predict.loess
Evaluation of Local Regression Surfaces
Description
Returns the surface values of local regression surfaces and/or standard errors.
Usage
predict.loess(object, newdata = NULL, se = FALSE,
na.action = na.pass, ...)
Arguments
| object |
a loess object.
|
| newdata |
a data frame specifying the values of the predictors at which
the evaluation is carried out. The default is to predict at the
data used to fit object. That is, if newdata = NULL
(the default), use the x component of the object
as newdata.
|
| se |
a logical value. If TRUE, estimates of the standard errors of the
surface values and other statistical information,
are returned along with the surface values. The default is FALSE.
|
| na.action |
a function to filter missing data. The default is the function na.pass.
|
| ... |
additional arguments for future functions.
|
Details
This function is a method for the generic function predict
for the class loess.
It can either be invoked by calling predict for an object x
of the appropriate class,
or it can be invoked directly by calling predict.loess,
regardless of the class of the object.
For one predictor, newdata can be a vector rather than a data frame.
For two or more predictors, newdata must include the names
of predictors used in formula as they appear in the database
from which they come. For example, if the right side of formula is log(E)*C,
then the names C and E must be in newdata.
Note that the specification of E in this example is not on the
transformed scale; rather, it is on the original scale.
For two or more predictors, one of two data structures
can be given to
newdata.
- The first data structure is an ordinary data frame, the result of which is
a vector whose length is equal to the number of rows of newdata,
and the element of the vector in position i is the evaluation
of the surface at row i of newdata.
- The second data structure can be used when the evaluation points form a grid.
In this case, newdata is the result of the function expand.grid.
If
se = FALSE, then the result of
predict.loess
is a numeric array whose dimension is equal to the number of predictors.
If
se = TRUE, then the components
fit
and
se.fit are both such arrays.
The computations of predict.loess that produce the component
se.fit are much more costly than those that produce fit,
so the number of points at which standard errors are computed should
be modest compared to those at which we do evaluations.
Often this means calling predict.loess twice:
once at a large number of points, with se equal to FALSE
to get a thorough description of the surface,
and once at a small number of points to get standard-error information.
Suppose the computation method for the loess argument surface is
interpolate (the default).
Then the evaluation values of a numeric predictor must lie
within the range of the values of the predictor used in the fit.
The evaluation values for a predictor that is a factor must
be one of the levels of the factor.
For any evaluation point for which these conditions are not met,
an NA is returned.
If se is TRUE or the computation method for loess surfaces
is direct,
then predict.loess must use the data originally used to fit
the loess model to compute the predictions.
If you fit the loess model using the data argument,
then the data set given by data should not be changed between the
fit and the prediction.
If you attached a data frame to supply data
for the model, then that same data frame must be attached to compute
the predicted values.
Value
- If se = FALSE, returns a vector or array of surface values evaluated at
newdata.
The evaluation is on the scale of the expression given
on the left side of formula.
For example, if the expression is log(temperature),
then the evaluation is on the log scale.
- If se = TRUE, returns a list with the following components.
|
fit | the evaluated loess surface at newdata. |
|
se.fit | the estimates of the standard errors of the surface values. |
|
residual.scale | the estimate of the scale of the residuals. |
|
df | the degrees of freedom of the t-distribution used to compute pointwise
confidence intervals for the evaluated surface. Use the function pointwise
to compute such intervals.
|
See Also
Examples
ethanol.cp <- loess(formula = NOx ~ C * E, data=Sdatasets::ethanol,
span = 1/2, degree = 2, parametric = "C", drop.square = "C")
# Example 1 - evaluation at 5 points
# newdata is a data frame with variables C and E
newdata <- Sdatasets::ethanol[c(1, 2, 3, 5, 8), c("C", "E")]
predict(ethanol.cp, newdata)
## 1 2 3 5 8
## 3.845377 2.345562 1.382060 0.745741 1.187618
# Example 2 - evaluation at 9 grid points
C.marginal <- with(Sdatasets::ethanol, seq(min(C), max(C), length = 3))
E.marginal <- with(Sdatasets::ethanol, seq(min(E), max(E), length = 3))
CE.grid <- expand.grid(list(C = C.marginal, E = E.marginal))
predict(ethanol.cp, CE.grid)
## E
## C E=0.5350 E=0.8835 E=1.2320
## C= 7.50 -0.1039991 3.399360 0.6823181
## C=12.75 0.2057837 3.850801 0.6481270
## C=18.00 0.5155665 4.302243 0.6139359
# Example 3 - evaluate and compute estimates of standard errors
gas.m <- loess(formula = NOx ~ E, data=Sdatasets::ethanol,
span = 2/3, degree = 2)
with(Sdatasets::ethanol,
predict(gas.m, newdata = seq(min(E), max(E), length = 5),
se = TRUE)$se.fit)
## [1] 0.18423300 0.07098989 0.07643326 0.07503963 0.11885234