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.
Natural language support but running in an English locale
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(106.7,0,110.2,0,125.9,0,100.1,0,106.4,0,114.8,0,81.3,0,87,0,104.2,0,108,0,105,0,94.5,0,92,0,95.9,0,108.8,0,103.4,0,102.1,0,110.1,0,83.2,0,82.7,0,106.8,0,113.7,0,102.5,0,96.6,0,92.1,0,95.6,0,102.3,0,98.6,0,98.2,0,104.5,0,84,0,73.8,0,103.9,0,106,0,97.2,0,102.6,0,89,0,93.8,0,116.7,1,106.8,1,98.5,1,118.7,1,90,1,91.9,1,113.3,1,113.1,1,104.1,1,108.7,1,96.7,1,101,1,116.9,1,105.8,1,99,1,129.4,1,83,1,88.9,1,115.9,1,104.2,1,113.4,1,112.2,1,100.8,1,107.3,1,126.6,1,102.9,1,117.9,1,128.8,1,87.5,1,93.8,1,122.7,1,126.2,1,124.6,1,116.7,1,115.2,1,111.1,1,129.9,1,113.3,1,118.5,1,133.5,1,102.1,1,102.4,1),dim=c(2,80),dimnames=list(c('y','x'),1:80))
> y <- array(NA,dim=c(2,80),dimnames=list(c('y','x'),1:80))
> 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 = '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
1 106.7 0
2 110.2 0
3 125.9 0
4 100.1 0
5 106.4 0
6 114.8 0
7 81.3 0
8 87.0 0
9 104.2 0
10 108.0 0
11 105.0 0
12 94.5 0
13 92.0 0
14 95.9 0
15 108.8 0
16 103.4 0
17 102.1 0
18 110.1 0
19 83.2 0
20 82.7 0
21 106.8 0
22 113.7 0
23 102.5 0
24 96.6 0
25 92.1 0
26 95.6 0
27 102.3 0
28 98.6 0
29 98.2 0
30 104.5 0
31 84.0 0
32 73.8 0
33 103.9 0
34 106.0 0
35 97.2 0
36 102.6 0
37 89.0 0
38 93.8 0
39 116.7 1
40 106.8 1
41 98.5 1
42 118.7 1
43 90.0 1
44 91.9 1
45 113.3 1
46 113.1 1
47 104.1 1
48 108.7 1
49 96.7 1
50 101.0 1
51 116.9 1
52 105.8 1
53 99.0 1
54 129.4 1
55 83.0 1
56 88.9 1
57 115.9 1
58 104.2 1
59 113.4 1
60 112.2 1
61 100.8 1
62 107.3 1
63 126.6 1
64 102.9 1
65 117.9 1
66 128.8 1
67 87.5 1
68 93.8 1
69 122.7 1
70 126.2 1
71 124.6 1
72 116.7 1
73 115.2 1
74 111.1 1
75 129.9 1
76 113.3 1
77 118.5 1
78 133.5 1
79 102.1 1
80 102.4 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x
99.57 10.20
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-26.762 -7.491 2.486 7.135 26.334
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 99.566 1.901 52.387 < 2e-16 ***
x 10.196 2.623 3.887 0.000212 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 11.72 on 78 degrees of freedom
Multiple R-Squared: 0.1623, Adjusted R-squared: 0.1515
F-statistic: 15.11 on 1 and 78 DF, p-value: 0.0002117
> postscript(file="/var/www/html/rcomp/tmp/15s2a1195502121.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/2iruz1195502121.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3427f1195502121.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4motu1195502121.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5rxgk1195502122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 80
Frequency = 1
1 2 3 4 5 6
7.1342105 10.6342105 26.3342105 0.5342105 6.8342105 15.2342105
7 8 9 10 11 12
-18.2657895 -12.5657895 4.6342105 8.4342105 5.4342105 -5.0657895
