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\name{formula-methods}
\docType{methods}
\alias{formula-methods}
\alias{formula,ANY-method}
\alias{formula,fGARCH-method}
\title{Extract GARCH Model formula}
\description{
Extracts formula from a formula GARCH object.
}
\section{Methods}{
\describe{
\item{object = "ANY"}{
Generic function.
}
\item{object = "fGARCH"}{
Extractor function for formula expression.
}
}
}
\details{
The function extracts the \code{@formula} expression slot from
an object of class \code{"fGARCH"} as returned by the function
\code{garchFit}.
Note, the returned formula has always a left hand
side. If the argument \code{data} was an univariate time series
and no name was specified to the series, then the left hand side
has assigned the name of the data.set. In the multivariate case
the rectangular \code{data} object must always have column names,
otherwise the fitting will be stopped and you get the error
message
The class of the returned value depends on the input to the
function \code{garchFit} who created the object. The returned
value is always of the same class as the input object to the
argument \code{data} in the function \code{garchFit}, i.e. if
you fit a \code{"timeSeries"} object, you will get back from
the function \code{fitted} also a \code{"timeSeries"} object,
if you fit an object of class \code{"zoo"}, you will get back
again a \code{"zoo"} object. The same holds for a \code{"numeric"}
vector, for a \code{"data.frame"}, and for objects of class
\code{"ts", "mts"}.
In contrast, the slot itself returns independent of the class
of the data input always a numceric vector, i.e. the function
call r\code{slot(object, "fitted")} will return a numeric vector.
}
\note{
\code{formula} is a generic function which extracts the formula
expression from objects returned by modeling functions.
}
\author{
Diethelm Wuertz for the Rmetrics \R-port.
}
\examples{
## garchFit -
fit = garchFit(~garch(1, 1), data = garchSim())
## formula -
formula(fit)
## A Bivariate series and mis-specified formula:
x = garchSim(n = 500)
y = garchSim(n = 500)
z = cbind(x, y)
colnames(z)
class(z)
\dontrun{
garchFit(z ~garch(1, 1), data = z, trace = FALSE)
}
# Returns:
# Error in .garchArgsParser(formula = formula, data = data, trace = FALSE) :
# Formula and data units do not match.
## Doubled column names in data set - formula can't fit:
colnames(z) <- c("x", "x")
z[1:6,]
\dontrun{
garchFit(x ~garch(1, 1), data = z, trace = FALSE)
}
# Again the error will be noticed:
# Error in garchFit(x ~ garch(1, 1), data = z) :
# Column names of data are not unique.
## Missing column names in data set - formula can't fit:
z.mat <- as.matrix(z)
colnames(z.mat) <- NULL
z.mat[1:6,]
\dontrun{
garchFit(x ~ garch(1, 1), data = z.mat, trace = FALSE)
}
# Again the error will be noticed:
# Error in .garchArgsParser(formula = formula, data = data, trace = FALSE) :
# Formula and data units do not match
}
\keyword{models}
|
