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fda

predict.fRegress

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

Author(s)

Spencer Graves

Examples

##
## vector response with functional explanatory variable
##

annualprec <- log10(apply(CanadianWeather$dailyAv[,,
                   "Precipitation.mm"], 2,sum))
smallbasis  <- create.fourier.basis(c(0, 365), 25)
tempfd <- smooth.basis(day.5,
   CanadianWeather$dailyAv[,,"Temperature.C"], smallbasis)$fd
precip.Temp.f <- fRegress(annualprec ~ tempfd)

precip.Temp.p <- predict(precip.Temp.f)

# plot response vs. fitted 
plot(annualprec, precip.Temp.p)

fda

Functional Data Analysis

v5.1.9

GPL (>= 2)

Authors

J. O. Ramsay <ramsay@psych.mcgill.ca> [aut,cre], Spencer Graves <spencer.graves@effectivedefense.org> [ctb], Giles Hooker <gjh27@cornell.edu> [ctb]

Initial release

2020-12-16