predictvglm {VGAM} | R Documentation |
Predicted values based on a vector generalized linear model (VGLM) object.
predictvglm(object, newdata = NULL, type = c("link", "response", "terms"), se.fit = FALSE, deriv = 0, dispersion = NULL, untransform = FALSE, extra = object@extra, ...)
object |
Object of class inheriting from |
newdata |
An optional data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used. |
type |
The value of this argument can be abbreviated. The type of prediction required. The default is the first one, meaning on the scale of the linear predictors. This should be a n x M matrix. The alternative The |
se.fit |
logical: return standard errors? |
deriv |
Non-negative integer. Currently this must be zero. Later, this may be implemented for general values. |
dispersion |
Dispersion parameter. This may be inputted at this stage, but the default is to use the dispersion parameter of the fitted model. |
extra |
A list containing extra information. This argument should be ignored. |
untransform |
Logical. Reverses any parameter link function.
This argument only works if
|
... |
Arguments passed into |
Obtains predictions and optionally estimates
standard errors of those predictions from a fitted
vglm
object.
This code implements smart prediction (see
smartpred
).
If se.fit = FALSE
, a vector or matrix of predictions.
If se.fit = TRUE
, a list with components
fitted.values |
Predictions |
se.fit |
Estimated standard errors |
df |
Degrees of freedom |
sigma |
The square root of the dispersion parameter |
This function may change in the future.
Setting se.fit = TRUE
and type = "response"
will generate an error.
Thomas W. Yee
Yee, T. W. and Hastie, T. J. (2003) Reduced-rank vector generalized linear models. Statistical Modelling, 3, 15–41.
predict
,
vglm
,
predictvlm
,
smartpred
.
# Illustrates smart prediction pneumo <- transform(pneumo, let = log(exposure.time)) fit <- vglm(cbind(normal, mild, severe) ~ poly(c(scale(let)), 2), propodds, data = pneumo, trace = TRUE, x.arg = FALSE) class(fit) (q0 <- head(predict(fit))) (q1 <- predict(fit, newdata = head(pneumo))) (q2 <- predict(fit, newdata = head(pneumo))) all.equal(q0, q1) # Should be TRUE all.equal(q1, q2) # Should be TRUE head(predict(fit)) head(predict(fit, untransform = TRUE)) p0 <- head(predict(fit, type = "response")) p1 <- head(predict(fit, type = "response", newdata = pneumo)) p2 <- head(predict(fit, type = "response", newdata = pneumo)) p3 <- head(fitted(fit)) all.equal(p0, p1) # Should be TRUE all.equal(p1, p2) # Should be TRUE all.equal(p2, p3) # Should be TRUE predict(fit, type = "terms", se = TRUE)