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(2490,0,3266,0,3475,0,3127,0,2955,0,3870,0,2852,0,3142,0,3029,0,3180,0,2560,0,2733,0,2452,0,2553,0,2777,0,2520,0,2318,0,2873,0,2311,0,2395,0,2099,0,2268,0,2316,0,2181,0,2175,0,2627,0,2578,0,3090,0,2634,0,3225,0,2938,0,3174,0,3350,0,2588,0,2061,0,2691,0,2061,0,2918,0,2223,0,2651,0,2379,0,3146,0,2883,0,2768,0,3258,0,2839,0,2470,0,5072,1,1463,1,1600,1,2203,1,2013,1,2169,1,2640,1,2411,1,2528,1,2292,1,1988,1,1774,1,2279,1),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> 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 = 'Include Monthly Dummies'
> par1 = '1'
> #'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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 2490 0 1 0 0 0 0 0 0 0 0 0 0
2 3266 0 0 1 0 0 0 0 0 0 0 0 0
3 3475 0 0 0 1 0 0 0 0 0 0 0 0
4 3127 0 0 0 0 1 0 0 0 0 0 0 0
5 2955 0 0 0 0 0 1 0 0 0 0 0 0
6 3870 0 0 0 0 0 0 1 0 0 0 0 0
7 2852 0 0 0 0 0 0 0 1 0 0 0 0
8 3142 0 0 0 0 0 0 0 0 1 0 0 0
9 3029 0 0 0 0 0 0 0 0 0 1 0 0
10 3180 0 0 0 0 0 0 0 0 0 0 1 0
11 2560 0 0 0 0 0 0 0 0 0 0 0 1
12 2733 0 0 0 0 0 0 0 0 0 0 0 0
13 2452 0 1 0 0 0 0 0 0 0 0 0 0
14 2553 0 0 1 0 0 0 0 0 0 0 0 0
15 2777 0 0 0 1 0 0 0 0 0 0 0 0
16 2520 0 0 0 0 1 0 0 0 0 0 0 0
17 2318 0 0 0 0 0 1 0 0 0 0 0 0
18 2873 0 0 0 0 0 0 1 0 0 0 0 0
19 2311 0 0 0 0 0 0 0 1 0 0 0 0
20 2395 0 0 0 0 0 0 0 0 1 0 0 0
21 2099 0 0 0 0 0 0 0 0 0 1 0 0
22 2268 0 0 0 0 0 0 0 0 0 0 1 0
23 2316 0 0 0 0 0 0 0 0 0 0 0 1
24 2181 0 0 0 0 0 0 0 0 0 0 0 0
25 2175 0 1 0 0 0 0 0 0 0 0 0 0
26 2627 0 0 1 0 0 0 0 0 0 0 0 0
27 2578 0 0 0 1 0 0 0 0 0 0 0 0
28 3090 0 0 0 0 1 0 0 0 0 0 0 0
29 2634 0 0 0 0 0 1 0 0 0 0 0 0
30 3225 0 0 0 0 0 0 1 0 0 0 0 0
31 2938 0 0 0 0 0 0 0 1 0 0 0 0
32 3174 0 0 0 0 0 0 0 0 1 0 0 0
33 3350 0 0 0 0 0 0 0 0 0 1 0 0
34 2588 0 0 0 0 0 0 0 0 0 0 1 0
35 2061 0 0 0 0 0 0 0 0 0 0 0 1
36 2691 0 0 0 0 0 0 0 0 0 0 0 0
37 2061 0 1 0 0 0 0 0 0 0 0 0 0
38 2918 0 0 1 0 0 0 0 0 0 0 0 0
39 2223 0 0 0 1 0 0 0 0 0 0 0 0
40 2651 0 0 0 0 1 0 0 0 0 0 0 0
41 2379 0 0 0 0 0 1 0 0 0 0 0 0
42 3146 0 0 0 0 0 0 1 0 0 0 0 0
43 2883 0 0 0 0 0 0 0 1 0 0 0 0
44 2768 0 0 0 0 0 0 0 0 1 0 0 0
45 3258 0 0 0 0 0 0 0 0 0 1 0 0
46 2839 0 0 0 0 0 0 0 0 0 0 1 0
47 2470 0 0 0 0 0 0 0 0 0 0 0 1
48 5072 1 0 0 0 0 0 0 0 0 0 0 0
49 1463 1 1 0 0 0 0 0 0 0 0 0 0
50 1600 1 0 1 0 0 0 0 0 0 0 0 0
51 2203 1 0 0 1 0 0 0 0 0 0 0 0
52 2013 1 0 0 0 1 0 0 0 0 0 0 0
53 2169 1 0 0 0 0 1 0 0 0 0 0 0
54 2640 1 0 0 0 0 0 1 0 0 0 0 0
55 2411 1 0 0 0 0 0 0 1 0 0 0 0
56 2528 1 0 0 0 0 0 0 0 1 0 0 0
57 2292 1 0 0 0 0 0 0 0 0 1 0 0
58 1988 1 0 0 0 0 0 0 0 0 0 1 0
59 1774 1 0 0 0 0 0 0 0 0 0 0 1
60 2279 1 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
3164.78 -433.94 -949.79 -485.19 -426.79 -397.79
M5 M6 M7 M8 M9 M10
-586.99 72.81 -398.99 -276.59 -272.39 -505.39
M11
-841.79
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-983.78 -250.19 -46.29 237.01 2341.16
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3164.78 240.70 13.148 < 2e-16 ***
X -433.94 163.77 -2.650 0.01094 *
M1 -949.79 329.18 -2.885 0.00589 **
M2 -485.19 329.18 -1.474 0.14717
M3 -426.79 329.18 -1.297 0.20113
M4 -397.79 329.18 -1.208 0.23293
M5 -586.99 329.18 -1.783 0.08101 .
