R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'license()' or 'licence()' for distribution details.
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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
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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 = 'Include Monthly 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.8 0 1 0 0 0 0 0 0 0 0 0 0 1
2 7.6 0 0 1 0 0 0 0 0 0 0 0 0 2
3 7.5 0 0 0 1 0 0 0 0 0 0 0 0 3
4 7.6 0 0 0 0 1 0 0 0 0 0 0 0 4
5 7.5 0 0 0 0 0 1 0 0 0 0 0 0 5
6 7.3 0 0 0 0 0 0 1 0 0 0 0 0 6
7 7.6 0 0 0 0 0 0 0 1 0 0 0 0 7
8 7.5 0 0 0 0 0 0 0 0 1 0 0 0 8
9 7.6 0 0 0 0 0 0 0 0 0 1 0 0 9
10 7.9 0 0 0 0 0 0 0 0 0 0 1 0 10
11 7.9 0 0 0 0 0 0 0 0 0 0 0 1 11
12 8.1 0 0 0 0 0 0 0 0 0 0 0 0 12
13 8.2 0 1 0 0 0 0 0 0 0 0 0 0 13
14 8.0 0 0 1 0 0 0 0 0 0 0 0 0 14
15 7.5 0 0 0 1 0 0 0 0 0 0 0 0 15
16 6.8 0 0 0 0 1 0 0 0 0 0 0 0 16
17 6.5 0 0 0 0 0 1 0 0 0 0 0 0 17
18 6.6 0 0 0 0 0 0 1 0 0 0 0 0 18
19 7.6 0 0 0 0 0 0 0 1 0 0 0 0 19
20 8.0 0 0 0 0 0 0 0 0 1 0 0 0 20
21 8.0 0 0 0 0 0 0 0 0 0 1 0 0 21
22 7.7 0 0 0 0 0 0 0 0 0 0 1 0 22
23 7.5 0 0 0 0 0 0 0 0 0 0 0 1 23
24 7.6 0 0 0 0 0 0 0 0 0 0 0 0 24
25 7.7 0 1 0 0 0 0 0 0 0 0 0 0 25
26 7.9 0 0 1 0 0 0 0 0 0 0 0 0 26
27 7.8 0 0 0 1 0 0 0 0 0 0 0 0 27
28 7.5 0 0 0 0 1 0 0 0 0 0 0 0 28
29 7.5 0 0 0 0 0 1 0 0 0 0 0 0 29
30 7.1 0 0 0 0 0 0 1 0 0 0 0 0 30
31 7.5 0 0 0 0 0 0 0 1 0 0 0 0 31
32 7.5 0 0 0 0 0 0 0 0 1 0 0 0 32
33 7.6 0 0 0 0 0 0 0 0 0 1 0 0 33
34 7.7 0 0 0 0 0 0 0 0 0 0 1 0 34
35 7.7 1 0 0 0 0 0 0 0 0 0 0 1 35
36 7.9 1 0 0 0 0 0 0 0 0 0 0 0 36
37 8.1 1 1 0 0 0 0 0 0 0 0 0 0 37
38 8.2 1 0 1 0 0 0 0 0 0 0 0 0 38
39 8.2 1 0 0 1 0 0 0 0 0 0 0 0 39
40 8.1 1 0 0 0 1 0 0 0 0 0 0 0 40
41 7.9 1 0 0 0 0 1 0 0 0 0 0 0 41
42 7.3 1 0 0 0 0 0 1 0 0 0 0 0 42
43 6.9 1 0 0 0 0 0 0 1 0 0 0 0 43
44 6.6 1 0 0 0 0 0 0 0 1 0 0 0 44
45 6.7 1 0 0 0 0 0 0 0 0 1 0 0 45
46 6.9 1 0 0 0 0 0 0 0 0 0 1 0 46
47 7.0 1 0 0 0 0 0 0 0 0 0 0 1 47
48 7.1 1 0 0 0 0 0 0 0 0 0 0 0 48
49 7.2 1 1 0 0 0 0 0 0 0 0 0 0 49
50 7.1 1 0 1 0 0 0 0 0 0 0 0 0 50
51 6.9 1 0 0 1 0 0 0 0 0 0 0 0 51
52 7.0 1 0 0 0 1 0 0 0 0 0 0 0 52
53 6.8 1 0 0 0 0 1 0 0 0 0 0 0 53
54 6.4 1 0 0 0 0 0 1 0 0 0 0 0 54
55 6.7 1 0 0 0 0 0 0 1 0 0 0 0 55
56 6.7 1 0 0 0 0 0 0 0 1 0 0 0 56
57 6.4 1 0 0 0 0 0 0 0 0 1 0 0 57
58 6.3 1 0 0 0 0 0 0 0 0 0 1 0 58
59 6.2 1 0 0 0 0 0 0 0 0 0 0 1 59
60 6.5 1 0 0 0 0 0 0 0 0 0 0 0 60
61 6.8 1 1 0 0 0 0 0 0 0 0 0 0 61
62 6.8 1 0 1 0 0 0 0 0 0 0 0 0 62
63 6.5 1 0 0 1 0 0 0 0 0 0 0 0 63
64 6.3 1 0 0 0 1 0 0 0 0 0 0 0 64
65 5.9 1 0 0 0 0 1 0 0 0 0 0 0 65
66 5.9 1 0 0 0 0 0 1 0 0 0 0 0 66
67 6.4 1 0 0 0 0 0 0 1 0 0 0 0 67
68 6.4 1 0 0 0 0 0 0 0 1 0 0 0 68
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x M1 M2 M3 M4
8.23900 0.24500 0.08644 0.07939 -0.09433 -0.25139
M5 M6 M7 M8 M9 M10
-0.42511 -0.64883 -0.27256 -0.24628 -0.20983 -0.14356
M11 t
-0.20628 -0.02628
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.86717 -0.23825 -0.05083 0.27317 0.91850
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.239000 0.223621 36.844 < 2e-16 ***
