Nothing
.check_args_fitme <- function(...,HLmethod, ranPars=NULL,ranFix=NULL, fixed=list(),
init=list(), lower=list(),upper=list(), what_checked="fitme() call") {
mc <- match.call(expand.dots = TRUE)
if ( missing(HLmethod)) {
mc$HLmethod <- mc$method
} else message(paste0("'HLmethod' argument in ",what_checked," may become obsolete: use 'method' instead. "))
if (is.null(fixed)) mc$fixed <- list() ## deep reason is that relist(., fixed) will need a list ## fitme-specific
## Preventing confusions
if (!is.null(mc$ranPars)) {
stop(paste0("incorrect 'ranPars' argument in ",what_checked,". Use 'fixed' (ranPars is for HLCor() only)"))
}
if (!is.null(mc$ranFix)) {
stop(paste0("incorrect 'ranFix' argument in ",what_checked,". Use 'fixed' (ranFix is for HLfit() and corrHLfit() only)"))
}
if ( ! (is.list(lower) && is.list(upper))) {
wrongclass <- setdiff(unique(c(class(lower),class(upper))),"list")
stop(paste("'lower' and 'upper' must be of class list, not",paste(wrongclass,collapse=" or ")))
## as.list() would flatten rho vectors
} # else if ((length(lower$phi) || length(upper$phi)) && ! length(init$phi)) {
# warning("'lower' or 'upper' specifications without matching 'init' have no effect",immediate. = TRUE)
# }
#
dotnames <- setdiff(names(mc)[-1],c(names(formals(fitme)), "what_checked"))
argcheck <- setdiff(dotnames, .spaMM.data$options$HLnames)
if (length(argcheck) && what_checked=="fitmv() call") argcheck <- setdiff(argcheck,"X2X")
if (length(argcheck)) {
warning(paste0("suspect argument(s) '",paste(argcheck, sep="'", collapse=","),"' in ",what_checked,"."))
if ("offset" %in% argcheck) {
stop("the offset should be a formula term, not a distinct argument.")
}
}
#
return(mc)
}
.preprocess_fitme <- function(formula,data,
family=gaussian(),
init=list(),
fixed=list(), ## replaces ranFix
lower=list(),upper=list(),
resid.model=~1,
init.HLfit=list(),
control=list(), ## optim.scale (private), nloptr, refit
control.dist=list(),
method="ML",
HLmethod=method, ## LRT fns assume HLmethod when they are called and when calling
processed=NULL,
nb_cores = NULL, # to be used by SEM...
objective=NULL,
For="fitme", # alternative is "fitmv"
...
) {
# Here if e.g. 'data' is a promise, str(data) is OK but eval(mc$data) fails. We can manipulate mc elements
# but cannot evaluate (hence, test) them easily in the current envir (nor in a fn called from here)
# Hence args that must be tested in a fn called from here cannot be passed through mc
# A fortiori, we must call .preprocess() in the current envir (where we created mc), not in a fn called from here
mc <- match.call(expand.dots = TRUE)
if (is.null(processed)) {
family <- .checkRespFam(family) ## beware negbin not shadowed by mgcv::negbin()
preprocess_args <- .get_inits_preprocess_args(For=For)
names_nondefault <- intersect(names(mc),names(preprocess_args)) ## mc including dotlist
preprocess_args[names_nondefault] <- mc[names_nondefault]
preprocess_args$family <- family ## checked version of 'family'
if ( ! is.null(mc$rand.family)) preprocess_args$rand.families <- mc$rand.family ## because preprocess expects $rand.families
preprocess_args$predictor <- mc$formula ## because preprocess still expects $predictor
preprocess_args$ranFix <- fixed ## because preprocess expects ranFix
preprocess_args$HLmethod <- HLmethod ## forces evaluation
if ( identical(family$family,"multi")) {
## then data are reformatted as a list. Both HLCor and HLfit can analyse such lists for given corrPars and return the joint likelihood
## By contrast HLCor should not fit different corrPars to each data, so it does not lapply("corrHLfit",...)
## Rather, it calls preprocess which will construct a list of processed objects, to be used conjointly with the data list.
