snha {snha}

Initialize a snha object with data.

Description

The main entry function to initialize a snha object with data where variables are in columns and items are in rows

Usage

snha( 
  data, 
  alpha=0.05, 
  method='pearson', 
  threshold=0.01, 
  check.singles=FALSE, 
  prob=FALSE, 
  prob.threshold=0.2, 
  prob.n=25) 

Arguments

data

a data frame where network nodes are the row names and data variables are in the columns.

alpha

confidence threshold for p-value edge cutting after all chains were generated, default: 0.05.

method

method to calculate correlation/association values, can be 'pearson', 'spearman' or 'kendall', default: 'pearson'.

threshold

R-squared correlation coefficient threshold for which r-square values should be used for chain generation, r=0.1 is r-square of 0.01, default: 0.01.

check.singles

should isolated nodes connected with sufficient high R^2 and significance, default: FALSE.

prob

should be probabilities computed for each edge using bootstrapping. Only in this case the parameters starting with prob are used, default: FALSE

prob.threshold

threshold to set an edge, a value of 0.5 means, that the edge must be found in 50% of all samplings, default: 0.2

prob.n

number of bootstrap samples to be taken, default: 25

Value

A snha graph data object with the folling components:

chains

association chains building the graph

data

representing the original input data

p.values

matrix with p-values for the pairwise correlations

probabilities

in case of re-samplings, the proportion how often the chain was found

sigma

correlation matrix used for the algorithm

theta

adjacency matrix found by the SNHA method

See Also

plot.snha

Examples

 
data(swiss) 
sw.g=snha(swiss,method='spearman') 
# what objects are there? 
ls(sw.g) 
sw.g$theta 
round(sw.g$sigma,2) 
sw.g=snha(swiss,method='spearman',check.singles=TRUE,prob=TRUE) 
sw.g$theta 
sw.g$probabilities