testMeanVariance: Test the equality of two normal distributions

In SourceSet: A Graphical Model Approach to Identify Primary Genes in Perturbed Biological Pathways

Test the equality of two normal distributions

Description

The function performs the test of equality of two multivariate normal distrbutions (class1 and class2).

Usage

testMeanVariance(S, S1, S2, n1, n2)

Arguments

S	estimated covariance matrix for pooled sample
S1	estimated covariance matrix in class 1
S2	estimated covariance matrix in class 2
n1	number of samples in class 1
n2	number of samples in class 2

Details

The criterion for testing the equality of two normal distributions is the following:

Λ_c = n_1 * log( |S| / |S^1| ) + n_2 * log( |S| / |S^2| )

The asymptotic null distribution of the criterion, when the maximum likelihood estimates of the covariance matrices are used, is Chi square with |Γ| * (|Γ|+3) / 2 degrees of freedom, where G is the dimension of the underlying distributions.

Value

The function returns a list that contain the test statistic (stat) and the p-value test obtained of equality, using the asymptotic distribution (alpha).

Note

The asymptotic null distributions holds only when the maximum likelihood estimates of the covariance matrices are supplied.

See Also

parameters

Examples

if(require(mvtnorm)){

  ## Generate two random samples of size 50 from two multivariate normal distributions
  # sample size
  n<-50
  # true parameters of class 1 and class 2
  param.class1<-simulation$condition1
  param.class2<-simulation$condition2$`5`$`2`
  # simulated dataset
  data.class1<-rmvnorm(n = n,mean =param.class1$mu ,sigma =param.class1$S)
  data.class2<-rmvnorm(n = n,mean =param.class2$mu ,sigma=param.class2$S)
  data<-rbind(data.class1,data.class2)
  classes<-c(rep(1,nrow(data.class1)),rep(2,nrow(data.class2)))

  s<-cov(data)
  s1<-cov(data.class1)
  s2<-cov(data.class2)
  testMeanVariance(S = s,S1 =s1, S2 = s2, n1 = n, n2 = n)

  ## equivalently...
  # estimated parameters: maximum likelihood estimate
  est.param<-parameters(data = data,classes =classes ,shrink = FALSE)
  testMeanVariance(est.param$S,est.param$S1,est.param$S2,est.param$n1,est.param$n2)
}

SourceSet documentation built on Nov. 21, 2022, 5:06 p.m.