zero | R Documentation |

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.

`CommonVGAMffArguments`

,
`constraints`

.

```
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)
```

VGAM documentation built on Sept. 19, 2023, 9:06 a.m.

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