MIread: To read PredictDivergence values

In MEET: MEET: Motif Elements Estimation Toolkit

Description

This function reads divergence values saved in memory. From the divergence values, MIread calculates the variation of the total divergence when the candidate sequence is added to the set.

Usage

MIread(training.set,val.set,iicc)

Arguments

training.set	A set of nucleotide sequences
val.set	A candidate sequence
iicc	A set of inicial conditions for the MEET-package

Author(s)

Joan Maynou <joan.maynouatupc.edu>

See Also

MImemory

Examples

require("MEET")
data(DrosophilaDivergence)
model<-list()
model$D<-iicc[["a1"]]$model$parameterModel$D
model$HXmax<-iicc[["a1"]]$model$parameterModel$HXmax
model$correctioc_1rOrdre<-iicc[["a1"]]$model$parameterModel$correction_1rOrdre
model$Entropy<-iicc[["a1"]]$model$parameterModel$HX
model$Mperfil<-iicc[["a1"]]$model$parameterModel$Mperfil
model$interA<-iicc[["a1"]]$model$parameterModel$interA
model$interB<-iicc[["a1"]]$model$parameterModel$interB
model$Divergence<-iicc[["a1"]]$model$model

test<-MIread(training.set=iicc[["a1"]]$Transcriptionfactor, val.set=iicc[["a1"]]$Transcriptionfactor[1,],iicc=model)

MEET documentation built on May 2, 2019, 1:45 p.m.