R/stanreg-methods.R

Defines functions justRE coef_mer .flist.stanreg .flist .cnms.stanreg .cnms .glmer_check terms.stanreg formula.stanreg model.matrix.stanreg model.frame.stanreg family.stanreg VarCorr.stanreg sigma.stanreg ranef_template ranef.stanreg nsamples.stanreg ngrps.stanreg fixef.stanreg vcov.stanreg update.stanreg se.stanreg se residuals.stanreg nobs.stanreg fitted.stanreg confint.stanreg coef.stanreg

Documented in coef.stanreg confint.stanreg family.stanreg fitted.stanreg fixef.stanreg formula.stanreg model.frame.stanreg model.matrix.stanreg ngrps.stanreg nobs.stanreg nsamples.stanreg ranef.stanreg residuals.stanreg se se.stanreg sigma.stanreg terms.stanreg update.stanreg VarCorr.stanreg vcov.stanreg

# Part of the rstanarm package for estimating model parameters
# Copyright (C) 2015, 2016, 2017 Trustees of Columbia University
# 
# 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.

#' Methods for stanreg objects
#' 
#' The methods documented on this page are actually some of the least important 
#' methods defined for \link[=stanreg-objects]{stanreg} objects. The most 
#' important methods are documented separately, each with its own page. Links to
#' those pages are provided in the \strong{See Also} section, below.
#' 
#' @name stanreg-methods
#' @aliases VarCorr fixef ranef ngrps sigma nsamples
#' 
#' @templateVar stanregArg object,x
#' @template args-stanreg-object
#' @param ... Ignored, except by the \code{update} method. See
#'   \code{\link{update}}.
#' 
#' @details The methods documented on this page are similar to the methods 
#'   defined for objects of class 'lm', 'glm', 'glmer', etc. However there are a
#'   few key differences:
#'   
#' \describe{
#' \item{\code{residuals}}{
#' Residuals are \emph{always} of type \code{"response"} (not \code{"deviance"}
#' residuals or any other type). However, in the case of \code{\link{stan_polr}}
#' with more than two response categories, the residuals are the difference 
#' between the latent utility and its linear predictor.
#' }
#' \item{\code{coef}}{
#' Medians are used for point estimates. See the \emph{Point estimates} section
#' in \code{\link{print.stanreg}} for more details.
#' }
#' \item{\code{se}}{
#' The \code{se} function returns standard errors based on 
#' \code{\link{mad}}. See the \emph{Uncertainty estimates} section in
#' \code{\link{print.stanreg}} for more details.
#' }
#' \item{\code{confint}}{
#' For models fit using optimization, confidence intervals are returned via a 
#' call to \code{\link[stats]{confint.default}}. If \code{algorithm} is 
#' \code{"sampling"}, \code{"meanfield"}, or \code{"fullrank"}, the
#' \code{confint} will throw an error because the
#' \code{\link{posterior_interval}} function should be used to compute Bayesian 
#' uncertainty intervals.
#' }
#' \item{\code{nsamples}}{
#' The number of draws from the posterior distribution obtained
#' }
#' }
#' 
#' @seealso 
#' \itemize{
#'  \item The \code{\link[=print.stanreg]{print}},
#'    \code{\link[=summary.stanreg]{summary}}, and \code{\link{prior_summary}} 
#'    methods for stanreg objects for information on the fitted model.
#'  \item \code{\link{launch_shinystan}} to use the ShinyStan GUI to explore a
#'    fitted \pkg{rstanarm} model.
#'  \item The \code{\link[=plot.stanreg]{plot}} method to plot estimates and
#'    diagnostics.
#'  \item The \code{\link{pp_check}} method for graphical posterior predictive
#'    checking.
#'  \item The \code{\link{posterior_predict}} and \code{\link{predictive_error}}
#'    methods for predictions and predictive errors.
#'  \item The \code{\link{posterior_interval}} and \code{\link{predictive_interval}}
#'    methods for uncertainty intervals for model parameters and predictions.
#'  \item The \code{\link[=loo.stanreg]{loo}}, \code{\link{kfold}}, and
#'  \code{\link{log_lik}} methods for leave-one-out or K-fold cross-validation, 
#'    model comparison, and computing the log-likelihood of (possibly new) data.
#'  \item The \code{\link[=as.matrix.stanreg]{as.matrix}}, \code{as.data.frame}, 
#'    and \code{as.array} methods to access posterior draws.
#' }
#' 
NULL

