Description Usage Arguments Value Author(s) References Examples

This routine tests whether the values in a vector x is distributed as uniform (0,1). The Neyman's smooth test of fit, as described by Ladwina (1994) is used. The p-values are obtained based on a resampling method from uniform (0,1).

1 |

`x` |
A vector of values, each in the interval [0,1]. |

`nrep` |
The number of replications used to simulate the Neyman distribution. |

`sim` |
A vector of simulated values from the Neyman distribution. If sim = NA this vector is generated by the function SimNev, otherwise the vector inputted is used. |

`n.min` |
The minimum sample size that triggers the use of asymptotic Chi distribution in place of the emprical distribution in the Neyman test of uniformity. |

`pn ` |
The p-value for the test. |

`n4 ` |
The value of the test statistics. |

Mortaza Jamshidian, Siavash Jalal, and Camden Jansen

Ledwina, T. (1994). “Data-driven version of neyman's smooth test of fit,”
*Journal of the American Statistical Association,* 89, 1000-1005.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
# Example 1
x <- runif(100)
TestUNey(x, nrep = 10000, sim = NA)
# Example 2
x <- runif(30,2,5)
x <- (x-min(x))/(max(x)-min(x))
TestUNey(x, nrep = 10000, sim = NA)
# Example 3
x <- c(0.6,0.6,0.5,0.7,0.3,0.4,0.5,0.4,0.2,0.4,0.2,0.5,0.7,0.1,0.7,0.1,0.5,0.5,0.4,0.6,0.3)
TestUNey(x, nrep = 10000, sim = NA)
``` |

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