Nothing
R/lme.R
In nlme: Linear and Nonlinear Mixed Effects Models
Defines functions
anova.lme
ACF.lme
MEdims
MEestimate
MEEM
MEdecomp
lmeApVar
lmeApVar.fullLmeLogLik
getFixDF
lme.formula
lme.lmList
lme.groupedData
Documented in
ACF.lme
anova.lme
lme.formula
lme.groupedData
lme.lmList
### Fit a general linear mixed effects model
###
### Copyright 2005-2022 The R Core team
### Copyright 1997-2003 Jose C. Pinheiro,
### Douglas M. Bates <bates@stat.wisc.edu>
###
### This program is free software; you can redistribute it and/or modify
### it under the terms of the GNU General Public License as published by
### the Free Software Foundation; either version 2 of the License, or
### (at your option) any later version.
###
### This program is distributed in the hope that it will be useful,
### but WITHOUT ANY WARRANTY; without even the implied warranty of
### MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
### GNU General Public License for more details.
###
### A copy of the GNU General Public License is available at
### http://www.r-project.org/Licenses/
lme <-
## fits general linear mixed effects model by maximum likelihood, or
## residual maximum likelihood using Newton-Raphson algorithm.
function(fixed,
data = sys.frame(sys.parent()),
random,
correlation = NULL,
weights = NULL,
subset,
method = c("REML", "ML"),
na.action = na.fail,
control = list(),
contrasts = NULL, keep.data = TRUE)
UseMethod("lme")
lme.groupedData <-
function(fixed,
data = sys.frame(sys.parent()),
random,
correlation = NULL,
weights = NULL,
subset,
method = c("REML", "ML"),
na.action = na.fail,
control = list(),
contrasts = NULL, keep.data = TRUE)
{
args <- as.list(match.call())[-1L]
names(args)[1L] <- "data"
form <- getResponseFormula(fixed)
form[[3]] <- getCovariateFormula(fixed)[[2L]]
do.call(lme, c(list(fixed = form), args))
}
lme.lmList <-
function(fixed,
data = sys.frame(sys.parent()),
random,
correlation = NULL,
weights = NULL,
subset,
method = c("REML", "ML"),
na.action = na.fail,
control = list(),
contrasts = NULL, keep.data = TRUE)
{
if (length(grpForm <- getGroupsFormula(fixed, asList = TRUE)) > 1) {
stop("can only fit \"lmList\" objects with single grouping variable")
}
this.call <- as.list(match.call())[-1L]
## warn "data" is passed to this function
if (!is.na(match("data", names(this.call)))) {
warning("'lme.lmList' will redefine 'data'")
}
## add object, data, and groups from the call that created object
last.call <- as.list(attr(fixed, "call"))[-1L]
whichLast <- match(c("object", "data", "na.action"), names(last.call))
whichLast <- whichLast[!is.na(whichLast)]
last.call <- last.call[whichLast]
names(last.call)[match(names(last.call), "object")] <- "fixed"
this.call[names(last.call)] <- last.call
this.call$fixed <- eval(substitute(L ~ R,
list(L = getResponseFormula (fixed)[[2L]],
R = getCovariateFormula(fixed)[[2L]])))
if (missing(random)) {
random <- eval(as.call(this.call[["fixed"]][-2]))
}
random <- reStruct(random, data = NULL)
mData <- this.call[["data"]]
if (is.null(mData)) { # will try to construct
allV <- all.vars(formula(random))
if (length(allV) > 0) {
alist <- lapply(as.list(allV), as.name)
names(alist) <- allV
alist <- c(as.list(quote(data.frame)), alist)
mode(alist) <- "call"
mData <- eval(alist, sys.parent(1))
}
} else {
if (mode(mData) == "name" || mode(mData) == "call") {
mData <- eval(mData)
}
}
reSt <- reStruct(random, data = mData) # getting random effects names
names(reSt) <- names(grpForm)
if (length(reSt) > 1) {
stop("can only fit \"lmList\" objects with single grouping variable")
}
rNames <- Names(reSt[[1L]])
if (all(match(rNames, names(cf <- na.omit(coef(fixed))), 0))) {
if (isInitialized(reSt)) {
warning("initial value for \"reStruct\" overwritten in 'lme.lmList'")
}
madRes <- mad(resid(fixed), na.rm = TRUE)
madRan <- unlist(lapply(cf, mad, na.rm = TRUE)[rNames])
names(madRan) <- rNames
matrix(reSt) <- diag((madRan/madRes)^2, ncol = length(rNames))
}
this.call[["random"]] <- reSt
val <- do.call(lme.formula, this.call)
val$origCall <- match.call()
val
}
lme.formula <-
function(fixed,
data = sys.frame(sys.parent()),
random = pdSymm( eval( as.call(fixed[-2]) ) ),
correlation = NULL,
weights = NULL,
subset,
method = c("REML", "ML"),
na.action = na.fail,
control = list(),
contrasts = NULL,
keep.data = TRUE)
{
Call <- match.call()
miss.data <- missing(data) || !is.data.frame(data)
## control parameters
controlvals <- lmeControl()
if (!missing(control)) {
controlvals[names(control)] <- control
}
fixedSigma <- controlvals$sigma > 0
## if(fixedSigma && controlvals$apVar) {
## if("apVar" %in% names(control))
## warning("for 'sigma' fixed, 'apVar' is set FALSE, as the cov approxmation is not yet available.")
## controlvals$apVar <- FALSE
## }
##
## checking arguments
##
if (!inherits(fixed, "formula") || length(fixed) != 3) {
stop("fixed-effects model must be a formula of the form \"resp ~ pred\"")
}
method <- match.arg(method)
REML <- method == "REML"
reSt <- reStruct(random, REML = REML, data = NULL)
groups <- getGroupsFormula(reSt)
if (is.null(groups)) {
if (inherits(data, "groupedData")) {
groups <- getGroupsFormula(data)
namGrp <- rev(names(getGroupsFormula(data, asList = TRUE)))
Q <- length(namGrp)
if (length(reSt) != Q) { # may need to repeat reSt
if (length(reSt) != 1) {
stop("incompatible lengths for 'random' and grouping factors")
}
randL <- vector("list", Q)
names(randL) <- rev(namGrp)
for(i in 1:Q) randL[[i]] <- random
reSt <- reStruct(as.list(randL), REML = REML, data = NULL)
} else {
names(reSt) <- namGrp
}
} else {
## will assume single group
groups <- ~ 1
names(reSt) <- "1"
}
}
## check if corStruct is present and assign groups to its formula,
## if necessary
if (!is.null(correlation)) {
add.form <- FALSE
if(!is.null(corGrpsForm <- getGroupsFormula(correlation, asList = TRUE))) {
corGrpsForm <- unlist(lapply(corGrpsForm,
function(el) deparse(el[[2L]])))
lmeGrpsForm <- unlist(lapply(splitFormula(groups),
function(el) deparse(el[[2L]])))
corQ <- length(corGrpsForm)
lmeQ <- length(lmeGrpsForm)
if (corQ <= lmeQ) {
if (any(corGrpsForm != lmeGrpsForm[1:corQ])) {
stop("incompatible formulas for groups in 'random' and 'correlation'")
}
if (corQ < lmeQ) {
warning("cannot use smaller level of grouping for 'correlation' than for 'random'. Replacing the former with the latter.")
add.form <- TRUE
}
} else if (any(lmeGrpsForm != corGrpsForm[1:lmeQ])) {
stop("incompatible formulas for groups in 'random' and 'correlation'")
}
} else {
add.form <- TRUE
corQ <- lmeQ <- 1
}
if(add.form)
## using the same grouping as in random
attr(correlation, "formula") <-
eval(substitute(~ COV | GRP,
list(COV = getCovariateFormula(formula(correlation))[[2L]],
GRP = groups[[2L]])))
} else {
corQ <- lmeQ <- 1
}
## create an lme structure containing the random effects model and plug-ins
lmeSt <- lmeStruct(reStruct = reSt, corStruct = correlation,
varStruct = varFunc(weights))
## extract a data frame with enough information to evaluate
## fixed, groups, reStruct, corStruct, and varStruct
mfArgs <- list(formula = asOneFormula(formula(lmeSt), fixed, groups),
data = data, na.action = na.action)
if (!missing(subset)) {
mfArgs[["subset"]] <- asOneSidedFormula(Call[["subset"]])[[2L]]
}
mfArgs$drop.unused.levels <- TRUE
dataMix <- do.call(model.frame, mfArgs)
origOrder <- row.names(dataMix) # preserve the original order
for(i in names(contrasts)) # handle contrasts statement
contrasts(dataMix[[i]]) <- contrasts[[i]]
## sort the model.frame by groups and get the matrices and parameters
## used in the estimation procedures
grps <- getGroups(dataMix, groups)
## ordering data by groups
if (inherits(grps, "factor")) { # single level
ord <- order(grps) #"order" treats a single named argument peculiarly
grps <- data.frame(grps)
row.names(grps) <- origOrder
names(grps) <- as.character(deparse((groups[[2L]])))
} else {
ord <- do.call(order, grps)
## making group levels unique
for(i in 2:ncol(grps)) {
grps[, i] <-
as.factor(paste(as.character(grps[, i-1]),
as.character(grps[, i ]), sep = "/"))
}
}
if (corQ > lmeQ) {
## may have to reorder by the correlation groups
ord <- do.call(order, getGroups(dataMix,
getGroupsFormula(correlation)))
}
grps <- grps[ord, , drop = FALSE]
dataMix <- dataMix[ord, ,drop = FALSE]
revOrder <- match(origOrder, row.names(dataMix)) # putting in orig. order
## obtaining basic model matrices
N <- nrow(grps)
Z <- model.matrix(reSt, dataMix) # stores contrasts in matrix form
ncols <- attr(Z, "ncols")
Names(lmeSt$reStruct) <- attr(Z, "nams")
## keeping the contrasts for later use in predict
contr <- attr(Z, "contr")
X <- model.frame(fixed, dataMix)
Terms <- attr(X, "terms")
if (length(attr(Terms, "offset")))
stop("offset() terms are not supported")
auxContr <- lapply(X, function(el)
if (inherits(el, "factor") &&
length(levels(el)) > 1) contrasts(el))
contr <- c(contr, auxContr[is.na(match(names(auxContr), names(contr)))])
contr <- contr[!unlist(lapply(contr, is.null))]
X <- model.matrix(fixed, data=X)
y <- eval(fixed[[2L]], dataMix)
ncols <- c(ncols, dim(X)[2L], 1)
Q <- ncol(grps)
## creating the condensed linear model
dims <- MEdims(grps, ncols)
attr(lmeSt, "conLin") <-
list(Xy = array(c(Z, X, y), c(N, sum(ncols)),
list(row.names(dataMix), c(colnames(Z), colnames(X),
deparse(fixed[[2L]])))),
dims = dims, logLik = 0,
## 17-11-2015; Fixed sigma:
sigma = controlvals$sigma, auxSigma = 0)
## checking if enough observations per group to estimate ranef
if(max(dims$ZXlen[[1L]]) < dims$qvec[1L] && !isTRUE(allow <- controlvals$allow.n.lt.q)) {
msg <- gettextf("fewer observations than random effects in all level %s groups", Q)
if(isFALSE(allow))
stop (msg, domain = NA)
else # typically NA, was hardwired default in nlme <= 3.1-137 [2018]
warning(msg, domain = NA)
}
## degrees of freedom for testing fixed effects
fixDF <- getFixDF(X, grps, dims$ngrps, terms = Terms)
## initialization
lmeSt <- Initialize(lmeSt, dataMix, grps, control = controlvals)
parMap <- attr(lmeSt, "pmap")
## Checking possibility of single decomposition
if (length(lmeSt) == 1) { # reStruct only, can do one decomposition
## need to save conLin for calculating fitted values and residuals
oldConLin <- attr(lmeSt, "conLin")
decomp <- TRUE
attr(lmeSt, "conLin") <- MEdecomp(attr(lmeSt, "conLin"))
} else decomp <- FALSE
## Setup for optimization iterations
if(controlvals$opt == "nlminb") {
control <- list(iter.max = controlvals$msMaxIter,
eval.max = controlvals$msMaxEval,
trace = controlvals$msVerbose)
keep <- c("abs.tol", "rel.tol", "x.tol", "xf.tol", "step.min",
"step.max", "sing.tol", "scale.init", "diff.g")
} else { ## "optim"
control <- list(maxit = controlvals$msMaxIter,
reltol = controlvals$msTol,# if(numIter == 0) controlvals$msTol else reltol
trace = controlvals$msVerbose)
keep <- c("fnscale", "parscale", "ndeps", "abstol", "alpha", "beta",
"gamma", "REPORT", "type", "lmm", "factr", "pgtol",
"temp", "tmax")
}
control <- c(control, controlvals[names(controlvals) %in% keep])
##
## getting the linear mixed effects fit object,
## possibly iterating for variance functions
##
numIter <- 0L
repeat {
oldPars <- coef(lmeSt)
optRes <-
if (controlvals$opt == "nlminb") {
nlminb(c(oldPars), function(lmePars) -logLik(lmeSt, lmePars),
control = control)
} else { ## "optim"
if(numIter == 1L) { # (yes, strange, but back-compatible ..) :
reltol <- controlvals$reltol
if(is.null(reltol)) reltol <- 100*.Machine$double.eps
control$reltol <- reltol
}
optim(c(oldPars), function(lmePars) -logLik(lmeSt, lmePars),
control = control, method = controlvals$optimMethod)
}
coef(lmeSt) <- optRes$par
attr(lmeSt, "lmeFit") <- MEestimate(lmeSt, grps)
## checking if any updating is needed
if (!needUpdate(lmeSt)) {
if (optRes$convergence) {
msg <- gettextf("%s problem, convergence error code = %s\n message = %s",
controlvals$opt, optRes$convergence,
paste(optRes$message, collapse = ""))
if(!controlvals$returnObject)
stop(msg, domain = NA)
else
warning(msg, domain = NA)
}
break
}
## updating the fit information
numIter <- numIter + 1L
lmeSt <- update(lmeSt, dataMix)
## calculating the convergence criterion
aConv <- coef(lmeSt)
conv <- abs((oldPars - aConv)/ifelse(aConv == 0, 1, aConv))
aConv <- NULL
for(i in names(lmeSt)) {
if (any(parMap[,i])) {
aConv <- c(aConv, max(conv[parMap[,i]]))
names(aConv)[length(aConv)] <- i
}
}
if (max(aConv) <= controlvals$tolerance) {
break
}
if (numIter > controlvals$maxIter) {
msg <- gettext("maximum number of iterations (lmeControl(maxIter)) reached without convergence")
if (controlvals$returnObject) {
warning(msg, domain = NA)
break
} else
stop(msg, domain = NA)
}
} ## end{repeat}
## wrapping up
lmeFit <- attr(lmeSt, "lmeFit")
names(lmeFit$beta) <- namBeta <- colnames(X)
attr(fixDF, "varFixFact") <- varFix <- lmeFit$sigma * lmeFit$varFix
varFix <- crossprod(varFix)
dimnames(varFix) <- list(namBeta, namBeta)
##
## fitted.values and residuals (in original order)
##
Fitted <- fitted(lmeSt, level = 0:Q,
conLin = if (decomp) oldConLin else attr(lmeSt, "conLin"))[
revOrder, , drop = FALSE]
Resid <- y[revOrder] - Fitted
rownames(Resid) <- rownames(Fitted) <- origOrder
attr(Resid, "std") <- lmeFit$sigma/(varWeights(lmeSt)[revOrder])
## putting groups back in original order
grps <- grps[revOrder, , drop = FALSE]
## making random effects estimates consistently ordered
## for(i in names(lmeSt$reStruct)) {
## lmeFit$b[[i]] <- lmeFit$b[[i]][unique(as.character(grps[, i])),, drop = F]
## NULL
## }
## inverting back reStruct
lmeSt$reStruct <- solve(lmeSt$reStruct)
## saving part of dims
dims <- attr(lmeSt, "conLin")$dims[c("N", "Q", "qvec", "ngrps", "ncol")]
## 17-11-2015; Fixed sigma patch; SH Heisterkamp; Quantitative Solutions
attr(lmeSt, "fixedSigma") <- fixedSigma
## getting the approximate var-cov of the parameters
apVar <-
if (controlvals$apVar) {
lmeApVar(lmeSt, lmeFit$sigma,
.relStep = controlvals[[".relStep"]],
minAbsPar = controlvals[["minAbsParApVar"]],
natural = controlvals[["natural"]])
} else {
"Approximate variance-covariance matrix not available"
}
## getting rid of condensed linear model and fit
attr(lmeSt, "conLin") <- NULL
attr(lmeSt, "lmeFit") <- NULL
grpDta <- inherits(data, "groupedData")
##
## creating the lme object
##
structure(class = "lme",
list(modelStruct = lmeSt,
dims = dims,
contrasts = contr,
coefficients = list(
fixed = lmeFit$beta,
random = lmeFit$b),
varFix = varFix,
sigma = lmeFit$sigma,
apVar = apVar,
logLik = lmeFit$logLik,
numIter = if (needUpdate(lmeSt)) numIter, # else NULL
groups = grps,
call = Call,
terms = Terms,
method = method,
fitted = Fitted,
residuals = Resid,
fixDF = fixDF,
na.action = attr(dataMix, "na.action"),
data = if (keep.data && !miss.data) data),
## saving labels and units for plots
units = if(grpDta) attr(data, "units"),
labels= if(grpDta) attr(data, "labels"))
}
### Auxiliary functions used internally in lme and its methods
getFixDF <- function(X, grps, ngrps, assign = attr(X, "assign"), terms)
{
## calculates degrees of freedom for fixed effects Wald tests
if (!is.list(assign)) { # in R
namTerms <- attr(terms, "term.labels")
if (attr(terms, "intercept") > 0) {
namTerms <- c("(Intercept)", namTerms)
}
namTerms <- factor(assign, labels = namTerms)
assign <- split(order(assign), namTerms)
}
## function to check if a vector is (nearly) a multiple of (1,1,...,1)
const <- function(x, tolerance = sqrt(.Machine$double.eps)) {
if (length(x) < 1) return(NA)
x <- as.numeric(x)
## return
all(abs(if(x[1L] == 0) x else x/x[1L] - 1) < tolerance)
}
N <- nrow(X)
p <- ncol(X)
Q <- ncol(grps)
Qp1 <- Q + 1L
namX <- colnames(X)
ngrps <- rev(ngrps)[-(1:2)]
stratNam <- c(names(ngrps), "Residual")
dfX <- dfTerms <- setNames(c(ngrps, N) - c(0, ngrps), stratNam)
valX <- setNames(double(p), namX)
namTerms <- names(assign)
valTerms <- double(length(assign))
names(valTerms) <- namTerms
if (any(notIntX <- !apply(X, 2, const))) {
## percentage of groups for which columns of X are inner
innP <- array(c(rep(1, p),
.C(inner_perc_table,
as.double(X),
as.integer(unlist(grps)),
as.integer(p),
as.integer(Q),
as.integer(N),
val = double(p * Q))[["val"]]),
dim = c(p, Qp1),
dimnames = list(namX, stratNam))
## strata in which columns of X are estimated
## ignoring fractional inner percentages for now
stratX <- stratNam[apply(innP, 1, function(el, index) max(index[el > 0]),
index = 1:Qp1)]
## strata in which terms are estimated
notIntTerms <- unlist(lapply(assign,
function(el, notIntX) {
any(notIntX[el])
}, notIntX = notIntX))
stratTerms <- stratNam[unlist(lapply(assign,
function(el) max(match(stratX[el], stratNam))
))][notIntTerms]
stratX <- stratX[notIntX]
xDF <- table(stratX)
dfX[names(xDF)] <- dfX[names(xDF)] - xDF
if (!all(notIntX)) { # correcting df for intercept
dfX[1L] <- dfX[1L] - 1L
} else {
dfX[-1L] <- dfX[-1L] + 1L
}
valX[notIntX] <- dfX[stratX]
## number of parameters in each term
pTerms <- lengths(assign)[notIntTerms]
tDF <- tapply(pTerms, stratTerms, sum)
dfTerms[names(tDF)] <- dfTerms[names(tDF)] - tDF
if (!all(notIntTerms)) {
dfTerms[1L] <- dfTerms[1L] - 1L
} else {
dfTerms[-1L] <- dfTerms[-1L] + 1L
}
valTerms[notIntTerms] <- dfTerms[stratTerms]
} else {
notIntTerms <- vapply(assign, function(el) any(notIntX[el]), NA)
}
if (!all(notIntX)) { #intercept included
valX[!notIntX] <- max(dfX)
if (!all(notIntTerms))
valTerms[!notIntTerms] <- max(dfTerms)
}
structure(list(X = valX, terms = valTerms),
assign = assign)
}
lmeApVar.fullLmeLogLik <- function(Pars, object, conLin, dims, N, settings) {
## logLik as a function of sigma and coef(lmeSt)
fixedSigma <- attr(object, "fixedSigma")
npar <- length(Pars)
if (!fixedSigma) {
sigma <- exp(Pars[npar]) # within-group std. dev.
Pars <- Pars[-npar]
lsigma <- 0
} else {
sigma <- lsigma <- conLin$sigma
}
coef(object) <- Pars
if ((lO <- length(object)) > 1) {
for(i in lO:2)
conLin <- recalc(object[[i]], conLin)
}
val <- .C(mixed_loglik,
as.double(conLin$Xy),
as.integer(unlist(dims)),
as.double(sigma * unlist(pdFactor(solve(object$reStruct)))),
as.integer(settings),
logLik = double(1L),
lRSS = double(1L), sigma=as.double(lsigma))[c("logLik", "lRSS")]
aux <- (exp(val[["lRSS"]])/sigma)^2
conLin[["logLik"]] + val[["logLik"]] + (N * log(aux) - aux)/2
}
lmeApVar <-
function(lmeSt, sigma, conLin = attr(lmeSt, "conLin"),
.relStep = .Machine$double.eps^(1/3), minAbsPar = 0,
natural = TRUE)
{
fixedSigma <- attr(lmeSt,"fixedSigma")
## calculate approximate variance-covariance matrix of all parameters
## except the fixed effects. By default, uses natural parametrization for
## for pdSymm matrices
dims <- conLin$dims
sett <- attr(lmeSt, "settings")
N <- dims$N - sett[1L] * dims$ncol[dims$Q + 1L]
sett[2:3] <- c(1, 0) # asDelta = TRUE and no grad/Hess
conLin[["logLik"]] <- 0 # making sure
sig2 <- sigma * sigma
reSt <- lmeSt[["reStruct"]]
for(i in seq_along(reSt)) {
matrix(reSt[[i]]) <- as.double(sig2) * pdMatrix(reSt[[i]])
if (inherits(reSt[[i]], "pdSymm") && natural) {
reSt[[i]] <- pdNatural(reSt[[i]])
}
if (inherits(reSt[[i]], "pdBlocked") && natural) {
for(j in seq_along(reSt[[i]])) {
if (inherits(reSt[[i]][[j]], "pdSymm")) {
reSt[[i]][[j]] <- pdNatural(reSt[[i]][[j]])
}
}
}
}
lmeSt[["reStruct"]] <- reSt
cSt <- lmeSt[["corStruct"]]
if (!is.null(cSt) && inherits(cSt, "corSymm") && natural) {
cStNatPar <- coef(cSt, unconstrained = FALSE)
class(cSt) <- c("corNatural", "corStruct")
coef(cSt) <- log((cStNatPar + 1)/(1 - cStNatPar))
lmeSt[["corStruct"]] <- cSt
}
Pars <- if(fixedSigma) coef(lmeSt) else c(coef(lmeSt), lSigma = log(sigma))
val <- fdHess(Pars, lmeApVar.fullLmeLogLik, lmeSt, conLin, dims, N, sett,
.relStep = .relStep, minAbsPar = minAbsPar)[["Hessian"]]
if (all(eigen(val, only.values=TRUE)$values < 0)) {
## negative definite - OK
val <- solve(-val)
nP <- names(Pars)
dimnames(val) <- list(nP, nP)
attr(val, "Pars") <- Pars
attr(val, "natural") <- natural
val
} else {
## problem - solution is not a maximum
"Non-positive definite approximate variance-covariance"
}
}
MEdecomp <-
function(conLin)
## decompose a condensed linear model. Returns another condensed
## linear model
{
dims <- conLin$dims
if (dims[["StrRows"]] >= dims[["ZXrows"]]) {
## no point in doing the decomposition
return(conLin)
}
dc <- array(.C(mixed_decomp,
as.double(conLin$Xy),
as.integer(unlist(dims)))[[1L]],
c(dims$StrRows, dims$ZXcols))
dims$ZXrows <- dims$StrRows
dims$ZXoff <- dims$DecOff
dims$ZXlen <- dims$DecLen
conLin[c("Xy", "dims")] <- list(Xy = dc, dims = dims)
conLin
}
MEEM <-
function(object, conLin, niter = 0)
## perform niter iterations of the EM algorithm for conLin
## assumes that object is in precision form
{
if (niter > 0) {
dd <- conLin$dims
pdCl <- attr(object, "settings")[-(1:3)]
pdCl[pdCl == -1] <- 0
precvec <- unlist(pdFactor(object))
zz <- .C(mixed_EM,
as.double(conLin$Xy),
as.integer(unlist(dd)),
precvec = as.double(precvec),
as.integer(niter),
as.integer(pdCl),
as.integer(attr(object, "settings")[1L]),
double(1),
double(length(precvec)),
double(1),
as.double(conLin$sigma))[["precvec"]]
Prec <- vector("list", length(object))
names(Prec) <- names(object)
for (i in seq_along(object)) {
len <- dd$qvec[i]^2
matrix(object[[i]]) <-
crossprod(matrix(zz[1:len + dd$DmOff[i]], ncol = dd$qvec[i]))
}
}
object
}
MEestimate <-
function(object, groups, conLin = attr(object, "conLin"))
{
dd <- conLin$dims
nc <- dd$ncol
REML <- attr(object$reStruct, "settings")[1L]
Q <- dd$Q
rConLin <- recalc(object, conLin)
zz <- .C(mixed_estimate,
as.double(rConLin$Xy),
as.integer(unlist(dd)),
as.double(unlist(pdFactor(object$reStruct))),
as.integer(REML),
double(1),
estimates = double(dd$StrRows * dd$ZXcols),
as.logical(FALSE),
## 17-11-2015; Fixed sigma patch; SH Heisterkamp; Quantitative Solutions
sigma = as.double(conLin$sigma))[["estimates"]]
estimates <- array(zz, c(dd$StrRows, dd$ZXcols))
resp <- estimates[ , dd$ZXcols]
reSt <- object$reStruct
nam <- names(reSt)
val <- vector(mode = "list", length = Q)
start <- dd$StrRows * c(0, cumsum(nc))
for (i in seq_along(reSt)) {
val[[i]] <-
matrix(resp[as.vector(outer(1:(nc[i]), dd$SToff[[i]] - start[i], "+"))],
ncol = nc[i], byrow = TRUE,
dimnames = list(unique(as.character(groups[, nam[i]])),
Names(reSt[[i]])))
}
names(val) <- nam
p <- nc[[Q + 1L]]
N <- dd$N - REML * p
dimE <- dim(estimates)
## 17-11-2015; Fixed sigma patch; ... modified by MM notably for p == 0:
Np <- N
auxSigma <- abs(resp[dimE[1]])/sqrt(Np)
if(conLin$sigma > 0) {
loglik <- (N * (-log(2 * pi)-2*log(conLin$sigma)))/2 + rConLin$logLik
sigma <- conLin$sigma
} else {
loglik <- (N * (log(N) - (1 + log(2 * pi))))/2 + rConLin$logLik
sigma <- auxSigma
}
list(logLik = loglik,
b = rev(val),
beta = if(p) resp[dimE[1] - (p:1)] else double(),
sigma = sigma,
auxSigma = auxSigma,
varFix = if(p)
t(solve(estimates[dimE[1] - (p:1), dimE[2] - (p:1), drop = FALSE]))
else
matrix(,p,p))
}
MEdims <- function(groups, ncols)
{
## define constants used in matrix decompositions and log-lik calculations
## first need some local functions
glengths <-
## returns the group lengths from a vector of last rows in the group
function(lstrow) diff(c(0, lstrow))
offsets <-
## converts total number of columns(N), columns per level(ncols), and
## a list of group lengths to offsets in C arrays
function(N, ncols, lstrow, triangle = FALSE)
{
pop <- function(x) x[-length(x)]
cstart <- c(0, cumsum(N * ncols))
