R version 3.4.3 (2017-11-30) -- "Kite-Eating Tree"
Copyright (C) 2017 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> source('/home/pw/wessanet/cretab')
>
>
>
> myrfcuid = 'account3'
>
> x <- c(97.7,88.9,96.5,89.5,85.4,84.3,83.7,86.2,90.7,95.7,95.6,97,97.2,86.6,88.4,81.4,86.9,84.9,83.7,86.8,88.3,92.5,94.7,94.5,98.7,88.6,95.2,91.3,91.7,89.3,88.7,91.2,88.6,94.6,96,94.3,102,93.4,96.7,93.7,91.6,89.6,92.9,94.1,92,97.5,92.7,100.7,105.9,95.3,99.8,91.3,90.8,87.1,91.4,86.1,87.1,92.6,96.6,105.3,102.4,98.2,98.6,92.6,87.9,84.1,86.7,84.4,86,90.4,92.9,105.8,106,99.1,99.9,88.1,87.8,87.1,85.9,86.5,84.1,92.1,93.3,98.9,103,98.4,100.7,92.3,89,88.9,85.5,90.1,87,97.1,101.5,103,106.1,96.1,94.2,89.1,85.2,86.5,88,88.4,87.9,95.7,94.8,105.2,108.7,96.1,98.3,88.6,90.8,88.1,91.9,98.5,98.6,100.3,98.7,110.7,115.4,105.4,108,94.5,96.5,91,94.1,96.4,93.1,97.5,102.5,105.7,109.1,97.2,100.3,91.3,94.3,89.5,89.3,93.4,91.9,92.9,93.7,100.1,105.5,110.5,89.5,90.4,89.9,84.6,86.2,83.4,82.9,81.8,87.6,94.6,99.6,96.7,99.8,83.8,82.4,86.8,91,85.3,83.6,94,100.3,107.1,100.7,95.5,92.9,79.2,82,79.3,81.5,76,73.1,80.4,82.1,90.5,98.1,89.5,86.5,77,74.7,73.4,72.5,69.3,75.2,83.5,90.5,92.2,110.5,101.8,107.4,95.5,84.5,81.1,86.2,91.5,84.7,92.2,99.2,104.5,113,100.4,101,84.8,86.5,91.7,94.8,95)
> par9 = '1'
> par8 = '2'
> par7 = '1'
> par6 = '0'
> par5 = '12'
> par4 = '1'
> par3 = '0'
> par2 = '1'
> par1 = 'FALSE'
> par9 <- '1'
> par8 <- '2'
> par7 <- '1'
> par6 <- '0'
> par5 <- '12'
> par4 <- '1'
> par3 <- '0'
> par2 <- '1'
> par1 <- 'FALSE'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 (Sat, 22 Jul 2017 18:50:53 +0200)
> #Author: root
> #To cite this work: Wessa P., (2017), ARIMA Backward Selection (v1.0.6) in Free Statistics Software (v$_version), Office for Research Development and Education, URL https://www.wessa.net/rwasp_arimabackwardselection.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> if (par1 == 'TRUE') par1 <- TRUE
> if (par1 == 'FALSE') par1 <- FALSE
> par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter
> par3 <- as.numeric(par3) #degree of non-seasonal differencing
> par4 <- as.numeric(par4) #degree of seasonal differencing
> par5 <- as.numeric(par5) #seasonal period
> par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial
> par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial
> par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial
> par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial
> armaGR <- function(arima.out, names, n){
+ try1 <- arima.out$coef
+ try2 <- sqrt(diag(arima.out$var.coef))
+ try.data.frame  <- data.frame(matrix(NA,ncol=4,nrow=length(names)))
+ dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv'))
+ try.data.frame[,1] <- try1
+ for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i]
+ try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2]
+ try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5)
+ vector <- rep(NA,length(names))
+ vector[is.na(try.data.frame[,4])] <- 0
+ maxi <- which.max(try.data.frame[,4])
+ continue <- max(try.data.frame[,4],na.rm=TRUE) > .05
+ vector[maxi] <- 0
+ list(summary=try.data.frame,next.vector=vector,continue=continue)
+ }
> arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){
+ nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3]
+ coeff <- matrix(NA, nrow=nrc*2, ncol=nrc)
+ pval <- matrix(NA, nrow=nrc*2, ncol=nrc)
+ mylist <- rep(list(NULL), nrc)
+ names  <- NULL
+ if(order[1] > 0) names <- paste('ar',1:order[1],sep='')
+ if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') )
+ if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep=''))
+ if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep=''))
+ arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML')
+ mylist[[1]] <- arima.out
