R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(7.8,0,7.6,0,7.5,0,7.6,0,7.5,0,7.3,0,7.6,0,7.5,0,7.6,0,7.9,0,7.9,0,8.1,0,8.2,0,8.0,0,7.5,0,6.8,0,6.5,0,6.6,0,7.6,0,8.0,0,8.0,0,7.7,0,7.5,0,7.6,0,7.7,0,7.9,0,7.8,0,7.5,0,7.5,0,7.1,0,7.5,0,7.5,0,7.6,0,7.7,0,7.7,1,7.9,1,8.1,1,8.2,1,8.2,1,8.1,1,7.9,1,7.3,1,6.9,1,6.6,1,6.7,1,6.9,1,7.0,1,7.1,1,7.2,1,7.1,1,6.9,1,7.0,1,6.8,1,6.4,1,6.7,1,6.7,1,6.4,1,6.3,1,6.2,1,6.5,1,6.8,1,6.8,1,6.5,1,6.3,1,5.9,1,5.9,1,6.4,1,6.4,1),dim=c(2,68),dimnames=list(c('y','x'),1:68))
> y <- array(NA,dim=c(2,68),dimnames=list(c('y','x'),1:68))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '0'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
y x
1 7.8 0
2 7.6 0
3 7.5 0
4 7.6 0
5 7.5 0
6 7.3 0
7 7.6 0
8 7.5 0
9 7.6 0
10 7.9 0
11 7.9 0
12 8.1 0
13 8.2 0
14 8.0 0
15 7.5 0
16 6.8 0
17 6.5 0
18 6.6 0
19 7.6 0
20 8.0 0
21 8.0 0
22 7.7 0
23 7.5 0
24 7.6 0
25 7.7 0
26 7.9 0
27 7.8 0
28 7.5 0
29 7.5 0
30 7.1 0
31 7.5 0
32 7.5 0
33 7.6 0
34 7.7 0
35 7.7 1
36 7.9 1
37 8.1 1
38 8.2 1
39 8.2 1
40 8.1 1
41 7.9 1
42 7.3 1
43 6.9 1
44 6.6 1
45 6.7 1
46 6.9 1
47 7.0 1
48 7.1 1
49 7.2 1
50 7.1 1
51 6.9 1
52 7.0 1
53 6.8 1
54 6.4 1
55 6.7 1
56 6.7 1
57 6.4 1
58 6.3 1
59 6.2 1
60 6.5 1
61 6.8 1
62 6.8 1
63 6.5 1
64 6.3 1
65 5.9 1
66 5.9 1
67 6.4 1
68 6.4 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x
7.5794 -0.6441
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.07941 -0.24632 -0.03529 0.23162 1.26471
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.57941 0.09132 82.999 < 2e-16 ***
x -0.64412 0.12915 -4.988 4.7e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5325 on 66 degrees of freedom
Multiple R-squared: 0.2737, Adjusted R-squared: 0.2627
F-statistic: 24.88 on 1 and 66 DF, p-value: 4.704e-06
> postscript(file="/var/www/html/rcomp/tmp/1bmrp1227554922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ug5k1227554922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3p6fb1227554922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4nw7v1227554922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5q2gh1227554922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 68
Frequency = 1
1 2 3 4 5 6
0.22058824 0.02058824 -0.07941176 0.02058824 -0.07941176 -0.27941176
7 8 9 10 11 12
0.02058824 -0.07941176 0.02058824 0.32058824 0.32058824 0.52058824
13 14 15 16 17 18
0.62058824 0.42058824 -0.07941176 -0.77941176 -1.07941176 -0.97941176
19 20 21 22 23 24
0.02058824 0.42058824 0.42058824 0.12058824 -0.07941176 0.02058824
25 26 27 28 29 30
0.12058824 0.32058824 0.22058824 -0.07941176 -0.07941176 -0.47941176
31 32 33 34 35 36
-0.07941176 -0.07941176 0.02058824 0.12058824 0.76470588 0.96470588
37 38 39 40 41 42
1.16470588 1.26470588 1.26470588 1.16470588 0.96470588 0.36470588
43 44 45 46 47 48
-0.03529412 -0.33529412 -0.23529412 -0.03529412 0.06470588 0.16470588
49 50 51 52 53 54
0.26470588 0.16470588 -0.03529412 0.06470588 -0.13529412 -0.53529412
55 56 57 58 59 60
-0.23529412 -0.23529412 -0.53529412 -0.63529412 -0.73529412 -0.43529412
61 62 63 64 65 66
-0.13529412 -0.13529412 -0.43529412 -0.63529412 -1.03529412 -1.03529412
67 68
-0.53529412 -0.53529412
> postscript(file="/var/www/html/rcomp/tmp/68il11227554922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 68
Frequency = 1
lag(myerror, k = 1) myerror
0 0.22058824 NA
1 0.02058824 0.22058824
2 -0.07941176 0.02058824
3 0.02058824 -0.07941176
