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plot.R
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plot.R
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#------------------------------------------------
# red-to-blue colours
#' @importFrom grDevices colorRampPalette
#' @noRd
col_hotcold <- function(n = 6) {
raw_cols <- c("#D73027", "#FC8D59", "#FEE090", "#E0F3F8", "#91BFDB", "#4575B4")
my_pal <- colorRampPalette(raw_cols)
return(my_pal(n))
}
#------------------------------------------------
# blue-to-red colours. Full credit to tim.colors from the fields package, from
# which these colours derive. Copied rather than including the fields package to
# avoid dependency on another package for the sake of a single colour scheme.
#' @importFrom grDevices colorRampPalette
#' @noRd
col_tim <- function(n = 10) {
raw_cols <- c("#00008F", "#00009F", "#0000AF", "#0000BF",
"#0000CF", "#0000DF", "#0000EF", "#0000FF", "#0010FF",
"#0020FF", "#0030FF", "#0040FF", "#0050FF", "#0060FF",
"#0070FF", "#0080FF", "#008FFF", "#009FFF", "#00AFFF",
"#00BFFF", "#00CFFF", "#00DFFF", "#00EFFF", "#00FFFF",
"#10FFEF", "#20FFDF", "#30FFCF", "#40FFBF", "#50FFAF",
"#60FF9F", "#70FF8F", "#80FF80", "#8FFF70", "#9FFF60",
"#AFFF50", "#BFFF40", "#CFFF30", "#DFFF20", "#EFFF10",
"#FFFF00", "#FFEF00", "#FFDF00", "#FFCF00", "#FFBF00",
"#FFAF00", "#FF9F00", "#FF8F00", "#FF8000", "#FF7000",
"#FF6000", "#FF5000", "#FF4000", "#FF3000", "#FF2000",
"#FF1000", "#FF0000", "#EF0000", "#DF0000", "#CF0000",
"#BF0000", "#AF0000", "#9F0000", "#8F0000", "#800000")
my_pal <- colorRampPalette(raw_cols)
return(my_pal(n))
}
#------------------------------------------------
#' @title Expand series of colours by interpolation
#'
#' @description Expand a series of colours by interpolation to produce any
#' number of colours from a given series. The pattern of interpolation is
#' designed so that (n+1)th value contains the nth value plus one more colour,
#' rather than being a completely different series. For example, running
#' \code{more_colours(5)} and \code{more_colours(4)}, the first 4 colours will
#' be shared between the two series.
#'
#' @param n how many colours to return.
#' @param raw_cols vector of colours to interpolate.
#'
#' @import RColorBrewer
#' @importFrom grDevices colorRampPalette
#' @export
more_colours <- function(n = 5,
raw_cols = brewer.pal(12, "Paired")) {
# check inputs
assert_single_pos_int(n, zero_allowed = FALSE)
assert_string(raw_cols)
assert_vector(raw_cols)
assert_greq(length(raw_cols), 2)
# simple case if n within raw cols
n_raw <- length(raw_cols)
if (n <= n_raw) {
return(raw_cols[1:n])
}
# generate colour palette from raw colours
my_palette <- colorRampPalette(raw_cols)
# interpolate colours by repeatedly splitting the [0,1] interval until we have
# enough values. n_steps is the number of times we have to do this. n_breaks
# is the number of breaks for each step
n_steps <- ceiling(log(n-1)/log(2) - log(n_raw-1)/log(2)) + 1
n_breaks <- (n_raw-1)*2^(1:n_steps - 1) + 1
# split the [0,1] interval this many times and drop duplicated values
s <- unlist(mapply(function(x) seq(0,1,l=x), n_breaks, SIMPLIFY = FALSE))
s <- s[!duplicated(s)]
# convert s to integer index
w <- match(s, seq(0,1,l = n_breaks[n_steps]))
w <- w[1:n]
# get final colours
all_cols <- my_palette(n_breaks[n_steps])
ret <- all_cols[w]
return(ret)
}
#------------------------------------------------
# ggplot theme with minimal objects
#' @import ggplot2
#' @noRd
theme_empty <- function() {
theme(axis.line = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank(),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.background = element_blank())
}
#------------------------------------------------
#' @title Plot loglikelihood 95\% credible intervals
#'
#' @description Plot loglikelihood 95\% credible intervals of current active set
#'
#' @param project an RgeoProfile project, as produced by the function
#' \code{rgeoprofile_project()}.
