# Plot Maps of Most Likely Quantiles 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. ```r PlotMostLikelyQuantileMap( probs, lon, lat, cat_dim = "bin", bar_titles = NULL, col_unknown_cat = "white", drawleg = TRUE, ... ) ``` ## 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' - `...`: Additional parameters to be sent to `PlotCombinedMap` and `PlotEquiMap`. ## Returns Produces a map showing the most likely category or quantile for each grid cell, based on the input probability maps. ## Examples ```r # 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) PlotMostLikelyQuantileMap(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, 1, 1, 1), fileout = file.path(tempdir(),"example1.pdf")) unlink(file.path(tempdir(),"example1.pdf")) # More complex example n_lons <- 40 n_lats <- 20 n_timesteps <- 100 n_bins <- 4 # 1. Generation of sample data lons <- seq(0, 359.5, length = n_lons) lats <- seq(-89.5, 89.5, length = n_lats) # This function builds a 3-D gaussian at a specified point in the map. make_gaussian <- function(lon, sd_lon, lat, sd_lat) { w <- outer(lons, lats, function(x, y) dnorm(x, lon, sd_lon) * dnorm(y, lat, sd_lat)) min_w <- min(w) w <- w - min_w w <- w / max(w) w <- t(w) names(dim(w)) <- c('lat', 'lon') w } # This function generates random time series (with values ranging 1 to 5) # according to 2 input weights. gen_data <- function(w1, w2, n) { r <- sample(1:5, n, prob = c(.05, .9 * w1, .05, .05, .9 * w2), replace = TRUE) r <- r + runif(n, -0.5, 0.5) dim(r) <- c(time = n) r } # We build two 3-D gaussians. w1 <- make_gaussian(120, 80, 20, 30) w2 <- make_gaussian(260, 60, -10, 40) # We generate sample data (with dimensions time, lat, lon) according # to the generated gaussians sample_data <- multiApply::Apply(list(w1, w2), NULL, gen_data, n = n_timesteps)$output1 # 2. Binning sample data prob_thresholds <- 1:n_bins / n_bins prob_thresholds <- prob_thresholds[1:(n_bins - 1)] thresholds <- quantile(sample_data, prob_thresholds) binning <- function(x, thresholds) { n_samples <- length(x) n_bins <- length(thresholds) + 1 thresholds <- c(thresholds, max(x)) result <- 1:n_bins lower_threshold <- min(x) - 1 for (i in 1:n_bins) { result[i] <- sum(x > lower_threshold & x <= thresholds[i]) / n_samples lower_threshold <- thresholds[i] } dim(result) <- c(bin = n_bins) result } bins <- multiApply::Apply(sample_data, 'time', binning, thresholds)$output1 # 3. Plotting most likely quantile/bin PlotMostLikelyQuantileMap(bins, lons, lats, toptitle = 'Most likely quantile map', bar_titles = paste('% of belonging to', letters[1:n_bins]), mask = 1 - (w1 + w2 / max(c(w1, w2))), brks = 20, width = 10, height = 8, bar_extra_margin = c(1, 1, 1, 1), fileout = file.path(tempdir(),"example2.pdf")) unlink(file.path(tempdir(),"example2.pdf")) ``` ## See Also `PlotCombinedMap` and `PlotEquiMap` ## Author(s) Veronica Torralba, veronica.torralba@bsc.es , Nicolau Manubens, nicolau.manubens@bsc.es

PlotMostLikelyQuantileMap function