theta.md {MASS}  R Documentation 
Given the estimated mean vector, estimate theta
of the
Negative Binomial Distribution.
theta.md(y, mu, dfr, weights, limit = 20, eps = .Machine$double.eps^0.25) theta.ml(y, mu, n, weights, limit = 10, eps = .Machine$double.eps^0.25, trace = FALSE) theta.mm(y, mu, dfr, weights, limit = 10, eps = .Machine$double.eps^0.25)
y 
Vector of observed values from the Negative Binomial. 
mu 
Estimated mean vector. 
n 
Number of data points (defaults to the sum of weights )

dfr 
Residual degrees of freedom (assuming theta known). For
a weighted fit this is the sum of the weights minus the number of
fitted parameters.

weights 
Case weights. If missing, taken as 1. 
limit 
Limit on the number of iterations. 
eps 
Tolerance to determine convergence. 
trace 
logical: should iteration progress be printed? 
theta.md
estimates by equating the deviance to the residual
degrees of freedom, an analogue of a moment estimator.
theta.ml
uses maximum likelihood.
theta.mm
calculates the moment estimator of theta
by
equating the Pearson chisquare
sum((yμ)^2/(μ+μ^2/theta)) to the residual
degrees of freedom.
The required estimate of theta
, as a scalar.
For theta.ml
, the standard error is given as attribute "SE"
.
quine.nb < glm.nb(Days ~ .^2, data = quine) theta.md(quine$Days, fitted(quine.nb), dfr = df.residual(quine.nb)) theta.ml(quine$Days, fitted(quine.nb)) theta.mm(quine$Days, fitted(quine.nb), dfr = df.residual(quine.nb)) ## weighted example yeast < data.frame(cbind(numbers = 0:5, fr = c(213, 128, 37, 18, 3, 1))) fit < glm.nb(numbers ~ 1, weights = fr, data = yeast) summary(fit) attach(yeast) mu < fitted(fit) theta.md(numbers, mu, dfr = 399, weights = fr) theta.ml(numbers, mu, weights = fr) theta.mm(numbers, mu, dfr = 399, weights = fr) detach()