ZeroTruncatedBinomial | R Documentation |
Density function, distribution function, quantile function and random
generation for the Zero-Truncated Binomial distribution with
parameters size
and prob
.
dztbinom(x, size, prob, log = FALSE)
pztbinom(q, size, prob, lower.tail = TRUE, log.p = FALSE)
qztbinom(p, size, prob, lower.tail = TRUE, log.p = FALSE)
rztbinom(n, size, prob)
x |
vector of (strictly positive integer) quantiles. |
q |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of observations. If |
size |
number of trials (strictly positive integer). |
prob |
probability of success on each trial. |
log, log.p |
logical; if |
lower.tail |
logical; if |
The zero-truncated binomial distribution with size
= n
and prob
= p
has probability mass function
%
p(x) = {n \choose x} \frac{p^x (1 - p)^{n-x}}{1 - (1 - p)^n}
for x = 1, \ldots, n
and 0 < p \le 1
, and p(1) = 1
when p = 0
.
The cumulative distribution function is
P(x) = \frac{F(x) - F(0)}{1 - F(0)},
where F(x)
is the distribution function of the standard binomial.
The mean is np/(1 - (1-p)^n)
and the variance is
np[(1-p) - (1-p+np)(1-p)^n]/[1 - (1-p)^n]^2
.
In the terminology of Klugman et al. (2012), the zero-truncated
binomial is a member of the (a, b, 1)
class of
distributions with a = -p/(1-p)
and b = (n+1)p/(1-p)
.
If an element of x
is not integer, the result of
dztbinom
is zero, with a warning.
The quantile is defined as the smallest value x
such that
P(x) \ge p
, where P
is the distribution function.
dztbinom
gives the probability mass function,
pztbinom
gives the distribution function,
qztbinom
gives the quantile function, and
rztbinom
generates random deviates.
Invalid size
or prob
will result in return value
NaN
, with a warning.
The length of the result is determined by n
for
rztbinom
, and is the maximum of the lengths of the
numerical arguments for the other functions.
Functions {d,p,q}ztbinom
use {d,p,q}binom
for all
but the trivial input values and p(0)
.
rztbinom
uses the simple inversion algorithm suggested by
Peter Dalgaard on the r-help mailing list on 1 May 2005
(https://stat.ethz.ch/pipermail/r-help/2005-May/070680.html).
Vincent Goulet vincent.goulet@act.ulaval.ca
Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2012), Loss Models, From Data to Decisions, Fourth Edition, Wiley.
dbinom
for the binomial distribution.
dztbinom(1:5, size = 5, prob = 0.4)
dbinom(1:5, 5, 0.4)/pbinom(0, 5, 0.4, lower = FALSE) # same
pztbinom(1, 2, prob = 0) # point mass at 1
qztbinom(pztbinom(1:10, 10, 0.6), 10, 0.6)
n <- 8; p <- 0.3
x <- 0:n
title <- paste("ZT Binomial(", n, ", ", p,
") and Binomial(", n, ", ", p,") PDF",
sep = "")
plot(x, dztbinom(x, n, p), type = "h", lwd = 2, ylab = "p(x)",
main = title)
points(x, dbinom(x, n, p), pch = 19, col = "red")
legend("topright", c("ZT binomial probabilities", "Binomial probabilities"),
col = c("black", "red"), lty = c(1, 0), lwd = 2, pch = c(NA, 19))
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