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zero
argument allows users to conveniently
model certain linear/additive predictors as intercepts
only.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.
zero
argument allows this to be fitted conveniently
without having to input all the constraint matrices explicitly. The zero
argument should 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.
Yee, T. W. and Hastie, T. J. (2003) Reduced-rank vector generalized linear models. Statistical Modelling, 3, 15--41.
constraints
.args(multinomial)
args(binom2.or)
args(gpd)
#LMS quantile regression example
fit = vglm(BMI ~ bs(age, df=4), fam=lms.bcg(zero=c(1,3)),
data=bminz, trace=TRUE)
coef(fit, matrix=TRUE)
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