[Lme4-commits] r1511 - in branches/roxygen: . R man
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Tue Jan 24 23:03:43 CET 2012
Author: dmbates
Date: 2012-01-24 23:03:43 +0100 (Tue, 24 Jan 2012)
New Revision: 1511
Removed:
branches/roxygen/NAMESPACE
branches/roxygen/R/drop1-etc.R
branches/roxygen/man/Dyestuff.Rd
branches/roxygen/man/GHrule.Rd
branches/roxygen/man/HPDinterval.bak
branches/roxygen/man/InstEval.Rd
branches/roxygen/man/NelderMead-class.Rd
branches/roxygen/man/NelderMead.Rd
branches/roxygen/man/Pastes.Rd
branches/roxygen/man/Penicillin.Rd
branches/roxygen/man/VarCorr.Rd
branches/roxygen/man/VerbAgg.Rd
branches/roxygen/man/bootMer.Rd
branches/roxygen/man/cake.Rd
branches/roxygen/man/cbpp.Rd
branches/roxygen/man/devcomp.Rd
branches/roxygen/man/findbars.Rd
branches/roxygen/man/fixef.Rd
branches/roxygen/man/getME.Rd
branches/roxygen/man/glmFamily-class.Rd
branches/roxygen/man/glmFamily.Rd
branches/roxygen/man/golden-class.Rd
branches/roxygen/man/golden.Rd
branches/roxygen/man/isNested.Rd
branches/roxygen/man/lmList-class.Rd
branches/roxygen/man/lmList.Rd
branches/roxygen/man/lmResp-class.Rd
branches/roxygen/man/lmResp.Rd
branches/roxygen/man/lmer.Rd
branches/roxygen/man/merMod-class.Rd
branches/roxygen/man/merPredD-class.Rd
branches/roxygen/man/merPredD.Rd
branches/roxygen/man/mkdevfun.Rd
branches/roxygen/man/profile-methods.Rd
branches/roxygen/man/ranef.Rd
branches/roxygen/man/refit.bak
branches/roxygen/man/refitML.Rd
branches/roxygen/man/sigma.Rd
branches/roxygen/man/sleepstudy.Rd
Log:
Remove files that are generated on the fly by roxygen
Deleted: branches/roxygen/NAMESPACE
===================================================================
--- branches/roxygen/NAMESPACE 2012-01-24 21:58:54 UTC (rev 1510)
+++ branches/roxygen/NAMESPACE 2012-01-24 22:03:43 UTC (rev 1511)
@@ -1,185 +0,0 @@
-useDynLib(lme4Eigen, .registration=TRUE)
-
-## Import non-base functions we need explicitly,
-## notably for which we define methods:
-
-## FIXME -- really need all these -- fully imported -- ???
-import("grid")
-import("lattice")
-import("splines")
-
-importFrom("graphics", plot)
-importFrom("nlme", fixef, ranef, VarCorr)
-importFrom("stats"
- , AIC
- , BIC
- , anova
- , coef
- , coefficients
- , confint
- , deviance
- , fitted
- , fitted.values
- , formula
- , getCall
- , logLik
- , model.frame
- , model.matrix
- , predict
- , profile
- , residuals
- , resid
- , simulate
- , terms
- , update
- , vcov
- )
-
-importFrom("minqa", bobyqa)
-
-importClassesFrom("Matrix",
- CHMfactor,
- CHMsimpl,
- CHMsuper,
- Cholesky,
- Matrix,
- corMatrix,
- dCHMsimpl,
- dCHMsuper,
- dMatrix,
- ddiMatrix,
- dgCMatrix,
- dgeMatrix,
- dpoMatrix,
- dsCMatrix,
- nCHMsimpl,
- nCHMsuper,
- symmetricMatrix)
-
-importFrom("MatrixModels", model.Matrix)
-
-importClassesFrom("MatrixModels",
- modelMatrix,
- denseModelMatrix, ddenseModelMatrix,
- sparseModelMatrix, dsparseModelMatrix
- )
-
-importMethodsFrom("Matrix"
- , "%*%"
- , Cholesky
- , as.vector
- , chol
- , chol2inv
- , coerce
- , crossprod
- , determinant
- , diag
- , rcond
- , solve
- , t
- , tcrossprod
- ## , update
- )
-
-# exportPattern("^[^\\.]")