+ last.arma <- armaGR(arima.out, names, length(series))
+ mystop <- FALSE
+ i <- 1
+ coeff[i,] <- last.arma[[1]][,1]
+ pval [i,] <- last.arma[[1]][,4]
+ i <- 2
+ aic <- arima.out$aic
+ while(!mystop){
+ mylist[[i]] <- arima.out
+ arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector)
+ aic <- c(aic, arima.out$aic)
+ last.arma <- armaGR(arima.out, names, length(series))
+ mystop <- !last.arma$continue
+ coeff[i,] <- last.arma[[1]][,1]
+ pval [i,] <- last.arma[[1]][,4]
+ i <- i+1
+ }
+ list(coeff, pval, mylist, aic=aic)
+ }
> arimaSelectplot <- function(arimaSelect.out,noms,choix){
+ noms <- names(arimaSelect.out[[3]][[1]]$coef)
+ coeff <- arimaSelect.out[[1]]
+ k <- min(which(is.na(coeff[,1])))-1
+ coeff <- coeff[1:k,]
+ pval  <- arimaSelect.out[[2]][1:k,]
+ aic   <- arimaSelect.out$aic[1:k]
+ coeff[coeff==0] <- NA
+ n <- ncol(coeff)
+ if(missing(choix)) choix <- k
+ layout(matrix(c(1,1,1,2,
+ 3,3,3,2,
+ 3,3,3,4,
+ 5,6,7,7),nr=4),
+ widths=c(10,35,45,15),
+ heights=c(30,30,15,15))
+ couleurs <- rainbow(75)[1:50]#(50)
+ ticks <- pretty(coeff)
+ par(mar=c(1,1,3,1))
+ plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA)
+ points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA)
+ title('aic',line=2)
+ par(mar=c(3,0,0,0))
+ plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1))
+ rect(xleft  = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)),
+ xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)),
+ ytop   = rep(1,50),
+ ybottom= rep(0,50),col=couleurs,border=NA)
+ axis(1,ticks)
+ rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0)
+ text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2)
+ par(mar=c(1,1,3,1))
+ image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks))
+ for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) {
+ if(pval[j,i]<.01)                            symb = 'green'
+ else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange'
+ else if( (pval[j,i]<.1)  & (pval[j,i]>=.05)) symb = 'red'
+ else                                         symb = 'black'
+ polygon(c(i+.5   ,i+.2   ,i+.5   ,i+.5),
+ c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5),
+ col=symb)
+ if(j==choix)  {
+ rect(xleft=i-.5,
+ xright=i+.5,
+ ybottom=k-j+1.5,
+ ytop=k-j+.5,
+ lwd=4)
+ text(i,
+ k-j+1,
+ round(coeff[j,i],2),
+ cex=1.2,
+ font=2)
+ }
+ else{
+ rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5)
+ text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1)
+ }
+ }
+ axis(3,1:n,noms)
+ par(mar=c(0.5,0,0,0.5))
+ plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8))
+ cols <- c('green','orange','red','black')
+ niv  <- c('0','0.01','0.05','0.1')
+ for(i in 0:3){
+ polygon(c(1+2*i   ,1+2*i   ,1+2*i-.5   ,1+2*i),
+ c(.4      ,.7      , .4        , .4),
+ col=cols[i+1])
+ text(2*i,0.5,niv[i+1],cex=1.5)
+ }
+ text(8,.5,1,cex=1.5)
+ text(4,0,'p-value',cex=2)
+ box()
+ residus <- arimaSelect.out[[3]][[choix]]$res
+ par(mar=c(1,2,4,1))
+ acf(residus,main='')
+ title('acf',line=.5)
+ par(mar=c(1,2,4,1))
+ pacf(residus,main='')
+ title('pacf',line=.5)
+ par(mar=c(2,2,4,1))
+ qqnorm(residus,main='')
+ title('qq-norm',line=.5)
+ qqline(residus)
+ residus
+ }
> if (par2 == 0) x <- log(x)
> if (par2 != 0) x <- x^par2
> (selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))
[[1]]
          [,1]      [,2]        [,3]       [,4]
[1,] 0.5271932 0.3214937 -0.04917977 -0.9999098
[2,] 0.5281285 0.3141671  0.00000000 -1.0000011
[3,]        NA        NA          NA         NA
[4,]        NA        NA          NA         NA
[5,]        NA        NA          NA         NA
[6,]        NA        NA          NA         NA
[7,]        NA        NA          NA         NA
[8,]        NA        NA          NA         NA

