R version 2.6.0 (2007-10-03)
Copyright (C) 2007 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(87.0,0,96.3,0,107.1,0,115.2,0,106.1,1,89.5,1,91.3,1,97.6,1,100.7,1,104.6,1,94.7,1,101.8,1,102.5,1,105.3,1,110.3,1,109.8,1,117.3,1,118.8,1,131.3,1,125.9,1,133.1,1,147.0,1,145.8,1,164.4,1,149.8,1,137.7,1,151.7,1,156.8,1,180.0,1,180.4,1,170.4,1,191.6,1,199.5,1,218.2,1,217.5,1,205.0,1,194.0,1,199.3,1,219.3,1,211.1,1,215.2,1,240.2,1,242.2,1,240.7,1,255.4,1,253.0,1,218.2,1,203.7,1,205.6,1,215.6,1,188.5,1,202.9,1,214.0,1,230.3,1,230.0,1,241.0,1,259.6,1,247.8,1,270.3,1,289.7,1),dim=c(2,60),dimnames=list(c('prijs/olie','war?
'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('prijs/olie','war?
'),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
prijs/olie war?\r M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 87.0 0 1 0 0 0 0 0 0 0 0 0 0
2 96.3 0 0 1 0 0 0 0 0 0 0 0 0
3 107.1 0 0 0 1 0 0 0 0 0 0 0 0
4 115.2 0 0 0 0 1 0 0 0 0 0 0 0
5 106.1 1 0 0 0 0 1 0 0 0 0 0 0
6 89.5 1 0 0 0 0 0 1 0 0 0 0 0
7 91.3 1 0 0 0 0 0 0 1 0 0 0 0
8 97.6 1 0 0 0 0 0 0 0 1 0 0 0
9 100.7 1 0 0 0 0 0 0 0 0 1 0 0
10 104.6 1 0 0 0 0 0 0 0 0 0 1 0
11 94.7 1 0 0 0 0 0 0 0 0 0 0 1
12 101.8 1 0 0 0 0 0 0 0 0 0 0 0
13 102.5 1 1 0 0 0 0 0 0 0 0 0 0
14 105.3 1 0 1 0 0 0 0 0 0 0 0 0
15 110.3 1 0 0 1 0 0 0 0 0 0 0 0
16 109.8 1 0 0 0 1 0 0 0 0 0 0 0
17 117.3 1 0 0 0 0 1 0 0 0 0 0 0
18 118.8 1 0 0 0 0 0 1 0 0 0 0 0
19 131.3 1 0 0 0 0 0 0 1 0 0 0 0
20 125.9 1 0 0 0 0 0 0 0 1 0 0 0
21 133.1 1 0 0 0 0 0 0 0 0 1 0 0
22 147.0 1 0 0 0 0 0 0 0 0 0 1 0
23 145.8 1 0 0 0 0 0 0 0 0 0 0 1
24 164.4 1 0 0 0 0 0 0 0 0 0 0 0
25 149.8 1 1 0 0 0 0 0 0 0 0 0 0
26 137.7 1 0 1 0 0 0 0 0 0 0 0 0
27 151.7 1 0 0 1 0 0 0 0 0 0 0 0
28 156.8 1 0 0 0 1 0 0 0 0 0 0 0
29 180.0 1 0 0 0 0 1 0 0 0 0 0 0
30 180.4 1 0 0 0 0 0 1 0 0 0 0 0
31 170.4 1 0 0 0 0 0 0 1 0 0 0 0
32 191.6 1 0 0 0 0 0 0 0 1 0 0 0
33 199.5 1 0 0 0 0 0 0 0 0 1 0 0
34 218.2 1 0 0 0 0 0 0 0 0 0 1 0
35 217.5 1 0 0 0 0 0 0 0 0 0 0 1
36 205.0 1 0 0 0 0 0 0 0 0 0 0 0
37 194.0 1 1 0 0 0 0 0 0 0 0 0 0
38 199.3 1 0 1 0 0 0 0 0 0 0 0 0
39 219.3 1 0 0 1 0 0 0 0 0 0 0 0
40 211.1 1 0 0 0 1 0 0 0 0 0 0 0
41 215.2 1 0 0 0 0 1 0 0 0 0 0 0
42 240.2 1 0 0 0 0 0 1 0 0 0 0 0
43 242.2 1 0 0 0 0 0 0 1 0 0 0 0
44 240.7 1 0 0 0 0 0 0 0 1 0 0 0
45 255.4 1 0 0 0 0 0 0 0 0 1 0 0
46 253.0 1 0 0 0 0 0 0 0 0 0 1 0
47 218.2 1 0 0 0 0 0 0 0 0 0 0 1
48 203.7 1 0 0 0 0 0 0 0 0 0 0 0
49 205.6 1 1 0 0 0 0 0 0 0 0 0 0
50 215.6 1 0 1 0 0 0 0 0 0 0 0 0
51 188.5 1 0 0 1 0 0 0 0 0 0 0 0
52 202.9 1 0 0 0 1 0 0 0 0 0 0 0
53 214.0 1 0 0 0 0 1 0 0 0 0 0 0
54 230.3 1 0 0 0 0 0 1 0 0 0 0 0
55 230.0 1 0 0 0 0 0 0 1 0 0 0 0
56 241.0 1 0 0 0 0 0 0 0 1 0 0 0
57 259.6 1 0 0 0 0 0 0 0 0 1 0 0
58 247.8 1 0 0 0 0 0 0 0 0 0 1 0
59 270.3 1 0 0 0 0 0 0 0 0 0 0 1
60 289.7 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) `war?\r` M1 M2 M3 M4
128.06 64.86 -32.17 -29.11 -24.57 -20.79
M5 M6 M7 M8 M9 M10
-26.40 -21.08 -19.88 -13.56 -3.26 1.20
M11
-3.62
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-94.60 -50.17 9.20 47.78 96.78
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 128.06 42.52 3.012 0.00417 **
`war?\r` 64.86 33.20 1.954 0.05671 .
