loggammag2 {MPS}

log gamma G type II distribution

Description

Computes the pdf, cdf, quantile, and random numbers, draws the q-q plot, and estimates the parameters of the log gamma type II G distribution. General form for the probability density function (pdf) of the log gamma type II G distribution due to Amini et al. (2013) is given by

f(x,{\Theta}) = \frac{{{b^a}}}{{\Gamma (a)}}g(x-\mu,\theta ){\left[ { - \log (G(x-\mu,\theta ))} \right]^{a - 1}}{\left( {G(x-\mu,\theta )} \right)^{b - 1}},

where \theta is the baseline family parameter vector. Also, a>0, b>0, d>0, and \mu are the extra parameters induced to the baseline cumulative distribution function (cdf) G whose pdf is g. The general form for the cumulative distribution function (cdf) of the log gamma type II G distribution is given by

F(x,{\Theta}) = 1 - \int_0^{ - b\log \left( {G(x-\mu,\theta )} \right)} {\frac{{{y^{a - 1}}{e^{ - y}}}}{{\Gamma (a)}}dy}.

Here, the baseline G refers to the cdf of famous families such as: Birnbaum-Saunders, Burr type XII, Exponential, Chen, Chisquare, F, Frechet, Gamma, Gompertz, Linear failure rate (lfr), Log-normal, Log-logistic, Lomax, Rayleigh, and Weibull. The parameter vector is \Theta=(a,b,\theta,\mu) where \theta is the baseline G family's parameter space. If \theta consists of the shape and scale parameters, the last component of \theta is the scale parameter (here, a and b are the first and second shape parameters). Always, the location parameter \mu is placed in the last component of \Theta.

Usage

dloggammag2(mydata, g, param, location = TRUE, log=FALSE)
ploggammag2(mydata, g, param, location = TRUE, log.p = FALSE, lower.tail = TRUE)
qloggammag2(p, g, param, location = TRUE, log.p = FALSE, lower.tail = TRUE)
rloggammag2(n, g, param, location = TRUE)
qqloggammag2(mydata, g, location = TRUE, method)
mpsloggammag2(mydata, g, location = TRUE, method, sig.level)

Arguments

g

The name of family's pdf including: "birnbaum-saunders", "burrxii", "chisq", "chen", "exp", "f", "frechet", "gamma", "gompetrz", "lfr", "log-normal", "log-logistic", "lomax", "rayleigh", and "weibull".

p

a vector of value(s) between 0 and 1 at which the quantile needs to be computed.

n

number of realizations to be generated.

mydata

Vector of observations.

param

parameter vector \Theta=(a,b,\theta,\mu)

location

If FALSE, then the location parameter will be omitted.

log

If TRUE, then log(pdf) is returned.

log.p

If TRUE, then log(cdf) is returned and quantile is computed for exp(-p).

lower.tail

If FALSE, then 1-cdf is returned and quantile is computed for 1-p.

method

The used method for maximizing the sum of log-spacing function. It will be "BFGS", "CG", "L-BFGS-B", "Nelder-Mead", or "SANN".

sig.level

Significance level for the Chi-square goodness-of-fit test.

Details

It can be shown that the Moran's statistic follows a normal distribution. Also, a chi-square approximation exists for small samples whose mean and variance approximately are m(log(m)+0.57722)-0.5-1/(12m) and m(\pi^2/6-1)-0.5-1/(6m), respectively, with m=n+1, see Cheng and Stephens (1989). So, a hypothesis tesing can be constructed based on a sample of n independent realizations at the given significance level, indicated in above as sig.level.

Value

1. A vector of the same length as mydata, giving the pdf values computed at mydata.

2. A vector of the same length as mydata, giving the cdf values computed at mydata.

3. A vector of the same length as p, giving the quantile values computed at p.

4. A vector of the same length as n, giving the random numbers realizations.

5. A sequence of goodness-of-fit statistics such as: Akaike Information Criterion (AIC), Consistent Akaike Information Criterion (CAIC), Bayesian Information Criterion (BIC), Hannan-Quinn information criterion (HQIC), Cramer-von Misses statistic (CM), Anderson Darling statistic (AD), log-likelihood statistic (log), and Moran's statistic (M). The Kolmogorov-Smirnov (KS) test statistic and corresponding p-value. The Chi-square test statistic, critical upper tail Chi-square distribution, related p-value, and the convergence status.

Author(s)

Mahdi Teimouri

References

Cheng, R. C. H. and Stephens, M. A. (1989). A goodness-of-fit test using Moran's statistic with estimated parameters, Biometrika, 76 (2), 385-392.

Amini, M., MirMostafaee, S. M. T. K., and Ahmadi, J. (2013). Log-gamma-generated families of distributions, Statistics, 48 (4), 913-932.

Examples

mydata<-rweibull(100,shape=2,scale=2)+3
dloggammag2(mydata, "weibull", c(1,1,2,2,3))
ploggammag2(mydata, "weibull", c(1,1,2,2,3))
qloggammag2(runif(100), "weibull", c(1,1,2,2,3))
rloggammag2(100, "weibull", c(1,1,2,2,3))
qqloggammag2(mydata, "weibull", TRUE, "Nelder-Mead")
mpsloggammag2(mydata, "weibull", TRUE, "Nelder-Mead", 0.05)