Convert Data To Class fv
Converts data into a function table (an object of class "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 'dppm' as.fv(x) ## S3 method for class 'kppm' as.fv(x) ## S3 method for class 'bw.optim' as.fv(x)
x |
Data which will be converted into a function table |
The dataset x
may be any of the following:
an object of class "fv"
;
a matrix or data frame with at least two columns;
an object of class "fasp"
, representing an array of
"fv"
objects.
an object of class "minconfit"
, giving the results
of a minimum contrast fit by
the command mincontrast
.
The
an object of class "kppm"
, representing a fitted
Cox or cluster point process model, obtained from the
model-fitting command kppm
;
an object of class "dppm"
, representing a fitted
determinantal point process model, obtained from the
model-fitting command dppm
;
an object of class "bw.optim"
, representing an optimal
choice of smoothing bandwidth by a cross-validation method, obtained
from commands like bw.diggle
.
The function as.fv
is generic, with methods for each of the
classes listed above. The behaviour is as follows:
If x
is an object of class "fv"
, it is
returned unchanged.
If 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.
If x
is an object of class "fasp"
representing an array of "fv"
objects,
these are combined into a single "fv"
object.
If x
is an object of class "minconfit"
,
or an object of class "kppm"
or "dppm"
,
the result is a function table containing the
observed summary function and the best fit summary function.
If 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.
An object of class "fv"
(see fv.object
).
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk
r <- seq(0, 1, length=101) x <- data.frame(r=r, y=r^2) as.fv(x)
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