canova: CANOVA (continuous analysis of variance)

In liyistat/canova: CANOVA: Efficient test for nonlinear dependence of two continuous variables

Description

CANOVA: Efficient test for nonlinear dependence of two continuous variables. Testing dependence/correlation of two variables is one of the fundamental tasks in statistics. In this work,we proposed a new way of testing nonlinear dependence between two continuous variables (X and Y). We addressed this research question by using CANOVA and developed its R package 'canova'. In the CANOVA framework, we first defined a neighborhood for each data point related to its X value, and then calculated the variance of the Y value within the neighborhood. Finally, we performed permutations to evaluate the significance of the observed values within the neighborhood variance.

Usage

canova(x, y, k = 2, perm = 1000, tie_shuffle = 100, clusters = 8)

Arguments

x	A vector containing values of a continuous variable (X).
y	A vector containing values of a continuous variable (Y).
k	the neighborhood structure or bin size of the dataset.
perm	the number of permutations to calculate permutation pvalues.
tie_shuffle	the number of random shuffle times when there are ties in X.
clusters	the number of "SOCK" cluster type slave nodes to implement parallel computing on the local machine.

Value

result A list of CANOVA pvalue, Parameter K and Number of Permutations.

Author(s)

Yi Li, [e-mail]

References

Wang Y, Li Y, Cao H, et al. Efficient test for nonlinear dependence of two continuous variables[J]. BMC bioinformatics, 2015, 16(1): 1.

Examples

#Example1
#Generate random variables X and Y
x<-rnorm(1000)
y<-rnorm(1000)
#Test whether X and Y are independent or not using canova function
#Load the canova package
library(canova)
#Set the parameters: k=2, perm=1000, tie_shuffle=100, clusters=8
t1<- Sys.time()
canova(x,y,k=2,perm=1000,tie_shuffle=100,clusters=8)
t2<- Sys.time()
#Output calculation time of canova
t2-t1

#Example2
#Generate random variables X and Y with sin function
x<-runif(1000)
noise<-rnorm(1000)
y<-sin(x)+noise
#Test whether X and Y are independent or not using canova function
library(canova)
t1<- Sys.time()
#Set the parameters: k=2, perm=10000, tie_shuffle=100, clusters=8
canova(x,y,k=2,perm=10000,tie_shuffle=100,clusters=8)
t2<- Sys.time()
#Output calculation time of canova
t2-t1

liyistat/canova documentation built on May 21, 2019, 7:33 a.m.