Predict method for functional linear model (fregre.fd class)
Computes predictions for regression between functional explanatory variables and scalar response using: basis representation, Principal Components Analysis, Partial least squares or nonparametric kernel estimation.
Predicts from a fitted fregre.basis object,see
fregre.basis or fregre.basis.cv
Predicts from
a fitted fregre.pc object,see fregre.pc or
fregre.pc.cv
Predicts from a fitted fregre.pls
object,see fregre.pls or fregre.pls.cv
Predicts from a fitted fregre.np object, see fregre.np
or fregre.np.cv.
## S3 method for class 'fregre.fd' predict( object, new.fdataobj = NULL, se.fit = FALSE, scale = NULL, df = df, interval = "none", level = 0.95, weights = 1, pred.var = res.var/weights, ... )
object |
|
new.fdataobj |
New functional explanatory data of |
se.fit |
=TRUE (not default) standard error estimates are returned for each prediction. |
scale |
Scale parameter for std.err. calculation. |
df |
Degrees of freedom for scale. |
interval |
Type of interval calculation. |
level |
Tolerance/confidence level. |
weights |
variance weights for prediction. This can be a numeric vector or a one-sided model formula. In the latter case, it is interpreted as an expression evaluated in newdata |
pred.var |
the variance(s) for future observations to be assumed for
prediction intervals. See |
... |
Further arguments passed to or from other methods. |
If se.fit = FALSE, a vector of predictions of scalar response
is returned or a matrix of predictions and bounds with column names fit,
lwr, and upr if interval is set.
If se.fit =TRUE a list with the following components is returned:
fit A vector of predictions or a matrix of predictions and bounds as above
se.fit Associated standard error estimates of predictions
residual.scale Residual standard deviations
df Degrees of freedom for residual
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es
Cai TT, Hall P. 2006. Prediction in functional linear regression. Annals of Statistics 34: 2159-2179.
Cardot H, Ferraty F, Sarda P. 1999. Functional linear model. Statistics and Probability Letters 45: 11-22.
Ferraty, F. and Vieu, P. (2006). Nonparametric functional data analysis. Springer Series in Statistics, New York.
Hall P, Hosseini-Nasab M. 2006. On properties of functional principal components analysis. Journal of the Royal Statistical Society B 68: 109-126.
Hardle, W. Applied Nonparametric Regression. Cambridge University Press, 1994.
Ramsay, James O., and Silverman, Bernard W. (2006), Functional Data Analysis, 2nd ed., Springer, New York.
Febrero-Bande, M., Oviedo de la Fuente, M. (2012). Statistical Computing in Functional Data Analysis: The R Package fda.usc. Journal of Statistical Software, 51(4), 1-28. http://www.jstatsoft.org/v51/i04/
See Also as: fregre.basis,
fregre.basis.cv, fregre.np,
fregre.np.cv, fregre.pc,
fregre.pc.cv, fregre.pls,
fregre.pls.cv
and summary.fregre.fd.
## Not run: data(tecator) absorp=tecator$absorp.fdata ind=1:129 x=absorp[ind,] y=tecator$y$Fat[ind] newx=absorp[-ind,] newy=matrix(tecator$y$Fat[-ind],ncol=1) ## Functional PC regression res.pc=fregre.pc(x,y,1:6) pred.pc=predict(res.pc,newx) # Functional PLS regression res.pls=fregre.pls(x,y,1:6) pred.pls=predict(res.pls,newx) # Functional nonparametric regression res.np=fregre.np(x,y,Ker=AKer.tri,metric=semimetric.deriv) pred.np=predict(res.np,newx) # Functional regression with basis representation res.basis=fregre.basis.cv(x,y) pred.basis=predict(res.basis[[1]],newx) dev.new() plot(pred.pc-newy) points(pred.pls-newy,col=2,pch=2) points(pred.np-newy,col=3,pch=3) points(pred.basis-newy,col=4,pch=4) sum((pred.pc-newy)^2,na.rm=TRUE)/sum((newy-mean(newy))^2,na.rm=TRUE) sum((pred.pls-newy)^2,na.rm=TRUE)/sum((newy-mean(newy))^2,na.rm=TRUE) sum((pred.np-newy)^2,na.rm=TRUE)/sum((newy-mean(newy))^2,na.rm=TRUE) sum((pred.basis-newy)^2,na.rm=TRUE)/sum((newy-mean(newy))^2,na.rm=TRUE) ## End(Not run)
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