Nothing
# Note: one of d, p, q, or n must be a **named** argument. This allows other things to
# be in ... without causing issues for the d/p/q/r function called.
#' Distribution wrapper
#'
#' Utility function wrapping up the d/p/q/r distribution functions
#'
#' @param dist a character description of a distribution, for example
#' `"norm"`, `"t"`, or `"chisq"`
#' @param type one of `"x"`, `"p"`, `"q"`, or `"r"`
#' @param ... additional arguments passed on to underlying distribution function.
#' Note that one of `d`, `p`, `q`, or `n` must
#' be a named argument in ...
#' @export
#' @examples
#' # 3 random draws from N(1,2)
#' dpqrdist("norm", "r", n = 3, mean = 1, sd = 2)
#' # These should all be the same
#' dpqrdist("norm", "d", x = 0) == dnorm(x = 0)
#' dpqrdist("norm", "p", q = 0, mean = 1, sd = 2) == pnorm(q = 0, mean = 1, sd = 2)
#' dpqrdist("norm", "q", p = 0.5, mean = 1, sd = 2) == qnorm(p = 0.5, mean = 1, sd = 2)
#' @importFrom purrr possibly map_dbl
dpqrdist <- function( dist, type = c("d","p","q","r"), ...) {
type <- match.arg(type)
dots <- list(...)
distFunName <- paste0(type, dist)
distFun <- rlang::as_function(distFunName)
dist_dots <- dots[names(dots) %in% names(formals(distFunName))]
first_name <- as.name(names(formals(distFunName))[[1]])
first_val <- dist_dots[[first_name]]
dist_dots[[first_name]] <- NULL
# for the sake of some distribution functions that throw errors, for example when p = 0 or 1
# we convert errors into NaNs [Note: is.na(NaN) is TRUE]
dpqrfun <- purrr::possibly(distFun, otherwise = NaN)
# make sure an output value is computed for each input, even if some throw errors
res <- do.call(sapply, c(list(first_val, dpqrfun), dist_dots))
if (length(res) > 0 & all(is.na(res))) {
stop("No values could be computed. Did you specify all the required parameters?")
}
res
}
#' Illustrated probability calculations from distributions
#'
#' Illustrated probability calculations from distributions
#'
#' @param dist a character description of a distribution, for example
#' `"norm"`, `"t"`, or `"chisq"`
#' @param q a vector of quantiles
#' @param plot a logical indicating whether a plot should be created
#' @param verbose a logical
#' @param invisible a logical
#' @param digits the number of digits desired
#' @param xlim x limits
#' @param ylim y limits
# #' @param vlwd width of vertical lines
# #' @param vcol color of vertical lines
# #' @param rot angle for rotating text indicating probability
#' @param resolution Number of points used for detecting discreteness and generating plots.
#' The default value of 5000 should work well except for discrete distributions
#' that have many distinct values, especially if these values are not evenly spaced.
#' @param ... Additional arguments, typically for fine tuning the plot.
#' @param return If `"plot"`, return a plot. If `"values"`, return a vector of numerical values.
#' @param refinements A list of refinements to the plot. See [ggformula::gf_refine()].
#' @details The most general function is `pdist` which can work with
#' any distribution for which a p-function exists. As a convenience, wrappers are
#' provided for several common distributions.
#' @return A vector of probabilities; a plot is printed as a side effect.
#' @examples
#' pdist("norm", -2:2)
#' pdist("norm", seq(80,120, by = 10), mean = 100, sd = 10)
#' pdist("chisq", 2:4, df = 3)
#' pdist("f", 1, df1 = 2, df2 = 10)
#' pdist("gamma", 2, shape = 3, rate = 4)
#' @export
pdist <- function (dist = "norm", q, plot = TRUE, verbose = FALSE, invisible = FALSE,
digits = 3L,
xlim, ylim,
resolution = 500L,
return = c("values", "plot"),
...,
refinements = list())
{
return <- match.arg(return)
dots <- list(...)