#' @param K which value of K to produce the plot for.
#' @param x_axis_type how to format the x-axis. 1 = integer rungs, 2 = values of
#' beta raised to the GTI power.
#' @param y_axis_type how to format the y-axis. 1 = raw values, 2 = truncated at
#' auto-chosen lower limit. 3 = double-log scale.
#' @param phase which phase to plot. Must be either "burnin" or "sampling".
#' @param legend switches the legend for thermodynamic power on or off
#'
#' @import ggplot2
#' @importFrom grDevices grey
#' @export
plot_loglike <- function(project,
K = 1,
x_axis_type = 1,
y_axis_type = 1,
phase = "sampling",
legend = TRUE) {
# check inputs
assert_custom_class(project, "rgeoprofile_project")
assert_single_pos_int(K, zero_allowed = FALSE)
assert_in(x_axis_type, 1:2)
assert_in(y_axis_type, 1:3)
assert_in(phase, c("burnin", "sampling"))
assert_single_logical(legend)
# get output
beta_vec <- get_output(project, type = "summary", name = "beta_vec", K = K)
rungs <- length(beta_vec)
s <- project$active_set
# define x-axis type
if (x_axis_type == 1) {
x_vec <- 1:rungs
x_lab <- "rung"
x_mid <- (2:rungs) - 0.5
} else {
x_vec <- beta_vec
x_lab <- "thermodynamic power"
x_mid <- beta_vec[-1] - diff(beta_vec)/2
}
# get plotting data
if (phase == "burnin") {
loglike <- get_output(project, "loglike_burnin", type = "raw", K = K)
} else {
loglike <- get_output(project, "loglike_sampling", type = "raw", K = K)
}
y_lab <- "log-likelihood"
# move to plotting deviance if specified
if (y_axis_type == 3) {
loglike <- -2 * loglike
y_lab <- "deviance"
# if needed, scale by adding/subtracting a power of ten until all values are
# positive
if (min(loglike) < 0) {
dev_scale_power <- ceiling(log(abs(min(loglike)))/log(10))
dev_scale_sign <- -sign(min(loglike))
loglike <- loglike + dev_scale_sign*10^dev_scale_power
dev_scale_base <- ifelse(dev_scale_power == 0, 1, 10)
dev_scale_power_char <- ifelse(dev_scale_power <= 1, "", paste("^", dev_scale_power))
dev_scale_sign_char <- ifelse(dev_scale_sign < 0, "-", "+")
y_lab <- parse(text = paste("deviance", dev_scale_sign_char, dev_scale_base, dev_scale_power_char))
}
}
# get 95% credible intervals over plotting values
loglike_intervals <- t(apply(loglike, 2, quantile_95))
# get data into ggplot format and define temperature colours
df <- as.data.frame(loglike_intervals)
df$col <- beta_vec
# produce plot
plot1 <- ggplot(df) + theme_bw() + theme(panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank())
plot1 <- plot1 + geom_vline(aes(xintercept = x_vec), col = grey(0.9))
plot1 <- plot1 + geom_segment(aes_(x = ~x_vec, y = ~Q2.5, xend = ~x_vec, yend = ~Q97.5))
plot1 <- plot1 + geom_point(aes_(x = ~x_vec, y = ~Q50, color = ~col))
plot1 <- plot1 + xlab(x_lab) + ylab(y_lab) + theme(legend.position = "none")
if(legend == TRUE){
plot1 <- plot1 + scale_colour_gradientn(colours = c("red", "blue"), name = "thermodynamic\npower", limits = c(0,1))
}
# define y-axis
if (y_axis_type == 2) {
y_min <- quantile(df$Q2.5, probs = 0.5)
y_max <- max(df$Q97.5)
plot1 <- plot1 + coord_cartesian(ylim = c(y_min, y_max))
} else if (y_axis_type == 3 & legend == TRUE) {
plot1 <- plot1 + scale_y_continuous(trans = "log10")
}
# return plot object
return(plot1)
}
#------------------------------------------------
#' @title Plot acceptance rate between rungs
#'
#' @description For each pair of rungs, plot the acceptance rate between them.
#'
#' @param project an RgeoProfile project, as produced by the function
#' \code{rgeoprofile_project()}.
#' @param K which value of K to produce the plot for.
