fda.usc: predict.fregre.gls – R documentation

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predict.fregre.gls

Predictions from a functional gls object

Description

The predictions for the functional generalized least squares fitted linear model represented by object are obtained at the covariate values defined in newx.

Usage

## S3 method for class 'fregre.gls'
predict(
  object,
  newx = NULL,
  type = "response",
  se.fit = FALSE,
  scale = NULL,
  df,
  interval = "none",
  ...
)

## S3 method for class 'fregre.igls'
predict(
  object,
  newx = NULL,
  data,
  df = df,
  weights = 1,
  pred.var,
  n.ahead = 1L,
  ...
)

Arguments

`object`	`fregre.gls` 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`	Type of prediction (response or model term).
`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.
`...`	Further arguments passed to or from other methods.
`data`	Data frame with the time or spatinal index
`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 `link{predict.lm}` for more details.
`n.ahead`	number of steps ahead at which to predict.

Value

a vector with the predicted values.

Author(s)

Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es

References

Oviedo de la Fuente, M., Febrero-Bande, M., Pilar Munoz, and Dominguez, A. Predicting seasonal influenza transmission using Functional Regression Models with Temporal Dependence. arXiv:1610.08718. https://arxiv.org/abs/1610.08718

Examples

## Not run: 
data(tecator)
ind<-1:190
x <-fdata.deriv(tecator$absorp.fdata,nderiv=1)
dataf=as.data.frame(tecator$y)
dataf$itime <- 1:nrow(x)
ldat=list("df"=dataf[ind,],"x"=x[ind])
newldat=list("df"=dataf[-ind,],"x"=x[-ind])
newy <- tecator$y$Fat[-ind]
ff <- Fat ~ x
res.lm <- fregre.lm(ff,data=ldat)
summary(res.lm)
res.gls <- fregre.gls(ff,data=ldat, correlation=corAR1())
summary(res.gls)
par.cor <- list("cor.ARMA"=list("p"=1))
par.cor <- list("cor.ARMA"=list("index"=c("itime"),"p"=1))
res.igls <- fregre.igls(ff,data=ldat,correlation=par.cor) 
pred.lm <- predict(res.lm,newldat)
pred.gls <- predict(res.gls,newldat)
pred.igls <- predict(res.igls,newldat)
mean((pred.lm-newldat$df$Fat)^2)
mean((pred.gls-newldat$df$Fat)^2)
mean((pred.igls-newldat$df$Fat)^2)

## End(Not run)

fda.usc

Functional Data Analysis and Utilities for Statistical Computing

v2.0.2

GPL-2

Authors

Manuel Febrero Bande [aut], Manuel Oviedo de la Fuente [aut, cre], Pedro Galeano [ctb], Alicia Nieto [ctb], Eduardo Garcia-Portugues [ctb]

Initial release

2020-02-17