Nothing
## **************************************************************************
## Probability density function(pdf) of Log-gamma distribution
dlog.gamma <- function (x, alpha, lambda, log = FALSE)
{
if((!is.numeric(alpha)) || (!is.numeric(lambda)) || (!is.numeric(x)))
stop("non-numeric argument to mathematical function")
if((min(alpha) <= 0) || (min(lambda) <= 0) || (x <= 0))
stop("Invalid arguments")
u <- exp(lambda * x)
pdf <- exp(log(alpha) + log(lambda) + lambda * x - alpha * u)
if(log)
pdf <- log(pdf)
return(pdf)
}
## **************************************************************************
## Cummulative distribution function(cdf) of Log-gamma distribution
plog.gamma <- function(q, alpha, lambda, lower.tail = TRUE, log.p = FALSE)
{
if((!is.numeric(alpha)) || (!is.numeric(lambda)) || (!is.numeric(q)))
stop("non-numeric argument to mathematical function")
if((min(alpha) <= 0) || (min(lambda) <= 0) || (q <= 0))
stop("Invalid arguments")
u <- exp(lambda * q)
cdf <- 1.0 - exp(- alpha * u)
if(!lower.tail)
cdf <- 1.0 - cdf
if(log.p)
cdf <- log(cdf)
return(cdf)
}
## **************************************************************************
## Quantile function of Log-gamma distribution
qlog.gamma <- function(p, alpha, lambda, lower.tail = TRUE, log.p = FALSE)
{
if((!is.numeric(alpha)) || (!is.numeric(lambda)) || (!is.numeric(p)))
stop("non-numeric argument to mathematical function")
if((min(alpha) <= 0) || (min(lambda) <= 0) || (p <= 0) || (p > 1))
stop("Invalid arguments")
tmp<-log(1.0-p)
qtl<- (1.0/lambda) * log(-tmp/alpha)
if(!lower.tail)
qtl<- (1.0/lambda) * log(-log(p)/alpha)
if(log.p)
qtl <- log(qtl)
return(qtl)
}
## **************************************************************************
## Random variate generation from Log-gamma distribution
rlog.gamma <- function(n, alpha, lambda)
{
if((!is.numeric(alpha)) || (!is.numeric(lambda)) || (!is.numeric(n)))
stop("non-numeric argument to mathematical function")
if((min(alpha) <= 0) || (min(lambda) <= 0) || (n <= 0))
stop("Invalid arguments")
u <- runif(n)
tmp <- log(u)
return((1.0/lambda) * log(-tmp/alpha))
}
## **************************************************************************
## Reliability function of Log-gamma distribution
slog.gamma <- function (x, alpha, lambda)
{
if((!is.numeric(alpha)) || (!is.numeric(lambda)) || (!is.numeric(x)))
stop("non-numeric argument to mathematical function")
if((min(alpha) <= 0) || (min(lambda) <= 0) || (x <= 0))
stop("Invalid arguments")
return(exp(- alpha * exp(lambda * x)))
}
## **************************************************************************
## Hazard function of Log-gamma distribution
hlog.gamma <- function (x, alpha, lambda)
{
if((!is.numeric(alpha)) || (!is.numeric(lambda)) || (!is.numeric(x)))
stop("non-numeric argument to mathematical function")
if((min(alpha) <= 0) || (min(lambda) <= 0) || (x <= 0))
stop("Invalid arguments")
return(alpha * lambda * exp(lambda * x))
}
## **************************************************************************
## Hazard rate average function of Log-gamma distribution
hra.log.gamma <- function(x, alpha, lambda)
{
r <- slog.gamma(x, alpha, lambda)
fra <- ((-1) * log(r))/x
return(fra)
}
## **************************************************************************
## Conditional Hazard rate function of Log-gamma distribution
crf.log.gamma <- function(x, t=0, alpha, lambda)
{
t <- t
x <- x
nume <- hlog.gamma(x+t, alpha, lambda)
deno <- hlog.gamma(x, alpha, lambda)
return(nume/deno)
}
## **************************************************************************
## Kolmogorov-Smirnov test (One-sample)for Log-gamma distribution
`ks.log.gamma` <-
function(x, alpha.est,lambda.est,
alternative=c("less","two.sided","greater"),plot=FALSE, ...)