#' @param phase which phase to plot. Must be either "burnin" or "sampling".
#'
#' @import ggplot2
#' @importFrom grDevices grey
#' @export
plot_coupling <- function(project,
K = 1,
phase = "sampling") {
# check inputs
assert_custom_class(project, "rgeoprofile_project")
assert_single_pos_int(K, zero_allowed = FALSE)
assert_in(phase, c("burnin", "sampling"))
# get plotting data
if (phase == "burnin") {
coupling <- get_output(project, "coupling_accept_burnin", type = "summary", K = K)
} else {
coupling <- get_output(project, "coupling_accept_sampling", type = "summary", K = K)
}
# get x values
x_vec <- 1:length(coupling) + 0.5
# create plotting dataframe
df_plot <- data.frame(x = x_vec, y = coupling)
# produce plot
plot1 <- ggplot(df_plot) + theme_bw() + theme(panel.grid.minor.x = element_blank(),
panel.grid.major.x = element_blank())
plot1 <- plot1 + geom_point(aes_(x = ~x, y = ~y))
plot1 <- plot1 + geom_vline(aes_(xintercept = ~x-0.5), col = grey(0.9))
plot1 <- plot1 + geom_vline(aes(xintercept = length(coupling)+1), col = grey(0.9))
plot1 <- plot1 + ylim(c(0, 1)) + xlim(c(1, length(coupling)+1))
plot1 <- plot1 + xlab("rung") + ylab("coupling acceptance rate")
# return plot object
return(plot1)
}
#------------------------------------------------
#' @title Posterior allocation plot
#'
#' @description Produce posterior allocation plot of current active set.
#'
#' @param project an RgeoProfile project, as produced by the function
#' \code{rgeoprofile_project()}.
#' @param K which value of K to produce the plot for.
#' @param divide_ind_on whether to add dividing lines between bars.
#'
#' @import ggplot2
#' @export
#'
#' @examples
#' \dontshow{p <- rgeoprofile_file("tutorial1_project.rds")}
#' # Plot the structure for a single K value.
#' plot_structure(project = p, K = 2)
#' # Similarly, plot the allocation structure for every K.
#' plot_structure(project = p, divide_ind_on = TRUE)
plot_structure <- function(project,
K = NULL,
divide_ind_on = FALSE) {
# check inputs
assert_custom_class(project, "rgeoprofile_project")
if (!is.null(K)) {
assert_pos_int(K)
}
assert_single_logical(divide_ind_on)
# get active set and check non-zero
s <- project$active_set
if (s == 0) {
stop("no active parameter set")
}
# set default K to all values with output
null_output <- mapply(function(x) {is.null(x$summary$qmatrix)}, project$output$single_set[[s]]$single_K)
if (all(null_output)) {
stop("no output for active parameter set")
}
K <- define_default(K, which(!null_output))
# check output exists for chosen K
qmatrix_list <- list()
for (i in 1:length(K)) {
qmatrix_list[[i]] <- project$output$single_set[[s]]$single_K[[K[i]]]$summary$qmatrix
if (is.null(qmatrix_list[[i]])) {
stop(sprintf("no qmatrix output for K = %s of active set", K[i]))
}
}
# get data into ggplot format
df <- NULL
for (i in 1:length(K)) {
m <- unclass(qmatrix_list[[i]])
m <- m[!is.na(m[,1]), , drop = FALSE]
n <- nrow(m)
df <- rbind(df,
data.frame(K = as.numeric(K[i]),
ind = rep(1:n, each = K[i]),
k = as.factor(rep(1:K[i], times = n)),
val = as.vector(t(m))))
}
# produce basic plot
plot1 <- ggplot(df) + theme_empty()
plot1 <- plot1 + geom_bar(aes_(x = ~ind, y = ~val, fill = ~k), width = 1, stat = "identity")
plot1 <- plot1 + scale_x_continuous(expand = c(0,0)) + scale_y_continuous(expand = c(0,0))
plot1 <- plot1 + xlab("positive sentinel site")
# arrange in rows
if (length(K) == 1) {
plot1 <- plot1 + facet_wrap(~K, ncol = 1)
plot1 <- plot1 + theme(strip.background = element_blank(), strip.text = element_blank())
plot1 <- plot1 + ylab("probability")
} else {
plot1 <- plot1 + facet_wrap(~K, ncol = 1, strip.position = "left")
plot1 <- plot1 + theme(strip.background = element_blank())
plot1 <- plot1 + ylab("K")
}
# add legends
plot1 <- plot1 + scale_fill_manual(values = more_colours(max(K)), name = "group")
plot1 <- plot1 + scale_colour_manual(values = "white")
plot1 <- plot1 + guides(colour = FALSE)
# add border
plot1 <- plot1 + theme(panel.border = element_rect(colour = "black", size = 2, fill = NA))
# optionally add dividing lines
if (divide_ind_on) {
plot1 <- plot1 + geom_segment(aes_(x = ~x, y = ~y, xend = ~x, yend = ~y+1, col = "white"), size = 0.3, data = data.frame(x = 1:n-0.5, y = rep(0,n)))
}
return(plot1)
}
#------------------------------------------------
#' @title Plot sigma 95\% credible intervals
#'
#' @description Plot credible intervals for a "single" (1) or "independent" (K)
#' sigma model.
