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> 
>  library(fuzzyRankTests)
> 
>  options(digits=5) # avoid rounding differences
> 
>  x <- c(-1.2, -0.7, -0.3, 0.1, 0.2, 0.3, 0.4, 0.9, 0.9, 1.0, 1.0,
+      1.1, 1.5, 1.7, 1.9, 3.5, 5.1)
> 
>  fuzzy.sign.test(x, alternative = "greater")

	sign test

data:  x 
alternative hypothesis: true mu is greater than 0 

fuzzy P-value has continuous, piecewise linear CDF with knots and
values

    knots values
 0.001175      0
 0.006363      1

> 
>  x <- c(-1.2, -0.7, 0.0, 0.0, 0.0, 0.3, 0.4, 0.9, 0.9, 1.0, 1.0,
+      1.1, 1.5, 1.7, 1.9, 3.5, 5.1)
> 
>  print(fuzzy.sign.test(x, alternative = "greater"), digits = 3)

	sign test

data:  x 
alternative hypothesis: true mu is greater than 0 

fuzzy P-value has continuous, piecewise linear CDF with knots and
values

    knots values
 0.000137  0.000
 0.001175  0.125
 0.006363  0.500
 0.024521  0.875
 0.071732  1.000

> 
>  print(fuzzy.sign.test(- x, alternative = "less"), digits = 3)

	sign test

data:  -x 
alternative hypothesis: true mu is less than 0 

fuzzy P-value has continuous, piecewise linear CDF with knots and
values

    knots values
 0.000137  0.000
 0.001175  0.125
 0.006363  0.500
 0.024521  0.875
 0.071732  1.000

> 
>  print(fuzzy.sign.test(x, alternative = "two.sided"), digits = 3)

	sign test

data:  x 
alternative hypothesis: true mu is not equal to 0 

fuzzy P-value has continuous, piecewise linear CDF with knots and
values

    knots values
 0.000275  0.000
 0.002350  0.125
 0.012726  0.500
 0.049042  0.875
 0.143463  1.000

> 
>  x2 <- c(-1.2, -0.7, 0.0, 0.0, 0.0, 0.3, 0.4, 0.9, 0.9, 1.0)
> 
>  print(fuzzy.sign.test(x2, alternative = "two.sided"), digits = 3)

	sign test

data:  x2 
alternative hypothesis: true mu is not equal to 0 

fuzzy P-value has continuous, piecewise linear CDF with knots and
values

  knots values
 0.0215  0.000
 0.1094  0.125
 0.3438  0.500
 0.7539  0.875
 1.0000  1.000

> 
>  print(fuzzy.sign.test(- x2, alternative = "two.sided"), digits = 3)

	sign test

data:  -x2 
alternative hypothesis: true mu is not equal to 0 

fuzzy P-value has continuous, piecewise linear CDF with knots and
values

  knots values
 0.0215  0.000
 0.1094  0.125
 0.3438  0.500
 0.7539  0.875
 1.0000  1.000

> 
>  x <- c(-3.5, -2.3, -1.2, -0.7, 0.0, 0.0, 0.0, 0.0, 0.4, 0.9, 0.9, 1.0, 1.0,
+      1.1, 1.9, 3.5, 5.1)
> 
>  print(fuzzy.sign.test(x, alternative = "two.sided"), digits = 3)

	sign test

data:  x 
alternative hypothesis: true mu is not equal to 0 

fuzzy P-value has continuous, piecewise linear CDF with knots and
values

  knots values
 0.0127 0.0000
 0.0490 0.0625
 0.1435 0.3125
 0.3323 0.6875
 0.6291 0.9375
 1.0000 1.0000

> 
>  x <- c(-4.1, -3.5, -2.3, -1.2, -0.7, 0.0, 0.0, 0.0, 0.0, 0.4, 0.9, 0.9, 1.0,
+      1.1, 1.9, 3.5)
> 
>  print(fuzzy.sign.test(x, alternative = "two.sided"), digits = 3)

	sign test

data:  x 
alternative hypothesis: true mu is not equal to 0 

fuzzy P-value has continuous, piecewise linear CDF with knots and
values

  knots values
 0.0768 0.0000
 0.2101 0.0625
 0.4545 0.3125
 0.8036 0.7500
 1.0000 1.0000

> 
>  x <- x[- length(x)]
> 
>  print(fuzzy.sign.test(x, alternative = "two.sided"), digits = 3)

	sign test

data:  x 
alternative hypothesis: true mu is not equal to 0 

fuzzy P-value has continuous, piecewise linear CDF with knots and
values

 knots values
 0.118 0.0000
 0.302 0.0625
 0.607 0.3750
 1.000 1.0000

> 
>  x <- seq(-2, 2)
> 
>  print(fuzzy.sign.test(x, alternative = "two.sided"), digits = 3)

	sign test

data:  x 
alternative hypothesis: true mu is not equal to 0 

fuzzy P-value has continuous, piecewise linear CDF with knots and
values

 knots values
 0.375      0
 1.000      1

> 
>  x <- x[x != 0]
> 
>  print(fuzzy.sign.test(x, alternative = "two.sided"), digits = 3)

	sign test

data:  x 
alternative hypothesis: true mu is not equal to 0 

fuzzy P-value has continuous, piecewise linear CDF with knots and
values

 knots values
 0.625      0
 1.000      1

> 
>  ##### now check with alpha #####
> 
>  print(fuzzy.sign.test(x, alternative = "two.sided", alpha = 0.75),
+      digits = 3)

	sign test

data:  x 
alternative hypothesis: true mu is not equal to 0 

randomized test rejects at level 0.75 with probability 0.333

> 
>  x <- c(-1.2, -0.7, 0.0, 0.0, 0.0, 0.3, 0.4, 0.9, 0.9, 1.0, 1.0,
+      1.1, 1.5, 1.7, 1.9, 3.5, 5.1)
> 
>  print(fuzzy.sign.test(x, alternative = "greater"), digits = 3)

	sign test

data:  x 
alternative hypothesis: true mu is greater than 0 

fuzzy P-value has continuous, piecewise linear CDF with knots and
values

    knots values
 0.000137  0.000
 0.001175  0.125
 0.006363  0.500
 0.024521  0.875
 0.071732  1.000

> 
>  print(fuzzy.sign.test(x, alternative = "greater", alpha = 0.10),
+      digits = 3)

	sign test

data:  x 
alternative hypothesis: true mu is greater than 0 

randomized test rejects at level 0.1 with probability 1

> 
>  print(fuzzy.sign.test(x, alternative = "greater", alpha = 0.05),
+      digits = 3)

	sign test

data:  x 
alternative hypothesis: true mu is greater than 0 

randomized test rejects at level 0.05 with probability 0.942

> 
>  print(fuzzy.sign.test(x, alternative = "greater", alpha = 0.01),
+      digits = 3)

	sign test

data:  x 
alternative hypothesis: true mu is greater than 0 

randomized test rejects at level 0.01 with probability 0.575

> 
>  print(fuzzy.sign.test(x, alternative = "greater", alpha = 0.001),
+      digits = 3)

	sign test

data:  x 
alternative hypothesis: true mu is greater than 0 

randomized test rejects at level 0.001 with probability 0.104

> 
>  print(fuzzy.sign.test(x, alternative = "greater", alpha = 0.0001),
+      digits = 3)

	sign test

data:  x 
alternative hypothesis: true mu is greater than 0 

randomized test rejects at level 1e-04 with probability 0

> 
> 
> proc.time()
   user  system elapsed 
   0.12    0.04    0.17