inst/other_stuff/old_r_functions/distribution.plot.R
In Auburngrads/SMRD: Statistical Methods For Reliability Data
#' Create a four-panel distribution plot
#'
#' @param distribution Generates an array of ggplot2 grobs showing
#' the cdf, pdf, survival function and hazard
#' function for a distribution
#' @param shape A vector of shape parameters
#' @param prob.range A vector (length 2) of values between 0 and 1
#' providing the lower and upper limits of the
#' probability range
#' @param number.points The number points to be used in the plot
#' @param scale A numeric value of the scale parameter (see Details)
#' @param location A numeric value of the location parameter (see Details)
#' @param shape2 A numeric value of the second shape parameter (see Details)
#' @param exponential2 Is this 2-param exponential
#' @param cex A positive numeric value giving the amount by which the
#' plot text should be magnified relative to the default.
#' @param lwd A positive numeric value giving the amount by which the
#' line widths should be magnified relative to the default.
#' @param ... Currently not used
#'
#' @importFrom ggplot2 ggplot geom_line aes theme_bw facet_wrap
#' @importFrom stats qlogis dlogis plogis
#' @importFrom stats qnorm dnorm pnorm
#' @importFrom stats qlnorm dlnorm plnorm
#' @importFrom stats qweibull dweibull pweibull
#' @importFrom stats qexp dexp pexp
#' @importFrom stats qgamma dgamma pgamma
#' @export
#'
#' @examples
#' \dontrun{
#' distribution.plot("Exponential",
#' shape = c(.5,1))
#'
#' distribution.plot("Lognormal",
#' shape = c(.3, .8))
#'
#' distribution.plot("Normal",
#' shape = c( .30, .5,.8),
#' location = 5)
#'
#' distribution.plot("Weibull",
#' shape = c(.8,1,1.5))
#'
#' distribution.plot("Smallest Extreme Value",
#' shape = c(5,6,7),
#' location = 50)
#'
#' distribution.plot("Largest Extreme Value",
#' shape = c(5, 6, 7),
#' location = 10)
#'
#' distribution.plot("Logistic",
#' shape = c(1, 2, 3),
#' location = 15)
#'
#' distribution.plot("Loglogistic",
#' shape = c(.2,.4,.6),
#' prob.range = c(0.001, 0.95))
#' }
distribution.plot <-
function (distribution,
shape,
prob.range = c(0.01, 0.99),
number.points = 1000,
type = "all",
axsi = F,
scale = rep(1, length(shape)),
plot.haz.log = F,
original.par = T,
my.title = NULL,
my.subtitle = NULL,
location = rep(0,length(shape)),
char.fudge = "",
shape2 = 1,
exponential2 = T,...)
{
invisible()
assign(envir = .frame0, inherits = TRUE,"my.title", my.title)
assign(envir = .frame0, inherits = TRUE,"my.subtitle", my.subtitle)
old.par <- par()
lwd = 2
par(err = -1, bg = NA)
if (original.par)
on.exit({
old.par[c("cin", "cra", "csi", "cxy", "din", "page")] <- NULL
par(old.par)
par(mfrow = c(1,1))
par(new = F)
})
if (is.null(scale)) scale <- rep(1, max(1, length(shape)))
distribution.plot.range <- function(distribution,
shape,
scale,
prob.range,
location,
shape2) {
switch(generic.distribution(distribution),
exponential = {
return(c(min(qexp(prob.range[1], shape)),
max(qexp(prob.range[2], 1 / shape))))
}, normal = {
return(c(min(qnorm(prob.range[1], mean = location, shape)),
max(qnorm(prob.range[2], mean = location, shape))))
}, logistic = {
return(c(min(qlogis(prob.range[1], location = location, shape)),
max(qlogis(prob.range[2], location = location, shape))))
}, lev = {
return(c(min(qlev(prob.range[1], location = location, shape)),
max(qlev(prob.range[2], location = location, shape))))
}, sev = {
return(c(min(qsev(prob.range[1], location = location, shape)),
max(qsev(prob.range[2], location = location, shape))))
}, weibull = {
return(c(min(qweibull(prob.range[1], shape)),
max(qweibull(prob.range[2], shape))))
}, gamma = {
return(c(min(qgamma(prob.range[1], shape)),
max(qgamma(prob.range[2], shape))))
}, igau = {
return(c(min(qigau(prob.range[1], shape)),
max(qigau(prob.range[2], shape))))
}, bisa = {
return(c(min(qbisa(prob.range[1], shape)),
max(qbisa(prob.range[2], shape))))
}, goma = {
return(c(min(qgoma(prob.range[1], shape, shape2)),
max(qgoma(prob.range[2], shape, shape2))))
}, lognormal = {
return(c(min(qlnorm(prob.range[1], 0, shape)),
max(qlnorm(prob.range[2], 0, shape))))
}, loglogistic = {
return(c(min(qloglogis(prob.range[1], 0, shape)),
max(qloglogis(prob.range[2], 0, shape))))
})
}
pdf.plot <- function(x,
distribution,
shape,
scale = 1,
location,
shape2,...) {
par(mar = c(4.25, 5, 3.75, 2) + 0.1)
switch(generic.distribution(distribution),
exponential = {
matplot(x, y = outer(x, 1/shape, dexp), lty = 1:length(shape),
type = "l", las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd,
...)
