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 = '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 t
1 7.8 0 1
2 7.6 0 2
3 7.5 0 3
4 7.6 0 4
5 7.5 0 5
6 7.3 0 6
7 7.6 0 7
8 7.5 0 8
9 7.6 0 9
10 7.9 0 10
11 7.9 0 11
12 8.1 0 12
13 8.2 0 13
14 8.0 0 14
15 7.5 0 15
16 6.8 0 16
17 6.5 0 17
18 6.6 0 18
19 7.6 0 19
20 8.0 0 20
21 8.0 0 21
22 7.7 0 22
23 7.5 0 23
24 7.6 0 24
25 7.7 0 25
26 7.9 0 26
27 7.8 0 27
28 7.5 0 28
29 7.5 0 29
30 7.1 0 30
31 7.5 0 31
32 7.5 0 32
33 7.6 0 33
34 7.7 0 34
35 7.7 1 35
36 7.9 1 36
37 8.1 1 37
38 8.2 1 38
39 8.2 1 39
40 8.1 1 40
41 7.9 1 41
42 7.3 1 42
43 6.9 1 43
44 6.6 1 44
45 6.7 1 45
46 6.9 1 46
47 7.0 1 47
48 7.1 1 48
49 7.2 1 49
50 7.1 1 50
51 6.9 1 51
52 7.0 1 52
53 6.8 1 53
54 6.4 1 54
55 6.7 1 55
56 6.7 1 56
57 6.4 1 57
58 6.3 1 58
59 6.2 1 59
60 6.5 1 60
61 6.8 1 61
62 6.8 1 62
63 6.5 1 63
64 6.3 1 64
65 5.9 1 65
66 5.9 1 66
67 6.4 1 67
68 6.4 1 68
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x t
8.08971 0.34731 -0.02916
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.093992 -0.303298 0.006387 0.315315 0.900210
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.089706 0.124154 65.158 < 2e-16 ***
x 0.347311 0.218234 1.591 0.116
t -0.029160 0.005559 -5.245 1.82e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4498 on 65 degrees of freedom
Multiple R-squared: 0.4897, Adjusted R-squared: 0.474
F-statistic: 31.19 on 2 and 65 DF, p-value: 3.190e-10
> postscript(file="/var/www/html/freestat/rcomp/tmp/1qmiq1227555269.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/freestat/rcomp/tmp/2u9ik1227555269.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3tavw1227555269.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/440rl1227555269.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/54bl91227555269.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-0.26054622 -0.43138655 -0.50222689 -0.37306723 -0.44390756 -0.61474790
7 8 9 10 11 12
-0.28558824 -0.35642857 -0.22726891 0.10189076 0.13105042 0.36021008
13 14 15 16 17 18
0.48936975 0.31852941 -0.15231092 -0.82315126 -1.09399160 -0.96483193
19 20 21 22 23 24
0.06432773 0.49348739 0.52264706 0.25180672 0.08096639 0.21012605
25 26 27 28 29 30
0.33928571 0.56844538 0.49760504 0.22676471 0.25592437 -0.11491597
31 32 33 34 35 36
0.31424370 0.34340336 0.47256303 0.60172269 0.28357143 0.51273109
37 38 39 40 41 42
0.74189076 0.87105042 0.90021008 0.82936975 0.65852941 0.08768908
43 44 45 46 47 48
-0.28315126 -0.55399160 -0.42483193 -0.19567227 -0.06651261 0.06264706
49 50 51 52 53 54
0.19180672 0.12096639 -0.04987395 0.07928571 -0.09155462 -0.46239496
55 56 57 58 59 60
-0.13323529 -0.10407563 -0.37491597 -0.44575630 -0.51659664 -0.18743697
61 62 63 64 65 66
0.14172269 0.17088235 -0.09995798 -0.27079832 -0.64163866 -0.61247899
67 68
-0.08331933 -0.05415966
> postscript(file="/var/www/html/freestat/rcomp/tmp/6ckd41227555269.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 -0.26054622 NA
1 -0.43138655 -0.26054622
2 -0.50222689 -0.43138655
3 -0.37306723 -0.50222689
4 -0.44390756 -0.37306723
5 -0.61474790 -0.44390756
6 -0.28558824 -0.61474790
7 -0.35642857 -0.28558824
8 -0.22726891 -0.35642857
9 0.10189076 -0.22726891
10 0.13105042 0.10189076
11 0.36021008 0.13105042
