LN0SCIs: LN0SCIs

In LN0SCIs: Simultaneous CIs for Ratios of Means of Log-Normal Populations with Zeros

Description

Construct the simultaneous confidence intervals for ratios of means of Log-normal populations with zeros. It also has a Python module that do the same thing, it can be applied to multiple comparisons of parameters of any k mixture distribution. And it provide four methods, the method based on generalized pivotal quantity with order statistics (GPQH and GPQW), and the method based on two-step MOVER intervals (FMW and FMWH).

Details

At present, these four function perform better than other methods that can be used to calculate the simultaneous confidence interval of log-normal populations with excess zeros.

Author(s)

Jing Xu, Xinmin Li, Hua Liang

See Also

[1] Besag I, Green P, Higdon D, Mengersen K, 1995. Bayesian computation and Stochastic-systems.

[2] Hannig J, Abdel-Karim A, Iyer H, 2006. Simultaneous fiducial generalized confidence intervals for ratios of means of lognormal distribution.

[3] Hannig J, Lee T C M, 2009.Generalized fiducial inference for wavelet regression.

[4] Li X, Zhou X, Tian L, 2013. Interval estimation for the mean of lognormal data with excess zeros.

[5] Schaarschmidt F, 2013. Simultaneous confidence intervals for multiple comparisons among expected values of log-normal variables.

[6] Jing Xu, Xinmin Li, Hua Liang. Simultaneous Confidence Intervals for Ratios of Means of Log-normal Populations with Zeros.

Examples

## Not run: 
#====================GPQW================



alpha <- 0.05

p <- c(0.1,0.15,0.1)
n <- c(30,15,10)
mu <- c(1,1.3,2)
sigma <- c(1,1,1)
N <- 1000
GPQW(n,p,mu,sigma,N)

p <- c(0.1,0.15,0.1,0.6)
n <- c(30,15,10,50)
mu <- c(1,1.3,2,0)
sigma <- c(1,1,1,2)
C2 <- rbind(c(-1,1,0,0),c(-1,0,1,0),c(-1,0,0,1),c(0,-1,1,0),c(0,-1,0,1),c(0,0,-1,1))

N <- 1000;
GPQW(n,p,mu,sigma,N,C2 = C2)



#====================GPQH===============


alpha <- 0.05

p <- c(0.1,0.15,0.1)
n <- c(30,15,10)
mu <- c(1,1.3,2)
sigma <- c(1,1,1)
N <- 1000
GPQH(n,p,mu,sigma,N)
p <- c(0.1,0.15,0.1,0.6)
n <- c(30,15,10,50)
mu <- c(1,1.3,2,0)
sigma <- c(1,1,1,2)
C2 <- rbind(c(-1,1,0,0),c(-1,0,1,0),c(-1,0,0,1),c(0,-1,1,0),c(0,-1,0,1),c(0,0,-1,1))

N<-1000;
GPQH(n,p,mu,sigma,N,C2 = C2)



#====================FMW=================


alpha <- 0.05

p <- c(0.1,0.15,0.1)
n <- c(30,15,10)
mu <- c(1,1.3,2)
sigma <- c(1,1,1)
N <- 1000

FMW(n,p,mu,sigma,N)

p <- c(0.1,0.15,0.1,0.6)
n <- c(30,15,10,50)
mu <- c(1,1.3,2,0)
sigma <- c(1,1,1,2)
C2 <- rbind(c(-1,1,0,0),c(-1,0,1,0),c(-1,0,0,1),c(0,-1,1,0),c(0,-1,0,1),c(0,0,-1,1))
N <- 1000

FMW(n,p,mu,sigma,N,C2 = C2)


#====================FMWH================


alpha<-0.05

p <- c(0.1,0.15,0.1)
n <- c(30,15,10)
mu <- c(1,1.3,2)
sigma <- c(1,1,1)
N <- 1000
FMWH(n,p,mu,sigma,N)
p <- c(0.1,0.15,0.1,0.6)
n <- c(30,15,10,50)
mu <- c(1,1.3,2,0)
sigma <- c(1,1,1,2)
C2 <- rbind(c(-1,1,0,0),c(-1,0,1,0),c(-1,0,0,1),c(0,-1,1,0),c(0,-1,0,1),c(0,0,-1,1))

N <- 1000;
FMWH(n,p,mu,sigma,N,C2 = C2)

## End(Not run)

LN0SCIs documentation built on May 1, 2019, 7:05 p.m.