#'
#' @param project an RgeoProfile project, as produced by the function
#' \code{rgeoprofile_project()}.
#' @param K which value of K to plot.
#'
#' @import ggplot2
#' @export
#'
#' @examples
#' \dontshow{p <- rgeoprofile_file("tutorial1_project.rds")}
#' plot_sigma(project = p)
plot_sigma <- function(project,
K = NULL) {
# check inputs
assert_custom_class(project, "rgeoprofile_project")
if (!is.null(K)) {
assert_single_pos_int(K, zero_allowed = FALSE)
}
# get active set and check non-zero
s <- project$active_set
if (s == 0) {
stop("no active parameter set")
}
# get sigma model
sigma_model <- project$parameter_sets[[s]]$sigma_model
# get output
sigma_intervals <- get_output(project, "sigma_intervals", K)
x_lab <- "source"
if (sigma_model == "single") {
sigma_intervals <- sigma_intervals[which(rowSums(sigma_intervals) != 0),]
rownames(sigma_intervals) <- ""
x_lab <- paste("K = ", K, sep = "")
}
# get properties
x_vec <- rownames(sigma_intervals)
# produce plot
plot1 <- ggplot(sigma_intervals) + theme_bw()
plot1 <- plot1 + geom_segment(aes_(x = ~x_vec, y = ~Q2.5, xend = ~x_vec, yend = ~Q97.5))
plot1 <- plot1 + geom_point(aes_(x = ~x_vec, y = ~Q50))
plot1 <- plot1 + scale_y_continuous(limits = c(0, max(sigma_intervals$Q97.5)*1.1), expand = c(0,0))
plot1 <- plot1 + xlab(x_lab) + ylab("sigma")
# return plot object
return(plot1)
}
#------------------------------------------------
#' @title Plot expected population size 95\% credible intervals
#'
#' @description Plot credible intervals for the entire expected population (single)
#' or individuals sources (independent).
#'
#' @param project an RgeoProfile project, as produced by the function
#' \code{rgeoprofile_project()}.
#' @param K which value of K to produce the plot for.
#'
#' @import ggplot2
#' @export
#'
#' @examples
#' \dontshow{p <- rgeoprofile_file("tutorial1_project.rds")}
#' plot_expected_popsize(project = p, K = 1)
plot_expected_popsize <- function(project,
K = NULL) {
# check inputs
assert_custom_class(project, "rgeoprofile_project")
assert_single_pos_int(K, zero_allowed = FALSE)
# single or independent expected population size?
s <- project$active_set
expected_popsize_model <- project$parameter_sets[[s]]$expected_popsize_model
# get output
expected_popsize_intervals <- get_output(project, "expected_popsize_intervals", K)
# set plotting params
if(expected_popsize_model == "single"){
K <- 1
xlabel <- ""
} else if(expected_popsize_model == "independent"){
xlabel <- as.character(1:K)
}
# produce plot
plot1 <- ggplot(expected_popsize_intervals) + theme_bw()
plot1 <- plot1 + geom_segment(aes_(x = 1:K, y = ~Q2.5, xend = 1:K, yend = ~Q97.5))
plot1 <- plot1 + geom_point(aes_(x = 1:K, y = ~Q50))
plot1 <- plot1 + scale_y_continuous(limits = c(0, max(expected_popsize_intervals$Q97.5)*1.1), expand = c(0,0))
plot1 <- plot1 + xlab(xlabel) + ylab("expected population size")
# return plot object
return(plot1)
}
#------------------------------------------------
#' @title Plot alpha 95\% credible intervals
#'
#' @description Plot the over dispersion parameter alpha, in accordance with
#' a negative binomial model
#'
#' @param project an RgeoProfile project, as produced by the function
#' \code{rgeoprofile_project()}.
