Dinorms {lingua} | R Documentation |
Dinorms are functions for aggregation of dichotomic values from the [-1,1] interval. The values are assumed to be supports for two opposite situations. It can be e.g. reasons for cold vs. hot situations, or underexpressed vs. overexpressed genes as reasons for positive vs. negative advices for a treatment.
dinorms.thresholds(thresh = NULL) dinorms.modes(modes = NULL) dinorms.aggregate(vect) dinorms.boost(vect, powers, count, limit = 0)
thresh |
either none or vector of two [0,1] values for addition, combination thresholds |
modes |
either none or name-pair for addition:
one of "maximum", "coproduct", "summation", and
combination: one of "maximum", "codivision", "subtraction",
"stabilized" modes |
vect |
[-1,1]-valued vector to aggregate |
powers |
[0,1]-valued vector of data strengths |
count |
number of resamples for the boosting |
limit |
to use data with >= limit power |
The Dinorms aggregation is based on fuzzy logic connectives, separated into twofold process. First, negative and positive values are aggregated separately in the natural way. Then the result values are combined into a final aggregation (e.g. prognosis).
thresholds
and modes
give the set option values,
aggregate
gives the sequence of negative, positive,
and combined results, boost
gives strengthen results,
with desc
being c(mean, variance).
Look at the Bioplexity and Enduce webs for more information. http://www.bioplexity.org/analysis/ http://www.tangloid.net/enduce/
Martin Saturka
Bioplexity www.bioplexity.org
See Also lingua
, contifiers
, clustions
library(lingua) ## initial settings dinorms.thresholds(c(0.1, 0.1)) dinorms.modes(c("cop", "sub")) ## aggregate some random vector vect <- runif(20, -1, 1) dinres <- dinorms.aggregate(vect) ## boosting the dinorms powers <- runif(length(vect)) dinorms.boost(vect, powers, 10, 0.5)