for (i in seq_along(lstrow)) {
lstrow[[i]] <- cstart[i] +
if (triangle) {
lstrow[[i]] - ncols[i] # storage offsets style
} else {
pop(c(0, lstrow[[i]])) # decomposition style
}
}
lstrow
}
Q <- ncol(groups) # number of levels
N <- nrow(groups) # number of observations
## 'isLast' indicates if the row is the last row in the group at that level.
## this version propagates changes from outer groups to inner groups
## isLast <- (array(unlist(lapply(c(rev(as.list(groups)),
## list(X = rep(0, N), y = rep(0, N))),
## function(x) c(0 != diff(codes(x)), TRUE))),
## c(N, Q+2), list(NULL, c(rev(names(groups)), "X", "y")))
## %*% (row(diag(Q+2)) >= col(diag(Q+2)))) != 0
## this version does not propagate changes from outer to inner.
isLast <- array(FALSE, dim(groups) + c(0, 2),
list(NULL, c(rev(names(groups)), "X", "y")))
for(i in 1:Q) {
isLast[, Q + 1L - i] <- c(0 != diff(as.integer(groups[[i]])), TRUE)
}
isLast[N, ] <- TRUE
lastRow <- apply(isLast, 2, function(x) seq_along(x)[x])
if(!is.list(lastRow)) {
nm <- names(lastRow)
lastRow <- as.list(lastRow)
names(lastRow) <- nm
}
isLast <- t(isLast)
strSizes <- cumsum(ncols * isLast) * isLast # required storage sizes
lastStr <- apply(t(strSizes), 2, function(x) x[x != 0])
if(!is.list(lastStr)) {
nm <- names(lastStr)
lastStr <- as.list(lastStr)
names(lastStr) <- nm
}
strRows <- max(lastStr[[length(lastStr)]])
lastBlock <- vector("list", Q)
names(lastBlock) <- rownames(strSizes)[1:Q]
for(i in 1:Q) lastBlock[[i]] <- c(strSizes[i, -N], strRows)
maxStr <- do.call(pmax, lastBlock)
for(i in 1:Q) lastBlock[[i]] <- maxStr[as.logical(lastBlock[[i]])]
lastBlock <- c(lastBlock, list(X = strRows, y = strRows))
list(N = N, # total number of rows in data
ZXrows = N, # no. of rows in array
ZXcols = sum(ncols), # no. of columns in array
Q = Q, # no. of levels of random effects
StrRows = strRows, # no. of rows required for storage
qvec = ncols * c(rep(1, Q), 0, 0), # lengths of random effects
# no. of groups at each level
### This looks wrong: ")" at wrong place: unlist(*, N, N) !!
ngrps = c(unlist(lapply(lastRow, length), N, N)),
###?ok ngrps = c(lengths(lastRow), N, N),# no. of groups at each level
DmOff = c(0, cumsum(ncols^2))[1:(Q+2)],# offsets into DmHalf array by level
ncol = ncols, # no. of columns decomposed per level
nrot = rev(c(0, cumsum(rev(ncols))))[-1L],# no. of columns rotated per level
ZXoff = offsets(N, ncols, lastRow), # offsets into ZXy
ZXlen = lapply(lastRow, glengths), # lengths of ZXy groups
# storage array offsets
SToff = offsets(strRows, ncols, lastStr, triangle = TRUE),
# decomposition offsets
DecOff = offsets(strRows, ncols, lastBlock),
# decomposition lengths
DecLen = lapply(lastBlock, glengths)
)
}
### Methods for standard generics
ACF.lme <-
function(object, maxLag,
resType = c("pearson", "response", "normalized"), ...)
{
resType <- match.arg(resType)
res <- resid(object, type = resType, asList = TRUE)
if(missing(maxLag)) {
maxLag <- min(c(maxL <- max(lengths(res)) - 1,
as.integer(10 * log10(maxL + 1))))
}
val <- lapply(res,
function(el, maxLag) {
N <- maxLag + 1L
tt <- double(N)
nn <- integer(N)
N <- min(c(N, n <- length(el)))
nn[1:N] <- n + 1L - 1:N
## el <- el - mean(el)
for(i in 1:N) {
tt[i] <- sum(el[1:(n-i+1)] * el[i:n])
}
array(c(tt,nn), c(length(tt), 2))
}, maxLag = maxLag)
val0 <- rowSums(sapply(val, function(x) x[,2]))
val1 <- rowSums(sapply(val, function(x) x[,1]))/val0
val2 <- val1/val1[1L]
z <- data.frame(lag = 0:maxLag, ACF = val2)
attr(z, "n.used") <- val0
class(z) <- c("ACF", "data.frame")
z
}
anova.lme <-
function(object, ..., test = TRUE, type = c("sequential", "marginal"),
adjustSigma = TRUE, Terms, L, verbose = FALSE)
{
fixSig <- attr(object$modelStruct, "fixedSigma")
fixSig <- !is.null(fixSig) && fixSig
## returns the likelihood ratio statistics, the AIC, and the BIC
Lmiss <- missing(L)
dots <- list(...)
if ((rt <- length(dots) + 1L) == 1L) { ## just one object
if (!inherits(object,"lme")) {
stop("object must inherit from class \"lme\" ")
}
vFix <- attr(object$fixDF, "varFixFact")
if (adjustSigma && object$method == "ML")
## using REML-like estimate of sigma under ML
vFix <- sqrt(object$dims$N/(object$dims$N - ncol(vFix))) * vFix
c0 <- solve(t(vFix), fixef(object))
assign <- attr(object$fixDF, "assign")
nTerms <- length(assign)
if (missing(Terms) && Lmiss) {
## returns the F.table (Wald) for the fixed effects
type <- match.arg(type)
Fval <- Pval <- double(nTerms)
nDF <- integer(nTerms)
dDF <- object$fixDF$terms
for(i in 1:nTerms) {
nDF[i] <- length(assign[[i]])
if (type == "sequential") { # type I SS
c0i <- c0[assign[[i]]]
} else {
c0i <- c(qr.qty(qr(vFix[, assign[[i]], drop = FALSE]), c0))[1:nDF[i]]
}
Fval[i] <- sum(c0i^2)/nDF[i]
Pval[i] <- 1 - pf(Fval[i], nDF[i], dDF[i])
}
##
## fixed effects F-values, df, and p-values
##
aod <- data.frame(numDF= nDF, denDF= dDF, "F-value"= Fval, "p-value"= Pval,
check.names = FALSE)
rownames(aod) <- names(assign)
attr(aod,"rt") <- rt
} else {
nX <- length(unlist(assign))
if (Lmiss) { # terms is given
if (is.numeric(Terms) && all(Terms == as.integer(Terms))) {
if (min(Terms) < 1 || max(Terms) > nTerms) {
stop(gettextf("'Terms' must be between 1 and %d", nTerms),
domain = NA)
}
} else {
if (is.character(Terms)) {
if (any(noMatch <- is.na(match(Terms, names(assign))))) {
stop(sprintf(ngettext(sum(noMatch),
"term %s not matched",
"terms %s not matched"),
paste(Terms[noMatch], collapse = ", ")),
domain = NA)
}
} else {
stop("terms can only be integers or characters")
}
}
dDF <- unique(object$fixDF$terms[Terms])
if (length(dDF) > 1) {
stop("terms must all have the same denominator DF")
}
lab <-
paste("F-test for:",paste(names(assign[Terms]),collapse=", "),"\n")
L <- diag(nX)[unlist(assign[Terms]),,drop=FALSE]
} else {
L <- as.matrix(L)
if (ncol(L) == 1) L <- t(L) # single linear combination
nrowL <- nrow(L)
ncolL <- ncol(L)
if (ncol(L) > nX) {
stop(sprintf(ngettext(nX,
"'L' must have at most %d column",
"'L' must have at most %d columns"),
nX), domain = NA)
}
dmsL1 <- rownames(L)
L0 <- array(0, c(nrowL, nX), list(NULL, names(object$fixDF$X)))
if (is.null(dmsL2 <- colnames(L))) {
## assume same order as effects
L0[, 1:ncolL] <- L
} else {
if (any(noMatch <- is.na(match(dmsL2, colnames(L0))))) {
stop(sprintf(ngettext(sum(noMatch),
"effect %s not matched",
"effects %s not matched"),
paste(dmsL2[noMatch],collapse=", ")),
domain = NA)
}
L0[, dmsL2] <- L
}
L <- L0[noZeroRowL <- as.logical((L0 != 0) %*% rep(1, nX)), , drop = FALSE]
nrowL <- nrow(L)
rownames(L) <- if(is.null(dmsL1)) 1:nrowL else dmsL1[noZeroRowL]
dDF <-
unique(object$fixDF$X[noZeroColL <-
as.logical(c(rep(1,nrowL) %*% (L != 0)))])
if (length(dDF) > 1) {
stop("L may only involve fixed effects with the same denominator DF")
}
lab <- "F-test for linear combination(s)\n"
}
nDF <- sum(svd.d(L) > 0)
c0 <- c(qr.qty(qr(vFix %*% t(L)), c0))[1:nDF]
Fval <- sum(c0^2)/nDF
Pval <- pf(Fval, nDF, dDF, lower.tail=FALSE)
aod <- data.frame(numDF = nDF, denDF = dDF, "F-value" = Fval, "p-value" = Pval,
check.names=FALSE)
attr(aod, "rt") <- rt
attr(aod, "label") <- lab
if (!Lmiss) {
attr(aod, "L") <-
if(nrow(L) > 1) L[, noZeroColL, drop = FALSE] else L[, noZeroColL]
}
}
}
##
## Otherwise construct the likelihood ratio and information table
## objects in ... may inherit from gls, gnls, lm, lmList, lme,
## nlme, nlsList, and nls
##
else {
ancall <- sys.call() # yuck.. hack
ancall$verbose <- ancall$test <- ancall$type <- NULL
object <- list(object, ...)
valid.cl <- c("gls", "gnls", "lm", "lmList", "lme","nlme","nlsList","nls")
if (!all(vapply(object, inherits, TRUE, what = valid.cl))) {
valid.cl <- paste0('"', valid.cl, '"')
stop(gettextf("objects must inherit from classes %s, or %s",
paste(head(valid.cl, -1), collapse=", "), tail(valid.cl, 1)),
domain=NA)
}
resp <- vapply(object,
function(el) deparse(getResponseFormula(el)[[2L]]), "")
## checking if responses are the same
subs <- as.logical(match(resp, resp[1L], FALSE))
if (!all(subs))
warning("some fitted objects deleted because response differs from the first model")
if (sum(subs) == 1)
stop("first model has a different response from the rest")
object <- object[subs]
rt <- length(object)
termsModel <- lapply(object, function(el) formula(el)[-2])
estMeth <- vapply(object, function(el)
if (is.null(val <- el[["method"]])) NA_character_ else val, "")
## checking consistency of estimation methods
if(length(uEst <- unique(estMeth[!is.na(estMeth)])) > 1) {
stop("all fitted objects must have the same estimation method")
}
estMeth[is.na(estMeth)] <- uEst
## checking if all models have same fixed effects when estMeth = "REML"
REML <- uEst == "REML"
if(REML) {
aux <- vapply(termsModel,
function(el) {
tt <- terms(el)
val <- paste(sort(attr(tt, "term.labels")), collapse = "&")
if (attr(tt, "intercept") == 1)
paste(val, "(Intercept)", sep = "&") else val
}, ".")
if(length(unique(aux)) > 1) {
warning("fitted objects with different fixed effects. REML comparisons are not meaningful.")
}
}
termsCall <-
lapply(object, function(el) {
if (is.null(val <- el$call) &&
is.null(val <- attr(el, "call")))
stop("objects must have a \"call\" component or attribute")
val
})
termsCall <- vapply(termsCall,
function(el) paste(deparse(el), collapse =""), "")
aux <- lapply(object, logLik, REML)
if (length(unique(vapply(aux, attr, 1, "nall"))) > 1) {
stop("all fitted objects must use the same number of observations")
}
dfModel <- vapply(aux, attr, 1, "df")
logLik <- vapply(aux, c, 1.1)
aod <- data.frame(call = termsCall,
Model = 1:rt,
df = dfModel,
AIC = vapply(aux, AIC, 1.),
BIC = vapply(aux, BIC, 1.),
logLik = logLik,
check.names = FALSE)
if (test) {
ddf <- diff(dfModel)
if (sum(abs(ddf)) > 0) {
effects <- rep("", rt)
for(i in 2:rt) {
if (ddf[i-1] != 0) {
effects[i] <- paste(i - 1, i, sep = " vs ")
}
}
pval <- rep(NA, rt - 1)
ldf <- as.logical(ddf)
lratio <- 2 * abs(diff(logLik))
lratio[!ldf] <- NA
pval[ldf] <- pchisq(lratio[ldf], abs(ddf[ldf]), lower.tail=FALSE)
aod <- data.frame(aod,
Test = effects,
"L.Ratio" = c(NA, lratio),
"p-value" = c(NA, pval),
check.names = FALSE,
stringsAsFactors = TRUE)
}
}
row.names(aod) <- vapply(as.list(ancall[-1L]), c_deparse, "")
attr(aod, "rt") <- rt
attr(aod, "verbose") <- verbose
}
class(aod) <- c("anova.lme", "data.frame")
aod
}
## (This is "cut'n'paste" similar to augPred.gls() in ./gls.R -- keep in sync!)
augPred.lme <-
function(object, primary = NULL, minimum = min(primary),
maximum = max(primary), length.out = 51L, level = Q, ...)
{
data <- eval.parent(object$call$data)
if (!inherits(data, "data.frame")) {
stop(gettextf("data in %s call must evaluate to a data frame",
sQuote(substitute(object))), domain = NA)
}
if(is.null(primary)) {
if (!inherits(data, "groupedData")) {
stop(gettextf(
"%s without \"primary\" can only be used with fits of \"groupedData\" objects",
sys.call()[[1L]]), domain = NA)
}
primary <- getCovariate(data)
pr.var <- getCovariateFormula(data)[[2L]]
} else{
pr.var <- asOneSidedFormula(primary)[[2L]]
primary <- eval(pr.var, data)
}
prName <- c_deparse(pr.var)
newprimary <- seq(from = minimum, to = maximum, length.out = length.out)
Q <- object$dims$Q # number of levels
if (is.null(level)) level <- Q
nL <- length(level) # number of requested levels
maxLev <- max(c(level, 1))
groups <- getGroups(object, level = maxLev)
if (!is.ordered(groups)) {
groups <- ordered(groups, levels = unique(as.character(groups)))
}
grName <- ".groups"
ugroups <- unique(groups)
value <- data.frame(rep(rep(newprimary, length(ugroups)), nL),
rep(rep(ugroups, rep(length(newprimary),
length(ugroups))), nL))
names(value) <- c(prName, grName)
## recovering other variables in data that may be needed for predictions
## varying variables will be replaced by their means
summData <- gsummary(data, groups = groups)
if (any(toAdd <- is.na(match(names(summData), names(value))))) {
summData <- summData[, toAdd, drop = FALSE]
}
value[, names(summData)] <- summData[value[, 2L], ]
pred <- predict(object, value[seq_len(nrow(value)/nL), , drop = FALSE],
level = level)
if (nL > 1) { # multiple levels
pred <- pred[, ncol(pred) - (nL - 1):0] # eliminating groups
predNames <- rep(names(pred), rep(nrow(pred), nL))
pred <- c(unlist(pred))
} else {
predNames <- rep("predicted", nrow(value))
}
newvals <- cbind(value[, 1:2], pred)
names(newvals)[3] <- respName <-
deparse(resp.var <- getResponseFormula(object)[[2L]])
orig <- data.frame(primary, groups, getResponse(object))
names(orig) <- names(newvals)
value <- rbind(orig, newvals)
attributes(value[, 2]) <- attributes(groups)
value[, ".type"] <- ordered(c(rep("original", nrow(data)), predNames),
levels = c(unique(predNames), "original"))
labs <- list(x = prName, y = respName)
unts <- list(x = "", y = "")
if(inherits(data, "groupedData")) {
labs[names(attr(data, "labels"))] <- attr(data, "labels")
unts[names(attr(data, "units"))] <- attr(data, "units")
attr(value, "units") <- attr(data, "units")
}
structure(value, class = c("augPred", class(value)),
labels = labs,
units = unts,
formula= eval(substitute(Y ~ X | G,
list(Y = resp.var, X = pr.var,
G = as.name(grName)))))
}
coef.lme <-
function(object, augFrame = FALSE, level = Q, data, which = 1:ncol(data),
FUN = mean, omitGroupingFactor = TRUE, subset = NULL, ...)
{
Q <- object$dims$Q
if (length(level) > 1) {
stop("only single level allowed")
}
fixed <- fixef(object)
p <- length(fixed)
value <- ranef(object, level = 1:level)
grps <- object[["groups"]]
if (Q > 1) {
grpNames <- t(array(rep(rev(names(grps)), Q), c(Q, Q)))
grpNames[lower.tri(grpNames)] <- ""
grpNames <-
rev(apply(grpNames, 1,
function(x) paste(x[x != ""], collapse = " %in% ")))[level]
} else {
grpNames <- names(grps)
}
grps <- grps[, 1:level, drop = FALSE]
grps <- gsummary(grps, groups = grps[, level])
if (level == 1) value <- list(value)
effNams <- unlist(lapply(value, names))
grps <- grps[row.names(value[[level]]), , drop = FALSE]
M <- nrow(grps)
effNams <- unique(c(names(fixed), effNams))
effs <- array(0, c(M, length(effNams)),
list(row.names(grps), effNams))
effs[, names(fixed)] <- array(rep(fixed, rep(M, p)), c(M, p))
for (i in 1:level) {
nami <- names(value[[i]])
effs[, nami] <- as.matrix(effs[, nami] + value[[i]][as.character(grps[, i]), ])
}
if (augFrame) { # can only do that for last level
if (missing(data)) {
data <- getData(object)
}
data <- as.data.frame(data)
data <- data[, which, drop = FALSE]
value <- ranef(object, TRUE, level, data, FUN = FUN,
omitGroupingFactor = omitGroupingFactor,
subset = subset)
whichKeep <- is.na(match(names(value), effNams))
if (any(whichKeep)) {
effs <- cbind(effs, value[, whichKeep, drop = FALSE])
}
}
effs <- as.data.frame(effs)
attr(effs, "level") <- level
attr(effs, "label") <- "Coefficients"
attr(effs, "effectNames") <- effNams
attr(effs, "standardized") <- FALSE
attr(effs, "grpNames") <- grpNames
class(effs) <- unique(c("coef.lme", "ranef.lme", class(effs)))
effs
}
fitted.lme <-
function(object, level = Q, asList = FALSE, ...)
{
Q <- object$dims$Q
val <- object[["fitted"]]
if (is.character(level)) { # levels must be given consistently
nlevel <- match(level, names(val))
if (any(aux <- is.na(nlevel))) {
stop(sprintf(ngettext(sum(aux),
"nonexistent level %s",
"nonexistent levels %s"),
level[aux]), domain = NA)
}
level <- nlevel
} else { # assuming integers
level <- 1 + level
}
if (length(level) == 1L) {
grp.nm <- row.names(object[["groups"]])
grps <- as.character(object[["groups"]][, max(c(1, level - 1))])
if (asList) {
val <- as.list(split(val, ordered(grps, levels = unique(grps))))
} else {
val <- napredict(object$na.action, val[, level])
names(val) <- grps[match(names(val), grp.nm)]
}
lab <- "Fitted values"
if (!is.null(aux <- attr(object, "units")$y))
lab <- paste(lab, aux)
attr(val, "label") <- lab
val
} else napredict(object$na.action, val[, level])
}
formula.lme <- function(x, ...) formula(x$terms)
fixef.lme <- function(object, ...) object$coefficients$fixed
getGroups.lme <- function(object, form, level = Q, data, sep)
{
Q <- object$dims$Q
val <- object[["groups"]][, level]
if (length(level) == 1) { # single group
attr(val, "label") <- names(object[["groups"]])[level]
}
val
}
getGroupsFormula.lme <-
function(object, asList = FALSE, sep)
{
getGroupsFormula(object$modelStruct$reStruct, asList)
}
getResponse.lme <-
function(object, form)
{
val <- resid(object) + fitted(object)
if (is.null(lab <- attr(object, "labels")$y)) {
lab <- deparse(getResponseFormula(object)[[2L]])
}
if (!is.null(aux <- attr(object, "units")$y)) {
lab <- paste(lab, aux)
}
attr(val, "label") <- lab
val
}
intervals.lme <-
function(object, level = 0.95, which = c("all", "var-cov", "fixed"), ...)
{
which <- match.arg(which)
val <- list()
## 17-11-2015; Fixed sigma patch; SH Heisterkamp; Quantitative Solutions
fixSig <- attr(object$modelStruct, "fixedSigma")
fixSig <- !is.null(fixSig) && fixSig
if (which != "var-cov") { # fixed effects included
est <- fixef(object)
len <- -qt((1-level)/2, object$fixDF$X) * sqrt(diag(object$varFix))
vfix <- array(c(est - len, est, est + len), c(length(est), 3),
list(names(est), c("lower", "est.", "upper")))
attr(vfix, "label") <- "Fixed effects:"
val <- list(fixed = vfix)
}
if (which != "fixed") { # variance-covariance included
if (is.character(aV <- object$apVar)) {
stop(gettextf("cannot get confidence intervals on var-cov components: %s\n Consider '%s'",
aV, "which = \"fixed\""), domain = NA)
}
est <- attr(aV, "Pars")
nP <- length(est)
len <- -qnorm((1-level)/2) * sqrt(diag(aV))
origInt <- # intervals in unconstrained parameters
array(c(est - len, est, est + len),
c(nP, 3), list(names(est), c("lower", "est.", "upper")))
lmeSt <- object$modelStruct
if (!all(whichKeep <- apply(attr(lmeSt, "pmap"), 2, any))) {
## need to deleted components with fixed coefficients
aux <- lmeSt[whichKeep]
class(aux) <- class(lmeSt)
attr(aux, "settings") <- attr(lmeSt, "settings")
attr(aux, "pmap") <- attr(lmeSt, "pmap")[, whichKeep, drop = FALSE]
lmeSt <- aux
}
cSt <- lmeSt[["corStruct"]]
if (!is.null(cSt) && inherits(cSt, "corSymm") && attr(aV, "natural")) {
## converting to corNatural
class(cSt) <- c("corNatural", "corStruct")
lmeSt[["corStruct"]] <- cSt
}
pmap <- attr(lmeSt, "pmap")
namL <- names(lmeSt)
## 17-11-2015; Fixed sigma patch; SH Heisterkamp; Quantitative Solutions
if (fixSig) {
natInt <- vector("list", length(namL))
names(natInt) <- namL
} else {
natInt <- vector("list", length(namL) + 1)
names(natInt) <- c(namL, "sigma") # list of intervals in natural pars
## intervals for sigma are stored separately and dropped from origInt
vsig <- exp(origInt[nP, ])
attr(vsig, "label") <- "Within-group standard error:"
natInt[["sigma"]] <- vsig
origInt <- origInt[ - nP,, drop = FALSE]
}
if (attr(aV, "natural")) { # convert any pdSymm's to pdNatural's
for(i in seq_along(lmeSt$reStruct)) {
if (inherits(s.i <- lmeSt$reStruct[[i]], "pdSymm")) {
s.i <- pdNatural(s.i)
} else if (inherits(s.i, "pdBlocked")) {
for(j in seq_along(s.i))
if (inherits(s.i[[j]], "pdSymm"))
s.i[[j]] <- pdNatural(s.i[[j]])
}
lmeSt$reStruct[[i]] <- s.i
}
}
rownames(origInt) <- # re-express names if necessary
## namP <-
names(coef(lmeSt, unconstrained = FALSE))
for(i in 1:3) { # re-express intervals in constrained pars
coef(lmeSt) <- origInt[,i]
origInt[,i] <- coef(lmeSt, unconstrained = FALSE)
}
for(i in namL) {
natInt[[i]] <- origInt[ pmap[ , i ], , drop = FALSE ]
switch(i,
"reStruct" = {
plen <- attr( lmeSt$reStruct, "plen" )
natInt[[i]] <-
rev(as.matrix( split( as.data.frame( natInt[[i]] ),
rep( seq_along(plen), plen ))))
names(natInt[[i]]) <- rev(names(plen))
for (j in names(plen)) {
dimnames(natInt[[i]][[j]])[[1L]] <-
names( coef( lmeSt[[i]][[j]], unconstrained = FALSE ) )
}
},
"corStruct" =,
"varStruct" = {
dimnames(natInt[[i]])[[1L]] <-
names(coef(lmeSt[[i]], unconstrained = FALSE))
}
)
attr(natInt[[i]], "label") <-
switch(i,
reStruct = "Random Effects:",
corStruct = "Correlation structure:",
varStruct = "Variance function:",
paste0(i,":"))
}
val <- c(val, natInt)
}
attr(val, "level") <- level
class(val) <- "intervals.lme"
val
}
logLik.lme <- function(object, REML, ...)