}, weibull = {
matplot(x, y = outer(x, shape, dweibull), lty = 1:length(shape),
type = "l", las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd,
...)
}, sev = {
matplot(x, y = dist.outer(x, shape, dummy = location,
dsev), lty = 1:length(shape), type = "l", las = 1,
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, lev = {
matplot(x, y = dist.outer(x, shape, dummy = location,
dlev), lty = 1:length(shape), type = "l", las = 1,
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, logistic = {
matplot(x, y = dist.outer(x, shape, dummy = location,
dlogis), lty = 1:length(shape), type = "l", las = 1,
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, normal = {
matplot(x, y = dist.outer(x, shape, dummy = location,
dnorm), lty = 1:length(shape), type = "l", las = 1,
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, lognormal = {
matplot(x, y = dist.outer(x, shape, 0, dlnorm), lty = 1:length(shape),
type = "l", las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd,
...)
}, loglogistic = {
matplot(x, y = dist.outer(x, shape, 0, dloglogis),
lty = 1:length(shape), type = "l", las = 1, cex = 1.2, cex.axis = 1.1,
xlab = "", ylab = "", lwd = lwd, ...)
}, gamma = {
matplot(x, y = outer(x, shape, dgamma), lty = 1:length(shape),
type = "l", las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd,
...)
}, igau = {
matplot(x, y = outer(x, shape, digau), lty = 1:length(shape),
type = "l", las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd,
...)
}, bisa = {
matplot(x, y = outer(x, shape, dbisa), lty = 1:length(shape),
type = "l", las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd,
...)
}, goma = {
matplot(x, y = dist.outer(x, shape, shape2, dgoma),
lty = 1:length(shape), type = "l", las = 1, cex = 1.2, cex.axis = 1.1,
xlab = "", ylab = "", lwd = lwd, ...)
})
if (is.null(my.subtitle))
title("Probability Density Function", cex = 1)
title(xlab = "t", cex.lab = 1.5)
usr.par <- par("usr")
xpos <- usr.par[1] - 0.25 * (usr.par[2] - usr.par[1])
ypos <- usr.par[3] + 0.5 * (usr.par[4] - usr.par[3])
mtext(side = 2, line = 3, srt = 90, "f(t)", cex = 1.1)
}
cdf.plot <- function(x,
distribution,
shape,
scale,
location,
shape2, ...) {
par(mar = c(4.25, 5, 3.75, 2) + 0.1)
switch(generic.distribution(distribution), exponential = {
matplot(x, y = outer(x, 1/shape, pexp), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, weibull = {
matplot(x, y = outer(x, shape, pweibull), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, sev = {
matplot(x, y = dist.outer(x, shape, dummy = location,
psev), lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, lev = {
matplot(x, y = dist.outer(x, shape, dummy = location,
plev), lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, logistic = {
matplot(x, y = dist.outer(x, shape, dummy = location,
plogis), lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, normal = {
matplot(x, y = dist.outer(x, shape, dummy = location,
pnorm), lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, lognormal = {
matplot(x, y = dist.outer(x, shape, 0, plnorm), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, loglogistic = {
matplot(x, y = dist.outer(x, shape, 0, ploglogis),
lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, gamma = {
matplot(x, y = outer(x, shape, pgamma), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, igau = {
matplot(x, y = outer(x, shape, pigau), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, bisa = {
matplot(x, y = outer(x, shape, pbisa), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, goma = {
matplot(x, y = dist.outer(x, shape, shape2, pgoma),
lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
})
cdf.ylabs <- c("0", ".5", "1")
axis(2, at = as.numeric(cdf.ylabs), labels = cdf.ylabs,
cex = 1.2, las = 1, hadj = .4)
if (is.null(my.subtitle))
title("Cumulative Distribution Function", cex = 1)
title(xlab = "t", cex.lab = 1.5)
usr.par <- par("usr")
xpos <- usr.par[1] - 0.2 * (usr.par[2] - usr.par[1])
ypos <- usr.par[3] + 0.5 * (usr.par[4] - usr.par[3])
mtext(side = 2, line = 3, srt = 90, "F(t)", cex = 1.1)
}
hf.plot <- function(x, distribution, shape, scale = rep(1, length(shape)),
plot.haz.log = F, location, shape2, ...) {
par(mar = c(4.25, 5, 3.25, 2) + 0.1)
if (plot.haz.log)
logger <- "y"
else logger <- ""
switch(generic.distribution(distribution), exponential = {
matplot(x,
y = outer(x, 1/shape, dexp)/(outer(1/x,1/shape, pexp)),
lty = 1:length(shape), type = "l",
log = logger, las = 1, cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, weibull = {
# ymat <- matrix(0, nrow = length(x), ncol = length(shape))
# for (i in 1:length(shape)) {
# ymat[, i] <- (shape[i]/scale[i]) * (x/scale[i])^(shape[i] - 1)
# }
matplot(x,
y = dist.outer(x, shape, dummy = scale, dweibull)/(dist.outer(x, shape, dummy = scale, pweibull)),
lty = 1:length(shape), type = "l",
log = logger, las = 1, cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, normal = {
matplot(x,
y = dist.outer(x, shape, dummy = location,dnorm)/(dist.outer(-x, shape, dummy = -location,pnorm)),
lty = 1:length(shape), type = "l", log = logger,
las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "",
lwd = lwd, ...)