4 -0.07941176 0.02058824
5 -0.27941176 -0.07941176
6 0.02058824 -0.27941176
7 -0.07941176 0.02058824
8 0.02058824 -0.07941176
9 0.32058824 0.02058824
10 0.32058824 0.32058824
11 0.52058824 0.32058824
12 0.62058824 0.52058824
13 0.42058824 0.62058824
14 -0.07941176 0.42058824
15 -0.77941176 -0.07941176
16 -1.07941176 -0.77941176
17 -0.97941176 -1.07941176
18 0.02058824 -0.97941176
19 0.42058824 0.02058824
20 0.42058824 0.42058824
21 0.12058824 0.42058824
22 -0.07941176 0.12058824
23 0.02058824 -0.07941176
24 0.12058824 0.02058824
25 0.32058824 0.12058824
26 0.22058824 0.32058824
27 -0.07941176 0.22058824
28 -0.07941176 -0.07941176
29 -0.47941176 -0.07941176
30 -0.07941176 -0.47941176
31 -0.07941176 -0.07941176
32 0.02058824 -0.07941176
33 0.12058824 0.02058824
34 0.76470588 0.12058824
35 0.96470588 0.76470588
36 1.16470588 0.96470588
37 1.26470588 1.16470588
38 1.26470588 1.26470588
39 1.16470588 1.26470588
40 0.96470588 1.16470588
41 0.36470588 0.96470588
42 -0.03529412 0.36470588
43 -0.33529412 -0.03529412
44 -0.23529412 -0.33529412
45 -0.03529412 -0.23529412
46 0.06470588 -0.03529412
47 0.16470588 0.06470588
48 0.26470588 0.16470588
49 0.16470588 0.26470588
50 -0.03529412 0.16470588
51 0.06470588 -0.03529412
52 -0.13529412 0.06470588
53 -0.53529412 -0.13529412
54 -0.23529412 -0.53529412
55 -0.23529412 -0.23529412
56 -0.53529412 -0.23529412
57 -0.63529412 -0.53529412
58 -0.73529412 -0.63529412
59 -0.43529412 -0.73529412
60 -0.13529412 -0.43529412
61 -0.13529412 -0.13529412
62 -0.43529412 -0.13529412
63 -0.63529412 -0.43529412
64 -1.03529412 -0.63529412
65 -1.03529412 -1.03529412
66 -0.53529412 -1.03529412
67 -0.53529412 -0.53529412
68 NA -0.53529412
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.02058824 0.22058824
[2,] -0.07941176 0.02058824
[3,] 0.02058824 -0.07941176
[4,] -0.07941176 0.02058824
[5,] -0.27941176 -0.07941176
[6,] 0.02058824 -0.27941176
[7,] -0.07941176 0.02058824
[8,] 0.02058824 -0.07941176
[9,] 0.32058824 0.02058824
[10,] 0.32058824 0.32058824
[11,] 0.52058824 0.32058824
[12,] 0.62058824 0.52058824
[13,] 0.42058824 0.62058824
[14,] -0.07941176 0.42058824
[15,] -0.77941176 -0.07941176
[16,] -1.07941176 -0.77941176
[17,] -0.97941176 -1.07941176
[18,] 0.02058824 -0.97941176
[19,] 0.42058824 0.02058824
[20,] 0.42058824 0.42058824
[21,] 0.12058824 0.42058824
[22,] -0.07941176 0.12058824
[23,] 0.02058824 -0.07941176
[24,] 0.12058824 0.02058824
[25,] 0.32058824 0.12058824
[26,] 0.22058824 0.32058824
[27,] -0.07941176 0.22058824
[28,] -0.07941176 -0.07941176
[29,] -0.47941176 -0.07941176
[30,] -0.07941176 -0.47941176
[31,] -0.07941176 -0.07941176
[32,] 0.02058824 -0.07941176
[33,] 0.12058824 0.02058824
[34,] 0.76470588 0.12058824
[35,] 0.96470588 0.76470588
[36,] 1.16470588 0.96470588
[37,] 1.26470588 1.16470588
[38,] 1.26470588 1.26470588
[39,] 1.16470588 1.26470588
[40,] 0.96470588 1.16470588
[41,] 0.36470588 0.96470588
[42,] -0.03529412 0.36470588
[43,] -0.33529412 -0.03529412
[44,] -0.23529412 -0.33529412
[45,] -0.03529412 -0.23529412
[46,] 0.06470588 -0.03529412
[47,] 0.16470588 0.06470588
[48,] 0.26470588 0.16470588
[49,] 0.16470588 0.26470588
[50,] -0.03529412 0.16470588
[51,] 0.06470588 -0.03529412
[52,] -0.13529412 0.06470588
[53,] -0.53529412 -0.13529412
[54,] -0.23529412 -0.53529412
[55,] -0.23529412 -0.23529412
[56,] -0.53529412 -0.23529412
[57,] -0.63529412 -0.53529412
