simul.bs.gme {bs} | R Documentation |
The function simul.bs.gme()
simulates three samples of size n from a
population T ~ BS(alpha,beta), one for each method (rbs1()
, rbs2()
, or rbs3()
), computes the GMES's
for alpha and beta, and establish goodness-of-fit for each sample.
simul.bs.gme(n, alpha, beta)
n |
Samples of size n . |
alpha |
Teorical shape parameter for simulations. |
beta |
Teorical scale parameter for simulations. |
In order to carry out simulation studies, we develop the functions simul.bs.gme()
,
simul.bs.mle()
, and simul.bs.mme()
. These functions generate random samples, estimate parameters,
and establish goodness-of-fit. The samples of size n, one for each method (G1, G2, or G3), are generated
by using rbs1()
, rbs2()
, and rbs3()
, respectively. The estimations, one for each method,
are obtained by using est1bs()
, est2bs()
, and est3bs()
, respectively. The goodness-of-fit
method is based on the statistic of Kolmogorov-Smirnov (KS), which is available through the
function ksbs()
. The generated observations by means of G1, G2, and G3 are saved as slots of the
R class simulBsClass
, which are named sample1
, sample2
, and sample3
, respectively. Furthermore,
the results of the simulation study are saved in a fourth slot of this class, named results
.
An object of class "simulBsClass"
(Slots).
Víctor Leiva <victor.leiva@uv.cl>, Hugo Hernández <hugo.hernande@msn.com>, and Marco Riquelme <mriquelm@ucm.cl>.
Leiva, V., Hernández, H., and Riquelme, M. (2006). A New Package for the Birnbaum-Saunders Distribution. Rnews, 6/4, 35-40. (http://www.r-project.org)
## Example: simul.bs.mle() simul.bs.mle(100,0.5,1.0) results<-simul.bs.mle(100,0.5,1.0) results@results sample<-results@sample1 ## Example: simul.bs.mme() simul.bs.mme(100,0.5,1.0) ## Example: simul.bs.gme() simul.bs.gme(100,0.5,1.0)