GFisher (version 0.2.0)

p.GFisher: Survival function of the generalized Fisher's p-value combination statistic.

Description

Survival function of the generalized Fisher's p-value combination statistic.

Usage

p.GFisher(q, df, w, M, p.type = "two", method = "HYB", nsim = NULL)

Arguments

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.

Value

p-value of the observed GFisher statistic.

References

Hong Zhang and Zheyang Wu. "Accurate p-Value Calculation for Generalized Fisher's Combination Tests Under Dependence", <arXiv:2003.01286>.

Examples

# NOT RUN {
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")
# }
# NOT RUN {
p.GFisher(gf1, df=2, w=1, M=M, method="MR", nsim=5e4)
# }
# NOT RUN {
p.GFisher(gf2, df=1:n, w=1:n, M=M, method="HYB")
# }
# NOT RUN {
p.GFisher(gf2, df=1:n, w=1:n, M=M, method="MR", nsim=5e4)
# }