#' @rdname stanreg-methods
#' @export
coef.stanreg <- function(object, ...) {
  if (is.mer(object)) 
    return(coef_mer(object, ...))
  
  object$coefficients
}

#' @rdname stanreg-methods
#' @export
#' @param parm For \code{confint}, an optional character vector of parameter
#'   names.
#' @param level For \code{confint}, a scalar between \eqn{0} and \eqn{1}
#'   indicating the confidence level to use.
#'
confint.stanreg <- function(object, parm, level = 0.95, ...) {
  if (!used.optimizing(object)) {
    stop("For models fit using MCMC or a variational approximation please use ", 
         "posterior_interval() to obtain Bayesian interval estimates.", 
         call. = FALSE)
  }
  confint.default(object, parm, level, ...)
}

#' @rdname stanreg-methods
#' @export
fitted.stanreg <- function(object, ...)  {
  object$fitted.values
}

#' @rdname stanreg-methods
#' @export 
nobs.stanreg <- function(object, ...) {
  nrow(model.frame(object))
}

#' @rdname stanreg-methods
#' @export 
residuals.stanreg <- function(object, ...) {
  object$residuals
}

#' Extract standard errors
#' 
#' Generic function for extracting standard errors from fitted models.
#' 
#' @export
#' @keywords internal
#' @param object A fitted model object.
#' @param ... Arguments to methods.
#' @return Standard errors of model parameters.
#' @seealso \code{\link{se.stanreg}}
#' 
se <- function(object, ...) UseMethod("se")

#' @rdname stanreg-methods
#' @export
se.stanreg <- function(object, ...) {
  object$ses
}

#' @rdname stanreg-methods
#' @export
#' @method update stanreg
#' @param formula.,evaluate See \code{\link[stats]{update}}.
#'
update.stanreg <- function(object, formula., ..., evaluate = TRUE) {
  call <- getCall(object)
  if (is.null(call)) 
    stop("'object' does not contain a 'call' component.", call. = FALSE)
  extras <- match.call(expand.dots = FALSE)$...
  if (!missing(formula.)) 
    call$formula <- update.formula(formula(object), formula.)
  if (length(extras)) {
    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)
  
  # do this like lme4 update.merMod instead of update.default
  ff <- environment(formula(object))
  pf <- parent.frame()
  sf <- sys.frames()[[1L]]
  tryCatch(eval(call, envir = ff),
           error = function(e) {
             tryCatch(eval(call, envir = sf),
                      error = function(e) {
                        eval(call, pf)
                      })
           })
}

#' @rdname stanreg-methods
#' @export 
#' @param correlation For \code{vcov}, if \code{FALSE} (the default) the
#'   covariance matrix is returned. If \code{TRUE}, the correlation matrix is
#'   returned instead.
#'
vcov.stanreg <- function(object, correlation = FALSE, ...) {
  out <- object$covmat
  if (!correlation) return(out)
  cov2cor(out)
}


#' @rdname stanreg-methods
#' @export
#' @export fixef
#' @importFrom lme4 fixef
#' 
fixef.stanreg <- function(object, ...) {
  coefs <- object$coefficients
  coefs[b_names(names(coefs), invert = TRUE)]
}

#' @rdname stanreg-methods
#' @export
#' @export ngrps
#' @importFrom lme4 ngrps
#' 
ngrps.stanreg <- function(object, ...) {
  vapply(.flist(object), nlevels, 1)  
}

#' @rdname stanreg-methods
#' @export
#' @export nsamples
#' @importFrom rstantools nsamples
nsamples.stanreg <- function(object, ...) {
  posterior_sample_size(object)
}