{
## 17-11-2015; Fixed sigma patch; SH Heisterkamp; Quantitative Solutions
fixSig <- attr(object[["modelStruct"]], "fixedSigma")
fixSig <- !is.null(fixSig) && fixSig
od <- object$dims
p <- od$ncol[[od$Q + 1L]]
N <- od$N
## Np <- N - p
estM <- object$method
if (missing(REML)) REML <- estM == "REML"
val <- object[["logLik"]]
if (REML && (estM == "ML")) { # have to correct logLik
val <- val + (p * (log(2 * pi) + 1L) + (N - p) * log(1 - p/N) +
sum(log(abs(svd.d(object$varFix))))) / 2
}
if (!REML && (estM == "REML")) { # have to correct logLik
val <- val - (p * (log(2*pi) + 1L) + N * log(1 - p/N) +
sum(log(abs(svd.d(object$varFix))))) / 2
}
structure(val, class = "logLik",
nall = N,
nobs = N - REML * p,
## 17-11-2015; Fixed sigma patch; SH Heisterkamp; Quantitative Solutions
df = p + length(coef(object[["modelStruct"]])) + as.integer(!fixSig))
}
nobs.lme <- function(object, ...) object$dims$N
pairs.lme <-
function(x, form = ~coef(.), label, id = NULL, idLabels = NULL,
grid = FALSE, ...)
{
object <- x
## scatter plot matrix plots, generally based on coef or ranef
if (!inherits(form, "formula")) {
stop("'form' must be a formula")
}
if (length(form) != 2) {
stop("'form' must be a one-sided formula")
}
## constructing data
allV <- all.vars(asOneFormula(form, id, idLabels))
allV <- allV[is.na(match(allV,c("T","F","TRUE","FALSE")))]
if (length(allV) > 0) {
data <- getData(object)
if (is.null(data)) { # try to construct data
alist <- lapply(as.list(allV), as.name)
names(alist) <- allV
alist <- c(as.list(quote(data.frame)), alist)
mode(alist) <- "call"
data <- eval(alist, sys.parent(1))
} else {
if (any(naV <- is.na(match(allV, names(data))))) {
stop(sprintf(ngettext(sum(naV),
"%s not found in data",
"%s not found in data"),
allV[naV]), domain = NA)
}
}
} else data <- NULL
## argument list
dots <- list(...)
args <- if(length(dots) > 0) dots else list()
## covariate - must be present as a data.frame
covF <- getCovariateFormula(form)
.x <- eval(covF[[2L]], list(. = object)) # only function of "."
if (!inherits(.x, "data.frame")) {
stop("covariate must be a data frame")
}
level <- attr(.x, "level")
if (!is.null(effNams <- attr(.x, "effectNames"))) {
.x <- .x[, effNams, drop = FALSE]
}
## eliminating constant effects
isFixed <- vapply(.x, function(el) length(unique(el)) == 1L, NA)
.x <- .x[, !isFixed, drop = FALSE]
nc <- ncol(.x)
if (nc == 1) {
stop("cannot do pairs of just one variable")
}
if (!missing(label)) {
names(.x) <- label
}
if (nc == 2) {
## will use xyplot
argForm <- .y ~ .x
argData <- .x
names(argData) <- c(".x", ".y")
if (is.null(args$xlab)) args$xlab <- names(.x)[1L]
if (is.null(args$ylab)) args$ylab <- names(.x)[2L]
} else { # splom
argForm <- ~ .x
argData <- list(.x = .x)
}
auxData <- list()
## groups - need not be present
grpsF <- getGroupsFormula(form)
if (!is.null(grpsF)) {
gr <- splitFormula(grpsF, sep = "*")
for(i in seq_along(gr)) {
for(j in all.vars(gr[[i]])) {
auxData[[j]] <- eval(as.name(j), data)
}
}
argForm <- eval(substitute(
if(length(argForm) == 2) ~ .x | R else .y ~ .x | R,
list(R = grpsF[[2L]])))
}
## id and idLabels - need not be present
if (!is.null(id)) { # identify points in plot
N <- object$dims$N
id <-
switch(mode(id),
numeric = {
if ((id <= 0) || (id >= 1)) {
stop("'id' must be between 0 and 1")
}
if (is.null(level)) {
stop("covariate must have a level attribute when groups are present")
}
aux <- t(as.matrix(ranef(object, level = level)))
aux <- as.logical(colSums(
(solve(t(pdMatrix(object$modelStruct$reStruct, factor = TRUE)[[level]]),
aux)/object$sigma)^2) > qchisq(1 - id, dim(aux)[1L]))
aux
},
call = eval(asOneSidedFormula(id)[[2L]], data),
stop("'id' can only be a formula or numeric")
)
if (length(id) == N) {
## id as a formula evaluated in data
if (is.null(level)) {
stop("covariate must have a level attribute when 'id' is a formula")
}
auxData[[".id"]] <- id
}
if (is.null(idLabels)) {
idLabels <- row.names(.x)
} else {
if (mode(idLabels) == "call") {
idLabels <-
as.character(eval(asOneSidedFormula(idLabels)[[2L]], data))
} else if (is.vector(idLabels)) {
if (length(idLabels <- unlist(idLabels)) != N) {
stop("'idLabels' of incorrect length")
}
idLabels <- as.character(idLabels)
} else {
stop("'idLabels' can only be a formula or a vector")
}
}
if (length(idLabels) == N) {
## idLabels as a formula evaluated in data
if (is.null(level)) {
stop("covariate must have a level attribute when 'idLabels' is a formula")
}
auxData[[".Lid"]] <- idLabels
}
}
if (length(auxData)) { # need collapsing
auxData <- gsummary(as.data.frame(auxData),
groups = getGroups(object, level = level))
auxData <- auxData[row.names(.x), , drop = FALSE]
if (!is.null(auxData[[".id"]])) {
id <- auxData[[".id"]]
}
if (!is.null(auxData[[".Lid"]])) {
idLabels <- auxData[[".Lid"]]
}
wchDat <- is.na(match(names(auxData), c(".id", ".idLabels")))
if (any(wchDat)) {
argData <- c(argData, as.list(auxData[, wchDat, drop = FALSE]))
}
}
if (!is.null(id)) id <- as.logical(as.character(id))
idLabels <- as.character(idLabels)
## adding to args list
args <- c(list(argForm, data = argData), args)
if (is.null(args$strip)) {
args$strip <- function(...) strip.default(..., style = 1)
}
if (is.null(args$cex)) args$cex <- par("cex")
if (is.null(args$adj)) args$adj <- par("adj")
## defining the type of plot
if (length(argForm) == 3) { # xyplot
plotFun <- "xyplot"
if(is.null(args$panel))
args$panel <- function(x, y, subscripts, ...) {
x <- as.numeric(x)
y <- as.numeric(y)
dots <- list(...)
if (grid) panel.grid()
panel.xyplot(x, y, ...)
if (any(ids <- id[subscripts])){
ltext(x[ids], y[ids], idLabels[subscripts][ids],
cex = dots$cex, adj = dots$adj)
}
}
} else { # splom
plotFun <- "splom"
if(is.null(args$panel)) {
args$panel <- function(x, y, subscripts, ...) {
x <- as.numeric(x)
y <- as.numeric(y)
dots <- list(...)
if (grid) panel.grid()
panel.xyplot(x, y, ...)
if (any(ids <- id[subscripts])){
ltext(x[ids], y[ids], idLabels[subscripts][ids],
cex = dots$cex, adj = dots$adj)
}
}
}
}
do.call(plotFun, as.list(args))
}
plot.ranef.lme <-
function(x, form = NULL, omitFixed = TRUE, level = Q,
grid = TRUE, control,
xlab = NULL, ylab = NULL, strip = NULL, ...)
{
plotControl <-
function(drawLine = TRUE, span.loess = 2/3, degree.loess = 1) {
list(drawLine = drawLine,
span.loess = span.loess,
degree.loess = degree.loess)
}
pControl <- plotControl()
if (!missing(control)) pControl[names(control)] <- control
if (!inherits(x, "data.frame")) {
## must be a list of data frames
Q <- length(x) # the default for 'level'
if (length(level) > 1) {
stop("only single level allowed")
}
oAttr <- attributes(x)[c("label", "standardized", "namsEff")]
x <- x[[level]]
oAttr$namsEff <- oAttr$namsEff[level]
attributes(x)[c("label", "standardized", "namsEff")] <- oAttr
}
if (omitFixed) { # eliminating constant effects
isFixed <- vapply(x, function(el) length(unique(el)) == 1L, NA)
if (any(isFixed)) {
oattr <- attributes(x)
oattr <- oattr[names(oattr) != "names"]
x <- x[, !isFixed, drop = FALSE]
oattr$effectNames <- oattr$effectNames[!is.na(match(oattr$effectNames,
names(x)))]
attributes(x)[names(oattr)] <- oattr
}
}
eNames <- attr(x, "effectNames")
if (is.null(form) || (inherits(form, "formula") && length(form) == 2)) {
## ~ x : dotplot
eLen <- length(eNames)
argData <- data.frame(.pars = as.vector(unlist(x[, eNames])),
.enames = ordered(rep(eNames, rep(nrow(x), eLen)),
levels = eNames), check.names = FALSE)
for(i in names(x)[is.na(match(names(x), eNames))]) {
argData[[i]] <- rep(x[[i]], eLen)
}
argForm <- .groups ~ .pars | .enames
argData[[".groups"]] <- rep(row.names(x), eLen)
if (inherits(form, "formula")) {
onames <- all.vars(form)
if (any(whichNA <- is.na(match(onames, names(argData))))) {
stop(sprintf(ngettext(sum(whichNA),
"%s not available for plotting",
"%s not available for plotting"),
onames[whichNA], collapse = ", "), domain = NA)
}
argData[[".groups"]] <-
as.character(argData[[as.character(onames[1L])]])
if (length(onames) > 1) {
for(i in onames[-1L]) {
argData[[".groups"]] <-
paste(as.character(argData[[".groups"]]),
as.character(argData[[i]]))
}
}
}
argData[[".groups"]] <- ordered(argData[[".groups"]],
levels = unique(argData[[".groups"]]))
args <- list(argForm, data = argData, ...)
args$xlab <- xlab %||% attr(x, "label")
args$ylab <- ylab %||% if (is.null(form)) attr(x, "grpNames")
else deparse(form[[2L]])
if (is.null(args$scales)) {
if (!is.null(attr(x, "standardized")) &&
!attr(x, "standardized")) {
args$scales <- list(x = list(relation = "free"))
}
}
args$strip <- strip %||% function(...) strip.default(..., style = 1)
do.call(dotplot, args)
} else { ## y ~ x ---> xyplot(): ------------------------------------------
if (!inherits(form, "formula")) stop("'form' must be a formula when not NULL")
reName <- form[[2L]]
if (length(reName) != 1 &&
substring(deparse(reName),
nchar(deparse(reName), "c") - 10) != "(Intercept)") {
stop("only single effects allowed in left side of 'form'")
}
reName <- deparse(reName)
if (is.na(match(reName, eNames))) {
stop(gettextf("%s is not a valid effect name", sQuote(reName)),
domain = NA)
}
vNames <- all.vars(form[[3]]) # variable names
if (any(!is.na(match(vNames, eNames)))) {
stop("no effects allowed in right side of formula")
}
if (any(whichNA <- is.na(match(vNames, names(x))))) {
stop(sprintf(ngettext(sum(whichNA),
"%s not available for plotting",
"%s not available for plotting"),
onames[whichNA], collapse = ", "), domain = NA)
}
nV <- length(vNames) # number of variables
nG <- nrow(x) # number of groups
reVal <- vNam <- vVal <- vector("list", nV)
vLevs <- vNam; names(vLevs) <- vNames
vType <- character(nV); names(vType) <- vNames
aux <- x[, reName]
for(i in 1:nV) {
obj <- x[, vNames[i]]
if (inherits(obj, "factor") || is.character(obj)) {
vType[i] <- "factor"
obj <- as.factor(obj)
vLevs[[i]] <- levels(obj)
reVal[[i]] <- c(NA, NA, aux)
vVal [[i]] <- c(0.5, length(levels(obj)) + 0.5, as.integer(obj))
vNam [[i]] <- rep(vNames[i], nG + 2)
} else { # numeric
vType[i] <- "numeric"
reVal[[i]] <- aux
vVal [[i]] <- obj
vNam [[i]] <- rep(vNames[i], nG)
}
}
vNam <- unlist(vNam)
argData <- data.frame(y = unlist(reVal), x = unlist(vVal),
g = ordered(vNam, levels = vNames))
## this is a hack to make this work, it's probably possible to
## implement the whole thing much more succintly -- ds
## The idea here is that the limits component of scales$x is going
## to be a list -- and character vectors have special meaning as
## limits, controlling both limits and the tick mark
## positions/labels
condvar <- eval(expression(g), argData)
xscales.lim <- as.list(levels(condvar))
subsc <- seq_along(condvar)
for (i in seq_along(xscales.lim)) {
subscripts <- subsc[condvar == xscales.lim[[i]]]
vN <- vNam[subscripts][1L]
xscales.lim[[i]] <-
if(vType[vN] == "numeric")
range(argData$x[subscripts])
else vLevs[vN][[1L]]
}
## --- further used from the panel() below: ---
.drawLine <- pControl$drawLine
.span <- pControl$span.loess
.degree <- pControl$degree.loess
## assign("panel.bwplot2", panel.bwplot2, where = 1)
## assign(".cex", pControl$cex.axis)#, where = 1)
## assign(".srt", pControl$srt.axis)#, where = 1)
## assign(".mgp", pControl$mgp.axis)#, where = 1)
xyplot(y ~ x | g, data = argData, subscripts = TRUE,
scales = list(x = list(relation = "free", limits = xscales.lim)),
panel = function(x, y, subscripts, ...) {
vN <- vNam[subscripts][1L]
if (grid) panel.grid()
if (vType[vN] == "numeric") {
panel.xyplot(x, y, ...)
if (.drawLine) {
panel.loess(x, y, span = .span, degree = .degree)
}
} else {
panel.bwplot(x, y, horizontal = FALSE)
if (.drawLine) {
plot.line <- trellis.par.get("plot.line")
panel.linejoin(x, y, fun = median, horizontal = FALSE,
col.line = plot.line$col,
lwd = plot.line$lwd,
lty = plot.line$lty)
}
}
},
xlab = xlab %||% "", ylab = ylab %||% reName,
strip = strip %||% strip.default, ...)
}
} ## {plot.ranef.lme}
predict.lme <-
function(object, newdata, level = Q, asList = FALSE,
na.action = na.fail, ...)
{
##
## method for predict() designed for objects inheriting from class lme
##
Q <- object$dims$Q
if (missing(newdata)) { # will return fitted values
val <- fitted(object, level, asList)
if (length(level) == 1) return(val)
return(data.frame(object[["groups"]][,level[level != 0], drop = FALSE],
predict = val))
}
maxQ <- max(level) # maximum level for predictions
nlev <- length(level)
mCall <- object$call
fixed <- eval(eval(mCall$fixed)[-2]) # RHS
Terms <- object$terms
newdata <- as.data.frame(newdata)
if (maxQ > 0) { # predictions with random effects
whichQ <- Q - (maxQ-1):0
reSt <- object$modelStruct$reStruct[whichQ]
lmeSt <- lmeStruct(reStruct = reSt)
groups <- getGroupsFormula(reSt)
if (any(is.na(match(all.vars(groups), names(newdata))))) {
## groups cannot be evaluated in newdata
stop("cannot evaluate groups for desired levels on 'newdata'")
}
} else {
reSt <- NULL
}
## use xlev to make sure factor levels are the same as in contrasts
## and to support character-type 'newdata' for factors
contr <- object$contrasts # these are in matrix form
dataMix <- model.frame(formula = asOneFormula(formula(reSt), fixed),
data = newdata, na.action = na.action,
drop.unused.levels = TRUE,
xlev = lapply(contr, rownames))
origOrder <- row.names(dataMix) # preserve the original order
whichRows <- match(origOrder, row.names(newdata))
if (maxQ > 0) {
## sort the model.frame by groups and get the matrices and parameters
## used in the estimation procedures
grps <- getGroups(newdata, # (unused levels are dropped here)
eval(substitute(~ 1 | GRPS,
list(GRPS = groups[[2]]))))
## ordering data by groups
if (inherits(grps, "factor")) { # single level
grps <- grps[whichRows]
oGrps <- data.frame(grps)
## checking if there are missing groups
if (any(naGrps <- is.na(grps))) {
grps[naGrps] <- levels(grps)[1L] # input with existing level
}
ord <- order(grps) #"order" treats a single named argument peculiarly
grps <- data.frame(grps)
row.names(grps) <- origOrder
names(grps) <- names(oGrps) <- as.character(deparse((groups[[2L]])))
} else {
grps <- oGrps <- grps[whichRows, ]
## checking for missing groups
if (any(naGrps <- is.na(grps))) {
## need to input missing groups
for(i in names(grps)) {
grps[naGrps[, i], i] <- levels(grps[,i])[1L]
}
naGrps <- t(apply(naGrps, 1, cumsum)) # propagating NAs
}
ord <- do.call(order, grps)
## making group levels unique
for(i in 2:ncol(grps)) {
grps[, i] <-
as.factor(paste(as.character(grps[, i-1]),
as.character(grps[, i ]), sep = "/"))
}
}
naGrps <- cbind(FALSE, naGrps)[ord, , drop = FALSE]
grps <- grps[ord, , drop = FALSE]
dataMix <- dataMix[ord, ,drop = FALSE]
}
## restore contrasts for the below model.matrix() calls (which may use
## different factor variables, so we avoid passing the whole contr list)
for(i in intersect(names(dataMix), names(contr))) {
attr(dataMix[[i]], "contrasts") <- contr[[i]]
}
if (maxQ > 0) {
revOrder <- match(origOrder, row.names(dataMix)) # putting in orig. order
Z <- model.matrix(reSt, dataMix)
ncols <- attr(Z, "ncols")
Names(lmeSt$reStruct) <- attr(Z, "nams")
}
N <- nrow(dataMix)
X <- if (length(all.vars(fixed)) > 0) {
model.matrix(fixed, model.frame(delete.response(Terms), dataMix))
} else if(attr(terms(fixed), "intercept")) {
array(1, c(N, 1), list(row.names(dataMix), "(Intercept)"))
} else {
array(, c(N, 0))
}
if (maxQ == 0) {
## only population predictions
val <- if(ncol(X)) c(X %*% fixef(object)) else rep(0, nrow(X))
attr(val, "label") <- "Predicted values"
return(val)
}
ncols <- c(ncols, dim(X)[2L], 1)
## creating the condensed linear model
attr(lmeSt, "conLin") <-
list(Xy = array(c(Z, X, double(N)), c(N, sum(ncols)),
list(row.names(dataMix), c(colnames(Z), colnames(X), "resp"))),
dims = MEdims(grps, ncols))
## Getting the appropriate BLUPs of the random effects
re <- object$coefficients$random[1:maxQ]
for(i in names(re)) {
ugrps <- unique(as.character(grps[, i]))
val <- array(NA, c(length(ugrps), ncol(re[[i]])),
list(ugrps, dimnames(re[[i]])[[2L]]))
mGrps <- match(ugrps, dimnames(re[[i]])[[1L]])
mGrps <- mGrps[!is.na(mGrps)]
re[[i]] <- re[[i]][mGrps, , drop = FALSE]
val[dimnames(re[[i]])[[1L]], ] <- re[[i]]
re[[i]] <- val
}
attr(lmeSt, "lmeFit") <- list(beta = fixef(object), b = re)
val <- fitted(lmeSt, level = 0:maxQ)
val[as.logical(naGrps)] <- NA # setting missing groups to NA
## putting back in original order and extracting levels
val <- val[revOrder, level + 1L] # predictions
if (maxQ > 1) { # making groups unique
for(i in 2:maxQ)
oGrps[, i] <-
as.factor(paste(as.character(oGrps[,i-1]),
as.character(oGrps[,i ]), sep = "/"))
}
if (nlev == 1) {
grps <- as.character(oGrps[, level])
if (asList) {
val <- split(val, ordered(grps, levels = unique(grps)))
} else {
names(val) <- grps
}
lab <- "Predicted values"
if (!is.null(aux <- attr(object, "units")$y)) {
lab <- paste(lab, aux)
}
attr(val, "label") <- lab
val
} else {
data.frame(oGrps, predict = val)
}
}
print.anova.lme <- function(x, verbose = attr(x, "verbose"), ...)
{
ox <- x
if ((rt <- attr(x,"rt")) == 1) { ## one object
if (!is.null(lab <- attr(x, "label"))) {
cat(lab)
if (!is.null(L <- attr(x, "L"))) {
print(zapsmall(L), ...)
}
}
pval <- format(round(x[, "p-value"],4))
pval[as.double(pval) == 0] <- "<.0001"
x[, "F-value"] <- format(zapsmall(x[, "F-value"]))
x[, "p-value"] <- pval
print(as.data.frame(x), ...)
} else { ## several objects
if (verbose) {
cat("Call:\n")
objNams <- row.names(x)
for(i in 1:rt) {
cat(" ",objNams[i],":\n", sep ="")
cat(" ",as.character(x[i,"call"]),"\n")
}
cat("\n")
}
x <- as.data.frame(x[,-1])
for(i in names(x)) {
xx <- x[[i]]
if (i == "p-value") {
xx <- round(xx, 4)
xna <- is.na(xx)
xx[!xna] <- format(xx[!xna])
xx[as.double(xx) == 0] <- "<.0001"
xx[xna] <- ""
} else {
if (match(i, c("AIC", "BIC", "logLik", "L.Ratio"), 0)) {
xna <- is.na(xx)
xx <- zapsmall(xx)
xx[xna] <- 0
xx <- format(xx)
xx[xna] <- ""
}
}
x[[i]] <- xx
}
print(as.data.frame(x), ...)
}
invisible(ox)
}
print.intervals.lme <- function(x, ...)
{
cat(paste0("Approximate ", attr(x,"level") *100, "% confidence intervals\n"))
for(i in names(x)) {
aux <- x[[i]]
cat("\n ",attr(aux, "label"), "\n", sep = "")
attr(aux, "label") <- NULL
if (i == "reStruct") {
for(j in names(aux)) {
cat(" Level:", j, "\n")
print(as.matrix(aux[[j]]), ...)
}
} else if (i == "sigma")
print(c(aux), ...)
else
print(as.matrix(aux), ...)
}
invisible(x)
}
print.lme <- function(x, ...)
{
dd <- x$dims
if (inherits(x, "nlme")) { # nlme object
cat( "Nonlinear mixed-effects model fit by " )
cat( if(x$method == "REML") "REML\n" else "maximum likelihood\n")
cat(" Model:", deparse(x$call$model),"\n")
} else { # lme objects
cat( "Linear mixed-effects model fit by " )
cat( if(x$method == "REML") "REML\n" else "maximum likelihood\n")
}
cat(" Data:", deparse( x$call$data ), "\n")
if (!is.null(x$call$subset)) {
cat(" Subset:", deparse(asOneSidedFormula(x$call$subset)[[2L]]),"\n")
}
cat(" Log-", if(x$method == "REML") "restricted-" else "",
"likelihood: ", format(x$logLik), "\n", sep = "")
fixF <- x$call$fixed
cat(" Fixed:",
deparse(
if(inherits(fixF, "formula") || is.call(fixF) || is.name(fixF))
fixF
else
lapply(fixF, function(el) as.name(deparse(el)))), "\n")
print(fixef(x), ...)
cat("\n")
print(summary(x$modelStruct), sigma = x$sigma, ...)
cat("Number of Observations:", dd[["N"]])
cat("\nNumber of Groups: ")
Ngrps <- dd$ngrps[1:dd$Q]
if ((lNgrps <- length(Ngrps)) == 1) { # single nesting
cat(Ngrps,"\n")
} else { # multiple nesting
sNgrps <- 1:lNgrps
aux <- rep(names(Ngrps), sNgrps)
aux <- split(aux, array(rep(sNgrps, lNgrps),
c(lNgrps, lNgrps))[!lower.tri(diag(lNgrps))])
names(Ngrps) <- unlist(lapply(aux, paste, collapse = " %in% "))
cat("\n")
print(rev(Ngrps), ...)
}
invisible(x)
}
print.ranef.lme <- function(x, ...)
{
if (!inherits(x[[1L]], "data.frame")) {
print.data.frame(x, ...)
} else { # list
for(i in seq_along(x)) {
cat("Level:", attr(x, "grpNames")[i],"\n")
print.data.frame(x[[i]])
if (i < length(x)) cat("\n")
}
}
invisible(x)
}
print.summary.lme <- function(x, verbose = FALSE, ...)
