predict.fRegress function

Predict method for Functional Regression

Model predictions for object of class fRegress.

## 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.

Returns

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

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.

See Also

fRegress

predict

Examples

##
## vector response with functional explanatory variable
##
## Not run:

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 
oldpar <- par(no.readonly=TRUE)
plot(annualprec, precip.Temp.p)
par(oldpar)
## End(Not run)