data(heights)
attach(heights)
# a symmetric two sided alternative:
tt <- triangular.test.norm(x=female[1:3],
y=male[1:3], mu1=170,mu2=176,mu0=164,
alpha=0.05, beta=0.2,sigma=7)
# Test is yet unfinished, add the remaining values step by step:
tt <- update(tt,x=female[4])
tt <- update(tt,y=male[4])
tt <- update(tt,x=female[5])
tt <- update(tt,y=male[5])
tt <- update(tt,x=female[6])
tt <- update(tt,y=male[6])
tt <- update(tt,x=female[7])
tt <- update(tt,y=male[7])
# Test is finished now
# an unsymmetric two sided alternative:
tt2 <- triangular.test.norm(x=female[1:3],
y=male[1:3], mu1=170,mu2=180,mu0=162,
alpha=0.05, beta=0.2,sigma=7)
tt2 <- update(tt2,x=female[4])
Results
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> library(OPDOE)
Loading required package: gmp
Attaching package: 'gmp'
The following objects are masked from 'package:base':
%*%, apply, crossprod, matrix, tcrossprod
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/OPDOE/triangular.test.norm.Rd_%03d_medium.png", width=480, height=480)
> ### Name: triangular.test.norm
> ### Title: Triangular Test for Normal Data
> ### Aliases: triangular.test.norm
> ### Keywords: test
>
> ### ** Examples
>
> data(heights)
> attach(heights)
> # a symmetric two sided alternative:
> tt <- triangular.test.norm(x=female[1:3],
+ y=male[1:3], mu1=170,mu2=176,mu0=164,
+ alpha=0.05, beta=0.2,sigma=7)
Triangular Test for normal distribution
Sigma known: 7
H0: mu1=mu2= 170 versus H1: mu1= 170 and mu2>= 176 or mu2<= 164
alpha: 0.05 beta: 0.2
Test not finished, continue by adding single data via update()
current sample size for x: 3
current sample size for y: 3
> # Test is yet unfinished, add the remaining values step by step:
> tt <- update(tt,x=female[4])
Triangular Test for normal distribution
Sigma known: 7
H0: mu1=mu2= 170 versus H1: mu1= 170 and mu2>= 176 or mu2<= 164
alpha: 0.05 beta: 0.2
Test not finished, continue by adding single data via update()
current sample size for x: 4
current sample size for y: 3
> tt <- update(tt,y=male[4])
Triangular Test for normal distribution
Sigma known: 7
H0: mu1=mu2= 170 versus H1: mu1= 170 and mu2>= 176 or mu2<= 164
alpha: 0.05 beta: 0.2
Test not finished, continue by adding single data via update()
current sample size for x: 4
current sample size for y: 4
> tt <- update(tt,x=female[5])
Triangular Test for normal distribution
Sigma known: 7
H0: mu1=mu2= 170 versus H1: mu1= 170 and mu2>= 176 or mu2<= 164
alpha: 0.05 beta: 0.2
Test not finished, continue by adding single data via update()
current sample size for x: 5
current sample size for y: 4
> tt <- update(tt,y=male[5])
Triangular Test for normal distribution
Sigma known: 7
H0: mu1=mu2= 170 versus H1: mu1= 170 and mu2>= 176 or mu2<= 164
alpha: 0.05 beta: 0.2
Test not finished, continue by adding single data via update()
current sample size for x: 5
current sample size for y: 5
> tt <- update(tt,x=female[6])
Triangular Test for normal distribution
Sigma known: 7
H0: mu1=mu2= 170 versus H1: mu1= 170 and mu2>= 176 or mu2<= 164
alpha: 0.05 beta: 0.2
Test not finished, continue by adding single data via update()
current sample size for x: 6
current sample size for y: 5
> tt <- update(tt,y=male[6])
Triangular Test for normal distribution
Sigma known: 7
H0: mu1=mu2= 170 versus H1: mu1= 170 and mu2>= 176 or mu2<= 164
alpha: 0.05 beta: 0.2
Test not finished, continue by adding single data via update()
current sample size for x: 6
current sample size for y: 6
> tt <- update(tt,x=female[7])
Triangular Test for normal distribution
Sigma known: 7
H0: mu1=mu2= 170 versus H1: mu1= 170 and mu2>= 176 or mu2<= 164
alpha: 0.05 beta: 0.2
Test not finished, continue by adding single data via update()
current sample size for x: 7
current sample size for y: 6
> tt <- update(tt,y=male[7])
Triangular Test for normal distribution
Sigma known: 7
H0: mu1=mu2= 170 versus H1: mu1= 170 and mu2>= 176 or mu2<= 164
alpha: 0.05 beta: 0.2
Test finished: accept H1
Sample size for x: 7
Sample size for y: 7
> # Test is finished now
> # an unsymmetric two sided alternative:
> tt2 <- triangular.test.norm(x=female[1:3],
+ y=male[1:3], mu1=170,mu2=180,mu0=162,
+ alpha=0.05, beta=0.2,sigma=7)
Triangular Test for normal distribution
Sigma known: 7
H0: mu1=mu2= 170 versus H1: mu1= 170 and mu2>= 180 or mu2<= 162
alpha: 0.05 beta: 0.2
Test not finished, continue by adding single data via update()
current sample size for x: 3
current sample size for y: 3
> tt2 <- update(tt2,x=female[4])
Triangular Test for normal distribution
Sigma known: 7
H0: mu1=mu2= 170 versus H1: mu1= 170 and mu2>= 180 or mu2<= 162
alpha: 0.05 beta: 0.2
Test finished: accept H1
Sample size for x: 4
Sample size for y: 3
>
>
>
>
>
> dev.off()
null device
1
>