13 14 15 16 17 18
-7.5657895 -3.6657895 9.2342105 3.8342105 2.5342105 10.5342105
19 20 21 22 23 24
-16.3657895 -16.8657895 7.2342105 14.1342105 2.9342105 -2.9657895
25 26 27 28 29 30
-7.4657895 -3.9657895 2.7342105 -0.9657895 -1.3657895 4.9342105
31 32 33 34 35 36
-15.5657895 -25.7657895 4.3342105 6.4342105 -2.3657895 3.0342105
37 38 39 40 41 42
-10.5657895 -5.7657895 6.9380952 -2.9619048 -11.2619048 8.9380952
43 44 45 46 47 48
-19.7619048 -17.8619048 3.5380952 3.3380952 -5.6619048 -1.0619048
49 50 51 52 53 54
-13.0619048 -8.7619048 7.1380952 -3.9619048 -10.7619048 19.6380952
55 56 57 58 59 60
-26.7619048 -20.8619048 6.1380952 -5.5619048 3.6380952 2.4380952
61 62 63 64 65 66
-8.9619048 -2.4619048 16.8380952 -6.8619048 8.1380952 19.0380952
67 68 69 70 71 72
-22.2619048 -15.9619048 12.9380952 16.4380952 14.8380952 6.9380952
73 74 75 76 77 78
5.4380952 1.3380952 20.1380952 3.5380952 8.7380952 23.7380952
79 80
-7.6619048 -7.3619048
> postscript(file="/var/www/html/rcomp/tmp/6btgv1195502122.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 = 80
Frequency = 1
lag(myerror, k = 1) myerror
0 7.1342105 NA
1 10.6342105 7.1342105
2 26.3342105 10.6342105
3 0.5342105 26.3342105
4 6.8342105 0.5342105
5 15.2342105 6.8342105
6 -18.2657895 15.2342105
7 -12.5657895 -18.2657895
8 4.6342105 -12.5657895
9 8.4342105 4.6342105
10 5.4342105 8.4342105
11 -5.0657895 5.4342105
12 -7.5657895 -5.0657895
13 -3.6657895 -7.5657895
14 9.2342105 -3.6657895
15 3.8342105 9.2342105
16 2.5342105 3.8342105
17 10.5342105 2.5342105
18 -16.3657895 10.5342105
19 -16.8657895 -16.3657895
20 7.2342105 -16.8657895
21 14.1342105 7.2342105
22 2.9342105 14.1342105
23 -2.9657895 2.9342105
24 -7.4657895 -2.9657895
25 -3.9657895 -7.4657895
26 2.7342105 -3.9657895
27 -0.9657895 2.7342105
28 -1.3657895 -0.9657895
29 4.9342105 -1.3657895
30 -15.5657895 4.9342105
31 -25.7657895 -15.5657895
32 4.3342105 -25.7657895
33 6.4342105 4.3342105
34 -2.3657895 6.4342105
35 3.0342105 -2.3657895
36 -10.5657895 3.0342105
37 -5.7657895 -10.5657895
38 6.9380952 -5.7657895
39 -2.9619048 6.9380952
40 -11.2619048 -2.9619048
41 8.9380952 -11.2619048
42 -19.7619048 8.9380952
43 -17.8619048 -19.7619048
44 3.5380952 -17.8619048
45 3.3380952 3.5380952
46 -5.6619048 3.3380952
47 -1.0619048 -5.6619048
48 -13.0619048 -1.0619048
49 -8.7619048 -13.0619048
50 7.1380952 -8.7619048
51 -3.9619048 7.1380952
52 -10.7619048 -3.9619048
53 19.6380952 -10.7619048
54 -26.7619048 19.6380952
55 -20.8619048 -26.7619048
56 6.1380952 -20.8619048
57 -5.5619048 6.1380952
58 3.6380952 -5.5619048
59 2.4380952 3.6380952
60 -8.9619048 2.4380952
61 -2.4619048 -8.9619048
62 16.8380952 -2.4619048
63 -6.8619048 16.8380952
64 8.1380952 -6.8619048
65 19.0380952 8.1380952
66 -22.2619048 19.0380952
67 -15.9619048 -22.2619048
68 12.9380952 -15.9619048
