
Examples with both continuous and discrete data.
data(ExampleMRPC)
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.
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.
# 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)
# }
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