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influence.merMod <- function(model, groups, data, maxfun=1000, do.coef = TRUE,
start=NULL,
parallel = c("no", "multicore", "snow"),
ncpus = getOption("influence.ncpus", 1L),
cl = NULL,
ncores,
...) {
if (!missing(ncores)) {
.Deprecated(msg = "argument 'ncores' is deprecated; please use 'parallel', 'ncpus', and 'cl' instead")
ncpus <- ncores
parallel <- "snow"
}
parallel <- match.arg(parallel)
# (parallel, ncpus, cl) -> (parallel)
eval(initialize.parallel)
.groups <- NULL ## avoid false-positive code checks
.vcov <- function(x) Matrix::as.matrix(vcov(x))
if (...length()>0) warning("disregarded additional arguments")
if (!do.coef) {
## simple/quick/trivial results
result <- list(hat=hatvalues(model))
class(result) <- "influence.merMod"
return(result)
}
if (missing(data)) {
data <- getCall(model)$data
data <- if (!is.null(data)) eval(data, parent.frame())
else stop("model did not use the data argument")
}
data <- as.data.frame(data) ## prevent tibble trouble
if (missing(groups)) {
groups <- ".case"
data$.case <- rownames(data)
}
else if (length(groups) > 1){
del.var <- paste0(groups, collapse=".")
data[, del.var] <- apply(data, 1, paste0, collapse=".")
groups <- del.var
}
unique.del <- unique(data[, groups])
data[[".groups"]] <- data[, groups]
if (!is.null(start)) {
par <- start
} else {
par <- list(theta=getME(model, "theta"))
if (inherits(model, "glmerMod") &&
getME(model, "devcomp")$dims[["nAGQ"]]>0) {
par$fixef <- fixef(model)
}
}
fixed <- fixef(model)
fixed.1 <- matrix(0, length(unique.del), length(fixed))
rownames(fixed.1) <- unique.del
colnames(fixed.1) <- names(fixed)
Vs <- VarCorr(model)
nms <- names(Vs)
sep <- ":"
if (length(nms) == 1) {
nms <- ""
sep <- ""
}
vc <- getME(model, "sigma")^2
names(vc) <- "sigma^2"
for (i in 1:length(Vs)){
V <- Vs[[i]]
c.names <- colnames(V)
e.names <- outer(c.names, c.names, function(a, b) paste0("C[", a, ",", b, "]"))
diag(e.names) <- paste0("v[", c.names, "]")
v <- V[lower.tri(V, diag=TRUE)]
names(v) <- paste0(nms[i], sep, e.names[lower.tri(e.names, diag=TRUE)])
vc <- c(vc, v)
}
vc.1 <- matrix(0, length(unique.del), length(vc))
rownames(vc.1) <- unique.del
colnames(vc.1) <- names(vc)
feval <- numeric(length(unique.del))
converged <- logical(length(unique.del))
vcov.1 <- vector(length(unique.del), mode="list")
names(vcov.1) <- names(feval) <- names(converged) <- unique.del
# control <- if (one.step){
# if (inherits(model, "lmerMod")) lmerControl(optimizer="optimx", optCtrl=list(method="L-BFGS-B", maxit=1))
# else if (inherits(model, "glmerMod")) glmerControl(optimizer="optimx", optCtrl=list(method="L-BFGS-B", maxit=1))
# } else {
control <- if (inherits(model, "lmerMod")) lmerControl(optCtrl=list(maxfun=maxfun))
else if (inherits(model, "glmerMod")) glmerControl(optCtrl=list(maxfun=maxfun))
# }
deleteGroup <- function(del) {
data$del <- del
mod.1 <- suppressWarnings(update(model, data=data,
subset=(.groups != del),
start=par,
control=control))
opt <- mod.1@optinfo
feval <- opt$feval
converged <- opt$conv$opt == 0 && length(opt$warnings) == 0
fixed.1 <- fixef(mod.1)
Vs.1 <- VarCorr(mod.1)
vc.0 <- getME(mod.1, "sigma")^2
for (V in Vs.1){
vc.0 <- c(vc.0, V[lower.tri(V, diag=TRUE)])
}
vc.1 <- vc.0
vcov.1 <<- .vcov(mod.1)
namedList(fixed.1, vc.1, vcov.1, converged, feval)
}
result <- if (parallel == "multicore") {
parallel::mclapply(unique.del, deleteGroup, mc.cores = ncpus)
} else if (parallel == "snow") {
if (is.null(cl)) {
cl <- parallel::makeCluster(ncpus)
on.exit(parallel::stopCluster(cl))
parallel::clusterEvalQ(cl, require("lme4"))
}
parallel::parLapply(cl, unique.del, deleteGroup)
} else {
lapply(unique.del, deleteGroup)
}
result <- combineLists(result)
fixed.1 <- result$fixed.1
rownames(fixed.1) <- unique.del
colnames(fixed.1) <- names(fixed)
vc.1 <- result$vc.1
rownames(vc.1) <- unique.del
colnames(vc.1) <- names(vc)
feval <- as.vector(result$feval)
converged <- as.vector(result$converged)
vcov.1 <- result$vcov.1
names(vcov.1) <- names(feval) <- names(converged) <- unique.del
left <- "[-"
right <- "]"
if (groups == ".case") {
groups <- "case"
}
nms <- c("fixed.effects", paste0("fixed.effects", left, groups, right),
"var.cov.comps", paste0("var.cov.comps", left, groups, right),
"vcov", paste0("vcov", left, groups, right),
"groups", "deleted", "converged", "function.evals")
result <- list(fixed, fixed.1, vc, vc.1,
.vcov(model),
vcov.1, groups, unique.del, converged, feval)
names(result) <- nms
class(result) <- "influence.merMod"
result
}
## lookup table for influence.merMod elements
ipos <- c("fixed"=1, "fixed.sub"=2,