# if (is.null(vlwd)) {
# vlwd <- if (is_discrete_dist( dist, ... )) 0 else 2
# }
p <- dpqrdist(dist, type = "p", q = q, ...)
if (verbose) {
cat("Verbose output not yet implemented.\n")
}
res_plot <-
do.call(
gf_refine,
c(list(
plot_multi_dist(
dist = dist, p = p,
xlim = xlim, ylim = ylim,
digits = digits,
resolution = resolution,
...)),
refinements)
)
if (return == "plot") {
return(res_plot)
}
if (plot) {
print(res_plot)
}
if (invisible) {
return(invisible(p))
}
return(p)
}
#' Illustrated quantile calculations from distributions
#'
#' Illustrated quantile calculations from distributions
#'
#' @param dist a character description of a distribution, for example
#' `"norm"`, `"t"`, or `"chisq"`
#' @param p a vector of probabilities
#' @param plot a logical indicating whether a plot should be created
#' @param verbose a logical
#' @param invisible a logical
#' @param digits the number of digits desired
#' @param xlim x limits. By default, these are chosen to show the central 99.8\%
#' of the distribution.
#' @param ylim y limits
# #' @param vlwd width of vertical lines
# #' @param vcol color of vertical lines
# #' @param rot angle for rotating text indicating probability
#' @param resolution number of points used for detecting discreteness and generating plots.
#' The default value of 5000 should work well except for discrete distributions
#' that have many distinct values, especially if these values are not evenly spaced.
#' @param refinements A list of refinements to the plot. See [ggformula::gf_refine()].
#' @param ... additional arguments, including parameters of the distribution
#' and additional options for the plot. To help with name collisions (eg `size` for binomial
#' distributions and `shape` for gamma distributions), argument names beginning `plot_` will
#' be renamed to remove `plot_` and passed only to the plot. The unprefixed version will
#' used as a parameter for the the distribution.
#' @param return If `"plot"`, return a plot. If `"values"`, return a vector of numerical values.
#' @details The most general function is `qdist` which can work with
#' any distribution for which a q-function exists. As a convenience, wrappers are
#' provided for several common distributions.
#'
#' @return a vector of quantiles; a plot is printed as a side effect
#' @examples
#' qdist("norm", seq(.1, .9, by = 0.10),
#' title = "Deciles of a normal distribution", show.legend = FALSE,
#' pattern = "rings")
#' xqnorm(seq(.2, .8, by = 0.20), mean = 100, sd = 10)
#' qdist("unif", .5)
#' xqgamma(.5, shape = 3, scale = 4)
#' xqgamma(.5, shape = 3, scale = 4, color = "black")
#' xqbeta(.5, shape1 = .9, shape2 = 1.4, dlwd = 1)
#' xqchisq(c(.25,.5,.75), df = 3)
#' xcbinom(c(0.80, 0.90), size = 1000, prob = 0.40)
#' # displayed as if continuous
#' xcbinom(c(0.80, 0.90), size = 5000, prob = 0.40)
#' xpbinom(c(480, 500, 520), size = 1000, prob = 0.48)
#' xpbinom(c(40, 60), size = 100, prob = 0.5)
#' xqpois(c(0.25, 0.5, 0.75), lambda = 12)
#' xcpois(0.50, lambda = 12)
#' xcpois(0.50, lambda = 12, refinements = list(scale_color_brewer(type = "qual", palette = 5)))
#' @export
qdist <- function (dist = "norm", p, plot = TRUE, verbose = FALSE, invisible = FALSE,
resolution = 500L,
digits = 3L,
xlim, ylim,
return = c("values", "plot"),
refinements = list(),
...
)
{
return = match.arg(return)
dots <- list(...)
# if (is.null(vlwd)) {
# vlwd <- if (is_discrete_dist( dist, ... )) 0 else 2
# }
#
q <- dpqrdist(dist, type = "q", p = p, ...)
# adjust probs for discrete dists where requested p's might not be (exactly) possible
p <- dpqrdist(dist, type = "p", q = q, ...)
if (verbose) {
cat("Verbose output not yet implemented.\n")
}
res_plot <-
do.call(
ggformula::gf_refine,
c(list(
plot_multi_dist(
dist = dist, p = p,
xlim = xlim, ylim = ylim,
digits = digits,
resolution = resolution,
...)
