fit.dist {gnlm}R Documentation

Fit a Probability Distribution to Frequency Data

Description

fit.dist fits the distributions in Chapter 4 of Lindsey (1995): binomial, Poisson, negative binomial, geometric, zeta, normal, log normal, inverse Gauss, logistic, exponential, Pareto, gamma, and Weibull.

Usage

fit.dist(y, ni, dist, breaks=F, delta=1, censor=F, exact=T, plot=F,
add=F, main, xlab, ...)

Arguments

y Vector of observations.
ni Corresponding vector of frequencies.
dist Character string specifying the distribution.
breaks If TRUE, y contains breaks between categories instead of mid-points.
delta Scalar or vector giving the unit of measurement (always one for discrete data) for each response value, set to unity by default. For example, if a response is measured to two decimals, delta=0.01.
censor If TRUE, the last category is right censored.
exact If FALSE, uses the approximations for certain distributions in Lindsey (1995).
plot If TRUE, plots the histogram of observed frequencies and the frequency polygon of fitted values.
add If TRUE, adds a new frequency polygon of fitted values without replotting the histogram.
others Plotting control options.

Author(s)

J.K. Lindsey

References

Lindsey, J.K. (1995) Introductory Statistics: A Modelling Approach. Oxford: Oxford University Press.

Examples

f <- c(215, 1485, 5331, 10649, 14959, 11929, 6678, 2092, 342)
y <- seq(0,8)
fit.dist(y, f, "binomial", plot=T, xlab="Number of males",
        main="Distribution of males in families of 8 children")
#
f <- c(1,1,6,3,4,3,9,6,5,16,4,11,6,11,3,4,5,6,4,4,5,1,1,4,1,2,
        0,2,0,0,1)
y <- seq(1100,4100,by=100)
fit.dist(y, f, "normal", delta=100, plot=T,
        xlab="Monthly salary (dollars)",
        main="Distribution of women mathematicians' salaries")
fit.dist(y, f, "log normal", delta=100, plot=T, add=T, lty=3)
fit.dist(y, f, "logistic", delta=100, exact=F, plot=T, add=T,
        lty=2)
[Package Contents]