12 0.48936975 0.36021008
13 0.31852941 0.48936975
14 -0.15231092 0.31852941
15 -0.82315126 -0.15231092
16 -1.09399160 -0.82315126
17 -0.96483193 -1.09399160
18 0.06432773 -0.96483193
19 0.49348739 0.06432773
20 0.52264706 0.49348739
21 0.25180672 0.52264706
22 0.08096639 0.25180672
23 0.21012605 0.08096639
24 0.33928571 0.21012605
25 0.56844538 0.33928571
26 0.49760504 0.56844538
27 0.22676471 0.49760504
28 0.25592437 0.22676471
29 -0.11491597 0.25592437
30 0.31424370 -0.11491597
31 0.34340336 0.31424370
32 0.47256303 0.34340336
33 0.60172269 0.47256303
34 0.28357143 0.60172269
35 0.51273109 0.28357143
36 0.74189076 0.51273109
37 0.87105042 0.74189076
38 0.90021008 0.87105042
39 0.82936975 0.90021008
40 0.65852941 0.82936975
41 0.08768908 0.65852941
42 -0.28315126 0.08768908
43 -0.55399160 -0.28315126
44 -0.42483193 -0.55399160
45 -0.19567227 -0.42483193
46 -0.06651261 -0.19567227
47 0.06264706 -0.06651261
48 0.19180672 0.06264706
49 0.12096639 0.19180672
50 -0.04987395 0.12096639
51 0.07928571 -0.04987395
52 -0.09155462 0.07928571
53 -0.46239496 -0.09155462
54 -0.13323529 -0.46239496
55 -0.10407563 -0.13323529
56 -0.37491597 -0.10407563
57 -0.44575630 -0.37491597
58 -0.51659664 -0.44575630
59 -0.18743697 -0.51659664
60 0.14172269 -0.18743697
61 0.17088235 0.14172269
62 -0.09995798 0.17088235
63 -0.27079832 -0.09995798
64 -0.64163866 -0.27079832
65 -0.61247899 -0.64163866
66 -0.08331933 -0.61247899
67 -0.05415966 -0.08331933
68 NA -0.05415966
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.43138655 -0.26054622
[2,] -0.50222689 -0.43138655
[3,] -0.37306723 -0.50222689
[4,] -0.44390756 -0.37306723
[5,] -0.61474790 -0.44390756
[6,] -0.28558824 -0.61474790
[7,] -0.35642857 -0.28558824
[8,] -0.22726891 -0.35642857
[9,] 0.10189076 -0.22726891
[10,] 0.13105042 0.10189076
[11,] 0.36021008 0.13105042
[12,] 0.48936975 0.36021008
[13,] 0.31852941 0.48936975
[14,] -0.15231092 0.31852941
[15,] -0.82315126 -0.15231092
[16,] -1.09399160 -0.82315126
[17,] -0.96483193 -1.09399160
[18,] 0.06432773 -0.96483193
[19,] 0.49348739 0.06432773
[20,] 0.52264706 0.49348739
[21,] 0.25180672 0.52264706
[22,] 0.08096639 0.25180672
[23,] 0.21012605 0.08096639
[24,] 0.33928571 0.21012605
[25,] 0.56844538 0.33928571
[26,] 0.49760504 0.56844538
[27,] 0.22676471 0.49760504
[28,] 0.25592437 0.22676471
[29,] -0.11491597 0.25592437
[30,] 0.31424370 -0.11491597
[31,] 0.34340336 0.31424370
[32,] 0.47256303 0.34340336
[33,] 0.60172269 0.47256303
[34,] 0.28357143 0.60172269
[35,] 0.51273109 0.28357143
[36,] 0.74189076 0.51273109
[37,] 0.87105042 0.74189076
[38,] 0.90021008 0.87105042
[39,] 0.82936975 0.90021008
[40,] 0.65852941 0.82936975
[41,] 0.08768908 0.65852941
[42,] -0.28315126 0.08768908
[43,] -0.55399160 -0.28315126
[44,] -0.42483193 -0.55399160
[45,] -0.19567227 -0.42483193
[46,] -0.06651261 -0.19567227
[47,] 0.06264706 -0.06651261
[48,] 0.19180672 0.06264706
[49,] 0.12096639 0.19180672
[50,] -0.04987395 0.12096639
[51,] 0.07928571 -0.04987395
[52,] -0.09155462 0.07928571
[53,] -0.46239496 -0.09155462
[54,] -0.13323529 -0.46239496
[55,] -0.10407563 -0.13323529
[56,] -0.37491597 -0.10407563
[57,] -0.44575630 -0.37491597
[58,] -0.51659664 -0.44575630
[59,] -0.18743697 -0.51659664
[60,] 0.14172269 -0.18743697
