MRPC (version 2.0.0)

ExampleMRPC: Example Data for Simple and Complex Model

Description

Examples with both continuous and discrete data.

Usage

data(ExampleMRPC)

Value

A list that containing the numeric data matrix and components of a graph.

• simple: Simple model.

• complex: Complex model.

• cont: Continuous.

• disc: Discrete.

• withGV: With genetic information.

• withoutGV: Without genetic information.

• data: Data matrix.

• graph: Components of a graph.

Details

Contains a numeric data matrix and a graph for both continuous and discrete data.

For continuous data with genetic information: 1000 samples in row and 6 variables in column. First two columns are the genetic variants and remaning columns are gene expression.

Continuous data without genetic information: 1000 samples in row and 8 variables in column.

Discrete data with genetic information: 1000 samples in row and 6 variables in column. First column is the genetic variant and remaning columns are the gene expression.

Discrete data without genetic information: 1000 samples in row and 5 variables in column.

Continuous data with genetic information for complex model: 1000 samples in row and 22 variables in column. First 14 column is the genetic variants and remaning columns are the genes expression.

Examples

# NOT RUN {
# Continuous data with genetic information
# load the data
data("ExampleMRPC")  

# extract the sample size
n <- nrow(ExampleMRPC$simple$cont$withGV$data)  

# extract the node/column names
V <- colnames(ExampleMRPC$simple$cont$withGV$data)  

# calculate robust correlation matrix
Rcor_R <- RobustCor(ExampleMRPC$simple$cont$withGV$data,
                    Beta = 0.005) 

suffStat_R <- list(C = Rcor_R$RR, n = n)

data.mrpc.cont.withGV <- MRPC(data = ExampleMRPC$simple$cont$withGV$data,
                              suffStat = suffStat_R, GV = 2,
                              FDR = 0.05, indepTest = 'gaussCItest',
                              labels = V, verbose = TRUE)

par(mfrow = c(1, 2))
# plot the true graph
plot(ExampleMRPC$simple$cont$withGV$graph,
     main = "truth") 
# plot the inferred graph
plot(data.mrpc.cont.withGV,
     main = "inferred")    


# Continuous data without genetic information
# load the data
data("ExampleMRPC")    

# extract the sample size
n <- nrow(ExampleMRPC$simple$cont$withoutGV$data) 

# extract the node/column names
V <- colnames(ExampleMRPC$simple$cont$withoutGV$data)

# calculate robust correlation matrix
Rcor_R <- RobustCor(ExampleMRPC$simple$cont$withoutGV$data,
                    Beta = 0.005) 
                    
suffStat_R <- list(C = Rcor_R$RR, n = n)

data.mrpc.cont.withoutGV <- MRPC(data = ExampleMRPC$simple$cont$withoutGV$data,
                                 suffStat = suffStat_R, GV = 0,
                                 FDR = 0.05, indepTest = 'gaussCItest',
                                 labels = V, verbose = TRUE)

par(mfrow = c(1, 2))
# plot the true graph
plot(ExampleMRPC$simple$cont$withoutGV$graph,
     main = "truth") 
# plot the inferred graph
plot(data.mrpc.cont.withoutGV,
     main = "inferred") 

# Discrete data with genetic information
# load the data
data("ExampleMRPC")     

# extract the sample size
n <- nrow(ExampleMRPC$simple$disc$withGV$data)  

# extract the node/column names
V <- colnames(ExampleMRPC$simple$disc$withGV$data)

# calculate robust correlation matrix
Rcor_R <- RobustCor(ExampleMRPC$simple$disc$withGV$data,
                    Beta = 0.005)
                    
suffStat_R <- list(C = Rcor_R$RR, n = n)

data.mrpc.disc.withGV <- MRPC(data = ExampleMRPC$simple$disc$withGV$data,
                              suffStat = suffStat_R, GV = 1,
                              FDR = 0.05, indepTest = 'gaussCItest',
                              labels = V, verbose = TRUE)

par (mfrow = c(1, 2))
# plot the true graph
plot(ExampleMRPC$simple$disc$withGV$graph,
     main = "truth") 
# plot the inferred causal graph
plot(data.mrpc.disc.withGV,
     main = "inferred") 

# Discrete data without genetic information
# load the data
data("ExampleMRPC")    

# extract the sample size
n <- nrow (ExampleMRPC$simple$disc$withoutGV$data)  

# extract the node/column names
V <- colnames(ExampleMRPC$simple$disc$withoutGV$data)   

# calculate robust correlation matrix
Rcor_R <- RobustCor(ExampleMRPC$simple$disc$withoutGV$data,
                    Beta = 0.005) 
                    
suffStat_R <- list(C = Rcor_R$RR, n = n)

data.mrpc.disc.withoutGV <- MRPC(data = ExampleMRPC$simple$disc$withoutGV$data,
                                 suffStat = suffStat_R, GV = 1,
                                 FDR = 0.05, indepTest = 'gaussCItest',
                                 labels = V, verbose = TRUE)

par(mfrow = c(1, 2))
# plot the true graph
plot(ExampleMRPC$simple$disc$withoutGV$graph,
     main = "truth") 
# plot the inferred graph
plot(data.mrpc.disc.withoutGV,
     main = "inferred") 

# Continuous data with genetic information for complex model
# load the data
data("ExampleMRPC")   

# graph without clustering
plot(ExampleMRPC$complex$cont$withGV$graph) 

Adj_directed <- as(ExampleMRPC$complex$cont$withGV$graph,
                   "matrix")

# dendrogram and graph with clustering
DendroModuleGraph(Adj_directed,
                  minModuleSize = 5,
                  GV = 14) 
# }