), refinements)
)
if (return == "plot") return (res_plot)
if (plot) {
print(res_plot)
}
if (invisible) {
return(invisible(q))
}
return(q)
}
utils::globalVariables(c("i", "name", "p_all"))
plot_multi_dist <-
function(dist, p, q, xlim, ylim, digits = 3L, resolution = 500L,
lower.tail = TRUE,
dlwd = 0,
pattern = c("stripes", "rings"),
...)
{
dots <- list(...)
pattern <- match.arg(pattern)
plot_dots <- dots[ ! names(dots) %in% names(formals(paste0("p",dist))) ]
dist_dots <- dots[ names(dots) %in% names(formals(paste0("p",dist))) ]
discrete <- do.call(is_discrete_dist, c(list(dist), dist_dots))
if (missing(p)) {
if (missing(q)) { stop( "one of p or q must be specified") }
q <- sort(q)
p <- dpqrdist(dist, type = "p", q = q, lower.tail = lower.tail, ...)
p_less <- dpqrdist(dist, type = "p", q = q, lower.tail = TRUE, ...)
} else {
if (!missing(q)) {
warning("Both p and q were specified. I'm using p")
}
p <- sort(p)
q <- sort(dpqrdist(dist, type = "q", p = p, lower.tail = lower.tail, ...))
p_less <- dpqrdist(dist, type = "p", q = q, ...)
}
names(plot_dots) <- gsub("plot_", "", names(plot_dots))
if ('lty' %in% names(plot_dots)) { plot_dots$linetype <- plot_dots$lty; plot_dots$lty <- NULL }
if ('pch' %in% names(plot_dots)) { plot_dots$shape <- plot_dots$pch; plot_dots$pch <- NULL }
if ('cex' %in% names(plot_dots)) { plot_dots$size <- plot_dots$cex; plot_dots$cex <- NULL }
if (! 'xlab' %in% names(plot_dots)) { plot_dots$xlab <- "" }
if (missing(xlim) || is.null(xlim)) {
xlim_opts <- dpqrdist(dist, type = "q", p = c(0, 0.0001, 0.001, 0.01, 0.99, 0.999, 0.9999, 1), ...)
if (is.matrix(xlim_opts)) {
xlim <- c(min(xlim_opts[, 4], na.rm = TRUE), max(xlim_opts[,5], na.rm = TRUE))
} else {
xlim <- xlim_opts[4:5]
}
# extend range if it doesn't extend too far
mid_width <- diff(xlim)
a <- xlim_opts[min(which(xlim[2] - xlim_opts < 1.8 * mid_width), na.rm = TRUE)]
b <- xlim_opts[max(which(xlim_opts - xlim[1] < 1.8 * mid_width), na.rm = TRUE)]
xlim = c(a,b)
}
if (discrete) {
xdata <- unique(dpqrdist(dist, type = "q", p = ppoints(resolution), ...))
step = min(diff(xdata))
xlim = c(-step/2, step/2) + xlim # widen by half step in each direction
fill <- seq( min(xdata) -1.5 * step , max(xdata) + 1.5*step, length.out = resolution)
xdata <- c(xdata, fill)
xdata <- dpqrdist(dist, type = "q", p = dpqrdist(dist, type = "p", q = xdata, ...), ...)
xdata <- sort(unique(xdata))
} else {
xdata <- seq(xlim[1], xlim[2], length.out = resolution)
xdata <- dpqrdist(dist, type = "q", p = dpqrdist(dist, type = "p", q = xdata, ...), ...)
xdata <- sort(unique(xdata))
}
# not a fix: xdata <- sort(xdata)
ydata <- dpqrdist(dist, type = "d", x = xdata, ...)