#' @rdname stanreg-methods
#' @export
#' @export ranef
#' @importFrom lme4 ranef
#' 
ranef.stanreg <- function(object, ...) {
  .glmer_check(object)
  point_estimates <- object$stan_summary[, select_median(object$algorithm)]
  out <- ranef_template(object)
  group_vars <- names(out)
  
  for (j in seq_along(out)) {
    tmp <- out[[j]]
    pars <- colnames(tmp) 
    levs <- rownames(tmp)
    levs <- gsub(" ", "_", levs) 
    for (p in seq_along(pars)) {
      stan_pars <- paste0("b[", pars[p], " ", group_vars[j],  ":", levs, "]")
      tmp[[pars[p]]] <- unname(point_estimates[stan_pars])
    }
    out[[j]] <- tmp
  }
  out
}

# Call lme4 to get the right structure for ranef objects
#' @importFrom lme4 lmerControl glmerControl nlmerControl lmer glmer nlmer
ranef_template <- function(object) {
  stan_fun <- object$stan_function %ORifNULL% "stan_glmer"
  
  if (stan_fun != "stan_gamm4") {
    new_formula <- formula(object)
  } else {
    # remove the part of the formula with s() terms just so we can call lme4
    # to get the ranef template without error
    new_formula_rhs <- as.character(object$call$random)[2]
    new_formula_lhs <- as.character(formula(object))[2]
    new_formula <- as.formula(paste(new_formula_lhs, "~", new_formula_rhs))
  }
  
  if (stan_fun != "stan_nlmer" && is.gaussian(object$family$family)) {
    stan_fun <- "stan_lmer"
  }
  lme4_fun <- switch(
    stan_fun,
    "stan_lmer" = "lmer",
    "stan_nlmer" = "nlmer",
    "glmer" # for stan_glmer, stan_glmer.nb, stan_gamm4 (unless gaussian)
  )
  cntrl_args <- list(optimizer = "Nelder_Mead", optCtrl = list(maxfun = 1))
  if (lme4_fun != "nlmer") { # nlmerControl doesn't allow these
    cntrl_args$check.conv.grad <- "ignore"
    cntrl_args$check.conv.singular <- "ignore"
    cntrl_args$check.conv.hess <- "ignore"
    cntrl_args$check.nlev.gtreq.5 <- "ignore"
    cntrl_args$check.nobs.vs.rankZ <- "ignore"
    cntrl_args$check.nobs.vs.nlev <- "ignore"
    cntrl_args$check.nobs.vs.nRE <- "ignore"
    if (lme4_fun == "glmer") {
      cntrl_args$check.response.not.const <- "ignore"
    }
  }
  
  cntrl <- do.call(paste0(lme4_fun, "Control"), cntrl_args)
  
  fit_args <- list(
    formula = new_formula,
    data = object$data,
    control = cntrl
  )
  
  if (lme4_fun == "nlmer") { # create starting values to avoid error
    fit_args$start <- unlist(getInitial(
      object = as.formula(as.character(formula(object))[2]),
      data = object$data,
      control = list(maxiter = 0, warnOnly = TRUE)
    ))
  }
  
  family <- family(object)
  fam <- family$family
  if (!(fam %in% c("gaussian", "beta"))) {
    if (fam == "neg_binomial_2") {
      family <- stats::poisson()
    } else if (fam == "beta_binomial") {
      family <- stats::binomial()
    } else if (fam == "binomial" && family$link == "clogit") {
      family <- stats::binomial()
    }
    fit_args$family <- family
  }
  
  lme4_fit <- suppressWarnings(do.call(lme4_fun, args = fit_args))
  ranef(lme4_fit)
}


