https://github.com/cran/bayestestR
Revision e1fa15d202de277bb07e58bb3013557724072b2b authored by Dominique Makowski on 22 September 2019, 15:30 UTC, committed by cran-robot on 22 September 2019, 15:30 UTC
1 parent aee422d
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Tip revision: e1fa15d202de277bb07e58bb3013557724072b2b authored by Dominique Makowski on 22 September 2019, 15:30 UTC
version 0.3.0
Tip revision: e1fa15d
utils.R
# trim leading / trailing whitespace
.trim <- function(x) gsub("^\\s+|\\s+$", "", x)


# safe depare, also for very long strings
.safe_deparse <- function(string) {
  paste0(sapply(deparse(string, width.cutoff = 500), .trim, simplify = TRUE), collapse = "")
}


# has object an element with given name?
#' @keywords internal
.obj_has_name <- function(x, name) {
  name %in% names(x)
}

# remove NULL elements from lists
#' @keywords internal
.compact_list <- function(x) x[!sapply(x, function(i) length(i) == 0 || is.null(i) || any(i == "NULL"))]

# is string empty?
#' @keywords internal
.is_empty_object <- function(x) {
  if (is.list(x)) {
    x <- tryCatch({
      .compact_list(x)
    },
    error = function(x) {
      x
    }
    )
  }
  # this is an ugly fix because of ugly tibbles
  if (inherits(x, c("tbl_df", "tbl"))) x <- as.data.frame(x)
  x <- suppressWarnings(x[!is.na(x)])
  length(x) == 0 || is.null(x)
}


# select rows where values in "variable" match "value"
#' @keywords internal
.select_rows <- function(data, variable, value) {
  data[which(data[[variable]] == value), ]
}

# remove column
#' @keywords internal
.remove_column <- function(data, variables) {
  data[variables] <- NULL
  data
}


#' @importFrom stats reshape
#' @keywords internal
.to_long <- function(x, names_to = "key", values_to = "value", columns = colnames(x)) {
  if (is.numeric(columns)) columns <- colnames(x)[columns]
  dat <- stats::reshape(
    as.data.frame(x),
    idvar = "id",
    ids = row.names(x),
    times = columns,
    timevar = names_to,
    v.names = values_to,
    varying = list(columns),
    direction = "long"
  )

  if (is.factor(dat[[values_to]])) {
    dat[[values_to]] <- as.character(dat[[values_to]])
  }

  dat[, 1:(ncol(dat) - 1), drop = FALSE]
}

#' select numerics columns
#' @keywords internal
.select_nums <- function(x) {
  x[unlist(lapply(x, is.numeric))]
}



## TODO remove?!?

# #' Used in describe_posterior
# #' @keywords internal
# .reorder_rows <- function(x, out, ci = NULL) {
#   if (!is.data.frame(out) || nrow(out) == 1) {
#     return(out)
#   }
#
#   if (is.null(ci)) {
#     refdata <- point_estimate(x, centrality = "median", dispersion = FALSE)
#     order <- refdata$Parameter
#     out <- out[match(order, out$Parameter), ]
#   } else {
#     uncertainty <- ci(x, ci = ci)
#     order <- paste0(uncertainty$Parameter, uncertainty$CI)
#     out <- out[match(order, paste0(out$Parameter, out$CI)), ]
#   }
#   rownames(out) <- NULL
#   out
# }


#' @keywords internal
.get_direction <- function(direction) {
  if (length(direction) > 1) warning("Using first 'direction' value.")

  if (is.numeric(direction[1])) {
    return(direction[1])
  }

  Value <- c(
    "left" = -1,
    "right" = 1,
    "two-sided" = 0,
    "twosided" = 0,
    "one-sided" = 1,
    "onesided" = 1,
    "<" = -1,
    ">" = 1,
    "=" = 0,
    "==" = 0,
    "-1" = -1,
    "0" = 0,
    "1" = 1,
    "+1" = 1
  )

  direction <- Value[tolower(direction[1])]

  if (is.na(direction)) {
    stop("Unrecognized 'direction' argument.")
  }
  direction
}

.prepare_output <- function(temp, cleaned_parameters) {
  merge_by <- intersect(c("Parameter", "Effects", "Component"), colnames(temp))
  temp$.roworder <- 1:nrow(temp)
  out <- merge(x = temp, y = cleaned_parameters, by = merge_by, all.x = TRUE)
  attr(out, "Cleaned_Parameter") <- out$Cleaned_Parameter[order(out$.roworder)]
  .remove_column(out[order(out$.roworder), ], c("Group", "Cleaned_Parameter", "Response", "Function", ".roworder"))
}


.merge_and_sort <- function(x, y, by, all) {
  x$.rowid <- 1:nrow(x)
  x <- merge(x, y, by = by, all = all)
  .remove_column(x[order(x$.rowid), ], ".rowid")
}
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