{
dd <- x$dims
verbose <- verbose || attr(x, "verbose")
if (inherits(x, "nlme")) { # nlme object
cat( "Nonlinear mixed-effects model fit by " )
cat( if(x$method == "REML") "REML\n" else "maximum likelihood\n")
cat(" Model:", deparse(x$call$model),"\n")
} else { # lme objects
cat( "Linear mixed-effects model fit by " )
cat( if(x$method == "REML") "REML\n" else "maximum likelihood\n")
}
## method <- x$method
cat(" Data:", deparse( x$call$data ), "\n")
if (!is.null(x$call$subset)) {
cat(" Subset:", deparse(asOneSidedFormula(x$call$subset)[[2L]]),"\n")
}
print(data.frame(AIC = x$AIC, BIC = x$BIC, logLik = c(x$logLik),
row.names = " "), ...)
if (verbose) cat("Convergence at iteration:",x$numIter,"\n")
cat("\n")
print(summary(x$modelStruct), sigma = x$sigma,
reEstimates = x$coefficients$random, verbose = verbose, ...)
fixF <- x$call$fixed
cat("Fixed effects: ",
deparse(
if(inherits(fixF, "formula") || is.call(fixF))
fixF
else
lapply(fixF, function(el) as.name(deparse(el)))),
"\n")
## fixed effects t-table and correlations
xtTab <- as.data.frame(x$tTable)
wchPval <- match("p-value", names(xtTab))
for(i in names(xtTab)[-wchPval]) {
xtTab[, i] <- format(zapsmall(xtTab[, i]))
}
xtTab[,wchPval] <- format(round(xtTab[,wchPval], 4))
if (any(wchLv <- (as.double(levels(xtTab[, wchPval])) == 0))) {
levels(xtTab[, wchPval])[wchLv] <- "<.0001"
}
row.names(xtTab) <- dimnames(x$tTable)[[1L]]
print(xtTab, ...)
if (nrow(x$tTable) > 1) {
corr <- x$corFixed
class(corr) <- "correlation"
print(corr, title = " Correlation:", ...)
}
cat("\nStandardized Within-Group Residuals:\n")
print(x$residuals, ...)
cat("\nNumber of Observations:",x$dims[["N"]])
cat("\nNumber of Groups: ")
Ngrps <- dd$ngrps[1:dd$Q]
if ((lNgrps <- length(Ngrps)) == 1) { # single nesting
cat(Ngrps,"\n")
} else { # multiple nesting
sNgrps <- 1:lNgrps
aux <- rep(names(Ngrps), sNgrps)
aux <- split(aux, array(rep(sNgrps, lNgrps),
c(lNgrps, lNgrps))[!lower.tri(diag(lNgrps))])
names(Ngrps) <- unlist(lapply(aux, paste, collapse = " %in% "))
cat("\n")
print(rev(Ngrps), ...)
}
invisible(x)
}
## coef(summary( obj )) # should work for "gls" or "lme" similarly as for lm():
getCTable <- function (object, ...) object$tTable
qqnorm.lme <-
function(y, form = ~ resid(., type = "p"), abline = NULL,
id = NULL, idLabels = NULL, grid = FALSE, ...)
## normal probability plots for residuals and random effects
{
if (!inherits(form, "formula")) stop("'form' must be a formula")
## object <- y
## constructing data
allV <- all.vars(asOneFormula(form, id, idLabels))
allV <- allV[is.na(match(allV,c("T","F","TRUE","FALSE")))]
if (length(allV) > 0) {
data <- getData(y)
if (is.null(data)) { # try to construct data
alist <- lapply(as.list(allV), as.name)
names(alist) <- allV
alist <- c(as.list(quote(data.frame)), alist)
mode(alist) <- "call"
data <- eval(alist, sys.parent(1))
} else {
if (any(naV <- is.na(match(allV, names(data))))) {
stop(sprintf(ngettext(sum(naV),
"%s not found in data",
"%s not found in data"),
allV[naV]), domain = NA)
}
}
} else data <- NULL
## argument list
args <- list(...) # may be empty list()
## appending object to data
data <- as.list(c(as.list(data), . = list(y)))
## covariate - must always be present
covF <- getCovariateFormula(form)
.x <- eval(covF[[2L]], data)
labs <- attr(.x, "label")
type <-
if (inherits(.x, "ranef.lme"))
"reff" # random effects
else if (!is.null(labs) && (labs == "Standardized residuals" ||
labs == "Normalized residuals" ||
substr(labs, 1, 9) == "Residuals"))
"res" # residuals
else
stop("only residuals and random effects allowed")
if (is.null(args$xlab)) args$xlab <- labs
if (is.null(args$ylab)) args$ylab <- "Quantiles of standard normal"
if(type == "res") { ## residuals ----------------------------------------
fData <- qqnorm(.x, plot.it = FALSE)
data[[".y"]] <- fData$x
data[[".x"]] <- fData$y
dform <-
if (!is.null(grp <- getGroupsFormula(form)))
eval(substitute(.y ~ .x | G, list(G = grp[[2L]])))
else
.y ~ .x
if (!is.null(id)) { # identify points in plot
id <-
switch(mode(id),
numeric = {
if (any(id <= 0) || any(id >= 1)) {
stop("'Id' must be between 0 and 1")
}
if (labs == "Normalized residuals") {
as.logical(abs(resid(y, type="normalized"))
> -qnorm(id / 2))
} else {
as.logical(abs(resid(y, type="pearson"))
> -qnorm(id / 2))
}
},
call = eval(asOneSidedFormula(id)[[2L]], data),
stop("'id' can only be a formula or numeric")
)
if (is.null(idLabels)) {
idLabels <- getGroups(y)
if (length(idLabels) == 0) idLabels <- seq_len(y$dims$N)
idLabels <- as.character(idLabels)
} else {
if (mode(idLabels) == "call") {
idLabels <-
as.character(eval(asOneSidedFormula(idLabels)[[2L]], data))
} else if (is.vector(idLabels)) {
if (length(idLabels <- unlist(idLabels)) != length(id)) {
stop("'idLabels' of incorrect length")
}
idLabels <- as.character(idLabels)
} else {
stop("'idLabels' can only be a formula or a vector")
}
}
}
} else { # type "ref" -- random.effects --------------------------------
level <- attr(.x, "level")
std <- attr(.x, "standardized")
if (!is.null(effNams <- attr(.x, "effectNames"))) {
.x <- .x[, effNams, drop = FALSE]
}
nc <- ncol(.x)
nr <- nrow(.x)
fData <- lapply(as.data.frame(.x), qqnorm, plot.it = FALSE)
fData <- data.frame(.x = unlist(lapply(fData, function(x) x[["y"]])),
.y = unlist(lapply(fData, function(x) x[["x"]])),
.g = ordered(rep(names(fData),rep(nr, nc)),
levels = names(fData)), check.names = FALSE)
if (!is.null(grp <- getGroupsFormula(form))) {
dform <- substitute(.y ~ .x | .g * GG, list(GG = deparse(grp[[2L]])))
auxData <- data[is.na(match(names(data), "."))]
} else {
dform <- .y ~ .x | .g
auxData <- list()
}
## id and idLabels - need not be present
if (!is.null(id)) { # identify points in plot
N <- y$dims$N
id <-
switch(mode(id),
numeric = {
if ((id <= 0) || (id >= 1)) {
stop("'id' must be between 0 and 1")
}
aux <- ranef(y, level = level, standard = TRUE)
as.logical(abs(c(unlist(aux))) > -qnorm(id / 2))
},
call = eval(asOneSidedFormula(id)[[2L]], data),
stop("'id' can only be a formula or numeric")
)
if (length(id) == N) {
## id as a formula evaluated in data
auxData[[".id"]] <- id
}
idLabels <-
if (is.null(idLabels)) {
rep(row.names(.x), nc)
} else if (mode(idLabels) == "call") {
as.character(eval(asOneSidedFormula(idLabels)[[2L]], data))
} else if (is.vector(idLabels)) {
if (length(idLabels <- unlist(idLabels)) != N)
stop("'idLabels' of incorrect length")
as.character(idLabels)
} else
stop("'idLabels' can only be a formula or a vector")
if (length(idLabels) == N) {
## idLabels as a formula evaluated in data
auxData[[".Lid"]] <- idLabels
}
}
data <-
if (length(auxData)) { # need collapsing
auxData <- gsummary(as.data.frame(auxData),
groups = getGroups(y, level = level))
auxData <- auxData[row.names(.x), , drop = FALSE]
if (!is.null(auxData[[".id"]]))
id <- rep(auxData[[".id"]], nc)
if (!is.null(auxData[[".Lid"]]))
idLabels <- rep(auxData[[".Lid"]], nc)
cbind(fData, do.call(rbind, rep(list(auxData), nc)))
} else
fData
}
id <- if (!is.null(id)) as.logical(as.character(id))
idLabels <- as.character(idLabels)
abl <- abline
if (is.null(args$strip))
args$strip <- function(...) strip.default(..., style = 1)
if (is.null(args$cex)) args$cex <- par("cex")
if (is.null(args$adj)) args$adj <- par("adj")
args <- c(list(dform, data = substitute(data)), args)
if (is.null(args$panel))
args$panel <- function(x, y, subscripts, ...) {
x <- as.numeric(x)
y <- as.numeric(y)
dots <- list(...)
if (grid) panel.grid()
panel.xyplot(x, y, ...)
if (any(ids <- id[subscripts])){
ltext(x[ids], y[ids], idLabels[subscripts][ids],
cex = dots$cex, adj = dots$adj)
}
if (!is.null(abl)) {
if (length(abl) == 2)
panel.abline(a = abl, ...)
else
panel.abline(h = abl, ...)
}
}
if(type == "reff" && !std) {
args[["scales"]] <- list(x = list(relation = "free"))
}
do.call(xyplot, as.list(args))
}
ranef.lme <-
## Extracts the random effects from an lme object.
## If aug.frame is true, the returned data frame is augmented with a
## values from the original data object, if available. The variables
## in the original data are collapsed over the cluster variable by the
## function fun.
function(object, augFrame = FALSE, level = 1:Q, data, which = 1:ncol(data),
FUN = mean, standard = FALSE , omitGroupingFactor = TRUE,
subset = NULL, ...)
{
Q <- object$dims$Q
effects <- object$coefficients$random
if (Q > 1) {
grpNames <- t(array(rep(rev(names(effects)), Q), c(Q, Q)))
grpNames[lower.tri(grpNames)] <- ""
grpNames <-
rev(apply(grpNames, 1, function(x) paste(x[x != ""], collapse = " %in% ")))
} else {
grpNames <- names(effects)
}
effects <- effects[level]
grpNames <- grpNames[level]
if (standard) {
for (i in names(effects)) {
effects[[i]] <-
t(t(effects[[i]]) /
(object$sigma *
sqrt(diag(as.matrix(object$modelStruct$reStruct[[i]])))))
}
}
effects <- lapply(effects, as.data.frame)
if (augFrame) {
if (length(level) > 1) {
stop("augmentation of random effects only available for single level")
}
effects <- effects[[1L]]
effectNames <- names(effects)
if (missing(data)) {
data <- getData(object)
}
data <- as.data.frame(data)
subset <- if (is.null(subset)) { # nlme case
eval(object$call[["naPattern"]])
} else {
asOneSidedFormula(as.list(match.call())[["subset"]])
}
if (!is.null(subset)) {
subset <- eval(subset[[2L]], data)
data <- data[subset, ,drop=FALSE]
}
data <- data[, which, drop = FALSE]
## eliminating columns with same names as effects
data <- data[, is.na(match(names(data), effectNames)), drop = FALSE]
grps <- as.character(object[["groups"]][, level])
data <- gsummary(data, FUN = FUN, groups = grps)
if (omitGroupingFactor) {
data <-
data[, is.na(match(names(data), names(object$modelStruct$reStruct))),
drop = FALSE]
}
if (length(data) > 0) {
effects <- cbind(effects, data[row.names(effects),, drop = FALSE])
}
attr(effects, "effectNames") <- effectNames
} else {
effects <- lapply(effects,
function(el) {
attr(el, "effectNames") <- names(el)
el
})
if (length(level) == 1) effects <- effects[[1L]]
}
structure(effects, class = c("ranef.lme", class(effects)),
label= if (standard)
"Standardized random effects"
else
"Random effects",
level = max(level),
standardized = standard,
grpNames = grpNames)
}
residuals.lme <-
function(object, level = Q, type = c("response", "pearson", "normalized"),
asList = FALSE, ...)
{
type <- match.arg(type)
Q <- object$dims$Q
val <- object[["residuals"]]
if (is.character(level)) { # levels must be given consistently
nlevel <- match(level, names(val))
if (any(aux <- is.na(nlevel))) {
stop(sprintf(ngettext(sum(aux),
"nonexistent level %s",
"nonexistent levels %s"),
level[aux]), domain = NA)
}
level <- nlevel
} else { # assuming integers
level <- 1 + level
}
if (type != "response") { # standardize
## have to standardize properly for when corStruct neq NULL
val <- val[, level]/attr(val, "std")
} else {
val <- val[, level]
}
if (type == "normalized") {
if (!is.null(cSt <- object$modelStruct$corStruct)) {
## normalize according to inv-trans factor
val <- recalc(cSt, list(Xy = as.matrix(val)))$Xy[, seq_along(level)]
} else { # will just standardized
type <- "pearson"
}
}
if (length(level) == 1) {
grps <- as.character(object[["groups"]][, max(c(1, level - 1))])
if (asList) {
val <- as.list(split(val, ordered(grps, levels = unique(grps))))
} else {
grp.nm <- row.names(object[["groups"]])
val <- naresid(object$na.action, val)
names(val) <- grps[match(names(val), grp.nm)]
}
attr(val, "label") <-
switch(type,
response = {
if (!is.null(aux <- attr(object, "units")$y))
paste("Residuals", aux)
else "Residuals"
},
pearson = "Standardized residuals",
normalized = "Normalized residuals"
)
val
} else naresid(object$na.action, val)
}
summary.lme <- function(object, adjustSigma = TRUE, verbose = FALSE, ...)
{
## variance-covariance estimates for fixed effects
fixed <- fixef(object)
stdFixed <- sqrt(diag(as.matrix(object$varFix)))
object$corFixed <- array(t(object$varFix/stdFixed)/stdFixed,
dim(object$varFix), list(names(fixed),names(fixed)))
if (adjustSigma && object$method == "ML")
stdFixed <- stdFixed *
sqrt(object$dims$N/(object$dims$N - length(stdFixed)))
## fixed effects coefficients, std. deviations and t-ratios
##
fDF <- object$fixDF[["X"]]
tVal <- fixed/stdFixed
object$tTable <- cbind(Value = fixed, Std.Error = stdFixed, DF = fDF,
"t-value" = tVal, "p-value" = 2 * pt(-abs(tVal), fDF))
##
## residuals
##
resd <- resid(object, type = "pearson")
if (length(resd) > 5) {
resd <- quantile(resd, na.rm = TRUE) # might have NAs from na.exclude
names(resd) <- c("Min","Q1","Med","Q3","Max")
}
object$residuals <- resd
##
## generating the final object
##
aux <- logLik(object)
object$BIC <- BIC(aux)
object$AIC <- AIC(aux)
structure(object, verbose = verbose, oClass = class(object),
class = c("summary.lme", class(object)))
}
## based on R's update.default
update.lme <-
function (object, fixed., ..., evaluate = TRUE)
{
call <- object$call
if (is.null(call))
stop("need an object with call component")
extras <- match.call(expand.dots = FALSE)$...
if (!missing(fixed.))
call$fixed <- update.formula(formula(object), fixed.)
if(length(extras) > 0) {
existing <- !is.na(match(names(extras), names(call)))
## do these individually to allow NULL to remove entries.
for (a in names(extras)[existing]) call[[a]] <- extras[[a]]
if(any(!existing))
call <- as.call(c(as.list(call), extras[!existing]))
}
if(evaluate) eval(call, parent.frame())
else call
}
## update.lme <-
## function(object, fixed, data, random, correlation, weights, subset,
## method, na.action, control, contrasts, ...)
## {
## thisCall <- as.list(match.call())[-(1:2)]
## if (is.null(nextCall <- object$origCall) ||
## !is.null(thisCall$fixed) ||
## is.null(thisCall$random)) {
## nextCall <- object$call
## }
## nextCall <- as.list(nextCall)[-1L]
## if (is.null(thisCall$random) && is.null(thisCall$subset)) {
## ## no changes in ranef model and no subsetting
## thisCall$random <- object$modelStruct$reStruct
## }
## if (is.na(match("correlation", names(thisCall))) &&
## !is.null(thCor <- object$modelStruct$corStruct)) {
## thisCall$correlation <- thCor
## }
## if (is.na(match("weights", names(thisCall))) &&
## !is.null(thWgt <- object$modelStruct$varStruct)) {
## thisCall$weights <- thWgt
## }
## argNams <- unique( c(names(nextCall), names(thisCall)) )
## args <- vector("list", length(argNams))
## names(args) <- argNams
## args[ names(nextCall) ] <- nextCall
## nextCall <- args
## if (!is.null(thisCall$fixed)) {
## thisCall$fixed <- update(as.formula(nextCall$fixed), fixed)
## }
## nextCall[names(thisCall)] <- thisCall
## do.call(lme, nextCall)
## }
Variogram.lme <-
function(object, distance, form = ~1,
resType = c("pearson", "response", "normalized"),
data, na.action = na.fail, maxDist, length.out = 50,
collapse = c("quantiles", "fixed", "none"), nint = 20, breaks,
robust = FALSE, metric = c("euclidean", "maximum", "manhattan"),
...)
{
resType <- match.arg(resType)
grps <- getGroups(object, level = object$dims$Q)
## checking if object has a corSpatial element
csT <- object$modelStruct$corStruct
wchRows <- NULL
if (missing(distance)) {
if (missing(form) && inherits(csT, "corSpatial")) {
distance <- getCovariate(csT)
} else {
metric <- match.arg(metric)
if (missing(data)) {
data <- getData(object)
}
if (is.null(data)) { # will try to construct
allV <- all.vars(form)
if (length(allV) > 0) {
alist <- lapply(as.list(allV), as.name)
names(alist) <- allV
alist <- c(as.list(quote(data.frame)), alist)
mode(alist) <- "call"
data <- eval(alist, sys.parent(1))
}
}
covForm <- getCovariateFormula(form)
if (length(all.vars(covForm)) > 0) {
if (attr(terms(covForm), "intercept") == 1) {
covForm <- eval(substitute( ~ cFORM - 1, list(cFORM = covForm[[2L]])))
}
covar <- model.frame(covForm, data, na.action = na.action)
## making sure grps is consistent
wchRows <- !is.na(match(row.names(data), row.names(covar)))
grps <- grps[wchRows, drop = TRUE]
covar <- as.data.frame(unclass(model.matrix(covForm, covar)))
} else {
covar <-
data.frame(dist = unlist(tapply(rep(1, nrow(data)), grps, cumsum)))
}
covar <- split(covar, grps)
## getting rid of 1-observation groups
covar <- covar[sapply(covar, function(el) nrow(as.matrix(el))) > 1]
distance <- lapply(covar,
function(el, metric) dist(as.matrix(el), metric),
metric = metric)
}
}
res <- resid(object, type = resType)
if (!is.null(wchRows)) {
res <- res[wchRows]
}
res <- split(res, grps)
res <- res[lengths(res) > 1L] # no 1-observation groups
## levGrps <- levels(grps)
val <- lapply(seq_along(res),
function(i) Variogram(res[[i]], distance[[i]]))
names(val) <- names(res)
val <- do.call(rbind, val)
if (!missing(maxDist)) {
val <- val[val$dist <= maxDist, ]
}
collapse <- match.arg(collapse)
if (collapse != "none") { # will collapse values
dst <- val$dist
udist <- sort(unique(dst))
ludist <- length(udist)
if (!missing(breaks)) {
if (min(breaks) > udist[1L]) breaks <- c(udist[1L], breaks)
if (max(breaks) < udist[2L]) breaks <- c(breaks, udist[2L])
if (!missing(nint) && nint != (length(breaks) - 1L)) {
stop("'nint' is not consistent with 'breaks'")
}
nint <- length(breaks) - 1
}
cutDist <-
if (nint < ludist) {
if (missing(breaks))
breaks <-
if (collapse == "quantiles") { # break into equal groups
unique(quantile(dst, seq(0, 1, 1/nint), names=FALSE))
} else { # fixed length intervals
seq(udist[1L], udist[length(udist)], length = nint + 1L)
}
cut(dst, breaks)
}
else
dst
val <- lapply(split(val, cutDist),
function(el) {
vrg <- el$variog
vrg <- if (robust)
((mean(vrg^0.25))^4) / (0.457+ 0.494/nrow(el))
else
mean(vrg)
data.frame(variog = vrg,
dist = median(el$dist))
})
val <- do.call(rbind, val)
val$n.pairs <- as.vector(table(na.omit(cutDist)))
val <- na.omit(val) # getting rid of NAs
}
row.names(val) <- 1:nrow(val)
if (inherits(csT, "corSpatial") && resType != "normalized") {
## will keep model variogram
sig2 <- if (resType == "pearson") 1 else object$sigma^2
attr(val, "modelVariog") <-
Variogram(csT, sig2 = sig2, length.out = length.out)
}
structure(val, class = c("Variogram", "data.frame"),
collapse = (collapse != "none"))
}
###*### lmeStruct - a model structure for lme fits
lmeStruct <-
## constructor for lmeStruct objects
function(reStruct, corStruct = NULL, varStruct = NULL)
{
val <- list(reStruct = reStruct, corStruct = corStruct,
varStruct = varStruct)
structure(val[!vapply(val, is.null, NA)], # removing NULL components
settings = attr(val$reStruct, "settings"),
class = c("lmeStruct", "modelStruct"))
}
##*## lmeStruct methods for standard generics
fitted.lmeStruct <-
function(object, level = Q, conLin = attr(object, "conLin"),
lmeFit = attr(object, "lmeFit"), ...)
{
if (is.null(conLin)) {
stop("no condensed linear model")
}
if (is.null(lmeFit)) {
stop("no fitted \"lme\" object")
}
dd <- conLin$dims
Q <- dd$Q
Qp1 <- Q + 1L
nc <- dd$ncol
fit <- array(0, c(dd$N, Qp1),
list(dimnames(conLin$Xy)[[1L]], c("fixed", rev(names(object$reStruct)))))
ZXstart <- rev(cumsum(c(1, nc[1:Q])))
ZXend <- rev(cumsum(nc[1:Qp1]))
ZXlen <- dd$ZXlen[Q:1]
ZXngrps <- dd$ngrps[Q:1]
ZXb <- lmeFit$b
nc <- nc[Q:1]
if(ZXstart[1L] <= ZXend[1L])
fit[, "fixed"] <- # population fitted values
conLin$Xy[, ZXstart[1L]:ZXend[1L], drop = FALSE] %*% lmeFit$beta
for(i in 1:Q) {
j <- i + 1L
fit[, j] <- fit[, i] +
(conLin$Xy[, ZXstart[j]:ZXend[j], drop = FALSE] *
ZXb[[i]][rep(1:ZXngrps[i], ZXlen[[i]]),,drop = FALSE]) %*% rep(1, nc[i])
}
## this is documented to return a vector for one level, matrix for more.
## So it should be a matrix if there is only one row, but not if
## there is only one columns.
if(length(level) > 1) fit[, level + 1L, drop = FALSE] else fit[, level+1]
}
Initialize.lmeStruct <-
function(object, data, groups, conLin = attr(object, "conLin"),
control= list(niterEM = 20, gradHess = TRUE), ...)
{
object[] <- lapply(object, Initialize, data, conLin, control)
theta <- lapply(object, coef)
len <- lengths(theta)
pmap <- if (sum(len) > 0) {
num <- seq_along(len)
outer(rep(num, len), num, "==")
} else {
array(FALSE, c(1, length(len)))
}
dimnames(pmap) <- list(NULL, names(object))
attr(object, "pmap") <- pmap
if (length(object) == 1 && # only reStruct
all(attr(object, "settings")[-(1:3)] >= 0) && # known pdMat class
control[["gradHess"]]) {
## can use numerical derivatives
attr(object, "settings")[2:3] <- c(0, 1)
class(object) <- c("lmeStructInt", class(object))
}
if (needUpdate(object)) {
attr(object, "lmeFit") <- MEestimate(object, groups)
update(object, data)
} else {
object
}
}
logLik.lmeStruct <-
function(object, Pars, conLin = attr(object, "conLin"), ...)
{
coef(object) <- Pars # updating parameter values
recalc(object, conLin)[["logLik"]] # updating conLin
}
logLik.lmeStructInt <-
function(object, Pars, conLin = attr(object, "conLin"), ...)
{
## logLik for objects with reStruct parameters only, with
## internally defined class
q <- length(Pars)
settings <- as.integer(attr(object, "settings"))
aux <- .C(mixed_loglik, # >> ../src/nlmefit.c
as.double(conLin[["Xy"]]), # ZXy
as.integer(unlist(conLin$dims)), # pdims[]
as.double(Pars), # pars[]
settings, # settings
val = double(1 + q * (q + 1)), # logLik[] = (value, gradient, Hessian)
double(1), # lRSS
as.double(conLin$sigma) # sigma (17-11-2015; Fixed sigma patch ..)
)[["val"]]
val <- aux[1L]
attr(val, "gradient") <- -aux[1 + (1:q)]
attr(val, "hessian") <- -array(aux[-(1:(q+1))], c(q, q))
val
}
residuals.lmeStruct <-
function(object, level = conLin$dims$Q, conLin = attr(object, "conLin"),
lmeFit = attr(object, "lmeFit"), ...)
{
conLin$Xy[, conLin$dims$ZXcols] - fitted(object, level, conLin, lmeFit)
}
varWeights.lmeStruct <- function(object)
{
if (is.null(object$varStruct)) rep(1, attr(object, "conLin")$dims$N)
else varWeights(object$varStruct)
}
## Auxiliary control functions
lmeControl <-
## Control parameters for lme
function(maxIter = 50, msMaxIter = 50, tolerance = 1e-6, niterEM = 25,
msMaxEval = 200,
msTol = 1e-7, msVerbose = FALSE,
returnObject = FALSE, gradHess = TRUE, apVar = TRUE,
.relStep = .Machine$double.eps^(1/3), minAbsParApVar = 0.05,
opt = c("nlminb", "optim"),
optimMethod = "BFGS", natural = TRUE,
sigma = NULL, ## 17-11-2015; Fixed sigma patch; SH Heisterkamp; Quantitative Solutions
allow.n.lt.q = FALSE, # 23-01-2019 (NA would be back compatible)
...)
{
if(is.null(sigma))
sigma <- 0
else if(!is.finite(sigma) || length(sigma) != 1 || sigma < 0)
stop("Within-group std. dev. must be a positive numeric value")
list(maxIter = maxIter, msMaxIter = msMaxIter, tolerance = tolerance,
niterEM = niterEM, msMaxEval = msMaxEval, msTol = msTol,
msVerbose = msVerbose, returnObject = returnObject,
gradHess = gradHess , apVar = apVar, .relStep = .relStep,
opt = match.arg(opt), optimMethod = optimMethod,
minAbsParApVar = minAbsParApVar, natural = natural,
sigma = sigma,
allow.n.lt.q = allow.n.lt.q,
...)
