,
Abir EL HAJ [aut, ctb]
, Alandra ZAKKOUR
[aut], Caroline BORDES [aut, ctb], Cyril PERRET [aut]
, Jean DUMONCEL [aut,
cre] | Title: | Quantile Maximization Likelihood Estimation and Bayesian Ex-Gaussian Estimation |
|---|---|
| Description: | Presents two methods to estimate the parameters 'mu', 'sigma', and 'tau' of an ex-Gaussian distribution. Those methods are Quantile Maximization Likelihood Estimation ('QMLE') and Bayesian. The 'QMLE' method allows a choice between three different estimation algorithms for these parameters : 'neldermead' ('NEMD'), 'fminsearch' ('FMIN'), and 'nlminb' ('NLMI'). For more details about the methods you can refer at the following list: Brown, S., & Heathcote, A. (2003) <doi:10.3758/BF03195527>; McCormack, P. D., & Wright, N. M. (1964) <doi:10.1037/h0083285>; Van Zandt, T. (2000) <doi:10.3758/BF03214357>; El Haj, A., Slaoui, Y., Solier, C., & Perret, C. (2021) <doi:10.19139/soic-2310-5070-1251>; Gilks, W. R., Best, N. G., & Tan, K. K. C. (1995) <doi:10.2307/2986138>. |
| Authors: | Yousri SLAOUI [aut] ,
Abir EL HAJ [aut, ctb]
, Alandra ZAKKOUR
[aut], Caroline BORDES [aut, ctb], Cyril PERRET [aut]
, Jean DUMONCEL [aut,
cre] |
| Maintainer: | Jean DUMONCEL <[email protected]> |
| License: | GPL-2 |
| Version: | 0.1.2 |
| Built: | 2025-02-19 03:43:52 UTC |
| Source: | https://github.com/cran/ExGaussEstim |
Estimates the mu, sigma, and tau parameters of an ex-Gaussian distribution using a bayesian method.
BayesianExgaussian(n, x, nSamples = 5000, Ti = 2500)BayesianExgaussian(n, x, nSamples = 5000, Ti = 2500)
n |
the data size |
x |
the data. Must be a vector, with no missing values |
nSamples |
number of Samples |
Ti |
burn-in |
BayesianExgaussian() returns an object "theta" which is a list with components: estimates of mu, sigma, and tau
Brown, S., & Heathcote, A. (2003). QMLE: Fast, robust, and efficient estimation of distribution functions based on quantiles. Behavior Research Methods, Instruments, & Computers, 35, 485-492.
McCormack, P. D., & Wright, N. M. (1964). The positive skew observed in reaction time distributions. Canadian Journal of Psychology/Revue canadienne de psychologie, 18, 43-51.
Van Zandt, T. (2000). How to fit a response time distribution. Psychonomic Bulletin & Review, 7, 424-465.
El Haj, A., Slaoui, Y., Solier, C., & Perret, C. (2021). Bayesian Estimation of The Ex-Gaussian distribution. Statistics, Optimization & Information Computing, 9(4), 809-819.
Gilks, W. R., Best, N. G., & Tan, K. K. C. (1995). Adaptive rejection Metropolis sampling within Gibbs sampling. Journal of the Royal Statistical Society: Series C (Applied Statistics), 44, 455-472.
library(gamlss.dist) set.seed(2703) data<-rexGAUS(n=100, mu = 500, sigma = 150, nu = 100) BayesianExgaussian(n = 100, x = data)library(gamlss.dist) set.seed(2703) data<-rexGAUS(n=100, mu = 500, sigma = 150, nu = 100) BayesianExgaussian(n = 100, x = data)
Estimates the mu, sigma, and tau parameters of an ex-Gaussian distribution. 3 different algorithms can be used : neldermead ('NEMD'), fminsearch ('FMIN') and nlminb ('NLMI').
QMLEEstim(y, func)QMLEEstim(y, func)
y |
the data. Must be a vector with no missing values |
func |
the function selected for the estimation. 'NEMD' for neldermead, 'FMIN' for fminsearch, and 'NLMI' for nlminb. |
QMLEEstim() returns an object "valEstim" which is a list with components: estimates of mu, sigma, and tau
Brown, S., & Heathcote, A. (2003). QMLE: Fast, robust, and efficient estimation of distribution functions based on quantiles. Behavior Research Methods, Instruments, & Computers, 35, 485-492.
McCormack, P. D., & Wright, N. M. (1964). The positive skew observed in reaction time distributions. Canadian Journal of Psychology/Revue canadienne de psychologie, 18, 43-51.
Van Zandt, T. (2000). How to fit a response time distribution. Psychonomic Bulletin & Review, 7, 424-465.
El Haj, A., Slaoui, Y., Solier, C., & Perret, C. (2021). Bayesian Estimation of The Ex-Gaussian distribution. Statistics, Optimization & Information Computing, 9(4), 809-819.
Gilks, W. R., Best, N. G., & Tan, K. K. C. (1995). Adaptive rejection Metropolis sampling within Gibbs sampling. Journal of the Royal Statistical Society: Series C (Applied Statistics), 44, 455-472.
library(gamlss.dist) set.seed(2703) data<-rexGAUS(n=100, mu = 500, sigma = 150, nu = 100) QMLEEstim(data, 'NEMD')library(gamlss.dist) set.seed(2703) data<-rexGAUS(n=100, mu = 500, sigma = 150, nu = 100) QMLEEstim(data, 'NEMD')