rbaes: R Bayesian Analysis and Estimation Stuff

ppi <- function(data, fit, fun = identity, form = NULL, prob = 0.9, n = 1) {
  pp <- do.call(rbind, lapply(
    1:n,
    function(i) {
      rstanarm::posterior_predict(
        fit, data,
        fun = fun,
        re.form = form
      )
    }))
  pp_int <- rstantools::posterior_interval(pp, prob = prob)
  yhat <- data.table(apply(pp, 2, median), pp_int)
  names(yhat) <- c("c", "l", "r")
  return(cbind(data, yhat))
}

n_post_draws <- function(stanfit) {
  pss <- stanfit$stanfit@sim$n_save
  sum(pss - stanfit$stanfit@sim$warmup2)
}

cdata_list <- function() {
  library(data.table)
  stanreg <- example_stanreg()
  cdata <- list(
    c1 = contrast_data(
      stanreg, TRUE,
      margin_ignore = "floor",
      subset_expression = . ~ log_uranium < -0.5
    ),
    c2 = contrast_data(stanreg, TRUE,
      margin_ignore = "floor",
      subset_expression = . ~
      log_uranium >= -0.5 &
        log_uranium <= 0.5),
    c3 = contrast_data(stanreg, TRUE,
      margin_ignore = "floor",
      subset_expression = . ~ log_uranium > 0.5))

  assign("stanreg", stanreg, .GlobalEnv)
  assign("cdata", cdata, .GlobalEnv)
  return(NULL)
}


# comparisons functions -----------------------------------------------------------------------

dec2pct <- function(x, digits = 1) {
  sprintf(paste0("%-", digits + 4, ".", digits, "f%%"), x * 100)
}

loo_ci <- function(x, se, N, p = 0.95) {
  if (N <= 1) {
    stop("n must be a sample size greater than 1")
  }

  if (p < 0.5 | p >= 1) {
    stop("p must be greater or equal to than 0.5 and less than 1")
  }

  one_mp <- (1 - p) / 2
  one_pp <- (1 - one_mp)
  q_scaled <- qt(one_pp, N - 1) * se
  ci <- c(x - q_scaled, x + q_scaled)
  dec2pct(one_mp)
  names(ci) <- c(dec2pct(one_mp), dec2pct(one_pp))
  return(ci)
}

get_contrast_rows <- function(form, data) {
  if (nchar(deparse(form[2])) != 3) {
    stop("improper contrast formula specification")
  }

  data[, eval(form[-1])][[1]]
}

apply_contrast2 <- function(pp_list, ccoef) {
  n_pp <- length(pp_list)

  if (length(ccoef) != n_pp) {
    stop("length coef must match length pp_list")
  }

  for (pp in seq_len(n_pp)) {
    pp_list[[pp]] <- pp_list[[pp]] * ccoef[pp]
  }

  as.matrix(apply(do.call(cbind, pp_list), 1, sum))
}

pp_cdata <- function(stanreg, cdata, row_stat = median, ...) {
  if (!"stanreg" %in% class(stanreg)) {
    stop('"stanreg" arg must be a stanreg object')
  }

  if (!is.list(cdata)) {
    stop('"cdata" must be a list of datasets to compare')
  }

  n_datasets <- length(cdata)

  if (n_datasets < 2) {
    stop("need at least 2 contrast data sets")
  }

  cdata <- lapply(
    cdata,
    function(d) {
      if (!is.data.table(d)) {
        d <- as.data.table(d)
      }
      return(d)
    })

  cnames <- names(cdata)

  if (is.null(cnames)) {
    cnames <- paste("contrast", 1:n_datasets)
  }

  no_names <- !nzchar(cnames)

  if (any(no_names)) {
    cnames[no_names] <- paste("contrast", which(no_names))
  }

  # combine all the data to use the same draws
  cdata_bind <- rbindlist(cdata,
    use.names = TRUE,
    fill = TRUE, idcol = "__cont_idx")

  posterior <- rstanarm::posterior_predict(stanreg, newdata = cdata_bind, ...)

  # get indices of which cols to pull from posterior matrix
  cont_grps <- dtbl2list(cdata_bind[, .I, by = "__cont_idx"], `__cont_idx`)

  # mean of rows at cols x
  pp <- lapply(
    cont_grps,
    function(i) {
      cidx <- i$I
      y <- posterior[, cidx]
      if (!is.null(dim(y))) {
        y <- apply(y, 1, row_stat)
      }

      matrix(y)
    })

  return(pp)
}

set_col_order <- function(x, first_names) {
  data.table::setcolorder(x, c(first_names, names(x)[!names(x) %chin%
    first_names]))
  invisible(x)
}

get_unit_attr <- function(x) {
  adj <- attr(x, "adj")
  m <- attr(x, "center")
  s <- attr(x, "scale")

  if (any(is.null(c(adj, m, s)))) {
    stop("could not find attributes from a unit_scale object")
  }

  nlist(adj, m, s)
}

.Random.seed <- c(
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