-
-# and the rest (S3 generics; regular functions):
-export(GHrule,
- GQdk,
- NelderMead,
- VarCorr,
- bootMer,
- devcomp,
- findbars,
- fixef,
- getL, getME,
- glmFamily,
- glmResp,
- glmer,
- golden,
- isNested,
- lmList,
- lmResp,
- lmer,
- lmerResp,
- mkdevfun,
- nlsResp,
- nlmer,
- nobars,
- merPredD,
- ranef,
- refitML,
- sigma,
- subbars
- )
-
-exportClasses(NelderMead,
- glmerMod,
- glmFamily,
- glmResp,
- golden,
- lmerMod,
- lmerResp,
- lmList,
- lmResp,
- merPredD,
- merMod,
- nlsResp
- )
-
-exportMethods(
- coef
- , coerce
- , getL
- , rcond
- , show
- , sigma
- ## , update
- )
-
-## S3 methods - S4 methods are not created when dispatch is on the first argument only
-
-S3method(confint, lmList)
-
-S3method(plot, lmList.confint)
-
-S3method(VarCorr, merMod)
-S3method(anova, merMod)
-S3method(coef, merMod)
-S3method(deviance, merMod)
-S3method(devcomp, merMod)
-S3method(drop1, merMod)
-S3method(extractAIC, merMod)
-S3method(fixef, merMod)
-S3method(fitted, merMod)
-S3method(formula, merMod)
-S3method(logLik, merMod)
-S3method(model.frame, merMod)
-S3method(model.matrix, merMod)
-S3method(print, merMod)
-S3method(profile, merMod)
-S3method(ranef, merMod)# <- hide inspite of extra args..
-S3method(residuals, merMod)
-S3method(simulate, merMod)
-S3method(summary, merMod)
-S3method(terms, merMod)
-S3method(vcov, merMod)
-S3method(refitML, merMod)
-
-S3method(print, summary.mer)
-S3method(summary, summary.mer)
-#S3method(vcov, summary.mer)
-
-S3method(plot, coef.mer)
-S3method(dotplot, coef.mer)
-
-S3method(plot, ranef.mer)
-S3method(qqmath, ranef.mer)
-S3method(dotplot, ranef.mer)
-
-## profile() related:
-S3method(xyplot, thpr)
-S3method(densityplot, thpr)
-S3method(confint, thpr)
-S3method(splom, thpr)
-S3method(log, thpr)
Deleted: branches/roxygen/R/drop1-etc.R
===================================================================
--- branches/roxygen/R/drop1-etc.R 2012-01-24 21:58:54 UTC (rev 1510)
+++ branches/roxygen/R/drop1-etc.R 2012-01-24 22:03:43 UTC (rev 1511)
@@ -1,83 +0,0 @@
-extractAIC.merMod <- function(fit, scale = 0, k = 2, ...) {
- L <- logLik(if (isREML(fit)) refitML(fit) else fit)
-### FIXME --- our logLik() gives NA because "dev" is NA
- edf <- attr(L,"df")
- c(edf,-2*L + k*edf)
-}
-
-## doesn't install properly in current form
-## should import nobs from stats?
-## setMethod("nobs","mer",
-## function(object,...) {
-## nrow(object at frame)
-## })
-
-## "Horribly" this is needed for stats::drop.scope() which does not see the S4 terms() method:
-terms.mer <- function(x,...) attr(x at frame,"terms")
-
-
-## hacked stats:::drop1.default
-## FIXME: add F test (with specified denom df)?
