gam.iso {UCS} | R Documentation |
Computes iso-surfaces for a generalised association measure (GAM) in standard or ebo-coordinates.
gam.iso(name, gamma, f1, f2, N, bsearch.min=NULL, bsearch.max=NULL) gam.iso(name, gamme, e, b=1, N=1e6, bsearch.min=NULL, bsearch.max=NULL)
name |
name of a generalised association measure (GAM) |
gamma |
a numerical constant that determines the desired iso-surface \{g = γ\} |
f1, f2, N |
numerical vectors specifying the f1 and
f2 coordinates of points in the standard coordinate space, as
well as the sample size N |
e, b |
numerical vectors specifying the e and b
coordinates of points in the ebo-coordinate space (if the
balance b is not specified, it defaults to 1 ) |
N |
optional numerical vector specifying the sample size N
when computing iso-surfaces for a GAM that is not size-invariant in
ebo-coordinates (defaults to 1e6 ) |
bsearch.min |
initial lower boundary for binary search algorithm, when no explicit equation for the iso-surface is available |
bsearch.max |
initial upper boundary for the binary search algorithm |
Note that all function arguments except for name
must be passed
explicitly by name in order to distinguish the two operating modes of
gam.iso
(standard vs. ebo-coordinates).
When ebo-coordinates are used, the argument N
(sample
size) can safely be omitted for any size-invariant GAM (in
ebo-coordinates). For other GAMs, a default value of 1e6
will
be used, corresponding to the typical size of a co-occurrence data
set. The argument b
(balance) can be omitted for any
central GAMs. Otherwise, it defaults to a value of 1
,
corresponding to the centralized version of the respective GAM.
Use gamma.nbest
to compute a suitable γ values for
n-best surfaces.
When no explicit equation for the iso-surface of a GAM is available,
the gam.iso
function uses a binary search algorithm to solve
the implicit equation \{g = γ\}. Since some GAMs are only
defined for valid frequency signatures (where all four cells of the
contingency table are non-negative), the binary search for the
o
coordinate is confined to the range from 0 to
\min\{f_1, f_2\}. When no solution can be found in this range,
gam.iso
returns NA
for the corresponding points. For
GAMs where it is safe to search a larger range (notably
Poisson.pv
and log.likelihood
), the boundaries of the
search interval can be adjusted with the bsearch.min
and
bsearch.max
parameters. Note that most other GAMs have
explicit iso-equations, so these parameters are rarely needed.
a vector of real numbers representing the f
or o
coordinates of the respective iso-surface; these are the values of
f
or o
that solve the implicit equation \{g =
γ\} for the specified values of f1, f2, N
or e, b
(and N
); this vector may contain missing values (NA
) for
points where no solution is found (see "Details" for more information)
gam.score
, builtin.gams
, gamma.nbest
e <- 10^seq(-2, 1, .1) # compute iso-line on logarithmic scale o <- gam.iso("t.score", 2, e=e) x <- 10^seq(0, 2, .1) # compute iso-surface over rectangular grid g <- expand.grid(f1=x, f2=x) g$f <- gam.iso("t.score", 2, f1=g$f1, f2=g$f2, N=1000) library(lattice) wireframe(f ~ f1 * f2, log(g))