' strip = FALSE, strip.left = TRUE, layout = c(1, 10),
-##' scales = list(y = list(relation = "free")),
-##' ylab = "Sample within batch",
-##' xlab = "Paste strength", jitter.y = TRUE)
-##' ## Four equivalent models differing only in specification
-##' (fm1 <- lmer(strength ~ (1|batch) + (1|sample), Pastes))
-##' (fm2 <- lmer(strength ~ (1|batch/cask), Pastes))
-##' (fm3 <- lmer(strength ~ (1|batch) + (1|batch:cask), Pastes))
-##' (fm4 <- lmer(strength ~ (1|batch/sample), Pastes))
-##' ## fm4 results in redundant labels on the sample:batch interaction
-##' head(ranef(fm4)[[1]])
-##' ## compare to fm1
-##' head(ranef(fm1)[[1]])
-##' ## This model is different and NOT appropriate for these data
-##' (fm5 <- lmer(strength ~ (1|batch) + (1|cask), Pastes))
-##'
-##' L <- getME(fm1, "L")
-##' Matrix::image(L, sub = "Structure of random effects interaction in pastes model")
-##'
-NULL
-
-##' Variation in penicillin testing
-##'
-##' Six samples of penicillin were tested using the \emph{B. subtilis} plate
-##' method on each of 24 plates. The response is the diameter (mm) of the zone
-##' of inhibition of growth of the organism.
-##'
-##' The data are described in Davies and Goldsmith (1972) as coming from an
-##' investigation to \dQuote{assess the variability between samples of
-##' penicillin by the \emph{B. subtilis} method. I this test method a
-##' bulk-innoculated nutrient agar medium is poured into a Petri dish of
-##' approximately 90 mm. diameter, known as a plate. When the medium has set,
-##' six small hollow cylinders or pots (about 4 mm. in diameter) are cemented
-##' onto the surface at equally spaced intervals. A few drops of the penicillin
-##' solutions to be compared are placed in the respective cylinders, and the
-##' whole plate is placed in an incubator for a given time. Penicillin diffuses
-##' from the pots into the agar, and this produces a clear circular zone of
-##' inhibition of growth of the organisms, which can be readily measured. The
-##' diameter of the zone is related in a known way to the concentration of
-##' penicillin in the solution.}
-##'
-##' @name Penicillin
-##' @docType data
-##' @format A data frame with 144 observations on the following 3 variables.
-##' \describe{
-##' \item{\code{diameter}}{diameter (mm) of the zone of inhibition of
-##' the growth of the organism.}
-##' \item{\code{plate}}{assay plate. A factor with levels \sQuote{a} to
-##' \sQuote{x}.}
-##' \item{\code{sample}}{penicillin sample. A factor with levels
-##' \sQuote{A} to \sQuote{F}.}
-##' }
-##' @source O.L. Davies and P.L. Goldsmith (eds), \emph{Statistical Methods in
-##' Research and Production, 4th ed.}, Oliver and Boyd, (1972), section 6.6
-##' @keywords datasets
-##' @examples
-##'
-##' str(Penicillin)
-##' dotplot(reorder(plate, diameter) ~ diameter, Penicillin, groups = sample,
-##' ylab = "Plate", xlab = "Diameter of growth inhibition zone (mm)",
-##' type = c("p", "a"), auto.key = list(columns = 3, lines = TRUE,
-##' title = "Penicillin sample"))
-##' (fm1 <- lmer(diameter ~ (1|plate) + (1|sample), Penicillin))
-##'
-##' L <- getME(fm1, "L")
-##' Matrix::image(L, main = "L",
-##' sub = "Penicillin: Structure of random effects interaction")
-##'
-NULL
-
-##' Reaction times in a sleep deprivation study
-##'
-##' The average reaction time per day for subjects in a sleep deprivation study.
-##' On day 0 the subjects had their normal amount of sleep. Starting that night
-##' they were restricted to 3 hours of sleep per night. The observations
-##' represent the average reaction time on a series of tests given each day to
-##' each subject.
-##'
-##' These data are from the study described in Belenky et al. (2003), for the
-##' sleep-deprived group and for the first 10 days of the study, up to the
-##' recovery period.
-##'
-##' @name sleepstudy
-##' @docType data
-##' @format A data frame with 180 observations on the following 3 variables.
-##' \describe{
-##' \item{\code{Reaction}}{Average reaction time (ms)}
-##' \item{\code{Days}}{Number of days of sleep deprivation}
-##' \item{\code{Subject}}{Subject number on which the observation was made.}
-##' }
-##' @references Gregory Belenky, Nancy J. Wesensten, David R. Thorne, Maria L.
-##' Thomas, Helen C. Sing, Daniel P. Redmond, Michael B. Russo and Thomas J.
-##' Balkin (2003) Patterns of performance degradation and restoration during
-##' sleep restriction and subsequent recovery: a sleep dose-response study.
-##' \emph{Journal of Sleep Research} \bold{12}, 1--12.
-##' @keywords datasets
-##' @examples
-##'
-##' str(sleepstudy)
-##' xyplot(Reaction ~ Days | Subject, sleepstudy, type = c("g","p","r"),
-##' index = function(x,y) coef(lm(y ~ x))[1],
-##' xlab = "Days of sleep deprivation",
-##' ylab = "Average reaction time (ms)", aspect = "xy")
-##' (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy))
-##' (fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy))
-##'
-NULL
-
-
-##' Verbal Aggression item responses
-##'
-##' These are the item responses to a questionaire on verbal aggression. These
-##' data are used throughout De Boeck and Wilson, \emph{Explanatory Item
-##' Response Models} (Springer, 2004) to illustrate various forms of item
-##' response models.