-drop1.merMod <- function(object, scope, scale = 0, test = c("none", "Chisq"),
- k = 2, trace = FALSE, ...)
-{
- tl <- attr(terms(object), "term.labels")
- if(missing(scope)) scope <- drop.scope(object)
- else {
- if(!is.character(scope))
- scope <- attr(terms(update.formula(object, scope)), "term.labels")
- if(!all(match(scope, tl, 0L) > 0L))
- stop("scope is not a subset of term labels")
- }
- ns <- length(scope)
- ans <- matrix(nrow = ns + 1L, ncol = 2L,
- dimnames = list(c("<none>", scope), c("df", "AIC")))
- ans[1, ] <- extractAIC(object, scale, k = k, ...)
- ## BMB: avoid nobs, to avoid dependence on 2.13
- ## n0 <- nobs(object, use.fallback = TRUE)
- n0 <- nrow(object at frame)
- env <- environment(formula(object))
- for(i in seq(ns)) {
- tt <- scope[i]
- if(trace > 1) {
- cat("trying -", tt, "\n", sep='')
- utils::flush.console()
- }
- nfit <- update(object, as.formula(paste("~ . -", tt)),
- evaluate = FALSE)
- nfit <- eval(nfit, envir = env) # was eval.parent(nfit)
- ans[i+1, ] <- extractAIC(nfit, scale, k = k, ...)
- ## BMB: avoid nobs, to avoid dependence on 2.13
- ## nnew <- nobs(nfit, use.fallback = TRUE)
- nnew <- nrow(nfit at frame)
- if(all(is.finite(c(n0, nnew))) && nnew != n0)
- stop("number of rows in use has changed: remove missing values?")
- }
- dfs <- ans[1L , 1L] - ans[, 1L]
- dfs[1L] <- NA
- aod <- data.frame(Df = dfs, AIC = ans[,2])
- test <- match.arg(test)
- if(test == "Chisq") {
- dev <- ans[, 2L] - k*ans[, 1L]
- dev <- dev - dev[1L] ; dev[1L] <- NA
- nas <- !is.na(dev)
- P <- dev
- ## BMB: hack to extract safe_pchisq
- P[nas] <- stats:::safe_pchisq(dev[nas], dfs[nas], lower.tail = FALSE)
- aod[, c("LRT", "Pr(Chi)")] <- list(dev, P)
- } else if (test == "F") {
- stop("F test STUB -- unfinished maybe forever")
- dev <- ans[, 2L] - k*ans[, 1L]
- dev <- dev - dev[1L] ; dev[1L] <- NA
- nas <- !is.na(dev)
- P <- dev
- ## BMB: hack to extract safe_pchisq
- P[nas] <- stats:::safe_pchisq(dev[nas], dfs[nas], lower.tail = FALSE)
- aod[, c("LRT", "Pr(F)")] <- list(dev, P)
- }
- head <- c("Single term deletions", "\nModel:", deparse(formula(object)),
- if(scale > 0) paste("\nscale: ", format(scale), "\n"))
- class(aod) <- c("anova", "data.frame")
- attr(aod, "heading") <- head
- aod
-}
Deleted: branches/roxygen/man/Dyestuff.Rd
===================================================================
--- branches/roxygen/man/Dyestuff.Rd 2012-01-24 21:58:54 UTC (rev 1510)
+++ branches/roxygen/man/Dyestuff.Rd 2012-01-24 22:03:43 UTC (rev 1511)
@@ -1,91 +0,0 @@
-\name{Dyestuff}
-\alias{Dyestuff}
-\alias{Dyestuff2}
-\docType{data}
-\title{Yield of dyestuff by batch}
-\description{
- 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.
