hMeanChiSqMu {ReplicationSuccess} | R Documentation |
The p-value from the harmonic mean chi-squared test is computed based on study-specific estimates and standard errors.
hMeanChiSqMu(thetahat, se, w=rep(1, length(thetahat)), mu=0, alternative="greater", bound=TRUE)
thetahat |
A vector of parameter estimates. |
se |
A vector of standard errors. |
w |
A vector of weights. |
mu |
The null hypothesis value. Defaults to 0. |
alternative |
Either |
bound |
Determines whether p-values that cannot be computed are
reported as "> bound" ( |
The p-value from the harmonic mean chi-squared test
Leonhard Held
Held, L. (2020). The harmonic mean chi-squared test to substantiate scientific findings. Journal of the Royal Statistical Society: Series C (Applied Statistics), 69, 697-708. https://doi.org/10.1111/rssc.12410
## Example from Fisher (1999) as discussed in Held (2020) ## but now based HR estimates lower <- c(0.04, 0.21, 0.12, 0.07, 0.41) upper <- c(1.14, 1.54, 0.60, 3.75, 1.27) se <- ci2se(lower, upper, ratio=TRUE) estimate <- ci2estimate(lower, upper, ratio=TRUE) hMeanChiSqMu(thetahat=estimate, se=se, alternative="two.sided") hMeanChiSqMu(thetahat=estimate, se=se, w=1/se^2, alternative="two.sided") hMeanChiSqMu(thetahat=estimate, se=se, alternative="two.sided", mu=-0.1)