}, sev = {
matplot(x,
y = dist.outer(x, shape, dummy = location,dsev)/(dist.outer(x, shape, dummy = location,ssev)),
lty = 1:length(shape), type = "l", log = logger,
las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "",
lwd = lwd, ...)
}, lev = {
matplot(x,
y = dist.outer(x, shape, dummy = location,dlev)/(dist.outer(x, shape, dummy = location,slev)),
lty = 1:length(shape), type = "l", log = logger,
las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "",
lwd = lwd, ...)
}, logistic = {
matplot(x,
y = dist.outer(x, shape, dummy = location,dlogis)/(dist.outer(-x, shape, dummy = -location,plogis)),
lty = 1:length(shape), type = "l",
log = logger, las = 1, cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, loglogistic = {
matplot(x,
y = dist.outer(x, shape, 0, dloglogis)/(dist.outer(1/(x), shape, 0, ploglogis)),
lty = 1:length(shape),
type = "l", log = logger, las = 1, cex = 1.2, cex.axis = 1.1,
xlab = "", ylab = "", lwd = lwd, ...)
}, gamma = {
matplot(x,
y = outer(x, shape, dgamma)/(outer(1/x, shape, pgamma)),
lty = 1:length(shape), type = "l",
log = logger, las = 1, cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, igau = {
matplot(x,
y = outer(x, shape, digau)/(outer(x, shape, sigau)),
lty = 1:length(shape), type = "l", log = logger,
las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "",
lwd = lwd, ...)
}, bisa = {
matplot(x,
y = outer(x, shape, dbisa)/(outer(x, shape,sbisa)),
lty = 1:length(shape), type = "l", log = logger,
las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "",
lwd = lwd, ...)
}, goma = {
matplot(x,
y = dist.outer(x, shape, shape2, dgoma)/(dist.outer(x,shape, shape2, sgoma)),
lty = 1:length(shape),
type = "l", log = logger, las = 1, cex = 1.2, cex.axis = 1.1,
xlab = "", ylab = "", lwd = lwd, ...)
}, lognormal = {
matplot(x,
y = dist.outer(x, shape, 0, dlnorm)/(dist.outer(1/(x),shape, 0, plnorm)),
lty = 1:length(shape), type = "l",
log = logger, xlab = "", ylab = "", lwd = lwd, las = 1,
cex = 1.2, cex.axis = 1.1, ...)
})
title(xlab = "t", cex.lab = 1.5)
usr.par <- par("usr")
mtext(side = 2, line = 3, srt = 90, "h(t)", cex = 1.1)
if (is.null(my.subtitle))
title("Hazard Function", cex = 1)
}
sf.plot <- function(x, distribution, shape, scale, location,
shape2, ...) {
par(mar = c(4.25, 5, 3.25, 2) + 0.1)
switch(generic.distribution(distribution), exponential = {
matplot(1/x, y = outer(x, 1/shape, pexp), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, weibull = {
matplot(1/x, y = outer(x, shape, pweibull), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, loglogistic = {
matplot(1/x, y = outer(x, shape, ploglogis), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, gamma = {
matplot(1/x, y = outer(x, shape, pgamma), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, igau = {
matplot(x, y = outer(x, shape, sigau), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, bisa = {
matplot(x, y = outer(x, shape, sbisa), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, goma = {
matplot(x, y = dist.outer(x, shape, shape2, sgoma),
lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, sev = {
matplot(x, y = dist.outer(x, shape, dummy = location,
ssev), lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, lev = {
matplot(x, y = dist.outer(x, shape, dummy = location,
slev), lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, logistic = {
matplot(x, y = dist.outer(-x, shape, dummy = location,
plogis), lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, ...)
}, normal = {
matplot(x, y = dist.outer(-x, shape, dummy = location,
pnorm), lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, ...)
