# Predict

##### Model Predictions

`Predict`

is a generic function with, at present, a single method for `"lm"`

objects,
`Predict.lm`

, which is a modification of the standard `predict.lm`

method in
the stats package, but with an additional `vcov.`

argument for a user-specified covariance matrix for intreval estimation.

- Keywords
- models

##### Usage

`Predict(object, ...)`# S3 method for lm
Predict(object, newdata, se.fit = FALSE,
scale = NULL, df = Inf,
interval = c("none", "confidence", "prediction"),
level = 0.95, type = c("response", "terms"),
terms = NULL, na.action = na.pass,
pred.var = res.var/weights, weights = 1, vcov., ...)

##### Arguments

- object
a model object for which predictions are desired.

- newdata, se.fit, scale, df, interval, level, type, terms, na.action, pred.var, weights
see

`predict.lm`

.- vcov.
optional, either a function to compute the coefficient covariance matrix of

`object`

(e.g.,`hccm`

) or a coefficient covariance matrix (as returned, e.g., by`hccm`

).- …
arguments to pass down to

`Predict`

or`predict`

methods.

##### Details

If there is no appropriate method for `Predict`

, then a `predict`

method
is invoked. If there is a *specific* `predict`

method for the primary class of `object`

but
only an *inherited* `Predict`

method, then the `predict`

method is invoked.
Thus an object of class `c("glm", "lm")`

will invoke method `predict.glm`

rather than
`Predict.lm`

, but an object of class `c("aov", "lm")`

will invoke `Predict.lm`

rather than `predict.lm`

.

##### Value

See `predict`

and `predict.lm`

.

##### References

Fox, J. and Weisberg, S. (2019)
*An R Companion to Applied Regression*, Third Edition, Sage.

##### See Also

##### Examples

```
# NOT RUN {
mod <- lm(interlocks ~ log(assets), data=Ornstein)
newd <- data.frame(assets=exp(4:12))
(p1 <- predict(mod, newd, interval="prediction"))
p2 <- Predict(mod, newd, interval="prediction", vcov.=vcov)
all.equal(p1, p2) # the same
(predict(mod, newd, se=TRUE))
(p3 <- Predict(mod, newd, se=TRUE, vcov.=hccm)) # larger SEs
p4 <- Predict(mod, newd, se=TRUE, vcov.=hccm(mod, type="hc3"))
all.equal(p3, p4) # the same
# }
```

*Documentation reproduced from package car, version 3.0-5, License: GPL (>= 2)*