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)

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`

. Thean 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) # }