ymax <- 1.1 * quantile(ydata, 0.95, na.rm = TRUE)
if (missing(ylim)) {
ylim = c(0, 1.4 * ymax) # quantile(ydata, 0.95, na.rm = TRUE))
# print(ylim)
}
# this could be funny if limits don't span q
p_less <- sort(unique(c(0, p_less, 1)))
q <- dpqrdist(dist, type = "q", p = p_less, ...) # c(xlim[1], q, xlim[2])
# if (discrete) {
# if (is.null(plot_dots$pch)) plot_dots$pch = 16
# }
# args <- c(
# list(
# ydata ~ xdata,
# xlim = xlim, ylim = ylim,
# xlab = "",
# ylab = if (discrete) "probability" else "density"
# ),
# plot_dots
# )
#
# res_plot <- do.call("xyplot", args)
Groups <-
tibble::tibble(p = diff(p_less)) |>
mutate(
i = 1 : n(),
name = if (pattern == "stripes") LETTERS[i] else LETTERS[ceiling(1 + abs(i - mean(i)))]
) |>
group_by(name) |>
mutate(
p_all = sum(p),
label = paste(first(name), ":", formatC(p_all, format = "f", digits = digits), sep = "")
)
if (length(xdata) != length(ydata)) {
stop("It looks like you have multiple parameter settingss specified for your distribution.
Plotting only works for one parameter setting.")
}
if (discrete) {
# q <- tail(q, -1)
Ddensity <-
tibble::tibble(
x = xdata, density = ydata,
group = sapply(xdata, function(x) {sum(x > q, na.rm = TRUE)})
) |>
mutate(
group = pmax(1, group),
probability = Groups$label[group]
)
# print(Ddensity)
plot <-
do.call(
gf_point,
c(list(density ~ x, color = ~ probability, group = ~ group, fill = ~ probability,
data = Ddensity), plot_dots)) |>
gf_segment(density + 0 ~ x + x) |>
gf_labs(x = "", y = "probability") |>
gf_refine(
scale_fill_viridis_d(end = 0.9),
scale_color_viridis_d(end = 0.9)
)
} else {
Ddensity <-
tibble::tibble(
x = xdata, density = ydata,
group = sapply(xdata, function(x) {sum(x > q, na.rm = TRUE)})
) |>
mutate(
group = pmax(1, group),
probability = Groups$label[group] # factor(group, labels = labels[sort(unique(group))])
) |>
dplyr::filter(!is.na(probability)) # avoids issues when xlim is wider than support
plot <-
do.call(gf_area,
c(list(density ~ x, fill = ~ probability,
group = ~ group, data = Ddensity), plot_dots)) |>
gf_refine(
scale_fill_viridis_d(end = 0.9),
scale_color_viridis_d(end = 0.9)
)
}
return(plot)
}
#' @rdname pdist
#' @inheritParams stats::pgamma
#' @param log.p A logical indicating whether probabilities should be
#' returned on the log scale.
#' @seealso [qdist()], [xpnorm()], [xqnorm()].
#' @export
xpgamma <- function(q, shape, rate = 1, scale = 1/rate,
lower.tail = TRUE, log.p = FALSE, ...) {
if (!missing(rate) && !missing(scale)) {
if (abs(rate * scale - 1) < 1e-15)
warning("specify 'rate' or 'scale' but not both")
else stop("specify 'rate' or 'scale' but not both")
}
pdist("gamma", q = q, shape = shape, scale = scale,
lower.tail = lower.tail, log.p = log.p, ...)
}
#' @rdname qdist
#' @inheritParams stats::qgamma
#' @param log.p A logical indicating whether probabilities should be
#' returned on the log scale.
#' @export
xqgamma <- function(p, shape, rate = 1, scale = 1/rate,
lower.tail = TRUE, log.p = FALSE, ...) {
if (!missing(rate) && !missing(scale)) {
if (abs(rate * scale - 1) < 1e-15)
warning("specify 'rate' or 'scale' but not both")
else stop("specify 'rate' or 'scale' but not both")
}
qdist("gamma", p = p, shape = shape, scale = scale,
lower.tail = lower.tail, log.p = log.p,
...)
}
#' @rdname cdist
#' @inheritParams stats::qgamma
#' @param log.p A logical indicating whether probabilities should be
#' returned on the log scale.
#' @export
xcgamma <- function(p, shape, rate = 1, scale = 1/rate,
lower.tail = TRUE, log.p = FALSE, ...) {
if (!missing(rate) && !missing(scale)) {
if (abs(rate * scale - 1) < 1e-15)
warning("specify 'rate' or 'scale' but not both")
else stop("specify 'rate' or 'scale' but not both")
}
cdist("gamma", q = q, shape = shape, scale = scale,
lower.tail = lower.tail, log.p = log.p,
...)