## But then we should not attempt to modify an element of 'pocessed' as if it was a single processed object
## We must use setProcessed / getProcessed to access list elements.
if ( ! inherits(data,"list")) {
familyargs <- family
familyargs$family <- NULL
familyargs$binfamily <- NULL
## we need the data list in the corrHLfit envir for the call to .makeCheckGeoMatrices
preprocess_args$data <- do.call(binomialize,c(list(data=data),familyargs)) ## if data not already binomialized
}
}
mc$processed <- do.call(.preprocess, preprocess_args, envir=parent.frame(1L))
## removing all elements that are matched in processed:
# We should remove all processed arguments, in particular those that go into the 'dotlist", otherwise their promises are evaluated again
## which is a waste of time (cf corrMatrix=as_precision(...))
pnames <- c("data","family","formula","prior.weights", "weights.form","HLmethod","method","rand.family","control.glm","REMLformula",
"resid.model", "verbose","distMatrix","adjMatrix", "control.dist", "corrMatrix","covStruct")
# control.HLfit" "init.HLfit" "etaFix" remain.
for (st in pnames) mc[st] <- NULL
} else mc$processed <- eval(mc$processed) # replace promise [for devel attempt ot recycle $processed in bootstraps]
return(mc)
}
.preprocess_fixed <- function(fixed) {
# Whether we can use a chol transfo (or some others) depend on the constraints... so ad hoc code for specific constraints seems justified
if (! is.null(ranCoefs <- fixed$ranCoefs)) {
for (it in seq_along(ranCoefs)) {
ranCoef <- ranCoefs[[it]]
names(ranCoef) <- NULL # In case the users added fancy names to the vector elements, which could break some post-fit name-matching code
Xi_ncol <- floor(sqrt(length(ranCoef)*2))
vdiagPos <- cumsum(c(1L,rev(seq(Xi_ncol-1L)+1L))) # diagpos on vector repre of half matrix, not on matrix
if (is.null(attr(ranCoefs[[it]],"isDiagFamily"))) {
attr(ranCoefs[[it]],"isDiagFamily") <- (! anyNA(ranCoef[ - vdiagPos])) & # If all non-diag pos are set by ranCoef, this may be 'is_diag".
(anyNA(ranCoef[vdiagPos]))
} # otherwise the user can avoid the ad hoc code for diag family by explitly setting the attribute to FALSE
attr(ranCoefs[[it]],"Xi_ncol") <- Xi_ncol # used by .constr_ranCoefsInv()
}
fixed$ranCoefs <- ranCoefs
}
if (! is.null(fixed$lambda) && is.null(names(fixed$lambda))) names(fixed$lambda) <- seq_along(fixed$lambda)
fixed
}
# This is tested by tests_private/test-.fix..n.R
..n_names2expr <- function(oricall) {
if (.spaMM.data$options$n_names2expr) {
for (argname in names(oricall)[-1]) {
putative_..n <- oricall[[argname]] # does not eval() objects of class "name"
if ( inherits(putative_..n, "name") && # paste() would fail on an environment
length(grep("\\.\\.[0-9]+$", paste(putative_..n)))) {
foo <- eval(str2lang(paste0("substitute(",argname,")")),
parent.frame())
if ( ! inherits(foo,"name")) oricall[[argname]] <- foo
}
}
}
oricall
}
fitme <- function(formula,data, ## matches minimal call of HLfit
family=gaussian(),
init=list(),
fixed=list(), ## replaces ranFix
lower=list(),upper=list(),
resid.model=~1,
init.HLfit=list(),
control=list(), ## optim.scale (private), nloptr, refit... ultimately passed to fitme_body
control.dist=list(),
method="ML",
HLmethod=method, ## LRT fns assume HLmethod when they are called and when calling
processed=NULL,
nb_cores = NULL, # to be used by SEM...