M1 -32.17 38.15 -0.843 0.40333
M2 -29.11 38.15 -0.763 0.44923
M3 -24.57 38.15 -0.644 0.52267
M4 -20.79 38.15 -0.545 0.58836
M5 -26.40 37.56 -0.703 0.48563
M6 -21.08 37.56 -0.561 0.57733
M7 -19.88 37.56 -0.529 0.59912
M8 -13.56 37.56 -0.361 0.71972
M9 -3.26 37.56 -0.087 0.93121
M10 1.20 37.56 0.032 0.97465
M11 -3.62 37.56 -0.096 0.92363
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 59.39 on 47 degrees of freedom
Multiple R-Squared: 0.1486, Adjusted R-squared: -0.06874
F-statistic: 0.6838 on 12 and 47 DF, p-value: 0.7582
> postscript(file="/var/www/html/rcomp/tmp/1ilip1197025408.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/2szdd1197025408.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3llww1197025408.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ut151197025408.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5v1eb1197025408.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-8.8900 -2.6500 3.6100 7.9300 -60.4200 -82.3400 -81.7400 -81.7600
9 10 11 12 13 14 15 16
-88.9600 -89.5200 -94.6000 -91.1200 -58.2525 -58.5125 -58.0525 -62.3325
17 18 19 20 21 22 23 24
-49.2200 -53.0400 -41.7400 -53.4600 -56.5600 -47.1200 -43.5000 -28.5200
25 26 27 28 29 30 31 32
-10.9525 -26.1125 -16.6525 -15.3325 13.4800 8.5600 -2.6400 12.2400
33 34 35 36 37 38 39 40
9.8400 24.0800 28.2000 12.0800 33.2475 35.4875 50.9475 38.9675
41 42 43 44 45 46 47 48
48.6800 68.3600 69.1600 61.3400 65.7400 58.8800 28.9000 10.7800
49 50 51 52 53 54 55 56
44.8475 51.7875 20.1475 30.7675 47.4800 58.4600 56.9600 61.6400
57 58 59 60
69.9400 53.6800 81.0000 96.7800
> postscript(file="/var/www/html/rcomp/tmp/6853z1197025409.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 -8.8900 NA
1 -2.6500 -8.8900
2 3.6100 -2.6500
3 7.9300 3.6100
4 -60.4200 7.9300
5 -82.3400 -60.4200
6 -81.7400 -82.3400
7 -81.7600 -81.7400
8 -88.9600 -81.7600
9 -89.5200 -88.9600
10 -94.6000 -89.5200
11 -91.1200 -94.6000
12 -58.2525 -91.1200
13 -58.5125 -58.2525
14 -58.0525 -58.5125
15 -62.3325 -58.0525
16 -49.2200 -62.3325
17 -53.0400 -49.2200
18 -41.7400 -53.0400
19 -53.4600 -41.7400
20 -56.5600 -53.4600
21 -47.1200 -56.5600
22 -43.5000 -47.1200
23 -28.5200 -43.5000
24 -10.9525 -28.5200
25 -26.1125 -10.9525
26 -16.6525 -26.1125
27 -15.3325 -16.6525
28 13.4800 -15.3325
29 8.5600 13.4800
30 -2.6400 8.5600
31 12.2400 -2.6400
32 9.8400 12.2400
33 24.0800 9.8400
34 28.2000 24.0800
35 12.0800 28.2000
36 33.2475 12.0800
37 35.4875 33.2475
38 50.9475 35.4875
39 38.9675 50.9475
40 48.6800 38.9675
41 68.3600 48.6800
42 69.1600 68.3600
43 61.3400 69.1600
44 65.7400 61.3400
45 58.8800 65.7400
46 28.9000 58.8800
47 10.7800 28.9000
48 44.8475 10.7800
49 51.7875 44.8475
50 20.1475 51.7875
51 30.7675 20.1475
52 47.4800 30.7675
53 58.4600 47.4800
54 56.9600 58.4600
55 61.6400 56.9600
56 69.9400 61.6400
57 53.6800 69.9400
58 81.0000 53.6800
59 96.7800 81.0000
60 NA 96.7800
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.6500 -8.8900
[2,] 3.6100 -2.6500
[3,] 7.9300 3.6100
[4,] -60.4200 7.9300
[5,] -82.3400 -60.4200
[6,] -81.7400 -82.3400
[7,] -81.7600 -81.7400
[8,] -88.9600 -81.7600
[9,] -89.5200 -88.9600
[10,] -94.6000 -89.5200
[11,] -91.1200 -94.6000
[12,] -58.2525 -91.1200
[13,] -58.5125 -58.2525
[14,] -58.0525 -58.5125
[15,] -62.3325 -58.0525
[16,] -49.2200 -62.3325
[17,] -53.0400 -49.2200