#' @rdname stanreg-methods
#' @export
#' @export sigma
#' @rawNamespace if(getRversion()>='3.3.0') importFrom(stats, sigma) else
#'   importFrom(lme4,sigma)
#'
sigma.stanreg <- function(object, ...) {
  if (!("sigma" %in% rownames(object$stan_summary))) 
    return(1)
  
  object$stan_summary["sigma", select_median(object$algorithm)]
}

#' @rdname stanreg-methods
#' @param sigma Ignored (included for compatibility with
#'   \code{\link[nlme]{VarCorr}}).
#' @export
#' @export VarCorr
#' @importFrom nlme VarCorr
#' @importFrom stats cov2cor
VarCorr.stanreg <- function(x, sigma = 1, ...) {
  dots <- list(...) # used to pass stanmat with a single draw for posterior_survfit
  mat <- if ("stanmat" %in% names(dots)) as.matrix(dots$stanmat) else as.matrix(x)
  cnms <- .cnms(x)
  useSc <- "sigma" %in% colnames(mat)
  if (useSc) sc <- mat[,"sigma"] else sc <- 1
  Sigma <- colMeans(mat[,grepl("^Sigma\\[", colnames(mat)), drop = FALSE])
  nc <- vapply(cnms, FUN = length, FUN.VALUE = 1L)
  nms <- names(cnms)
  ncseq <- seq_along(nc)
  if (length(Sigma) == sum(nc * nc)) { # stanfit contains all Sigma entries
    spt <- split(Sigma, rep.int(ncseq, nc * nc))
    ans <- lapply(ncseq, function(i) {
      Sigma <- matrix(0, nc[i], nc[i])
      Sigma[,] <- spt[[i]]
      rownames(Sigma) <- colnames(Sigma) <- cnms[[i]]
      stddev <- sqrt(diag(Sigma))
      corr <- cov2cor(Sigma)
      structure(Sigma, stddev = stddev, correlation = corr)
    })       
  } else { # stanfit contains lower tri Sigma entries
    spt <- split(Sigma, rep.int(ncseq, (nc * (nc + 1)) / 2))
    ans <- lapply(ncseq, function(i) {
      Sigma <- matrix(0, nc[i], nc[i])
      Sigma[lower.tri(Sigma, diag = TRUE)] <- spt[[i]]
      Sigma <- Sigma + t(Sigma)
      diag(Sigma) <- diag(Sigma) / 2
      rownames(Sigma) <- colnames(Sigma) <- cnms[[i]]
      stddev <- sqrt(diag(Sigma))
      corr <- cov2cor(Sigma)
      structure(Sigma, stddev = stddev, correlation = corr)
    })    
  }
  names(ans) <- nms
  structure(ans, sc = mean(sc), useSc = useSc, class = "VarCorr.merMod")
}

# Exported but doc kept internal ----------------------------------------------

#' family method for stanreg objects
#'
#' @keywords internal
#' @export
#' @param object,... See \code{\link[stats]{family}}.
family.stanreg <- function(object, ...) object$family

#' model.frame method for stanreg objects
#' 
#' @keywords internal
#' @export
#' @param formula,... See \code{\link[stats]{model.frame}}.
#' @param fixed.only See \code{\link[lme4]{model.frame.merMod}}.
#' 
model.frame.stanreg <- function(formula, fixed.only = FALSE, ...) {
  if (is.mer(formula)) {
    fr <- formula$glmod$fr
    if (fixed.only) {
      ff <- formula(formula, fixed.only = TRUE)
      vars <- rownames(attr(terms.formula(ff), "factors"))
      fr <- fr[vars]
    }
    return(fr)
  }
  
  NextMethod("model.frame")
}

#' model.matrix method for stanreg objects
#' 
#' @keywords internal
#' @export
#' @param object,... See \code{\link[stats]{model.matrix}}.
#' 
model.matrix.stanreg <- function(object, ...) {
  if (inherits(object, "gamm4")) return(object$jam$X)
  if (is.mer(object)) return(object$glmod$X)
    
  NextMethod("model.matrix")
}

#' formula method for stanreg objects
#' 
#' @keywords internal
#' @export
#' @param x A stanreg object.
#' @param ... Can contain \code{fixed.only} and \code{random.only} arguments 
#'   that both default to \code{FALSE}.
#' 
formula.stanreg <- function(x, ..., m = NULL) {
  if (is.mer(x) && !isTRUE(x$stan_function == "stan_gamm4")) return(formula_mer(x, ...))
  x$formula
}

