Examples

The examples are carried out using "iris" data.

Calculate the MaxNMI between two variables

  • maxNMI(iris$Sepal.Length,iris$Petal.Length)
  • corCouples<-multiBivariateCorrelation(iris)
  • print(corCouples)

Extract a correlation matrix from the correlation dataframe

The Pearson correlation matrix :

  • corMatrixPearson<-corCouplesToMatrix(x1_x2_val = corCouples[,c('X1','X2',"pearson")])
  • print(corMatrixPearson)

The MaxNMI matrix:

  • corMatrixMaxNMI<-corCouplesToMatrix(x1_x2_val = corCouples[,c('X1','X2',"MaxNMI")])
  • print(corMatrixMaxNMI)

Clustering of variables using a correlation matrix

  • cl<-clusterVariables(correlationMatrix = corMatrixMaxNMI)
  • print(cl)

Visualize the graph using Pearson correlation

  • linkspotterGraph(corDF = corCouples, variablesClustering = cl,corMethod = "pearson", minCor = 0.25, smoothEdges = FALSE,dynamicNodes = FALSE)

Visualize the graph using MaxNMI

  • linkspotterGraph(corDF = corCouples, variablesClustering = cl,corMethod = "MaxNMI", minCor = 0.25, smoothEdges = F,dynamicNodes = TRUE)

Launch the costumizable user interface

  • linkspotterUI(dataset = iris, corDF = corCouples,variablesClustering = cl, appTitle = "Linkspotter example")

Additional features

Complete Linkspotter computation:

  • lsiris<-linkspotterComplete(iris)

Complete Linkspotter computation from an external file:

  • lsiris<-linkspotterOnFile("iris.csv")
  • summary(lsiris)

Then launch the user interface using:

  • lsiris$run_it