[58,] -0.73529412 -0.63529412
[59,] -0.43529412 -0.73529412
[60,] -0.13529412 -0.43529412
[61,] -0.13529412 -0.13529412
[62,] -0.43529412 -0.13529412
[63,] -0.63529412 -0.43529412
[64,] -1.03529412 -0.63529412
[65,] -1.03529412 -1.03529412
[66,] -0.53529412 -1.03529412
[67,] -0.53529412 -0.53529412
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.02058824 0.22058824
2 -0.07941176 0.02058824
3 0.02058824 -0.07941176
4 -0.07941176 0.02058824
5 -0.27941176 -0.07941176
6 0.02058824 -0.27941176
7 -0.07941176 0.02058824
8 0.02058824 -0.07941176
9 0.32058824 0.02058824
10 0.32058824 0.32058824
11 0.52058824 0.32058824
12 0.62058824 0.52058824
13 0.42058824 0.62058824
14 -0.07941176 0.42058824
15 -0.77941176 -0.07941176
16 -1.07941176 -0.77941176
17 -0.97941176 -1.07941176
18 0.02058824 -0.97941176
19 0.42058824 0.02058824
20 0.42058824 0.42058824
21 0.12058824 0.42058824
22 -0.07941176 0.12058824
23 0.02058824 -0.07941176
24 0.12058824 0.02058824
25 0.32058824 0.12058824
26 0.22058824 0.32058824
27 -0.07941176 0.22058824
28 -0.07941176 -0.07941176
29 -0.47941176 -0.07941176
30 -0.07941176 -0.47941176
31 -0.07941176 -0.07941176
32 0.02058824 -0.07941176
33 0.12058824 0.02058824
34 0.76470588 0.12058824
35 0.96470588 0.76470588
36 1.16470588 0.96470588
37 1.26470588 1.16470588
38 1.26470588 1.26470588
39 1.16470588 1.26470588
40 0.96470588 1.16470588
41 0.36470588 0.96470588
42 -0.03529412 0.36470588
43 -0.33529412 -0.03529412
44 -0.23529412 -0.33529412
45 -0.03529412 -0.23529412
46 0.06470588 -0.03529412
47 0.16470588 0.06470588
48 0.26470588 0.16470588
49 0.16470588 0.26470588
50 -0.03529412 0.16470588
51 0.06470588 -0.03529412
52 -0.13529412 0.06470588
53 -0.53529412 -0.13529412
54 -0.23529412 -0.53529412
55 -0.23529412 -0.23529412
56 -0.53529412 -0.23529412
57 -0.63529412 -0.53529412
58 -0.73529412 -0.63529412
59 -0.43529412 -0.73529412
60 -0.13529412 -0.43529412
61 -0.13529412 -0.13529412
62 -0.43529412 -0.13529412
63 -0.63529412 -0.43529412
64 -1.03529412 -0.63529412
65 -1.03529412 -1.03529412
66 -0.53529412 -1.03529412
67 -0.53529412 -0.53529412
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/765vw1227554922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8zkht1227554922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9xd3q1227554922.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
hat values (leverages) are all = 0.02941176
and there are no factor predictors; no plot no. 5
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/10us5p1227554922.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11xda81227554922.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12kuny1227554922.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13edrc1227554922.tab")
>
> system("convert tmp/1bmrp1227554922.ps tmp/1bmrp1227554922.png")
> system("convert tmp/2ug5k1227554922.ps tmp/2ug5k1227554922.png")
> system("convert tmp/3p6fb1227554922.ps tmp/3p6fb1227554922.png")
> system("convert tmp/4nw7v1227554922.ps tmp/4nw7v1227554922.png")
> system("convert tmp/5q2gh1227554922.ps tmp/5q2gh1227554922.png")
> system("convert tmp/68il11227554922.ps tmp/68il11227554922.png")
> system("convert tmp/765vw1227554922.ps tmp/765vw1227554922.png")
> system("convert tmp/8zkht1227554922.ps tmp/8zkht1227554922.png")
> system("convert tmp/9xd3q1227554922.ps tmp/9xd3q1227554922.png")
>
>
> proc.time()
user system elapsed
1.898 1.382 2.267