trc_cor_test {trc}R Documentation

Procedure for calculating p-values

Description

Procedure for calculating p-values of Pearson's rho, Kendall's tau, TRC tau for two-sided test for the null hypothesis correaltion is equal to 0 based on the estimated null distribution by permutation.

Usage

trc_cor_test(X,Y, nperm=10000,start=3,range_m=0.8, span=0.5, seed=21, m0=NULL)

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: 10000).

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 (default: 0.5).

seed

An initial seed for the permutation (default: 21).

m0

a specific m value for p-value of the TRC tau with m (defalut: NULL (not reported)).

Details

The p-values are caculated based on the estimated null distributions of the TRC tau with a given m value, the Kendall's tau, and Pearson's correlation with the permuted samples, respectively.

Value

measure

a vector of calculated Pearson's rho, Kendall's tau, and TRC tau with m chosen by the proposed rule if m0 = NULL; a vector of calculated Pearson's rho, Kendall's tau, TRC tau with m0, TRC tau with m chosen by the proposed rule if m0 is specified.

p_val

a vector of p-values for Pearson's rho, Kendall's tau, and TRC tau with m chosen by the proposed rule if m0 = NULL; a vector of p-values for Pearson's rho, Kendall's tau, TRC tau with m0, TRC tau with m chosen by the proposed rule if m0 is specified.

chs_m

the chosen m value by the proposed procedure.

mean_perm_trc

a mean value of the estimated null distribution of TRC tau by permutation.

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)

trc_cor_test(S1,S2, nperm=1000,start=3,range_m=0.8, span=0.2, seed=21, m0=NULL)



[Package trc version 0.2 Index]