severity (version 2.0)

severity-package: Mayo's Post-data Severity Evaluation

Description

This package contains functions for calculating severity and generating severity curves. Specifically, the simple case of the one-parameter Normal distribution (i.e., with known variance) is considered.

Details

ll{ Package: severity Type: Package Version: 2.0 Date: 2013-03-27 License: GPL (>= 2) } There is one function in this package, which is called severity: it (1) computes severity at various discrepancies (from the null hypothesis) for the hypothesis test $H_{0}: \mu = \mu_{0}$ vs $H_{1}: \mu > \mu_{0}$, where $\mu_{0}$ is the hypothesized value; and (2) plots both the severity curve(s) and the power curve on a single plot. *** The difference between this version and previous versions is that one more input is added for additional flexibility: the user is now able to control the hypothesized value of the (unknown) parameter $\mu$. ***

References

Mayo, Deborah G. 2012. Statistical Science Meets Philosophy of Science Part 2: Shallow Versus Deep Explorations. Rationality, Markets and Morals: Studies at the Intersection of Philosophy and Economics 3 (Special Topic: Statistical Science and Philosophy of Science) (September 26): 71-107. http://www.rmm-journal.com/downloads/Article_Mayo2.pdf. Mayo, Deborah G., and David R. Cox. 2010. Frequentist Statistics as a Theory of Inductive Inference. In Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science, edited by Deborah G. Mayo and Aris Spanos, 247-274. Cambridge: Cambridge University Press. Mayo, Deborah G., and Aris Spanos. 2006. Severe Testing as a Basic Concept in a Neyman-Pearson Philosophy of Induction. The British Journal for the Philosophy of Science 57 (2) (June 1): 323-357. doi:10.2307/3873470. http://www.jstor.org/stable/3873470. Mayo, Deborah G., and Aris Spanos. 2011. Error Statistics. In Philosophy of Statistics, edited by Prasanta S. Bandyopadhyay and Malcom R. Forster, 7:153-198. Elsevier.

Examples

library(severity)
mu0 <- 5
sigma <- 2
n <- 100
alpha <- 0.025
xbar <- c(5.39, 5.30, 5.10, 5.40, 5.60, 5.85)
(sev <- severity(mu0 = mu0, xbar = xbar, sigma = sigma, n = n, alpha = alpha))