bivgeom-package {bivgeom} | R Documentation |
Implements Roy's bivariate geometric model (Roy (1993) <doi:10.1006/jmva.1993.1065>): joint probability mass function, distribution function, survival function, random generation, parameter estimation, and more.
The DESCRIPTION file:
Package: | bivgeom |
Type: | Package |
Title: | Roy's Bivariate Geometric Distribution |
Version: | 1.0 |
Date: | 2018-10-17 |
Author: | Alessandro Barbiero |
Maintainer: | Alessandro Barbiero <alessandro.barbiero@unimi.it> |
Imports: | methods, stats, utils, bbmle, copula |
Description: | Implements Roy's bivariate geometric model (Roy (1993) <doi:10.1006/jmva.1993.1065>): joint probability mass function, distribution function, survival function, random generation, parameter estimation, and more. |
License: | GPL |
NeedsCompilation: | no |
Packaged: | 2018-10-16 12:34:47 UTC; Barbiero |
Index of help topics:
EyxbivgeomRoy Conditional moment FbivgeomRoy Joint distribution function FyxbivgeomRoy Conditional distribution RelbivgeomRoy Reliability parameter S.n Empirical joint survival function SbivgeomRoy Joint survival function bivgeom-package Roy's Bivariate Geometric Distribution corbivgeomRoy Linear correlation dbivgeomRoy Joint probability mass function estbivgeomRoy Parameter estimation lambda1Roy Bivariate failure rates lambda2Roy Bivariate failure rate loglikgeomRoy Log-likelihood function minuslogRoy Log-likelihood function rbivgeomRoy Pseudo-random generation
Alessandro Barbiero
Maintainer: Alessandro Barbiero (alessandro.barbiero@unimi.it)
Roy, D. (1993) Reliability measures in the discrete bivariate set-up and related characterization results for a bivariate geometric distribution, Journal of Multivariate Analysis 46(2), 362-373.
Barbiero, A. (2018) Properties and estimation of a bivariate geometric model with locally constant failure rates, submitted
dbivgeomRoy
, rbivgeomRoy
, estbivgeomRoy
, FbivgeomRoy
##################################### #### MONTE CARLO SIMULATION PLAN #### ##################################### # setting the parameters' values theta1 <- 0.3 theta2 <- 0.7 theta3 <- 0.6 N <- 20 # number of Monte Carlo runs n <- 100 # sample size # arranging the array containig the simulation results # N runs, 7 methods, 3 estimates h <- array(0,c(N,7,3)) # setting the seed set.seed(12345) # function for handling missing values # when computing the mean and standard deviation of the estimates: meanrm <- function(x){mean(x,na.rm=TRUE)} sdrm <- function(x){sd(x,na.rm=TRUE)} colnames <- c("ML","MMP","MM1","MM2","MM3","MM4","LS") dimnames(h)[[2]] <- colnames # Monte Carlo simulation: for(i in 1:N) { d <- rbivgeomRoy(n,theta1,theta2,theta3) cat("MC run #",i,"\n") x<-d[,1] y<-d[,2] # implementing all the estimation methods # and saving the point estimates in the array h[i,1,] <- estbivgeomRoy(x, y, "ML") h[i,2,] <- estbivgeomRoy(x, y, "MMP") h[i,3,] <- estbivgeomRoy(x, y, "MM1") h[i,4,] <- estbivgeomRoy(x, y, "MM2") h[i,5,] <- estbivgeomRoy(x, y, "MM3") h[i,6,] <- estbivgeomRoy(x, y, "MM4") h[i,7,] <- estbivgeomRoy(x, y, "LS") } # printing MC expected values and standard errors # for each of the proposed estimation methods cat("hattheta1:","\n") cbind(mean=apply(h,c(2,3),meanrm)[,1],se=apply(h,c(2,3),sdrm)[,1]) cat("hattheta2:","\n") cbind(mean=apply(h,c(2,3),meanrm)[,2],se=apply(h,c(2,3),sdrm)[,2]) cat("hattheta3:","\n") cbind(mean=apply(h,c(2,3),meanrm)[,3],se=apply(h,c(2,3),sdrm)[,3]) # boxplots of MC distribution of the estimators of theta3 boxplot(h[,,3]) abline(h=theta3, lty=3)