fit.dist: Fit Probability Distributions to Frequency Data

In swihart/gnlm: Generalized Nonlinear Regression Models

Description

fit.dist fits the distributions in Chapter 4 of Lindsey (1995, 2003 2nd edn): binomial, beta-binomial, Poisson, negative binomial, geometric, zeta, normal, log normal, inverse Gauss, logistic, Laplace, Cauchy, Student t, exponential, Pareto, gamma, and Weibull to frequency (histogram) data, possibly plotting the frequency polygon of fitted values with the histogram.

Usage

fit.dist(y, ni, distribution = "normal", breaks = FALSE, delta = 1,
  censor = FALSE, exact = TRUE, plot = FALSE, add = FALSE,
  xlab = deparse(substitute(y)), ylab = "Probability",
  xlim = range(y), main = paste("Histogram of",
  deparse(substitute(y))), ...)

Arguments

y	Vector of observations.
ni	Corresponding vector of frequencies.
distribution	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.
xlab	Plotting control options.
ylab	Plotting control options.
xlim	Plotting control options.
main	Plotting control options.
...	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=TRUE, 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=TRUE,
	xlab="Monthly salary (dollars)",
	main="Distribution of women mathematicians' salaries")
fit.dist(y, f, "log normal", delta=100, plot=TRUE, add=TRUE, lty=3)
fit.dist(y, f, "logistic", delta=100, exact=FALSE, plot=TRUE, add=TRUE, lty=2)

swihart/gnlm documentation built on May 30, 2019, 9:38 p.m.