model.matrixvlm {VGAM} | R Documentation |
Creates a design matrix. Two types can be
returned: a large one (class "vlm"
or one that inherits
from this such as "vglm"
) or a small one
(such as returned if it were of class "lm"
).
model.matrixvlm(object, type=c("vlm","lm","lm2","bothlmlm2"), ...)
object |
an object of a class that inherits from the vector linear model (VLM). |
type |
Type of design matrix returned. The first is the default.
The value "vlm" is the VLM model matrix corresponding
to the formula argument.
The value "lm" is the LM model matrix corresponding
to the formula argument.
The value "lm2" is the second (LM) model matrix corresponding
to the form2 argument.
The value "bothlmlm2" means both LM and VLM model matrices.
|
... |
further arguments passed to or from other methods.
These include data (which
is a data frame created with model.framevlm ),
contrasts.arg , and xlev .
See model.matrix for more information.
|
This function creates a design matrix from object
.
This can be a small LM object or a big VLM object (default).
The latter is constructed from the former and the constraint
matrices.
This code implements smart prediction
(see smartpred
).
The design matrix for a regression model with the specified formula
and data.
If type="bothlmlm2"
then a list is returned with components
"X"
and "Xm2"
.
Yee, T. W. and Hastie, T. J. (2003) Reduced-rank vector generalized linear models. Statistical Modelling, 3, 15–41.
Chambers, J. M. (1992) Data for models. Chapter 3 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
model.matrix
,
model.framevlm
,
predict.vglm
,
smartpred
.
# Illustrates smart prediction pneumo = transform(pneumo, let=log(exposure.time)) fit = vglm(cbind(normal,mild, severe) ~ poly(c(scale(let)), 2), fam=multinomial, data=pneumo, trace=TRUE, x=FALSE) class(fit) fit@x model.matrix(fit) Check1 = head(model.matrix(fit, type="lm")) Check1 Check2 = model.matrix(fit, data=head(pneumo), type="lm") Check2 all.equal(c(Check1), c(Check2)) q0 = head(predict(fit)) q1 = head(predict(fit, newdata=pneumo)) q2 = predict(fit, newdata=head(pneumo)) all.equal(q0, q1) # Should be TRUE all.equal(q1, q2) # Should be TRUE