Converts data into a function table (an object of class "fv").
as.fv(x) # S3 method for fv
as.fv(x)
# S3 method for data.frame
as.fv(x)
# S3 method for matrix
as.fv(x)
# S3 method for fasp
as.fv(x)
# S3 method for minconfit
as.fv(x)
# S3 method for dppm
as.fv(x)
# S3 method for kppm
as.fv(x)
# S3 method for bw.optim
as.fv(x)
Data which will be converted into a function table
An object of class "fv" (see fv.object).
This command converts data 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:
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.
# NOT RUN {
r <- seq(0, 1, length=101)
x <- data.frame(r=r, y=r^2)
as.fv(x)
# }
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