library("bgmm") data("genotypes") str(genotypes) modelSupervised <- supervised(knowns=genotypes$knowns, class=genotypes$labels) plot(modelSupervised) modelSemiSupervised <- semisupervised(X=genotypes$X, knowns=genotypes$knowns, class=genotypes$labels) plot(modelSemiSupervised) modelBelief <- belief(X=genotypes$X, knowns=genotypes$knowns, B=genotypes$B) plot(modelBelief) modelSoft <- soft(X=genotypes$X, knowns=genotypes$knowns, P=genotypes$B) plot(modelSoft) modelUnSupervised <- unsupervised(X=genotypes$X, k=3) plot(modelUnSupervised) str(modelBelief) modelBelief$likelihood model.structure.m.E <- getModelStructure(mean="E") str(model.structure.m.E) modelBelief <- belief(X=genotypes$X, knowns=genotypes$knowns, B=genotypes$B, model.structure=model.structure.m.E) modelBelief$likelihood preds <- predict(modelSoft, X=genotypes$X, knowns=genotypes$knowns, B=genotypes$B) str(preds) initial.params <- init.model.params(X=genotypes$X, knowns=genotypes$knowns, class=genotypes$labels, method="knowns") str(initial.params) model <- soft(X=genotypes$X, knowns=genotypes$knowns, P=genotypes$B, init.params=initial.params) set.seed(1313) simulated <- simulateData(d=2, k=3, n=300, m=60, cov="0", within="E", n.labels=2) str(simulated) model <- belief(X=simulated$X, knowns=simulated$knowns, B=simulated$B) plot(model) models1 <- mModelList(X=simulated$X, knowns=simulated$knowns, B=simulated$B, kList=3, mean=c("D","E"), between=c("D","E"), within=c("D","E"), cov=c("D","0"), funct=belief) plot(models1) plotGIC(models1, penalty="BIC") plotGIC(models1, penalty="CLC") plotGIC(models1, penalty="AIC3") plotGIC(models1, penalty="AICu") bestModel <- chooseOptimal(models1, penalty="BIC") bestModel2 <- chooseOptimal(models1, penalty="CLC") bestModel3 <- chooseOptimal(models1, penalty="AIC3") bestModel4 <- chooseOptimal(models1, penalty="AICu") plot(bestModel) plot(bestModel2) plot(bestModel3) plot(bestModel4) models2 <- beliefList(X=simulated$X, knowns=simulated$knowns, B=simulated$B, kList=2:7, mean="D", between="D", within="E", cov="0") plot(models2) plotGIC(models2, penalty="BIC") plotGIC(models2, penalty="CLC") models3 <- beliefList(X=simulated$X, knowns=simulated$knowns, B=simulated$B, kList=2:7, mean="D") plotGIC(models3, penalty="BIC", plot.it=FALSE) models4 <- chooseModels(models3, kList=2:5, struct=c("DDDD","DDED","DDE0")) plot(models4) plotGIC(models4, penalty="BIC")