x 0.245000 0.219278 1.117 0.2688
M1 0.086444 0.265919 0.325 0.7464
M2 0.079389 0.265794 0.299 0.7663
M3 -0.094333 0.265785 -0.355 0.7240
M4 -0.251389 0.265894 -0.945 0.3486
M5 -0.425111 0.266120 -1.597 0.1160
M6 -0.648833 0.266463 -2.435 0.0182 *
M7 -0.272556 0.266922 -1.021 0.3118
M8 -0.246278 0.267497 -0.921 0.3613
M9 -0.209833 0.279024 -0.752 0.4553
M10 -0.143556 0.279510 -0.514 0.6096
M11 -0.206278 0.277423 -0.744 0.4604
t -0.026278 0.005583 -4.707 1.79e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4386 on 54 degrees of freedom
Multiple R-squared: 0.5969, Adjusted R-squared: 0.4999
F-statistic: 6.151 on 13 and 54 DF, p-value: 7.154e-07
> postscript(file="/var/www/html/rcomp/tmp/1kek21227545849.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/27jt51227545849.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3ks181227545849.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/41myb1227545849.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5wyko1227545849.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-0.499166667 -0.665833333 -0.565833333 -0.282500000 -0.182500000 -0.132500000
7 8 9 10 11 12
-0.182500000 -0.282500000 -0.192666667 0.067333333 0.156333333 0.176333333
13 14 15 16 17 18
0.216166667 0.049500000 -0.250500000 -0.767166667 -0.867166667 -0.517166667
19 20 21 22 23 24
0.132833333 0.532833333 0.522666667 0.182666667 0.071666667 -0.008333333
25 26 27 28 29 30
0.031500000 0.264833333 0.364833333 0.248166667 0.448166667 0.298166667
31 32 33 34 35 36
0.348166667 0.348166667 0.438000000 0.498000000 0.342000000 0.362000000
37 38 39 40 41 42
0.501833333 0.635166667 0.835166667 0.918500000 0.918500000 0.568500000
43 44 45 46 47 48
-0.181500000 -0.481500000 -0.391666667 -0.231666667 -0.042666667 -0.122666667
49 50 51 52 53 54
-0.082833333 -0.149500000 -0.149500000 0.133833333 0.133833333 -0.016166667
55 56 57 58 59 60
-0.066166667 -0.066166667 -0.376333333 -0.516333333 -0.527333333 -0.407333333
61 62 63 64 65 66
-0.167500000 -0.134166667 -0.234166667 -0.250833333 -0.450833333 -0.200833333
67 68
-0.050833333 -0.050833333
> postscript(file="/var/www/html/rcomp/tmp/614iq1227545849.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 -0.499166667 NA
1 -0.665833333 -0.499166667
2 -0.565833333 -0.665833333
3 -0.282500000 -0.565833333
4 -0.182500000 -0.282500000
5 -0.132500000 -0.182500000
6 -0.182500000 -0.132500000
7 -0.282500000 -0.182500000
8 -0.192666667 -0.282500000
9 0.067333333 -0.192666667
10 0.156333333 0.067333333
11 0.176333333 0.156333333
12 0.216166667 0.176333333
13 0.049500000 0.216166667
14 -0.250500000 0.049500000
15 -0.767166667 -0.250500000
16 -0.867166667 -0.767166667
17 -0.517166667 -0.867166667
18 0.132833333 -0.517166667
19 0.532833333 0.132833333
20 0.522666667 0.532833333
21 0.182666667 0.522666667
22 0.071666667 0.182666667
23 -0.008333333 0.071666667
24 0.031500000 -0.008333333
25 0.264833333 0.031500000
26 0.364833333 0.264833333
27 0.248166667 0.364833333
28 0.448166667 0.248166667
29 0.298166667 0.448166667
30 0.348166667 0.298166667
31 0.348166667 0.348166667
32 0.438000000 0.348166667
33 0.498000000 0.438000000
