conTest {ConNEcT}

Test significance

Description

Test significance

Usage

conTest(
  data,
  lag = 0,
  conFun,
  typeOfTest = "permut",
  adCor = TRUE,
  nBlox = 10,
  nReps = 100
)

Arguments

data

Binary time-points-by-variable matrix

lag

Non-negative integer indicating how much the second variable is lagged (default 0)

conFun

Contingency measure function (calculating the contingency value between two binary vectors). Built in: FunClassJacc, FunCorrJacc, FunKappa, FunOdds, FunLogOdds, FunPropAgree,FunPhiCC

typeOfTest

String indicating whether a model-based ('model') or a permutation-based ('permut'; default) data generation approach is used.

adCor

Logic indicating the auto-dependence correction should be applied (TRUE; default) or not (FALSE)

nBlox

Number indicating the number of segments (default 10). Necessary for permutation-based test, accounting for auto-dependence (typeOfTest='permut'; adCor=TRUE)

nReps

Number of replicates/samples that is used to generate the test distribution

Value

A conTest-object including

allLinks Matrix of pairwise calculated contingency measures

percentile Matrix of raw percentiles, situating the observed value in the sample distribution

pValue Matrix of the p-values (upper one-sided significance test) calculated by subtracting the percentile from 1.

para: Saving the parameter settings for typeOfTest, adCor, nBlox, nReps, funName, lag, varNames

samples Saved generated replicates/samples for each variable combination under $NameVariable1$NameVariable2

Examples

 signdata=cbind(c(1,0,1,0,1,0,1,0),c(1,1,1,1,0,0,0,0),c(0,0,0,0,0,0,1,1))
           colnames(signdata) <-c ('momangry', 'momsad','adoangry')
           conTest(data=signdata,lag=1,conFun=funClassJacc,typeOfTest='model',
           adCor=FALSE)