p.GFisher | R Documentation |
Survival function of the generalized Fisher's p-value combination statistic.
p.GFisher(q, df, w, M, p.type = "two", method = "HYB", nsim = NULL)
q |
- observed GFisher statistic. |
df |
- vector of degrees of freedom for inverse chi-square transformation for each p-value. If all df's are equal, it can be defined by the constant. |
w |
- vector of weights. |
M |
- correlation matrix of the input statistics. |
p.type |
- "two" = two-sided p-values, "one" = one-sided p-values. |
method |
- "MR" = simulation-assisted moment ratio matching, "HYB" = moment ratio matching by quadratic approximation, "GB" = Brown's method with calculated variance. See details in the reference. |
nsim |
- number of simulation used in the "MR" method, default = 5e4. |
p-value of the observed GFisher statistic.
Hong Zhang and Zheyang Wu. "Accurate p-Value Calculation for Generalized Fisher's Combination Tests Under Dependence", <arXiv:2003.01286>.
set.seed(123) n = 10 M = matrix(0.3, n, n) + diag(0.7, n, n) zscore = matrix(rnorm(n),nrow=1)%*%chol(M) pval = 2*(1-pnorm(abs(zscore))) gf1 = stat.GFisher(pval, df=2, w=1) gf2 = stat.GFisher(pval, df=1:n, w=1:n) p.GFisher(gf1, df=2, w=1, M=M, method="HYB") p.GFisher(gf1, df=2, w=1, M=M, method="MR", nsim=5e4) p.GFisher(gf2, df=1:n, w=1:n, M=M, method="HYB") p.GFisher(gf2, df=1:n, w=1:n, M=M, method="MR", nsim=5e4)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.