quasibinomialff {VGAM} | R Documentation |
Family function for fitting generalized linear models to binomial responses, where the dispersion parameters are unknown.
quasibinomialff(link = "logit", mv = FALSE, onedpar = !mv, parallel = FALSE, zero = NULL)
link |
Link function. See Links for more choices.
|
mv |
Multivariate response? If TRUE , then the response is interpreted
as M binary responses, where M is the number of columns
of the response matrix. In this case, the response matrix should have
zero/one values only.
If FALSE and the response is a (2-column) matrix, then the
number of successes is given in the first column and the second column
is the number of failures.
|
onedpar |
One dispersion parameter? If mv , then a separate dispersion
parameter will be computed for each response (column), by default.
Setting onedpar=TRUE will pool them so that there is only one
dispersion parameter to be estimated.
|
parallel |
A logical or formula. Used only if mv is TRUE . This
argument allows for the parallelism assumption whereby the regression
coefficients for a variable is constrained to be equal over the M
linear/additive predictors.
|
zero |
An integer-valued vector specifying which linear/additive predictors
are modelled as intercepts only. The values must be from the set
{1,2,...,M}, where M is the number of columns of
the matrix response.
|
The final model is not fully estimated by maximum likelihood since the dispersion parameter is unknown (see pp.124–8 of McCullagh and Nelder (1989) for more details).
A dispersion parameter that is less/greater than unity corresponds to under-/over-dispersion relative to the binomial model. Over-dispersion is more common in practice.
Setting mv=TRUE
is necessary when fitting a Quadratic RR-VGLM
(see cqo
) because the response will be a matrix of
M columns (e.g., one column per species). Then there will be
M dispersion parameters (one per column of the response).
An object of class "vglmff"
(see vglmff-class
).
The object is used by modelling functions such as
vglm
,
vgam
,
rrvglm
,
cqo
,
and cao
.
If mv
is FALSE
(the default), then the response can be
of one of three formats: a factor (first level taken as success), a
vector of proportions of success, or a 2-column matrix (first column =
successes) of counts. The argument weights
in the modelling
function can also be specified. In particular, for a general vector
of proportions, you will need to specify weights
because the
number of trials is needed.
If mv
is TRUE
, then the matrix response can only be of
one format: a matrix of 1's and 0's (1=success).
This function is only a front-end to the VGAM family function
binomialff()
; indeed, quasibinomialff(...)
is equivalent
to binomialff(..., dispersion=0)
. Here, the argument
dispersion=0
signifies that the dispersion parameter is to
be estimated.
Regardless of whether the dispersion parameter is to be estimated or
not, its value can be seen from the output from the summary()
of the object.
Thomas W. Yee
McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models, 2nd ed. London: Chapman & Hall.
binomialff
,
rrvglm
,
cqo
,
cao
,
logit
,
probit
,
cloglog
,
cauchit
,
poissonff
,
quasipoissonff
,
quasibinomial
.
quasibinomialff() quasibinomialff(link="probit") # Nonparametric logistic regression data(hunua) hunua = transform(hunua, a.5 = sqrt(altitude)) # Transformation of altitude fit1 = vglm(agaaus ~ poly(a.5, 2), quasibinomialff, hunua) fit2 = vgam(agaaus ~ s(a.5, df=2), quasibinomialff, hunua) ## Not run: plot(fit2, se=TRUE, llwd=2, lcol="red", scol="red", xlab="sqrt(altitude)", ylim=c(-3,1), main="GAM and quadratic GLM fitted to species data") plotvgam(fit1, se=TRUE, lcol="blue", scol="blue", add=TRUE, llwd=2) ## End(Not run) fit1@misc$dispersion # dispersion parameter logLik(fit1) # Here, the dispersion parameter defaults to 1 fit0 = vglm(agaaus ~ poly(a.5, 2), binomialff, hunua) fit0@misc$dispersion # dispersion parameter