#' @param K which value of K to produce the plot for.
#'
#' @import ggplot2
#' @export
#'
#' @examples
#' # \dontshow{p <- rgeoprofile_file("tutorial1_project.rds")}
#' # plot_alpha(project = p)
plot_alpha <- function(project,
K = NULL) {
# check inputs
assert_custom_class(project, "rgeoprofile_project")
assert_single_pos_int(K, zero_allowed = FALSE)
# single or independent expected population size?
s <- project$active_set
# get output
alpha_intervals <- get_output(project, "alpha_intervals", K)
# produce plot
plot1 <- ggplot(alpha_intervals) + theme_bw()
plot1 <- plot1 + geom_segment(aes_(x = "", y = ~Q2.5, xend = "", yend = ~Q97.5))
plot1 <- plot1 + geom_point(aes_(x = "", y = ~Q50))
plot1 <- plot1 + scale_y_continuous(limits = c(0, max(alpha_intervals$Q97.5)*1.1), expand = c(0,0))
plot1 <- plot1 + xlab("") + ylab("alpha") + ggtitle("Alpha - 95% confidence interval")
# return plot object
return(plot1)
}
#------------------------------------------------
#' @title Produce MCMC trace plot
#'
#' @description Produce MCMC trace plot of the log-likelihood at each iteration.
#'
#' @param project an RgeoProfile project, as produced by the function
#' \code{rgeoprofile_project()}.
#' @param K which value of K to plot.
#' @param rung which rung to plot. Defaults to the cold chain.
#' @param col colour of the trace.
#' @param phase plot the trace during the burnin or sampling phase
#'
#' @import ggplot2
#' @export
#'
#' @examples
#' \dontshow{p <- rgeoprofile_file("tutorial1_project.rds")}
#' plot_trace(project = p, K = 2)
#' # Similarly, plot the trace for every K value.
#' plot_trace(project = p)
plot_trace <- function(project,
K = NULL,
rung = NULL,
col = "black",
phase = "sampling") {
# check inputs
assert_custom_class(project, "rgeoprofile_project")
if (!is.null(K)) {
assert_single_pos_int(K, zero_allowed = FALSE)
}
if (!is.null(rung)) {
assert_single_pos_int(rung)
}
assert_in(phase, c("burnin", "sampling"))
# get output
if(phase == "sampling"){
loglike_mat <- get_output(project, "loglike_sampling", K, "raw")
} else if(phase == "burnin"){
loglike_mat <- get_output(project, "loglike_burnin", K, "raw")
}
# use cold rung by default
rungs <- ncol(loglike_mat)
rung <- define_default(rung, rungs)
assert_leq(rung, rungs)
loglike <- as.vector(loglike_mat[,rung])
# get into ggplot format
df <- data.frame(x = 1:length(loglike), y = loglike)
# produce plot
plot1 <- ggplot(df) + theme_bw() + ylab("log-likelihood")
# complete plot
plot1 <- plot1 + geom_line(aes_(x = ~x, y = ~y, colour = "col1"))
plot1 <- plot1 + coord_cartesian(xlim = c(0,nrow(df)))
plot1 <- plot1 + scale_x_continuous(expand = c(0,0))
plot1 <- plot1 + scale_colour_manual(values = col)
plot1 <- plot1 + guides(colour = FALSE)
plot1 <- plot1 + xlab("iteration")
# return plot object
return(plot1)
}
#------------------------------------------------
#' @title Produce MCMC autocorrelation plot
#'
#' @description Produce MCMC autocorrelation plot of the log-likelihood
#'
#' @param project an RgeoProfile project, as produced by the function
#' \code{rgeoprofile_project()}.
#' @param K which value of K to plot.
#' @param rung which rung to plot. Defaults to the cold chain.
#' @param col colour of the trace.
