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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