null_perm {trc}R Documentation

Procedure for estimating the null distribution of the TRC tau with the m value chosen by the proposed rule.

Description

Procedure for estimating the null distribution of the TRC tau with the m value chosen by the proposed rule.

Usage

null_perm(X,Y,nperm=1000,start=3,range_m=0.5,span=0.5,seed=21,all_m=FALSE)

Arguments

X

An observed data vector from the first condition.

Y

An observed data vector from the second condition.

nperm

the number of permutations to estimate the null distribution (default: 1000).

start

A lower bound of a search region for the threshold rank m (default: 3).

range_m

A proportion of length of X for specifying the end of the search region for m (default: 0.8).

span

A parameter alpha which controls the degree of smoothing in loess function.

seed

An initial seed for the permutation.

all_m

a logical flag for returning permuted TRC tau values for all m values (default: FALSE).

Details

Null distributions of the TRC tau with a given m value, the Kendall's tau, and Pearson's correlation are estimated by the permuted samples.

Value

perm_trc

A vector of TRC tau values from the permuted samples with the m value chosen by the proposed rule.

hist_m

A vector of the chosen m values for permutations.

perm_ktau

A vector of Kendall's tau values from the permuted samples.

perm_rho

A vector of Pearson's correlation values from the permuted samples.

perm_trc_all_m

A matrix of permuted TRC tau values for all m values, in which each column stores the permuted TRC tau values for corresponding m value.

References

Lim, J., Yu, D., Kuo, H., Choi, H., and Walmsely, S. (2019). Truncated Rank Correlation as a robust measure of test-retest reliability in mass spectrometry data. Statistical Applications in Genetics and Molecular Biology, 18(4).

Examples

p = 100
sig_z = 1.15
sig_e = 1
mu_z = 2
mu_e = 8
m0 = 30

S1 = rnorm(p,mean=mu_e,sd=sig_e)
S2 = rnorm(p,mean=mu_e,sd=sig_e)
    
if(m0!=0)
{
   X = mu_z + rnorm(m0,mean=0,sd=sig_z)
   indx = 1:p
   s_indx = sort(sample(indx,m0))
   S1[s_indx] = S1[s_indx] + X
   S2[s_indx] = S2[s_indx] + X
}
      
S1 = exp(S1)
S2 = exp(S2)

null_res = null_perm(S1,S2,nperm=1000,start=3,range_m=0.5,span=0.2,seed=21,all_m=FALSE)


[Package trc version 0.2 Index]