#' @param phase plot the acf during the burnin or sampling phase
#'
#' @import ggplot2
#' @export
#'
#' @examples
#' \dontshow{p <- rgeoprofile_file("tutorial1_project.rds")}
#' plot_acf(project = p)
plot_acf <- function(project,
K = NULL,
rung = NULL,
col = "black",
phase = "sampling") {
# check inputs
assert_custom_class(project, "rgeoprofile_project")
if (!is.null(K)) {
assert_single_pos_int(K, zero_allowed = FALSE)
}
if (!is.null(rung)) {
assert_single_pos_int(rung)
}
assert_in(phase, c("burnin", "sampling"))
# get output
if(phase == "sampling"){
loglike_mat <- get_output(project, "loglike_sampling", K, "raw")
} else if(phase == "burnin"){
loglike_mat <- get_output(project, "loglike_burnin", K, "raw")
}
# use cold rung by default
rungs <- ncol(loglike_mat)
rung <- define_default(rung, rungs)
assert_leq(rung, rungs)
loglike <- as.vector(loglike_mat[,rung])
# store variable to plot
v <- loglike
# get autocorrelation
lag_max <- round(3*length(v)/effectiveSize(v))
lag_max <- max(lag_max, 20)
lag_max <- min(lag_max, length(v))
# get into ggplot format
a <- acf(v, lag.max = lag_max, plot = FALSE)
acf <- as.vector(a$acf)
df <- data.frame(lag = (1:length(acf))-1, ACF = acf)
# produce plot
plot1 <- ggplot(df) + theme_bw()
plot1 <- plot1 + geom_segment(aes_(x = ~lag, y = 0, xend = ~lag, yend = ~ACF, colour = "col1"))
plot1 <- plot1 + scale_colour_manual(values = col)
plot1 <- plot1 + guides(colour = FALSE)
plot1 <- plot1 + xlab("lag") + ylab("ACF")
# return plot object
return(plot1)
}
#------------------------------------------------
#' @title Produce MCMC density plot
#'
#' @description Produce MCMC density plot of the log-likelihood
#'
#' @param project an RgeoProfile project, as produced by the function
#' \code{rgeoprofile_project()}.
#' @param K value of K to plot.
#' @param rung which rung to plot. Defaults to the cold chain.
#' @param col colour of the trace.
#' @param phase plot the acf during the burnin or sampling phase.
#'
#' @import ggplot2
#' @export
#'
#' @examples
#' \dontshow{p <- rgeoprofile_file("tutorial1_project.rds")}
#' plot_density(project = p)
plot_density <- function(project,
K = NULL,
rung = NULL,
col = "black",
phase = "sampling") {
# check inputs
assert_custom_class(project, "rgeoprofile_project")
if (!is.null(K)) {
assert_single_pos_int(K, zero_allowed = FALSE)
}
if (!is.null(rung)) {
assert_single_pos_int(rung)
}
assert_in(phase, c("burnin", "sampling"))
# get output
if(phase == "sampling"){
loglike_mat <- get_output(project, "loglike_sampling", K, "raw")
} else if(phase == "burnin"){
loglike_mat <- get_output(project, "loglike_burnin", K, "raw")
}
# use cold rung by default
rungs <- ncol(loglike_mat)
rung <- define_default(rung, rungs)
assert_leq(rung, rungs)
loglike <- as.vector(loglike_mat[,rung])
# get into ggplot format
df <- data.frame(v = loglike)
# produce plot
plot1 <- ggplot(df) + theme_bw() + xlab("log-likelihood")
# produce plot
#plot1 <- ggplot(df) + theme_bw()
plot1 <- plot1 + geom_histogram(aes_(x = ~v, y = ~..density.., fill = "col1"), bins = 50)
plot1 <- plot1 + scale_fill_manual(values = col)
plot1 <- plot1 + guides(fill = FALSE)
plot1 <- plot1 + ylab("density")
# return plot object
return(plot1)
}
#------------------------------------------------
#' @title Produce diagnostic plots of log-likelihood
#'
#' @description Produce diagnostic plots of the log-likelihood.
#'
#' @details For a value of K produce the auto-correlation, trace and density plots of the MCMC.
#'
#' @param project an RgeoProfile project, as produced by the function
#' \code{rgeoprofile_project()}.
#' @param K which value of K to plot.
#' @param rung which rung to plot. Defaults to the cold chain.
#' @param phase check the burnin or sampling phase of the MCMC chain
#' @param col colour of the trace.
