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)