Predict method for functional linear model
Computes predictions for regression between functional (and non functional) explanatory variables and scalar response.
predict.fregre.lm, Predict method for functional linear model of
fregre.lm fits object using basis or principal component
representation.
predict.fregre.plm, Predict method for
semi-functional linear regression model of fregre.plm fits
object using using asymmetric kernel estimation.
predict.fregre.glm, Predict method for functional generalized linear
model of fregre.glm fits object using basis or principal
component representation.
predict.fregre.gsam, Predict method for functional generalized
spectral additive model of fregre.gsam fits object using basis
or principal component representation.
predict.fregre.gkam, Predict method for functional generalized
kernel additive model of fregre.gkam fits object using
backfitting algorithm.
These functions use the model fitting function lm,
glm or gam properties.
If using functional
data derived, is recommended to use a number of bases to represent beta
lower than the number of bases used to represent the functional data.
The first item in the data list of newx argument is called
"df" and is a data frame with the response and non functional
explanatory variables, as lm, glm or
gam. Functional variables (fdata and fd class)
are introduced in the following items in the data list of newx
argument.
## S3 method for class 'fregre.gkam' predict(object, newx = NULL, type = "response", ...) ## S3 method for class 'fregre.glm' predict(object, newx = NULL, type = "response", ...) ## S3 method for class 'fregre.gsam' predict(object, newx = NULL, type = "response", ...) ## S3 method for class 'fregre.lm' predict( object, newx = NULL, type = "response", se.fit = FALSE, scale = NULL, df = df, interval = "none", level = 0.95, weights = 1, pred.var = res.var/weights, ... ) ## S3 method for class 'fregre.plm' predict(object, newx = NULL, ...)
object |
|
newx |
An optional data list in which to look for variables with which to predict. If omitted, the fitted values are used. List of new explanatory data. |
type |
a character vector, Type of prediction: ( |
... |
Further arguments passed to or from other methods. |
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 |
Return the predicted values and optionally:
predict.lm,predict.glm,predict.gam produces a vector of predictions or a matrix of predictions and bounds with column names fit, lwr, and upr if interval is set. If se.fit is TRUE, a list with the following components is returned: fit vector or matrix as above.
se.fit standard error of predicted means.
residual.scale residual standard deviations.
df degrees of freedom for residual.
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es
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.lm, fregre.plm,
fregre.glm, fregre.gsam and
fregre.gkam.
## Not run:
data(tecator)
ind<-1:129
x=tecator$absorp.fdata
x.d2<-fdata.deriv(x,nderiv=2)
tt<-x[["argvals"]]
dataf=as.data.frame(tecator$y)
nbasis.x=11;nbasis.b=7
basis1=create.bspline.basis(rangeval=range(tt),nbasis=nbasis.x)
basis2=create.bspline.basis(rangeval=range(tt),nbasis=nbasis.b)
basis.x=list("x.d2"=basis1)
basis.b=list("x.d2"=basis2)
ldata=list("df"=dataf[ind,],"x.d2"=x.d2[ind])
res=fregre.gsam(Fat~s(Water,k=3)+s(x.d2,k=3),data=ldata,
family=gaussian(),basis.x=basis.x,basis.b=basis.b)
newldata=list("df"=dataf[-ind,],"x.d2"=x.d2[-ind])
pred<-predict(res,newldata)
plot(pred,tecator$y$Fat[-ind])
res.glm=fregre.glm(Fat~Water+x.d2,data=ldata,family=gaussian(),
basis.x=basis.x,basis.b=basis.b)
pred.glm<-predict(res.glm,newldata)
newy<-tecator$y$Fat[-ind]
points(pred.glm,tecator$y$Fat[-ind],col=2)
# Time-consuming
res.gkam=fregre.gkam(Fat~x.d2,data=ldata)
pred.gkam=predict(res.gkam,newldata)
points(pred.gkam,tecator$y$Fat[-ind],col=4)
((1/length(newy))*sum((drop(newy)-pred)^2))/var(newy)
((1/length(newy))*sum((newy-pred.glm)^2))/var(newy)
((1/length(newy))*sum((newy-pred.gkam)^2))/var(newy)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.