GLDEX (version 2.0.0.9.3)

fun.bimodal.init: Finds the initial values for optimisation in fitting the bimodal generalised lambda distribution.

Description

After classifying the data using fun.class.regime.bi, this function evaluates the temporary or initial solutions by estimating each part of the bimodal distribution using the maximum likelihood estimation and starship method. These initial solutions are then passed onto fun.bimodal.fit.ml or fun.bimodal.fit.pml to obtain the final fits.

Usage

fun.bimodal.init(data1, data2, rs.leap1, fmkl.leap1, rs.init1, fmkl.init1, 
rs.leap2, fmkl.leap2, rs.init2, fmkl.init2,fun1="runif.sobol",
fun2="runif.sobol",no=10000)

Value

prop

Proportion of the number of observations in the first data in relation to the entire data.

first.fit

A matrix comprising the parameters of GLD obtained from RPRS, RMFMKL and STAR for the first dataset.

second.fit

A matrix comprising the parameters of GLD obtained from RPRS, RMFMKL and STAR for the second dataset.

Arguments

data1

The first data obtained by the clustering algorithm.

data2

The second data obtained by the clustering algorithm.

rs.leap1

See scrambling argument in fun.gen.qrn.

fmkl.leap1

See scrambling argument in fun.gen.qrn.

rs.init1

Inititial values (lambda3 and lambda4) for the first RS generalised lambda distribution. c(-1.5,1.5) tends to work well.

fmkl.init1

Inititial values (lambda3 and lambda4) for the first FMKL generalised lambda distribution. c(-0.25,1.5) tends to work well

rs.leap2

See scrambling argument in fun.gen.qrn.

fmkl.leap2

See scrambling argument in fun.gen.qrn.

rs.init2

Inititial values (lambda3 and lambda4) for the second RS generalised lambda distribution. c(-1.5,1.5) tends to work well.

fmkl.init2

Inititial values (lambda3 and lambda4) for the second FMKL generalised lambda distribution. c(-0.25,1.5) tends to work well

fun1

A character string of either "runif.sobol" (default), "runif.sobol.owen", "runif.halton" or "QUnif".

fun2

A character string of either "runif.sobol" (default), "runif.sobol.owen", "runif.halton" or "QUnif".

no

Number of initial random values to find the best initial values for optimisation.

Author

Steve Su

Details

All three methods of fitting (RPRS, RMFMKL and STAR) will be given for each part of the bimodal distribution.

References

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.class.regime.bi,fun.bimodal.fit.pml, fun.bimodal.fit.ml

Examples

# \donttest{
 # Split the first column of the faithful data into two using 

 fun.class.regime.bi
 faithful1.mod<-fun.class.regime.bi(faithful[,1], 0.1, clara)

 # Save the datasets
 qqqq1.faithful1.cc1<-faithful1.mod$data.a
 qqqq2.faithful1.cc1<-faithful1.mod$data.b

 # Find the initial values for secondary optimisation.
 
 result.faithful1.init1<-fun.bimodal.init(data1=qqqq1.faithful1.cc1,
 data2=qqqq2.faithful1.cc1, rs.leap1=3,fmkl.leap1=3,rs.init1 = c(-1.5, 1.5), 
 fmkl.init1 = c(-0.25, 1.5), rs.leap2=3,fmkl.leap2=3,rs.init2 = c(-1.5, 1.5), 
 fmkl.init2 = c(-0.25, 1.5))

 # These initial values are then passed onto fun,bimodal.fit.ml to obtain the 
 # final fits.
# }