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.
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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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> x <- array(list(86.5,109.2,104.1,126.3,110.9,104,114.5,96,112.2,262,96.4,89.8,92,86,102,92.7,99.7,126.8,102,92.8,98.9,87.8,87.4,100,94.4,72.4,109.3,104.9,116.4,52.3,101,65.3,105.5,110.2,97.8,54.4,95.5,47.5,113.7,65.2,103.7,69.8,100.8,53.6,113.8,116.1,84.6,56.6,95.3,47.2,110,90.6,107.5,60.4,107.6,59.3,116,131.6,96.9,59.4,97,65.5,108.1,70.5,101.9,81,107.2,73.3,110.2,107.5,78.7,88.9,96.5,55.8,115.2,80.5,104.7,86.3,109.1,112.6,108.4,148.6,95.5,47.1,97.8,57.8,115.1,81,96.2,60.1,112,76.1,111.8,82.5,82.5,66.8,100.8,58.7,116,54.2,116.3,103.3,116.6,77.8,112.9,118.4,100.9,64.9,104.1,40.8,117.4,77.7,103.3,66.8,111.6,69.2,115,82.4,92.6,62.7,105.2,58.2),dim=c(2,61),dimnames=list(c('Indpr','Inv'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Indpr','Inv'),1:61))
> 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 = '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
Indpr Inv M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 86.5 109.2 1 0 0 0 0 0 0 0 0 0 0 1
2 104.1 126.3 0 1 0 0 0 0 0 0 0 0 0 2
3 110.9 104.0 0 0 1 0 0 0 0 0 0 0 0 3
4 114.5 96.0 0 0 0 1 0 0 0 0 0 0 0 4
5 112.2 262.0 0 0 0 0 1 0 0 0 0 0 0 5
6 96.4 89.8 0 0 0 0 0 1 0 0 0 0 0 6
7 92.0 86.0 0 0 0 0 0 0 1 0 0 0 0 7
8 102.0 92.7 0 0 0 0 0 0 0 1 0 0 0 8
9 99.7 126.8 0 0 0 0 0 0 0 0 1 0 0 9
10 102.0 92.8 0 0 0 0 0 0 0 0 0 1 0 10
11 98.9 87.8 0 0 0 0 0 0 0 0 0 0 1 11
12 87.4 100.0 0 0 0 0 0 0 0 0 0 0 0 12
13 94.4 72.4 1 0 0 0 0 0 0 0 0 0 0 13
14 109.3 104.9 0 1 0 0 0 0 0 0 0 0 0 14
15 116.4 52.3 0 0 1 0 0 0 0 0 0 0 0 15
16 101.0 65.3 0 0 0 1 0 0 0 0 0 0 0 16
17 105.5 110.2 0 0 0 0 1 0 0 0 0 0 0 17
18 97.8 54.4 0 0 0 0 0 1 0 0 0 0 0 18
19 95.5 47.5 0 0 0 0 0 0 1 0 0 0 0 19
20 113.7 65.2 0 0 0 0 0 0 0 1 0 0 0 20
21 103.7 69.8 0 0 0 0 0 0 0 0 1 0 0 21
22 100.8 53.6 0 0 0 0 0 0 0 0 0 1 0 22
23 113.8 116.1 0 0 0 0 0 0 0 0 0 0 1 23
24 84.6 56.6 0 0 0 0 0 0 0 0 0 0 0 24
25 95.3 47.2 1 0 0 0 0 0 0 0 0 0 0 25
26 110.0 90.6 0 1 0 0 0 0 0 0 0 0 0 26
27 107.5 60.4 0 0 1 0 0 0 0 0 0 0 0 27
28 107.6 59.3 0 0 0 1 0 0 0 0 0 0 0 28
29 116.0 131.6 0 0 0 0 1 0 0 0 0 0 0 29
30 96.9 59.4 0 0 0 0 0 1 0 0 0 0 0 30
31 97.0 65.5 0 0 0 0 0 0 1 0 0 0 0 31
32 108.1 70.5 0 0 0 0 0 0 0 1 0 0 0 32
33 101.9 81.0 0 0 0 0 0 0 0 0 1 0 0 33
34 107.2 73.3 0 0 0 0 0 0 0 0 0 1 0 34
35 110.2 107.5 0 0 0 0 0 0 0 0 0 0 1 35
36 78.7 88.9 0 0 0 0 0 0 0 0 0 0 0 36
37 96.5 55.8 1 0 0 0 0 0 0 0 0 0 0 37
38 115.2 80.5 0 1 0 0 0 0 0 0 0 0 0 38
39 104.7 86.3 0 0 1 0 0 0 0 0 0 0 0 39
40 109.1 112.6 0 0 0 1 0 0 0 0 0 0 0 40
41 108.4 148.6 0 0 0 0 1 0 0 0 0 0 0 41
42 95.5 47.1 0 0 0 0 0 1 0 0 0 0 0 42
43 97.8 57.8 0 0 0 0 0 0 1 0 0 0 0 43
44 115.1 81.0 0 0 0 0 0 0 0 1 0 0 0 44
45 96.2 60.1 0 0 0 0 0 0 0 0 1 0 0 45
46 112.0 76.1 0 0 0 0 0 0 0 0 0 1 0 46
47 111.8 82.5 0 0 0 0 0 0 0 0 0 0 1 47
48 82.5 66.8 0 0 0 0 0 0 0 0 0 0 0 48