}
#' @rdname pdist
#' @inheritParams stats::pt
#' @param log.p A logical indicating whether probabilities should be
#' returned on the log scale.
#' @export
xpt <- function(q, df , ncp, lower.tail = TRUE, log.p = FALSE, ...)
if (missing(ncp)) {
pdist("t", q = q, df = df, lower.tail = lower.tail,
log.p = log.p, ...)
} else {
pdist("t", q = q, df = df, ncp = ncp, lower.tail = lower.tail,
log.p = log.p, ...)
}
#' @rdname qdist
#' @inheritParams stats::qt
#' @param log.p A logical indicating whether probabilities should be
#' returned on the log scale.
#' @export
xqt <- function(p, df , ncp, lower.tail = TRUE, log.p = FALSE, ...)
if (missing(ncp)) {
qdist("t", p = p, df = df, lower.tail = lower.tail,
log.p = log.p, ...)
} else {
qdist("t", p = p, df = df, ncp = ncp, lower.tail = lower.tail,
log.p = log.p, ...)
}
#' @rdname cdist
#' @inheritParams stats::pt
#' @param log.p A logical indicating whether probabilities should be
#' returned on the log scale.
#' @export
xct <- function(p, df , ncp, lower.tail = TRUE, log.p = FALSE, ...)
if (missing(ncp)) {
cdist("t", p = p, df = df, lower.tail = lower.tail,
log.p = log.p, ...)
} else {
cdist("t", p = p, df = df, ncp = ncp, lower.tail = lower.tail,
log.p = log.p, ...)
}
#' @rdname pdist
#' @export
xpchisq <- function(q, df, ncp = 0, lower.tail = TRUE, log.p = FALSE, ...)
pdist("chisq", q = q, df = df, ncp = ncp, lower.tail = lower.tail,
log.p = log.p, ...)
#' @rdname qdist
#' @export
xqchisq <- function(p, df, ncp = 0, lower.tail = TRUE, log.p = FALSE, ...)
qdist("chisq", p = p, df = df, ncp = ncp, lower.tail = lower.tail,
log.p = log.p, ...)
#' @rdname cdist
#' @export
xcchisq <- function(p, df, ncp = 0, lower.tail = TRUE, log.p = FALSE, ...)
cdist("chisq", p = p, df = df, ncp = ncp, lower.tail = lower.tail,
log.p = log.p, ...)
#' @rdname pdist
#' @inheritParams stats::pf
#' @export
xpf <- function(q, df1, df2, lower.tail = TRUE, log.p = FALSE, ...)
pdist("f", q = q, df1 = df1, df2 =df2,
lower.tail = lower.tail, log.p = log.p, ...)
#' @rdname qdist
#' @inheritParams stats::qf
#' @export
xqf <- function(p, df1, df2, lower.tail = TRUE, log.p = FALSE, ...)
qdist("f", p = p, df1 = df1, df2 =df2,
lower.tail = lower.tail, log.p = log.p, ...)
#' @rdname cdist
#' @inheritParams stats::qf
#' @export
xcf <- function(p, df1, df2, lower.tail = TRUE, log.p = FALSE, ...)
cdist("f", p = p, df1 = df1, df2 =df2,
lower.tail = lower.tail, log.p = log.p, ...)
#' @rdname pdist
#' @inheritParams stats::pbinom
#' @export
xpbinom <- function(q, size, prob, lower.tail = TRUE, log.p = FALSE, ...)
pdist("binom", q = q, size = size, prob = prob,
lower.tail = lower.tail, log.p = log.p, ...)
#' @rdname qdist
#' @inheritParams stats::qbinom
#' @export
xqbinom <- function(p, size, prob, lower.tail = TRUE, log.p = FALSE, ...)
qdist("binom", p = p, size = size, prob = prob,
lower.tail = lower.tail, log.p = log.p, ...)
#' @rdname cdist
#' @inheritParams stats::qbinom
#' @export
xcbinom <- function(p, size, prob, lower.tail = TRUE, log.p = FALSE, ...)
cdist("binom", p = p, size = size, prob = prob,
lower.tail = lower.tail, log.p = log.p, ...)
