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# Methods for lmer and for the objects that it produces## Some utilities## Return the pairs of expressions separated by vertical barsfindbars <- function(term){if (is.name(term) || is.numeric(term)) return(NULL)if (term[[1]] == as.name("(")) return(findbars(term[[2]]))if (!is.call(term)) stop("term must be of class call")if (term[[1]] == as.name('|')) return(term)if (length(term) == 2) return(findbars(term[[2]]))c(findbars(term[[2]]), findbars(term[[3]]))}## Return the formula omitting the pairs of expressions## that are separated by vertical barsnobars <- function(term){if (!('|' %in% all.names(term))) return(term)if (is.call(term) && term[[1]] == as.name('|')) return(NULL)if (length(term) == 2) {nb <- nobars(term[[2]])if (is.null(nb)) return(NULL)term[[2]] <- nbreturn(term)}nb2 <- nobars(term[[2]])nb3 <- nobars(term[[3]])if (is.null(nb2)) return(nb3)if (is.null(nb3)) return(nb2)term[[2]] <- nb2term[[3]] <- nb3term}## Substitute the '+' function for the '|' functionsubbars <- function(term){if (is.name(term) || is.numeric(term)) return(term)if (length(term) == 2) {term[[2]] <- subbars(term[[2]])return(term)}stopifnot(length(term) == 3)if (is.call(term) && term[[1]] == as.name('|'))term[[1]] <- as.name('+')term[[2]] <- subbars(term[[2]])term[[3]] <- subbars(term[[3]])term}## Expand an expression with colons to the sum of the lhs## and the current expressioncolExpand <- function(term){if (is.name(term) || is.numeric(term)) return(term)if (length(term) == 2) {term[[2]] <- colExpand(term[[2]])return(term)}stopifnot(length(term) == 3)if (is.call(term) && term[[1]] == as.name(':')) {return(substitute(A+B, list(A = term, B = colExpand(term[[2]]))))}term[[2]] <- colExpand(term[[2]])term[[3]] <- colExpand(term[[3]])term}abbrvNms <- function(gnm, cnms){ans <- paste(abbreviate(gnm), abbreviate(cnms), sep = '.')if (length(cnms) > 1) {anms <- lapply(cnms, abbreviate, minlength = 3)nmmat <- outer(anms, anms, paste, sep = '.')ans <- c(ans, paste(abbreviate(gnm, minlength = 3),nmmat[upper.tri(nmmat)], sep = '.'))}ans}## Control parameters for lmerlmerControl <-function(maxIter = 200, # used in ../src/lmer.c onlytolerance = sqrt(.Machine$double.eps),# dittomsMaxIter = 200,## msTol = sqrt(.Machine$double.eps),## FIXME: should be able to pass tolerances to nlminb()msVerbose = getOption("verbose"),niterEM = 15,EMverbose = getOption("verbose"),PQLmaxIt = 30,# FIXME: unused; PQL currently uses 'maxIter' insteadanalyticGradient = TRUE,analyticHessian = FALSE # unused _FIXME_){list(maxIter = as.integer(maxIter),tolerance = as.double(tolerance),msMaxIter = as.integer(msMaxIter),## msTol = as.double(msTol),msVerbose = as.integer(msVerbose),# "integer" on purposeniterEM = as.integer(niterEM),EMverbose = as.logical(EMverbose),PQLmaxIt = as.integer(PQLmaxIt),analyticGradient = as.logical(analyticGradient),analyticHessian = as.logical(analyticHessian))}setMethod("coef", signature(object = "lmer"),function(object, ...){fef <- data.frame(rbind(object@fixed), check.names = FALSE)ref <- as(ranef(object), "list")names(ref) <- names(object@flist)val <- lapply(ref, function(x) fef[rep(1, nrow(x)),])for (i in seq(a = val)) {refi <- ref[[i]]row.names(val[[i]]) <- row.names(refi)if (!all(names(refi) %in% names(fef)))stop("unable to align random and fixed effects")val[[i]][ , names(refi)] <- val[[i]][ , names(refi)] + refi}new("lmer.coef", val)})## setMethod("plot", signature(x = "lmer.coef"),## function(x, y, ...)