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20416 15100 13500 19900 23760 20000 20000 12858 20000 18560 16590 16240 26919 27700 20124 24152 23921 25513 25761 14813 23701 23701 23100 19508 24270 21960 21500 21500 17000 20826 20844 21060 17010 17342 17342 15957 19600 19600 17493 17800 19400 19400 15400 18900 18900 16580 16580 17800 18000 18520
minimum value of shape parameter
maximum value of shape parameter
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R Code
par2 <- '90' par1 <- '1' library(MASS) PPCCWeibull <- function(shape, scale, x) { x <- sort(x) pp <- ppoints(x) cor(qweibull(pp, shape=shape, scale=scale), x) } par1 <- as.numeric(par1) par2 <- as.numeric(par2) if (par1 < 0.1) par1 <- 0.1 if (par1 > 50) par1 <- 50 if (par2 < 0.1) par2 <- 0.1 if (par2 > 50) par2 <- 50 par1h <- par1*10 par2h <- par2*10 sortx <- sort(x) c <- array(NA,dim=c(par2h)) for (i in par1h:par2h) { c[i] <- cor(qweibull(ppoints(x), shape=i/10,scale=2),sortx) } bitmap(file='test1.png') plot((par1h:par2h)/10,c[par1h:par2h],xlab='shape',ylab='correlation',main='PPCC Plot - Weibull') dev.off() f<-fitdistr(x, 'weibull') f$estimate f$sd xlab <- paste('Weibull(shape=',round(f$estimate[[1]],2)) xlab <- paste(xlab,', scale=') xlab <- paste(xlab,round(f$estimate[[2]],2)) xlab <- paste(xlab,')') bitmap(file='test2.png') qqplot(qweibull(ppoints(x), shape=f$estimate[[1]], scale=f$estimate[[2]]), x, main='QQ plot (Weibull)', xlab=xlab ) grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Parameter',1,TRUE) a<-table.element(a,'Estimated Value',1,TRUE) a<-table.element(a,'Standard Deviation',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'shape',header=TRUE) a<-table.element(a,f$estimate[1]) a<-table.element(a,f$sd[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'scale',header=TRUE) a<-table.element(a,f$estimate[2]) a<-table.element(a,f$sd[2]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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