The zero Argument in VGAM Family Functions
The zero argument allows users to conveniently
model certain linear/additive predictors as intercept-only.
Often a certain parameter needs to be modelled simply while other
parameters in the model may be more complex, for example, the
lambda parameter in LMS-Box-Cox quantile regression
should be modelled more simply compared to its mu parameter.
Another example is the xi parameter in a GEV distribution
which is should be modelled simpler than its mu parameter.
Using the zero argument allows this to be fitted conveniently
without having to input all the constraint matrices explicitly.
The zero argument can be assigned an integer vector from the
set {1:M} where M is the number of linear/additive
predictors. Full details about constraint matrices can be found in
the references.
See CommonVGAMffArguments for more information.
Nothing is returned. It is simply a convenient argument for constraining certain linear/additive predictors to be an intercept only.
The use of other arguments may conflict with the zero
argument. For example, using constraints to input constraint
matrices may conflict with the zero argument.
Another example is the argument parallel.
In general users
should not assume any particular order of precedence when
there is potential conflict of definition.
Currently no checking for consistency is made.
The argument zero may be renamed in the future to
something better.
The argument creates the appropriate constraint matrices internally.
In all VGAM family functions zero = NULL means
none of the linear/additive predictors are modelled as
intercepts-only.
Almost all VGAM family function have zero = NULL
as the default, but there are some exceptions, e.g.,
binom2.or.
Typing something like coef(fit, matrix = TRUE) is a useful
way to ensure that the zero argument has worked as expected.
T. W. Yee
Yee, T. W. and Wild, C. J. (1996). Vector generalized additive models. Journal of the Royal Statistical Society, Series B, Methodological, 58, 481–493.
Yee, T. W. and Hastie, T. J. (2003). Reduced-rank vector generalized linear models. Statistical Modelling, 3, 15–41.
args(multinomial)
args(binom2.or)
args(gpd)
#LMS quantile regression example
fit <- vglm(BMI ~ sm.bs(age, df = 4), lms.bcg(zero = c(1, 3)),
data = bmi.nz, trace = TRUE)
coef(fit, matrix = TRUE)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.