-}
-\usage{data(Dyestuff)}
-\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).}
- }
-}
-\details{
- 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.}
-}
-\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
-}
-%\references{}
-\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("minqa")
- pkgI("MatrixModels")
- 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))
-}
-\keyword{datasets}
Deleted: branches/roxygen/man/GHrule.Rd
===================================================================
--- branches/roxygen/man/GHrule.Rd 2012-01-24 21:58:54 UTC (rev 1510)
+++ branches/roxygen/man/GHrule.Rd 2012-01-24 22:03:43 UTC (rev 1511)
@@ -1,56 +0,0 @@
-\name{GHrule}
-\alias{GHrule}
-\title{
- Nodes and weights for Gauss-Hermite quadrature
-}
-\description{
- Returns the nodes, weights and the logarithm of the standard Gaussian
- density for a Gauss-Hermite quadrature rule, with the standard
- Gaussian density as the kernel, of order \code{ord}.
-}
-\usage{
-GHrule(ord)
-}
-\arguments{
- \item{ord}{
- positive integer not exceeding 25 - the order of the rule.
- }
-}
-\details{
- Univariate Gauss-Hermite quadrature evaluates the integral of a
- function that is multiplied by a \dQuote{kernel} where the kernel is a
- multiple of \eqn{e^{-z^2}}{exp(-z^2)} or
- \eqn{e^{-z^2/2}}{exp(-z^2/2)}. For statisticians the natural
- candidate is the standard normal density,
- \eqn{\phi(z)=e^{-z^2/2}/\sqrt(2\pi)}{exp(-z^2/2)/sqrt(2*pi)}.
- A \eqn{k}{k}th-order Gauss-Hermite formula provides knots,
- \eqn{z_i,i=1,...,k}{z[i]}, and weights, \eqn{w_i,i=1,\dots,k}{w[i]}, such that
- \deqn{\int_{\mathbb{R}}t(z)\phi(z)\,dz\approx\sum_{i=1}^kw_it(z_i)}{int[-Inf]^Inf t(z) phi(z) dz ~ sum[i=1]^k w[i] t(z[i])}
-}
-\note{
- The choice of the value of \eqn{k} depends on the behavior of the function
- \eqn{t(z)}. If \eqn{t(z)} is a polynomial of degree \eqn{k-1} then the
- Gauss-Hermite formula for order \eqn{k} or greater provides the exact
- value of the integral. The fact that we want \eqn{t(z)} to behave
- like a low-order polynomial is often neglected in the formulation of
- a Gauss-Hermite approximation to an integral.
-}
-\value{
- a numeric matrix with \code{ord} rows and three columns named
- \dQuote{z}, \dQuote{w} and \dQuote{ldnorm}.
-}
-\references{
- Based on code in the \dQuote{SparseGrid} package.
-}
-%\author{}
-%\note{}
-%\seealso{}
-\examples{
-(rr <- as.data.frame(GHrule(5)))
-sum(rr$w * (rr$z)^2) # second moment of standard Gaussian
-sum(rr$w * (rr$z)^4) # fourth moment of standard Gaussian
-sum(rr$w * (rr$z)^6) # sixth moment of standard Gaussian (approx)
-sum(rr$w * (rr$z)^8) # eighth moment of standard Gaussian (approx)
-sum(rr$w * (rr$z)^10) # tenth moment of standard Gaussian (approx)
-}
-\keyword{math}
Deleted: branches/roxygen/man/HPDinterval.bak
===================================================================
--- branches/roxygen/man/HPDinterval.bak 2012-01-24 21:58:54 UTC (rev 1510)
+++ branches/roxygen/man/HPDinterval.bak 2012-01-24 22:03:43 UTC (rev 1511)
@@ -1,40 +0,0 @@
-\name{HPDinterval}
-\alias{HPDinterval}
-\docType{genericFunction}
-\alias{HPDinterval,merMCMC-method}
-\alias{HPDinterval,matrix-method}
-\title{Highest Posterior Density intervals}
-\description{
- Create Highest Posterior Density (HPD) intervals for the parameters in
- an MCMC sample.
