MRPC (version 2.2.0)

data_with_outliers: Example data with outliers (noises)

Description

The data contain two genotype nodes, V1 and V2, and three phenotype nodes, T1, T2 and T3. The genotype nodes are discrete, whereas the phenotype nodes are continuous. The data matrix includes 10 outliers (noises) generated from a uniform distribution. The code below compares the performance of MRPC, mmhc and pc on this data set.

Value

Matrix

Examples

# NOT RUN {
# Load packages

library(MRPC)     # MRPC
library(pcalg)    # pc
library(bnlearn)  # mmhc

# Truth without outlier
tarmat <- matrix(0,
                 nrow = ncol(data_with_outliers),
                 ncol = ncol(data_with_outliers))
                
colnames(tarmat) <- colnames(data_with_outliers)

rownames(tarmat) <- colnames(data_with_outliers)

tarmat[1,2] <- 1
tarmat[2,1] <- 1
tarmat[1,3] <- 1
tarmat[4,3] <- 1
tarmat[4,5] <- 1

Truth <- as(tarmat,
            "graphNEL")

# Data without outliers
n <- nrow(data_without_outliers)     # Number of rows
V <- colnames(data_without_outliers) # Column names

# Calculate Pearson correlation
suffStat_C1 <- list(C = cor(data_without_outliers),
                    n = n)

# Calculate robust correlation (Beta = 0.005)
Rcor_R1 <- RobustCor(data_without_outliers,
                     Beta=0.005) 
suffStat_R1 <- list(C = Rcor_R1$RR,
                    n = n)

# Infer the graph by MRPC robust correlation
MRPC.fit_withoutoutlier <- MRPC(data_without_outliers, 
                                suffStat = suffStat_R1,
                                GV = 2, 
                                FDR = 0.05,
                                indepTest ='gaussCItest',
                                labels = V, 
                                FDRcontrol = TRUE,
                                verbose = TRUE)

# Infer the graph by pc with Pearson correlation
pc.fit_withoutoutlier <- pc(suffStat = suffStat_C1,
                            indepTest = gaussCItest,
                            alpha = 0.05, 
                            labels = V,
                            verbose = TRUE)

# Infer the graph by mmhc
data <- data.frame(data_without_outliers)
mmhc_withoutoutlier <- mmhc(data)


# Data with outliers
n <- nrow (data_with_outliers)    # Number of rows
V <- colnames(data_with_outliers) # Column names

# Calculate Pearson correlation
suffStat_C1 <- list(C = cor(data_with_outliers),
                    n = n)

# Calculate robust correlation (Beta = 0.005)
Rcor_R1 <- RobustCor(data_with_outliers,
                     Beta=0.005) 
suffStat_R1 <- list(C = Rcor_R1$RR,
                    n = n)

# Infer the graph by MRPC with robust correlation
MRPC.fit_withoutlier <- MRPC(data_with_outliers, 
                             suffStat = suffStat_R1,
                             GV = 2, 
                             FDR = 0.05,
                             indepTest ='gaussCItest',
                             labels = V, 
                             FDRcontrol = TRUE,
                             verbose = TRUE)

# Infer the graph by pc with Pearson correlation
pc.fit_withoutlier <- pc( suffStat = suffStat_C1,
                         indepTest = gaussCItest,
                         alpha = 0.05, 
                         labels = V,
                         verbose = TRUE)

# Infer the graph by mmhc
data <- data.frame(data_with_outliers)
mmhc_withoutlier <- mmhc(data)

# Plot the inferred graphs
par(mfrow = c(2, 4))
plot(Truth,
     main = "Truth")
plot(MRPC.fit_withoutoutlier,
     main = "MRPC")
plot(pc.fit_withoutoutlier,
     main = "pc")
graphviz.plot(mmhc_withoutoutlier,
              main = "mmhc")
plot(Truth,
     main = " ")
plot(MRPC.fit_withoutlier,
     main = " ")
plot(pc.fit_withoutlier,
     main = " ")
graphviz.plot(mmhc_withoutlier,
              main = " ")
# }