}
## Local Variables:
## ess-indent-offset: 2
## End:
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Defines functions anova.lme ACF.lme MEdims MEestimate MEEM MEdecomp lmeApVar lmeApVar.fullLmeLogLik getFixDF lme.formula lme.lmList lme.groupedData
Documented in ACF.lme anova.lme lme.formula lme.groupedData lme.lmList
### Fit a general linear mixed effects model
###
### Copyright 2005-2022 The R Core team
### Copyright 1997-2003 Jose C. Pinheiro,
### Douglas M. Bates <bates@stat.wisc.edu>
###
### This program is free software; you can redistribute it and/or modify
### it under the terms of the GNU General Public License as published by
### the Free Software Foundation; either version 2 of the License, or
### (at your option) any later version.
###
### This program is distributed in the hope that it will be useful,
### but WITHOUT ANY WARRANTY; without even the implied warranty of
### MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
### GNU General Public License for more details.
###
### A copy of the GNU General Public License is available at
### http://www.r-project.org/Licenses/
lme <-
## fits general linear mixed effects model by maximum likelihood, or
## residual maximum likelihood using Newton-Raphson algorithm.
function(fixed,
data = sys.frame(sys.parent()),
random,
correlation = NULL,
weights = NULL,
subset,
method = c("REML", "ML"),
na.action = na.fail,
control = list(),
contrasts = NULL, keep.data = TRUE)
UseMethod("lme")
lme.groupedData <-
function(fixed,
data = sys.frame(sys.parent()),
random,
correlation = NULL,
weights = NULL,
subset,
method = c("REML", "ML"),
na.action = na.fail,
control = list(),
contrasts = NULL, keep.data = TRUE)
{
args <- as.list(match.call())[-1L]
names(args)[1L] <- "data"
form <- getResponseFormula(fixed)
form[[3]] <- getCovariateFormula(fixed)[[2L]]
do.call(lme, c(list(fixed = form), args))
}
lme.lmList <-
function(fixed,
data = sys.frame(sys.parent()),
random,
correlation = NULL,
weights = NULL,
subset,
method = c("REML", "ML"),
na.action = na.fail,
control = list(),
contrasts = NULL, keep.data = TRUE)
{
if (length(grpForm <- getGroupsFormula(fixed, asList = TRUE)) > 1) {
stop("can only fit \"lmList\" objects with single grouping variable")
}
this.call <- as.list(match.call())[-1L]
## warn "data" is passed to this function
if (!is.na(match("data", names(this.call)))) {
warning("'lme.lmList' will redefine 'data'")
}
## add object, data, and groups from the call that created object
last.call <- as.list(attr(fixed, "call"))[-1L]
whichLast <- match(c("object", "data", "na.action"), names(last.call))
whichLast <- whichLast[!is.na(whichLast)]
last.call <- last.call[whichLast]
names(last.call)[match(names(last.call), "object")] <- "fixed"
this.call[names(last.call)] <- last.call
this.call$fixed <- eval(substitute(L ~ R,
list(L = getResponseFormula (fixed)[[2L]],
R = getCovariateFormula(fixed)[[2L]])))
if (missing(random)) {
random <- eval(as.call(this.call[["fixed"]][-2]))
}
random <- reStruct(random, data = NULL)
mData <- this.call[["data"]]
if (is.null(mData)) { # will try to construct
allV <- all.vars(formula(random))
if (length(allV) > 0) {
alist <- lapply(as.list(allV), as.name)
names(alist) <- allV
alist <- c(as.list(quote(data.frame)), alist)
mode(alist) <- "call"
mData <- eval(alist, sys.parent(1))
}
} else {
if (mode(mData) == "name" || mode(mData) == "call") {
mData <- eval(mData)
}
}
reSt <- reStruct(random, data = mData) # getting random effects names
names(reSt) <- names(grpForm)
if (length(reSt) > 1) {
stop("can only fit \"lmList\" objects with single grouping variable")
}
rNames <- Names(reSt[[1L]])
if (all(match(rNames, names(cf <- na.omit(coef(fixed))), 0))) {
if (isInitialized(reSt)) {
warning("initial value for \"reStruct\" overwritten in 'lme.lmList'")
}
madRes <- mad(resid(fixed), na.rm = TRUE)
madRan <- unlist(lapply(cf, mad, na.rm = TRUE)[rNames])
names(madRan) <- rNames
matrix(reSt) <- diag((madRan/madRes)^2, ncol = length(rNames))
}
this.call[["random"]] <- reSt
val <- do.call(lme.formula, this.call)
val$origCall <- match.call()
val
}
lme.formula <-
function(fixed,
data = sys.frame(sys.parent()),
random = pdSymm( eval( as.call(fixed[-2]) ) ),
correlation = NULL,
weights = NULL,
subset,
method = c("REML", "ML"),
na.action = na.fail,
control = list(),
contrasts = NULL,
keep.data = TRUE)
{
Call <- match.call()
miss.data <- missing(data) || !is.data.frame(data)
## control parameters
controlvals <- lmeControl()
if (!missing(control)) {
controlvals[names(control)] <- control
}
fixedSigma <- controlvals$sigma > 0
## if(fixedSigma && controlvals$apVar) {
## if("apVar" %in% names(control))
## warning("for 'sigma' fixed, 'apVar' is set FALSE, as the cov approxmation is not yet available.")
## controlvals$apVar <- FALSE
## }
##
## checking arguments
##
if (!inherits(fixed, "formula") || length(fixed) != 3) {
stop("fixed-effects model must be a formula of the form \"resp ~ pred\"")
}
method <- match.arg(method)
REML <- method == "REML"
reSt <- reStruct(random, REML = REML, data = NULL)
groups <- getGroupsFormula(reSt)
if (is.null(groups)) {
if (inherits(data, "groupedData")) {
groups <- getGroupsFormula(data)
namGrp <- rev(names(getGroupsFormula(data, asList = TRUE)))
Q <- length(namGrp)
if (length(reSt) != Q) { # may need to repeat reSt
if (length(reSt) != 1) {
stop("incompatible lengths for 'random' and grouping factors")
}
randL <- vector("list", Q)
names(randL) <- rev(namGrp)
for(i in 1:Q) randL[[i]] <- random
reSt <- reStruct(as.list(randL), REML = REML, data = NULL)
} else {
names(reSt) <- namGrp
}
} else {
## will assume single group
groups <- ~ 1
names(reSt) <- "1"
}
}
## check if corStruct is present and assign groups to its formula,
## if necessary
if (!is.null(correlation)) {
add.form <- FALSE
if(!is.null(corGrpsForm <- getGroupsFormula(correlation, asList = TRUE))) {
corGrpsForm <- unlist(lapply(corGrpsForm,
function(el) deparse(el[[2L]])))
lmeGrpsForm <- unlist(lapply(splitFormula(groups),
function(el) deparse(el[[2L]])))
corQ <- length(corGrpsForm)
lmeQ <- length(lmeGrpsForm)
if (corQ <= lmeQ) {
if (any(corGrpsForm != lmeGrpsForm[1:corQ])) {
stop("incompatible formulas for groups in 'random' and 'correlation'")
}
if (corQ < lmeQ) {
warning("cannot use smaller level of grouping for 'correlation' than for 'random'. Replacing the former with the latter.")
add.form <- TRUE
}
} else if (any(lmeGrpsForm != corGrpsForm[1:lmeQ])) {
stop("incompatible formulas for groups in 'random' and 'correlation'")
}
} else {
add.form <- TRUE
corQ <- lmeQ <- 1
}
if(add.form)
## using the same grouping as in random
attr(correlation, "formula") <-
eval(substitute(~ COV | GRP,
list(COV = getCovariateFormula(formula(correlation))[[2L]],
GRP = groups[[2L]])))
} else {
corQ <- lmeQ <- 1
}
## create an lme structure containing the random effects model and plug-ins
lmeSt <- lmeStruct(reStruct = reSt, corStruct = correlation,
varStruct = varFunc(weights))
## extract a data frame with enough information to evaluate
## fixed, groups, reStruct, corStruct, and varStruct
mfArgs <- list(formula = asOneFormula(formula(lmeSt), fixed, groups),
data = data, na.action = na.action)
if (!missing(subset)) {
mfArgs[["subset"]] <- asOneSidedFormula(Call[["subset"]])[[2L]]
}
mfArgs$drop.unused.levels <- TRUE
dataMix <- do.call(model.frame, mfArgs)
origOrder <- row.names(dataMix) # preserve the original order
for(i in names(contrasts)) # handle contrasts statement
contrasts(dataMix[[i]]) <- contrasts[[i]]
## sort the model.frame by groups and get the matrices and parameters
## used in the estimation procedures
grps <- getGroups(dataMix, groups)
## ordering data by groups
if (inherits(grps, "factor")) { # single level
ord <- order(grps) #"order" treats a single named argument peculiarly
grps <- data.frame(grps)
row.names(grps) <- origOrder
names(grps) <- as.character(deparse((groups[[2L]])))
} else {
ord <- do.call(order, grps)
## making group levels unique
for(i in 2:ncol(grps)) {
grps[, i] <-
as.factor(paste(as.character(grps[, i-1]),
as.character(grps[, i ]), sep = "/"))
}
}
if (corQ > lmeQ) {
## may have to reorder by the correlation groups
ord <- do.call(order, getGroups(dataMix,
getGroupsFormula(correlation)))
}
grps <- grps[ord, , drop = FALSE]
dataMix <- dataMix[ord, ,drop = FALSE]
revOrder <- match(origOrder, row.names(dataMix)) # putting in orig. order
## obtaining basic model matrices
N <- nrow(grps)
Z <- model.matrix(reSt, dataMix) # stores contrasts in matrix form
ncols <- attr(Z, "ncols")
Names(lmeSt$reStruct) <- attr(Z, "nams")
## keeping the contrasts for later use in predict
contr <- attr(Z, "contr")
X <- model.frame(fixed, dataMix)
Terms <- attr(X, "terms")
if (length(attr(Terms, "offset")))
stop("offset() terms are not supported")
auxContr <- lapply(X, function(el)
if (inherits(el, "factor") &&
length(levels(el)) > 1) contrasts(el))
contr <- c(contr, auxContr[is.na(match(names(auxContr), names(contr)))])
contr <- contr[!unlist(lapply(contr, is.null))]
X <- model.matrix(fixed, data=X)
y <- eval(fixed[[2L]], dataMix)
ncols <- c(ncols, dim(X)[2L], 1)
Q <- ncol(grps)
## creating the condensed linear model
dims <- MEdims(grps, ncols)
attr(lmeSt, "conLin") <-
list(Xy = array(c(Z, X, y), c(N, sum(ncols)),
list(row.names(dataMix), c(colnames(Z), colnames(X),
deparse(fixed[[2L]])))),
dims = dims, logLik = 0,
## 17-11-2015; Fixed sigma:
sigma = controlvals$sigma, auxSigma = 0)
## checking if enough observations per group to estimate ranef
if(max(dims$ZXlen[[1L]]) < dims$qvec[1L] && !isTRUE(allow <- controlvals$allow.n.lt.q)) {
msg <- gettextf("fewer observations than random effects in all level %s groups", Q)
if(isFALSE(allow))
stop (msg, domain = NA)
else # typically NA, was hardwired default in nlme <= 3.1-137 [2018]
warning(msg, domain = NA)
}
## degrees of freedom for testing fixed effects
fixDF <- getFixDF(X, grps, dims$ngrps, terms = Terms)
## initialization
lmeSt <- Initialize(lmeSt, dataMix, grps, control = controlvals)
parMap <- attr(lmeSt, "pmap")
## Checking possibility of single decomposition
if (length(lmeSt) == 1) { # reStruct only, can do one decomposition
## need to save conLin for calculating fitted values and residuals
oldConLin <- attr(lmeSt, "conLin")
decomp <- TRUE
attr(lmeSt, "conLin") <- MEdecomp(attr(lmeSt, "conLin"))
} else decomp <- FALSE
## Setup for optimization iterations
if(controlvals$opt == "nlminb") {
control <- list(iter.max = controlvals$msMaxIter,
eval.max = controlvals$msMaxEval,
trace = controlvals$msVerbose)
keep <- c("abs.tol", "rel.tol", "x.tol", "xf.tol", "step.min",
"step.max", "sing.tol", "scale.init", "diff.g")
} else { ## "optim"
control <- list(maxit = controlvals$msMaxIter,
reltol = controlvals$msTol,# if(numIter == 0) controlvals$msTol else reltol
trace = controlvals$msVerbose)
keep <- c("fnscale", "parscale", "ndeps", "abstol", "alpha", "beta",
"gamma", "REPORT", "type", "lmm", "factr", "pgtol",
"temp", "tmax")
}
control <- c(control, controlvals[names(controlvals) %in% keep])
##
## getting the linear mixed effects fit object,
## possibly iterating for variance functions
##
numIter <- 0L
repeat {
oldPars <- coef(lmeSt)
optRes <-
if (controlvals$opt == "nlminb") {
nlminb(c(oldPars), function(lmePars) -logLik(lmeSt, lmePars),
control = control)
} else { ## "optim"
if(numIter == 1L) { # (yes, strange, but back-compatible ..) :
reltol <- controlvals$reltol
if(is.null(reltol)) reltol <- 100*.Machine$double.eps
control$reltol <- reltol
}
optim(c(oldPars), function(lmePars) -logLik(lmeSt, lmePars),
control = control, method = controlvals$optimMethod)
}
coef(lmeSt) <- optRes$par
attr(lmeSt, "lmeFit") <- MEestimate(lmeSt, grps)
## checking if any updating is needed
if (!needUpdate(lmeSt)) {
if (optRes$convergence) {
msg <- gettextf("%s problem, convergence error code = %s\n message = %s",
controlvals$opt, optRes$convergence,
paste(optRes$message, collapse = ""))
if(!controlvals$returnObject)
stop(msg, domain = NA)
else
warning(msg, domain = NA)
}
break
}
## updating the fit information
numIter <- numIter + 1L
lmeSt <- update(lmeSt, dataMix)
## calculating the convergence criterion
aConv <- coef(lmeSt)
conv <- abs((oldPars - aConv)/ifelse(aConv == 0, 1, aConv))
aConv <- NULL
for(i in names(lmeSt)) {
if (any(parMap[,i])) {
aConv <- c(aConv, max(conv[parMap[,i]]))
names(aConv)[length(aConv)] <- i
}
}
if (max(aConv) <= controlvals$tolerance) {
break
}
if (numIter > controlvals$maxIter) {
msg <- gettext("maximum number of iterations (lmeControl(maxIter)) reached without convergence")
if (controlvals$returnObject) {
warning(msg, domain = NA)
break
} else
stop(msg, domain = NA)
}
} ## end{repeat}
## wrapping up
lmeFit <- attr(lmeSt, "lmeFit")
names(lmeFit$beta) <- namBeta <- colnames(X)
attr(fixDF, "varFixFact") <- varFix <- lmeFit$sigma * lmeFit$varFix
varFix <- crossprod(varFix)
dimnames(varFix) <- list(namBeta, namBeta)
##
## fitted.values and residuals (in original order)
##
Fitted <- fitted(lmeSt, level = 0:Q,
conLin = if (decomp) oldConLin else attr(lmeSt, "conLin"))[
revOrder, , drop = FALSE]
Resid <- y[revOrder] - Fitted
rownames(Resid) <- rownames(Fitted) <- origOrder
attr(Resid, "std") <- lmeFit$sigma/(varWeights(lmeSt)[revOrder])
## putting groups back in original order
grps <- grps[revOrder, , drop = FALSE]
## making random effects estimates consistently ordered
## for(i in names(lmeSt$reStruct)) {
## lmeFit$b[[i]] <- lmeFit$b[[i]][unique(as.character(grps[, i])),, drop = F]
## NULL
## }
## inverting back reStruct
lmeSt$reStruct <- solve(lmeSt$reStruct)
## saving part of dims
dims <- attr(lmeSt, "conLin")$dims[c("N", "Q", "qvec", "ngrps", "ncol")]
## 17-11-2015; Fixed sigma patch; SH Heisterkamp; Quantitative Solutions
attr(lmeSt, "fixedSigma") <- fixedSigma
## getting the approximate var-cov of the parameters
apVar <-
if (controlvals$apVar) {
lmeApVar(lmeSt, lmeFit$sigma,
.relStep = controlvals[[".relStep"]],
minAbsPar = controlvals[["minAbsParApVar"]],
natural = controlvals[["natural"]])
} else {
"Approximate variance-covariance matrix not available"
}
## getting rid of condensed linear model and fit
attr(lmeSt, "conLin") <- NULL
attr(lmeSt, "lmeFit") <- NULL
grpDta <- inherits(data, "groupedData")
##
## creating the lme object
##
structure(class = "lme",
list(modelStruct = lmeSt,
dims = dims,
contrasts = contr,
coefficients = list(
fixed = lmeFit$beta,
random = lmeFit$b),
varFix = varFix,
sigma = lmeFit$sigma,
apVar = apVar,
logLik = lmeFit$logLik,
numIter = if (needUpdate(lmeSt)) numIter, # else NULL
groups = grps,
call = Call,
terms = Terms,
method = method,
fitted = Fitted,
residuals = Resid,
fixDF = fixDF,
na.action = attr(dataMix, "na.action"),
data = if (keep.data && !miss.data) data),
## saving labels and units for plots
units = if(grpDta) attr(data, "units"),
labels= if(grpDta) attr(data, "labels"))
}
### Auxiliary functions used internally in lme and its methods
getFixDF <- function(X, grps, ngrps, assign = attr(X, "assign"), terms)
{
## calculates degrees of freedom for fixed effects Wald tests
if (!is.list(assign)) { # in R
namTerms <- attr(terms, "term.labels")
if (attr(terms, "intercept") > 0) {
namTerms <- c("(Intercept)", namTerms)
}
namTerms <- factor(assign, labels = namTerms)
assign <- split(order(assign), namTerms)
}
## function to check if a vector is (nearly) a multiple of (1,1,...,1)
const <- function(x, tolerance = sqrt(.Machine$double.eps)) {
if (length(x) < 1) return(NA)
x <- as.numeric(x)
## return
all(abs(if(x[1L] == 0) x else x/x[1L] - 1) < tolerance)
}
N <- nrow(X)
p <- ncol(X)
Q <- ncol(grps)
Qp1 <- Q + 1L
namX <- colnames(X)
ngrps <- rev(ngrps)[-(1:2)]
stratNam <- c(names(ngrps), "Residual")
dfX <- dfTerms <- setNames(c(ngrps, N) - c(0, ngrps), stratNam)
valX <- setNames(double(p), namX)
namTerms <- names(assign)
valTerms <- double(length(assign))
names(valTerms) <- namTerms
if (any(notIntX <- !apply(X, 2, const))) {
## percentage of groups for which columns of X are inner
innP <- array(c(rep(1, p),
.C(inner_perc_table,
as.double(X),
as.integer(unlist(grps)),
as.integer(p),
as.integer(Q),
as.integer(N),
val = double(p * Q))[["val"]]),
dim = c(p, Qp1),
dimnames = list(namX, stratNam))
## strata in which columns of X are estimated
## ignoring fractional inner percentages for now
stratX <- stratNam[apply(innP, 1, function(el, index) max(index[el > 0]),
index = 1:Qp1)]
## strata in which terms are estimated
notIntTerms <- unlist(lapply(assign,
function(el, notIntX) {
any(notIntX[el])
}, notIntX = notIntX))
stratTerms <- stratNam[unlist(lapply(assign,
function(el) max(match(stratX[el], stratNam))
))][notIntTerms]
stratX <- stratX[notIntX]
xDF <- table(stratX)
dfX[names(xDF)] <- dfX[names(xDF)] - xDF
if (!all(notIntX)) { # correcting df for intercept
dfX[1L] <- dfX[1L] - 1L
} else {
dfX[-1L] <- dfX[-1L] + 1L
}
valX[notIntX] <- dfX[stratX]
## number of parameters in each term
pTerms <- lengths(assign)[notIntTerms]
tDF <- tapply(pTerms, stratTerms, sum)
dfTerms[names(tDF)] <- dfTerms[names(tDF)] - tDF
if (!all(notIntTerms)) {
dfTerms[1L] <- dfTerms[1L] - 1L
} else {
dfTerms[-1L] <- dfTerms[-1L] + 1L
}
valTerms[notIntTerms] <- dfTerms[stratTerms]
} else {
notIntTerms <- vapply(assign, function(el) any(notIntX[el]), NA)
}
if (!all(notIntX)) { #intercept included
valX[!notIntX] <- max(dfX)
if (!all(notIntTerms))
valTerms[!notIntTerms] <- max(dfTerms)
}
structure(list(X = valX, terms = valTerms),
assign = assign)
}
lmeApVar.fullLmeLogLik <- function(Pars, object, conLin, dims, N, settings) {
## logLik as a function of sigma and coef(lmeSt)
fixedSigma <- attr(object, "fixedSigma")
npar <- length(Pars)
if (!fixedSigma) {
sigma <- exp(Pars[npar]) # within-group std. dev.
Pars <- Pars[-npar]
lsigma <- 0
} else {
sigma <- lsigma <- conLin$sigma
}
coef(object) <- Pars
if ((lO <- length(object)) > 1) {
for(i in lO:2)
conLin <- recalc(object[[i]], conLin)
}
val <- .C(mixed_loglik,
as.double(conLin$Xy),
as.integer(unlist(dims)),
as.double(sigma * unlist(pdFactor(solve(object$reStruct)))),
as.integer(settings),
logLik = double(1L),
lRSS = double(1L), sigma=as.double(lsigma))[c("logLik", "lRSS")]
aux <- (exp(val[["lRSS"]])/sigma)^2
conLin[["logLik"]] + val[["logLik"]] + (N * log(aux) - aux)/2
}
lmeApVar <-
function(lmeSt, sigma, conLin = attr(lmeSt, "conLin"),
.relStep = .Machine$double.eps^(1/3), minAbsPar = 0,
natural = TRUE)
{
fixedSigma <- attr(lmeSt,"fixedSigma")
## calculate approximate variance-covariance matrix of all parameters
## except the fixed effects. By default, uses natural parametrization for
## for pdSymm matrices
dims <- conLin$dims
sett <- attr(lmeSt, "settings")
N <- dims$N - sett[1L] * dims$ncol[dims$Q + 1L]
sett[2:3] <- c(1, 0) # asDelta = TRUE and no grad/Hess
conLin[["logLik"]] <- 0 # making sure
sig2 <- sigma * sigma
reSt <- lmeSt[["reStruct"]]
for(i in seq_along(reSt)) {
matrix(reSt[[i]]) <- as.double(sig2) * pdMatrix(reSt[[i]])
if (inherits(reSt[[i]], "pdSymm") && natural) {
reSt[[i]] <- pdNatural(reSt[[i]])
}
if (inherits(reSt[[i]], "pdBlocked") && natural) {
for(j in seq_along(reSt[[i]])) {
if (inherits(reSt[[i]][[j]], "pdSymm")) {
reSt[[i]][[j]] <- pdNatural(reSt[[i]][[j]])
}
}
}
}
lmeSt[["reStruct"]] <- reSt
cSt <- lmeSt[["corStruct"]]
if (!is.null(cSt) && inherits(cSt, "corSymm") && natural) {
cStNatPar <- coef(cSt, unconstrained = FALSE)
class(cSt) <- c("corNatural", "corStruct")
coef(cSt) <- log((cStNatPar + 1)/(1 - cStNatPar))
lmeSt[["corStruct"]] <- cSt
}
Pars <- if(fixedSigma) coef(lmeSt) else c(coef(lmeSt), lSigma = log(sigma))
val <- fdHess(Pars, lmeApVar.fullLmeLogLik, lmeSt, conLin, dims, N, sett,
.relStep = .relStep, minAbsPar = minAbsPar)[["Hessian"]]
if (all(eigen(val, only.values=TRUE)$values < 0)) {
## negative definite - OK
val <- solve(-val)
nP <- names(Pars)
dimnames(val) <- list(nP, nP)
attr(val, "Pars") <- Pars
attr(val, "natural") <- natural
val
} else {
## problem - solution is not a maximum
"Non-positive definite approximate variance-covariance"
}
}
MEdecomp <-
function(conLin)
## decompose a condensed linear model. Returns another condensed
## linear model
{
dims <- conLin$dims
if (dims[["StrRows"]] >= dims[["ZXrows"]]) {
## no point in doing the decomposition
return(conLin)
}
dc <- array(.C(mixed_decomp,
as.double(conLin$Xy),
as.integer(unlist(dims)))[[1L]],
c(dims$StrRows, dims$ZXcols))
dims$ZXrows <- dims$StrRows
dims$ZXoff <- dims$DecOff
dims$ZXlen <- dims$DecLen
conLin[c("Xy", "dims")] <- list(Xy = dc, dims = dims)
conLin
}
MEEM <-
function(object, conLin, niter = 0)
## perform niter iterations of the EM algorithm for conLin
## assumes that object is in precision form
{
if (niter > 0) {
dd <- conLin$dims
pdCl <- attr(object, "settings")[-(1:3)]
pdCl[pdCl == -1] <- 0
precvec <- unlist(pdFactor(object))
zz <- .C(mixed_EM,
as.double(conLin$Xy),
as.integer(unlist(dd)),
precvec = as.double(precvec),
as.integer(niter),
as.integer(pdCl),
as.integer(attr(object, "settings")[1L]),
double(1),
double(length(precvec)),
double(1),
as.double(conLin$sigma))[["precvec"]]
Prec <- vector("list", length(object))
names(Prec) <- names(object)
for (i in seq_along(object)) {
len <- dd$qvec[i]^2
matrix(object[[i]]) <-
crossprod(matrix(zz[1:len + dd$DmOff[i]], ncol = dd$qvec[i]))
}
}
object
}
MEestimate <-
function(object, groups, conLin = attr(object, "conLin"))
{
dd <- conLin$dims
nc <- dd$ncol
REML <- attr(object$reStruct, "settings")[1L]
Q <- dd$Q
rConLin <- recalc(object, conLin)
zz <- .C(mixed_estimate,
as.double(rConLin$Xy),
as.integer(unlist(dd)),
as.double(unlist(pdFactor(object$reStruct))),
as.integer(REML),
double(1),
estimates = double(dd$StrRows * dd$ZXcols),
as.logical(FALSE),
## 17-11-2015; Fixed sigma patch; SH Heisterkamp; Quantitative Solutions
sigma = as.double(conLin$sigma))[["estimates"]]
estimates <- array(zz, c(dd$StrRows, dd$ZXcols))
resp <- estimates[ , dd$ZXcols]
reSt <- object$reStruct
nam <- names(reSt)
val <- vector(mode = "list", length = Q)
start <- dd$StrRows * c(0, cumsum(nc))
for (i in seq_along(reSt)) {
val[[i]] <-
matrix(resp[as.vector(outer(1:(nc[i]), dd$SToff[[i]] - start[i], "+"))],
ncol = nc[i], byrow = TRUE,
dimnames = list(unique(as.character(groups[, nam[i]])),
Names(reSt[[i]])))
}
names(val) <- nam
p <- nc[[Q + 1L]]
N <- dd$N - REML * p
dimE <- dim(estimates)
## 17-11-2015; Fixed sigma patch; ... modified by MM notably for p == 0:
Np <- N
auxSigma <- abs(resp[dimE[1]])/sqrt(Np)
if(conLin$sigma > 0) {
loglik <- (N * (-log(2 * pi)-2*log(conLin$sigma)))/2 + rConLin$logLik
sigma <- conLin$sigma
} else {
loglik <- (N * (log(N) - (1 + log(2 * pi))))/2 + rConLin$logLik
sigma <- auxSigma
}
list(logLik = loglik,
b = rev(val),
beta = if(p) resp[dimE[1] - (p:1)] else double(),
sigma = sigma,
auxSigma = auxSigma,
varFix = if(p)
t(solve(estimates[dimE[1] - (p:1), dimE[2] - (p:1), drop = FALSE]))
else
matrix(,p,p))
}
MEdims <- function(groups, ncols)
{
## define constants used in matrix decompositions and log-lik calculations
## first need some local functions
glengths <-
## returns the group lengths from a vector of last rows in the group
function(lstrow) diff(c(0, lstrow))
offsets <-
## converts total number of columns(N), columns per level(ncols), and
## a list of group lengths to offsets in C arrays
function(N, ncols, lstrow, triangle = FALSE)
{
pop <- function(x) x[-length(x)]
cstart <- c(0, cumsum(N * ncols))
