lgamma1 {VGAM} | R Documentation

## Log-gamma Distribution Family Function

### Description

Estimation of the parameter of the standard and nonstandard log-gamma distribution.

### Usage

    lgamma1(lshape = "loge", ishape = NULL)
    lgamma3(llocation = "identitylink", lscale = "loge", lshape = "loge",
            ilocation = NULL, iscale = NULL, ishape = 1,
            zero = c("scale", "shape"))

### Arguments

`llocation, lscale` | Parameter link function applied to the location parameter a and the positive scale parameter b. See `Links` for more choices.
`lshape` | Parameter link function applied to the positive shape parameter k. See `Links` for more choices.
`ishape` | Initial value for k. If given, it must be positive. If failure to converge occurs, try some other value. The default means an initial value is determined internally.
`ilocation, iscale` | Initial value for a and b. The defaults mean an initial value is determined internally for each.
`zero` | An integer-valued vector specifying which linear/additive predictors are modelled as intercepts only. The values must be from the set {1,2,3}. The default value means none are modelled as intercept-only terms. See `CommonVGAMffArguments` for more information.

### Details

The probability density function of the standard log-gamma distribution is given by

f(y;k) = exp[ky - exp(y)]/gamma(k),

for parameter k>0 and all real y. The mean of Y is `digamma(k)` (returned as the fitted values) and its variance is `trigamma(k)`.

For the non-standard log-gamma distribution, one replaces y by (y-a)/b, where a is the location parameter and b is the positive scale parameter. Then the density function is

f(y) = exp[k(y-a)/b - exp((y-a)/b)]/(b*gamma(k)).

The mean and variance of Y are `a + b*digamma(k)` (returned as the fitted values) and `b^2 * trigamma(k)`, respectively.

### Value

An object of class `"vglmff"` (see `vglmff-class`). The object is used by modelling functions such as `vglm`, and `vgam`.

### Note

The standard log-gamma distribution can be viewed as a generalization of the standard type 1 extreme value density: when k = 1 the distribution of -Y is the standard type 1 extreme value distribution.

The standard log-gamma distribution is fitted with `lgamma1` and the non-standard (3-parameter) log-gamma distribution is fitted with `lgamma3`.

### Author(s)

T. W. Yee

### References

Kotz, S. and Nadarajah, S. (2000) Extreme Value Distributions: Theory and Applications, pages 48–49, London: Imperial College Press.

Johnson, N. L. and Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, 2nd edition, Volume 2, p.89, New York: Wiley.

### See Also

`rlgamma`, `gengamma.stacy`, `prentice74`, `gamma1`, `lgamma`.

### Examples

    ldata <- data.frame(y = rlgamma(100, shape = exp(1)))
    fit <- vglm(y ~ 1, lgamma1, data = ldata, trace = TRUE, crit = "coef")
    summary(fit)
    coef(fit, matrix = TRUE)
    Coef(fit)

    ldata <- data.frame(x2 = runif(nn <- 5000))  # Another example
    ldata <- transform(ldata, loc = -1 + 2 * x2, Scale = exp(1))
    ldata <- transform(ldata, y = rlgamma(nn, loc, scale = Scale, shape = exp(0)))
    fit2 <- vglm(y ~ x2, lgamma3, data = ldata, trace = TRUE, crit = "c")
    coef(fit2, matrix = TRUE)

[Package VGAM version 1.0-4 Index]