VGAM (version 1.1-14)

zero: The zero Argument in VGAM Family Functions

Description

The zero argument allows users to conveniently model certain linear/additive predictors as intercept-only.

Value

Nothing is returned. It is simply a convenient argument for constraining certain linear/additive predictors to be an intercept only.

Details

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.

References

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.

See Also

CommonVGAMffArguments, constraints.

Examples

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)