}, lognormal = {
matplot(x, y = dist.outer(1/(x), shape, 0, plnorm),
lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
})
cdf.ylabs <- c("0", ".5", "1")
axis(2, at = as.numeric(cdf.ylabs), labels = cdf.ylabs,
cex = 1.2, las = 1, hadj = .4)
usr.par <- par("usr")
xpos <- usr.par[1] - 0.18 * (usr.par[2] - usr.par[1])
ypos <- usr.par[3] + 0.5 * (usr.par[4] - usr.par[3])
mtext(side = 2, line = 3, srt = 90, "S(t)", cex = 1.1)
title(xlab = "t", cex.lab = 1.5)
if (is.null(my.subtitle))
title("Survival Function", cex = 1)
}
parameter.plot <- function(distribution, shape, scale, location,
shape2, cexchar = 2, char.fudge = "", exponential2 = T) {
cexchar <- cexchar * 0.5
par(mar = c(4.25, 5, 3.25, 2) + 0.1)
par(xaxt = "n", yaxt = "n", bty = "n")
parameter2.name <- NULL
parameter1 <- shape
char.eta <- "eta "
char.mu <- "mu "
char.nu <- "nu "
char.sigma <- "sigma"
char.kappa <- "kappa"
char.theta <- "theta"
char.zeta <- "zeta "
char.gamma <- "gamma"
char.eta <- "eta "
char.beta <- "beta "
char.xi <- "xi "
switch(generic.distribution(distribution), exponential = {
parameter1.name <- char.theta
if (exponential2) {
textxy1 <- c(0.1, 0.92)
parameter2.name <- char.gamma
} else {
textxy1 <- c(0.3, 0.9)
parameter2.name <- NULL
}
parameter2 <- rep(0, length(parameter1))
}, normal = {
parameter2 <- location
parameter2.name <- char.mu
parameter1.name <- char.sigma
}, sev = {
parameter2 <- location
parameter2.name <- char.mu
parameter1.name <- char.sigma
}, lev = {
parameter2 <- location
parameter2.name <- char.mu
parameter1.name <- char.sigma
}, logistic = {
parameter2 <- location
parameter2.name <- char.mu
parameter1.name <- char.sigma
}, loglogistic = {
parameter2 <- location
parameter1.name <- char.sigma
parameter2.name <- char.mu
}, gamma = {
parameter2 <- scale
parameter1.name <- char.kappa
parameter2.name <- char.theta
}, igau = {
parameter2 <- scale
parameter1.name <- char.beta
parameter2.name <- char.theta
}, bisa = {
parameter2 <- scale
parameter1.name <- char.beta
parameter2.name <- char.theta
}, goma = {
parameter2 <- shape2
parameter3 <- scale
parameter1.name <- char.zeta
parameter2.name <- char.eta
parameter3.name <- char.theta
}, weibull = {
parameter2 <- scale
parameter1.name <- char.beta
parameter2.name <- char.eta
}, lognormal = {
parameter2 <- 0
parameter1.name <- char.sigma
parameter2.name <- char.mu
}, )
plot(c(0, 1), c(0, 1), type = "n", xlab = "", ylab = "", lwd = lwd,
cex = 1.2, cex.axis = 1.1, frame.plot = FALSE)
if (is.null(parameter2.name)) {
legend("top",
parse(text = paste(parameter1.name,"~~",format(parameter1, nsmall = 2), sep = "~")),
col = c(0,1:length(parameter1)), lty = c(0,1:length(parameter1)),
cex = 1.25, lwd = 2, y.intersp = 1.25-0.05*length(parameter1),
bty = "n", inset = -0.05)
} else {
legend("top",
c(parse(text = paste(parameter1.name,"~~~~~~~~~~", parameter2.name,sep = "~")),
paste(format(parameter1, nsmall = 2),format(parameter2, nsmall = 2), sep = " ")),
col = c(0,1:length(parameter1)), lty = c(0,1:length(parameter1)),
cex = 1.5-0.05*length(parameter1), y.intersp = 1.25-0.05*length(parameter1), lwd = 2,
bty = "n", inset = -0.05)
}
}
if (axsi)
old.par <- par(yaxs = "i", xaxs = "i")
else old.par <- par()
if (original.par)
on.exit({
old.par[c("cin", "cra", "csi", "cxy", "din", "page")] <- NULL
par(old.par)
par(mfrow = c(1,1))
par(new = F)
})
xx <- distribution.plot.range(distribution = distribution,
shape = shape, scale = scale, prob.range = prob.range,
location = location, shape2 = shape2)
x <- seq(xx[1], xx[2], length = number.points)
switch(type, all = {
if (!is.null(my.title) && my.title == "") {
par(mfrow = c(2, 2), oma = c(0, 0, 0, 0))
} else {
par(mfrow = c(2, 2), oma = c(0, 0, 0, 0))
}
cdf.plot(x, distribution, shape, scale, location, shape2,
...)
pdf.plot(x, distribution, shape, scale, location, shape2,
...)
if (length(shape) > 1) {
hf.plot(x, distribution, shape, scale, plot.haz.log,
location, shape2, ...)
parameter.plot(distribution, shape, scale, location,
shape2, char.fudge = char.fudge, exponential2 = exponential2)
} else {
sf.plot(x, distribution, shape, scale, location,
shape2, ...)
hf.plot(x, distribution, shape, scale, plot.haz.log,
location, shape2, ...)
}
if (is.null(my.title)) mtext(side = 3, line = 0, cex = 1.5,
outer = TRUE, paste(distribution, " Distribution"))
}, pdfcdf = {
par(mfrow = c(1, 2), oma = c(0, 0, 0, 0))
cdf.plot(x, distribution, shape, scale, location, shape2,
...)
pdf.plot(x, distribution, shape, scale, location, shape2,
...)
if (is.null(my.title)) mtext(side = 3, line = 0, cex = 1.5,
outer = TRUE, paste(distribution, " Distribution"))
}, pdf = {
par(oma = c(0, 0, 0, 0))
pdf.plot(x, distribution, shape, scale, location, shape2,
...)
}, cdf = {
par(oma = c(0, 0, 0, 0))
cdf.plot(x, distribution, shape, scale, location, shape2,
...)
}, sf = {
par(oma = c(0, 0, 0, 0))
sf.plot(x, distribution, shape, scale, location, shape2,
...)
}, hf = {
par(oma = c(0, 0, 0, 0))
hf.plot(x, distribution, shape, scale, plot.haz.log,
location, shape2, ...)