49 100.8 58.7 1 0 0 0 0 0 0 0 0 0 0 49
50 116.0 54.2 0 1 0 0 0 0 0 0 0 0 0 50
51 116.3 103.3 0 0 1 0 0 0 0 0 0 0 0 51
52 116.6 77.8 0 0 0 1 0 0 0 0 0 0 0 52
53 112.9 118.4 0 0 0 0 1 0 0 0 0 0 0 53
54 100.9 64.9 0 0 0 0 0 1 0 0 0 0 0 54
55 104.1 40.8 0 0 0 0 0 0 1 0 0 0 0 55
56 117.4 77.7 0 0 0 0 0 0 0 1 0 0 0 56
57 103.3 66.8 0 0 0 0 0 0 0 0 1 0 0 57
58 111.6 69.2 0 0 0 0 0 0 0 0 0 1 0 58
59 115.0 82.4 0 0 0 0 0 0 0 0 0 0 1 59
60 92.6 62.7 0 0 0 0 0 0 0 0 0 0 0 60
61 105.2 58.2 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Inv M1 M2 M3 M4
76.95083 0.02732 12.36642 27.02595 27.36909 25.77229
M5 M6 M7 M8 M9 M10
24.87543 13.69126 13.39848 26.71838 16.15219 21.95688
M11 t
24.39768 0.17112
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.6511 -2.2457 0.4153 2.3610 8.4699
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 76.95083 3.31804 23.192 < 2e-16 ***
Inv 0.02732 0.02569 1.063 0.293
M1 12.36642 2.57319 4.806 1.62e-05 ***
M2 27.02595 2.69689 10.021 2.99e-13 ***
M3 27.36909 2.68076 10.209 1.63e-13 ***
M4 25.77229 2.67828 9.623 1.09e-12 ***
M5 24.87543 3.29499 7.549 1.21e-09 ***
M6 13.69126 2.70218 5.067 6.71e-06 ***
M7 13.39848 2.71263 4.939 1.03e-05 ***
M8 26.71838 2.66892 10.011 3.09e-13 ***
M9 16.15219 2.66970 6.050 2.26e-07 ***
M10 21.95688 2.66803 8.230 1.16e-10 ***
M11 24.39768 2.71357 8.991 8.83e-12 ***
t 0.17112 0.03483 4.914 1.13e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.215 on 47 degrees of freedom
Multiple R-Squared: 0.8417, Adjusted R-squared: 0.7979
F-statistic: 19.23 on 13 and 47 DF, p-value: 1.517e-14
> postscript(file="/var/www/html/rcomp/tmp/17xix1199490842.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/2u3nc1199490842.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3u97a1199490842.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4gpyj1199490842.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/54v731199490842.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5 6
-5.97140411 -3.66917537 3.22573244 8.46994743 2.36104614 2.27810301
7 8 9 10 11 12
-1.89643356 -5.57048190 1.59306811 -1.15395525 -6.72929076 5.66399576
13 14 15 16 17 18
0.88040385 0.06194906 8.08456538 -6.24487900 -2.24567793 2.59166702
19 20 21 22 23 24
0.60181349 4.82727691 5.09668174 -3.33658622 5.34417316 1.99609667
25 26 27 28 29 30
0.41533330 -0.90087802 -3.09016503 -1.53443812 5.61627406 -0.49838034
31 32 33 34 35 36
-0.44335633 -2.97096558 0.93726829 0.47180487 -0.07436146 -6.83970787
37 38 39 40 41 42
-0.67305566 2.52156303 -8.65114004 -3.54390202 -4.50157864 -3.61584164
43 44 45 46 47 48
-1.48647636 1.68874294 -6.24526585 3.14185516 0.15510457 -4.48946147
49 50 51 52 53 54
1.49426292 1.98654130 0.43100725 2.85327171 -1.23006363 -0.75554804
55 56 57 58 59 60
3.22445277 2.02542763 -1.38175229 0.87688145 1.30437449 3.66907690
61
3.85445969
> postscript(file="/var/www/html/rcomp/tmp/6prey1199490843.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -5.97140411 NA
1 -3.66917537 -5.97140411
2 3.22573244 -3.66917537
3 8.46994743 3.22573244