#'
#' @import ggplot2
#' @importFrom gridExtra grid.arrange
#' @export
#'
#' @examples
#' \dontshow{p <- rgeoprofile_file("tutorial1_project.rds")}
#' plot_loglike_diagnostic(project = p, K = 2)
plot_loglike_diagnostic <- function(project,
K = NULL,
rung = NULL,
phase = "sampling",
col = "black") {
# check inputs
assert_custom_class(project, "rgeoprofile_project")
if (!is.null(K)) {
assert_single_pos_int(K, zero_allowed = FALSE)
}
if (!is.null(rung)) {
assert_single_pos_int(rung)
}
assert_in(phase, c("sampling", "burnin"))
# get active set and check non-zero
s <- project$active_set
if (s == 0) {
stop("no active parameter set")
}
# set default K to first value with output
null_output <- mapply(function(x) {is.null(x$raw$loglike_sampling)}, project$output$single_set[[s]]$single_K)
if (all(null_output)) {
stop("no loglike_sampling output for active parameter set")
}
if (is.null(K)) {
K <- which(!null_output)[1]
message(sprintf("using K = %s by default", K))
}
# get output
mcmc_phase <- paste0("loglike_", phase)
loglike_sampling <- get_output(project, mcmc_phase, K, "raw")
# use cold rung by default
rungs <- ncol(loglike_sampling)
rung <- define_default(rung, rungs)
assert_leq(rung, rungs)
# produce individual diagnostic plots and add features
plot1 <- plot_trace(project, K = K, rung = rung, col = col, phase = phase)
plot1 <- plot1 + ggtitle("MCMC trace")
plot2 <- plot_acf(project, K = K, rung = rung, col = col, phase = phase)
plot2 <- plot2 + ggtitle("autocorrelation")
plot3 <- plot_density(project, K = K, rung = rung, col = col, phase = phase)
plot3 <- plot3 + ggtitle("density")
# produce grid of plots
ret <- gridExtra::grid.arrange(plot1, plot2, plot3, layout_matrix = rbind(c(1,1), c(2,3)))
}
#------------------------------------------------
#' @title Create dynamic map
#'
#' @description Create dynamic map
#'
#' @param map_type an index from 1 to 137 indicating the type of base map. The
#' map types are taken from \code{leaflet::providers}. Defaults to "CartoDB".
#'
#' @import leaflet
#' @export
#'
#' @examples
#' # Standard OSM format is given by map_type = 1, though
#' # this value can take anywhere between 1 and 137.
#' plot_map(map_type = 1)
plot_map <- function(map_type = 110) {
# check inputs
assert_in(map_type, 1:137, message = "map_type must be in 1:137")
# produce plot
myplot <- leaflet()
myplot <- addProviderTiles(myplot, leaflet::providers[[map_type]])
# return plot object
return(myplot)
}
#------------------------------------------------
#' @title Plot DIC over all K
#'
#' @description Plot DIC over all K for the current active parameter set.
#'
#' @param project an RgeoProfile project, as produced by the function
#' \code{rgeoprofile_project()}.
#'
#' @import ggplot2
#' @export
#'
#' @examples
#' \dontshow{p <- rgeoprofile_file("tutorial1_project.rds")}
#' plot_DIC_gelman(p)
plot_DIC_gelman <- function(project) {
# check inputs
assert_custom_class(project, "rgeoprofile_project")
# get active set and check non-zero
s <- project$active_set
if (s == 0) {
stop("no active parameter set")
}
# get DIC values
df <- project$output$single_set[[s]]$all_K$DIC_gelman
# produce plot
plot1 <- ggplot(data = df) + theme_bw()
plot1 <- plot1 + geom_point(aes_(x = ~as.factor(K), y = ~DIC_gelman))
plot1 <- plot1 + xlab("K") + ylab("DIC (Gelman)")
return(plot1)
}
#------------------------------------------------
#' @title Produce Lorenz plot of hitscores
#'
#' @description Produce Lorenz plot of hitscores
#'
#' @details The hs object is obtained from the \code{get_hitscores()} function.
#'
#' @param hs dataframe of hitscores.
#' @param col vector of group colours. Uses \code{more_colours()} by default.
#' @param counts optional vector of counts corresponding to each source. If
#' specified, the y-axis is in terms of total counts found, rather than total
#' sources found.
