# Predict method for Functional Regression Model predictions for object of class `fRegress`. ```r ## 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 ```r ## ## 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) ```

predict.fRegress function