34 0.342000000 0.498000000
35 0.362000000 0.342000000
36 0.501833333 0.362000000
37 0.635166667 0.501833333
38 0.835166667 0.635166667
39 0.918500000 0.835166667
40 0.918500000 0.918500000
41 0.568500000 0.918500000
42 -0.181500000 0.568500000
43 -0.481500000 -0.181500000
44 -0.391666667 -0.481500000
45 -0.231666667 -0.391666667
46 -0.042666667 -0.231666667
47 -0.122666667 -0.042666667
48 -0.082833333 -0.122666667
49 -0.149500000 -0.082833333
50 -0.149500000 -0.149500000
51 0.133833333 -0.149500000
52 0.133833333 0.133833333
53 -0.016166667 0.133833333
54 -0.066166667 -0.016166667
55 -0.066166667 -0.066166667
56 -0.376333333 -0.066166667
57 -0.516333333 -0.376333333
58 -0.527333333 -0.516333333
59 -0.407333333 -0.527333333
60 -0.167500000 -0.407333333
61 -0.134166667 -0.167500000
62 -0.234166667 -0.134166667
63 -0.250833333 -0.234166667
64 -0.450833333 -0.250833333
65 -0.200833333 -0.450833333
66 -0.050833333 -0.200833333
67 -0.050833333 -0.050833333
68 NA -0.050833333
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.665833333 -0.499166667
[2,] -0.565833333 -0.665833333
[3,] -0.282500000 -0.565833333
[4,] -0.182500000 -0.282500000
[5,] -0.132500000 -0.182500000
[6,] -0.182500000 -0.132500000
[7,] -0.282500000 -0.182500000
[8,] -0.192666667 -0.282500000
[9,] 0.067333333 -0.192666667
[10,] 0.156333333 0.067333333
[11,] 0.176333333 0.156333333
[12,] 0.216166667 0.176333333
[13,] 0.049500000 0.216166667
[14,] -0.250500000 0.049500000
[15,] -0.767166667 -0.250500000
[16,] -0.867166667 -0.767166667
[17,] -0.517166667 -0.867166667
[18,] 0.132833333 -0.517166667
[19,] 0.532833333 0.132833333
[20,] 0.522666667 0.532833333
[21,] 0.182666667 0.522666667
[22,] 0.071666667 0.182666667
[23,] -0.008333333 0.071666667
[24,] 0.031500000 -0.008333333
[25,] 0.264833333 0.031500000
[26,] 0.364833333 0.264833333
[27,] 0.248166667 0.364833333
[28,] 0.448166667 0.248166667
[29,] 0.298166667 0.448166667
[30,] 0.348166667 0.298166667
[31,] 0.348166667 0.348166667
[32,] 0.438000000 0.348166667
[33,] 0.498000000 0.438000000
[34,] 0.342000000 0.498000000
[35,] 0.362000000 0.342000000
[36,] 0.501833333 0.362000000
[37,] 0.635166667 0.501833333
[38,] 0.835166667 0.635166667
[39,] 0.918500000 0.835166667
[40,] 0.918500000 0.918500000
[41,] 0.568500000 0.918500000
[42,] -0.181500000 0.568500000
[43,] -0.481500000 -0.181500000
[44,] -0.391666667 -0.481500000
[45,] -0.231666667 -0.391666667
[46,] -0.042666667 -0.231666667
[47,] -0.122666667 -0.042666667
[48,] -0.082833333 -0.122666667
[49,] -0.149500000 -0.082833333
[50,] -0.149500000 -0.149500000
[51,] 0.133833333 -0.149500000
[52,] 0.133833333 0.133833333
[53,] -0.016166667 0.133833333
[54,] -0.066166667 -0.016166667
[55,] -0.066166667 -0.066166667
[56,] -0.376333333 -0.066166667
[57,] -0.516333333 -0.376333333
[58,] -0.527333333 -0.516333333
[59,] -0.407333333 -0.527333333
[60,] -0.167500000 -0.407333333
[61,] -0.134166667 -0.167500000
[62,] -0.234166667 -0.134166667
[63,] -0.250833333 -0.234166667
[64,] -0.450833333 -0.250833333
[65,] -0.200833333 -0.450833333
[66,] -0.050833333 -0.200833333