69 16.4380952 12.9380952
70 14.8380952 16.4380952
71 6.9380952 14.8380952
72 5.4380952 6.9380952
73 1.3380952 5.4380952
74 20.1380952 1.3380952
75 3.5380952 20.1380952
76 8.7380952 3.5380952
77 23.7380952 8.7380952
78 -7.6619048 23.7380952
79 -7.3619048 -7.6619048
80 NA -7.3619048
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 10.6342105 7.1342105
[2,] 26.3342105 10.6342105
[3,] 0.5342105 26.3342105
[4,] 6.8342105 0.5342105
[5,] 15.2342105 6.8342105
[6,] -18.2657895 15.2342105
[7,] -12.5657895 -18.2657895
[8,] 4.6342105 -12.5657895
[9,] 8.4342105 4.6342105
[10,] 5.4342105 8.4342105
[11,] -5.0657895 5.4342105
[12,] -7.5657895 -5.0657895
[13,] -3.6657895 -7.5657895
[14,] 9.2342105 -3.6657895
[15,] 3.8342105 9.2342105
[16,] 2.5342105 3.8342105
[17,] 10.5342105 2.5342105
[18,] -16.3657895 10.5342105
[19,] -16.8657895 -16.3657895
[20,] 7.2342105 -16.8657895
[21,] 14.1342105 7.2342105
[22,] 2.9342105 14.1342105
[23,] -2.9657895 2.9342105
[24,] -7.4657895 -2.9657895
[25,] -3.9657895 -7.4657895
[26,] 2.7342105 -3.9657895
[27,] -0.9657895 2.7342105
[28,] -1.3657895 -0.9657895
[29,] 4.9342105 -1.3657895
[30,] -15.5657895 4.9342105
[31,] -25.7657895 -15.5657895
[32,] 4.3342105 -25.7657895
[33,] 6.4342105 4.3342105
[34,] -2.3657895 6.4342105
[35,] 3.0342105 -2.3657895
[36,] -10.5657895 3.0342105
[37,] -5.7657895 -10.5657895
[38,] 6.9380952 -5.7657895
[39,] -2.9619048 6.9380952
[40,] -11.2619048 -2.9619048
[41,] 8.9380952 -11.2619048
[42,] -19.7619048 8.9380952
[43,] -17.8619048 -19.7619048
[44,] 3.5380952 -17.8619048
[45,] 3.3380952 3.5380952
[46,] -5.6619048 3.3380952
[47,] -1.0619048 -5.6619048
[48,] -13.0619048 -1.0619048
[49,] -8.7619048 -13.0619048
[50,] 7.1380952 -8.7619048
[51,] -3.9619048 7.1380952
[52,] -10.7619048 -3.9619048
[53,] 19.6380952 -10.7619048
[54,] -26.7619048 19.6380952
[55,] -20.8619048 -26.7619048
[56,] 6.1380952 -20.8619048
[57,] -5.5619048 6.1380952
[58,] 3.6380952 -5.5619048
[59,] 2.4380952 3.6380952
[60,] -8.9619048 2.4380952
[61,] -2.4619048 -8.9619048
[62,] 16.8380952 -2.4619048
[63,] -6.8619048 16.8380952
[64,] 8.1380952 -6.8619048
[65,] 19.0380952 8.1380952
[66,] -22.2619048 19.0380952
[67,] -15.9619048 -22.2619048
[68,] 12.9380952 -15.9619048
[69,] 16.4380952 12.9380952
[70,] 14.8380952 16.4380952
[71,] 6.9380952 14.8380952
[72,] 5.4380952 6.9380952
[73,] 1.3380952 5.4380952
[74,] 20.1380952 1.3380952
[75,] 3.5380952 20.1380952
[76,] 8.7380952 3.5380952
[77,] 23.7380952 8.7380952
[78,] -7.6619048 23.7380952
[79,] -7.3619048 -7.6619048
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 10.6342105 7.1342105
2 26.3342105 10.6342105
3 0.5342105 26.3342105
4 6.8342105 0.5342105
5 15.2342105 6.8342105
6 -18.2657895 15.2342105
7 -12.5657895 -18.2657895
8 4.6342105 -12.5657895
9 8.4342105 4.6342105
10 5.4342105 8.4342105
11 -5.0657895 5.4342105
12 -7.5657895 -5.0657895
