initialize,D2C.descriptor-method | R Documentation |
creation of a D2C.descriptor
## S4 method for signature 'D2C.descriptor'
initialize(.Object, lin = TRUE, acc = TRUE,
struct = TRUE, pq = c(0.1, 0.25, 0.5, 0.75, 0.9), bivariate = FALSE,
ns = 4)
.Object |
: the D2C.descriptor object |
lin |
: TRUE OR FALSE: if TRUE it uses a linear model to assess a dependency, otherwise a local learning algorithm |
acc |
: TRUE OR FALSE: if TRUE it uses the accuracy of the regression as a descriptor |
struct |
: TRUE or FALSE to use the ranking in the markov blanket as a descriptor |
pq |
:a vector of quantiles used to compute the descriptors |
bivariate |
:TRUE OR FALSE: if TRUE it includes also the descriptors of the bivariate dependence |
ns |
: size of the Markov Blanket returned by the mIMR algorithm |
Gianluca Bontempi, Maxime Flauder (2015) From dependency to causality: a machine learning approach. JMLR, 2015, http://jmlr.org/papers/v16/bontempi15a.html
require(RBGL)
require(gRbase)
require(foreach)
descr.example<-new("D2C.descriptor",bivariate=FALSE,ns=3,acc=TRUE)
trainDAG<-new("simulatedDAG",NDAG=2, N=50,noNodes=10,
functionType = "linear", seed=0,sdn=0.5)
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