VizMostLikelyQuantileMap: Plot Maps of Most Likely Quantiles

Description

This function receives as main input (via the parameter probs) a collection of longitude-latitude maps, each containing the probabilities (from 0 to 1) of the different grid cells of belonging to a category. As many categories as maps provided as inputs are understood to exist. The maps of probabilities must be provided on a common rectangular regular grid, and a vector with the longitudes and a vector with the latitudes of the grid must be provided. The input maps can be provided in two forms, either as a list of multiple two-dimensional arrays (one for each category) or as a three-dimensional array, where one of the dimensions corresponds to the different categories.

Usage

VizMostLikelyQuantileMap(
  probs,
  lon,
  lat,
  cat_dim = "bin",
  bar_titles = NULL,
  col_unknown_cat = "white",
  drawleg = TRUE,
  margin_scale = NULL,
  bar_extra_margin = c(1.5, 0, 1.5, 0),
  ...
)

Value

Invisibly returns NULL after producing the plot.

Arguments

probs: A list of bi-dimensional arrays with the named dimensions 'latitude' (or 'lat') and 'longitude' (or 'lon'), with equal size and in the same order, or a single tri-dimensional array with an additional dimension (e.g. 'bin') for the different categories. The arrays must contain probability values between 0 and 1, and the probabilities for all categories of a grid cell should not exceed 1 when added.
lon: A numeric vector with the longitudes of the map grid, in the same order as the values along the corresponding dimension in probs.
lat: A numeric vector with the latitudes of the map grid, in the same order as the values along the corresponding dimension in probs.
cat_dim: The name of the dimension along which the different categories are stored in probs. This only applies if probs is provided in the form of 3-dimensional array. The default expected name is 'bin'.
bar_titles: Vector of character strings with the names to be drawn on top of the color bar for each of the categories. As many titles as categories provided in probs must be provided.
col_unknown_cat: Character string with a colour representation of the colour to be used to paint the cells for which no category can be clearly assigned. Takes the value 'white' by default.
drawleg: Where to draw the common colour bar. Can take values TRUE, FALSE or:
'up', 'u', 'U', 'top', 't', 'T', 'north', 'n', 'N'
'down', 'd', 'D', 'bottom', 'b', 'B', 'south', 's', 'S' (default)
'right', 'r', 'R', 'east', 'e', 'E'
'left', 'l', 'L', 'west', 'w', 'W'
margin_scale: Numeric vector of length 4 specifying the plot margins (excluding the color bar), in the order: bottom, left, top, and right. If not specified (NULL), the default margins from par("mar"), which are c(5.1, 4.1, 4.1, 2.1), are used. Default is NULL.
bar_extra_margin: Numeric vector of length 4 specifying the margins to be added around the color bar, in the order: bottom, left, top, and right. The units are margin lines. The default values are c(1.5, 0, 1.5, 0).
...: Additional parameters to be sent to VizCombinedMap and VizEquiMap.

Author

Veronica Torralba, veronica.torralba@bsc.es, Nicolau Manubens, nicolau.manubens@bsc.es

Examples

Run this code


# Simple example:

x <- array(1:(20 * 10), dim = c(lat = 10, lon = 20)) / 200
a <- x * 0.6
b <- (1 - x) * 0.6
c <- 1 - (a + b)
lons <- seq(0, 359.5, length = 20)
lats <- seq(-89.5, 89.5, length = 10)

VizMostLikelyQuantileMap(list(a, b, c), lons, lats, 
                         toptitle = 'Most likely tercile map',
                         bar_titles = paste('% of belonging to', c('a', 'b', 'c')), 
                         brks = 20, width = 10, height = 8,
                         bar_extra_margin = c(1, 0, 1.5, 0))


# More complex example:

# 1. Generation of sample data
lons <- seq(0, 359.5, length = 40)
lats <- seq(-89.5, 89.5, length = 20)

# Generate sample data (with dimensions time, lat, lon)
sample_data <- sample(1:5, 50 * 20 * 40, replace = TRUE)
dim(sample_data) <- c(time = 50, lat = 20, lon = 40)

# 2. Binning sample data
n_bins <- 4
prob_thresholds <- 1:n_bins / n_bins
prob_thresholds <- prob_thresholds[1:(n_bins - 1)]
thresholds <- quantile(sample_data, prob_thresholds)

binning <- function(x, thresholds) {
  n_samples <- length(x)
  n_bins <- length(thresholds) + 1
  thresholds <- c(thresholds, max(x))
  result <- 1:n_bins
  lower_threshold <- min(x) - 1
  for (i in 1:n_bins) {
    result[i] <- sum(x > lower_threshold & x <= thresholds[i]) / n_samples
    lower_threshold <- thresholds[i]
  }
  dim(result) <- c(bin = n_bins)
  result
}
bins <- apply(sample_data, 2:3, binning, thresholds)
names(dim(bins))[1] <- "bin"

VizMostLikelyQuantileMap(bins, lons, lats, 
                         toptitle = 'Most likely quantile map',
                         bar_titles = paste('% of belonging to', letters[1:n_bins]),
                         brks = 20, width = 10, height = 10,
                         bar_extra_margin = c(1, 0, 1.5, 0))