R/bootMer.R

Defines functions confint.bootMer print.bootMer print.bootWarnings as.data.frame.bootMer bootMer .simpleCap

Documented in bootMer

.simpleCap <- function(x) {
  paste0(toupper(substr(x, 1,1)), substr(x, 2, 1000000L), collapse=" ")
}

### bootMer() --- <==>  (TODO: semi-*)parametric bootstrap
### -------------------------------------------------------
## Doc: show how  this is equivalent - but faster than
##              boot(*, R = nsim, sim = "parametric", ran.gen = simulate(.,1,.), mle = x)
## --> return a "boot" object -- so we could use boot.ci() etc
## TODO: also allow "semi-parametric" model-based bootstrap:
##    resampling the (centered!) residuals (for use.u=TRUE) or for use.u=FALSE,
##    *both* the centered u's + centered residuals
##    instead of using  rnorm()
##  BUT see:
## @article{morris_blups_2002,
##      title = {The {BLUPs} are not "best" when it comes to bootstrapping},
##      volume = {56},
##      issn = {0167-7152},
##      url = {http://www.sciencedirect.com/science/article/S016771520200041X},
##      doi = {10.1016/S0167-7152(02)00041-X},
##      journal = {Statistics \& Probability Letters},
##      author = {Morris, Jeffrey S},
##      year = {2002},
## }
## for an indication of why this is not necessarily a good idea!

##' Perform model-based (Semi-)parametric bootstrap for mixed models.
##'
##' The semi-parametric variant is not yet implemented, and we only
##' provide a method for \code{\link{lmer}}  and \code{\link{glmer}} results.
bootMer <- function(x, FUN, nsim = 1, seed = NULL,
                    use.u = FALSE, re.form = NA,
                    type = c("parametric","semiparametric"),
                    verbose = FALSE,
                    .progress = "none", PBargs=list(),
                    parallel = c("no", "multicore", "snow"),
                    ncpus = getOption("boot.ncpus", 1L), cl = NULL)
{
    stopifnot((nsim <- as.integer(nsim[1])) > 0)
    if (.progress!="none") { ## progress bar
        pbfun <- get(paste0(.progress,"ProgressBar"))
        setpbfun <- get(paste0("set",.simpleCap(.progress),"ProgressBar"))
        pb <- do.call(pbfun,PBargs)
    }

    do_parallel <- have_mc <- have_snow <- NULL # '-Wall', set here:
    eval(initialize.parallel)

    if (do_parallel && .progress != "none")
        message("progress bar disabled for parallel operations")

    FUN <- match.fun(FUN)
    type <- match.arg(type)
    if(!is.null(seed)) set.seed(seed)
    else if(!exists(".Random.seed", envir = .GlobalEnv))
        runif(1) # initialize the RNG if necessary

    mc <- match.call()
    t0 <- FUN(x)
    if (!is.numeric(t0))
        stop("bootMer currently only handles functions that return numeric vectors")

    mle <- list(beta = getME(x,"beta"), theta = getME(x,"theta"))
    if (isLMM(x)) mle <- c(mle,list(sigma = sigma(x)))
    ## FIXME: what about GLMMs with scale parameters??
    ## FIXME: remove prefix when incorporated in package

    if (type=="parametric") {
        argList <- list(x, nsim=nsim, na.action=na.exclude)
        if (!missing(re.form)) {
            argList <- c(argList,list(re.form=re.form))
        } else {
            argList <- c(argList,list(use.u=use.u))
        }
        ss <- do.call(simulate,argList)
    } else {
        if (!missing(re.form))
            stop(paste(sQuote("re.form")),
                 "cannot be used with semiparametric bootstrapping")
        if (use.u) {
            if (isGLMM(x)) warning("semiparametric bootstrapping is questionable for GLMMs")
            ss <- replicate(nsim,fitted(x)+sample(residuals(x,"response"),
                                                  replace=TRUE),
                            simplify=FALSE)
        } else {
            stop("semiparametric bootstrapping with use.u=FALSE not yet implemented")
        }
    }