for (i in seq_along(lstrow)) {
lstrow[[i]] <- cstart[i] +
if (triangle) {
lstrow[[i]] - ncols[i] # storage offsets style
} else {
pop(c(0, lstrow[[i]])) # decomposition style
}
}
lstrow
}
Q <- ncol(groups) # number of levels
N <- nrow(groups) # number of observations
## 'isLast' indicates if the row is the last row in the group at that level.
## this version propagates changes from outer groups to inner groups
## isLast <- (array(unlist(lapply(c(rev(as.list(groups)),
## list(X = rep(0, N), y = rep(0, N))),
## function(x) c(0 != diff(codes(x)), TRUE))),
## c(N, Q+2), list(NULL, c(rev(names(groups)), "X", "y")))
## %*% (row(diag(Q+2)) >= col(diag(Q+2)))) != 0
## this version does not propagate changes from outer to inner.
isLast <- array(FALSE, dim(groups) + c(0, 2),
list(NULL, c(rev(names(groups)), "X", "y")))
for(i in 1:Q) {
isLast[, Q + 1L - i] <- c(0 != diff(as.integer(groups[[i]])), TRUE)
}
isLast[N, ] <- TRUE
lastRow <- apply(isLast, 2, function(x) seq_along(x)[x])
if(!is.list(lastRow)) {
nm <- names(lastRow)
lastRow <- as.list(lastRow)
names(lastRow) <- nm
}
isLast <- t(isLast)
strSizes <- cumsum(ncols * isLast) * isLast # required storage sizes
lastStr <- apply(t(strSizes), 2, function(x) x[x != 0])
if(!is.list(lastStr)) {
nm <- names(lastStr)
lastStr <- as.list(lastStr)
names(lastStr) <- nm
}
strRows <- max(lastStr[[length(lastStr)]])
lastBlock <- vector("list", Q)
names(lastBlock) <- rownames(strSizes)[1:Q]
for(i in 1:Q) lastBlock[[i]] <- c(strSizes[i, -N], strRows)
maxStr <- do.call(pmax, lastBlock)
for(i in 1:Q) lastBlock[[i]] <- maxStr[as.logical(lastBlock[[i]])]
lastBlock <- c(lastBlock, list(X = strRows, y = strRows))
list(N = N, # total number of rows in data
ZXrows = N, # no. of rows in array
ZXcols = sum(ncols), # no. of columns in array
Q = Q, # no. of levels of random effects
StrRows = strRows, # no. of rows required for storage
qvec = ncols * c(rep(1, Q), 0, 0), # lengths of random effects
# no. of groups at each level
### This looks wrong: ")" at wrong place: unlist(*, N, N) !!
ngrps = c(unlist(lapply(lastRow, length), N, N)),
###?ok ngrps = c(lengths(lastRow), N, N),# no. of groups at each level
DmOff = c(0, cumsum(ncols^2))[1:(Q+2)],# offsets into DmHalf array by level
ncol = ncols, # no. of columns decomposed per level
nrot = rev(c(0, cumsum(rev(ncols))))[-1L],# no. of columns rotated per level
ZXoff = offsets(N, ncols, lastRow), # offsets into ZXy
ZXlen = lapply(lastRow, glengths), # lengths of ZXy groups
# storage array offsets
SToff = offsets(strRows, ncols, lastStr, triangle = TRUE),
# decomposition offsets
DecOff = offsets(strRows, ncols, lastBlock),
# decomposition lengths
DecLen = lapply(lastBlock, glengths)
)
}
### Methods for standard generics
ACF.lme <-
function(object, maxLag,
resType = c("pearson", "response", "normalized"), ...)
{
resType <- match.arg(resType)
res <- resid(object, type = resType, asList = TRUE)
if(missing(maxLag)) {
maxLag <- min(c(maxL <- max(lengths(res)) - 1,
as.integer(10 * log10(maxL + 1))))
}
val <- lapply(res,
function(el, maxLag) {
N <- maxLag + 1L
tt <- double(N)
nn <- integer(N)
N <- min(c(N, n <- length(el)))
nn[1:N] <- n + 1L - 1:N
## el <- el - mean(el)
for(i in 1:N) {
tt[i] <- sum(el[1:(n-i+1)] * el[i:n])
}
array(c(tt,nn), c(length(tt), 2))
}, maxLag = maxLag)
val0 <- rowSums(sapply(val, function(x) x[,2]))
val1 <- rowSums(sapply(val, function(x) x[,1]))/val0
val2 <- val1/val1[1L]
z <- data.frame(lag = 0:maxLag, ACF = val2)
attr(z, "n.used") <- val0
class(z) <- c("ACF", "data.frame")
z
}
anova.lme <-
function(object, ..., test = TRUE, type = c("sequential", "marginal"),
adjustSigma = TRUE, Terms, L, verbose = FALSE)
{
fixSig <- attr(object$modelStruct, "fixedSigma")
fixSig <- !is.null(fixSig) && fixSig
## returns the likelihood ratio statistics, the AIC, and the BIC
Lmiss <- missing(L)
dots <- list(...)
if ((rt <- length(dots) + 1L) == 1L) { ## just one object
if (!inherits(object,"lme")) {
stop("object must inherit from class \"lme\" ")
}
vFix <- attr(object$fixDF, "varFixFact")
if (adjustSigma && object$method == "ML")
## using REML-like estimate of sigma under ML
vFix <- sqrt(object$dims$N/(object$dims$N - ncol(vFix))) * vFix
c0 <- solve(t(vFix), fixef(object))
assign <- attr(object$fixDF, "assign")
nTerms <- length(assign)
if (missing(Terms) && Lmiss) {
## returns the F.table (Wald) for the fixed effects
type <- match.arg(type)
Fval <- Pval <- double(nTerms)
nDF <- integer(nTerms)
dDF <- object$fixDF$terms
for(i in 1:nTerms) {
nDF[i] <- length(assign[[i]])
if (type == "sequential") { # type I SS
c0i <- c0[assign[[i]]]
} else {
c0i <- c(qr.qty(qr(vFix[, assign[[i]], drop = FALSE]), c0))[1:nDF[i]]
}
Fval[i] <- sum(c0i^2)/nDF[i]
Pval[i] <- 1 - pf(Fval[i], nDF[i], dDF[i])
}
##
## fixed effects F-values, df, and p-values
##
aod <- data.frame(numDF= nDF, denDF= dDF, "F-value"= Fval, "p-value"= Pval,
check.names = FALSE)
rownames(aod) <- names(assign)
attr(aod,"rt") <- rt
} else {
nX <- length(unlist(assign))
if (Lmiss) { # terms is given
if (is.numeric(Terms) && all(Terms == as.integer(Terms))) {
if (min(Terms) < 1 || max(Terms) > nTerms) {
stop(gettextf("'Terms' must be between 1 and %d", nTerms),
domain = NA)
}
} else {
if (is.character(Terms)) {
if (any(noMatch <- is.na(match(Terms, names(assign))))) {
stop(sprintf(ngettext(sum(noMatch),
"term %s not matched",
"terms %s not matched"),
paste(Terms[noMatch], collapse = ", ")),
domain = NA)
}
} else {
stop("terms can only be integers or characters")
}
}
dDF <- unique(object$fixDF$terms[Terms])
if (length(dDF) > 1) {
stop("terms must all have the same denominator DF")
}
lab <-
paste("F-test for:",paste(names(assign[Terms]),collapse=", "),"\n")
L <- diag(nX)[unlist(assign[Terms]),,drop=FALSE]
} else {
L <- as.matrix(L)
if (ncol(L) == 1) L <- t(L) # single linear combination
nrowL <- nrow(L)
ncolL <- ncol(L)
if (ncol(L) > nX) {
stop(sprintf(ngettext(nX,
"'L' must have at most %d column",
"'L' must have at most %d columns"),
nX), domain = NA)
}
dmsL1 <- rownames(L)
L0 <- array(0, c(nrowL, nX), list(NULL, names(object$fixDF$X)))
if (is.null(dmsL2 <- colnames(L))) {
## assume same order as effects
L0[, 1:ncolL] <- L
} else {
if (any(noMatch <- is.na(match(dmsL2, colnames(L0))))) {
stop(sprintf(ngettext(sum(noMatch),
"effect %s not matched",
"effects %s not matched"),
paste(dmsL2[noMatch],collapse=", ")),
domain = NA)
}
L0[, dmsL2] <- L
}
L <- L0[noZeroRowL <- as.logical((L0 != 0) %*% rep(1, nX)), , drop = FALSE]
nrowL <- nrow(L)
rownames(L) <- if(is.null(dmsL1)) 1:nrowL else dmsL1[noZeroRowL]
dDF <-
unique(object$fixDF$X[noZeroColL <-
as.logical(c(rep(1,nrowL) %*% (L != 0)))])
if (length(dDF) > 1) {
stop("L may only involve fixed effects with the same denominator DF")
}
lab <- "F-test for linear combination(s)\n"
}
nDF <- sum(svd.d(L) > 0)
c0 <- c(qr.qty(qr(vFix %*% t(L)), c0))[1:nDF]
Fval <- sum(c0^2)/nDF
Pval <- pf(Fval, nDF, dDF, lower.tail=FALSE)
aod <- data.frame(numDF = nDF, denDF = dDF, "F-value" = Fval, "p-value" = Pval,
check.names=FALSE)
attr(aod, "rt") <- rt
attr(aod, "label") <- lab
if (!Lmiss) {
attr(aod, "L") <-
if(nrow(L) > 1) L[, noZeroColL, drop = FALSE] else L[, noZeroColL]
}
}
}
##
## Otherwise construct the likelihood ratio and information table
## objects in ... may inherit from gls, gnls, lm, lmList, lme,
## nlme, nlsList, and nls
##
else {
ancall <- sys.call() # yuck.. hack
ancall$verbose <- ancall$test <- ancall$type <- NULL
object <- list(object, ...)
valid.cl <- c("gls", "gnls", "lm", "lmList", "lme","nlme","nlsList","nls")
if (!all(vapply(object, inherits, TRUE, what = valid.cl))) {
valid.cl <- paste0('"', valid.cl, '"')
stop(gettextf("objects must inherit from classes %s, or %s",
paste(head(valid.cl, -1), collapse=", "), tail(valid.cl, 1)),
domain=NA)
}
resp <- vapply(object,
function(el) deparse(getResponseFormula(el)[[2L]]), "")
## checking if responses are the same
subs <- as.logical(match(resp, resp[1L], FALSE))
if (!all(subs))
warning("some fitted objects deleted because response differs from the first model")
if (sum(subs) == 1)
stop("first model has a different response from the rest")
object <- object[subs]
rt <- length(object)
termsModel <- lapply(object, function(el) formula(el)[-2])
estMeth <- vapply(object, function(el)
if (is.null(val <- el[["method"]])) NA_character_ else val, "")
## checking consistency of estimation methods
if(length(uEst <- unique(estMeth[!is.na(estMeth)])) > 1) {
stop("all fitted objects must have the same estimation method")
}
estMeth[is.na(estMeth)] <- uEst
## checking if all models have same fixed effects when estMeth = "REML"
REML <- uEst == "REML"
if(REML) {
aux <- vapply(termsModel,
function(el) {
tt <- terms(el)
val <- paste(sort(attr(tt, "term.labels")), collapse = "&")
if (attr(tt, "intercept") == 1)
paste(val, "(Intercept)", sep = "&") else val
}, ".")
if(length(unique(aux)) > 1) {
warning("fitted objects with different fixed effects. REML comparisons are not meaningful.")
}
}
termsCall <-
lapply(object, function(el) {
if (is.null(val <- el$call) &&
is.null(val <- attr(el, "call")))
stop("objects must have a \"call\" component or attribute")
val
})
termsCall <- vapply(termsCall,
function(el) paste(deparse(el), collapse =""), "")
aux <- lapply(object, logLik, REML)
if (length(unique(vapply(aux, attr, 1, "nall"))) > 1) {
stop("all fitted objects must use the same number of observations")
}
dfModel <- vapply(aux, attr, 1, "df")
logLik <- vapply(aux, c, 1.1)
aod <- data.frame(call = termsCall,
Model = 1:rt,
df = dfModel,
AIC = vapply(aux, AIC, 1.),
BIC = vapply(aux, BIC, 1.),
logLik = logLik,
check.names = FALSE)
if (test) {
ddf <- diff(dfModel)
if (sum(abs(ddf)) > 0) {
effects <- rep("", rt)
for(i in 2:rt) {
if (ddf[i-1] != 0) {
effects[i] <- paste(i - 1, i, sep = " vs ")
}
}
pval <- rep(NA, rt - 1)
ldf <- as.logical(ddf)
lratio <- 2 * abs(diff(logLik))
lratio[!ldf] <- NA
pval[ldf] <- pchisq(lratio[ldf], abs(ddf[ldf]), lower.tail=FALSE)
aod <- data.frame(aod,
Test = effects,
"L.Ratio" = c(NA, lratio),
"p-value" = c(NA, pval),
check.names = FALSE,
stringsAsFactors = TRUE)
}
}
row.names(aod) <- vapply(as.list(ancall[-1L]), c_deparse, "")
attr(aod, "rt") <- rt
attr(aod, "verbose") <- verbose
}
class(aod) <- c("anova.lme", "data.frame")
aod
}
## (This is "cut'n'paste" similar to augPred.gls() in ./gls.R -- keep in sync!)
augPred.lme <-
function(object, primary = NULL, minimum = min(primary),
maximum = max(primary), length.out = 51L, level = Q, ...)
{
data <- eval.parent(object$call$data)
if (!inherits(data, "data.frame")) {
stop(gettextf("data in %s call must evaluate to a data frame",
sQuote(substitute(object))), domain = NA)
}
if(is.null(primary)) {
if (!inherits(data, "groupedData")) {
stop(gettextf(
"%s without \"primary\" can only be used with fits of \"groupedData\" objects",
sys.call()[[1L]]), domain = NA)
}
primary <- getCovariate(data)
pr.var <- getCovariateFormula(data)[[2L]]
} else{
pr.var <- asOneSidedFormula(primary)[[2L]]
primary <- eval(pr.var, data)
}
prName <- c_deparse(pr.var)
newprimary <- seq(from = minimum, to = maximum, length.out = length.out)
Q <- object$dims$Q # number of levels
if (is.null(level)) level <- Q
nL <- length(level) # number of requested levels
maxLev <- max(c(level, 1))
groups <- getGroups(object, level = maxLev)
if (!is.ordered(groups)) {
groups <- ordered(groups, levels = unique(as.character(groups)))
}
grName <- ".groups"
ugroups <- unique(groups)
value <- data.frame(rep(rep(newprimary, length(ugroups)), nL),
rep(rep(ugroups, rep(length(newprimary),
length(ugroups))), nL))
names(value) <- c(prName, grName)
## recovering other variables in data that may be needed for predictions
## varying variables will be replaced by their means
summData <- gsummary(data, groups = groups)
if (any(toAdd <- is.na(match(names(summData), names(value))))) {
summData <- summData[, toAdd, drop = FALSE]
}
value[, names(summData)] <- summData[value[, 2L], ]
pred <- predict(object, value[seq_len(nrow(value)/nL), , drop = FALSE],
level = level)
if (nL > 1) { # multiple levels
pred <- pred[, ncol(pred) - (nL - 1):0] # eliminating groups
predNames <- rep(names(pred), rep(nrow(pred), nL))
pred <- c(unlist(pred))
} else {
predNames <- rep("predicted", nrow(value))
}
newvals <- cbind(value[, 1:2], pred)
names(newvals)[3] <- respName <-
deparse(resp.var <- getResponseFormula(object)[[2L]])
orig <- data.frame(primary, groups, getResponse(object))
names(orig) <- names(newvals)
value <- rbind(orig, newvals)
attributes(value[, 2]) <- attributes(groups)
value[, ".type"] <- ordered(c(rep("original", nrow(data)), predNames),
levels = c(unique(predNames), "original"))
labs <- list(x = prName, y = respName)
unts <- list(x = "", y = "")
if(inherits(data, "groupedData")) {
labs[names(attr(data, "labels"))] <- attr(data, "labels")
unts[names(attr(data, "units"))] <- attr(data, "units")
attr(value, "units") <- attr(data, "units")
}
structure(value, class = c("augPred", class(value)),
labels = labs,
units = unts,
formula= eval(substitute(Y ~ X | G,
list(Y = resp.var, X = pr.var,
G = as.name(grName)))))
}
coef.lme <-
function(object, augFrame = FALSE, level = Q, data, which = 1:ncol(data),
FUN = mean, omitGroupingFactor = TRUE, subset = NULL, ...)
{
Q <- object$dims$Q
if (length(level) > 1) {
stop("only single level allowed")
}
fixed <- fixef(object)
p <- length(fixed)
value <- ranef(object, level = 1:level)
grps <- object[["groups"]]
if (Q > 1) {
grpNames <- t(array(rep(rev(names(grps)), Q), c(Q, Q)))
grpNames[lower.tri(grpNames)] <- ""
grpNames <-
rev(apply(grpNames, 1,
function(x) paste(x[x != ""], collapse = " %in% ")))[level]
} else {
grpNames <- names(grps)
}
grps <- grps[, 1:level, drop = FALSE]
grps <- gsummary(grps, groups = grps[, level])
if (level == 1) value <- list(value)
effNams <- unlist(lapply(value, names))
grps <- grps[row.names(value[[level]]), , drop = FALSE]
M <- nrow(grps)
effNams <- unique(c(names(fixed), effNams))
effs <- array(0, c(M, length(effNams)),
list(row.names(grps), effNams))
effs[, names(fixed)] <- array(rep(fixed, rep(M, p)), c(M, p))
for (i in 1:level) {
nami <- names(value[[i]])
effs[, nami] <- as.matrix(effs[, nami] + value[[i]][as.character(grps[, i]), ])
}
if (augFrame) { # can only do that for last level
if (missing(data)) {
data <- getData(object)
}
data <- as.data.frame(data)
data <- data[, which, drop = FALSE]
value <- ranef(object, TRUE, level, data, FUN = FUN,
omitGroupingFactor = omitGroupingFactor,
subset = subset)
whichKeep <- is.na(match(names(value), effNams))
if (any(whichKeep)) {
effs <- cbind(effs, value[, whichKeep, drop = FALSE])
}
}
effs <- as.data.frame(effs)
attr(effs, "level") <- level
attr(effs, "label") <- "Coefficients"
attr(effs, "effectNames") <- effNams
attr(effs, "standardized") <- FALSE
attr(effs, "grpNames") <- grpNames
class(effs) <- unique(c("coef.lme", "ranef.lme", class(effs)))
effs
}
fitted.lme <-
function(object, level = Q, asList = FALSE, ...)
{
Q <- object$dims$Q
val <- object[["fitted"]]
if (is.character(level)) { # levels must be given consistently
nlevel <- match(level, names(val))
if (any(aux <- is.na(nlevel))) {
stop(sprintf(ngettext(sum(aux),
"nonexistent level %s",
"nonexistent levels %s"),
level[aux]), domain = NA)
}
level <- nlevel
} else { # assuming integers
level <- 1 + level
}
if (length(level) == 1L) {
grp.nm <- row.names(object[["groups"]])
grps <- as.character(object[["groups"]][, max(c(1, level - 1))])
if (asList) {
val <- as.list(split(val, ordered(grps, levels = unique(grps))))
} else {
val <- napredict(object$na.action, val[, level])
names(val) <- grps[match(names(val), grp.nm)]
}
lab <- "Fitted values"
if (!is.null(aux <- attr(object, "units")$y))
lab <- paste(lab, aux)
attr(val, "label") <- lab
val
} else napredict(object$na.action, val[, level])
}
formula.lme <- function(x, ...) formula(x$terms)
fixef.lme <- function(object, ...) object$coefficients$fixed
getGroups.lme <- function(object, form, level = Q, data, sep)
{
Q <- object$dims$Q
val <- object[["groups"]][, level]
if (length(level) == 1) { # single group
attr(val, "label") <- names(object[["groups"]])[level]
}
val
}
getGroupsFormula.lme <-
function(object, asList = FALSE, sep)
{
getGroupsFormula(object$modelStruct$reStruct, asList)
}
getResponse.lme <-
function(object, form)
{
val <- resid(object) + fitted(object)
if (is.null(lab <- attr(object, "labels")$y)) {
lab <- deparse(getResponseFormula(object)[[2L]])
}
if (!is.null(aux <- attr(object, "units")$y)) {
lab <- paste(lab, aux)
}
attr(val, "label") <- lab
val
}
intervals.lme <-
function(object, level = 0.95, which = c("all", "var-cov", "fixed"), ...)
{
which <- match.arg(which)
val <- list()
## 17-11-2015; Fixed sigma patch; SH Heisterkamp; Quantitative Solutions
fixSig <- attr(object$modelStruct, "fixedSigma")
fixSig <- !is.null(fixSig) && fixSig
if (which != "var-cov") { # fixed effects included
est <- fixef(object)
len <- -qt((1-level)/2, object$fixDF$X) * sqrt(diag(object$varFix))
vfix <- array(c(est - len, est, est + len), c(length(est), 3),
list(names(est), c("lower", "est.", "upper")))
attr(vfix, "label") <- "Fixed effects:"
val <- list(fixed = vfix)
}
if (which != "fixed") { # variance-covariance included
if (is.character(aV <- object$apVar)) {
stop(gettextf("cannot get confidence intervals on var-cov components: %s\n Consider '%s'",
aV, "which = \"fixed\""), domain = NA)
}
est <- attr(aV, "Pars")
nP <- length(est)
len <- -qnorm((1-level)/2) * sqrt(diag(aV))
origInt <- # intervals in unconstrained parameters
array(c(est - len, est, est + len),
c(nP, 3), list(names(est), c("lower", "est.", "upper")))
lmeSt <- object$modelStruct
if (!all(whichKeep <- apply(attr(lmeSt, "pmap"), 2, any))) {
## need to deleted components with fixed coefficients
aux <- lmeSt[whichKeep]
class(aux) <- class(lmeSt)
attr(aux, "settings") <- attr(lmeSt, "settings")
attr(aux, "pmap") <- attr(lmeSt, "pmap")[, whichKeep, drop = FALSE]
lmeSt <- aux
}
cSt <- lmeSt[["corStruct"]]
if (!is.null(cSt) && inherits(cSt, "corSymm") && attr(aV, "natural")) {
## converting to corNatural
class(cSt) <- c("corNatural", "corStruct")
lmeSt[["corStruct"]] <- cSt
}
pmap <- attr(lmeSt, "pmap")
namL <- names(lmeSt)
## 17-11-2015; Fixed sigma patch; SH Heisterkamp; Quantitative Solutions
if (fixSig) {
natInt <- vector("list", length(namL))
names(natInt) <- namL
} else {
natInt <- vector("list", length(namL) + 1)
names(natInt) <- c(namL, "sigma") # list of intervals in natural pars
## intervals for sigma are stored separately and dropped from origInt
vsig <- exp(origInt[nP, ])
attr(vsig, "label") <- "Within-group standard error:"
natInt[["sigma"]] <- vsig
origInt <- origInt[ - nP,, drop = FALSE]
}
if (attr(aV, "natural")) { # convert any pdSymm's to pdNatural's
for(i in seq_along(lmeSt$reStruct)) {
if (inherits(s.i <- lmeSt$reStruct[[i]], "pdSymm")) {
s.i <- pdNatural(s.i)
} else if (inherits(s.i, "pdBlocked")) {
for(j in seq_along(s.i))
if (inherits(s.i[[j]], "pdSymm"))
s.i[[j]] <- pdNatural(s.i[[j]])
}
lmeSt$reStruct[[i]] <- s.i
}
}
rownames(origInt) <- # re-express names if necessary
## namP <-
names(coef(lmeSt, unconstrained = FALSE))
for(i in 1:3) { # re-express intervals in constrained pars
coef(lmeSt) <- origInt[,i]
origInt[,i] <- coef(lmeSt, unconstrained = FALSE)
}
for(i in namL) {
natInt[[i]] <- origInt[ pmap[ , i ], , drop = FALSE ]
switch(i,
"reStruct" = {
plen <- attr( lmeSt$reStruct, "plen" )
natInt[[i]] <-
rev(as.matrix( split( as.data.frame( natInt[[i]] ),
rep( seq_along(plen), plen ))))
names(natInt[[i]]) <- rev(names(plen))
for (j in names(plen)) {
dimnames(natInt[[i]][[j]])[[1L]] <-
names( coef( lmeSt[[i]][[j]], unconstrained = FALSE ) )
}
},
"corStruct" =,
"varStruct" = {
dimnames(natInt[[i]])[[1L]] <-
names(coef(lmeSt[[i]], unconstrained = FALSE))
}
)
attr(natInt[[i]], "label") <-
switch(i,
reStruct = "Random Effects:",
corStruct = "Correlation structure:",
varStruct = "Variance function:",
paste0(i,":"))
}
val <- c(val, natInt)
}
attr(val, "level") <- level
class(val) <- "intervals.lme"
val
}
logLik.lme <- function(object, REML, ...)
