spatstat.core (version 2.3-1)

as.fv: Convert Data To Class fv

Description

Converts data into a function table (an object of class "fv").

Usage

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)

Arguments

x

Data which will be converted into a function table

Value

An object of class "fv" (see fv.object).

Details

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.

Examples

# NOT RUN {
  r <- seq(0, 1, length=101)
  x <- data.frame(r=r, y=r^2)
  as.fv(x)
# }