})
if (type != "all") {
if (is.null(my.title))
mtext(side = 3, line = 0, cex = 1.5, outer = TRUE,
paste(distribution, " Distribution"))
}
}
Auburngrads/SMRD documentation built on Sept. 14, 2020, 2:21 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#' Create a four-panel distribution plot
#'
#' @param distribution Generates an array of ggplot2 grobs showing
#' the cdf, pdf, survival function and hazard
#' function for a distribution
#' @param shape A vector of shape parameters
#' @param prob.range A vector (length 2) of values between 0 and 1
#' providing the lower and upper limits of the
#' probability range
#' @param number.points The number points to be used in the plot
#' @param scale A numeric value of the scale parameter (see Details)
#' @param location A numeric value of the location parameter (see Details)
#' @param shape2 A numeric value of the second shape parameter (see Details)
#' @param exponential2 Is this 2-param exponential
#' @param cex A positive numeric value giving the amount by which the
#' plot text should be magnified relative to the default.
#' @param lwd A positive numeric value giving the amount by which the
#' line widths should be magnified relative to the default.
#' @param ... Currently not used
#'
#' @importFrom ggplot2 ggplot geom_line aes theme_bw facet_wrap
#' @importFrom stats qlogis dlogis plogis
#' @importFrom stats qnorm dnorm pnorm
#' @importFrom stats qlnorm dlnorm plnorm
#' @importFrom stats qweibull dweibull pweibull
#' @importFrom stats qexp dexp pexp
#' @importFrom stats qgamma dgamma pgamma
#' @export
#'
#' @examples
#' \dontrun{
#' distribution.plot("Exponential",
#' shape = c(.5,1))
#'
#' distribution.plot("Lognormal",
#' shape = c(.3, .8))
#'
#' distribution.plot("Normal",
#' shape = c( .30, .5,.8),
#' location = 5)
#'
#' distribution.plot("Weibull",
#' shape = c(.8,1,1.5))
#'
#' distribution.plot("Smallest Extreme Value",
#' shape = c(5,6,7),
#' location = 50)
#'
#' distribution.plot("Largest Extreme Value",
#' shape = c(5, 6, 7),
#' location = 10)
#'
#' distribution.plot("Logistic",
#' shape = c(1, 2, 3),
#' location = 15)
#'
#' distribution.plot("Loglogistic",
#' shape = c(.2,.4,.6),
#' prob.range = c(0.001, 0.95))
#' }
distribution.plot <-
function (distribution,
shape,
prob.range = c(0.01, 0.99),
number.points = 1000,
type = "all",
axsi = F,
scale = rep(1, length(shape)),
plot.haz.log = F,
original.par = T,
my.title = NULL,
my.subtitle = NULL,
location = rep(0,length(shape)),
char.fudge = "",
shape2 = 1,
exponential2 = T,...)
{
invisible()
assign(envir = .frame0, inherits = TRUE,"my.title", my.title)
assign(envir = .frame0, inherits = TRUE,"my.subtitle", my.subtitle)
old.par <- par()
lwd = 2
par(err = -1, bg = NA)
if (original.par)
on.exit({
old.par[c("cin", "cra", "csi", "cxy", "din", "page")] <- NULL
par(old.par)
par(mfrow = c(1,1))
par(new = F)
})
if (is.null(scale)) scale <- rep(1, max(1, length(shape)))
distribution.plot.range <- function(distribution,
shape,
scale,
prob.range,
location,
shape2) {
switch(generic.distribution(distribution),
exponential = {
return(c(min(qexp(prob.range[1], shape)),
max(qexp(prob.range[2], 1 / shape))))
}, normal = {
return(c(min(qnorm(prob.range[1], mean = location, shape)),
max(qnorm(prob.range[2], mean = location, shape))))
}, logistic = {
return(c(min(qlogis(prob.range[1], location = location, shape)),
max(qlogis(prob.range[2], location = location, shape))))
}, lev = {
return(c(min(qlev(prob.range[1], location = location, shape)),
max(qlev(prob.range[2], location = location, shape))))
}, sev = {
return(c(min(qsev(prob.range[1], location = location, shape)),
max(qsev(prob.range[2], location = location, shape))))
}, weibull = {
return(c(min(qweibull(prob.range[1], shape)),
max(qweibull(prob.range[2], shape))))
}, gamma = {
return(c(min(qgamma(prob.range[1], shape)),
max(qgamma(prob.range[2], shape))))
}, igau = {
return(c(min(qigau(prob.range[1], shape)),
max(qigau(prob.range[2], shape))))
}, bisa = {
return(c(min(qbisa(prob.range[1], shape)),
max(qbisa(prob.range[2], shape))))
}, goma = {
return(c(min(qgoma(prob.range[1], shape, shape2)),
max(qgoma(prob.range[2], shape, shape2))))
}, lognormal = {
return(c(min(qlnorm(prob.range[1], 0, shape)),
max(qlnorm(prob.range[2], 0, shape))))
}, loglogistic = {
return(c(min(qloglogis(prob.range[1], 0, shape)),
max(qloglogis(prob.range[2], 0, shape))))
})
}
pdf.plot <- function(x,
distribution,
shape,
scale = 1,
location,
shape2,...) {
par(mar = c(4.25, 5, 3.75, 2) + 0.1)
switch(generic.distribution(distribution),
exponential = {
matplot(x, y = outer(x, 1/shape, dexp), lty = 1:length(shape),
type = "l", las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd,
...)
}, weibull = {
matplot(x, y = outer(x, shape, dweibull), lty = 1:length(shape),
type = "l", las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd,
...)