{
## 17-11-2015; Fixed sigma patch; SH Heisterkamp; Quantitative Solutions
fixSig <- attr(object[["modelStruct"]], "fixedSigma")
fixSig <- !is.null(fixSig) && fixSig
od <- object$dims
p <- od$ncol[[od$Q + 1L]]
N <- od$N
## Np <- N - p
estM <- object$method
if (missing(REML)) REML <- estM == "REML"
val <- object[["logLik"]]
if (REML && (estM == "ML")) { # have to correct logLik
val <- val + (p * (log(2 * pi) + 1L) + (N - p) * log(1 - p/N) +
sum(log(abs(svd.d(object$varFix))))) / 2
}
if (!REML && (estM == "REML")) { # have to correct logLik
val <- val - (p * (log(2*pi) + 1L) + N * log(1 - p/N) +
sum(log(abs(svd.d(object$varFix))))) / 2
}
structure(val, class = "logLik",
nall = N,
nobs = N - REML * p,
## 17-11-2015; Fixed sigma patch; SH Heisterkamp; Quantitative Solutions
df = p + length(coef(object[["modelStruct"]])) + as.integer(!fixSig))
}
nobs.lme <- function(object, ...) object$dims$N
pairs.lme <-
function(x, form = ~coef(.), label, id = NULL, idLabels = NULL,
grid = FALSE, ...)
{
object <- x
## scatter plot matrix plots, generally based on coef or ranef
if (!inherits(form, "formula")) {
stop("'form' must be a formula")
}
if (length(form) != 2) {
stop("'form' must be a one-sided formula")
}
## constructing data
allV <- all.vars(asOneFormula(form, id, idLabels))
allV <- allV[is.na(match(allV,c("T","F","TRUE","FALSE")))]
if (length(allV) > 0) {
data <- getData(object)
if (is.null(data)) { # try to construct data
alist <- lapply(as.list(allV), as.name)
names(alist) <- allV
alist <- c(as.list(quote(data.frame)), alist)
mode(alist) <- "call"
data <- eval(alist, sys.parent(1))
} else {
if (any(naV <- is.na(match(allV, names(data))))) {
stop(sprintf(ngettext(sum(naV),
"%s not found in data",
"%s not found in data"),
allV[naV]), domain = NA)
}
}
} else data <- NULL
## argument list
dots <- list(...)
args <- if(length(dots) > 0) dots else list()
## covariate - must be present as a data.frame
covF <- getCovariateFormula(form)
.x <- eval(covF[[2L]], list(. = object)) # only function of "."
if (!inherits(.x, "data.frame")) {
stop("covariate must be a data frame")
}
level <- attr(.x, "level")
if (!is.null(effNams <- attr(.x, "effectNames"))) {
.x <- .x[, effNams, drop = FALSE]
}
## eliminating constant effects
isFixed <- vapply(.x, function(el) length(unique(el)) == 1L, NA)
.x <- .x[, !isFixed, drop = FALSE]
nc <- ncol(.x)
if (nc == 1) {
stop("cannot do pairs of just one variable")
}
if (!missing(label)) {
names(.x) <- label
}
if (nc == 2) {
## will use xyplot
argForm <- .y ~ .x
argData <- .x
names(argData) <- c(".x", ".y")
if (is.null(args$xlab)) args$xlab <- names(.x)[1L]
if (is.null(args$ylab)) args$ylab <- names(.x)[2L]
} else { # splom
argForm <- ~ .x
argData <- list(.x = .x)
}
auxData <- list()
## groups - need not be present
grpsF <- getGroupsFormula(form)
if (!is.null(grpsF)) {
gr <- splitFormula(grpsF, sep = "*")
for(i in seq_along(gr)) {
for(j in all.vars(gr[[i]])) {
auxData[[j]] <- eval(as.name(j), data)
}
}
argForm <- eval(substitute(
if(length(argForm) == 2) ~ .x | R else .y ~ .x | R,
list(R = grpsF[[2L]])))
}
## id and idLabels - need not be present
if (!is.null(id)) { # identify points in plot
N <- object$dims$N
id <-
switch(mode(id),
numeric = {
if ((id <= 0) || (id >= 1)) {
stop("'id' must be between 0 and 1")
}
if (is.null(level)) {
stop("covariate must have a level attribute when groups are present")
}
aux <- t(as.matrix(ranef(object, level = level)))
aux <- as.logical(colSums(
(solve(t(pdMatrix(object$modelStruct$reStruct, factor = TRUE)[[level]]),
aux)/object$sigma)^2) > qchisq(1 - id, dim(aux)[1L]))
aux
},
call = eval(asOneSidedFormula(id)[[2L]], data),
stop("'id' can only be a formula or numeric")
)
if (length(id) == N) {
## id as a formula evaluated in data
if (is.null(level)) {
stop("covariate must have a level attribute when 'id' is a formula")
}
auxData[[".id"]] <- id
}
if (is.null(idLabels)) {
idLabels <- row.names(.x)
} else {
if (mode(idLabels) == "call") {
idLabels <-
as.character(eval(asOneSidedFormula(idLabels)[[2L]], data))
} else if (is.vector(idLabels)) {
if (length(idLabels <- unlist(idLabels)) != N) {
stop("'idLabels' of incorrect length")
}
idLabels <- as.character(idLabels)
} else {
stop("'idLabels' can only be a formula or a vector")
}
}
if (length(idLabels) == N) {
## idLabels as a formula evaluated in data
if (is.null(level)) {
stop("covariate must have a level attribute when 'idLabels' is a formula")
}
auxData[[".Lid"]] <- idLabels
}
}
if (length(auxData)) { # need collapsing
auxData <- gsummary(as.data.frame(auxData),
groups = getGroups(object, level = level))
auxData <- auxData[row.names(.x), , drop = FALSE]
if (!is.null(auxData[[".id"]])) {
id <- auxData[[".id"]]
}
if (!is.null(auxData[[".Lid"]])) {
idLabels <- auxData[[".Lid"]]
}
wchDat <- is.na(match(names(auxData), c(".id", ".idLabels")))
if (any(wchDat)) {
argData <- c(argData, as.list(auxData[, wchDat, drop = FALSE]))
}
}
if (!is.null(id)) id <- as.logical(as.character(id))
idLabels <- as.character(idLabels)
## adding to args list
args <- c(list(argForm, data = argData), args)
if (is.null(args$strip)) {
args$strip <- function(...) strip.default(..., style = 1)
}
if (is.null(args$cex)) args$cex <- par("cex")
if (is.null(args$adj)) args$adj <- par("adj")
## defining the type of plot
if (length(argForm) == 3) { # xyplot
plotFun <- "xyplot"
if(is.null(args$panel))
args$panel <- function(x, y, subscripts, ...) {
x <- as.numeric(x)
y <- as.numeric(y)
dots <- list(...)
if (grid) panel.grid()
panel.xyplot(x, y, ...)
if (any(ids <- id[subscripts])){
ltext(x[ids], y[ids], idLabels[subscripts][ids],
cex = dots$cex, adj = dots$adj)
}
}
} else { # splom
plotFun <- "splom"
if(is.null(args$panel)) {
args$panel <- function(x, y, subscripts, ...) {
x <- as.numeric(x)
y <- as.numeric(y)
dots <- list(...)
if (grid) panel.grid()
panel.xyplot(x, y, ...)
if (any(ids <- id[subscripts])){
ltext(x[ids], y[ids], idLabels[subscripts][ids],
cex = dots$cex, adj = dots$adj)
}
}
}
}
do.call(plotFun, as.list(args))
}
plot.ranef.lme <-
function(x, form = NULL, omitFixed = TRUE, level = Q,
grid = TRUE, control,
xlab = NULL, ylab = NULL, strip = NULL, ...)
{
plotControl <-
function(drawLine = TRUE, span.loess = 2/3, degree.loess = 1) {
list(drawLine = drawLine,
span.loess = span.loess,
degree.loess = degree.loess)
}
pControl <- plotControl()
if (!missing(control)) pControl[names(control)] <- control
if (!inherits(x, "data.frame")) {
## must be a list of data frames
Q <- length(x) # the default for 'level'
if (length(level) > 1) {
stop("only single level allowed")
}
oAttr <- attributes(x)[c("label", "standardized", "namsEff")]
x <- x[[level]]
oAttr$namsEff <- oAttr$namsEff[level]
attributes(x)[c("label", "standardized", "namsEff")] <- oAttr
}
if (omitFixed) { # eliminating constant effects
isFixed <- vapply(x, function(el) length(unique(el)) == 1L, NA)
if (any(isFixed)) {
oattr <- attributes(x)
oattr <- oattr[names(oattr) != "names"]
x <- x[, !isFixed, drop = FALSE]
oattr$effectNames <- oattr$effectNames[!is.na(match(oattr$effectNames,
names(x)))]
attributes(x)[names(oattr)] <- oattr
}
}
eNames <- attr(x, "effectNames")
if (is.null(form) || (inherits(form, "formula") && length(form) == 2)) {
## ~ x : dotplot
eLen <- length(eNames)
argData <- data.frame(.pars = as.vector(unlist(x[, eNames])),
.enames = ordered(rep(eNames, rep(nrow(x), eLen)),
levels = eNames), check.names = FALSE)
for(i in names(x)[is.na(match(names(x), eNames))]) {
argData[[i]] <- rep(x[[i]], eLen)
}
argForm <- .groups ~ .pars | .enames
argData[[".groups"]] <- rep(row.names(x), eLen)
if (inherits(form, "formula")) {
onames <- all.vars(form)
if (any(whichNA <- is.na(match(onames, names(argData))))) {
stop(sprintf(ngettext(sum(whichNA),
"%s not available for plotting",
"%s not available for plotting"),
onames[whichNA], collapse = ", "), domain = NA)
}
argData[[".groups"]] <-
as.character(argData[[as.character(onames[1L])]])
if (length(onames) > 1) {
for(i in onames[-1L]) {
argData[[".groups"]] <-
paste(as.character(argData[[".groups"]]),
as.character(argData[[i]]))
}
}
}
argData[[".groups"]] <- ordered(argData[[".groups"]],
levels = unique(argData[[".groups"]]))
args <- list(argForm, data = argData, ...)
args$xlab <- xlab %||% attr(x, "label")
args$ylab <- ylab %||% if (is.null(form)) attr(x, "grpNames")
else deparse(form[[2L]])
if (is.null(args$scales)) {
if (!is.null(attr(x, "standardized")) &&
!attr(x, "standardized")) {
args$scales <- list(x = list(relation = "free"))
}
}
args$strip <- strip %||% function(...) strip.default(..., style = 1)
do.call(dotplot, args)
} else { ## y ~ x ---> xyplot(): ------------------------------------------
if (!inherits(form, "formula")) stop("'form' must be a formula when not NULL")
reName <- form[[2L]]
if (length(reName) != 1 &&
substring(deparse(reName),
nchar(deparse(reName), "c") - 10) != "(Intercept)") {
stop("only single effects allowed in left side of 'form'")
}
reName <- deparse(reName)
if (is.na(match(reName, eNames))) {
stop(gettextf("%s is not a valid effect name", sQuote(reName)),
domain = NA)
}
vNames <- all.vars(form[[3]]) # variable names
if (any(!is.na(match(vNames, eNames)))) {
stop("no effects allowed in right side of formula")
}
if (any(whichNA <- is.na(match(vNames, names(x))))) {
stop(sprintf(ngettext(sum(whichNA),
"%s not available for plotting",
"%s not available for plotting"),
onames[whichNA], collapse = ", "), domain = NA)
}
nV <- length(vNames) # number of variables
nG <- nrow(x) # number of groups
reVal <- vNam <- vVal <- vector("list", nV)
vLevs <- vNam; names(vLevs) <- vNames
vType <- character(nV); names(vType) <- vNames
aux <- x[, reName]
for(i in 1:nV) {
obj <- x[, vNames[i]]
if (inherits(obj, "factor") || is.character(obj)) {
vType[i] <- "factor"
obj <- as.factor(obj)
vLevs[[i]] <- levels(obj)
reVal[[i]] <- c(NA, NA, aux)
vVal [[i]] <- c(0.5, length(levels(obj)) + 0.5, as.integer(obj))
vNam [[i]] <- rep(vNames[i], nG + 2)
} else { # numeric
vType[i] <- "numeric"
reVal[[i]] <- aux
vVal [[i]] <- obj
vNam [[i]] <- rep(vNames[i], nG)
}
}
vNam <- unlist(vNam)
argData <- data.frame(y = unlist(reVal), x = unlist(vVal),
g = ordered(vNam, levels = vNames))
## this is a hack to make this work, it's probably possible to
## implement the whole thing much more succintly -- ds
## The idea here is that the limits component of scales$x is going
## to be a list -- and character vectors have special meaning as
## limits, controlling both limits and the tick mark
## positions/labels
condvar <- eval(expression(g), argData)
xscales.lim <- as.list(levels(condvar))
subsc <- seq_along(condvar)
for (i in seq_along(xscales.lim)) {
subscripts <- subsc[condvar == xscales.lim[[i]]]
vN <- vNam[subscripts][1L]
xscales.lim[[i]] <-
if(vType[vN] == "numeric")
range(argData$x[subscripts])
else vLevs[vN][[1L]]
}
## --- further used from the panel() below: ---
.drawLine <- pControl$drawLine
.span <- pControl$span.loess
.degree <- pControl$degree.loess
## assign("panel.bwplot2", panel.bwplot2, where = 1)
## assign(".cex", pControl$cex.axis)#, where = 1)
## assign(".srt", pControl$srt.axis)#, where = 1)
## assign(".mgp", pControl$mgp.axis)#, where = 1)
xyplot(y ~ x | g, data = argData, subscripts = TRUE,
scales = list(x = list(relation = "free", limits = xscales.lim)),
panel = function(x, y, subscripts, ...) {
vN <- vNam[subscripts][1L]
if (grid) panel.grid()
if (vType[vN] == "numeric") {
panel.xyplot(x, y, ...)
if (.drawLine) {
panel.loess(x, y, span = .span, degree = .degree)
}
} else {
panel.bwplot(x, y, horizontal = FALSE)
if (.drawLine) {
plot.line <- trellis.par.get("plot.line")
panel.linejoin(x, y, fun = median, horizontal = FALSE,
col.line = plot.line$col,
lwd = plot.line$lwd,
lty = plot.line$lty)
}
}
},
xlab = xlab %||% "", ylab = ylab %||% reName,
strip = strip %||% strip.default, ...)
}
} ## {plot.ranef.lme}
predict.lme <-
function(object, newdata, level = Q, asList = FALSE,
na.action = na.fail, ...)
{
##
## method for predict() designed for objects inheriting from class lme
##
Q <- object$dims$Q
if (missing(newdata)) { # will return fitted values
val <- fitted(object, level, asList)
if (length(level) == 1) return(val)
return(data.frame(object[["groups"]][,level[level != 0], drop = FALSE],
predict = val))
}
maxQ <- max(level) # maximum level for predictions
nlev <- length(level)
mCall <- object$call
fixed <- eval(eval(mCall$fixed)[-2]) # RHS
Terms <- object$terms
newdata <- as.data.frame(newdata)
if (maxQ > 0) { # predictions with random effects
whichQ <- Q - (maxQ-1):0
reSt <- object$modelStruct$reStruct[whichQ]
lmeSt <- lmeStruct(reStruct = reSt)
groups <- getGroupsFormula(reSt)
if (any(is.na(match(all.vars(groups), names(newdata))))) {
## groups cannot be evaluated in newdata
stop("cannot evaluate groups for desired levels on 'newdata'")
}
} else {
reSt <- NULL
}
## use xlev to make sure factor levels are the same as in contrasts
## and to support character-type 'newdata' for factors
contr <- object$contrasts # these are in matrix form
dataMix <- model.frame(formula = asOneFormula(formula(reSt), fixed),
data = newdata, na.action = na.action,
drop.unused.levels = TRUE,
xlev = lapply(contr, rownames))
origOrder <- row.names(dataMix) # preserve the original order
whichRows <- match(origOrder, row.names(newdata))
if (maxQ > 0) {
## sort the model.frame by groups and get the matrices and parameters
## used in the estimation procedures
grps <- getGroups(newdata, # (unused levels are dropped here)
eval(substitute(~ 1 | GRPS,
list(GRPS = groups[[2]]))))
## ordering data by groups
if (inherits(grps, "factor")) { # single level
grps <- grps[whichRows]
oGrps <- data.frame(grps)
## checking if there are missing groups
if (any(naGrps <- is.na(grps))) {
grps[naGrps] <- levels(grps)[1L] # input with existing level
}
ord <- order(grps) #"order" treats a single named argument peculiarly
grps <- data.frame(grps)
row.names(grps) <- origOrder
names(grps) <- names(oGrps) <- as.character(deparse((groups[[2L]])))
} else {
grps <- oGrps <- grps[whichRows, ]
## checking for missing groups
if (any(naGrps <- is.na(grps))) {
## need to input missing groups
for(i in names(grps)) {
grps[naGrps[, i], i] <- levels(grps[,i])[1L]
}
naGrps <- t(apply(naGrps, 1, cumsum)) # propagating NAs
}
ord <- do.call(order, grps)
## making group levels unique
for(i in 2:ncol(grps)) {
grps[, i] <-
as.factor(paste(as.character(grps[, i-1]),
as.character(grps[, i ]), sep = "/"))
}
}
naGrps <- cbind(FALSE, naGrps)[ord, , drop = FALSE]
grps <- grps[ord, , drop = FALSE]
dataMix <- dataMix[ord, ,drop = FALSE]
}
## restore contrasts for the below model.matrix() calls (which may use
## different factor variables, so we avoid passing the whole contr list)
for(i in intersect(names(dataMix), names(contr))) {
attr(dataMix[[i]], "contrasts") <- contr[[i]]
}
if (maxQ > 0) {
revOrder <- match(origOrder, row.names(dataMix)) # putting in orig. order
Z <- model.matrix(reSt, dataMix)
ncols <- attr(Z, "ncols")
Names(lmeSt$reStruct) <- attr(Z, "nams")
}
N <- nrow(dataMix)
X <- if (length(all.vars(fixed)) > 0) {
model.matrix(fixed, model.frame(delete.response(Terms), dataMix))
} else if(attr(terms(fixed), "intercept")) {
array(1, c(N, 1), list(row.names(dataMix), "(Intercept)"))
} else {
array(, c(N, 0))
}
if (maxQ == 0) {
## only population predictions
val <- if(ncol(X)) c(X %*% fixef(object)) else rep(0, nrow(X))
attr(val, "label") <- "Predicted values"
return(val)
}
ncols <- c(ncols, dim(X)[2L], 1)
## creating the condensed linear model
attr(lmeSt, "conLin") <-
list(Xy = array(c(Z, X, double(N)), c(N, sum(ncols)),
list(row.names(dataMix), c(colnames(Z), colnames(X), "resp"))),
dims = MEdims(grps, ncols))
## Getting the appropriate BLUPs of the random effects
re <- object$coefficients$random[1:maxQ]
for(i in names(re)) {
ugrps <- unique(as.character(grps[, i]))
val <- array(NA, c(length(ugrps), ncol(re[[i]])),
list(ugrps, dimnames(re[[i]])[[2L]]))
mGrps <- match(ugrps, dimnames(re[[i]])[[1L]])
mGrps <- mGrps[!is.na(mGrps)]
re[[i]] <- re[[i]][mGrps, , drop = FALSE]
val[dimnames(re[[i]])[[1L]], ] <- re[[i]]
re[[i]] <- val
}
attr(lmeSt, "lmeFit") <- list(beta = fixef(object), b = re)
val <- fitted(lmeSt, level = 0:maxQ)
val[as.logical(naGrps)] <- NA # setting missing groups to NA
## putting back in original order and extracting levels
val <- val[revOrder, level + 1L] # predictions
if (maxQ > 1) { # making groups unique
for(i in 2:maxQ)
oGrps[, i] <-
as.factor(paste(as.character(oGrps[,i-1]),
as.character(oGrps[,i ]), sep = "/"))
}
if (nlev == 1) {
grps <- as.character(oGrps[, level])
if (asList) {
val <- split(val, ordered(grps, levels = unique(grps)))
} else {
names(val) <- grps
}
lab <- "Predicted values"
if (!is.null(aux <- attr(object, "units")$y)) {
lab <- paste(lab, aux)
}
attr(val, "label") <- lab
val
} else {
data.frame(oGrps, predict = val)
}
}
print.anova.lme <- function(x, verbose = attr(x, "verbose"), ...)
{
ox <- x
if ((rt <- attr(x,"rt")) == 1) { ## one object
if (!is.null(lab <- attr(x, "label"))) {
cat(lab)
if (!is.null(L <- attr(x, "L"))) {
print(zapsmall(L), ...)
}
}
pval <- format(round(x[, "p-value"],4))
pval[as.double(pval) == 0] <- "<.0001"
x[, "F-value"] <- format(zapsmall(x[, "F-value"]))
x[, "p-value"] <- pval
print(as.data.frame(x), ...)
} else { ## several objects
if (verbose) {
cat("Call:\n")
objNams <- row.names(x)
for(i in 1:rt) {
cat(" ",objNams[i],":\n", sep ="")
cat(" ",as.character(x[i,"call"]),"\n")
}
cat("\n")
}
x <- as.data.frame(x[,-1])
for(i in names(x)) {
xx <- x[[i]]
if (i == "p-value") {
xx <- round(xx, 4)
xna <- is.na(xx)
xx[!xna] <- format(xx[!xna])
xx[as.double(xx) == 0] <- "<.0001"
xx[xna] <- ""
} else {
if (match(i, c("AIC", "BIC", "logLik", "L.Ratio"), 0)) {
xna <- is.na(xx)
xx <- zapsmall(xx)
xx[xna] <- 0
xx <- format(xx)
xx[xna] <- ""
}
}
x[[i]] <- xx
}
print(as.data.frame(x), ...)
}
invisible(ox)
}
print.intervals.lme <- function(x, ...)
{
cat(paste0("Approximate ", attr(x,"level") *100, "% confidence intervals\n"))
for(i in names(x)) {
aux <- x[[i]]
cat("\n ",attr(aux, "label"), "\n", sep = "")
attr(aux, "label") <- NULL
if (i == "reStruct") {
for(j in names(aux)) {
cat(" Level:", j, "\n")
print(as.matrix(aux[[j]]), ...)
}
} else if (i == "sigma")
print(c(aux), ...)
else
print(as.matrix(aux), ...)
}
invisible(x)
}
print.lme <- function(x, ...)
{
dd <- x$dims
if (inherits(x, "nlme")) { # nlme object
cat( "Nonlinear mixed-effects model fit by " )
cat( if(x$method == "REML") "REML\n" else "maximum likelihood\n")
cat(" Model:", deparse(x$call$model),"\n")
} else { # lme objects
cat( "Linear mixed-effects model fit by " )
cat( if(x$method == "REML") "REML\n" else "maximum likelihood\n")
}
cat(" Data:", deparse( x$call$data ), "\n")
if (!is.null(x$call$subset)) {
cat(" Subset:", deparse(asOneSidedFormula(x$call$subset)[[2L]]),"\n")
}
cat(" Log-", if(x$method == "REML") "restricted-" else "",
"likelihood: ", format(x$logLik), "\n", sep = "")
fixF <- x$call$fixed
cat(" Fixed:",
deparse(
if(inherits(fixF, "formula") || is.call(fixF) || is.name(fixF))
fixF
else
lapply(fixF, function(el) as.name(deparse(el)))), "\n")
print(fixef(x), ...)
cat("\n")
print(summary(x$modelStruct), sigma = x$sigma, ...)
cat("Number of Observations:", dd[["N"]])
cat("\nNumber of Groups: ")
Ngrps <- dd$ngrps[1:dd$Q]
if ((lNgrps <- length(Ngrps)) == 1) { # single nesting
cat(Ngrps,"\n")
} else { # multiple nesting
sNgrps <- 1:lNgrps
aux <- rep(names(Ngrps), sNgrps)
aux <- split(aux, array(rep(sNgrps, lNgrps),
c(lNgrps, lNgrps))[!lower.tri(diag(lNgrps))])
names(Ngrps) <- unlist(lapply(aux, paste, collapse = " %in% "))
cat("\n")
print(rev(Ngrps), ...)
}
invisible(x)
}
print.ranef.lme <- function(x, ...)
{
if (!inherits(x[[1L]], "data.frame")) {
print.data.frame(x, ...)
} else { # list
for(i in seq_along(x)) {
cat("Level:", attr(x, "grpNames")[i],"\n")
print.data.frame(x[[i]])
if (i < length(x)) cat("\n")
}
}
invisible(x)
}
print.summary.lme <- function(x, verbose = FALSE, ...)
{
dd <- x$dims
verbose <- verbose || attr(x, "verbose")
if (inherits(x, "nlme")) { # nlme object
cat( "Nonlinear mixed-effects model fit by " )
cat( if(x$method == "REML") "REML\n" else "maximum likelihood\n")
cat(" Model:", deparse(x$call$model),"\n")
} else { # lme objects
cat( "Linear mixed-effects model fit by " )
cat( if(x$method == "REML") "REML\n" else "maximum likelihood\n")
}
## method <- x$method
cat(" Data:", deparse( x$call$data ), "\n")
if (!is.null(x$call$subset)) {
cat(" Subset:", deparse(asOneSidedFormula(x$call$subset)[[2L]]),"\n")
}
print(data.frame(AIC = x$AIC, BIC = x$BIC, logLik = c(x$logLik),
row.names = " "), ...)
if (verbose) cat("Convergence at iteration:",x$numIter,"\n")
cat("\n")
print(summary(x$modelStruct), sigma = x$sigma,
reEstimates = x$coefficients$random, verbose = verbose, ...)
fixF <- x$call$fixed
cat("Fixed effects: ",
deparse(
if(inherits(fixF, "formula") || is.call(fixF))
fixF
else
lapply(fixF, function(el) as.name(deparse(el)))),
"\n")
## fixed effects t-table and correlations
xtTab <- as.data.frame(x$tTable)
wchPval <- match("p-value", names(xtTab))
for(i in names(xtTab)[-wchPval]) {
xtTab[, i] <- format(zapsmall(xtTab[, i]))
}
xtTab[,wchPval] <- format(round(xtTab[,wchPval], 4))
if (any(wchLv <- (as.double(levels(xtTab[, wchPval])) == 0))) {
levels(xtTab[, wchPval])[wchLv] <- "<.0001"
}
row.names(xtTab) <- dimnames(x$tTable)[[1L]]
print(xtTab, ...)
if (nrow(x$tTable) > 1) {
corr <- x$corFixed
class(corr) <- "correlation"
print(corr, title = " Correlation:", ...)