4 2.36104614 8.46994743
5 2.27810301 2.36104614
6 -1.89643356 2.27810301
7 -5.57048190 -1.89643356
8 1.59306811 -5.57048190
9 -1.15395525 1.59306811
10 -6.72929076 -1.15395525
11 5.66399576 -6.72929076
12 0.88040385 5.66399576
13 0.06194906 0.88040385
14 8.08456538 0.06194906
15 -6.24487900 8.08456538
16 -2.24567793 -6.24487900
17 2.59166702 -2.24567793
18 0.60181349 2.59166702
19 4.82727691 0.60181349
20 5.09668174 4.82727691
21 -3.33658622 5.09668174
22 5.34417316 -3.33658622
23 1.99609667 5.34417316
24 0.41533330 1.99609667
25 -0.90087802 0.41533330
26 -3.09016503 -0.90087802
27 -1.53443812 -3.09016503
28 5.61627406 -1.53443812
29 -0.49838034 5.61627406
30 -0.44335633 -0.49838034
31 -2.97096558 -0.44335633
32 0.93726829 -2.97096558
33 0.47180487 0.93726829
34 -0.07436146 0.47180487
35 -6.83970787 -0.07436146
36 -0.67305566 -6.83970787
37 2.52156303 -0.67305566
38 -8.65114004 2.52156303
39 -3.54390202 -8.65114004
40 -4.50157864 -3.54390202
41 -3.61584164 -4.50157864
42 -1.48647636 -3.61584164
43 1.68874294 -1.48647636
44 -6.24526585 1.68874294
45 3.14185516 -6.24526585
46 0.15510457 3.14185516
47 -4.48946147 0.15510457
48 1.49426292 -4.48946147
49 1.98654130 1.49426292
50 0.43100725 1.98654130
51 2.85327171 0.43100725
52 -1.23006363 2.85327171
53 -0.75554804 -1.23006363
54 3.22445277 -0.75554804
55 2.02542763 3.22445277
56 -1.38175229 2.02542763
57 0.87688145 -1.38175229
58 1.30437449 0.87688145
59 3.66907690 1.30437449
60 3.85445969 3.66907690
61 NA 3.85445969
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.66917537 -5.97140411
[2,] 3.22573244 -3.66917537
[3,] 8.46994743 3.22573244
[4,] 2.36104614 8.46994743
[5,] 2.27810301 2.36104614
[6,] -1.89643356 2.27810301
[7,] -5.57048190 -1.89643356
[8,] 1.59306811 -5.57048190
[9,] -1.15395525 1.59306811
[10,] -6.72929076 -1.15395525
[11,] 5.66399576 -6.72929076
[12,] 0.88040385 5.66399576
[13,] 0.06194906 0.88040385
[14,] 8.08456538 0.06194906
[15,] -6.24487900 8.08456538
[16,] -2.24567793 -6.24487900
[17,] 2.59166702 -2.24567793
[18,] 0.60181349 2.59166702
[19,] 4.82727691 0.60181349
[20,] 5.09668174 4.82727691
[21,] -3.33658622 5.09668174
[22,] 5.34417316 -3.33658622
[23,] 1.99609667 5.34417316
[24,] 0.41533330 1.99609667
[25,] -0.90087802 0.41533330
[26,] -3.09016503 -0.90087802
[27,] -1.53443812 -3.09016503
[28,] 5.61627406 -1.53443812
[29,] -0.49838034 5.61627406
[30,] -0.44335633 -0.49838034
[31,] -2.97096558 -0.44335633
[32,] 0.93726829 -2.97096558
[33,] 0.47180487 0.93726829
[34,] -0.07436146 0.47180487
[35,] -6.83970787 -0.07436146
[36,] -0.67305566 -6.83970787
[37,] 2.52156303 -0.67305566
[38,] -8.65114004 2.52156303
[39,] -3.54390202 -8.65114004
[40,] -4.50157864 -3.54390202
[41,] -3.61584164 -4.50157864
[42,] -1.48647636 -3.61584164
[43,] 1.68874294 -1.48647636
[44,] -6.24526585 1.68874294
[45,] 3.14185516 -6.24526585
[46,] 0.15510457 3.14185516
[47,] -4.48946147 0.15510457
[48,] 1.49426292 -4.48946147
[49,] 1.98654130 1.49426292
[50,] 0.43100725 1.98654130
[51,] 2.85327171 0.43100725