}, sev = {
matplot(x, y = dist.outer(x, shape, dummy = location,
dsev), lty = 1:length(shape), type = "l", las = 1,
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, lev = {
matplot(x, y = dist.outer(x, shape, dummy = location,
dlev), lty = 1:length(shape), type = "l", las = 1,
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, logistic = {
matplot(x, y = dist.outer(x, shape, dummy = location,
dlogis), lty = 1:length(shape), type = "l", las = 1,
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, normal = {
matplot(x, y = dist.outer(x, shape, dummy = location,
dnorm), lty = 1:length(shape), type = "l", las = 1,
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, lognormal = {
matplot(x, y = dist.outer(x, shape, 0, dlnorm), lty = 1:length(shape),
type = "l", las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd,
...)
}, loglogistic = {
matplot(x, y = dist.outer(x, shape, 0, dloglogis),
lty = 1:length(shape), type = "l", las = 1, cex = 1.2, cex.axis = 1.1,
xlab = "", ylab = "", lwd = lwd, ...)
}, gamma = {
matplot(x, y = outer(x, shape, dgamma), lty = 1:length(shape),
type = "l", las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd,
...)
}, igau = {
matplot(x, y = outer(x, shape, digau), lty = 1:length(shape),
type = "l", las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd,
...)
}, bisa = {
matplot(x, y = outer(x, shape, dbisa), lty = 1:length(shape),
type = "l", las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd,
...)
}, goma = {
matplot(x, y = dist.outer(x, shape, shape2, dgoma),
lty = 1:length(shape), type = "l", las = 1, cex = 1.2, cex.axis = 1.1,
xlab = "", ylab = "", lwd = lwd, ...)
})
if (is.null(my.subtitle))
title("Probability Density Function", cex = 1)
title(xlab = "t", cex.lab = 1.5)
usr.par <- par("usr")
xpos <- usr.par[1] - 0.25 * (usr.par[2] - usr.par[1])
ypos <- usr.par[3] + 0.5 * (usr.par[4] - usr.par[3])
mtext(side = 2, line = 3, srt = 90, "f(t)", cex = 1.1)
}
cdf.plot <- function(x,
distribution,
shape,
scale,
location,
shape2, ...) {
par(mar = c(4.25, 5, 3.75, 2) + 0.1)
switch(generic.distribution(distribution), exponential = {
matplot(x, y = outer(x, 1/shape, pexp), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, weibull = {
matplot(x, y = outer(x, shape, pweibull), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, sev = {
matplot(x, y = dist.outer(x, shape, dummy = location,
psev), lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, lev = {
matplot(x, y = dist.outer(x, shape, dummy = location,
plev), lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, logistic = {
matplot(x, y = dist.outer(x, shape, dummy = location,
plogis), lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, normal = {
matplot(x, y = dist.outer(x, shape, dummy = location,
pnorm), lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, lognormal = {
matplot(x, y = dist.outer(x, shape, 0, plnorm), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, loglogistic = {
matplot(x, y = dist.outer(x, shape, 0, ploglogis),
lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, gamma = {
matplot(x, y = outer(x, shape, pgamma), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, igau = {
matplot(x, y = outer(x, shape, pigau), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, bisa = {
matplot(x, y = outer(x, shape, pbisa), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, goma = {
matplot(x, y = dist.outer(x, shape, shape2, pgoma),
lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
})
cdf.ylabs <- c("0", ".5", "1")
axis(2, at = as.numeric(cdf.ylabs), labels = cdf.ylabs,
cex = 1.2, las = 1, hadj = .4)
if (is.null(my.subtitle))
title("Cumulative Distribution Function", cex = 1)
title(xlab = "t", cex.lab = 1.5)
usr.par <- par("usr")
xpos <- usr.par[1] - 0.2 * (usr.par[2] - usr.par[1])
ypos <- usr.par[3] + 0.5 * (usr.par[4] - usr.par[3])
mtext(side = 2, line = 3, srt = 90, "F(t)", cex = 1.1)
}
hf.plot <- function(x, distribution, shape, scale = rep(1, length(shape)),
plot.haz.log = F, location, shape2, ...) {
par(mar = c(4.25, 5, 3.25, 2) + 0.1)
if (plot.haz.log)
logger <- "y"
else logger <- ""
switch(generic.distribution(distribution), exponential = {
matplot(x,
y = outer(x, 1/shape, dexp)/(outer(1/x,1/shape, pexp)),
lty = 1:length(shape), type = "l",
log = logger, las = 1, cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, weibull = {
# ymat <- matrix(0, nrow = length(x), ncol = length(shape))
# for (i in 1:length(shape)) {
# ymat[, i] <- (shape[i]/scale[i]) * (x/scale[i])^(shape[i] - 1)
# }
matplot(x,
y = dist.outer(x, shape, dummy = scale, dweibull)/(dist.outer(x, shape, dummy = scale, pweibull)),
lty = 1:length(shape), type = "l",
log = logger, las = 1, cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, normal = {
matplot(x,
y = dist.outer(x, shape, dummy = location,dnorm)/(dist.outer(-x, shape, dummy = -location,pnorm)),
lty = 1:length(shape), type = "l", log = logger,
las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "",
lwd = lwd, ...)
}, sev = {
matplot(x,
y = dist.outer(x, shape, dummy = location,dsev)/(dist.outer(x, shape, dummy = location,ssev)),
lty = 1:length(shape), type = "l", log = logger,
las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "",
lwd = lwd, ...)