M6 72.81 329.18 0.221 0.82590
M7 -398.99 329.18 -1.212 0.23154
M8 -276.59 329.18 -0.840 0.40503
M9 -272.39 329.18 -0.827 0.41215
M10 -505.39 329.18 -1.535 0.13142
M11 -841.79 329.18 -2.557 0.01384 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 517.9 on 47 degrees of freedom
Multiple R-squared: 0.3351, Adjusted R-squared: 0.1654
F-statistic: 1.974 on 12 and 47 DF, p-value: 0.04882
> postscript(file="/var/www/html/rcomp/tmp/1gihz1229786306.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/2tz9g1229786306.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/34x8i1229786306.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4m2211229786306.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/54bjy1229786306.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5 6 7 8
275.012 586.412 737.012 360.012 377.212 632.412 86.212 253.812
9 10 11 12 13 14 15 16
136.612 520.612 237.012 -431.776 237.012 -126.588 39.012 -246.988
17 18 19 20 21 22 23 24
-259.788 -364.588 -454.788 -493.188 -793.388 -391.388 -6.988 -983.776
25 26 27 28 29 30 31 32
-39.988 -52.588 -159.988 323.012 56.212 -12.588 172.212 285.812
33 34 35 36 37 38 39 40
457.612 -71.388 -261.988 -473.776 -153.988 238.412 -514.988 -115.988
41 42 43 44 45 46 47 48
-198.788 -91.588 117.212 -120.188 365.612 179.612 147.012 2341.164
49 50 51 52 53 54 55 56
-318.048 -645.648 -101.048 -320.048 25.152 -163.648 79.152 73.752
57 58 59 60
-166.448 -237.448 -115.048 -451.836
> postscript(file="/var/www/html/rcomp/tmp/6k0gr1229786306.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 275.012 NA
1 586.412 275.012
2 737.012 586.412
3 360.012 737.012
4 377.212 360.012
5 632.412 377.212
6 86.212 632.412
7 253.812 86.212
8 136.612 253.812
9 520.612 136.612
10 237.012 520.612
11 -431.776 237.012
12 237.012 -431.776
13 -126.588 237.012
14 39.012 -126.588
15 -246.988 39.012
16 -259.788 -246.988
17 -364.588 -259.788
18 -454.788 -364.588
19 -493.188 -454.788
20 -793.388 -493.188
21 -391.388 -793.388
22 -6.988 -391.388
23 -983.776 -6.988
24 -39.988 -983.776
25 -52.588 -39.988
26 -159.988 -52.588
27 323.012 -159.988
28 56.212 323.012
29 -12.588 56.212
30 172.212 -12.588
31 285.812 172.212
32 457.612 285.812
33 -71.388 457.612
34 -261.988 -71.388
35 -473.776 -261.988
36 -153.988 -473.776
37 238.412 -153.988
38 -514.988 238.412
39 -115.988 -514.988
40 -198.788 -115.988
41 -91.588 -198.788
42 117.212 -91.588
43 -120.188 117.212
44 365.612 -120.188
45 179.612 365.612
46 147.012 179.612
47 2341.164 147.012
48 -318.048 2341.164
49 -645.648 -318.048
50 -101.048 -645.648
51 -320.048 -101.048
52 25.152 -320.048
53 -163.648 25.152
54 79.152 -163.648
55 73.752 79.152
56 -166.448 73.752
57 -237.448 -166.448
58 -115.048 -237.448
59 -451.836 -115.048
60 NA -451.836
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 586.412 275.012
[2,] 737.012 586.412
[3,] 360.012 737.012
[4,] 377.212 360.012
[5,] 632.412 377.212
[6,] 86.212 632.412
[7,] 253.812 86.212
[8,] 136.612 253.812
[9,] 520.612 136.612
[10,] 237.012 520.612
[11,] -431.776 237.012
[12,] 237.012 -431.776
[13,] -126.588 237.012
[14,] 39.012 -126.588
[15,] -246.988 39.012
[16,] -259.788 -246.988
[17,] -364.588 -259.788
[18,] -454.788 -364.588
[19,] -493.188 -454.788
[20,] -793.388 -493.188