[67,] -0.050833333 -0.050833333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.665833333 -0.499166667
2 -0.565833333 -0.665833333
3 -0.282500000 -0.565833333
4 -0.182500000 -0.282500000
5 -0.132500000 -0.182500000
6 -0.182500000 -0.132500000
7 -0.282500000 -0.182500000
8 -0.192666667 -0.282500000
9 0.067333333 -0.192666667
10 0.156333333 0.067333333
11 0.176333333 0.156333333
12 0.216166667 0.176333333
13 0.049500000 0.216166667
14 -0.250500000 0.049500000
15 -0.767166667 -0.250500000
16 -0.867166667 -0.767166667
17 -0.517166667 -0.867166667
18 0.132833333 -0.517166667
19 0.532833333 0.132833333
20 0.522666667 0.532833333
21 0.182666667 0.522666667
22 0.071666667 0.182666667
23 -0.008333333 0.071666667
24 0.031500000 -0.008333333
25 0.264833333 0.031500000
26 0.364833333 0.264833333
27 0.248166667 0.364833333
28 0.448166667 0.248166667
29 0.298166667 0.448166667
30 0.348166667 0.298166667
31 0.348166667 0.348166667
32 0.438000000 0.348166667
33 0.498000000 0.438000000
34 0.342000000 0.498000000
35 0.362000000 0.342000000
36 0.501833333 0.362000000
37 0.635166667 0.501833333
38 0.835166667 0.635166667
39 0.918500000 0.835166667
40 0.918500000 0.918500000
41 0.568500000 0.918500000
42 -0.181500000 0.568500000
43 -0.481500000 -0.181500000
44 -0.391666667 -0.481500000
45 -0.231666667 -0.391666667
46 -0.042666667 -0.231666667
47 -0.122666667 -0.042666667
48 -0.082833333 -0.122666667
49 -0.149500000 -0.082833333
50 -0.149500000 -0.149500000
51 0.133833333 -0.149500000
52 0.133833333 0.133833333
53 -0.016166667 0.133833333
54 -0.066166667 -0.016166667
55 -0.066166667 -0.066166667
56 -0.376333333 -0.066166667
57 -0.516333333 -0.376333333
58 -0.527333333 -0.516333333
59 -0.407333333 -0.527333333
60 -0.167500000 -0.407333333
61 -0.134166667 -0.167500000
62 -0.234166667 -0.134166667
63 -0.250833333 -0.234166667
64 -0.450833333 -0.250833333
65 -0.200833333 -0.450833333
66 -0.050833333 -0.200833333
67 -0.050833333 -0.050833333
> 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/7pgal1227545849.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8nelb1227545849.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/92c7q1227545849.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10bilq1227545849.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/11q91o1227545849.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/12ndrh1227545849.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/13t62l1227545849.tab")
>
> system("convert tmp/1kek21227545849.ps tmp/1kek21227545849.png")
> system("convert tmp/27jt51227545849.ps tmp/27jt51227545849.png")
> system("convert tmp/3ks181227545849.ps tmp/3ks181227545849.png")
> system("convert tmp/41myb1227545849.ps tmp/41myb1227545849.png")
> system("convert tmp/5wyko1227545849.ps tmp/5wyko1227545849.png")
> system("convert tmp/614iq1227545849.ps tmp/614iq1227545849.png")
> system("convert tmp/7pgal1227545849.ps tmp/7pgal1227545849.png")
> system("convert tmp/8nelb1227545849.ps tmp/8nelb1227545849.png")
> system("convert tmp/92c7q1227545849.ps tmp/92c7q1227545849.png")
>
>
> proc.time()
user system elapsed
1.937 1.410 2.371