}, lev = {
matplot(x,
y = dist.outer(x, shape, dummy = location,dlev)/(dist.outer(x, shape, dummy = location,slev)),
lty = 1:length(shape), type = "l", log = logger,
las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "",
lwd = lwd, ...)
}, logistic = {
matplot(x,
y = dist.outer(x, shape, dummy = location,dlogis)/(dist.outer(-x, shape, dummy = -location,plogis)),
lty = 1:length(shape), type = "l",
log = logger, las = 1, cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, loglogistic = {
matplot(x,
y = dist.outer(x, shape, 0, dloglogis)/(dist.outer(1/(x), shape, 0, ploglogis)),
lty = 1:length(shape),
type = "l", log = logger, las = 1, cex = 1.2, cex.axis = 1.1,
xlab = "", ylab = "", lwd = lwd, ...)
}, gamma = {
matplot(x,
y = outer(x, shape, dgamma)/(outer(1/x, shape, pgamma)),
lty = 1:length(shape), type = "l",
log = logger, las = 1, cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, igau = {
matplot(x,
y = outer(x, shape, digau)/(outer(x, shape, sigau)),
lty = 1:length(shape), type = "l", log = logger,
las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "",
lwd = lwd, ...)
}, bisa = {
matplot(x,
y = outer(x, shape, dbisa)/(outer(x, shape,sbisa)),
lty = 1:length(shape), type = "l", log = logger,
las = 1, cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "",
lwd = lwd, ...)
}, goma = {
matplot(x,
y = dist.outer(x, shape, shape2, dgoma)/(dist.outer(x,shape, shape2, sgoma)),
lty = 1:length(shape),
type = "l", log = logger, las = 1, cex = 1.2, cex.axis = 1.1,
xlab = "", ylab = "", lwd = lwd, ...)
}, lognormal = {
matplot(x,
y = dist.outer(x, shape, 0, dlnorm)/(dist.outer(1/(x),shape, 0, plnorm)),
lty = 1:length(shape), type = "l",
log = logger, xlab = "", ylab = "", lwd = lwd, las = 1,
cex = 1.2, cex.axis = 1.1, ...)
})
title(xlab = "t", cex.lab = 1.5)
usr.par <- par("usr")
mtext(side = 2, line = 3, srt = 90, "h(t)", cex = 1.1)
if (is.null(my.subtitle))
title("Hazard Function", cex = 1)
}
sf.plot <- function(x, distribution, shape, scale, location,
shape2, ...) {
par(mar = c(4.25, 5, 3.25, 2) + 0.1)
switch(generic.distribution(distribution), exponential = {
matplot(1/x, y = outer(x, 1/shape, pexp), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, weibull = {
matplot(1/x, y = outer(x, shape, pweibull), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, loglogistic = {
matplot(1/x, y = outer(x, shape, ploglogis), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, gamma = {
matplot(1/x, y = outer(x, shape, pgamma), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, igau = {
matplot(x, y = outer(x, shape, sigau), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, bisa = {
matplot(x, y = outer(x, shape, sbisa), lty = 1:length(shape),
type = "l", yaxt = "n", cex = 1.2, cex.axis = 1.1, xlab = "",
ylab = "", lwd = lwd, ...)
}, goma = {
matplot(x, y = dist.outer(x, shape, shape2, sgoma),
lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, sev = {
matplot(x, y = dist.outer(x, shape, dummy = location,
ssev), lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, lev = {
matplot(x, y = dist.outer(x, shape, dummy = location,
slev), lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
}, logistic = {
matplot(x, y = dist.outer(-x, shape, dummy = location,
plogis), lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, ...)
}, normal = {
matplot(x, y = dist.outer(-x, shape, dummy = location,
pnorm), lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, ...)
}, lognormal = {
matplot(x, y = dist.outer(1/(x), shape, 0, plnorm),
lty = 1:length(shape), type = "l", yaxt = "n",
cex = 1.2, cex.axis = 1.1, xlab = "", ylab = "", lwd = lwd, ...)
