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$double.eps^0.25)
y |
Vector of observed values from the Negative Binomial. |
mu |
Estimated mean vector. |
n |
Number of data points (defaults to the sum of |
dfr |
Residual degrees of freedom (assuming |
weights |
Case weights. If missing, taken as 1. |
limit |
Limit on the number of iterations. |
eps |
Tolerance to determine convergence. |
trace |
logical: should iteration progress be printed? |
theta.md
estimates by equating the deviance to the residual
degrees of freedom, an analogue of a moment estimator.
theta.ml
uses maximum likelihood.
theta.mm
calculates the moment estimator of theta
by
equating the Pearson chi-square
\sum (y-\mu)^2/(\mu+\mu^2/\theta)
to the residual degrees of freedom.
The required estimate of theta
, as a scalar.
For theta.ml
, the standard error is given as attribute "SE"
.
quine.nb <- glm.nb(Days ~ .^2, data = quine)
theta.md(quine$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)
mu <- fitted(fit)
theta.md(yeast$numbers, mu, dfr = 399, weights = yeast$fr)
theta.ml(yeast$numbers, mu, limit = 15, weights = yeast$fr)
theta.mm(yeast$numbers, mu, dfr = 399, weights = yeast$fr)