Zero-Inflated Geometric Distribution Family Function
Fits a zero-inflated geometric distribution by maximum likelihood estimation.
zigeometric(lpstr0 = "logitlink", lprob = "logitlink",
type.fitted = c("mean", "prob", "pobs0", "pstr0", "onempstr0"),
ipstr0 = NULL, iprob = NULL,
imethod = 1, bias.red = 0.5, zero = NULL)
zigeometricff(lprob = "logitlink", lonempstr0 = "logitlink",
type.fitted = c("mean", "prob", "pobs0", "pstr0", "onempstr0"),
iprob = NULL, ionempstr0 = NULL,
imethod = 1, bias.red = 0.5, zero = "onempstr0")lpstr0, lprob |
Link functions for the parameters
phi
and
prob ( |
lonempstr0, ionempstr0 |
Corresponding arguments for the other parameterization. See details below. |
bias.red |
A constant used in the initialization process of |
type.fitted |
See |
ipstr0, iprob |
See |
zero, imethod |
See |
Function zigeometric() is based on
P(Y=0) = phi + (1-phi) * prob,
for y=0, and
P(Y=y) = (1-phi) * prob * (1 - prob)^y.
for y=1,2,…. The parameter phi satisfies 0 < phi < 1. The mean of Y is E(Y) = (1-phi) * prob / (1-prob) and these are returned as the fitted values by default. By default, the two linear/additive predictors are (logit(phi), logit(prob))^T. Multiple responses are handled.
The VGAM family function zigeometricff() has a few
changes compared to zigeometric().
These are:
(i) the order of the linear/additive predictors is switched so the
geometric probability comes first;
(ii) argument onempstr0 is now 1 minus
the probability of a structural zero, i.e.,
the probability of the parent (geometric) component,
i.e., onempstr0 is 1-pstr0;
(iii) argument zero has a new default so that the onempstr0
is intercept-only by default.
Now zigeometricff() is generally recommended over
zigeometric().
Both functions implement Fisher scoring and can handle
multiple responses.
An object of class "vglmff" (see vglmff-class).
The object is used by modelling functions such as vglm
and vgam.
The zero-deflated geometric distribution might
be fitted by setting lpstr0 = identitylink, albeit,
not entirely reliably. See zipoisson
for information that can be applied here. Else
try the zero-altered geometric distribution (see
zageometric).
T. W. Yee
gdata <- data.frame(x2 = runif(nn <- 1000) - 0.5)
gdata <- transform(gdata, x3 = runif(nn) - 0.5,
x4 = runif(nn) - 0.5)
gdata <- transform(gdata, eta1 = 1.0 - 1.0 * x2 + 2.0 * x3,
eta2 = -1.0,
eta3 = 0.5)
gdata <- transform(gdata, prob1 = logitlink(eta1, inverse = TRUE),
prob2 = logitlink(eta2, inverse = TRUE),
prob3 = logitlink(eta3, inverse = TRUE))
gdata <- transform(gdata, y1 = rzigeom(nn, prob1, pstr0 = prob3),
y2 = rzigeom(nn, prob2, pstr0 = prob3),
y3 = rzigeom(nn, prob2, pstr0 = prob3))
with(gdata, table(y1))
with(gdata, table(y2))
with(gdata, table(y3))
head(gdata)
fit1 <- vglm(y1 ~ x2 + x3 + x4, zigeometric(zero = 1), data = gdata, trace = TRUE)
coef(fit1, matrix = TRUE)
head(fitted(fit1, type = "pstr0"))
fit2 <- vglm(cbind(y2, y3) ~ 1, zigeometric(zero = 1), data = gdata, trace = TRUE)
coef(fit2, matrix = TRUE)
summary(fit2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.