fit.dist(y, ni, dist, breaks=F, delta=1, censor=F, exact=T, plot=F, add=F, main, xlab, ...)
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. |
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.
Lindsey, J.K. (1995) Introductory Statistics: A Modelling Approach. Oxford: Oxford University Press.
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)