[[2]]
     [,1]  [,2]    [,3] [,4]
[1,]    0 3e-05 0.52239    0
[2,]    0 3e-05      NA    0
[3,]   NA    NA      NA   NA
[4,]   NA    NA      NA   NA
[5,]   NA    NA      NA   NA
[6,]   NA    NA      NA   NA
[7,]   NA    NA      NA   NA
[8,]   NA    NA      NA   NA

[[3]]
[[3]][[1]]

Call:
arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean,
    method = "ML")

Coefficients:
         ma1    sar1     sar2     sma1
      0.5272  0.3215  -0.0492  -0.9999
s.e.  0.0513  0.0746   0.0768   0.1286

sigma^2 estimated as 18.85:  log likelihood = -591.83,  aic = 1193.66

[[3]][[2]]

Call:
arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean,
    method = "ML")

Coefficients:
         ma1    sar1     sar2     sma1
      0.5272  0.3215  -0.0492  -0.9999
s.e.  0.0513  0.0746   0.0768   0.1286

sigma^2 estimated as 18.85:  log likelihood = -591.83,  aic = 1193.66

[[3]][[3]]
NULL

[[3]][[4]]
NULL


$aic
[1] 1193.655 1192.063

Warning message:
In arima(series, order = order, seasonal = seasonal, include.mean = include.mean,  :
  some AR parameters were fixed: setting transform.pars = FALSE
> postscript(file="/home/pw/wessanet/rcomp/tmp/1nrx11516787106.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> resid <- arimaSelectplot(selection)
> dev.off()
null device
          1
> resid
Time Series:
Start = 1
End = 212
Frequency = 1
  [1]   0.09769990   0.08889983   0.09649983   0.08949983   0.08539984
  [6]   0.08429985   0.08369985   0.08619985   0.09069984   0.09569983
 [11]   0.09559983   0.09699985  -0.35705332  -1.64125592  -5.65398563
 [16]  -3.57369093   3.08909186  -1.14294096   0.60249929   0.16682206
 [21]  -2.02531352  -1.51546107   0.07248059  -2.12205930   2.19628766
 [26]  -0.06050797   3.64463341   4.33122994   2.20072806   2.74253724
 [31]   2.80660341   2.42310541  -1.66450297   1.80722045  -0.08791617
 [36]  -0.79054493   3.70734995   2.45145951   0.47152692   3.41122052
 [41]   0.18759141   1.87810106   4.50494119   1.90316778   1.52467515
 [46]   1.72575669  -3.58861425   6.96148170   1.69928771   3.04405183
 [51]   2.48606121  -0.80739690   1.53657589  -1.38161898   2.77598793
 [56]  -5.93296184  -0.26388905  -3.02535675   4.00125759   4.32801940
 [61]  -2.04085541   6.68357992  -2.06004642   3.52275467  -3.36850476
 [66]  -0.71147923  -1.61039661  -1.94921198  -1.21319021  -2.44756150
 [71]  -1.41356428   5.47243064   1.89600852   3.88035094   0.92232584
 [76]  -3.08706585   1.06860614   0.88141368  -1.57557833   0.52793991
 [81]  -3.86831653   1.40765149  -1.40354501  -1.55576678   0.91490085
 [86]   2.99711419   1.32395379   2.39203609  -0.72625990   2.29567767
 [91]  -2.53527156   3.74269941  -1.86879169   4.51712031   4.45668746
 [96]   1.59689204   3.26315869  -0.54307710  -3.46548933   0.05985015
[101]  -3.52257937   0.87933063   0.80826796  -0.89190033   0.44399432
[106]   0.14129801  -2.80826063   5.52761466   2.17891837   0.58161394
[111]   2.24179848  -2.07361858   4.49698866  -0.91011926   4.55376469
[116]   7.48120524   6.07660729   2.17515797   2.53634277   7.09052860
[121]   6.64550875   6.85412406   6.41334363   1.45139613   5.88118897
[126]   0.39627652   4.51978150   1.41617889  -0.13404129   0.80784032
[131]   5.04424629  -1.33622252   2.23050899  -2.41247948   0.28094742
[136]  -0.62988374   2.82350012  -0.53944736  -0.47863390   1.98286681
[141]   0.56675150  -2.95578003  -2.85657691  -0.84387697   0.65771663
[146]  14.28829710 -15.99961838   8.39265013  -5.40811611  -0.38760588
[151]  -1.89488354  -6.19979746  -3.79506067  -9.84732540  -1.85489548
[156]  -5.02361466  -2.03333959  -3.16576613   6.72791512  -9.82848702
[161]  -1.71062398   1.46570355   2.64418036  -3.28738354  -1.41781549
[166]   4.74181775   4.68722307   5.46484582  -4.51374512   2.18551107
[171]  -6.82074462  -4.71034867  -2.21582759  -6.47891425  -4.22546887
[176]  -9.60477239  -8.68320806  -9.17323004 -10.30173337  -7.38105100
[181]  -0.79687782  -5.86095189  -5.79007970  -5.75147520  -8.68514524
[186]  -5.87071349  -9.60690856  -9.53894276  -2.52198658  -3.74119764
[191]   2.19669722  -5.76158272  11.52826554   1.77267041  12.67252228