    # define ffun as a closure containing the referenced variables
    # in its scope to avoid explicit clusterExport statement
    # in the PSOCKcluster case
    ffun <- local({
      FUN
      refit
      x
      ss
      verbose
      do_parallel
      length.t0 <- length(t0)
      f1 <- factory(function(i) FUN(refit(x,ss[[i]])), errval = rep(NA, length.t0))
      function(i) {
          ret <- f1(i)
          if (verbose) { cat(sprintf("%5d :",i)); str(ret) }
          if (!do_parallel && .progress!="none") { setpbfun(pb,i/nsim) }
          ret
      }})

    simvec <- seq_len(nsim)
    res <- if (do_parallel) {
        if (have_mc) {
            parallel::mclapply(simvec, ffun, mc.cores = ncpus)
        } else if (have_snow) {
            if (is.null(cl)) {
                cl <- parallel::makePSOCKcluster(rep("localhost", ncpus))
                ## explicit export of the lme4 namespace since most FUNs will probably
                ## use some of them
                parallel::clusterExport(cl, varlist=getNamespaceExports("lme4"))
                if(RNGkind()[1L] == "L'Ecuyer-CMRG")
                    parallel::clusterSetRNGStream(cl)
                res <- parallel::parLapply(cl, simvec, ffun)
                parallel::stopCluster(cl)
                res
            } else parallel::parLapply(cl, simvec, ffun)
        }
    } else lapply(simvec, ffun)

    t.star <- do.call(cbind,res)
    rownames(t.star) <- names(t0)
    msgs <- list()
    for (mtype in paste0("factory-",c("message","warning","error"))) {
        msgs[[mtype]] <- trimws(unlist(lapply(res, attr, mtype)))
        msgs[[mtype]] <- table(msgs[[mtype]])
    }
    if ((numFail <- sum(msgs[["factory-error"]])) > 0) {
        warning("some bootstrap runs failed (",numFail,"/",nsim,")")
    }
    fail.msgs <- if (numFail==0) NULL else msgs[["factory-error"]]

    ## mimic ending of boot() construction
    s <- structure(list(t0 = t0, t = t(t.star), R = nsim, data = model.frame(x),
                   seed = .Random.seed,
                   statistic = FUN, sim = "parametric", call = mc,
                   ## these two are dummies
                   ran.gen = "simulate(<lmerMod>, 1, *)", mle = mle),
                   class = c("bootMer", "boot"))
    ## leave these for back-compat
    attr(s,"bootFail") <- numFail
    attr(s,"boot.fail.msgs") <- fail.msgs
    attr(s,"boot.all.msgs") <- msgs ## store all messages (tabulated)
    attr(s,"boot_type") <- "boot"
    s
} ## {bootMer}

##' @S3method as.data.frame boot
as.data.frame.bootMer <- function(x,...) {
  as.data.frame(x$t)
}

## FIXME: collapse convergence warnings (ignore numeric values
## when tabulating) ?
print.bootWarnings <- function(x, verbose=FALSE) {
    msgs <- attr(x, "boot.all.msgs")
    if (is.null(msgs) || all(lengths(msgs)==0)) {
        return(invisible(NULL))
    }
    wstr <- "\n"
    for (i in c("message","warning","error")) {
        f <- paste0("factory-",i)
        m <- sort(msgs[[f]])
        if (length(m)>0) {
            if (!verbose) {
                wstr <- c(wstr,
                          paste0(sum(m)," ",i,"(s): ",names(m)[1]))
                if (length(m)>1) {
                    wstr <- c(wstr," (and others)")
                }
                wstr <- c(wstr,"\n")
            } else {
                wstr <- paste0(i,"(s):\n")
                wstr <- c(wstr,capture.output(cat(cbind("  ",m,names(m)),sep="\n")))
                wstr <- c(wstr,"\n")
            }
        }
    }
    message(wstr)
    return(invisible(NULL))
}

print.bootMer <- function(x,...) {
    NextMethod(x,...)
    print.bootWarnings(x, verbose=FALSE)
    return(invisible(x))
}

confint.bootMer <- function(object, parm=seq(length(object$t0)), level=0.95,
                             type=c("perc","norm","basic"), ...) {
    type <- match.arg(type)
    bnms <- c(norm="normal",basic="basic",perc="percent")
    blens <- c(norm=3,basic=5,perc=5)
    bnm <- bnms[[type]]
    blen <- blens[[type]]
    btab0 <- t(vapply(parm,
                      function(i)
        boot::boot.ci(object,index=i,conf=level, type=type)[[bnm]],
        FUN.VALUE=numeric(blen)))
    btab <- btab0[,(blen-1):blen,drop=FALSE]
    rownames(btab) <- names(object$t0)
    a <- (1 - level)/2
    a <- c(a, 1 - a)
    ## replicate stats::format.perc
    pct <- paste(format(100 * a, trim = TRUE,
                        scientific = FALSE, digits = 3), "%")
    colnames(btab) <- pct
    return(btab)
}

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lme4 documentation built on June 22, 2021, 9:07 a.m.