Calculates the z-score required such that confidence intervals do not overlap under the null hypothesis withe a specified probability.
gen_z(b, v, alpha = 0.05, df = Inf, ...)A list with two elements:
ave_z: A data frame with one row for each estimate in b and the following variables:
vij: observation number
s_zb: standard deviation of the z-scores across all pairs of intervals containing that estimate.
min_zb, max_zb: The minimum and maximum z-scores for the pairs of intervals containing that estimate.
zb: The mean z-score for the pairs of intervals containing that estimate.
ci: The confidence level corresponding to zb.
all_z: A data frame with one row for each pair of estimates in b and the following variables:
i, j: The indices of the two estimates in the pair.
s_i, s_j: The standard errors of the two estimates in the pair.
theta: The ratio of the standard errors of the two estimates.
rho: The correlation between the two estimates.
zb: The z-score for the pair of estimates.
ci : The confidence level corresponding to zb.
olap_ave The probability that the two intervals do not overlap under the null hypothesis.
olap_84 The probability that two 84% confidence intervals for the estimates in the pair would not overlap under the null hypothesis.
A vector of estiamtes
The variance-covariance matrix for b.
The desired probability at which the confidence intervals do not overlap under the null hypothesis.
Degrees of freedom for the t-distribution, defaults to Inf indicating a normal distribution.
Other arguments passed down, currently not implemented.
Harvey Goldstein and Michael J.R. Healy. (1995) "The Graphical Presentation of A Collection of Means." Journal of the Royal Statistical Society, Series A 158(1): 175-177 doi:10.2307/2983411. David Afshartous and Richard A. Preston. (2010) "Confidence Intervals for Dependent Data: Equating Non-overlap with Statistical Significance." Computational Statistics and Data Analysis 54: 2296-2305 doi:10.1016/j.csda.2010.04.011
data(mtcars)
mod <- lm(mpg ~ wt + hp + disp + vs, data=mtcars)
gen_z(coef(mod), vcov(mod))
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