#' terms method for stanreg objects
#' @export
#' @keywords internal
#' @param x,fixed.only,random.only,... See lme4:::terms.merMod.
#' 
terms.stanreg <- function(x, ..., fixed.only = TRUE, random.only = FALSE) {
  if (!is.mer(x))
    return(NextMethod("terms"))
  
  fr <- x$glmod$fr
  if (missing(fixed.only) && random.only) 
    fixed.only <- FALSE
  if (fixed.only && random.only) 
    stop("'fixed.only' and 'random.only' can't both be TRUE.", call. = FALSE)
  
  Terms <- attr(fr, "terms")
  if (fixed.only) {
    Terms <- terms.formula(formula(x, fixed.only = TRUE))
    attr(Terms, "predvars") <- attr(terms(fr), "predvars.fixed")
  } 
  if (random.only) {
    Terms <- terms.formula(lme4::subbars(formula.stanreg(x, random.only = TRUE)))
    attr(Terms, "predvars") <- attr(terms(fr), "predvars.random")
  }
  
  return(Terms)
}



# internal ----------------------------------------------------------------
.glmer_check <- function(object) {
  if (!is.mer(object))
    stop("This method is for stan_glmer and stan_lmer models only.", 
         call. = FALSE)
}
.cnms <- function(object, ...) UseMethod(".cnms")
.cnms.stanreg <- function(object, ...) {
  .glmer_check(object)
  object$glmod$reTrms$cnms
}
.flist <- function(object, ...) UseMethod(".flist")
.flist.stanreg <- function(object, ...) {
  .glmer_check(object)
  as.list(object$glmod$reTrms$flist)
}

coef_mer <- function(object, ...) {
  if (length(list(...))) 
    warning("Arguments named \"", paste(names(list(...)), collapse = ", "), 
            "\" ignored.", call. = FALSE)
  fef <- data.frame(rbind(fixef(object)), check.names = FALSE)
  ref <- ranef(object)
  refnames <- unlist(lapply(ref, colnames))
  missnames <- setdiff(refnames, names(fef))
  nmiss <- length(missnames)
  if (nmiss > 0) {
    fillvars <- setNames(data.frame(rbind(rep(0, nmiss))), missnames)
    fef <- cbind(fillvars, fef)
  }
  val <- lapply(ref, function(x) fef[rep.int(1L, nrow(x)), , drop = FALSE])
  for (i in seq(a = val)) {
    refi <- ref[[i]]
    row.names(val[[i]]) <- row.names(refi)
    nmsi <- colnames(refi)
    if (!all(nmsi %in% names(fef))) 
      stop("Unable to align random and fixed effects.", call. = FALSE)
    for (nm in nmsi) 
      val[[i]][[nm]] <- val[[i]][[nm]] + refi[, nm]
  }
  structure(val, class = "coef.mer")
}

justRE <- function(f, response = FALSE) {
  response <- if (response && length(f) == 3) f[[2]] else NULL
  reformulate(paste0("(", vapply(lme4::findbars(f), 
                                 function(x) paste(deparse(x, 500L), 
                                                   collapse = " "), 
                                 ""), ")"), 
              response = response)
}
formula_mer <- function (x, fixed.only = FALSE, random.only = FALSE, ...) {
  if (missing(fixed.only) && random.only) 
    fixed.only <- FALSE
  if (fixed.only && random.only) 
    stop("'fixed.only' and 'random.only' can't both be TRUE.", call. = FALSE)
  
  fr <- x$glmod$fr
  if (is.null(form <- attr(fr, "formula"))) {
    if (!grepl("lmer$", deparse(getCall(x)[[1L]]))) 
      stop("Can't find formula stored in model frame or call.", call. = FALSE)
    form <- as.formula(formula(getCall(x), ...))
  }
  if (fixed.only) {
    form <- attr(fr, "formula")
    form[[length(form)]] <- lme4::nobars(form[[length(form)]])
  }
  if (random.only)
    form <- justRE(form, response = TRUE)
  
  return(form)
}

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rstanarm documentation built on Oct. 4, 2019, 1:04 a.m.