truncdist {Ecfun} R Documentation

Truncated distribution

Description

The cumulative distribution function for a truncated distribution is 0 for x <= truncmin, 1 for truncmax < x, and in between is as follows:

(pdist(x, ...) - pdist(truncmin, ...)) / (pdist(truncmax, ...) - pdist(truncmin, ...))

The density, quantile, and random number generation functions are similarly defined from this.

Usage

dtruncdist(x, ..., dist='norm', truncmin=-Inf,
truncmax=Inf)
ptruncdist(q, ..., dist='norm', truncmin=-Inf,
truncmax=Inf)
qtruncdist(p, ..., dist='norm', truncmin=-Inf,
truncmax=Inf)
rtruncdist(n, ..., dist='norm', truncmin=-Inf,
truncmax=Inf)


Arguments

 x, q numeric vector of quantiles p numeric vector of probabilities n number of observations. If length(n) > 1, the length is taken to be the number required. ... other arguments to be passed to the corresponding function for the indicated dist dist Standard R name for the family of functions for the desired distribution. By default, this is norm, so the corresponding function for dtruncdist is dnorm, the corresponding function for ptrunctist is pnorm, etc. truncmin, truncmax lower and upper truncation points, respectively.

Details

NOTE: Truncation is different from "censoring", where it's known that an observation lies between certain limits; it's just not known exactly where it lies between those limits.

By contrast, with a truncated distribution, events below truncmin and above truncmax may exist but are not observed. Thus, it's not known how many events occur outside the given range, truncmin to truncmax, if any. Given data believed to come from a truncated distribution, estimating the parameters provide a means of estimating the number of unobserved events, assuming a particular form for their distribution.

1. Setup

dots <- list(...)

2. For dtruncdist, return 0 for all x outside truncmin and truncmax. For all others, compute as follows:

dots$x <- truncmin ddist <- paste0('d', dist) pdist <- paste0('p', dist) p.min <- do.call(pdist, dots) dots$x <- truncmax p.max <- do.call(pdist, dots) dots\$x <- x dx <- do.call(ddist, dots)

return(dx / (p.max-p.min))

NOTE: Adjustments must be made if 'log' appears in names(dots)

3. The computations for ptruncdist are similar.

4. The computations for qtruncdist are complementary.

5. For rtruncdist, use qtruncdist(runif(n), ...).

Value

dtruncdist gives the density, ptruncdist gives the distribution function, qtruncdist gives the quantile function, and rtruncdist generates random deviates.

The length of the result is determined by n for rtruncdist and is the maximum of the lengths of the numerical arguments for the other functions.

Author(s)

Spencer Graves

Distributions Normal

Examples

##
## 1.  dtruncdist
##
#  1.1.  Normal
dx <- dtruncdist(1:4)

# check

all.equal(dx, dnorm(1:4))

#  1.2.  Truncated normal between 0 and 1
dx01 <- dtruncdist(seq(-1, 2, .5), truncmin=0, truncmax=1)

# check
dx01. <- c(0, 0, 0, dnorm(c(.5, 1))/(pnorm(1)-pnorm(0)),
0, 0)

all.equal(dx01, dx01.)

#  1.3.  lognormal meanlog=log(100), sdlog = 2, truncmin=500
x10 <- 10^(0:9)
dx10 <- dtruncdist(x10, log(100), 2, dist='lnorm',
truncmin=500)

# check
dx10. <- (dtruncdist(log(x10), log(100), 2,
truncmin=log(500)) / x10)

all.equal(dx10, dx10.)

#  1.4.  log density of the previous example
dx10log <- dtruncdist(x10, log(100), 2, log=TRUE,
dist='lnorm', truncmin=500)

all.equal(dx10log, log(dx10))

#  1.5.  Poisson without 0.

dPois0.9 <-dtruncdist(0:9, lambda=1, dist='pois', truncmin=0)

# check
dP0.9 <- c(0, dpois(1:9, lambda=1)/ppois(0, lambda=1, lower.tail=FALSE))

all.equal(dPois0.9, dP0.9)

##
## 2.  ptruncdist
##
#  2.1.  Normal
px <- ptruncdist(1:4)

# check

all.equal(px, pnorm(1:4))

#  2.2.  Truncated normal between 0 and 1
px01 <- ptruncdist(seq(-1, 2, .5), truncmin=0, truncmax=1)

# check
px01. <- c(0, 0, (pnorm(c(0, .5, 1)) - pnorm(0))
/(pnorm(1)-pnorm(0)), 1, 1)

all.equal(px01, px01.)

#  2.3.  lognormal meanlog=log(100), sdlog = 2, truncmin=500
x10 <- 10^(0:9)
px10 <- ptruncdist(x10, log(100), 2, dist='lnorm',
truncmin=500)

# check
px10. <- (ptruncdist(log(x10), log(100), 2,
truncmin=log(500)))

all.equal(px10, px10.)

#  2.4.  log of the previous probabilities
px10log <- ptruncdist(x10, log(100), 2, log=TRUE,
dist='lnorm', truncmin=500)

all.equal(px10log, log(px10))

##
## 3.  qtruncdist
##
#  3.1.  Normal
qx <- qtruncdist(seq(0, 1, .2))

# check

all.equal(qx, qnorm(seq(0, 1, .2)))

#  3.2.  Normal truncated outside (0, 1)
qx01 <- qtruncdist(seq(0, 1, .2),
truncmin=0, truncmax=1)

# check
pxmin <- pnorm(0)
pxmax <- pnorm(1)
unp <- (pxmin + seq(0, 1, .2)*(pxmax-pxmin))
qx01. <- qnorm(unp)

all.equal(qx01, qx01.)

#  3.3.  lognormal meanlog=log(100),
#             sdlog=2, truncmin=500
qlx10 <- qtruncdist(seq(0, 1, .2), log(100), 2,
dist='lnorm', truncmin=500)

# check
plxmin <- plnorm(500, log(100), 2)
unp. <- (plxmin + seq(0, 1, .2)*(1-plxmin))

qlx10. <- qlnorm(unp., log(100), 2)

all.equal(qlx10, qlx10.)

#  3.4.  previous example with log probabilities
qlx10l <- qtruncdist(log(seq(0, 1, .2)),
log(100), 2, log.p=TRUE, dist='lnorm',
truncmin=500)

# check

all.equal(qlx10, qlx10l)

##
## 4.  rtruncdist
##
#  4.1.  Normal
set.seed(1)
rx <- rtruncdist(9)

# check
set.seed(1)

all.equal(rx[1], rnorm(1))

# Only the first observation matches;  check that.

#  4.2.  Normal truncated outside (0, 1)
set.seed(1)
rx01 <- rtruncdist(9, truncmin=0, truncmax=1)

# check
pxmin <- pnorm(0)
pxmax <- pnorm(1)
set.seed(1)
rnp <- (pxmin + runif(9)*(pxmax-pxmin))
rx01. <- qnorm(rnp)

all.equal(rx01, rx01.)

#  4.3.  lognormal meanlog=log(100), sdlog=2, truncmin=500
set.seed(1)
rlx10 <- rtruncdist(9, log(100), 2,
dist='lnorm', truncmin=500)

# check
plxmin <- plnorm(500, log(100), 2)
set.seed(1)
rnp. <- (plxmin + runif(9)*(1-plxmin))

rlx10. <- qlnorm(rnp., log(100), 2)

all.equal(rlx10, rlx10.)



[Package Ecfun version 0.3-2 Index]