[61,] 0.17088235 0.14172269
[62,] -0.09995798 0.17088235
[63,] -0.27079832 -0.09995798
[64,] -0.64163866 -0.27079832
[65,] -0.61247899 -0.64163866
[66,] -0.08331933 -0.61247899
[67,] -0.05415966 -0.08331933
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.43138655 -0.26054622
2 -0.50222689 -0.43138655
3 -0.37306723 -0.50222689
4 -0.44390756 -0.37306723
5 -0.61474790 -0.44390756
6 -0.28558824 -0.61474790
7 -0.35642857 -0.28558824
8 -0.22726891 -0.35642857
9 0.10189076 -0.22726891
10 0.13105042 0.10189076
11 0.36021008 0.13105042
12 0.48936975 0.36021008
13 0.31852941 0.48936975
14 -0.15231092 0.31852941
15 -0.82315126 -0.15231092
16 -1.09399160 -0.82315126
17 -0.96483193 -1.09399160
18 0.06432773 -0.96483193
19 0.49348739 0.06432773
20 0.52264706 0.49348739
21 0.25180672 0.52264706
22 0.08096639 0.25180672
23 0.21012605 0.08096639
24 0.33928571 0.21012605
25 0.56844538 0.33928571
26 0.49760504 0.56844538
27 0.22676471 0.49760504
28 0.25592437 0.22676471
29 -0.11491597 0.25592437
30 0.31424370 -0.11491597
31 0.34340336 0.31424370
32 0.47256303 0.34340336
33 0.60172269 0.47256303
34 0.28357143 0.60172269
35 0.51273109 0.28357143
36 0.74189076 0.51273109
37 0.87105042 0.74189076
38 0.90021008 0.87105042
39 0.82936975 0.90021008
40 0.65852941 0.82936975
41 0.08768908 0.65852941
42 -0.28315126 0.08768908
43 -0.55399160 -0.28315126
44 -0.42483193 -0.55399160
45 -0.19567227 -0.42483193
46 -0.06651261 -0.19567227
47 0.06264706 -0.06651261
48 0.19180672 0.06264706
49 0.12096639 0.19180672
50 -0.04987395 0.12096639
51 0.07928571 -0.04987395
52 -0.09155462 0.07928571
53 -0.46239496 -0.09155462
54 -0.13323529 -0.46239496
55 -0.10407563 -0.13323529
56 -0.37491597 -0.10407563
57 -0.44575630 -0.37491597
58 -0.51659664 -0.44575630
59 -0.18743697 -0.51659664
60 0.14172269 -0.18743697
61 0.17088235 0.14172269
62 -0.09995798 0.17088235
63 -0.27079832 -0.09995798
64 -0.64163866 -0.27079832
65 -0.61247899 -0.64163866
66 -0.08331933 -0.61247899
67 -0.05415966 -0.08331933
> 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/freestat/rcomp/tmp/7foz51227555269.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8rdx41227555269.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9y0g31227555269.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/10grj81227555269.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/freestat/rcomp/tmp/11rcku1227555269.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/freestat/rcomp/tmp/12vd8q1227555269.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/freestat/rcomp/tmp/13y4781227555269.tab")
>
> system("convert tmp/1qmiq1227555269.ps tmp/1qmiq1227555269.png")
> system("convert tmp/2u9ik1227555269.ps tmp/2u9ik1227555269.png")
> system("convert tmp/3tavw1227555269.ps tmp/3tavw1227555269.png")
> system("convert tmp/440rl1227555269.ps tmp/440rl1227555269.png")
> system("convert tmp/54bl91227555269.ps tmp/54bl91227555269.png")
> system("convert tmp/6ckd41227555269.ps tmp/6ckd41227555269.png")
> system("convert tmp/7foz51227555269.ps tmp/7foz51227555269.png")
> system("convert tmp/8rdx41227555269.ps tmp/8rdx41227555269.png")
> system("convert tmp/9y0g31227555269.ps tmp/9y0g31227555269.png")
>
>
> proc.time()
user system elapsed
3.001 2.259 3.749