-}
-\usage{
-HPDinterval(object, prob = 0.95, \dots)
-}
-\arguments{
- \item{object}{The object containing the MCMC sample - usually of class
- \code{\linkS4class{merMCMC}} or a numeric matrix.}
- \item{prob}{A numeric scalar in the interval (0,1) giving the target
- probability content of the intervals. The nominal probability
- content of the intervals is the multiple of \code{1/nrow(obj)}
- nearest to \code{prob}.}
- \item{\dots}{Optional additional arguments for methods. None are used
- at present.}
-}
-\details{
- For each parameter the interval is constructed from the empirical cdf
- of the sample as the shortest interval for which the difference in
- the ecdf values of the endpoints is the nominal probability. Assuming
- that the distribution is not severely multimodal, this is the HPD interval.
-}
-
-\value{
- For an \code{\linkS4class{merMCMC}} object, a list of matrices with
- columns \code{"lower"} and \code{"upper"} and rows corresponding to
- the parameters. The attribute \code{"Probability"} is the nominal
- probability content of the intervals.
-}
-%\author{Douglas Bates}
-%\examples{}
-\keyword{univar}
-\keyword{htest}
Deleted: branches/roxygen/man/InstEval.Rd
===================================================================
--- branches/roxygen/man/InstEval.Rd 2012-01-24 21:58:54 UTC (rev 1510)
+++ branches/roxygen/man/InstEval.Rd 2012-01-24 22:03:43 UTC (rev 1511)
@@ -1,56 +0,0 @@
-\name{InstEval}
-\alias{InstEval}
-\docType{data}
-\title{
- University Lecture/Instructor Evaluations by Students at ETH
-}
-\description{
- 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.
-}
-\usage{data(InstEval)}
-\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}.}
- }
- One specific observation is one student rating for a specific lecture
- (of one lecturer, during one semester in the past).
-}
-\details{
- 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.
-}
-% \source{
-% not public; anonymized on purpose
-% }
-\examples{
-str(InstEval)
-
-head(InstEval, 16)
-xtabs(~ service + dept, InstEval)
-}
-\keyword{datasets}
Deleted: branches/roxygen/man/NelderMead-class.Rd
===================================================================
--- branches/roxygen/man/NelderMead-class.Rd 2012-01-24 21:58:54 UTC (rev 1510)
+++ branches/roxygen/man/NelderMead-class.Rd 2012-01-24 22:03:43 UTC (rev 1511)
@@ -1,63 +0,0 @@
-\name{NelderMead-class}
-\Rdversion{1.1}
-\docType{class}
-\alias{NelderMead-class}
-\title{Class \code{"NelderMead"}}
-\description{
- A reference class for a Nelder-Mead simplex optimizer allowing box
- constraints on the parameters and using reverse communication.
-}
-\section{Extends}{
- All reference classes extend and inherit methods from \code{"\linkS4class{envRefClass}"}.
-}
-\references{
- Based on code in the NLopt collection.
