library(MASS) PPCCGamma <- function(shape, rate, x) { x <- sort(x) pp <- ppoints(x) cor(qgamma(pp, shape=shape, rate=rate), 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(qgamma(ppoints(x), shape=i/10,rate=2),sortx) } bitmap(file='test1.png') plot((par1h:par2h)/10,c[par1h:par2h],xlab='shape',ylab='correlation',main='PPCC Plot - Gamma') dev.off() f<-fitdistr(x, 'gamma') f$estimate f$sd xlab <- paste('Gamma(shape=',round(f$estimate[[1]],2)) xlab <- paste(xlab,', rate=') xlab <- paste(xlab,round(f$estimate[[2]],2)) xlab <- paste(xlab,')') bitmap(file='test2.png') qqplot(qgamma(ppoints(x), shape=f$estimate[[1]], rate=f$estimate[[2]]), x, main='QQ plot (Gamma)', 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,'rate',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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