}
cat("\nStandardized Within-Group Residuals:\n")
print(x$residuals, ...)
cat("\nNumber of Observations:",x$dims[["N"]])
cat("\nNumber of Groups: ")
Ngrps <- dd$ngrps[1:dd$Q]
if ((lNgrps <- length(Ngrps)) == 1) { # single nesting
cat(Ngrps,"\n")
} else { # multiple nesting
sNgrps <- 1:lNgrps
aux <- rep(names(Ngrps), sNgrps)
aux <- split(aux, array(rep(sNgrps, lNgrps),
c(lNgrps, lNgrps))[!lower.tri(diag(lNgrps))])
names(Ngrps) <- unlist(lapply(aux, paste, collapse = " %in% "))
cat("\n")
print(rev(Ngrps), ...)
}
invisible(x)
}
## coef(summary( obj )) # should work for "gls" or "lme" similarly as for lm():
getCTable <- function (object, ...) object$tTable
qqnorm.lme <-
function(y, form = ~ resid(., type = "p"), abline = NULL,
id = NULL, idLabels = NULL, grid = FALSE, ...)
## normal probability plots for residuals and random effects
{
if (!inherits(form, "formula")) stop("'form' must be a formula")
## object <- y
## constructing data
allV <- all.vars(asOneFormula(form, id, idLabels))
allV <- allV[is.na(match(allV,c("T","F","TRUE","FALSE")))]
if (length(allV) > 0) {
data <- getData(y)
if (is.null(data)) { # try to construct data
alist <- lapply(as.list(allV), as.name)
names(alist) <- allV
alist <- c(as.list(quote(data.frame)), alist)
mode(alist) <- "call"
data <- eval(alist, sys.parent(1))
} else {
if (any(naV <- is.na(match(allV, names(data))))) {
stop(sprintf(ngettext(sum(naV),
"%s not found in data",
"%s not found in data"),
allV[naV]), domain = NA)
}
}
} else data <- NULL
## argument list
args <- list(...) # may be empty list()
## appending object to data
data <- as.list(c(as.list(data), . = list(y)))
## covariate - must always be present
covF <- getCovariateFormula(form)
.x <- eval(covF[[2L]], data)
labs <- attr(.x, "label")
type <-
if (inherits(.x, "ranef.lme"))
"reff" # random effects
else if (!is.null(labs) && (labs == "Standardized residuals" ||
labs == "Normalized residuals" ||
substr(labs, 1, 9) == "Residuals"))
"res" # residuals
else
stop("only residuals and random effects allowed")
if (is.null(args$xlab)) args$xlab <- labs
if (is.null(args$ylab)) args$ylab <- "Quantiles of standard normal"
if(type == "res") { ## residuals ----------------------------------------
fData <- qqnorm(.x, plot.it = FALSE)
data[[".y"]] <- fData$x
data[[".x"]] <- fData$y
dform <-
if (!is.null(grp <- getGroupsFormula(form)))
eval(substitute(.y ~ .x | G, list(G = grp[[2L]])))
else
.y ~ .x
if (!is.null(id)) { # identify points in plot
id <-
switch(mode(id),
numeric = {
if (any(id <= 0) || any(id >= 1)) {
stop("'Id' must be between 0 and 1")
}
if (labs == "Normalized residuals") {
as.logical(abs(resid(y, type="normalized"))
> -qnorm(id / 2))
} else {
as.logical(abs(resid(y, type="pearson"))
> -qnorm(id / 2))
}
},
call = eval(asOneSidedFormula(id)[[2L]], data),
stop("'id' can only be a formula or numeric")
)
if (is.null(idLabels)) {
idLabels <- getGroups(y)
if (length(idLabels) == 0) idLabels <- seq_len(y$dims$N)
idLabels <- as.character(idLabels)
} else {
if (mode(idLabels) == "call") {
idLabels <-
as.character(eval(asOneSidedFormula(idLabels)[[2L]], data))
} else if (is.vector(idLabels)) {
if (length(idLabels <- unlist(idLabels)) != length(id)) {
stop("'idLabels' of incorrect length")
}
idLabels <- as.character(idLabels)
} else {
stop("'idLabels' can only be a formula or a vector")
}
}
}
} else { # type "ref" -- random.effects --------------------------------
level <- attr(.x, "level")
std <- attr(.x, "standardized")
if (!is.null(effNams <- attr(.x, "effectNames"))) {
.x <- .x[, effNams, drop = FALSE]
}
nc <- ncol(.x)
nr <- nrow(.x)
fData <- lapply(as.data.frame(.x), qqnorm, plot.it = FALSE)
fData <- data.frame(.x = unlist(lapply(fData, function(x) x[["y"]])),
.y = unlist(lapply(fData, function(x) x[["x"]])),
.g = ordered(rep(names(fData),rep(nr, nc)),
levels = names(fData)), check.names = FALSE)
if (!is.null(grp <- getGroupsFormula(form))) {
dform <- substitute(.y ~ .x | .g * GG, list(GG = deparse(grp[[2L]])))
auxData <- data[is.na(match(names(data), "."))]
} else {
dform <- .y ~ .x | .g
auxData <- list()
}
## id and idLabels - need not be present
if (!is.null(id)) { # identify points in plot
N <- y$dims$N
id <-
switch(mode(id),
numeric = {
if ((id <= 0) || (id >= 1)) {
stop("'id' must be between 0 and 1")
}
aux <- ranef(y, level = level, standard = TRUE)
as.logical(abs(c(unlist(aux))) > -qnorm(id / 2))
},
call = eval(asOneSidedFormula(id)[[2L]], data),
stop("'id' can only be a formula or numeric")
)
if (length(id) == N) {
## id as a formula evaluated in data
auxData[[".id"]] <- id
}
idLabels <-
if (is.null(idLabels)) {
rep(row.names(.x), nc)
} else if (mode(idLabels) == "call") {
as.character(eval(asOneSidedFormula(idLabels)[[2L]], data))
} else if (is.vector(idLabels)) {
if (length(idLabels <- unlist(idLabels)) != N)
stop("'idLabels' of incorrect length")
as.character(idLabels)
} else
stop("'idLabels' can only be a formula or a vector")
if (length(idLabels) == N) {
## idLabels as a formula evaluated in data
auxData[[".Lid"]] <- idLabels
}
}
data <-
if (length(auxData)) { # need collapsing
auxData <- gsummary(as.data.frame(auxData),
groups = getGroups(y, level = level))
auxData <- auxData[row.names(.x), , drop = FALSE]
if (!is.null(auxData[[".id"]]))
id <- rep(auxData[[".id"]], nc)
if (!is.null(auxData[[".Lid"]]))
idLabels <- rep(auxData[[".Lid"]], nc)
cbind(fData, do.call(rbind, rep(list(auxData), nc)))
} else
fData
}
id <- if (!is.null(id)) as.logical(as.character(id))
idLabels <- as.character(idLabels)
abl <- abline
if (is.null(args$strip))
args$strip <- function(...) strip.default(..., style = 1)
if (is.null(args$cex)) args$cex <- par("cex")
if (is.null(args$adj)) args$adj <- par("adj")
args <- c(list(dform, data = substitute(data)), args)
if (is.null(args$panel))
args$panel <- function(x, y, subscripts, ...) {
x <- as.numeric(x)
y <- as.numeric(y)
dots <- list(...)
if (grid) panel.grid()
panel.xyplot(x, y, ...)
if (any(ids <- id[subscripts])){
ltext(x[ids], y[ids], idLabels[subscripts][ids],
cex = dots$cex, adj = dots$adj)
}
if (!is.null(abl)) {
if (length(abl) == 2)
panel.abline(a = abl, ...)
else
panel.abline(h = abl, ...)
}
}
if(type == "reff" && !std) {
args[["scales"]] <- list(x = list(relation = "free"))
}
do.call(xyplot, as.list(args))
}
ranef.lme <-
## Extracts the random effects from an lme object.
## If aug.frame is true, the returned data frame is augmented with a
## values from the original data object, if available. The variables
## in the original data are collapsed over the cluster variable by the
## function fun.
function(object, augFrame = FALSE, level = 1:Q, data, which = 1:ncol(data),
FUN = mean, standard = FALSE , omitGroupingFactor = TRUE,
subset = NULL, ...)
{
Q <- object$dims$Q
effects <- object$coefficients$random
if (Q > 1) {
grpNames <- t(array(rep(rev(names(effects)), Q), c(Q, Q)))
grpNames[lower.tri(grpNames)] <- ""
grpNames <-
rev(apply(grpNames, 1, function(x) paste(x[x != ""], collapse = " %in% ")))
} else {
grpNames <- names(effects)
}
effects <- effects[level]
grpNames <- grpNames[level]
if (standard) {
for (i in names(effects)) {
effects[[i]] <-
t(t(effects[[i]]) /
(object$sigma *
sqrt(diag(as.matrix(object$modelStruct$reStruct[[i]])))))
}
}
effects <- lapply(effects, as.data.frame)
if (augFrame) {
if (length(level) > 1) {
stop("augmentation of random effects only available for single level")
}
effects <- effects[[1L]]
effectNames <- names(effects)
if (missing(data)) {
data <- getData(object)
}
data <- as.data.frame(data)
subset <- if (is.null(subset)) { # nlme case
eval(object$call[["naPattern"]])
} else {
asOneSidedFormula(as.list(match.call())[["subset"]])
}
if (!is.null(subset)) {
subset <- eval(subset[[2L]], data)
data <- data[subset, ,drop=FALSE]
}
data <- data[, which, drop = FALSE]
## eliminating columns with same names as effects
data <- data[, is.na(match(names(data), effectNames)), drop = FALSE]
grps <- as.character(object[["groups"]][, level])
data <- gsummary(data, FUN = FUN, groups = grps)
if (omitGroupingFactor) {
data <-
data[, is.na(match(names(data), names(object$modelStruct$reStruct))),
drop = FALSE]
}
if (length(data) > 0) {
effects <- cbind(effects, data[row.names(effects),, drop = FALSE])
}
attr(effects, "effectNames") <- effectNames
} else {
effects <- lapply(effects,
function(el) {
attr(el, "effectNames") <- names(el)
el
})
if (length(level) == 1) effects <- effects[[1L]]
}
structure(effects, class = c("ranef.lme", class(effects)),
label= if (standard)
"Standardized random effects"
else
"Random effects",
level = max(level),
standardized = standard,
grpNames = grpNames)
}
residuals.lme <-
function(object, level = Q, type = c("response", "pearson", "normalized"),
asList = FALSE, ...)
{
type <- match.arg(type)
Q <- object$dims$Q
val <- object[["residuals"]]
if (is.character(level)) { # levels must be given consistently
nlevel <- match(level, names(val))
if (any(aux <- is.na(nlevel))) {
stop(sprintf(ngettext(sum(aux),
"nonexistent level %s",
"nonexistent levels %s"),
level[aux]), domain = NA)
}
level <- nlevel
} else { # assuming integers
level <- 1 + level
}
if (type != "response") { # standardize
## have to standardize properly for when corStruct neq NULL
val <- val[, level]/attr(val, "std")
} else {
val <- val[, level]
}
if (type == "normalized") {
if (!is.null(cSt <- object$modelStruct$corStruct)) {
## normalize according to inv-trans factor
val <- recalc(cSt, list(Xy = as.matrix(val)))$Xy[, seq_along(level)]
} else { # will just standardized
type <- "pearson"
}
}
if (length(level) == 1) {
grps <- as.character(object[["groups"]][, max(c(1, level - 1))])
if (asList) {
val <- as.list(split(val, ordered(grps, levels = unique(grps))))
} else {
grp.nm <- row.names(object[["groups"]])
val <- naresid(object$na.action, val)
names(val) <- grps[match(names(val), grp.nm)]
}
attr(val, "label") <-
switch(type,
response = {
if (!is.null(aux <- attr(object, "units")$y))
paste("Residuals", aux)
else "Residuals"
},
pearson = "Standardized residuals",
normalized = "Normalized residuals"
)
val
} else naresid(object$na.action, val)
}
summary.lme <- function(object, adjustSigma = TRUE, verbose = FALSE, ...)
{
## variance-covariance estimates for fixed effects
fixed <- fixef(object)
stdFixed <- sqrt(diag(as.matrix(object$varFix)))
object$corFixed <- array(t(object$varFix/stdFixed)/stdFixed,
dim(object$varFix), list(names(fixed),names(fixed)))
if (adjustSigma && object$method == "ML")
stdFixed <- stdFixed *
sqrt(object$dims$N/(object$dims$N - length(stdFixed)))
## fixed effects coefficients, std. deviations and t-ratios
##
fDF <- object$fixDF[["X"]]
tVal <- fixed/stdFixed
object$tTable <- cbind(Value = fixed, Std.Error = stdFixed, DF = fDF,
"t-value" = tVal, "p-value" = 2 * pt(-abs(tVal), fDF))
##
## residuals
##
resd <- resid(object, type = "pearson")
if (length(resd) > 5) {
resd <- quantile(resd, na.rm = TRUE) # might have NAs from na.exclude
names(resd) <- c("Min","Q1","Med","Q3","Max")
}
object$residuals <- resd
##
## generating the final object
##
aux <- logLik(object)
object$BIC <- BIC(aux)
object$AIC <- AIC(aux)
structure(object, verbose = verbose, oClass = class(object),
class = c("summary.lme", class(object)))
}
## based on R's update.default
update.lme <-
function (object, fixed., ..., evaluate = TRUE)
{
call <- object$call
if (is.null(call))
stop("need an object with call component")
extras <- match.call(expand.dots = FALSE)$...
if (!missing(fixed.))
call$fixed <- update.formula(formula(object), fixed.)
if(length(extras) > 0) {
existing <- !is.na(match(names(extras), names(call)))
## do these individually to allow NULL to remove entries.
for (a in names(extras)[existing]) call[[a]] <- extras[[a]]
if(any(!existing))
call <- as.call(c(as.list(call), extras[!existing]))
}
if(evaluate) eval(call, parent.frame())
else call
}
## update.lme <-
## function(object, fixed, data, random, correlation, weights, subset,
## method, na.action, control, contrasts, ...)
## {
## thisCall <- as.list(match.call())[-(1:2)]
## if (is.null(nextCall <- object$origCall) ||
## !is.null(thisCall$fixed) ||
## is.null(thisCall$random)) {
## nextCall <- object$call
## }
## nextCall <- as.list(nextCall)[-1L]
## if (is.null(thisCall$random) && is.null(thisCall$subset)) {
## ## no changes in ranef model and no subsetting
## thisCall$random <- object$modelStruct$reStruct
## }
## if (is.na(match("correlation", names(thisCall))) &&
## !is.null(thCor <- object$modelStruct$corStruct)) {
## thisCall$correlation <- thCor
## }
## if (is.na(match("weights", names(thisCall))) &&
## !is.null(thWgt <- object$modelStruct$varStruct)) {
## thisCall$weights <- thWgt
## }
## argNams <- unique( c(names(nextCall), names(thisCall)) )
## args <- vector("list", length(argNams))
## names(args) <- argNams
## args[ names(nextCall) ] <- nextCall
## nextCall <- args
## if (!is.null(thisCall$fixed)) {
## thisCall$fixed <- update(as.formula(nextCall$fixed), fixed)
## }
## nextCall[names(thisCall)] <- thisCall
## do.call(lme, nextCall)
## }
Variogram.lme <-
function(object, distance, form = ~1,
resType = c("pearson", "response", "normalized"),
data, na.action = na.fail, maxDist, length.out = 50,
collapse = c("quantiles", "fixed", "none"), nint = 20, breaks,
robust = FALSE, metric = c("euclidean", "maximum", "manhattan"),
...)
{
resType <- match.arg(resType)
grps <- getGroups(object, level = object$dims$Q)
## checking if object has a corSpatial element
csT <- object$modelStruct$corStruct
wchRows <- NULL
if (missing(distance)) {
if (missing(form) && inherits(csT, "corSpatial")) {
distance <- getCovariate(csT)
} else {
metric <- match.arg(metric)
if (missing(data)) {
data <- getData(object)
}
if (is.null(data)) { # will try to construct
allV <- all.vars(form)
if (length(allV) > 0) {
alist <- lapply(as.list(allV), as.name)
names(alist) <- allV
alist <- c(as.list(quote(data.frame)), alist)
mode(alist) <- "call"
data <- eval(alist, sys.parent(1))
}
}
covForm <- getCovariateFormula(form)
if (length(all.vars(covForm)) > 0) {
if (attr(terms(covForm), "intercept") == 1) {
covForm <- eval(substitute( ~ cFORM - 1, list(cFORM = covForm[[2L]])))
}
covar <- model.frame(covForm, data, na.action = na.action)
## making sure grps is consistent
wchRows <- !is.na(match(row.names(data), row.names(covar)))
grps <- grps[wchRows, drop = TRUE]
covar <- as.data.frame(unclass(model.matrix(covForm, covar)))
} else {
covar <-
data.frame(dist = unlist(tapply(rep(1, nrow(data)), grps, cumsum)))
}
covar <- split(covar, grps)
## getting rid of 1-observation groups
covar <- covar[sapply(covar, function(el) nrow(as.matrix(el))) > 1]
distance <- lapply(covar,
function(el, metric) dist(as.matrix(el), metric),
metric = metric)
}
}
res <- resid(object, type = resType)
if (!is.null(wchRows)) {
res <- res[wchRows]
}
res <- split(res, grps)
res <- res[lengths(res) > 1L] # no 1-observation groups
## levGrps <- levels(grps)
val <- lapply(seq_along(res),
function(i) Variogram(res[[i]], distance[[i]]))
names(val) <- names(res)
val <- do.call(rbind, val)
if (!missing(maxDist)) {
val <- val[val$dist <= maxDist, ]
}
collapse <- match.arg(collapse)
if (collapse != "none") { # will collapse values
dst <- val$dist
udist <- sort(unique(dst))
ludist <- length(udist)
if (!missing(breaks)) {
if (min(breaks) > udist[1L]) breaks <- c(udist[1L], breaks)
if (max(breaks) < udist[2L]) breaks <- c(breaks, udist[2L])
if (!missing(nint) && nint != (length(breaks) - 1L)) {
stop("'nint' is not consistent with 'breaks'")
}
nint <- length(breaks) - 1
}
cutDist <-
if (nint < ludist) {
if (missing(breaks))
breaks <-
if (collapse == "quantiles") { # break into equal groups
unique(quantile(dst, seq(0, 1, 1/nint), names=FALSE))
} else { # fixed length intervals
seq(udist[1L], udist[length(udist)], length = nint + 1L)
}
cut(dst, breaks)
}
else
dst
val <- lapply(split(val, cutDist),
function(el) {
vrg <- el$variog
vrg <- if (robust)
((mean(vrg^0.25))^4) / (0.457+ 0.494/nrow(el))
else
mean(vrg)
data.frame(variog = vrg,
dist = median(el$dist))
})
val <- do.call(rbind, val)
val$n.pairs <- as.vector(table(na.omit(cutDist)))
val <- na.omit(val) # getting rid of NAs
}
row.names(val) <- 1:nrow(val)
if (inherits(csT, "corSpatial") && resType != "normalized") {
## will keep model variogram
sig2 <- if (resType == "pearson") 1 else object$sigma^2
attr(val, "modelVariog") <-
Variogram(csT, sig2 = sig2, length.out = length.out)
}
structure(val, class = c("Variogram", "data.frame"),
collapse = (collapse != "none"))
}
###*### lmeStruct - a model structure for lme fits
lmeStruct <-
## constructor for lmeStruct objects
function(reStruct, corStruct = NULL, varStruct = NULL)
{
val <- list(reStruct = reStruct, corStruct = corStruct,
varStruct = varStruct)
structure(val[!vapply(val, is.null, NA)], # removing NULL components
settings = attr(val$reStruct, "settings"),
class = c("lmeStruct", "modelStruct"))
}
##*## lmeStruct methods for standard generics
fitted.lmeStruct <-
function(object, level = Q, conLin = attr(object, "conLin"),
lmeFit = attr(object, "lmeFit"), ...)
{
if (is.null(conLin)) {
stop("no condensed linear model")
}
if (is.null(lmeFit)) {
stop("no fitted \"lme\" object")
}
dd <- conLin$dims
Q <- dd$Q
Qp1 <- Q + 1L
nc <- dd$ncol
fit <- array(0, c(dd$N, Qp1),
list(dimnames(conLin$Xy)[[1L]], c("fixed", rev(names(object$reStruct)))))
ZXstart <- rev(cumsum(c(1, nc[1:Q])))
ZXend <- rev(cumsum(nc[1:Qp1]))
ZXlen <- dd$ZXlen[Q:1]
ZXngrps <- dd$ngrps[Q:1]
ZXb <- lmeFit$b
nc <- nc[Q:1]
if(ZXstart[1L] <= ZXend[1L])
fit[, "fixed"] <- # population fitted values
conLin$Xy[, ZXstart[1L]:ZXend[1L], drop = FALSE] %*% lmeFit$beta
for(i in 1:Q) {
j <- i + 1L
fit[, j] <- fit[, i] +
(conLin$Xy[, ZXstart[j]:ZXend[j], drop = FALSE] *
ZXb[[i]][rep(1:ZXngrps[i], ZXlen[[i]]),,drop = FALSE]) %*% rep(1, nc[i])
}
## this is documented to return a vector for one level, matrix for more.
## So it should be a matrix if there is only one row, but not if
## there is only one columns.
if(length(level) > 1) fit[, level + 1L, drop = FALSE] else fit[, level+1]
}
Initialize.lmeStruct <-
function(object, data, groups, conLin = attr(object, "conLin"),
control= list(niterEM = 20, gradHess = TRUE), ...)
{
object[] <- lapply(object, Initialize, data, conLin, control)
theta <- lapply(object, coef)
len <- lengths(theta)
pmap <- if (sum(len) > 0) {
num <- seq_along(len)
outer(rep(num, len), num, "==")
} else {
array(FALSE, c(1, length(len)))
}
dimnames(pmap) <- list(NULL, names(object))
attr(object, "pmap") <- pmap
if (length(object) == 1 && # only reStruct
all(attr(object, "settings")[-(1:3)] >= 0) && # known pdMat class
control[["gradHess"]]) {
## can use numerical derivatives
attr(object, "settings")[2:3] <- c(0, 1)
class(object) <- c("lmeStructInt", class(object))
}
if (needUpdate(object)) {
attr(object, "lmeFit") <- MEestimate(object, groups)
update(object, data)
} else {
object
}
}
logLik.lmeStruct <-
function(object, Pars, conLin = attr(object, "conLin"), ...)
{
coef(object) <- Pars # updating parameter values
recalc(object, conLin)[["logLik"]] # updating conLin
}
logLik.lmeStructInt <-
function(object, Pars, conLin = attr(object, "conLin"), ...)
{
## logLik for objects with reStruct parameters only, with
## internally defined class
q <- length(Pars)
settings <- as.integer(attr(object, "settings"))
aux <- .C(mixed_loglik, # >> ../src/nlmefit.c
as.double(conLin[["Xy"]]), # ZXy
as.integer(unlist(conLin$dims)), # pdims[]
as.double(Pars), # pars[]
settings, # settings
val = double(1 + q * (q + 1)), # logLik[] = (value, gradient, Hessian)
double(1), # lRSS
as.double(conLin$sigma) # sigma (17-11-2015; Fixed sigma patch ..)
)[["val"]]
val <- aux[1L]
attr(val, "gradient") <- -aux[1 + (1:q)]
attr(val, "hessian") <- -array(aux[-(1:(q+1))], c(q, q))
val
}
residuals.lmeStruct <-
function(object, level = conLin$dims$Q, conLin = attr(object, "conLin"),
lmeFit = attr(object, "lmeFit"), ...)
{
conLin$Xy[, conLin$dims$ZXcols] - fitted(object, level, conLin, lmeFit)
}
varWeights.lmeStruct <- function(object)
{
if (is.null(object$varStruct)) rep(1, attr(object, "conLin")$dims$N)
else varWeights(object$varStruct)
}
## Auxiliary control functions
lmeControl <-
## Control parameters for lme
function(maxIter = 50, msMaxIter = 50, tolerance = 1e-6, niterEM = 25,
msMaxEval = 200,
msTol = 1e-7, msVerbose = FALSE,
returnObject = FALSE, gradHess = TRUE, apVar = TRUE,
.relStep = .Machine$double.eps^(1/3), minAbsParApVar = 0.05,
opt = c("nlminb", "optim"),
optimMethod = "BFGS", natural = TRUE,
sigma = NULL, ## 17-11-2015; Fixed sigma patch; SH Heisterkamp; Quantitative Solutions
allow.n.lt.q = FALSE, # 23-01-2019 (NA would be back compatible)
...)
{
if(is.null(sigma))
sigma <- 0
else if(!is.finite(sigma) || length(sigma) != 1 || sigma < 0)
stop("Within-group std. dev. must be a positive numeric value")
list(maxIter = maxIter, msMaxIter = msMaxIter, tolerance = tolerance,
niterEM = niterEM, msMaxEval = msMaxEval, msTol = msTol,
msVerbose = msVerbose, returnObject = returnObject,
gradHess = gradHess , apVar = apVar, .relStep = .relStep,
opt = match.arg(opt), optimMethod = optimMethod,
minAbsParApVar = minAbsParApVar, natural = natural,
sigma = sigma,
allow.n.lt.q = allow.n.lt.q,
...)
}
## Local Variables:
## ess-indent-offset: 2
## End:
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