-}
-%\author{}
-\note{This is the default optimizer for the second stage of
- \code{\link{glmer}} and \code{\link{nlmer}} fits. We found that it
- was more reliable and often faster than more sophisticated optimizers.}
-\seealso{\code{\link{glmer}}, \code{\link{nlmer}}}
-\examples{
-showClass("NelderMead")
-}
-\keyword{classes}
-\section{Fields}{
- \describe{
- \item{\code{Ptr}:}{\code{externalptr} to the instance of the C++ class}
- \item{\code{lowerbd}:}{\code{numeric} vector of lower bounds - some
- or all of the elements may be \code{-Inf} for parameters with no
- lower bound.}
- \item{\code{upperbd}:}{\code{numeric} vector of upper bounds - some
- or all of the elements may be \code{Inf} for parameters with no
- upper bound.}
- \item{\code{xstep}:}{\code{numeric} vector of initial steps to
- generate the simplex. All the elements must be non-zero.}
- \item{\code{xtol}:}{\code{numeric} vector of tolerances on the parameters}
- }
-}
-\section{Methods}{
- \describe{
- \item{\code{setFtolAbs(fta)}:}{set the absolute tolerance on the
- function value.}
- \item{\code{newf(value)}:}{provide the function value at the current
- \code{xeval} vector.}
- \item{\code{setMaxeval(mxev)}:}{set the maximum number of function evaluations.}
- \item{\code{value()}:}{return the lowest function value previously recorded}
- \item{\code{setMinfMax(minf)}:}{set the maximum value to be declared
- as the minimum function value. Defaults to \code{-Inf}.}
- \item{\code{setFtolRel(ftr)}:}{set the relative tolerance on the
- function value.}
- \item{\code{setForceStop(stp)}:}{Force the iterations to stop. This
- method is unlikely to be used in an algorithm using reverse communication.}
- \item{\code{ptr()}:}{return the external pointer, \code{Ptr},
- regenerating the C++ object if necessary.}
- \item{\code{xeval()}:}{next value of the parameters at which to
- evaluate the objective function}
- \item{\code{setIprint(iprint)}:}{set the parameter controlling
- printing of intermediate results. If \code{iprint > 0} then the
- function and parameter values are printed every \code{iprint} evaluations.}
- \item{\code{xpos()}:}{position of the minimum objective value seen
- to this point.}
- }
-}
Deleted: branches/roxygen/man/NelderMead.Rd
===================================================================
--- branches/roxygen/man/NelderMead.Rd 2012-01-24 21:58:54 UTC (rev 1510)
+++ branches/roxygen/man/NelderMead.Rd 2012-01-24 22:03:43 UTC (rev 1511)
@@ -1,26 +0,0 @@
-\name{NelderMead}
-\alias{NelderMead}
-\title{Generator object for the Nelder-Mead optimizer class.}
-\section{Methods}{
- \describe{
- \item{\code{new(lower, upper, xst, x0, xt, \dots)}:}{Create a new
- \code{\linkS4class{NelderMead}} object.}
- }
-}
-\arguments{
- \item{lower}{numeric vector of lower bounds - elements may be
- \code{-Inf}.}
- \item{upper}{numeric vector of upper bounds - elements may be
- \code{Inf}.}
- \item{xst}{numeric vector of initial step sizes to establish the
- simplex - all elements must be non-zero.}
- \item{x0}{numeric vector of starting values for the parameters.}
- \item{xt}{numeric vector of tolerances on the parameters.}
- \item{\dots}{additional, optional arguments. None are used at present.}
-}
-\note{Arguments to the \code{new} methods must be named arguments.}
-\description{The generator objects for the
- \code{\linkS4class{NelderMead}} class of optimizers subject to box
- constraints and using reverse communications.}
-\seealso{\code{\linkS4class{NelderMead}}}
-\keyword{classes}
Deleted: branches/roxygen/man/Pastes.Rd
===================================================================
--- branches/roxygen/man/Pastes.Rd 2012-01-24 21:58:54 UTC (rev 1510)
+++ branches/roxygen/man/Pastes.Rd 2012-01-24 22:03:43 UTC (rev 1511)
@@ -1,70 +0,0 @@
-\name{Pastes}
-\alias{Pastes}
-\docType{data}
-\title{Paste strength by batch and cask}
-\description{
- Strength of a chemical paste product; its quality depending on the
- delivery batch, and the cask within the delivery.
-}
-\usage{data(Pastes)}
-\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}.}
- }
-}
-\details{
- 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}.