[52,] -1.23006363 2.85327171
[53,] -0.75554804 -1.23006363
[54,] 3.22445277 -0.75554804
[55,] 2.02542763 3.22445277
[56,] -1.38175229 2.02542763
[57,] 0.87688145 -1.38175229
[58,] 1.30437449 0.87688145
[59,] 3.66907690 1.30437449
[60,] 3.85445969 3.66907690
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.66917537 -5.97140411
2 3.22573244 -3.66917537
3 8.46994743 3.22573244
4 2.36104614 8.46994743
5 2.27810301 2.36104614
6 -1.89643356 2.27810301
7 -5.57048190 -1.89643356
8 1.59306811 -5.57048190
9 -1.15395525 1.59306811
10 -6.72929076 -1.15395525
11 5.66399576 -6.72929076
12 0.88040385 5.66399576
13 0.06194906 0.88040385
14 8.08456538 0.06194906
15 -6.24487900 8.08456538
16 -2.24567793 -6.24487900
17 2.59166702 -2.24567793
18 0.60181349 2.59166702
19 4.82727691 0.60181349
20 5.09668174 4.82727691
21 -3.33658622 5.09668174
22 5.34417316 -3.33658622
23 1.99609667 5.34417316
24 0.41533330 1.99609667
25 -0.90087802 0.41533330
26 -3.09016503 -0.90087802
27 -1.53443812 -3.09016503
28 5.61627406 -1.53443812
29 -0.49838034 5.61627406
30 -0.44335633 -0.49838034
31 -2.97096558 -0.44335633
32 0.93726829 -2.97096558
33 0.47180487 0.93726829
34 -0.07436146 0.47180487
35 -6.83970787 -0.07436146
36 -0.67305566 -6.83970787
37 2.52156303 -0.67305566
38 -8.65114004 2.52156303
39 -3.54390202 -8.65114004
40 -4.50157864 -3.54390202
41 -3.61584164 -4.50157864
42 -1.48647636 -3.61584164
43 1.68874294 -1.48647636
44 -6.24526585 1.68874294
45 3.14185516 -6.24526585
46 0.15510457 3.14185516
47 -4.48946147 0.15510457
48 1.49426292 -4.48946147
49 1.98654130 1.49426292
50 0.43100725 1.98654130
51 2.85327171 0.43100725
52 -1.23006363 2.85327171
53 -0.75554804 -1.23006363
54 3.22445277 -0.75554804
55 2.02542763 3.22445277
56 -1.38175229 2.02542763
57 0.87688145 -1.38175229
58 1.30437449 0.87688145
59 3.66907690 1.30437449
60 3.85445969 3.66907690
> 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/74wht1199490843.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8xdwp1199490843.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jx3i1199490843.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/107j351199490843.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/113wp71199490843.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/12y5p31199490843.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/13rgpk1199490844.tab")
>
> system("convert tmp/17xix1199490842.ps tmp/17xix1199490842.png")
> system("convert tmp/2u3nc1199490842.ps tmp/2u3nc1199490842.png")
> system("convert tmp/3u97a1199490842.ps tmp/3u97a1199490842.png")
> system("convert tmp/4gpyj1199490842.ps tmp/4gpyj1199490842.png")
> system("convert tmp/54v731199490842.ps tmp/54v731199490842.png")
> system("convert tmp/6prey1199490843.ps tmp/6prey1199490843.png")
> system("convert tmp/74wht1199490843.ps tmp/74wht1199490843.png")
> system("convert tmp/8xdwp1199490843.ps tmp/8xdwp1199490843.png")
> system("convert tmp/9jx3i1199490843.ps tmp/9jx3i1199490843.png")
>
>
> proc.time()
user system elapsed
2.238 1.414 3.415