fun.diag.ks.g.bimodal {GLDEX}

Compute the simulated Kolmogorov-Smirnov tests for the bimodal dataset

Description

This function counts the number of times the p-value exceed 0.05 for the null hypothesis that the observations simulated from the fitted distribution is the same as the observations simulated from the bimodal data set.

Usage

fun.diag.ks.g.bimodal(result1, result2, prop1, prop2, data, no.test = 1000, 
len = floor(0.9 * length(data)), param1, param2, alpha = 0.05)

Arguments

result1

A vector representing the four parameters of the first generalised lambda distribution.

result2

A vector representing the four parameters of the second generalised lambda distribution.

prop1

Proportion of the first distribution fitted to the bimodal dataset.

prop2

Proportion of the second distribution fitted to the bimodal dataset.

data

The bimodal dataset.

no.test

Total number of tests required.

len

Number of data to sample.

param1

Type of first generalised lambda distribution, can be "rs" or "fmkl".

param2

Type of second generalised lambda distribution, can be "rs" or "fmkl".

alpha

Significance level of KS test.

Value

A numerical value representing number of times the p-value exceeds alpha.

Note

If there are ties, jittering is used in ks.gof.

Author(s)

Steve Su

References

Stephens, M. A. (1986). Tests based on EDF statistics. In Goodness- of-Fit Techniques. D'Agostino, R. B. and Stevens, M. A., eds. New York: Marcel Dekker.

Su, S. (2005). A Discretized Approach to Flexibly Fit Generalized Lambda Distributions to Data. Journal of Modern Applied Statistical Methods (November): 408-424.

Su (2007). Nmerical Maximum Log Likelihood Estimation for Generalized Lambda Distributions. Computational Statistics and Data Analysis: *51*, 8, 3983-3998.

Su (2007). Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R. Journal of Statistical Software: *21* 9.

See Also

fun.diag.ks.g

Examples

# Fit the faithful[,1] data from the MASS library
 fit1<-fun.auto.bimodal.ml(faithful[,1],init1.sel="rprs",init2.sel="rmfmkl",
 init1=c(-1.5,1,5),init2=c(-0.25,1.5),leap1=3,leap2=3)
# Run diagnostic KS tests
 fun.diag.ks.g.bimodal(fit1$par[1:4],fit1$par[5:8],prop1=fit1$par[9],
 data=faithful[,1],param1="rs",param2="fmkl")