{
alpha <- alpha.est
lambda <- lambda.est
res<-ks.test(x,plog.gamma, alpha, lambda, alternative=alternative)
if(plot){
plot(ecdf(x), do.points = FALSE, main = 'Empirical and Theoretical cdfs',
xlab = 'x', ylab = 'Fn(x)', ...)
mini <- min(x)
maxi <- max(x)
t <- seq(mini, maxi, by = 0.01)
y <- plog.gamma(t, alpha, lambda)
lines(t, y, lwd = 2, col = 2)
}
return(res)
}
## **************************************************************************
## Quantile-Quantile(QQ) plot for Log-gamma distribution
qq.log.gamma <- function(x, alpha.est, lambda.est, main = ' ', line.qt = FALSE, ...)
{
xlab <- 'Empirical quantiles'
ylab <- 'Theoretical quantiles'
alpha <- alpha.est
lambda <- lambda.est
n <- length(x)
k <- seq(1, n, by = 1)
P <- (k - 0.5)/n
limx <- c(min(x), max(x))
Finv <- qlog.gamma(P, alpha, lambda)
quantiles <- sort(x)
plot(quantiles, Finv, xlab = xlab, ylab = ylab, xlim = limx,
ylim = limx, main = main, col = 4, lwd = 2, ...)
lines(c(0,limx), c(0,limx), col = 2, lwd = 2)
if(line.qt){
quant <- quantile(x)
x1 <- quant[2]
x2 <- quant[4]
y1 <- qlog.gamma(0.25, alpha, lambda)
y2 <- qlog.gamma(0.75, alpha, lambda)
m <- ((y2-y1) / (x2-x1))
inter <- y1 - (m * x1)
abline(inter, m, col = 2, lwd = 2)
}
invisible(list(x = quantiles, y = Finv))
}
## **************************************************************************
## Probability-Probability(PP) plot for Log-gamma distribution
pp.log.gamma <- function(x, alpha.est, lambda.est, main=' ', line = FALSE, ...)
{
xlab <- 'Empirical distribution function'
ylab <- 'Theoretical distribution function'
alpha <- alpha.est
lambda <- lambda.est
F <- plog.gamma(x,alpha, lambda)
Pemp <- sort(F)
n <- length(x)
k <- seq(1, n, by = 1)
Pteo <- (k - 0.5) / n
plot(Pemp, Pteo, xlab = xlab, ylab = ylab, col = 4,
xlim = c(0, 1), ylim = c(0, 1), main = main, lwd = 2, ...)
if(line)
lines(c(0, 1), c(0, 1), col = 2, lwd = 2)
Cor.Coeff <- cor(Pemp, Pteo)
Determination.Coeff <- (Cor.Coeff^2) * 100
return(list(Cor.Coeff = Cor.Coeff, Determination.Coeff = Determination.Coeff))
}
## **************************************************************************
## Akaike information criterium (AIC) and
## Schwartz/Bayesian information criterion (BIC)
## for Log-gamma distribution
abic.log.gamma <- function(x, alpha.est, lambda.est)
{
alpha <- alpha.est
lambda <- lambda.est
n <- length(x)
p <- 2
f <- dlog.gamma(x, alpha, lambda)
l <- log(f)
LogLik <- sum(l)
AIC <- - 2 * LogLik + 2 * p
BIC <- - 2 * LogLik + p * log(n)
return(list(LogLik = LogLik, AIC = AIC, BIC = BIC))
}
## **************************************************************************
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