-##'
-##'
-##' @name VerbAgg
-##' @docType data
-##' @format A data frame with 7584 observations on the following 13 variables.
-##' \describe{
-##' \item{\code{Anger}}{the subject's Trait Anger score as measured on
-##' the State-Trait Anger Expression Inventory (STAXI)}
-##' \item{\code{Gender}}{the subject's gender - a factor with levels
-##' \code{M} and \code{F}}
-##' \item{\code{item}}{the item on the questionaire, as a factor}
-##' \item{\code{resp}}{the subject's response to the item - an ordered
-##' factor with levels \code{no} < \code{perhaps} < \code{yes}}
-##' \item{\code{id}}{the subject identifier, as a factor}
-##' \item{\code{btype}}{behavior type - a factor with levels
-##' \code{curse}, \code{scold} and \code{shout}}
-##' \item{\code{situ}}{situation type - a factor with levels
-##' \code{other} and \code{self} indicating other-to-blame and self-to-blame}
-##' \item{\code{mode}}{behavior mode - a factor with levels \code{want}
-##' and \code{do}}
-##' \item{\code{r2}}{dichotomous version of the response - a factor with
-##' levels \code{N} and \code{Y}}
-##' }
-##' @references De Boeck and Wilson (2004), \emph{Explanatory Item Response
-##' Models}, Springer.
-##' @source \url{http://bear.soe.berkeley.edu/EIRM/}
-##' @keywords datasets
-##' @examples
-##'
-##' str(VerbAgg)
-##' ## Show how r2 := h(resp) is defined:
-##' with(VerbAgg, stopifnot( identical(r2, {
-##' r <- factor(resp, ordered=FALSE); levels(r) <- c("N","Y","Y"); r})))
-##'
-##' xtabs(~ item + resp, VerbAgg)
-##' xtabs(~ btype + resp, VerbAgg)
-##' round(100 * ftable(prop.table(xtabs(~ situ + mode + resp, VerbAgg), 1:2), 1))
-##' person <- unique(subset(VerbAgg, select = c(id, Gender, Anger)))
-##' if (require(lattice)) { # is this necessary when the package depends on lattice?
-##' densityplot(~ Anger, person, groups = Gender, auto.key = list(columns = 2),
-##' xlab = "Trait Anger score (STAXI)")
-##' }
-##'
-##' \dontrun{## takes about 15 sec
-##' print(fmVA <- glmer(r2 ~ (Anger + Gender + btype + situ)^2 +
-##' (1|id) + (1|item), family = binomial, data =
-##' VerbAgg), corr=FALSE)
-##' }
-##' ## much faster but less accurate
-##' print(fmVA0 <- glmer(r2 ~ (Anger + Gender + btype + situ)^2 +
-##' (1|id) + (1|item), family = binomial, data =
-##' VerbAgg, nAGQ=0L), corr=FALSE)
-##'
-NULL
-
-
-
Modified: pkg/lme4Eigen/man/merMod-class.Rd
===================================================================
--- pkg/lme4Eigen/man/merMod-class.Rd 2012-02-10 23:41:09 UTC (rev 1598)
+++ pkg/lme4Eigen/man/merMod-class.Rd 2012-02-13 17:47:32 UTC (rev 1599)
@@ -1,29 +1,29 @@
\docType{class}
\name{merMod-class}
-\alias{anova,merMod-method}
-\alias{coef,merMod-method}
-\alias{deviance,merMod-method}
-\alias{fitted,merMod-method}
-\alias{formula,merMod-method}
+\alias{anova.merMod}
+\alias{coef.merMod}
+\alias{deviance.merMod}
+\alias{fitted.merMod}
+\alias{formula.merMod}
\alias{glmerMod-class}
\alias{lmerMod-class}
-\alias{logLik,merMod-method}
+\alias{logLik.merMod}
\alias{merMod-class}
-\alias{model.frame,merMod-method}
-\alias{model.matrix,merMod-method}
+\alias{model.frame.merMod}
+\alias{model.matrix.merMod}
\alias{nlmerMod-class}
-\alias{print,merMod-method}
-\alias{print,summary.mer-method}
-\alias{show,merMod-method}
-\alias{show,summary.mer-method}
-\alias{simulate,merMod-method}
-\alias{summary,merMod-method}
-\alias{summary,summary.mer-method}
-\alias{terms,merMod-method}
-\alias{update,merMod-method}
-\alias{VarCorr,merMod-method}
-\alias{vcov,merMod-method}
-\alias{vcov,summary.mer-method}
+\alias{print.merMod}
+\alias{print.summary.mer}
+\alias{show.merMod}
+\alias{show.summary.mer}
+\alias{simulate.merMod}
+\alias{summary.merMod}
+\alias{summary.summary.mer}
+\alias{terms.merMod}
+\alias{update.merMod}
+\alias{VarCorr.merMod}
+\alias{vcov.merMod}
+\alias{vcov.summary.mer}
\title{Class "merMod" of Fitted Mixed-Effect Models}
\description{
A mixed-effects model represented as a
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