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Number of values (?)


Mean (?)


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R Code
library(MASS) par1 <- as.numeric(par1) par2 <- as.numeric(par2) par2 <- round(par2,2) #rounded (we want to be able to display 10 columns) par3 <- as.numeric(par3) par3 <- round(par3,2) #rounded (we want to be able to display 10 columns) par4 <- as.numeric(par4) if (par6 == '0') par6 = 'Sturges' else par6 <- as.numeric(par6) par7 <- as.numeric(par7) x <- array(NA,dim=c(par7,par1)) rest.mean <- array(NA,dim=c(par7)) rest.sd <- array(NA,dim=c(par7)) rsd.mean <- array(NA,dim=c(par7)) rsd.sd <- array(NA,dim=c(par7)) for (i in 1:par7) { x[i,] <- rlnorm(par1,par2,par3) x[i,] <- as.ts(x[i,]) #otherwise the fitdistr function does not work properly dum <- fitdistr(x[i,],'log-normal') rest.mean[i] <- dum$estimate[1] rest.sd[i] <- dum$estimate[2] rsd.mean[i] <- dum$sd[1] rsd.sd[i] <- dum$sd[2] } nc <- par7 if (nc > 10) nc = 10 if (par5 == 'Y') { load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Index',1,TRUE) for (j in 1:nc) { a<-table.element(a,paste('X',j),1,TRUE) } a<-table.row.end(a) if (nc < par7) { a<-table.row.start(a) a<-table.element(a,'Note: only the first 10 series are displayed',nc+1,TRUE) a<-table.row.end(a) } for (i in 1:par1) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) for (j in 1:nc) { a<-table.element(a,round(x[j,i],2)) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab') } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Parameter',1,TRUE) for (j in 1:nc) { a<-table.element(a,paste('X',j,sep=''),1,TRUE) } a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',1,TRUE) for (j in 1:nc) { a<-table.element(a,'(SD)',1,TRUE) } a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'# simulated values',header=TRUE) for (j in 1:nc) { a<-table.element(a,par1) } a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'true mean',header=TRUE) for (j in 1:nc) { a<-table.element(a,par2) } a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'true standard deviation',header=TRUE) for (j in 1:nc) { a<-table.element(a,par3) } a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'mean',header=TRUE) for (j in 1:nc) { a<-table.element(a,round(rest.mean[j],2)) } a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (j in 1:nc) { a<-table.element(a,round(rsd.mean[j],2)) } a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'standard deviation',header=TRUE) for (j in 1:nc) { a<-table.element(a,round(rest.sd[j],2)) } a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (j in 1:nc) { a<-table.element(a,round(rsd.sd[j],2)) } a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab') bitmap(file='test0.png') myhist<-hist(x[1,],col=par4,breaks=par6,main='Histogram of 1st simulated series',ylab='density',xlab='simulated values',freq=F) dev.off() bitmap(file='test1.png') myhist<-hist(rest.mean[],col=par4,breaks=par6,main='Histogram of Estimated Means',ylab='density',xlab='estimated means',freq=F) x <- rest.mean[] dummean <- mean(x) dumsd <- sd(x) curve(1/(dumsdsqrt(2pi))exp(-1/2((x-dummean)/dumsd)^2),min(x),max(x),add=T) dev.off() bitmap(file='test2.png') myhist<-hist(rest.sd[],col=par4,breaks=par6,main='Histogram of Estimated SDs',ylab='density',xlab='estimated standard deviations',freq=F) x <- rest.sd[] dummean <- mean(x) dumsd <- sd(x) curve(1/(dumsdsqrt(2pi))exp(-1/2((x-dummean)/dumsd)^2),min(x),max(x),add=T) dev.off()

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	Big Analytics Cloud Computing Center