genlog_simu_sk {genlogis} | R Documentation |
Simulating the Generalized logistic distribution with skewness
Description
Creating a simulation of the generalized logistic distribution with skewness maximum likelihood estimation of the parameters
with parallelized processing code using the foreach
package.
Usage
genlog_simu_sk(real.par, init.par, sample.size = 100,
k = 1000, seed = 555, threads = 1, progress.bar = T)
Arguments
real.par |
the real parameters value of the distribution wich the random sample will be taken. It has to be a vector of length 5,
the parameters are the values of |
init.par |
Initial values for the parameters to be optimized over in the following order |
sample.size |
the sample size to be taken in each |
k |
the number of simulations. |
seed |
seed to be given to |
threads |
the numbers of CPU threads to be used for parallel computing. If the threads number is higher than the available the maximum allowed will be used. |
progress.bar |
show progress bar for each thread during simulations, default value |
Details
The used distribution for this package is given by:
f(x) = 2*((a + b*(1+p)*(abs(x-mu)^p))*exp(-(x-mu)*(a+b*(abs(x-mu)^p))))/
((exp(-(x-mu)*(a + b* (abs(x-mu)^p)))+1)^2) *
((exp(-(skew*(x-mu))*(a+b*(abs(skew*(x-mu))^p)))+1)^(-1))
Value
It returns a data.frame with k
rows (each simulation) and 7 columns with the following information:
a, b, p
and mu
are estimations using maximum likelihood estimation, for more info help(genlogis_mle)
sample.size
The sample size used for each k
simulation.
convergence
The estimation's convergence status.
References
Rathie, P. N. and Swamee, P. K (2006) On a new invertible generalized logistic distribution approximation to normal distribution, Technical Research Report in Statistics, 07/2006, Dept. of Statistics, Univ. of Brasilia, Brasilia, Brazil.
Azzalini, A. (1985) A class of distributions which includes the normal ones. Scandinavian Journal of Statistics.
Examples
genlog_simu_sk(real.par = c(0.3, 0.9, 1.5, 0.0, .9), init.par = c(0.9, 0.3, 0.2, 0.0, .9),
sample.size = 100, k = 50, threads = 2, seed = 200)