# TestUNey: Test of Goodness of Fit (Uniformity) In MissMech: Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random

## Description

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).

## Usage

 `1` ```TestUNey(x, nrep = 10000, sim = NA, n.min = 30) ```

## Arguments

 `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.

## Value

 `pn ` The p-value for the test. `n4 ` The value of the test statistics.

## Author(s)

Mortaza Jamshidian, Siavash Jalal, and Camden Jansen

## References

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

## Examples

 ``` 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) ```

MissMech documentation built on May 2, 2019, 1:08 p.m.