#'
#' @import ggplot2
#' @export
#'
#' @examples
#' \dontshow{hs <- rgeoprofile_file("tutorial1_hitscore.rds")}
#' plot_lorenz(hs)
plot_lorenz <- function(hs,
col = NULL,
counts = NULL) {
# check inputs
assert_dataframe(hs)
if (!is.null(counts)) {
assert_pos_int(counts, zero_allowed = FALSE)
assert_vector(counts)
assert_length(counts, nrow(hs))
}
# drop lon/lat columns
hs <- hs[ , !names(hs) %in% c("longitude", "latitude"), drop = FALSE]
# get properties
ns <- nrow(hs)
hs_names <- colnames(hs)
# set default colours
col <- define_default(col, more_colours(ncol(hs)))
# get sorted hitscores on x-axis
x_list <- mapply(function(x) c(0, sort(x, na.last = NA), 100), as.list(hs), SIMPLIFY = FALSE)
# get number of sources found on y axis
if (is.null(counts)) {
y_list <- mapply(function(x) {
l <- length(x)
c(0:(l-2), l-2)/ns*100
}, x_list, SIMPLIFY = FALSE)
} else {
print("bar")
y_list <- mapply(function(x) {
l <- length(x)
c(0, cumsum(counts), sum(counts))/sum(counts)*100
}, x_list, SIMPLIFY = FALSE)
}
# get number of points in each group
z_list <- rep(hs_names, times = mapply(length, x_list))
# get into ggplot format
df <- data.frame(x = unlist(x_list), y = unlist(y_list), col = unlist(z_list))
# make ggplot object
plot1 <- ggplot(data = df, aes_(x = ~x, y = ~y, color = ~col)) + theme_bw()
plot1 <- plot1 + theme_bw() + geom_point() + geom_line()
plot1 <- plot1 + geom_abline(slope = 1, linetype = "dashed")
plot1 <- plot1 + scale_colour_manual(values = col, name = "Search method")
plot1 <- plot1 + scale_x_continuous(limits = c(0,100)) + scale_y_continuous(limits = c(0,100))
plot1 <- plot1 + xlab("search effort (%)") + ylab("found (%)")
return(plot1)
}
#------------------------------------------------
#' @title Add sentinel sites to dynamic map
#'
#' @description Add sentinel sites to dynamic map
#'
#' @param myplot dynamic map produced by \code{plot_map()} function
#' @param project an RgeoProfile project, as produced by the function
#' \code{rgeoprofile_project()}.
#' @param sentinel_radius the radius of sentinel sites. Taken from the active
#' parameter set if unspecified.
#' @param fill whether to fill circles.
#' @param fill_colour colour of circle fill.
#' @param fill_opacity fill opacity.
#' @param border whether to add border to circles.
#' @param border_colour colour of circle borders.
#' @param border_weight thickness of circle borders.
#' @param border_opacity opacity of circle borders.
#' @param legend whether to add a legend for site count.
#' @param label whether to label sentinel sites with densities.
#' @param label_size size of the label.
#'
#' @import leaflet
#' @importFrom grDevices grey
#' @export
#'
#' @examples
#' \dontshow{p <- rgeoprofile_file("tutorial1_project.rds")}
#' plot1 <- plot_map()
#' plot1 <- overlay_sentinels(plot1, project = p, fill_opacity = 0.9, fill = TRUE,
#' fill_colour = c(grey(0.7), "red"), border = c(FALSE, TRUE),
#' border_colour = "black",border_weight = 0.5)
#' plot1
overlay_sentinels <- function(myplot,
project,
sentinel_radius = NULL,
fill = TRUE,
fill_colour = c(grey(0.5), "red"),
fill_opacity = 0.5,
border = FALSE,
border_colour = "black",
border_weight = 1,
border_opacity = 1.0,
legend = FALSE,
label = FALSE,
label_size = 15) {
# check inputs
assert_custom_class(myplot, "leaflet")
assert_custom_class(project, "rgeoprofile_project")
if (!is.null(sentinel_radius)) {
assert_single_pos(sentinel_radius)
}
assert_logical(fill)
assert_vector(fill)
assert_in(length(fill), c(1,2))
if (length(fill) == 1) {
fill <- rep(fill, 2)
}
assert_string(fill_colour)
assert_vector(fill_colour)
# assert_in(length(fill_colour), c(1,2))