lamme (version 0.0.1)

boot.es: Boostrapped CI for Effect Size measures

Description

Compute the bias-corrected and expanded percentile boostrapped confidence intervals for effect size estimates zetas and the overall signal-to-noise ratio. Additionally, if pretest scores are provided, boostrapped CI on beta is also given.

Usage

boot.es(y, g, x = 0, nrep = 1000, alpha = 0.05)

Arguments

y

the raw posttest scores of a continuous outcome variable.

g

the categorical variable that denotes the group membership.

x

(optional) the raw pretest scores of a continuous outcome variable.

nrep

the number of boostrapped samples. (default=1000)

alpha

the significance level (default=.05)

Value

a table of lower and upper limit from bias-corrected and accelerated and expanded percentile boostrapped confidence interval. The first row is on the geometric mean of the control group (default group of comparison). After that, zeta estimates are given of the each respective group versus the control group (default group of comparison). Then, if pretest scores are given, CI on the beta estimate is given. Lastly, CI on the signal-to-noise ratio, an overall effect size measure, is provided.

BCa LL

the lower limit of the Bias-Corrected and accelerated boostrapped Confidence Interval

BCa UL

the upper limit of the Bias-Corrected and accelerated boostrapped Confidence Interval

exp LL

the lower limit of the expanded percentile boostrapped Confidence Interval

exp UL

the upper limit of the expanded percentile boostrapped Confidence Interval

References

Efron, B. (1987). "Better Bootstrap Confidence Intervals". Journal of the American Statistical Association. Journal of the American Statistical Association, Vol. 82, No. 397. 82 (397): 171<U+2013>185. doi:10.2307/2289144. JSTOR 2289144.

Examples

# NOT RUN {
data("schoene")
attach(schoene)
boot.es(post_HRT,group,pre_HRT,1000,.05)
# }