pVals {RPDTest}

Randomized phi-divergence test: simulated p-value part

Description

This is one of the auxiliary functions used to execute the rpdTest function. This function can be used to calculate p-values based on Monte Carlo simulation. Users generally do not need to call this function except for testing purposes. For more detailed description one can find inrpdTest.

Usage

pVals(x, p, lambda = 1, B = 200, z = 40, rs = 1250, n.cores, nDim, r)

Arguments

x

the obtained multinomial distribution data.Same data structure as the data parameter in rpdTest.

p

the probability vector in the null hypothesis. It is necessary to ensure beforehand that the vectors are valid.

lambda

a control parameter of the statistic calculation, adjusting it will significantly change the final obtained statistic.

B

an integer specifying the number of simulation data on the expected null distribution (p) of the Monte Carlo simulation.

z

an integer specifying the number by which to divide the observation data group in a Monte Carlo simulation.

rs

an integer that adjusts the number of statistics calculated in simulation.

n.cores

an integer used to specify the number of cores used to perform parallel operations. The default is to use the maximum number of cores available to the computer minus one.

nDim

an integer indicating the dimension of the uniformly distributed vectors generated during the computation of the statistic. It is equal to the number of experiments for the multinomial distribution.

r

an integer indicating the dimension of the data parameter. It is equal to the number of possible outcomes of the multinomial distribution.

Value

an numeric value indicating simulated p-value.

Examples

d <- c(20,40)
#The next line is equivalent to rpdTest(d,sim.pValue = TRUE,n.cores = 2)$p.value
#It usually takes 1-2 minutes to perform this calculation process

pVals(d, c(1/2,1/2), B = 200, z = 40, rs = 1250, n.cores = 2, nDim = sum(d), r = length(d))