MHEdge: mhEdge
In Evatar/baycn: Bayesian Inference for Causal Networks

Description Usage Arguments Value References Examples

The main function for the Metropolis-Hastings algorithm. It returns the posterior distribution of the edge directions.

mhEdge(
  data,
  adjMatrix,
  prior = c(0.05, 0.05, 0.9),
  nCPh = 0,
  nGV = 0,
  pmr = FALSE,
  burnIn = 0.2,
  iterations = 1000,
  thinTo = 200,
  progress = TRUE
)

`data`	A matrix with the variables across the columns and the observations down the rows. If there are genetic variants in the data these variables must come before the remaining variables. If there are clinical phenotypes in the data these variables must come after all other variables. For example, if there is a data set with one genetic variant variable, three gene expression variables, and one clinical phenotype variable the first column in the data matrix must contain the genetic variant data, the next three columns will contain the gene expression data, and the last column will contain the clinical phenotype data.
`adjMatrix`	An adjacency matrix indicating the edges that will be considered by the Metropolis-Hastings algorithm. An adjacency matrix is a matrix of zeros and ones. The ones represent an edge and its direction between two nodes.
`prior`	A vector containing the prior probability for the three edge states.
`nCPh`	The number of clinical phenotypes in the graph.
`nGV`	The number of genetic variants in the graph.
`pmr`	Logical. If true the Metropolis-Hastings algorithm will use the Principle of Mendelian Randomization (PMR). This prevents the direction of an edge pointing from a gene expression or a clinical phenotype node to a genetic variant node.
`burnIn`	A number between 0 and 1 indicating the percentage of the sample that will be discarded.
`iterations`	An integer for the number of iterations to run the MH algorithm.
`thinTo`	An integer indicating the number of observations the chain should be thinned to.
`progress`	Logical. If TRUE the runtime in seconds for the cycle finder and log likelihood functions and a progress bar will be printed.

An object of class baycn containing 9 elements:

burnIn – The percentage of MCMC iterations that will be discarded from the beginning of the chain.
chain – A matrix where each row contains the vector of edge states for the accepted graph.
decimal – A vector of decimal numbers. Each element in the vector is the decimal of the accepted graph.
iterations – The number of iterations for which the Metropolis-Hastings algorithm is run.
posteriorES – A matrix of posterior probabilities for all three edge states for each edge in the network.
posteriorPM – A posterior probability adjacency matrix.
likelihood – A vector of log likelihood values. Each element in the vector is the log likelihood of the accepted graph.
stepSize – The number of MCMC iterations discarded between each accepted graph.
time – The runtime of the Metropolis-Hastings algorithm in seconds.

Martin, E. A. and Fu, A. Q. (2019). A Bayesian approach to directed acyclic graphs with a candidate graph. arXiv preprint arXiv:1909.10678.

# Generate data under topology m1_gv.
# Use ?simdata for a description and graph of m1_gv.
data_m1 <- simdata(b0 = 0,
                   N = 200,
                   s = 1,
                   graph = 'm1_gv',
                   ss = 1,
                   q = 0.27)

# Create an adjacency matrix with the true edges.
am_m1 <- matrix(c(0, 1, 0,
                  0, 0, 1,
                  0, 0, 0),
                byrow = TRUE,
                nrow = 3)

# Run the Metropolis-Hastings algorithm on the data from m1_gv using the
# Principle of Mendelian Randomization (PMR) and the true edges as the input.
mh_m1_pmr <- mhEdge(data = data_m1,
                    adjMatrix = am_m1,
                    prior = c(0.05,
                              0.05,
                              0.9),
                    nCPh = 0,
                    nGV = 1,
                    pmr = TRUE,
                    burnIn = 0.2,
                    iterations = 1000,
                    thinTo = 200,
                    progress = FALSE)

summary(mh_m1_pmr)

# Generate data under topology gn4.
# Use ?simdata for a description and graph of gn4.
data_gn4 <- simdata(b0 = 0,
                    N = 200,
                    s = 1,
                    graph = 'gn4',
                    ss = 1)

# Create an adjacency matrix with the true edges.
am_gn4 <- matrix(c(0, 1, 1, 0,
                   0, 0, 0, 1,
                   0, 0, 0, 0,
                   0, 0, 1, 0),
                 byrow = TRUE,
                 nrow = 4)

# Run the Metropolis-Hastings algorithm on the data from gn4 with the true
# edges as the input.
mh_gn4 <- mhEdge(data = data_gn4,
                 adjMatrix = am_gn4,
                 prior = c(0.05,
                           0.05,
                           0.9),
                 nCPh = 0,
                 nGV = 0,
                 pmr = FALSE,
                 burnIn = 0.2,
                 iterations = 1000,
                 thinTo = 200,
                 progress = FALSE)

summary(mh_gn4)