ChoR (version 0.0-4)

ChoR: Getting started with the ChoR package

Description

The chordalysis algorithm allows to learn the structure of graphical models from datasets with thousands of variables. More information about the research papers detailing the theory behind Chordalysis is available at http://www.francois-petitjean.com/Research

If you have problems using ChoR, find a bug, or have suggestions, please contact the package maintainer by email. Do not write to the general R lists or contact the authors of the original chordalysis software.

If you use the package, please cite references in your publications.

Details

Chordalysis allows to learn the structure of graphical models from datasets with thousands of variables. There are 3 differentes algorithms versions: SMT, Budget and MML. SMT, standing for Subfamiliwize Multiple Testing, is generally the method of choice. It superseeds Budget and is always superior to it. Demonstration is in our KDD'16 paper (see CITATION). Both SMT and Budget are based on statistical testing, while MML uses information theory to decide upon a model. The objective of the different techniques is slightly different: SMT controls the familywise error rate (FWER) while MML is a probabilistic method. Our experiments (again in KDD'16) indicate that SMT is superior to MML for most datasets.

References

See citation("ChoR")

Examples

# NOT RUN {
# Warning: RJava requires to **copy** your data from R into a JVM.
# If you need extra memory, use this option (here, for 4Gb) **before** loading choR.
# Note: not needed in our case, kept for the example
options( java.parameters = "-Xmx4g" )
library(ChoR)

# Helper function for graph printing. Require Rgraphviz:
# source("https://bioconductor.org/biocLite.R")
# biocLite("Rgraphviz")
printGraph = function(x){
  if(requireNamespace("Rgraphviz", quietly=TRUE)){
    attrs <- list(node=list(shape="ellipse", fixedsize=FALSE, fontsize=25))
    Rgraphviz::plot(x, attrs=attrs)
  } else { stop("Rgraphviz required for graph printing.") }
}


###### MUSHROOM #####
# We are using a partial UCI mushroom data set (the example should not be too long)
MR.url = system.file("extdata", "mushrooms.csv", package = "ChoR", mustWork = TRUE)

MR.data =
  read.csv(
              MR.url,
              header            = TRUE,             # Here, we have a header
              na.strings        = c("NA","?",""),   # Configure the missing values
              stringsAsFactors  = FALSE,            # Keep strings for now
              check.names       = TRUE              # Replace some special characters
            )

# This file has a special line with types. You can check this with MR.data[1,].
# Let's remove it:
MR.data = MR.data[-1, ]

# Launch the SMT analysis, with:
# ## default pValueThreshold=0.05
# ## computation of attributes cardinality from the data
MR.res = ChoR.SMT(MR.data)

# Access the result:
# ## As a list of cliques:
NR.cl = ChoR.as.cliques(MR.res)
print(NR.cl)
# ## As a formula
NR.fo = ChoR.as.formula(MR.res)
print(NR.fo)
# ## As a graph
if(requireNamespace("graph", quietly=TRUE)){
  NR.gr = ChoR.as.graph(MR.res)
  printGraph(NR.gr)
} else {
  print("'graph' package not installed; Skipping 'as graph' example.")
}



###### Titanic #####
# We are using the titanix data set

MR.url = system.file("extdata", "titanic.dat.txt", package = "ChoR", mustWork = TRUE)

T.data =
  read.csv( MR.url,
            sep               = "",       # White spaces
            header            = FALSE,
            stringsAsFactors  = FALSE
          )

# Give meaningful names
colnames(T.data) = c(   "Class", "Age", "Sex", "Survived" )
# Chordalysis
T.res = ChoR.SMT(T.data, card = c(4, 2, 2, 2))

if(requireNamespace("graph", quietly=TRUE)){
  T.gr = ChoR.as.graph(T.res)
  printGraph(T.gr)
}
# }