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 pobs1 or phi,
called |
lshape |
See |
gshape, type.fitted |
See |
ipobs1, zero |
See |
The response Y is one with probability pobs1, or Y has a 1-truncated logarithmic distribution with probability 1-pobs1. Thus 0 < pobs1 < 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(shape))^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 pobs1 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, inverse = TRUE), shape = logitlink(-2 + 3*x2, inverse = TRUE)) odata <- transform(odata, y1 = roalog(nn, shape = shape, pobs1 = pobs1), y2 = roalog(nn, shape = 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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