oalog | R Documentation |
Fits a one-altered logarithmic distribution based on a conditional model involving a Bernoulli distribution and a 1-truncated logarithmic distribution.
oalog(lpobs1 = "logitlink", lshape = "logitlink",
type.fitted = c("mean", "shape", "pobs1", "onempobs1"),
ipobs1 = NULL, gshape = ppoints(8), zero = NULL)
lpobs1 |
Link function for the parameter |
lshape |
See |
gshape , type.fitted |
See |
ipobs1 , zero |
See |
The response Y
is one with probability p_1
,
or Y
has a 1-truncated logarithmic distribution with
probability 1-p_1
.
Thus 0 < p_1 < 1
,
which is modelled as a function of the covariates. The one-altered
logarithmic distribution differs from the one-inflated
logarithmic distribution in that the former has ones coming from one
source, whereas the latter has ones coming from the logarithmic
distribution too. The one-inflated logarithmic distribution
is implemented in the VGAM package. Some people
call the one-altered logarithmic a hurdle model.
The input can be a matrix (multiple responses).
By default, the two linear/additive predictors
of oalog
are (logit(\phi), logit(s))^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
logarithmic distribution.
If type.fitted = "pobs1"
then p_1
is
returned.
This family function effectively combines
binomialff
and
otlog
into
one family function.
T. W. Yee
Gaitdlog
,
Oalog
,
logff
,
oilog
,
CommonVGAMffArguments
,
simulate.vlm
.
## Not run: odata <- data.frame(x2 = runif(nn <- 1000))
odata <- transform(odata, pobs1 = logitlink(-1 + 2*x2, inv = TRUE),
shape = logitlink(-2 + 3*x2, inv = TRUE))
odata <- transform(odata, y1 = roalog(nn, shape, pobs1 = pobs1),
y2 = roalog(nn, shape, pobs1 = pobs1))
with(odata, table(y1))
ofit <- vglm(cbind(y1, y2) ~ x2, oalog, data = odata, trace = TRUE)
coef(ofit, matrix = TRUE)
head(fitted(ofit))
head(predict(ofit))
summary(ofit)
## End(Not run)
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