## {## varying <- unique(do.call("c",## lapply(x, function(el)## names(el)[sapply(el,## function(col)## any(col != col[1]))])))## gf <- do.call("rbind", lapply(x, "[", j = varying))## gf$.grp <- factor(rep(names(x), sapply(x, nrow)))## switch(min(length(varying), 3),## qqmath(eval(substitute(~ x | .grp,## list(x = as.name(varying[1])))), gf, ...),## xyplot(eval(substitute(y ~ x | .grp,## list(y = as.name(varying[1]),## x = as.name(varying[2])))), gf, ...),## splom(~ gf | .grp, ...))## })## setMethod("plot", signature(x = "lmer.ranef"),## function(x, y, ...)## {## lapply(x, function(x) {## cn <- lapply(colnames(x), as.name)## switch(min(ncol(x), 3),## qqmath(eval(substitute(~ x, list(x = cn[[1]]))), x, ...),## xyplot(eval(substitute(y ~ x,## list(y = cn[[1]],## x = cn[[2]]))), x, ...),## splom(~ x, ...))## })## })setMethod("with", signature(data = "lmer"),function(data, expr, ...) {dat <- eval(data@call$data)if (!is.null(na.act <- attr(data@frame, "na.action")))dat <- dat[-na.act, ]lst <- c(list(. = data), data@flist, data@frame, dat)eval(substitute(expr), lst[unique(names(lst))])})setMethod("terms", signature(x = "lmer"),function(x, ...) x@terms)rWishart <- function(n, df, invScal).Call("Matrix_rWishart", n, df, invScal, PACKAGE = "Matrix")setMethod("lmer", signature(formula = "formula"),function(formula, data, family,method = c("REML", "ML", "PQL", "Laplace", "AGQ"),control = list(), start,subset, weights, na.action, offset,model = TRUE, x = FALSE, y = FALSE , ...){## match and check parametersif (length(formula) < 3) stop("formula must be a two-sided formula")cv <- do.call("lmerControl", control)## Must evaluate the model frame first and then fit the glm using## that frame. Otherwise missing values in the grouping factors## cause inconsistent numbers of observations.mf <- match.call()m <- match(c("family", "data", "subset", "weights","na.action", "offset"), names(mf), 0)mf <- fe <- mf[c(1, m)]frame.form <- subbars(formula) # substitute `+' for `|'fixed.form <- nobars(formula) # remove any terms with `|'if (inherits(fixed.form, "name")) # RHS is empty - use a constantfixed.form <- substitute(foo ~ 1, list(foo = fixed.form))environment(fixed.form) <- environment(frame.form) <- environment(formula)## evaluate a model frame for fixed and random effectsmf$formula <- frame.formmf$family <- NULLmf$drop.unused.levels <- TRUEmf[[1]] <- as.name("model.frame")frm <- eval(mf, parent.frame())## fit a glm model to the fixed formulafe$formula <- fixed.formfe$subset <- NULL # subset has already been created in call to data.framefe$data <- frmfe$x <- fe$model <- fe$y <- TRUEfe[[1]] <- as.name("glm")glm.fit <- eval(fe, parent.frame())x <- glm.fit$xy <- as.double(glm.fit$y)family <- glm.fit$family## check for a linear mixed modellmm <- family$family == "gaussian" && family$link == "identity"if (lmm) { # linear mixed modelmethod <- match.arg(method)if (method %in% c("PQL", "Laplace", "AGQ")) {warning(paste('Argument method = "', method,'" is not meaningful for a linear mixed model.