Evaluates any expression involving one or more function value (fv) objects, and returns another object of the same kind.

`eval.fv(expr, envir, dotonly=TRUE, equiv=NULL, relabel=TRUE)`

Another object of class `"fv"`

.

- expr
An expression.

- envir
Optional. The environment in which to evaluate the expression, or a named list containing

`"fv"`

objects to be used in the expression.- dotonly
Logical. See Details.

- equiv
Mapping between column names of different objects that are deemed to be equivalent. See Details.

- relabel
Logical value indicating whether to compute appropriate labels for the resulting function. This should normally be

`TRUE`

(the default). See Details.

Adrian Baddeley Adrian.Baddeley@curtin.edu.au and Rolf Turner r.turner@auckland.ac.nz

This is a wrapper to make it easier to perform pointwise calculations with the summary functions used in spatial statistics.

An object of class `"fv"`

is essentially a data frame
containing several different statistical estimates of the same
function. Such objects are returned by `Kest`

and its
relatives.

For example, suppose `X`

is an object of class `"fv"`

containing several different estimates of the Ripley's K function \(K(r)\),
evaluated at a sequence of values of \(r\).
Then `eval.fv(X+3)`

effectively adds 3 to
each function estimate in `X`

, and returns
the resulting object.

Suppose `X`

and `Y`

are two objects of class `"fv"`

which are compatible (in particular they have the same vector
of \(r\) values). Then
`eval.im(X + Y)`

will add the corresponding function values in
`X`

and `Y`

, and return the resulting function.

In general, `expr`

can be any expression involving
(a) the *names* of objects of class `"fv"`

, (b) scalar
constants, and (c) functions which are vectorised.
See the Examples.

First `eval.fv`

determines which of the *variable names*
in the expression `expr`

refer to objects of class `"fv"`

.
Each such name is replaced by a vector containing the function values.
The expression is then evaluated. The result should be a vector;
it is taken as the new vector of function values.

The expression `expr`

must be vectorised.
There must be at least one object of class `"fv"`

in the expression.
If the objects are not compatible, they will be made compatible
by `harmonise.fv`

.

If `dotonly=TRUE`

(the default), the expression will be
evaluated only for those columns of an `"fv"`

object
that contain values of the function itself (rather than
values of the derivative of the function, the hazard rate, etc).
If `dotonly=FALSE`

, the expression will be evaluated for all columns.

For example the result of `Fest`

includes several columns
containing estimates of the empty space function \(F(r)\),
but also includes an estimate of the
*hazard* \(h(r)\) of \(F(r)\). Transformations that are valid
for \(F\) may not be valid for \(h\). Accordingly, \(h\) would
normally be omitted from the calculation.

The columns of an object `x`

that represent the function itself
are identified by its “dot” names, `fvnames(x, ".")`

.
They are the columns normally plotted by `plot.fv`

and identified by the symbol `"."`

in plot formulas
in `plot.fv`

.

The argument `equiv`

can be used to specify that
two different column names in different function objects
are mathematically equivalent or cognate.
It should be a list of `name=value`

pairs, or a named vector of
character strings, indicating the pairing of equivalent names.
(Without this argument, these columns would be discarded.)
See the Examples.

The argument `relabel`

should normally be `TRUE`

(the default).
It determines whether to compute appropriate mathematical labels and
descriptions for the resulting function object
(used when the object is printed or plotted).
If `relabel=FALSE`

then this does not occur,
and the mathematical labels and descriptions
in the result are taken from the function object
that appears first in the expression. This reduces computation time
slightly (for advanced use only).

`fv.object`

,
`Kest`

```
# manipulating the K function
X <- runifrect(42)
Ks <- Kest(X)
eval.fv(Ks + 3)
Ls <- eval.fv(sqrt(Ks/pi))
# manipulating two K functions
Y <- runifrect(20)
Kr <- Kest(Y)
Kdif <- eval.fv(Ks - Kr)
Z <- eval.fv(sqrt(Ks/pi) - sqrt(Kr/pi))
## Use of 'envir'
U <- eval.fv(sqrt(K), list(K=Ks))
## Use of 'equiv'
Fc <- Fest(cells)
Gc <- Gest(cells)
# Hanisch and Chiu-Stoyan estimators are cognate
Dc <- eval.fv(Fc - Gc, equiv=list(cs="han"))
```

Run the code above in your browser using DataLab