[18,] -41.7400 -53.0400
[19,] -53.4600 -41.7400
[20,] -56.5600 -53.4600
[21,] -47.1200 -56.5600
[22,] -43.5000 -47.1200
[23,] -28.5200 -43.5000
[24,] -10.9525 -28.5200
[25,] -26.1125 -10.9525
[26,] -16.6525 -26.1125
[27,] -15.3325 -16.6525
[28,] 13.4800 -15.3325
[29,] 8.5600 13.4800
[30,] -2.6400 8.5600
[31,] 12.2400 -2.6400
[32,] 9.8400 12.2400
[33,] 24.0800 9.8400
[34,] 28.2000 24.0800
[35,] 12.0800 28.2000
[36,] 33.2475 12.0800
[37,] 35.4875 33.2475
[38,] 50.9475 35.4875
[39,] 38.9675 50.9475
[40,] 48.6800 38.9675
[41,] 68.3600 48.6800
[42,] 69.1600 68.3600
[43,] 61.3400 69.1600
[44,] 65.7400 61.3400
[45,] 58.8800 65.7400
[46,] 28.9000 58.8800
[47,] 10.7800 28.9000
[48,] 44.8475 10.7800
[49,] 51.7875 44.8475
[50,] 20.1475 51.7875
[51,] 30.7675 20.1475
[52,] 47.4800 30.7675
[53,] 58.4600 47.4800
[54,] 56.9600 58.4600
[55,] 61.6400 56.9600
[56,] 69.9400 61.6400
[57,] 53.6800 69.9400
[58,] 81.0000 53.6800
[59,] 96.7800 81.0000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.6500 -8.8900
2 3.6100 -2.6500
3 7.9300 3.6100
4 -60.4200 7.9300
5 -82.3400 -60.4200
6 -81.7400 -82.3400
7 -81.7600 -81.7400
8 -88.9600 -81.7600
9 -89.5200 -88.9600
10 -94.6000 -89.5200
11 -91.1200 -94.6000
12 -58.2525 -91.1200
13 -58.5125 -58.2525
14 -58.0525 -58.5125
15 -62.3325 -58.0525
16 -49.2200 -62.3325
17 -53.0400 -49.2200
18 -41.7400 -53.0400
19 -53.4600 -41.7400
20 -56.5600 -53.4600
21 -47.1200 -56.5600
22 -43.5000 -47.1200
23 -28.5200 -43.5000
24 -10.9525 -28.5200
25 -26.1125 -10.9525
26 -16.6525 -26.1125
27 -15.3325 -16.6525
28 13.4800 -15.3325
29 8.5600 13.4800
30 -2.6400 8.5600
31 12.2400 -2.6400
32 9.8400 12.2400
33 24.0800 9.8400
34 28.2000 24.0800
35 12.0800 28.2000
36 33.2475 12.0800
37 35.4875 33.2475
38 50.9475 35.4875
39 38.9675 50.9475
40 48.6800 38.9675
41 68.3600 48.6800
42 69.1600 68.3600
43 61.3400 69.1600
44 65.7400 61.3400
45 58.8800 65.7400
46 28.9000 58.8800
47 10.7800 28.9000
48 44.8475 10.7800
49 51.7875 44.8475
50 20.1475 51.7875
51 30.7675 20.1475
52 47.4800 30.7675
53 58.4600 47.4800
54 56.9600 58.4600
55 61.6400 56.9600
56 69.9400 61.6400
57 53.6800 69.9400
58 81.0000 53.6800
59 96.7800 81.0000
> 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/7o5rm1197025409.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8pscv1197025409.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9abby1197025409.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10i5kb1197025409.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/11ji9l1197025409.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/12wac71197025409.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/13qvhe1197025409.tab")
>
> system("convert tmp/1ilip1197025408.ps tmp/1ilip1197025408.png")
> system("convert tmp/2szdd1197025408.ps tmp/2szdd1197025408.png")
> system("convert tmp/3llww1197025408.ps tmp/3llww1197025408.png")
> system("convert tmp/4ut151197025408.ps tmp/4ut151197025408.png")
> system("convert tmp/5v1eb1197025408.ps tmp/5v1eb1197025408.png")
> system("convert tmp/6853z1197025409.ps tmp/6853z1197025409.png")
> system("convert tmp/7o5rm1197025409.ps tmp/7o5rm1197025409.png")
> system("convert tmp/8pscv1197025409.ps tmp/8pscv1197025409.png")
> system("convert tmp/9abby1197025409.ps tmp/9abby1197025409.png")
>
>
> proc.time()
user system elapsed
2.222 1.417 2.671