cor.confint {GeneNT} | R Documentation |
This function returns confidence intervals for a list of Pearson correlation estimates given a sample size N and significant level alpha.
cor.confint(cor, N, alpha)
cor |
Pearson correlation coefficient estimate, can be single value or vector. |
N |
Sample size. |
alpha |
The significant level |
The function use Fisher transformation to approximate p-values and confidence intervals.
The function returns asymptotic lower and upper bound(s) of Pearson correlation coefficient(s).
conf.int1 |
Lower bound(s) of Pearson correlation coefficient(s) |
conf.int2 |
Upper bound(s) of Pearson correlation coefficient(s) |
Dongxiao Zhu (http://dongxiaozhu.googlepages.com/home)
Zhu, D., Hero, A.O., Qin, Z.S. and Swaroop, A. High throughput screening of co-expressed gene pairs with controlled False Discovery Rate (FDR) and Minimum Acceptable Strength (MAS). J Comput Biol.
Schäfer, J., and K. Strimmer. (2004) An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics, 1, 1-13.
## simulate a vector of correlation coefficients cor <- runif(20, -1, 1) g6 <- cor.confint(cor, 20, 0.05) g6$conf.int1 g6$conf.int2