/
dataprep.R
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dataprep.R
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# Using advice from read-and-delete-me:
# "Be sure to add useDynLib(mypackage, .registration = TRUE) to the NAMESPACE file
# which you can do by placing the line #' @useDynLib rstanarm, .registration = TRUE
# in one of your .R files
# see rstanarm's 'rstanarm-package.R' file"
#' Preprocess data for BAM estimation
#'
#' Produces a bamdata object that can be passed to bam_estimate function
#'
#' @useDynLib bamr, .registration = TRUE
#' @param w Matrix (or data frame) of widths: time as columns, space as rows
#' @param s Matrix of slopes: time as columns, space as rows
#' @param dA Matrix of area above base area: time as columns, space as rows
#' @param Qhat Vector of Q estimates. Needed to create prior on Q.
#' @param max_xs Maximum number of cross-sections to allow in data. Used to reduce
#' sampling time. Defaults to 30.
#' @param seed RNG seed to use for sampling cross-sections, if nx > max_xs.
#' @export
bam_data <- function(w,
s = NULL,
dA = NULL,
Qhat,
max_xs = 30L,
seed = NULL) {
force(Qhat)
manning_ready <- !is.null(s) && !is.null(dA)
if (!manning_ready) {
s <- dA <- matrix(1, nrow = nrow(w), ncol = ncol(w))
}
datalist <- list(Wobs = w,
Sobs = s,
dAobs = dA,
logQ_hat = log(Qhat))
datalist <- bam_check_args(datalist)
datalist <- bam_check_nas(datalist)
nx <- nrow(datalist$Wobs)
nt <- ncol(datalist$Wobs)
out <- structure(c(datalist,
nx = nx,
nt = nt),
manning_ready = manning_ready,
class = c("bamdata"))
if (nx > max_xs)
out <- sample_xs(out, n = max_xs, seed = seed)
out
}
#' Performs the following checks:
#' - types:
#' - logQ_hat is numeric vector
#' - everything else matrix
#' - dimensions:
#' - all matrices have same dims
#' - logQ_hat has length equal to ncol of matrices
#'
#' @param datalist A list of BAM data inputs
bam_check_args <- function(datalist) {
logQ_hat <- datalist$logQ_hat
matlist <- datalist[names(datalist) != "logQ_hat"]
# Check types
if (!(is(logQ_hat, "numeric") && is(logQ_hat, "vector")))
stop("Qhat must be a numeric vector.\n")
if (!all(vapply(matlist, is, logical(1), "matrix")))
stop("All data must be a supplied as a matrix.\n")
# Check dims
nr <- nrow(matlist[[1]])
nc <- ncol(matlist[[1]])
if (!(all(vapply(matlist, nrow, 0L) == nr) &&
all(vapply(matlist, ncol, 0L) == nc)))
stop("All data must have same dimensions.\n")
if (!length(logQ_hat) == nc)
logQ_hat <- rep(logQ_hat, length.out = nc)
out <- c(matlist, list(logQ_hat = logQ_hat))
out
}
#' Add missing-data inputs to data list
#'
#' Binary matrices indicating where data are/aren't missing are
#' added to the data list. This is required in order to run
#' ragged-array data structures in the stanfile.
#'
#' Previously this function omitted any times with missing data,
#' but now that ragged arrays are accommodated in the stanfile the
#' operations are entirely different.
#'
#' @param datalist a list of BAM inputs
#' @importFrom stats median
bam_check_nas <- function(datalist) {
mats <- vapply(datalist, is.matrix, logical(1))
nonas <- lapply(datalist[mats], function(x) !is.na(x))
# AMHG has-data matrix
hasdat_amhg <- (!is.na(datalist[["Wobs"]])) * 1
# Replace NA's with zeros so Stan will accept the data
datalist[["Wobs"]][!hasdat_amhg] <- 0
# Manning has-data matrix (only nonzero if all Manning obs present)
if (identical(setdiff(c("Wobs", "Sobs", "dAobs"),
names(datalist[mats])),
character(0))) {
hasdat_s <- (!is.na(datalist[["Sobs"]])) * 1
hasdat_a <- (!is.na(datalist[["dAobs"]])) * 1
hasdat_man <- hasdat_amhg * hasdat_s * hasdat_a
# Replace NA's with zeros so Stan will accept the data
datalist[["Sobs"]][!hasdat_man] <- 0
datalist[["dAobs"]][!hasdat_man] <- 0
} else {
hasdat_man <- matrix(0, nrow = nrow(hasdat_amhg), ncol = ncol(hasdat_amhg))
}
if (!is.null(datalist[["dAobs"]])) {
dA_shift <- apply(datalist[["dAobs"]], 1, function(x) median(x) - min(x))
} else {
dA_shift <- rep(0, nrow(datalist[["Wobs"]]))
}
newbits <- list(
hasdat_man = hasdat_man,
hasdat_amhg = hasdat_amhg,
ntot_man = sum(hasdat_man),
ntot_amhg = sum(hasdat_amhg),
dA_shift = dA_shift
)
out <- c(datalist, newbits)
out
}
#' Establish prior hyperparameters for BAM estimation
#'
#' Produces a bampriors object that can be passed to bam_estimate function
#'
#' @useDynLib bamr, .registration = TRUE
#' @param bamdata An object of class bamdata, as returned by \code{bam_data}
#' @param variant Which BAM variant to use. Options are "manning_amhg" (default),
#' "manning", or "amhg".