"var.cov"=3, "var.cov.sub"=4,
"vcov"=5, "vcov.sub"=6)
dfbeta.influence.merMod <- function(model, which=c("fixed", "var.cov"), ...){
which <- match.arg(which)
b <- model[[ipos[sprintf("%s.sub",which)]]]
b0 <- model[[ipos[which]]]
b - matrix(b0, nrow=nrow(b), ncol=ncol(b), byrow=TRUE)
}
dfbetas.influence.merMod <- function(model, ...){
vList <- model[[ipos["vcov.sub"]]]
n <- nrow(vList[[1]])
vmat <- t(vapply(vList, function(x) sqrt(diag(x)),
numeric(n)))
dfbeta(model)/vmat
}
cooks.distance.merMod <- function(model, ...) {
p <- Matrix::rankMatrix(getME(model,"X"))
hat <- hatvalues(model)
dispersion <- sigma(model)^2
res <- residuals(model,type="pearson")
res <- (res/(1 - hat))^2 * hat/(dispersion * p)
res[is.infinite(res)] <- NaN
res
}
cooks.distance.influence.merMod <- function(model, ...) {
db <- dfbeta(model)
n <- nrow(db)
p <- ncol(db)
d <- numeric(n)
names(d) <- names(model[["vcov[-case]"]])
vcov.inv <- (n - p)/(n*p)*solve(model$vcov)
for (i in 1:n) {
d[i] <- db[i, ] %*% vcov.inv %*% db[i, ]
}
d
}
## from ?lm.influence:
## wt.res: a vector of _weighted_ (or for class 'glm' rather _deviance_)
## residuals.
##
## residuals.lm gives r*sqrt(object$weights) (if non-NULL weights)
## for type %in% c("deviance","pearson")
##
## residuals.glm gives (y - mu) * sqrt(wts)/sqrt(object$family$variance(mu))
##
rstudent.merMod <- function (model, ...) {
r <- residuals(model, type="deviance")
hat <- hatvalues(model)
pr <- residuals(model, type="pearson")
r <- sign(r) * sqrt(r^2 + (hat * pr^2)/(1 - hat))
r[is.infinite(r)] <- NaN
r/sigma(model)
}
rstandard.glmerMod <- function (model,
infl = influence(model, do.coef = FALSE),
type = c("pearson", "deviance"), ...)
{
type <- match.arg(type)
res <- residuals(model, type = type)
res <- res/(sigma(model) * sqrt(1 - hatvalues(model)))
res[is.infinite(res)] <- NaN
res
}
rstudent.glmerMod <- function(model, infl = influence(model, do.coef=FALSE), ...) {
useSc <- model@devcomp$dims[["useSc"]]
if (useSc) return(rstandard(model, infl, type="pearson"))
residuals(model, type = "pearson")/
(sigma(model)*sqrt(1 - hatvalues(model)))
}
##' @S3method sigma merMod
sigma.merMod <- function(object, ...) {
dc <- object@devcomp
dd <- dc$dims
if(dd[["useSc"]])
dc$cmp[[if(dd[["REML"]]) "sigmaREML" else "sigmaML"]] else 1.
}
##' @importFrom stats terms
##' @S3method terms merMod
terms.merMod <- function(x, fixed.only=TRUE, random.only=FALSE, ...) {
if (missing(fixed.only) && random.only) fixed.only <- FALSE
if (fixed.only && random.only) stop("can't specify 'only fixed' and 'only random' terms")
tt <- attr(x@frame,"terms")
if (fixed.only) {
## ... may contain 'data' (needed when formula contains .)
tt <- terms.formula(formula(x,fixed.only=TRUE), ...)
attr(tt,"predvars") <- attr(terms(x@frame),"predvars.fixed")
}
if (random.only) {
ff <- reformulas::subbars(formula(x, random.only=TRUE))
ff <- noSpecials(ff, specials = lme4_specials, delete = FALSE)
tt <- terms.formula(ff)
attr(tt,"predvars") <- attr(terms(x@frame),"predvars.random")
}
tt
}
##' @importFrom stats update
##' @S3method update merMod
update.merMod <- function(object, formula., ..., evaluate = TRUE) {
if (is.null(call <- getCall(object)))
stop("object should contain a 'call' component")
extras <- match.call(expand.dots = FALSE)$...
if (!missing(formula.))
call$formula <- update.formula(formula(object), formula.)
if (length(extras) > 0) {
existing <- !is.na(match(names(extras), names(call)))
for (a in names(extras)[existing]) call[[a]] <- extras[[a]]
if (any(!existing)) {
call <- c(as.list(call), extras[!existing])
call <- as.call(call)
}
}
if (!evaluate) return(call)
## should be able to find model components somewhere in (1) formula env; (2) calling env;
## (3) parent frame [plus its parent frames]
## see discusion at https://stackoverflow.com/questions/64268994/evaluate-call-when-components-may-be-scattered-among-environments
## FIXME: suppressWarnings(update(model)) will give
## Error in as.list.environment(X[[i]], ...) :
## promise already under evaluation: recursive default argument reference or earlier problems?
ff <- environment(formula(object))
pf <- parent.frame()
sf <- sys.frames()[[1]]
tryCatch(eval(call, envir = ff), ## try formula environment
error = function(e) {
tryCatch(eval(call, envir = sf), ## try stack frame
error = function(e) {
eval(call, envir=pf) ## try parent frame
})
})
##
## combf <- tryCatch(
## do.call("c", lapply(list(ff, sf), as.list)),
## error=function(e) as.list(ff)
## )
## eval(call,combf, enclos=pf)
}
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