[196]   3.46546573  -1.08046962  -0.30611640   3.86481926   7.42235827
[201]  -2.95271703   3.61466876   3.26319794   4.58485704   4.04330872
[206]  -0.21210904  -0.10139253  -6.66116676   2.74832705   5.34508005
[211]   4.33180588   2.79468097
> postscript(file="/home/pw/wessanet/rcomp/tmp/2ksm61516787106.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(resid,length(resid)/2, main='Residual Autocorrelation Function')
> dev.off()
null device
          1
> postscript(file="/home/pw/wessanet/rcomp/tmp/3u2i81516787106.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')
> dev.off()
null device
          1
> postscript(file="/home/pw/wessanet/rcomp/tmp/4xis51516787106.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> cpgram(resid, main='Residual Cumulative Periodogram')
> dev.off()
null device
          1
> postscript(file="/home/pw/wessanet/rcomp/tmp/5o0ao1516787106.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(resid, main='Residual Histogram', xlab='values of Residuals')
> dev.off()
null device
          1
> postscript(file="/home/pw/wessanet/rcomp/tmp/658821516787106.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
          1
> postscript(file="/home/pw/wessanet/rcomp/tmp/7483o1516787106.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(resid, main='Residual Normal Q-Q Plot')
> qqline(resid)
> dev.off()
null device
          1
> ncols <- length(selection[[1]][1,])
> nrows <- length(selection[[2]][,1])-1
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Iteration', header=TRUE)
> for (i in 1:ncols) {
+ a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE)
+ }
> a<-table.row.end(a)
> for (j in 1:nrows) {
+ a<-table.row.start(a)
+ mydum <- 'Estimates ('
+ mydum <- paste(mydum,j)
+ mydum <- paste(mydum,')')
+ a<-table.element(a,mydum, header=TRUE)
+ for (i in 1:ncols) {
+ a<-table.element(a,round(selection[[1]][j,i],4))
+ }
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'(p-val)', header=TRUE)
+ for (i in 1:ncols) {
+ mydum <- '('
+ mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='')
+ mydum <- paste(mydum,')')
+ a<-table.element(a,mydum)
+ }
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/home/pw/wessanet/rcomp/tmp/8h2o11516787106.tab")
> a <-table.start()
> a <- table.row.start(a)
> a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <- table.element(a,'Description',1,TRUE)
> a <- table.element(a,'Link',1,TRUE)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Histogram',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Central Tendency',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'QQ Plot',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Spectral Analysis',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a <- table.row.start(a)
> a <- table.element(a,'Summary Statistics',1,header=TRUE)
> a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
> a <- table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/home/pw/wessanet/rcomp/tmp/9eshs1516787106.tab")
>
> try(system("convert /home/pw/wessanet/rcomp/tmp/1nrx11516787106.ps /home/pw/wessanet/rcomp/tmp/1nrx11516787106.png",intern=TRUE))
character(0)
> try(system("convert /home/pw/wessanet/rcomp/tmp/2ksm61516787106.ps /home/pw/wessanet/rcomp/tmp/2ksm61516787106.png",intern=TRUE))
character(0)
> try(system("convert /home/pw/wessanet/rcomp/tmp/3u2i81516787106.ps /home/pw/wessanet/rcomp/tmp/3u2i81516787106.png",intern=TRUE))
character(0)
> try(system("convert /home/pw/wessanet/rcomp/tmp/4xis51516787106.ps /home/pw/wessanet/rcomp/tmp/4xis51516787106.png",intern=TRUE))
character(0)
> try(system("convert /home/pw/wessanet/rcomp/tmp/5o0ao1516787106.ps /home/pw/wessanet/rcomp/tmp/5o0ao1516787106.png",intern=TRUE))
character(0)
> try(system("convert /home/pw/wessanet/rcomp/tmp/658821516787106.ps /home/pw/wessanet/rcomp/tmp/658821516787106.png",intern=TRUE))
character(0)
> try(system("convert /home/pw/wessanet/rcomp/tmp/7483o1516787106.ps /home/pw/wessanet/rcomp/tmp/7483o1516787106.png",intern=TRUE))
character(0)
>
> proc.time()
   user  system elapsed
  9.728   0.956  11.699