spotSeverity: spotSeverity

In SPOT: Sequential Parameter Optimization Toolbox

spotSeverity

Description

spotSeverity

Usage

spotSeverity(xbar, mu0, mu1, n, sigma, alpha, tdist = FALSE, paired = TRUE)

Arguments

xbar	sample mean value
mu0	mean value of the null hypothesis (usually referred to as H0)
mu1	mean value of the alternative hypothesis (usually referred to as H1)
n	sample size in each arm, e.g., if 20 samples are available, then n=10 regardless whether the samples are paired/blocked (paired=TRUE) or independent (paired=FALSE). Degrees of freedom will be modified internally according to the setting of the paired argument.
sigma	sample s.d. Will be used to determine s.d. of the differences (if paired==TRUE) or s.d. of the pooled s.d (if paired==FALSE).
alpha	probability of a type I error, given H0 is true
tdist	logical. Use Student t Distribution. Default: FALSE
paired	logical. Paired (blocked) data. Default: TRUE

Value

an object of class "spotSeverity", with a summary method and a print method.

Examples

 
s0 <- spotSeverity(xbar=0.4, mu0=0.0, mu1=0.6, n=25, sigma=1, alpha=0.03)
print(s0)
s1 <- spotSeverity(xbar=0.4, mu0=0.6, mu1=0.6, n=25, sigma=1, alpha=0.03)
print(s1)
s2 <- spotSeverity(xbar=0, mu0=0.6, mu1=0.6, n=25, sigma=1, alpha=0.03)
print(s2)

## Example from Mayo, p345
spotSeverity(xbar=90, mu0=0, mu1= 200, n=200, sigma = 450, alpha = 0.025, 
paired = FALSE, tdist = FALSE)

## Example from Vena02a to compare with results from t.test()
## library("BHH2")
## data(shoes.data)
## A <- shoes.data$matA
## B <- shoes.data$matB
 A <- c(13.2, 8.2, 10.9, 14.3, 10.7, 6.6, 9.5, 10.8, 8.8, 13.3)
 B <- c(14, 8.8, 11.2, 14.2, 11.8, 6.4, 9.8, 11.3, 9.3, 13.6)
t.paired <- t.test(x = A, y = B, var.equal = TRUE, paired = TRUE, 
alternative = "greater",  conf.level = 0.95)
xbar <- mean(A-B)
n <- length(A)
sigma <- sd(A-B) 
s.paired <- spotSeverity(xbar=xbar,mu0=0, mu1= 1, n=n, sigma = sigma, 
alpha = 0.025, tdist = TRUE)

SPOT documentation built on June 26, 2022, 1:06 a.m.