Evaluate an Expression in a Function Table
Evaluate an R expression in a
function value table (object of class
## S3 method for class 'fv': with(data, expr, ..., drop = TRUE)
- A function value table (object of class
"fv") in which the expression will be evaluated.
- The expression to be evaluated. An Rlanguage
expression, which may involve the names of columns in
data, the special abbreviations
.y, and global constants or functions.
- Logical value. If the result of evaluating the expression
expris a vector (rather than a matrix or data frame) then the result will be returned as a vector if
drop=TRUE. Otherwise it will be returned as another funct
This is a method for the generic command
for an object of class
"fv" (function value table).
An object of class
"fv" is a convenient way of storing and
plotting several different estimates of the same function. It is
effectively a data frame with extra attributes.
fv.object for further explanation.
This command makes it possible to perform computations that involve different estimates of the same function. For example we use it to compute the arithmetic difference between two different edge-corrected estimates of the $K$ function of a point pattern.
expr should be an Rlanguage expression. The expression
- the name of any column in
data, referring to one of the estimates of the function;
- the symbol
.which stands for all the available estimates of the function;
- the symbol
.ywhich stands for the recommended estimate of the function (in an
"fv"object, one of the estimates is always identified as the recommended estimate);
- the symbol
.xwhich stands for the argument of the function;
- global constants or functions.
The expression should be capable of handling vectors and matrices.
If the result of evaluating the expression is a matrix or data frame,
then it is returned as a new function value table
(object of class
"fv"). If the result of evaluation is
a vector and
drop=TRUE then the result is returned as a vector.
To perform calculations involving several objects of
- Either a function value table (object of class
"fv") or a vector.
# compute 4 estimates of the K function X <- rpoispp(42) K <- Kest(X) plot(K) # derive 4 estimates of the L function L(r) = sqrt(K(r)/pi) L <- with(K, sqrt(./pi)) plot(L) # compute 4 estimates of V(r) = L(r)/r V <- with(L, ./.x) plot(V) # compute the maximum absolute difference between # the isotropic and translation correction estimates of K(r) D <- with(K, max(abs(iso - trans)))