prentice74: Prentice (1974) Log-gamma Distribution

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prentice74R Documentation

Prentice (1974) Log-gamma Distribution


Estimation of a 3-parameter log-gamma distribution described by Prentice (1974).


prentice74(llocation = "identitylink", lscale = "loglink",
           lshape = "identitylink", ilocation = NULL, iscale = NULL,
           ishape = NULL, imethod = 1,
           glocation.mux = exp((-4:4)/2), gscale.mux = exp((-4:4)/2),
           gshape = qt(ppoints(6), df = 1), probs.y = 0.3,
           zero = c("scale", "shape"))


llocation, lscale, lshape

Parameter link function applied to the location parameter a, positive scale parameter b and the shape parameter q, respectively. See Links for more choices.

ilocation, iscale

Initial value for a and b, respectively. The defaults mean an initial value is determined internally for each.


Initial value for q. If failure to converge occurs, try some other value. The default means an initial value is determined internally.

imethod, zero

See CommonVGAMffArguments for information.

glocation.mux, gscale.mux, gshape, probs.y

See CommonVGAMffArguments for information.


The probability density function is given by

f(y;a,b,q) = |q| * exp(w/q^2 - e^w) / (b*gamma(1/q^2)),

for shape parameter q != 0, positive scale parameter b > 0, location parameter a, and all real y. Here, w = (y-a)*q/b+psi(1/q^2) where psi is the digamma function, digamma. The mean of Y is a (returned as the fitted values). This is a different parameterization compared to lgamma3.

Special cases: q = 0 is the normal distribution with standard deviation b, q = -1 is the extreme value distribution for maximums, q = 1 is the extreme value distribution for minima (Weibull). If q > 0 then the distribution is left skew, else q < 0 is right skew.


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


The special case q = 0 is not handled, therefore estimates of q too close to zero may cause numerical problems.


The notation used here differs from Prentice (1974): alpha = a, sigma = b. Fisher scoring is used.


T. W. Yee


Prentice, R. L. (1974). A log gamma model and its maximum likelihood estimation. Biometrika, 61, 539–544.

See Also

lgamma3, lgamma, gengamma.stacy.


pdata <- data.frame(x2 = runif(nn <- 1000))
pdata <- transform(pdata, loc = -1 + 2*x2, Scale = exp(1))
pdata <- transform(pdata, y = rlgamma(nn, loc = loc, scale = Scale, shape = 1))
fit <- vglm(y ~ x2, prentice74(zero = 2:3), data = pdata, trace = TRUE)
coef(fit, matrix = TRUE)  # Note the coefficients for location

VGAM documentation built on July 6, 2022, 5:05 p.m.