ddst.uniform.test: Data Driven Smooth Test for Uniformity
In ddst: Data Driven Smooth Tests

Description Usage Arguments Details Value Author(s) References Examples

Performs data driven smooth tests for simple hypothesis of uniformity on [0,1].

1 2	ddst.uniform.test(x, base = ddst.base.legendre, c = 2.4, B = 1000, compute.p = F, Dmax = 10, ...)

`x`	a (non-empty) numeric vector of data values.
`base`	a function which returns orthogonal system, might be `ddst.base.legendre` for Legendre polynomials or `ddst.base.cos` for cosine system, see package description.
`c`	a parameter for model selection rule, see package description.
`B`	an integer specifying the number of replicates used in p-value computation.
`compute.p`	a logical value indicating whether to compute a p-value.
`Dmax`	an integer specifying the maximum number of coordinates, only for advanced users.
`...`	further arguments.

Embeding null model into the original exponential family introduced by Neyman (1937) leads to the information matrix I being identity and smooth test statistic with k components $W_k=[1/sqrt(n) sum_j=1^k sum_i=1^n phi_j(Z_i)]^2$, where $phi_j$ is jth degree normalized Legendre polynomial on [0,1] (default value of parameter base = ‘ddst.base.legendre’). Alternatively, in our implementation, cosine system can be selected (base = ‘ddst.base.cos’). For details see Ledwina (1994) and Inglot and Ledwina (2006).

An application of the pertaining selection rule T for choosing k gives related ‘ddst.uniform.test()’ based on statistic $W_T$.

Similar approach applies to testing goodness-of-fit to any fully specified continuous distribution function F. For this purpose it is enough to apply the above solution to transformed observations $F(z_1),...,F(z_n)$.

For more details see: http://www.biecek.pl/R/ddst/description.pdf.

An object of class htest

`statistic`	the value of the test statistic.
`parameter`	the number of choosen coordinates (k).
`method`	a character string indicating the parameters of performed test.
`data.name`	a character string giving the name(s) of the data.
`p.value`	the p-value for the test, computed only if `compute.p=T`.

Przemyslaw Biecek and Teresa Ledwina

Inglot, T., Ledwina, T. (2006). Towards data driven selection of a penalty function for data driven Neyman tests. Linear Algebra and its Appl. 417, 579–590.

Ledwina, T. (1994). Data driven version of Neyman's smooth test of fit. J. Amer. Statist. Assoc. 89 1000-1005.

Neyman, J. (1937). ‘Smooth test’ for goodness of fit. Skand. Aktuarietidskr. 20, 149-199.

# H0 is true
z = runif(80)
ddst.uniform.test(z, compute.p=TRUE)

# known fixed alternative
z = rnorm(80,10,16)
ddst.uniform.test(pnorm(z, 10, 16), compute.p=TRUE)


# H0 is false
z = rbeta(80,4,2)
(t = ddst.uniform.test(z, compute.p=TRUE))
t$p.value