#' @rdname pdist
#' @inheritParams stats::ppois
#' @export
xppois <- function(q, lambda, lower.tail = TRUE, log.p = FALSE, ...)
pdist("pois", q = q, lambda = lambda,
lower.tail = lower.tail, log.p = log.p, ...)
#' @rdname qdist
#' @inheritParams stats::qpois
#' @export
xqpois <- function(p, lambda, lower.tail = TRUE, log.p = FALSE, ...)
qdist("pois", p = p, lambda = lambda,
lower.tail = lower.tail, log.p = log.p, ...)
#' @rdname cdist
#' @inheritParams stats::qpois
#' @export
xcpois <- function(p, lambda, lower.tail = TRUE, log.p = FALSE, ...)
cdist("pois", p = p, lambda = lambda,
lower.tail = lower.tail, log.p = log.p, ...)
#' @rdname pdist
#' @inheritParams stats::pgeom
#' @export
xpgeom <- function(q, prob, lower.tail = TRUE, log.p = FALSE, ...)
pdist("geom", q = q, prob = prob,
lower.tail = lower.tail, log.p = log.p, ...)
#' @rdname qdist
#' @inheritParams stats::qgeom
#' @export
xqgeom <- function(p, prob, lower.tail = TRUE, log.p = FALSE, ...)
qdist("geom", p = p, prob = prob,
lower.tail = lower.tail, log.p = log.p, ...)
#' @rdname cdist
#' @inheritParams stats::qgeom
#' @export
xcgeom <- function(p, prob, lower.tail = TRUE, log.p = FALSE, ...)
cdist("geom", p = p, prob = prob,
lower.tail = lower.tail, log.p = log.p, ...)
#' @rdname pdist
#' @inheritParams stats::pnbinom
#' @export
xpnbinom <- function(q, size, prob, mu, lower.tail = TRUE, log.p = FALSE, ...)
pdist("nbinom", q = q, size = size, prob =prob, mu = mu,
lower.tail = lower.tail, log.p = log.p, ...)
#' @rdname qdist
#' @inheritParams stats::qnbinom
#' @export
xqnbinom <- function(p, size, prob, mu, lower.tail = TRUE, log.p = FALSE, ...)
qdist("nbinom", p = p, size = size, prob =prob, mu = mu,
lower.tail = lower.tail, log.p = log.p, ...)
#' @rdname cdist
#' @inheritParams stats::qnbinom
#' @export
xcnbinom <- function(p, size, prob, mu, lower.tail = TRUE, log.p = FALSE, ...)
cdist("nbinom", p = p, size = size, prob =prob, mu = mu,
lower.tail = lower.tail, log.p = log.p, ...)
#' @rdname pdist
#' @inheritParams stats::pbeta
#' @export
xpbeta <- function(q, shape1, shape2, ncp = 0,
lower.tail = TRUE, log.p = FALSE, ...)
pdist("beta", q = q, shape1 = shape1, shape2 = shape2, ncp = 0,
lower.tail = lower.tail, log.p = log.p, ...)
#' @rdname qdist
#' @inheritParams stats::qbeta
#' @export
xqbeta <- function(p, shape1, shape2, ncp = 0,
lower.tail = TRUE, log.p = FALSE, ...)
qdist("beta", p = p, shape1 = shape1, shape2 = shape2, ncp = 0,
lower.tail = lower.tail, log.p = log.p, ...)
#' @rdname cdist
#' @inheritParams stats::qbeta
#' @export
xcbeta <- function(p, shape1, shape2, ncp = 0,
lower.tail = TRUE, log.p = FALSE, ...)
cdist("beta", p = p, shape1 = shape1, shape2 = shape2, ncp = 0,
lower.tail = lower.tail, log.p = log.p, ...)
is_discrete_dist <- function(dist, resolution = 100L, ... ) {
q <- dpqrdist(dist, type = "q", p = ppoints(resolution), ...)
# continuous dists should have all unique values
# discrete dists will typically have many ties
# discrete dists with many values may look continuous
length(unique(q)) / length(q) <= 0.95
}
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