#' @param ... Optional manually set parameters. Unquoted expressions are allowed,
#' e.g. \code{logQ_sd = cv2sigma(0.8)}. Additionally, any variables present in
#' \code{bamdata} may be referenced, e.g. \code{lowerbound_logQ = log(mean(Wobs)) + log(5)}
#' @export
bam_priors <- function(bamdata,
variant = c("manning_amhg", "manning", "amhg"),
...) {
variant <- match.arg(variant)
if (variant != "amhg" && !attr(bamdata, "manning_ready"))
stop("bamdata must have slope and dA data for non-amhg variants.")
force(bamdata)
paramset <- bam_settings("paramnames")
myparams0 <- rlang::quos(..., .named = TRUE)
myparams <- do.call(settings::clone_and_merge,
args = c(list(options = bam_settings), myparams0))
quoparams <- myparams()[-1] # first one is parameter set
params <- lapply(quoparams, rlang::eval_tidy, data = bamdata)
if (!length(params[["logQ_sd"]]) == bamdata$nt)
params$logQ_sd <- rep(params$logQ_sd, length.out = bamdata$nt)
if (!identical(dim(params[["sigma_man"]]),
as.integer(c(bamdata$nx, bamdata$nt)))) {
params$sigma_man <- matrix(rep(params$sigma_man,
length.out = bamdata$nt * bamdata$nx),
nrow = bamdata$nx)
}
if (!identical(dim(params[["sigma_amhg"]]),
as.integer(c(bamdata$nx, bamdata$nt)))) {
params$sigma_amhg <- matrix(rep(params$sigma_amhg,
length.out = bamdata$nt * bamdata$nx),
nrow = bamdata$nx)
}
out <- structure(params[paramset],
class = c("bampriors"))
out
}
compose_bam_inputs <- function(bamdata, priors = bam_priors(bamdata)) {
inps <- c(bamdata, priors)
out <- inps
out
}
#' Take a random sample of a bamdata object's cross-sections.
#'
#' @param bamdata a bamdata object, as returned by \code{bam_data()}
#' @param n Number of cross-sections to
#' @param seed option RNG seed, for reproducibility.
#' @importFrom methods is
#' @export
sample_xs <- function(bamdata, n, seed = NULL) {
stopifnot(is(bamdata, "bamdata"))
if (n >= bamdata$nx)
return(bamdata)
if (!is.null(seed))
set.seed(seed)
keepxs <- sort(sample(1:bamdata$nx, size = n, replace = FALSE))
bamdata$nx <- n
bamdata$Wobs <- bamdata$Wobs[keepxs, ]
if (!is.null(bamdata$Sobs)) {
bamdata$Sobs <- bamdata$Sobs[keepxs, ]
bamdata$dAobs <- bamdata$dAobs[keepxs, ]
}
bamdata
}
#' Calculate lognormal moments based on truncated normal parameters
#'
#' Used to put measurement errors into original log-normal parameterization.
#'
#' @param obs A numeric vector of observations
#' @param err_sigma Standard deviation of measurement error
#' @param a zero-reference for method of moments.
#' @importFrom stats dnorm pnorm
ln_moms <- function(obs, err_sigma, a = 0) {
alpha <- (a - obs) / err_sigma
Z <- 1 - pnorm(alpha)
mean <- obs + (dnorm(alpha)) / Z * err_sigma
sdquan <- 1 + (alpha * dnorm(alpha)) / Z -
(dnorm(alpha) / Z)^2
sd <- err_sigma * sqrt(sdquan)
out <- list(mean = mean, sd = sd)
out
}
#' Calculate lognormal sigma parameter based on truncated normal parameters
#'
#' Used to put measurement errors into original log-normal parameterization.
#'
#' @param obs A numeric vector of observations
#' @param err_sigma Standard deviation of measurement error
#' @param a zero-reference for method of moments.
ln_sigsq <- function(obs, err_sigma, a = 0) {
moms <- ln_moms(obs = obs, err_sigma = err_sigma, a = a)
mn <- unname(moms[["mean"]])
sd <- unname(moms[["sd"]])
mu <- 2 * log(mn) - 0.5 * log(sd^2 + mn^2)
sigsq <- 2 * log(mn) - 2 * mu
sigsq
}