\n','Using method = "REML".\n', sep = ''))method <- "REML"}} else { # generalized linear mixed modelif (missing(method)) method <- "PQL"else {method <- match.arg(method)if (method == "ML") method <- "PQL"if (method == "REML")warning('Argument method = "REML" is not meaningful ','for a generalized linear mixed model.','\nUsing method = "PQL".\n')}}## create factor list for the random effectsbars <- findbars(formula[[3]])names(bars) <- unlist(lapply(bars, function(x) deparse(x[[3]])))fl <- lapply(bars,function(x)eval(substitute(as.factor(fac)[,drop = TRUE],list(fac = x[[3]])), frm))## order factor list by decreasing number of levelsnlev <- sapply(fl, function(x) length(levels(x)))if (any(diff(nlev) > 0)) {ord <- rev(order(nlev))bars <- bars[ord]fl <- fl[ord]}## create list of transposed model matrices for random effectsZtl <- lapply(bars, function(x)t(model.matrix(eval(substitute(~ expr,list(expr = x[[2]]))),frm)))## Create the mixed-effects representation (mer) objectmer <- .Call("mer_create", fl,.Call("Zt_create", fl, Ztl, PACKAGE = "Matrix"),x, y, method, sapply(Ztl, nrow),c(lapply(Ztl, rownames), list(.fixed = colnames(x))),!(family$family %in% c("binomial", "poisson")),match.call(), family,PACKAGE = "Matrix")if (lmm) {.Call("mer_ECMEsteps", mer, cv$niterEM, cv$EMverbose, PACKAGE = "Matrix")LMEoptimize(mer) <- cvreturn(mer)}## The rest of the function applies to generalized linear mixed modelsgVerb <- getOption("verbose")eta <- glm.fit$linear.predictorswts <- glm.fit$prior.weightswtssqr <- wts * wtsoffset <- glm.fit$offsetif (is.null(offset)) offset <- numeric(length(eta))linkinv <- quote(family$linkinv(eta))mu.eta <- quote(family$mu.eta(eta))mu <- family$linkinv(eta)variance <- quote(family$variance(mu))dev.resids <- quote(family$dev.resids(y, mu, wtssqr))LMEopt <- get("LMEoptimize<-")doLMEopt <- quote(LMEopt(x = mer, value = cv))GSpt <- .Call("glmer_init", environment(), PACKAGE = "Matrix").Call("glmer_PQL", GSpt, PACKAGE = "Matrix") # obtain PQL estimatesfixInd <- seq(ncol(x))## pars[fixInd] == beta, pars[-fixInd] == thetaPQLpars <- c(fixef(mer),.Call("mer_coef", mer, 2, PACKAGE = "Matrix")).Call("glmer_devLaplace", PQLpars, GSpt, PACKAGE = "Matrix")## indicator of constrained parametersconst <- c(rep(FALSE, length(fixInd)),unlist(lapply(mer@nc[seq(along = fl)],function(k) 1:((k*(k+1))/2) <= k)))devLaplace <- function(pars).Call("glmer_devLaplace", pars, GSpt, PACKAGE = "Matrix")optimRes <-nlminb(PQLpars, devLaplace,lower = ifelse(const, 5e-10, -Inf),control = list(trace = getOption("verbose"),iter.max = cv$msMaxIter)).Call("glmer_finalize", GSpt, PACKAGE = "Matrix")return(mer)deviance <- devAGQ(PQLpars, 1)### FIXME: For nf == 1 change this to an AGQ evaluation. Needs### AGQ for nc > 1 first.fxd <- PQLpars[fixInd]loglik <- logLik(mer)if (method %in% c("Laplace", "AGQ")) {nAGQ <- 1if (method == "AGQ") { # determine nAGQ at PQL estimatesdev11 <- devAGQ(PQLpars, 11)## FIXME: Should this be an absolute or a relative tolerance?devTol <- sqrt(.Machine$double.eps) * abs(dev11)for (nAGQ in c(9, 7, 5, 3, 1))if (abs(dev11 - devAGQ(PQLpars, nAGQ - 2)) > devTol) breaknAGQ <- nAGQ + 2if (gVerb)cat(paste("Using", nAGQ, "quadrature points per column\n"))}obj <- function(pars).Call("glmer_devAGQ", pars, GSpt, nAGQ, PACKAGE = "Matrix")optimRes <-nlminb(PQLpars, obj,lower = ifelse(const, 5e-10, -Inf),control = list(trace = getOption("verbose"),iter.max = cv$msMaxIter))optpars <- optimRes$parif (optimRes$convergence != 0)warning("nlminb failed to converge")deviance <- optimRes$objectiveif (gVerb)cat(paste("convergence message", optimRes$message, "\n"))fxd[] <- optpars[fixInd] ## preserve the names.Call("lmer_coefGets", mer, optpars[-fixInd], 2, PACKAGE = "Matrix")}.Call("glmer_finalize", GSpt, PACKAGE = "Matrix")loglik[] <- -deviance/2new("lmer", mer,frame = if (model) frm else data.frame(),terms = glm.fit$terms,assign = attr(glm.fit$x, "assign"),call = match.call(), family = family,logLik = loglik, fixed = fxd)})## Extract the permutationsetAs("mer", "pMatrix", function(from).Call("mer_pMatrix", from, PACKAGE = "Matrix"))## Extract the L matrixsetAs("mer", "dtCMatrix", function(from).Call("mer_dtCMatrix", from, PACKAGE = "Matrix"))## Extract the fixed effectssetMethod("fixef", signature(object = "mer"),function(object, ...).Call("mer_fixef", object, PACKAGE = "Matrix"))## Extract the random effectssetMethod("ranef", signature(object = "mer"),function(object, ...).Call("mer_ranef", object, PACKAGE = "Matrix"))## Optimization for mer objectssetReplaceMethod("LMEoptimize", signature(x="mer", value="list"),function(x, value){if (value$msMaxIter < 1) return(x)nc <- x@ncconstr <- unlist(lapply(nc, function(k) 1:((k*(k+1))/2) <= k))fn <- function(pars)deviance(.Call("mer_coefGets", x, pars, 2, PACKAGE = "Matrix"))gr <- if (value$analyticGradient)function(pars) {if (!isTRUE(all.equal(pars,.Call("mer_coef", x,2, PACKAGE = "Matrix")))).Call("mer_coefGets", x, pars, 2, PACKAGE = "Matrix").Call("mer_gradient", x, 2, PACKAGE = "Matrix")}else NULLoptimRes <- nlminb(.Call("mer_coef", x, 2, PACKAGE = "Matrix"),fn, gr,lower = ifelse(constr, 5e-10, -Inf),control = list(iter.max = value$msMaxIter,trace = as.integer(value$msVerbose))).Call("mer_coefGets", x, optimRes$par, 2, PACKAGE = "Matrix")if (optimRes$convergence != 0) {warning(paste("nlminb returned message",optimRes$message,"\n"))}return(x)})setMethod("deviance", signature(object = "mer"),function(object, ...) {.Call("mer_factor", object, PACKAGE = "Matrix")object@deviance[[ifelse(object@method == "REML", "REML", "ML")]]})setMethod("mcmcsamp", signature(object = "mer"),function(object, n = 1, verbose = FALSE, saveb = FALSE,trans = TRUE, ...){ans <- t(.Call("mer_MCMCsamp", object, saveb, n,trans, PACKAGE = "Matrix"))attr(ans, "mcpar") <- as.integer(c(1, n, 1))class(ans) <- "mcmc"glmer <- FALSEgnms <- names(object@flist)cnms <- object@cnamesff <- fixef(object)colnms <- c(names(ff), if (glmer) character(0) else "sigma^2",unlist(lapply(seq(along = gnms),function(i)abbrvNms(gnms[i],cnms[[i]]))))if (trans) {## parameter type: 0 => fixed effect, 1 => variance,## 2 => covarianceptyp <- c(integer(length(ff)), if (glmer) integer(0) else 1:1,unlist(lapply(seq(along = gnms),function(i){k <- length(cnms[[i]])rep(1:2, c(k, (k*(k-1))/2))})))colnms[ptyp == 1] <-paste("log(", colnms[ptyp == 1], ")", sep = "")colnms[ptyp == 2] <-paste("atanh(", colnms[ptyp == 2], ")", sep = "")}colnames(ans) <- colnmsans})setMethod("simulate", signature(object = "mer"),function(object, nsim = 1, seed = NULL, ...){if(!exists(".Random.seed", envir = .GlobalEnv))runif(1) # initialize the RNG if necessaryif(is.null(seed))RNGstate <- .Random.seedelse {R.seed <- .Random.seedset.seed(seed)RNGstate <- structure(seed, kind = as.list(RNGkind()))on.exit(assign(".Random.seed", R.seed, envir = .GlobalEnv))}family <- object@familyif (family$family != "gaussian" ||family$link != "identity")stop("simulation of generalized linear mixed models not yet implemented")## similate the linear predictorslpred <- .Call("mer_simulate", object, nsim, PACKAGE = "Matrix")sc <- 1if (object@useScale)sc <- .Call("mer_sigma", object, object@method == "REML",PACKAGE = "Matrix")## add fixed-effects contribution and per-observation noise termlpred <- as.data.frame(lpred + drop(object@X %*% fixef(object)) +rnorm(prod(dim(lpred)), sd = sc))## save the seedattr(lpred, "seed") <- RNGstatelpred})setMethod("show", "mer",function(object) {vcShow <- function(varc, useScale){digits <- max(3, getOption("digits") - 2)sc <- attr(varc, "sc")recorr <- lapply(varc, function(el) el@factors$correlation)reStdDev <- c(lapply(recorr, slot, "sd"), list(Residual = sc))reLens <- unlist(c(lapply(reStdDev, length)))reMat <- array('', c(sum(reLens), 4),list(rep('', sum(reLens)),c("Groups", "Name", "Variance", "Std.Dev.")))reMat[1+cumsum(reLens)-reLens, 1] <- names(reLens)reMat[,2] <- c(unlist(lapply(reStdDev, names)), "")reMat[,3] <- format(unlist(reStdDev)^2, digits = digits)reMat[,4] <- format(unlist(reStdDev), digits = digits)if (any(reLens > 1)) {maxlen <- max(reLens)corr <-do.call("rbind",lapply(recorr,function(x, maxlen) {x <- as(x, "matrix")cc <- format(round(x, 3), nsmall = 3)cc[!lower.tri(cc)] <- ""nr <- dim(cc)[1]if (nr >= maxlen) return(cc)cbind(cc, matrix("", nr, maxlen-nr))}, maxlen))colnames(corr) <- c("Corr", rep("", maxlen - 1))reMat <- cbind(reMat, rbind(corr, rep("", ncol(corr))))}if (!useScale) reMat <- reMat[-nrow(reMat),]print(reMat, quote = FALSE)}fcoef <- .Call("mer_fixef", object, PACKAGE = "Matrix")useScale <- object@useScalecorF <- vcov(object)@factors$correlationDF <- getFixDF(object)coefs <- cbind(fcoef, corF@sd, DF)dimnames(coefs) <-list(names(fcoef), c("Estimate", "Std. Error", "DF"))digits <- max(3, getOption("digits") - 2)REML <- object@method == "REML"llik <- logLik(object, REML)dev <- object@deviancedevc <- object@devComprdig <- 5if (glz <- !(object@method %in% c("REML", "ML"))) {cat(paste("Generalized linear mixed model fit using",object@method, "\n"))} else {cat("Linear mixed-effects model fit by ")cat(if(REML) "REML\n" else "maximum likelihood\n")}if (!is.null(object@call$formula)) {cat("Formula:", deparse(object@call$formula),"\n")}if (!is.null(object@call$data)) {cat(" Data:", deparse(object@call$data), "\n")}if (!is.null(object@call$subset)) {cat(" Subset:",deparse(asOneSidedFormula(object@call$subset)[[2]]),"\n")}if (glz) {cat(" Family: ", object@family$family, "(",object@family$link, " link)\n", sep = "")print(data.frame(AIC = AIC(llik), BIC = BIC(llik),logLik = c(llik),deviance = -2*llik,row.names = ""))} else {print(data.frame(AIC = AIC(llik), BIC = BIC(llik),logLik = c(llik),MLdeviance = dev["ML"],REMLdeviance = dev["REML"],row.names = ""))}cat("Random effects:\n")vcShow(VarCorr(object), useScale)ngrps <- lapply(object@flist, function(x) length(levels(x)))cat(sprintf("# of obs: %d, groups: ", devc[1]))cat(paste(paste(names(ngrps), ngrps, sep = ", "), collapse = "; "))cat("\n")if (!useScale)cat("\nEstimated scale (compare to 1) ",.Call("mer_sigma", object, FALSE, PACKAGE = "Matrix"),"\n")if (nrow(coefs) > 0) {if (useScale) {stat <- coefs[,1]/coefs[,2]pval <- 2*pt(abs(stat), coefs[,3], lower = FALSE)nms <- colnames(coefs)coefs <- cbind(coefs, stat, pval)colnames(coefs) <- c(nms, "t value", "Pr(>|t|)")} else {coefs <- coefs[, 1:2, drop = FALSE]stat <- coefs[,1]/coefs[,2]pval <- 2*pnorm(abs(stat), lower = FALSE)nms <- colnames(coefs)coefs <- cbind(coefs, stat, pval)colnames(coefs) <- c(nms, "z value", "Pr(>|z|)")}cat("\nFixed effects:\n")printCoefmat(coefs, tst.ind = 4, zap.ind = 3)rn <- rownames(coefs)if (!is.null(corF)) {p <- ncol(corF)if (p > 1) {cat("\nCorrelation of Fixed Effects:\n")corF <- matrix(format(round(corF@x, 3), nsmall = 3),nc = p)dimnames(corF) <- list(abbreviate(rn, minlen=11),abbreviate(rn, minlen=6))corF[!lower.tri(corF)] <- ""print(corF[-1, -p, drop=FALSE], quote = FALSE)}}}invisible(object)})setMethod("vcov", signature(object = "mer"),function(object, REML = object@method == "REML",useScale = object@useScale,...) {sc <- if (object@useScale) {.Call("mer_sigma", object, REML, PACKAGE = "Matrix")} else { 1 }rr <- as(sc^2 * tcrossprod(solve(object@RXX)), "dpoMatrix")rr@factors$correlation <- as(rr, "correlation")rr})## calculates degrees of freedom for fixed effects Wald tests## This is a placeholder. The answers are generally wrong. It will## be very tricky to decide what a 'right' answer should be with## crossed random effects.setMethod("getFixDF", signature(object="mer"),function(object, ...) {devc <- object@devComprep(as.integer(devc[1]- devc[2]), devc[2])})setMethod("logLik", signature(object="mer"),function(object, REML = object@method == "REML", ...) {val <- -deviance(object, REML = REML)/2devc <- as.integer(object@devComp[1:2])attr(val, "nall") <- attr(val, "nobs") <- devc[1]attr(val, "df") <- abs(devc[2]) +length(.Call("mer_coef", object, 0, PACKAGE = "Matrix"))attr(val, "REML") <- REMLclass(val) <- "logLik"val})setMethod("VarCorr", signature(x = "mer"),function(x, REML = x@method == "REML", useScale = x@useScale, ...){sc <- 1if (useScale)sc <- .Call("mer_sigma", x, REML, PACKAGE = "Matrix")sc2 <- sc * scans <- lapply(x@Omega, function(el) {el <- as(sc2 * solve(el), "dpoMatrix")el@factors$correlation <- as(el, "correlation")el})attr(ans, "sc") <- scans})setMethod("anova", signature(object = "mer"),function(object, ...){mCall <- match.call(expand.dots = TRUE)dots <- list(...)modp <- logical(0)if (length(dots))modp <- sapply(dots, inherits, "mer") | sapply(dots, inherits, "lm")if (any(modp)) { # multiple