# predict.fRegress: Predict method for Functional Regression In fda: Functional Data Analysis

 predict.fRegress R Documentation

## Predict method for Functional Regression

### Description

Model predictions for object of class `fRegress`.

### Usage

``````## S3 method for class 'fRegress'
predict(object, newdata=NULL, se.fit = FALSE,
interval = c("none", "confidence", "prediction"),
level = 0.95, ...)
``````

### Arguments

 `object` Object of class inheriting from `fRegress` `newdata` Either NULL or a list matching object\$xfdlist. If(is.null(newdata)) predictions <- object\$yhatfdobj If newdata is a list, predictions = the sum of either newdata[i] * betaestfdlist[i] if object\$yfdobj has class `fd` or inprod(newdata[i], betaestfdlist[i]) if class(object\$yfdobj) = `numeric`. `se.fit` a switch indicating if standard errors of predictions are required NOTE: se.fit = TRUE is NOT IMPLEMENTED YET. `interval` type of prediction (response or model term) NOTE: Only "intervale = 'none'" has been implemented so far. `level` Tolerance/confidence level `...` additional arguments for other methods

### Details

1. Without `newdata`, fit <- object\$yhatfdobj.

2. With `newdata`, if(class(object\$y) == 'numeric'), fit <- sum over i of inprod(betaestlist[i], newdata[i]). Otherwise, fit <- sum over i of betaestlist[i] * newdata[i].

3. If(se.fit | (interval != 'none')) compute `se.fit`, then return whatever is desired.

### Value

The predictions produced by `predict.fRegress` are either a vector or a functional parameter (class `fdPar`) object, matching the class of `object\$y`.

If `interval` is not "none", the predictions will be multivariate for `object\$y` and the requested `lwr` and `upr` bounds. If `object\$y` is a scalar, these predictions are returned as a matrix; otherwise, they are a multivariate functional parameter object (class `fdPar`).

If `se.fit` is `TRUE`, `predict.fRegress` returns a list with the following components:

 `fit` vector or matrix or univariate or multivariate functional parameter object depending on the value of `interval` and the class of `object\$y`. `se.fit` standard error of predicted means

Spencer Graves

### References

Ramsay, James O., Hooker, Giles, and Graves, Spencer (2009), Functional data analysis with R and Matlab, Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2005), Functional Data Analysis, 2nd ed., Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York.

`fRegress` `predict`

### Examples

``````##
## vector response with functional explanatory variable
##
## Not run:
"Precipitation.mm"], 2,sum))
smallbasis  <- create.fourier.basis(c(0, 365), 25)
tempfd <- smooth.basis(day.5,