gaitdlog: Generally Altered, Inflated, Truncated and Deflated Logarithmic Regression

Description

Fits a generally altered, inflated, truncated and deflated logarithmic regression by MLE. The GAITD combo model having 7 types of special values is implemented. This allows logarithmic mixtures on nested and/or partitioned support as well as a multinomial logit model for altered, inflated and deflated values. Truncation may include the upper tail.

Usage

gaitdlog(a.mix = NULL, i.mix = NULL, d.mix = NULL,
         a.mlm = NULL, i.mlm = NULL, d.mlm = NULL,
         truncate = NULL, max.support = Inf,
         zero = c("pobs", "pstr", "pdip"), eq.ap = TRUE, eq.ip = TRUE,
         eq.dp = TRUE, parallel.a = FALSE,
         parallel.i = FALSE, parallel.d = FALSE,
         lshape.p = "logitlink", lshape.a = lshape.p,
         lshape.i = lshape.p, lshape.d = lshape.p,
         type.fitted = c("mean", "shapes", "pobs.mlm", "pstr.mlm",
         "pdip.mlm", "pobs.mix", "pstr.mix", "pdip.mix", "Pobs.mix",
         "Pstr.mix", "Pdip.mix", "nonspecial",
         "Numer", "Denom.p", "sum.mlm.i", "sum.mix.i", "sum.mlm.d",
         "sum.mix.d", "ptrunc.p", "cdf.max.s"),
         gshape.p = -expm1(-7 * ppoints(12)), gpstr.mix = ppoints(7) / 3,
         gpstr.mlm = ppoints(7) / (3 + length(i.mlm)),
         imethod = 1, mux.init = c(0.75, 0.5, 0.75),
         ishape.p = NULL, ishape.a = ishape.p,
         ishape.i = ishape.p, ishape.d = ishape.p,
         ipobs.mix = NULL, ipstr.mix = NULL, ipdip.mix = NULL,
         ipobs.mlm = NULL, ipstr.mlm = NULL, ipdip.mlm = NULL,
         byrow.aid = FALSE, ishrinkage = 0.95, probs.y = 0.35)

Arguments

Value

An object of class "vglmff"

(see vglmff-class). The object is used by modelling functions such as vglm,

rrvglm

and vgam.

Details

Many details to this family function can be found in gaitdpoisson because it is also a 1-parameter discrete distribution. This function currently does not handle multiple responses. Further details are at Gaitdlog.

As alluded to above, when there are covariates it is much more interpretable to model the mean rather than the shape parameter. Hence logffMlink is recommended. (This might become the default in the future.) So installing VGAMextra is a good idea.

Apart from the order of the linear/additive predictors, the following are (or should be) equivalent: gaitdlog() and logff(), gaitdlog(a.mix = 1) and oalog(zero = "pobs1"), gaitdlog(i.mix = 1) and oilog(zero = "pstr1"), gaitdlog(truncate = 1) and otlog(). The functions oalog, oilog and otlog have been placed in VGAMdata.

Examples

Run this code

if (FALSE)  avec <- c(5, 10)  # Alter these values parametrically
ivec <- c(3, 15)  # Inflate these values
tvec <- c(6, 7)   # Truncate these values
max.support <- 20; set.seed(1)
pobs.a <- pstr.i <- 0.1
gdata <- data.frame(x2 = runif(nn <- 1000))
gdata <- transform(gdata, shape.p = logitlink(2+0.5*x2, inverse = TRUE))
gdata <- transform(gdata,
  y1 = rgaitdlog(nn, shape.p, a.mix = avec, pobs.mix = pobs.a,
                i.mix = ivec, pstr.mix = pstr.i, truncate = tvec,
                max.support = max.support))
gaitdlog(a.mix = avec, i.mix = ivec, max.support = max.support)
with(gdata, table(y1))
spikeplot(with(gdata, y1), las = 1)
fit7 <- vglm(y1 ~ x2, trace = TRUE, data = gdata,
             gaitdlog(i.mix = ivec, truncate = tvec,
                      max.support = max.support, a.mix = avec,
                      eq.ap = TRUE, eq.ip = TRUE))
head(fitted(fit7, type.fitted = "Pstr.mix"))
head(predict(fit7))
t(coef(fit7, matrix = TRUE))  # Easier to see with t()
summary(fit7)
spikeplot(with(gdata, y1), lwd = 2, ylim = c(0, 0.4))
plotdgaitd(fit7, new.plot = FALSE, offset.x = 0.2, all.lwd = 2)