| 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"), ...)
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. |
... |
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.
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
data(pneumo)
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 = model.matrix(fit, type="lm")[1:3,]
Check1
Check2 = model.matrix(fit, data=pneumo[1:3,], type="lm")
Check2
all.equal(c(Check1), c(Check2))
q0 = predict(fit)[1:3,]
q1 = predict(fit, newdata=pneumo)[1:3,]
q2 = predict(fit, newdata=pneumo[1:3,])
all.equal(q0, q1) # Should be TRUE
all.equal(q1, q2) # Should be TRUE