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