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# Demonstrate the problem with gls model
library(nlme)
data(Ovary)
gls_raw <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), data = Ovary,
correlation = corAR1(form = ~ 1 | Mare),
weights = varPower())
Mares <- levels(gls_raw$groups)
V_raw <- lapply(Mares, function(g) getVarCov(gls_raw, individual = g))
Ovary_sorted <- Ovary[with(Ovary, order(Mare, Time)),]
gls_sorted <- update(gls_raw, data = Ovary_sorted)
V_sorted <- lapply(Mares, function(g) getVarCov(gls_sorted, individual = g))
all.equal(gls_raw$modelStruct, gls_sorted$modelStruct)
all.equal(V_raw, V_sorted)
nlme:::getVarCov.gls
identical(gls_raw$groups, gls_sorted$groups)
identical(varWeights(gls_raw$modelStruct$varStruct),
varWeights(gls_sorted$modelStruct$varStruct))
# proposed patch for getVarCov.gls
getVarCov_revised_gls <- function (obj, individual = 1, ...) {
S <- corMatrix(obj$modelStruct$corStruct)[[individual]]
if (!is.null(obj$modelStruct$varStruct)) {
ind <- sort(obj$groups) == individual # sort the groups before matching index
vw <- 1 / varWeights(obj$modelStruct$varStruct)[ind]
}
else vw <- rep(1, nrow(S))
vars <- (obj$sigma * vw)^2
result <- t(S * sqrt(vars)) * sqrt(vars)
class(result) <- c("marginal", "VarCov")
attr(result, "group.levels") <- names(obj$groups)
result
}
V_raw <- lapply(Mares, function(g) getVarCov_revised_gls(gls_raw, individual = g))
V_sorted <- lapply(Mares, function(g) getVarCov_revised_gls(gls_sorted, individual = g))
all.equal(V_raw, V_sorted)
# proposed patch for getVarCov.lme
getVarCov_revised_lme <- function (obj, individuals, type = c("random.effects", "conditional", "marginal"), ...) {
type <- match.arg(type)
if (any("nlme" == class(obj)))
stop("not implemented for \"nlme\" objects")
if (length(obj$group) > 1)
stop("not implemented for multiple levels of nesting")
sigma <- obj$sigma
D <- as.matrix(obj$modelStruct$reStruct[[1]]) * sigma^2
if (type == "random.effects") {
result <- D
}
else {
result <- list()
groups <- sort(obj$groups[[1]]) # sort the groups before matching index
ugroups <- unique(groups)
if (missing(individuals))
individuals <- as.matrix(ugroups)[1, ]
if (is.numeric(individuals))
individuals <- ugroups[individuals]
for (individ in individuals) {
indx <- which(individ == ugroups)
if (!length(indx))
stop(gettextf("individual %s was not used in the fit",
sQuote(individ)), domain = NA)
if (is.na(indx))
stop(gettextf("individual %s was not used in the fit",
sQuote(individ)), domain = NA)
ind <- groups == individ
if (!is.null(obj$modelStruct$corStruct)) {
V <- corMatrix(obj$modelStruct$corStruct)[[as.character(individ)]]
}
else V <- diag(sum(ind))
if (!is.null(obj$modelStruct$varStruct))
sds <- 1/varWeights(obj$modelStruct$varStruct)[ind]
else sds <- rep(1, sum(ind))
sds <- obj$sigma * sds
cond.var <- t(V * sds) * sds
dimnames(cond.var) <- list(1:nrow(cond.var), 1:ncol(cond.var))
if (type == "conditional")
result[[as.character(individ)]] <- cond.var
else {
Z <- model.matrix(obj$modelStruct$reStruc, getData(obj))[ind,
, drop = FALSE]
result[[as.character(individ)]] <- cond.var +
Z %*% D %*% t(Z)
}
}
}
class(result) <- c(type, "VarCov")
attr(result, "group.levels") <- names(obj$groups)
result
}
lme_raw <- lme(follicles ~ sin(2*pi*Time) + cos(2*pi*Time),
random = ~ 1 | Mare,
correlation = corExp(form = ~ Time),
weights = varPower(),
data=Ovary)
lme_sorted <- update(lme_raw, data = Ovary_sorted)
all.equal(lme_raw$modelStruct, lme_sorted$modelStruct)
# current getVarCov
V_raw <- lapply(Mares, function(g) getVarCov(lme_raw, individual = g, type = "marginal"))
V_sorted <- lapply(Mares, function(g) getVarCov(lme_sorted, individual = g, type = "marginal"))
all.equal(V_raw, V_sorted)
# revised getVarCov
V_raw <- lapply(Mares, function(g) getVarCov_revised_lme(lme_raw, individual = g, type = "marginal"))
V_sorted <- lapply(Mares, function(g) getVarCov_revised_lme(lme_sorted, individual = g, type = "marginal"))
all.equal(V_raw, V_sorted)
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