gen_z {VizTest} | R Documentation |
Calculate z-score for Confidence Interval Overlap
Description
Calculates the z-score required such that confidence intervals do not overlap under the null hypothesis withe a specified probability.
Usage
gen_z(b, v, alpha = 0.05, df = Inf, ...)
Arguments
b |
A vector of estiamtes |
v |
The variance-covariance matrix for |
alpha |
The desired probability at which the confidence intervals do not overlap under the null hypothesis. |
df |
Degrees of freedom for the t-distribution, defaults to |
... |
Other arguments passed down, currently not implemented. |
Value
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 tozb
.all_z
: A data frame with one row for each pair of estimates inb
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 tozb
. -
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.
References
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
Examples
data(mtcars)
mod <- lm(mpg ~ wt + hp + disp + vs, data=mtcars)
gen_z(coef(mod), vcov(mod))