| oapospoisson | R Documentation |
Fits a one-altered positive-Poisson distribution based on a conditional model involving a Bernoulli distribution and a 1-truncated positive-Poisson distribution.
oapospoisson(lpobs1 = "logitlink", llambda = "loglink",
type.fitted = c("mean", "lambda", "pobs1", "onempobs1"),
ipobs1 = NULL, zero = NULL)
lpobs1 |
Link function for the parameter |
llambda |
See |
type.fitted |
See |
ipobs1, zero |
See |
The response Y is one with probability p_1,
or Y has a 1-truncated positive-Poisson distribution with
probability 1-p_1. Thus 0 < p_1 < 1, which is modelled as a function of the covariates.
The one-altered positive-Poisson distribution differs from the
one-inflated positive-Poisson distribution in that the former has
ones coming from one source, whereas the latter has ones coming
from the positive-Poisson distribution too. The one-inflated
positive-Poisson distribution is implemented in the VGAM
package. Some people call the one-altered positive-Poisson a
hurdle model.
The input can be a matrix (multiple responses).
By default, the two linear/additive predictors
of oapospoisson
are (logit(\phi), log(\lambda))^T.
An object of class "vglmff"
(see vglmff-class).
The object is used by modelling functions
such as vglm,
and vgam.
The fitted.values slot of the fitted object,
which should be extracted by the generic function fitted,
returns
the mean \mu (default) which is given by
\mu = \phi + (1-\phi) A
where A is the mean of the one-truncated
positive-Poisson distribution.
If type.fitted = "pobs1" then p_1 is
returned.
This family function effectively combines
binomialff and
otpospoisson into
one family function.
T. W. Yee
Oapospois,
pospoisson,
oipospoisson,
CommonVGAMffArguments,
simulate.vlm.
## Not run: odata <- data.frame(x2 = runif(nn <- 1000))
odata <- transform(odata, pobs1 = logitlink(-1 + 2*x2, inv = TRUE),
lambda = loglink( 1 + 1*x2, inv = TRUE))
odata <- transform(odata, y1 = roapospois(nn, lambda, pobs1 = pobs1),
y2 = roapospois(nn, lambda, pobs1 = pobs1))
with(odata, table(y1))
ofit <- vglm(cbind(y1, y2) ~ x2, oapospoisson, odata, trace = TRUE)
coef(ofit, matrix = TRUE)
head(fitted(ofit))
head(predict(ofit))
summary(ofit)
## End(Not run)
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