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# Part of the rstanarm package for estimating model parameters
# Copyright (C) 2015, 2016, 2017 Trustees of Columbia University
# Copyright (C) 1995-2015 The R Core Team
# Copyright (C) 1998 B. D. Ripley
#
# 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 3
# 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.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#' @rdname stan_lm
#' @export
#' @param projections For \code{stan_aov}, a logical scalar (defaulting to
#' \code{FALSE}) indicating whether \code{\link[stats]{proj}} should be called
#' on the fit.
#' @examples
#' if (.Platform$OS.type != "windows" || .Platform$r_arch != "i386") {
#' \donttest{
#' op <- options(contrasts = c("contr.helmert", "contr.poly"))
#' fit_aov <- stan_aov(yield ~ block + N*P*K, data = npk,
#' prior = R2(0.5), seed = 12345)
#' options(op)
#' print(fit_aov)
#' }
#' }
stan_aov <- function(formula, data, projections = FALSE,
contrasts = NULL, ...,
prior = R2(stop("'location' must be specified")),
prior_PD = FALSE,
algorithm = c("sampling", "meanfield", "fullrank"),
adapt_delta = NULL) {
# parse like aov() does
Terms <- if (missing(data))
terms(formula, "Error") else terms(formula, "Error", data = data)
indError <- attr(Terms, "specials")$Error
## NB: this is only used for n > 1, so singular form makes no sense
## in English. But some languages have multiple plurals.
if(length(indError) > 1L)
stop(sprintf(ngettext(length(indError),
"there are %d Error terms: only 1 is allowed",
"there are %d Error terms: only 1 is allowed"),
length(indError)), domain = NA)
lmcall <- Call <- match.call()
## need rstanarm:: for non-standard evaluation
lmcall[[1L]] <- quote(stan_lm)
lmcall$singular.ok <- TRUE
if (projections)
qr <- lmcall$qr <- TRUE
lmcall$projections <- NULL
if (is.null(indError)) {
## no Error term
fit <- eval(lmcall, parent.frame())
fit$terms <- Terms
fit$qr <- qr(model.matrix(Terms, data = fit$data, contrasts.arg = contrasts))
R <- qr.R(fit$qr)
beta <- extract(fit$stanfit, pars = "beta", permuted = FALSE)
pnames <- dimnames(beta)$parameters
rownames(R) <- colnames(R)
R <- R[pnames, pnames, drop = FALSE]
effects <- apply(beta, 1:2, FUN = function(x) R %*% x)
if (length(dim(effects)) == 2) {
dim(effects) <- c(1L, dim(effects))
}
effects <- aperm(effects, c(2,3,1))
fit$effects <- effects
class(fit) <- c("stanreg", "aov", "lm")
if (projections)
fit$projections <- proj(fit)
fit$call <- Call
fit$stan_function <- "stan_aov"
return(fit)
} else { # nocov start
stop("Error terms not supported yet")
if(pmatch("weights", names(match.call()), 0L))
stop("weights are not supported in a multistratum aov() fit")
## Helmert contrasts can be helpful: do we want to force them?
## this version does for the Error model.
opcons <- options("contrasts")
options(contrasts = c("contr.helmert", "contr.poly"))
on.exit(options(opcons))
allTerms <- Terms
errorterm <- attr(Terms, "variables")[[1 + indError]]
eTerm <- deparse(errorterm[[2L]], width.cutoff = 500L, backtick = TRUE)
intercept <- attr(Terms, "intercept")
ecall <- lmcall
ecall$formula <-
as.formula(paste(deparse(formula[[2L]], width.cutoff = 500L,
backtick = TRUE), "~", eTerm,
if(!intercept) "- 1"),
env = environment(formula))
ecall$method <- "qr"
ecall$qr <- TRUE
ecall$contrasts <- NULL
er.fit <- eval(ecall, parent.frame())
options(opcons)
nmstrata <- attr(terms(er.fit), "term.labels")
## remove backticks from simple labels for strata (only)
nmstrata <- sub("^`(.*)`$", "\\1", nmstrata)
nmstrata <- c("(Intercept)", nmstrata)
qr.e <- er.fit$qr
rank.e <- er.fit$rank
if(rank.e < NROW(er.fit$coefficients))
warning("Error() model is singular")
qty <- er.fit$residuals
maov <- is.matrix(qty)
asgn.e <- er.fit$assign[qr.e$pivot[1L:rank.e]]
## we want this to label the rows of qtx, not cols of x.
maxasgn <- length(nmstrata) - 1L
nobs <- NROW(qty)
len <- if(nobs > rank.e) {
asgn.e[(rank.e+1):nobs] <- maxasgn + 1L
nmstrata <- c(nmstrata, "Within")
maxasgn + 2L
} else maxasgn + 1L
result <- setNames(vector("list", len), nmstrata)
lmcall$formula <- form <-
update(formula, paste(". ~ .-", deparse(errorterm, width.cutoff = 500L, backtick = TRUE)))
Terms <- terms(form)
lmcall$method <- "model.frame"
mf <- eval(lmcall, parent.frame())
xlev <- .getXlevels(Terms, mf)
resp <- model.response(mf)
qtx <- model.matrix(Terms, mf, contrasts)
cons <- attr(qtx, "contrasts")
dnx <- colnames(qtx)
asgn.t <- attr(qtx, "assign")
if(length(wts <- model.weights(mf))) {
wts <- sqrt(wts)
resp <- resp * wts
qtx <- qtx * wts
}
qty <- as.matrix(qr.qty(qr.e, resp))
if((nc <- ncol(qty)) > 1L) {
dny <- colnames(resp)
if(is.null(dny)) dny <- paste0("Y", 1L:nc)
dimnames(qty) <- list(seq(nrow(qty)), dny)
} else dimnames(qty) <- list(seq(nrow(qty)), NULL)
qtx <- qr.qty(qr.e, qtx)
dimnames(qtx) <- list(seq(nrow(qtx)) , dnx)
for(i in seq_along(nmstrata)) {
select <- asgn.e == (i-1L)
ni <- sum(select)
if(!ni) next
## helpful to drop constant columns.
xi <- qtx[select, , drop = FALSE]
cols <- colSums(xi^2) > 1e-5
if(any(cols)) {
xi <- xi[, cols, drop = FALSE]
attr(xi, "assign") <- asgn.t[cols]
fiti <- lm.fit(xi, qty[select,,drop=FALSE])
fiti$terms <- Terms
} else {
y <- qty[select,,drop=FALSE]
fiti <- list(coefficients = numeric(), residuals = y,
fitted.values = 0 * y, weights = wts, rank = 0L,
df.residual = NROW(y))
}
if(projections) fiti$projections <- proj(fiti)
class(fiti) <- c(if(maov) "maov", "aov", oldClass(er.fit))
result[[i]] <- fiti
}
## drop empty strata
result <- result[!sapply(result, is.null)]
class(result) <- c("aovlist", "listof")
if(qr) attr(result, "error.qr") <- qr.e
attr(result, "call") <- Call
if(length(wts)) attr(result, "weights") <- wts
attr(result, "terms") <- allTerms
attr(result, "contrasts") <- cons
attr(result, "xlevels") <- xlev
result
} # nocov end
}
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