flexorhtest.pvalue {GeneF}

Significance Assessment for the Flexible Order Restricted Hypothesis Testing

Description

These functions evaluate the p-values from an individual or multiple flexible order restricted hypothesis testing.

Usage

flexisoreg.pvalue(y, x, lambda=0, alpha.location=1, alpha.adjacency=0.5, B=100)
flexisoreg.poolpvalues(m, x, lambda=0, alpha.location=1, alpha.adjacency=0.5, B=100)
flexmonoreg.pvalue(y, x, lambda=0, alpha.location=1, alpha.adjacency=0.5, B=100)
flexmonoreg.poolpvalues(m, x, lambda=0, alpha.location=1, alpha.adjacency=0.5, B=100)

Arguments

m

a matrix of observed data, where samples are in columns and variables are in rows

y

a vector of observed data

x

a vector of ordinal group labels correponding to y or rows of m but not necessarily sorted

lambda

a lower location bound for partitioned groups other than the first one

alpha.location

\alpha level for the upper-tailed one-sample t-test with lower bound lambda

alpha.adjacency

\alpha level for the upper-tailed two-sample t-test to evaluate the magnitude of nondecreasing order

B

the number of permutations for p-value assessment

Details

flexisoreg.pvalue and flexmonoreg.pvalue provide the permutation p-value for an individual flexible order restricted hypothesis testing. flexisoreg.poolpvalues and flexmonoreg.poolpvalues provide the pooled permutation p-values for multiple flexible order restricted hypothesis testing.

Value

flexisoreg.pvalue and flexmonoreg.pvalue return a permutation p-value. flexisoreg.poolpvalues and flexmonoreg.poolpvalues return a vector of pooled permutation p-values.

Note

These functions are used in conjunction with flexisoreg, flexisoreg.stat, flexmonoreg and flexmonoreg.stat.

Author(s)

Yinglei Lai [e-mail]

References

Yinglei Lai (2007) A flexible order restricted hypothesis testing and its application to gene expression data. Technical Report

Examples

#generate ordinal group lables x
x <- runif(100)*6
x <- round(x,0)/3
#generate true values z
z <- round(x^2,0)
#generate 6 vectors in a matrix for observed values, some noises and some not
m <- array(double(6*100), dim=c(6,100))
for(k in 1:3)
m[k,] <- rnorm(100)
for(k in 4:6)
m[k,] <- z + rnorm(100)


#print default results
par(mfrow=c(2,3))
for(k in 1:6){
print(paste("The ", k, "-th vector", sep=""))
y <- m[k,]
plot(x,y,main=k)
print(flexisoreg.stat(y,x))
print(flexisoreg.pvalue(y,x,B=20))
print(flexisoreg.stat(y,0-x))
print(flexisoreg.pvalue(y,0-x,B=20))
print(flexmonoreg.stat(y,x))
print(flexmonoreg.pvalue(y,x,B=20))
}

flexisoreg.poolpvalues(m, x, B=20)
flexmonoreg.poolpvalues(m, x, B=20)