-}
-\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
-}
-%\references{}
-\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")
-image(L, sub = "Structure of random effects interaction in pastes model")
-}
-\keyword{datasets}
Deleted: branches/roxygen/man/Penicillin.Rd
===================================================================
--- branches/roxygen/man/Penicillin.Rd 2012-01-24 21:58:54 UTC (rev 1510)
+++ branches/roxygen/man/Penicillin.Rd 2012-01-24 22:03:43 UTC (rev 1511)
@@ -1,56 +0,0 @@
-\name{Penicillin}
-\alias{Penicillin}
-\docType{data}
-\title{Variation in penicillin testing}
-\description{
- 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.
-}
-\usage{data(Penicillin)}
-\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}.}
- }
-}
-\details{
- 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.}
-}
-\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
-}
-%\references{}
-\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")
-image(L, main = "L",
- sub = "Penicillin: Structure of random effects interaction")
-}
-\keyword{datasets}
Deleted: branches/roxygen/man/VarCorr.Rd
===================================================================
--- branches/roxygen/man/VarCorr.Rd 2012-01-24 21:58:54 UTC (rev 1510)
+++ branches/roxygen/man/VarCorr.Rd 2012-01-24 22:03:43 UTC (rev 1511)
@@ -1,49 +0,0 @@
-\name{VarCorr}
-\title{Extract variance and correlation components}
-\usage{
-VarCorr(x, sigma = 1, rdig = 3)
-}
-\alias{VarCorr}
-\alias{VarCorr.merMod}
-\arguments{
- \item{x}{a fitted model object, usually an object inheriting from
- class \code{\linkS4class{merMod}}.}
- \item{sigma}{an optional numeric value used as a multiplier for the
- standard deviations. Default is \code{1}.}
- \item{rdig}{an optional integer value specifying the number of digits
- used to represent correlation estimates. Default is \code{3}.}
-}
-\description{
- This function calculates the estimated variances, standard
- deviations, and correlations between the random-effects terms in a
- mixed-effects model, of class \code{\linkS4class{merMod}} (linear,
- generalized or nonlinear). The within-group error
- variance and standard deviation are also calculated.
-}
-\value{
- a matrix with the estimated variances, standard deviations, and
- correlations for the random effects. The first two columns, named
- \code{Variance} and \code{StdDev}, give, respectively, the variance
- and the standard deviations. If there are correlation components in
- the random effects model, the third column, named \code{Corr},
- and the remaining unnamed columns give the estimated correlations
- among random effects within the same level of grouping. The
- within-group error variance and standard deviation are included as
- the last row in the matrix.
-}
-%\references{
- % Pinheiro, J.C., and Bates, D.M. (2000)
- % \emph{Mixed-Effects Models in S and S-PLUS},
- % Springer, esp. pp. 100, 461.
-%}
-\author{This is modeled after \code{\link[nlme]{VarCorr}} from package
- \pkg{nlme}, by Jose Pinheiro and Douglas Bates.}
-\seealso{
- \code{\link{lmer}}, \code{\link{nlmer}}
-}
-\examples{
-data(Orthodont, package="nlme")
-fm1 <- lmer(distance ~ age + (age|Subject), data = Orthodont)
-VarCorr(fm1)
-}
-\keyword{models}
Deleted: branches/roxygen/man/VerbAgg.Rd
===================================================================
--- branches/roxygen/man/VerbAgg.Rd 2012-01-24 21:58:54 UTC (rev 1510)
+++ branches/roxygen/man/VerbAgg.Rd 2012-01-24 22:03:43 UTC (rev 1511)
@@ -1,65 +0,0 @@
-\name{VerbAgg}
-\alias{VerbAgg}
-\docType{data}
-\title{Verbal Aggression item responses}
-\description{
- 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.
-}
-%\usage{data(VerbAgg)}
-\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}}
- }
-}
-%\details{}
-\source{
- \url{http://bear.soe.berkeley.edu/EIRM/}
-}
-\references{
- De Boeck and Wilson (2004), \emph{Explanatory Item Response Models}, Springer.
-}
-\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)) {
- 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)
-}
-\keyword{datasets}
[TRUNCATED]
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