predict.lm
Predict Method for a Linear Model
Description
Make predictions based on an lm object.
Usage
## S3 method for class 'lm':
predict(object, newdata = NULL, se.fit = FALSE, scale = NULL, df = Inf,
interval = "none", level = 0.95,
type = "response", terms = NULL, na.action = na.pass,
pred.var = res.var/weights, weights = 1, ...)
## S3 method for class 'mlm':
predict(object, newdata = NULL, se.fit = FALSE, na.action = na.pass,
...)
Arguments
| object |
a fitted lm (or mlm) object.
|
| newdata |
An environemt, data frame, or list containing the values of the
predictor variables at which predictions are required.
This argument can be missing, in which case predictions are made at
the same values used to compute the object.
The predictors referred to in the right side of the formula in
object must be present by name in newdata.
|
| se.fit |
if TRUE, pointwise standard errors are computed along with the predictions.
This is not available for multiresponse (mlm) models and se.fit=TRUE
will cause an error for them.
|
| scale |
If not NULL this is used instead of the scale recorded the lm object.
|
| df |
If scale is not NULL, this is used as the number of residual
degrees of freedom.
|
| interval |
the interval type. it can be "none" (the default), "confidence" or "prediction".
|
| level |
confidence or prediction level.
|
| type |
type of predictions, with choices "response" (the default) or "terms".
If "response" is selected, the predictions are on the scale of the
response.
If type="terms" is selected, the predictions are broken down into the contributions from each term. A
matrix of predictions is produced, one column for each term in the model.
|
| terms |
if type="terms", the terms= argument can be used to specify which terms should be included;
the default is NULL. It means all terms are included. This argument is ignored
when type is "response".
|
| na.action |
the function to handle missing values (NAs) for newdata. The default is na.pass.
|
| pred.var |
prediction variance used instead of the estimated residual variance.
Only used when interval is "prediction".
|
| weights |
a numeric vector.
Another way to specify pred.var: the estimated residual variance
will be divided by weights to produce prev.var
|
| ... |
further arguments passed to or from other methods
|
Details
This function is a method for the generic function predict for class lm.
It can be invoked by callingpredict for an object x of the appropriate class,
or directly by calling predict.lm regardless of the class of the object.
(In the latter case an error will occur or the results will be incorrect if
the object is not sufficiently like an "lm" object.)
Only the listed arguments are uses for predict.mlm -- any others
are silently ignored.
Value
If type = "response", the default, a vector of predictions
or, if interval is "confidence" or "prediction",
a matrix of predictions with columns named "fit", "lwr" and "upr"
giving the fit and its lower and upper confidence or prediction intervals.
If type="terms", a matrix of term-wise fitted values is produced, with one
column for each term in the model (or subset of these if the terms=
argument is used). There will be no column for the intercept but its
value will be attached as the attributed named "constant".
The row sums of this matrix, plus the constant term, will be the same
as the predicted values given in the type="response" case.
If se.fit=TRUE, the above fitted values will be the the "fit"
component of a list. The other components of the returned list will
be "se.fit", a vector or matrix that same shape as "fit" containing
the standard errors of the predicted values, "df", the number of residual
degrees of freedom, and "residual.scale", the scale of the residuals.
When type is "terms" and interval is "confidence" or "prediction",
things are further rearranged. You will get a list with matrix components
(with one column per term) "fit", "se.fit", "lrw", and "upr" and
scalar components "df" and "residual.scale".
Warning
predict can produce incorrect predictions when the newdata
argument is used if the formula in object involves
data-dependenttransformations, such as sqrt(Age - min(Age)).
However, certain data-dependent transformations known to lm,
such as poly, scale, and splines::ns are safe to
use because lm uses makepredictcall to store the data-dependent information
for the transformation in the attr(terms,"predvars") part of its output.
See Also
Examples
fit <- lm(Fuel ~ Weight + Disp. + Type, data=Sdatasets::fuel.frame)
predict(fit)
predict(fit, newdata=data.frame(Weight=2750, Disp.=110, Type="Small"))
predict(fit, interval = "prediction",
newdata=data.frame(Weight=2700, Disp.=300, Type="Sporty"))
predict(fit, type = "terms")