trc_m_search {trc}R Documentation

Procedure for the choice of m for the TRC tau

Description

Procedure for the choice of m for the TRC tau.

Usage

trc_m_search(X,Y,start=3,range_m=0.8,span=0.3)

Arguments

X

An observed data vector from the first condition.

Y

An observed data vector from the second condition.

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.

Details

The thresholding rank m is chosen by the proposed procedure in Lim et al. (2019).

Value

tau

A calculated TRC tau value with the chosen m value (chs_m).

chs_m

the chosen m value.

km_tau_vec

A vector of calculated k_m * TRC tau values for the given values of m [start, floor(range_m*n)]

km_tau_loess

A fitted values by the local regression with loess function for km_tau_vec .

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)

# tau_m
trc_res = trc_m_search(S1,S2,start=3,range_m=0.8,span=0.2)
trc_res$tau
trc_res$chs_m


[Package trc version 0.2 Index]