[21,] -391.388 -793.388
[22,] -6.988 -391.388
[23,] -983.776 -6.988
[24,] -39.988 -983.776
[25,] -52.588 -39.988
[26,] -159.988 -52.588
[27,] 323.012 -159.988
[28,] 56.212 323.012
[29,] -12.588 56.212
[30,] 172.212 -12.588
[31,] 285.812 172.212
[32,] 457.612 285.812
[33,] -71.388 457.612
[34,] -261.988 -71.388
[35,] -473.776 -261.988
[36,] -153.988 -473.776
[37,] 238.412 -153.988
[38,] -514.988 238.412
[39,] -115.988 -514.988
[40,] -198.788 -115.988
[41,] -91.588 -198.788
[42,] 117.212 -91.588
[43,] -120.188 117.212
[44,] 365.612 -120.188
[45,] 179.612 365.612
[46,] 147.012 179.612
[47,] 2341.164 147.012
[48,] -318.048 2341.164
[49,] -645.648 -318.048
[50,] -101.048 -645.648
[51,] -320.048 -101.048
[52,] 25.152 -320.048
[53,] -163.648 25.152
[54,] 79.152 -163.648
[55,] 73.752 79.152
[56,] -166.448 73.752
[57,] -237.448 -166.448
[58,] -115.048 -237.448
[59,] -451.836 -115.048
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 586.412 275.012
2 737.012 586.412
3 360.012 737.012
4 377.212 360.012
5 632.412 377.212
6 86.212 632.412
7 253.812 86.212
8 136.612 253.812
9 520.612 136.612
10 237.012 520.612
11 -431.776 237.012
12 237.012 -431.776
13 -126.588 237.012
14 39.012 -126.588
15 -246.988 39.012
16 -259.788 -246.988
17 -364.588 -259.788
18 -454.788 -364.588
19 -493.188 -454.788
20 -793.388 -493.188
21 -391.388 -793.388
22 -6.988 -391.388
23 -983.776 -6.988
24 -39.988 -983.776
25 -52.588 -39.988
26 -159.988 -52.588
27 323.012 -159.988
28 56.212 323.012
29 -12.588 56.212
30 172.212 -12.588
31 285.812 172.212
32 457.612 285.812
33 -71.388 457.612
34 -261.988 -71.388
35 -473.776 -261.988
36 -153.988 -473.776
37 238.412 -153.988
38 -514.988 238.412
39 -115.988 -514.988
40 -198.788 -115.988
41 -91.588 -198.788
42 117.212 -91.588
43 -120.188 117.212
44 365.612 -120.188
45 179.612 365.612
46 147.012 179.612
47 2341.164 147.012
48 -318.048 2341.164
49 -645.648 -318.048
50 -101.048 -645.648
51 -320.048 -101.048
52 25.152 -320.048
53 -163.648 25.152
54 79.152 -163.648
55 73.752 79.152
56 -166.448 73.752
57 -237.448 -166.448
58 -115.048 -237.448
59 -451.836 -115.048
> 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/76pue1229786306.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8do3e1229786306.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ai5s1229786306.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10n0qz1229786306.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/11bwjg1229786306.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/12waet1229786306.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/13c55o1229786306.tab")
>
> system("convert tmp/1gihz1229786306.ps tmp/1gihz1229786306.png")
> system("convert tmp/2tz9g1229786306.ps tmp/2tz9g1229786306.png")
> system("convert tmp/34x8i1229786306.ps tmp/34x8i1229786306.png")
> system("convert tmp/4m2211229786306.ps tmp/4m2211229786306.png")
> system("convert tmp/54bjy1229786306.ps tmp/54bjy1229786306.png")
> system("convert tmp/6k0gr1229786306.ps tmp/6k0gr1229786306.png")
> system("convert tmp/76pue1229786306.ps tmp/76pue1229786306.png")
> system("convert tmp/8do3e1229786306.ps tmp/8do3e1229786306.png")
> system("convert tmp/9ai5s1229786306.ps tmp/9ai5s1229786306.png")
>
>
> proc.time()
user system elapsed
2.070 1.519 5.033