models - form tableopts <- dots[!modp]mods <- c(list(object), dots[modp])names(mods) <- sapply(as.list(mCall)[c(FALSE, TRUE, modp)], as.character)mods <- mods[order(sapply(lapply(mods, logLik, REML = FALSE), attr, "df"))]calls <- lapply(mods, slot, "call")data <- lapply(calls, "[[", "data")if (any(data != data[[1]])) stop("all models must be fit to the same data object")header <- paste("Data:", data[[1]])subset <- lapply(calls, "[[", "subset")if (any(subset != subset[[1]])) stop("all models must use the same subset")if (!is.null(subset[[1]]))header <-c(header, paste("Subset", deparse(subset[[1]]), sep = ": "))llks <- lapply(mods, logLik, REML = FALSE)Df <- sapply(llks, attr, "df")llk <- unlist(llks)chisq <- 2 * pmax(0, c(NA, diff(llk)))dfChisq <- c(NA, diff(Df))val <- data.frame(Df = Df,AIC = sapply(llks, AIC),BIC = sapply(llks, BIC),logLik = llk,"Chisq" = chisq,"Chi Df" = dfChisq,"Pr(>Chisq)" = pchisq(chisq, dfChisq, lower = FALSE),check.names = FALSE)class(val) <- c("anova", class(val))attr(val, "heading") <-c(header, "Models:",paste(names(mods),unlist(lapply(lapply(calls, "[[", "formula"), deparse)),sep = ": "))return(val)} else {foo <- objectfoo@status["factored"] <- FALSE.Call("mer_factor", foo, PACKAGE="Matrix")dfr <- getFixDF(foo)ss <- foo@rXy^2ssr <- exp(foo@devComp["logryy2"])ss <- ss[seq(along = dfr)]names(ss) <- object@cnames[[".fixed"]][seq(along = dfr)]asgn <- foo@assignterms <- foo@termsnmeffects <- attr(terms, "term.labels")if ("(Intercept)" %in% names(ss))nmeffects <- c("(Intercept)", nmeffects)ss <- unlist(lapply(split(ss, asgn), sum))df <- unlist(lapply(split(asgn, asgn), length))#dfr <- unlist(lapply(split(dfr, asgn), function(x) x[1]))ms <- ss/df#f <- ms/(ssr/dfr)#P <- pf(f, df, dfr, lower.tail = FALSE)#table <- data.frame(df, ss, ms, dfr, f, P)table <- data.frame(df, ss, ms)dimnames(table) <-list(nmeffects,# c("Df", "Sum Sq", "Mean Sq", "Denom", "F value", "Pr(>F)"))c("Df", "Sum Sq", "Mean Sq"))if ("(Intercept)" %in% nmeffects) table <- table[-1,]attr(table, "heading") <- "Analysis of Variance Table"class(table) <- c("anova", "data.frame")table}})setMethod("confint", signature(object = "mer"),function(object, parm, level = 0.95, ...)stop("not yet implemented"))setMethod("fitted", signature(object = "mer"),function(object, ...).Call("mer_fitted", object, TRUE, TRUE, PACKAGE = "Matrix"))setMethod("formula", signature(x = "mer"),function(x, ...)x@call$formula)setMethod("residuals", signature(object = "mer"),function(object, ...)stop("not yet implemented"))setMethod("resid", signature(object = "mer"),function(object, ...)stop("not yet implemented"))setMethod("summary", signature(object = "mer"),function(object, ...)stop("not yet implemented"))setMethod("update", signature(object = "mer"),function(object, ...)stop("not yet implemented"))simss <- function(fm0, fma, nsim){ysim <- simulate(fm0, nsim)cv <- list(analyticGradient = FALSE, msMaxIter = 200:200,msVerbose = 0:0)sapply(ysim, function(yy) {.Call("mer_update_y", fm0, yy, PACKAGE = "Matrix")LMEoptimize(fm0) <- cv.Call("mer_update_y", fma, yy, PACKAGE = "Matrix")LMEoptimize(fma) <- cvexp(c(H0 = fm0@devComp[["logryy2"]],Ha = fma@devComp[["logryy2"]]))})}