objective=NULL,
weights.form=NULL,
# verbose=NULL,
... # control.HLfit passed through the dots to .preprocess_fitme() -> .preprocess()
) {
.spaMM.data$options$xLM_conv_crit <- list(max=-Inf)
time1 <- Sys.time()
oricall <- match.call(expand.dots=TRUE) ## mc including dotlist
oricall <- ..n_names2expr(oricall) #
oricall$"control.HLfit" <- eval(oricall$control.HLfit, parent.frame()) # to evaluate variables in the formula_env, otherwise there are bugs in waiting
oricall$fixed <- eval(oricall$fixed, parent.frame()) # allows modif in post-fit code (cf get_HLCorcall: .modify_list needs a list, not a promise)
oricall$init <- eval(oricall[["init"]], parent.frame()) # allows modif in post-fit code (cf get_HLCorcall). Better way ? One should be in principle able
# to provide the arguments again to the post-fit call => argument o post-fit fn to provide control of eval envir.
# cf also alternative strategy of trying 3 envirs in lme4:::update.merMod(), incl. sys.frames()[[1]]
mc <- oricall
if ( ! is.null(weights.form)) {
mc[["prior.weights"]] <- weights.form[[2]]
#} else if ("prior.weights" %in% evalq(names(substitute(...())))) { # ~ R >= 4.1's ...names()
# substitute(...()) trick found in a Dunlap post to R-devel, 22/05/2020, 20:53
} else if ("prior.weights" %in% ...names()) {
p_weights <- substitute(alist(...))$prior.weights # necessary when prior weights has been passed to fitme
# through the '...' of another function. In that case we reconstruct the call argument as if they had not been passed in this way.
# is user quoted the pw, the str() of the result of the substitute() calls is language quote(...) ~ doubly quoted stuff... => eval
if ( (inherits(p_weights,"call") && p_weights[[1L]] == "quote") ) p_weights <- eval(p_weights)
mc[["prior.weights"]] <- p_weights
}
#
mc[[1L]] <- get(".preprocess_formula", asNamespace("spaMM"), inherits=FALSE) ## https://stackoverflow.com/questions/10022436/do-call-in-combination-with
oricall$formula <- mc$formula <- eval(mc,parent.frame()) #
## : among other effects, forces eval of promise for formula, so re-evaluating the call later will work
## [cf probitgem re-evaluating fitme(form,.....) in eval_smoothtest()];
## likewise, associates the evaluated formula_env to the formula
mc[[1L]] <- get(".check_args_fitme", asNamespace("spaMM"), inherits=FALSE)
mc <- eval(mc,parent.frame()) #
mc[["fixed"]] <- .preprocess_fixed(fixed)
mc[[1L]] <- get(".preprocess_fitme", asNamespace("spaMM"), inherits=FALSE)
mc <- eval(mc,parent.frame()) # returns modified call including an element 'processed'
#
if (identical(control$processed_only, TRUE)) return(mc$processed) # [for devel attempt ot recycle $processed in bootstraps]
#
mc[[1L]] <- get("fitme_body", asNamespace("spaMM"), inherits=FALSE)
hlcor <- eval(mc,parent.frame())
.check_conv_dispGammaGLM_reinit()
if (inherits(hlcor,"HLfitlist")) {
attr(hlcor,"call") <- oricall
} else {
oricall$control.dist <- mc$processed$control_dist ## but never in the fitme_body() call
hlcor$call <- oricall ## this is a call to fitme()
}
# attr(hlcor,"HLCorcall") <- NULL # presumably no more needed
lsv <- c("lsv",ls())
if ( ! is.call(hlcor) ) {
if ( inherits(hlcor,"HLfitlist") ) {
attr(hlcor,"how") <- list(fit_time=.timerraw(time1),fnname="fitme", spaMM.version=hlcor[[1L]]$how$spaMM.version)
} else {
hlcor$how$fit_time <- .timerraw(time1)
hlcor$how$fnname <- "fitme"
hlcor$fit_time <- structure(hlcor$how$fit_time,
message="Please use how(<fit object>)[['fit_time']] to extract this information cleanly.")
}
if ( ! is.null(mc$control.HLfit$NbThreads)) .setNbThreads(thr=.spaMM.data$options$NbThreads)
}
rm(list=setdiff(lsv,"hlcor")) ## empties the whole local envir except the return value
return(hlcor)
}
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