})
cdf.ylabs <- c("0", ".5", "1")
axis(2, at = as.numeric(cdf.ylabs), labels = cdf.ylabs,
cex = 1.2, las = 1, hadj = .4)
usr.par <- par("usr")
xpos <- usr.par[1] - 0.18 * (usr.par[2] - usr.par[1])
ypos <- usr.par[3] + 0.5 * (usr.par[4] - usr.par[3])
mtext(side = 2, line = 3, srt = 90, "S(t)", cex = 1.1)
title(xlab = "t", cex.lab = 1.5)
if (is.null(my.subtitle))
title("Survival Function", cex = 1)
}
parameter.plot <- function(distribution, shape, scale, location,
shape2, cexchar = 2, char.fudge = "", exponential2 = T) {
cexchar <- cexchar * 0.5
par(mar = c(4.25, 5, 3.25, 2) + 0.1)
par(xaxt = "n", yaxt = "n", bty = "n")
parameter2.name <- NULL
parameter1 <- shape
char.eta <- "eta "
char.mu <- "mu "
char.nu <- "nu "
char.sigma <- "sigma"
char.kappa <- "kappa"
char.theta <- "theta"
char.zeta <- "zeta "
char.gamma <- "gamma"
char.eta <- "eta "
char.beta <- "beta "
char.xi <- "xi "
switch(generic.distribution(distribution), exponential = {
parameter1.name <- char.theta
if (exponential2) {
textxy1 <- c(0.1, 0.92)
parameter2.name <- char.gamma
} else {
textxy1 <- c(0.3, 0.9)
parameter2.name <- NULL
}
parameter2 <- rep(0, length(parameter1))
}, normal = {
parameter2 <- location
parameter2.name <- char.mu
parameter1.name <- char.sigma
}, sev = {
parameter2 <- location
parameter2.name <- char.mu
parameter1.name <- char.sigma
}, lev = {
parameter2 <- location
parameter2.name <- char.mu
parameter1.name <- char.sigma
}, logistic = {
parameter2 <- location
parameter2.name <- char.mu
parameter1.name <- char.sigma
}, loglogistic = {
parameter2 <- location
parameter1.name <- char.sigma
parameter2.name <- char.mu
}, gamma = {
parameter2 <- scale
parameter1.name <- char.kappa
parameter2.name <- char.theta
}, igau = {
parameter2 <- scale
parameter1.name <- char.beta
parameter2.name <- char.theta
}, bisa = {
parameter2 <- scale
parameter1.name <- char.beta
parameter2.name <- char.theta
}, goma = {
parameter2 <- shape2
parameter3 <- scale
parameter1.name <- char.zeta
parameter2.name <- char.eta
parameter3.name <- char.theta
}, weibull = {
parameter2 <- scale
parameter1.name <- char.beta
parameter2.name <- char.eta
}, lognormal = {
parameter2 <- 0
parameter1.name <- char.sigma
parameter2.name <- char.mu
}, )
plot(c(0, 1), c(0, 1), type = "n", xlab = "", ylab = "", lwd = lwd,
cex = 1.2, cex.axis = 1.1, frame.plot = FALSE)
if (is.null(parameter2.name)) {
legend("top",
parse(text = paste(parameter1.name,"~~",format(parameter1, nsmall = 2), sep = "~")),
col = c(0,1:length(parameter1)), lty = c(0,1:length(parameter1)),
cex = 1.25, lwd = 2, y.intersp = 1.25-0.05*length(parameter1),
bty = "n", inset = -0.05)
} else {
legend("top",
c(parse(text = paste(parameter1.name,"~~~~~~~~~~", parameter2.name,sep = "~")),
paste(format(parameter1, nsmall = 2),format(parameter2, nsmall = 2), sep = " ")),
col = c(0,1:length(parameter1)), lty = c(0,1:length(parameter1)),
cex = 1.5-0.05*length(parameter1), y.intersp = 1.25-0.05*length(parameter1), lwd = 2,
bty = "n", inset = -0.05)
}
}
if (axsi)
old.par <- par(yaxs = "i", xaxs = "i")
else old.par <- par()
if (original.par)
on.exit({
old.par[c("cin", "cra", "csi", "cxy", "din", "page")] <- NULL
par(old.par)
par(mfrow = c(1,1))
par(new = F)
})
xx <- distribution.plot.range(distribution = distribution,
shape = shape, scale = scale, prob.range = prob.range,
location = location, shape2 = shape2)
x <- seq(xx[1], xx[2], length = number.points)
switch(type, all = {
if (!is.null(my.title) && my.title == "") {
par(mfrow = c(2, 2), oma = c(0, 0, 0, 0))
} else {
par(mfrow = c(2, 2), oma = c(0, 0, 0, 0))
}
cdf.plot(x, distribution, shape, scale, location, shape2,
...)
pdf.plot(x, distribution, shape, scale, location, shape2,
...)
if (length(shape) > 1) {
hf.plot(x, distribution, shape, scale, plot.haz.log,
location, shape2, ...)
parameter.plot(distribution, shape, scale, location,
shape2, char.fudge = char.fudge, exponential2 = exponential2)
} else {
sf.plot(x, distribution, shape, scale, location,
shape2, ...)
hf.plot(x, distribution, shape, scale, plot.haz.log,
location, shape2, ...)
}
if (is.null(my.title)) mtext(side = 3, line = 0, cex = 1.5,
outer = TRUE, paste(distribution, " Distribution"))
}, pdfcdf = {
par(mfrow = c(1, 2), oma = c(0, 0, 0, 0))
cdf.plot(x, distribution, shape, scale, location, shape2,
...)
pdf.plot(x, distribution, shape, scale, location, shape2,
...)
if (is.null(my.title)) mtext(side = 3, line = 0, cex = 1.5,
outer = TRUE, paste(distribution, " Distribution"))
}, pdf = {
par(oma = c(0, 0, 0, 0))
pdf.plot(x, distribution, shape, scale, location, shape2,
...)
}, cdf = {
par(oma = c(0, 0, 0, 0))
cdf.plot(x, distribution, shape, scale, location, shape2,
...)
}, sf = {
par(oma = c(0, 0, 0, 0))
sf.plot(x, distribution, shape, scale, location, shape2,
...)
}, hf = {
par(oma = c(0, 0, 0, 0))
hf.plot(x, distribution, shape, scale, plot.haz.log,
location, shape2, ...)
})
if (type != "all") {
if (is.null(my.title))
mtext(side = 3, line = 0, cex = 1.5, outer = TRUE,
paste(distribution, " Distribution"))
}
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.