13 -3.6657895 -7.5657895
14 9.2342105 -3.6657895
15 3.8342105 9.2342105
16 2.5342105 3.8342105
17 10.5342105 2.5342105
18 -16.3657895 10.5342105
19 -16.8657895 -16.3657895
20 7.2342105 -16.8657895
21 14.1342105 7.2342105
22 2.9342105 14.1342105
23 -2.9657895 2.9342105
24 -7.4657895 -2.9657895
25 -3.9657895 -7.4657895
26 2.7342105 -3.9657895
27 -0.9657895 2.7342105
28 -1.3657895 -0.9657895
29 4.9342105 -1.3657895
30 -15.5657895 4.9342105
31 -25.7657895 -15.5657895
32 4.3342105 -25.7657895
33 6.4342105 4.3342105
34 -2.3657895 6.4342105
35 3.0342105 -2.3657895
36 -10.5657895 3.0342105
37 -5.7657895 -10.5657895
38 6.9380952 -5.7657895
39 -2.9619048 6.9380952
40 -11.2619048 -2.9619048
41 8.9380952 -11.2619048
42 -19.7619048 8.9380952
43 -17.8619048 -19.7619048
44 3.5380952 -17.8619048
45 3.3380952 3.5380952
46 -5.6619048 3.3380952
47 -1.0619048 -5.6619048
48 -13.0619048 -1.0619048
49 -8.7619048 -13.0619048
50 7.1380952 -8.7619048
51 -3.9619048 7.1380952
52 -10.7619048 -3.9619048
53 19.6380952 -10.7619048
54 -26.7619048 19.6380952
55 -20.8619048 -26.7619048
56 6.1380952 -20.8619048
57 -5.5619048 6.1380952
58 3.6380952 -5.5619048
59 2.4380952 3.6380952
60 -8.9619048 2.4380952
61 -2.4619048 -8.9619048
62 16.8380952 -2.4619048
63 -6.8619048 16.8380952
64 8.1380952 -6.8619048
65 19.0380952 8.1380952
66 -22.2619048 19.0380952
67 -15.9619048 -22.2619048
68 12.9380952 -15.9619048
69 16.4380952 12.9380952
70 14.8380952 16.4380952
71 6.9380952 14.8380952
72 5.4380952 6.9380952
73 1.3380952 5.4380952
74 20.1380952 1.3380952
75 3.5380952 20.1380952
76 8.7380952 3.5380952
77 23.7380952 8.7380952
78 -7.6619048 23.7380952
79 -7.3619048 -7.6619048
> 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/7b0za1195502122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80fma1195502122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9luaq1195502122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/103l0x1195502122.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/11xb9n1195502122.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/12yq3z1195502122.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/13hlzx1195502123.tab")
>
> system("convert tmp/15s2a1195502121.ps tmp/15s2a1195502121.png")
> system("convert tmp/2iruz1195502121.ps tmp/2iruz1195502121.png")
> system("convert tmp/3427f1195502121.ps tmp/3427f1195502121.png")
> system("convert tmp/4motu1195502121.ps tmp/4motu1195502121.png")
> system("convert tmp/5rxgk1195502122.ps tmp/5rxgk1195502122.png")
> system("convert tmp/6btgv1195502122.ps tmp/6btgv1195502122.png")
> system("convert tmp/7b0za1195502122.ps tmp/7b0za1195502122.png")
> system("convert tmp/80fma1195502122.ps tmp/80fma1195502122.png")
> system("convert tmp/9luaq1195502122.ps tmp/9luaq1195502122.png")
>
>
> proc.time()
user system elapsed
4.205 2.467 4.552