| ci.sm | R Documentation |
Function to obtain the exact confidence interval for the standardized mean.
ci.sm(sm = NULL, Mean = NULL, SD = NULL, ncp = NULL, N = NULL,
conf.level = 0.95, alpha.lower = NULL, alpha.upper = NULL, ...)
sm |
standardized mean |
Mean |
mean |
SD |
standard deviation |
ncp |
noncentral parameter |
N |
sample size |
conf.level |
confidence interval coverage (i.e., 1 - Type I error rate); default is .95 |
alpha.lower |
Type I error for the lower confidence limit |
alpha.upper |
Type I error for the upper confidence limit |
... |
allows one to potentially include parameter values for inner functions |
The user must specify the standardized mean in one and only one of the three ways: a) mean and standard deviation (Mean and SD), b) standardized
mean (sm), and c) noncentral parameter (ncp). The confidence level must be specified in one of following two ways: using confidence interval
coverage (conf.level), or lower and upper confidence limits (alpha.lower and alpha.upper).
This function uses the exact confidence interval method based on noncentral t-distributions. The confidence interval for noncentral t-parameter can be obtained from the conf.limits.nct function in MBESS.
Lower.Conf.Limit.Standardized.Mean |
lower confidence limit of the standardized mean |
Standardized.Mean |
standardized mean |
Upper.Conf.Limit.Standardized.Mean |
upper confidence limit of the standardized mean |
The standardized mean is the mean divided by the standard deviation.
Ken Kelley (University of Notre Dame; KKelley@ND.Edu)
Kelley, K. (2007). Constructing confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20 (8), 1–24.
Steiger, J. H., & Fouladi, R. T. (1997). Noncentrality interval estimation and the evaluation of statistical methods. In L. L. Harlow, S. A. Mulaik, & J.H. Steiger (Eds.), What if there were no significance tests? (pp. 221–257). Mahwah, NJ: Lawrence Erlbaum.
conf.limits.nct
ci.sm(sm=2.037905, N=13, conf.level=.95)
ci.sm(Mean=30, SD=14.721, N=13, conf.level=.95)
ci.sm(ncp=7.347771, N=13, conf.level=.95)
ci.sm(sm=2.037905, N=13, alpha.lower=.05, alpha.upper=0)
ci.sm(Mean=50, SD=10, N=25, conf.level=.95)
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