Perform sign test on one-sample data, which is one of the oldest non-parametric statistical methods. Assume that X comes from a continuous distribution with median = v ( unknown ). Test the null hypothesis H0: median of X v = mu ( mu is the location parameter and is given in the test ) v.s. the alternative hypothesis H1: v > mu ( or v < mu or v != mu ) and calculate the p-value. When the sample size is large, perform the asymptotic sign test. In both ways, calculate the R-estimate of location of X and the distribution free confidence interval for mu.
Yeyun Yu ,Ting Yang
Maintainer: Ting Yang<email@example.com>
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##One-sample test x<-c(-5,-3,-2,1,5,6,3,9,10,15,20,21) signmedian.test(x,alternative = "greater",exact=TRUE) signmedian.test(x,mu=3,alternative="two.sided",exact=FALSE) ##Two-sample test(paired data) x<-c(-5,-3,-2,1,5,6,3,9,10,15,20,21) y<-c(-1,-2,-3,1,2,3,4,2,6,8,9,10) x<-y-x signmedian.test(x,alternative = "greater",exact=TRUE)
Exact sign test data: x #(x>0) = 9, mu = 0, p-value = 0.073 alternative hypothesis: the median of x is greater than mu 96.14258 percent confidence interval: -2 15 sample estimates: point estimator 5.5 Asymptotic sign test(with continuity correction) data: x #(x!=3) = 11, mu = 3, p-value = 0.5465 alternative hypothesis: the median of x is not equal to mu 94.79071 percent confidence interval: -3 20 sample estimates: point estimator 5.5 Exact sign test data: x #(x>0) = 3, mu = 0, p-value = 0.9673 alternative hypothesis: the median of x is greater than mu 93.45703 percent confidence interval: -7 1 sample estimates: point estimator -3
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