"fv"
).as.fv(x) ## S3 method for class 'fv':
as.fv(x)
## S3 method for class 'data.frame':
as.fv(x)
## S3 method for class 'matrix':
as.fv(x)
## S3 method for class 'fasp':
as.fv(x)
## S3 method for class 'minconfit':
as.fv(x)
## S3 method for class 'kppm':
as.fv(x)
## S3 method for class 'bw.optim':
as.fv(x)
"fv"
(see fv.object
).x
, that
could be interpreted as the values of a function,
into a function value table (object of the class "fv"
as described in fv.object
). This object can then
be plotted easily using plot.fv
. The dataset x
may be any of the following:
"fv"
;"fasp"
, representing an array of"fv"
objects."minconfit"
, giving the results
of a minimum contrast fit by the commandmincontrast
.
The"kppm"
, representing a fitted
Cox or cluster point process model, obtained from the
model-fitting commandkppm
;"bw.optim"
, representing an optimal
choice of smoothing bandwidth by a cross-validation method, obtained
from commands likebw.diggle
.as.fv
is generic, with methods for each of the
classes listed above. The behaviour is as follows:
x
is an object of class"fv"
, it is
returned unchanged.x
is a matrix or data frame,
the first column is interpreted
as the function argument, and subsequent columns are interpreted as
values of the function computed by different methods.x
is an object of class"fasp"
representing an array of"fv"
objects,
these are combined into a single"fv"
object.x
is an object of class"minconfit"
,
or an object of class"kppm"
that was fitted by minimum contrast,
the result is a function table containing the
observed summary function and the best fit summary function.x
is an object of class"bw.optim"
,
the result is a function table of the optimisation criterion
as a function of the smoothing bandwidth.r <- seq(0, 1, length=101)
x <- data.frame(r=r, y=r^2)
as.fv(x)
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