predict.fRegress | R Documentation |
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, ...)
object |
Object of class inheriting from |
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$yfdobj has class or inprod(newdata[i], betaestfdlist[i]) if class(object$yfdobj) =
|
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 |
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.
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$y
is a scalar, these predictions
are returned as a matrix; otherwise, they are a multivariate
functional parameter object (class fdPar
).
If se.fit
is TRUE
, predict.fRegress
returns a
list with the following components:
fit |
vector or matrix or univariate or multivariate functional parameter
object depending on the value of |
se.fit |
standard error of predicted means |
Spencer Graves
Ramsay, James O., Hooker, Giles, and Graves, Spencer (2009), Functional data analysis with R and Matlab, Springer, New York.
Ramsay, James O., and Silverman, Bernard W. (2005), Functional Data Analysis, 2nd ed., Springer, New York.
Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York.
fRegress
predict
##
## vector response with functional explanatory variable
##
## Not run:
annualprec <- log10(apply(CanadianWeather$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)
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