R: Estimate the Shape Parameter of the Gamma Distribution in a GLM Fit

gamma.shape {MASS} | R Documentation

## Estimate the Shape Parameter of the Gamma Distribution in a GLM Fit

### Description

Find the maximum likelihood estimate of the shape parameter of the gamma distribution after fitting a `Gamma` generalized linear model.

### Usage

    ## S3 method for class 'glm':
    gamma.shape(object, it.lim = 10,
                eps.max = .Machine$double.eps^0.25, verbose = FALSE, ...)

### Arguments

`object` | Fitted model object from a `Gamma` family or `quasi` family with `variance = "mu^2"`.
`it.lim` | Upper limit on the number of iterations.
`eps.max` | Maximum discrepancy between approximations for the iteration process to continue.
`verbose` | If `TRUE`, causes successive iterations to be printed out. The initial estimate is taken from the deviance.
`...` | further arguments passed to or from other methods.

### Details

A glm fit for a Gamma family correctly calculates the maximum likelihood estimate of the mean parameters but provides only a crude estimate of the dispersion parameter. This function takes the results of the glm fit and solves the maximum likelihood equation for the reciprocal of the dispersion parameter, which is usually called the shape (or exponent) parameter.

### Value

List of two components

`alpha` | the maximum likelihood estimate
`SE` | the approximate standard error, the square-root of the reciprocal of the observed information.

### References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

### See Also

`gamma.dispersion`

### Examples

    clotting <- data.frame(
        u = c(5,10,15,20,30,40,60,80,100),
        lot1 = c(118,58,42,35,27,25,21,19,18),
        lot2 = c(69,35,26,21,18,16,13,12,12))
    clot1 <- glm(lot1 ~ log(u), data = clotting, family = Gamma)
    gamma.shape(clot1)
    ## Not run:
    Alpha: 538.13
       SE: 253.60
    ## End(Not run)
    gm <- glm(Days + 0.1 ~ Age*Eth*Sex*Lrn,
              quasi(link=log, variance="mu^2"), quine,
              start = c(3, rep(0,31)))
    gamma.shape(gm, verbose = TRUE)
    ## Not run:
    Initial estimate: 1.0603
    Iter.  1  Alpha: 1.23840774338543
    Iter.  2  Alpha: 1.27699745778205
    Iter.  3  Alpha: 1.27834332265501
    Iter.  4  Alpha: 1.27834485787226

    Alpha: 1.27834
       SE: 0.13452
    ## End(Not run)
    summary(gm, dispersion = gamma.dispersion(gm))  # better summary

[Package MASS version 7.2-45 Index]