plotResPaths: Plot mean and variance trajectory

In andreamrau/GBNcausal: Causal estimation for Gaussian Bayesian networks

Description

Plot the trajectory of the estimations of mean and variance of the GBNetwork estimated.

Usage

plotResPaths(full.run, trueM = NULL, trueS = NULL)

Arguments

full.run	list - full.run can be a result of MCMC.GBN function. It's a list of gaussian bayesian networks. They must have the same number of nodes and the same names.
trueM	vector - Mean of the true GBNetwork.
trueS	vector - Variance of the true GBNetwork.

Value

Plot as many graphs as there are elements in variance and mean vectors.

See Also

plotCausalPaths

Examples

# Data creation

seed = 1990
n = 3000
p <- 10
m<-rep(0,10)
sigma<-rep(0.1,10) 

W <- 1*upper.tri(matrix(0,p,p))

data <- dataCreate(nbData = 2*p, p = 10,KO = list(1,9), nbKO = c(p,p), W = W , m = m,sigma = sigma, seed = seed)$data

# Initial Value

W1=1*upper.tri(matrix(0,p,p)) 
m1=rep(0,p)
s1=rep(10e-4,p)
colnames(W1)=names(m1)=names(s1)=rownames(W1)=paste("N",1:p,sep="")

firstGBN = new("GBNetwork",WeightMatrix=W1,resMean=m1,resSigma=s1)
firstGBN = GBNmle(firstGBN,data,lambda= 0,sigmapre=s1)$GBN

# Algorithm

results=MCMC.GBN(data, firstGBN, nbSimulation=2000, burnIn=20, seq=1, verbose=TRUE,verbose.index=100, 
              alpha=1,lambda=0) 

alphaRes <- causalEffects(results$full.run)$alphaRes

plotResPaths(results$full.run, m1, s1)

andreamrau/GBNcausal documentation built on May 12, 2019, 3:34 a.m.