"fv"
).
as.fv(x)
"as.fv"(x)
"as.fv"(x)
"as.fv"(x)
"as.fv"(x)
"as.fv"(x)
"as.fv"(x)
"as.fv"(x)
"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 command mincontrast
.
The
"kppm"
, representing a fitted
Cox or cluster point process model, obtained from the
model-fitting command kppm
;
"dppm"
, representing a fitted
determinantal point process model, obtained from the
model-fitting command dppm
;
"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:
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"
or "dppm"
,
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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