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(97.3,0,101,0,113.2,0,101,0,105.7,0,113.9,0,86.4,0,96.5,0,103.3,0,114.9,0,105.8,0,94.2,0,98.4,0,99.4,0,108.8,0,112.6,0,104.4,0,112.2,0,81.1,0,97.1,0,112.6,0,113.8,0,107.8,0,103.2,0,103.3,0,101.2,0,107.7,0,110.4,0,101.9,0,115.9,0,89.9,0,88.6,0,117.2,0,123.9,0,100,0,103.6,0,94.1,0,98.7,0,119.5,0,112.7,0,104.4,0,124.7,0,89.1,0,97,0,121.6,0,118.8,0,114,0,111.5,0,97.2,0,102.5,0,113.4,0,109.8,0,104.9,0,126.1,0,80,0,96.8,0,117.2,1,112.3,1,117.3,1,111.1,1,102.2,1,104.3,1,122.9,1,107.6,1,121.3,1,131.5,1,89,1,104.4,1,128.9,1,135.9,1,133.3,1,121.3,1,120.5,1,120.4,1,137.9,1,126.1,1,133.2,1,146.6,1,103.4,1,117.2,1),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 = '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\r t
1 97.3 0 1
2 101.0 0 2
3 113.2 0 3
4 101.0 0 4
5 105.7 0 5
6 113.9 0 6
7 86.4 0 7
8 96.5 0 8
9 103.3 0 9
10 114.9 0 10
11 105.8 0 11
12 94.2 0 12
13 98.4 0 13
14 99.4 0 14
15 108.8 0 15
16 112.6 0 16
17 104.4 0 17
18 112.2 0 18
19 81.1 0 19
20 97.1 0 20
21 112.6 0 21
22 113.8 0 22
23 107.8 0 23
24 103.2 0 24
25 103.3 0 25
26 101.2 0 26
27 107.7 0 27
28 110.4 0 28
29 101.9 0 29
30 115.9 0 30
31 89.9 0 31
32 88.6 0 32
33 117.2 0 33
34 123.9 0 34
35 100.0 0 35
36 103.6 0 36
37 94.1 0 37
38 98.7 0 38
39 119.5 0 39
40 112.7 0 40
41 104.4 0 41
42 124.7 0 42
43 89.1 0 43
44 97.0 0 44
45 121.6 0 45
46 118.8 0 46
47 114.0 0 47
48 111.5 0 48
49 97.2 0 49
50 102.5 0 50
51 113.4 0 51
52 109.8 0 52
53 104.9 0 53
54 126.1 0 54
55 80.0 0 55
56 96.8 0 56
57 117.2 1 57
58 112.3 1 58
59 117.3 1 59
60 111.1 1 60
61 102.2 1 61
62 104.3 1 62
63 122.9 1 63
64 107.6 1 64
65 121.3 1 65
66 131.5 1 66
67 89.0 1 67
68 104.4 1 68
69 128.9 1 69
70 135.9 1 70
71 133.3 1 71
72 121.3 1 72
73 120.5 1 73
74 120.4 1 74
75 137.9 1 75
76 126.1 1 76
77 133.2 1 77
78 146.6 1 78
79 103.4 1 79
80 117.2 1 80
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `x\r` t
101.1121 8.7370 0.1396
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-30.1990 -6.1980 0.3439 8.7458 25.8659
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 101.11211 3.00393 33.660 <2e-16 ***
`x\r` 8.73705 4.57647 1.909 0.060 .
t 0.13955 0.09082 1.537 0.128
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.41 on 77 degrees of freedom
Multiple R-Squared: 0.2724, Adjusted R-squared: 0.2535
F-statistic: 14.41 on 2 and 77 DF, p-value: 4.819e-06
> postscript(file="/var/www/html/rcomp/tmp/1ukqc1196781536.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/2pv2f1196781536.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/39y801196781536.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4anbf1196781536.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5nqzq1196781536.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-3.9516590 -0.3912090 11.6692409 -0.6703092 3.8901408 11.9505907
7 8 9 10 11 12
-15.6889594 -5.7285094 0.9319405 12.3923905 3.1528404 -8.5867097
13 14 15 16 17 18
-4.5262597 -3.6658098 5.5946401 9.2550901 0.9155400 8.5759900
19 20 21 22 23 24
-22.6635601 -6.8031102 8.5573398 9.6177897 3.4782396 -1.2613104
25 26 27 28 29 30
-1.3008605 -3.5404106 2.8200394 5.3804893 -3.2590607 10.6013892
31 32 33 34 35 36
-15.5381609 -16.9777109 11.4827390 18.0431889 -5.9963611 -2.5359112
37 38 39 40 41 42
-12.1754613 -7.7150113 12.9454386 6.0058886 -2.4336615 17.7267884
43 44 45 46 47 48
-18.0127616 -10.2523117 14.2081382 11.2685882 6.3290381 3.6894880
49 50 51 52 53 54
-10.7500620 -5.5896121 5.1708379 1.4312878 -3.6082623 17.4521877
55 56 57 58 59 60
-28.7873624 -12.1269125 -0.6035076 -5.6430577 -0.7826077 -7.1221578
61 62 63 64 65 66
-16.1617079 -14.2012579 4.2591920 -11.1803580 2.3800919 12.4405418
67 68 69 70 71 72
-30.1990082 -14.9385583 9.4218916 16.2823416 13.5427915 1.4032414
73 74 75 76 77 78
0.4636914 0.2241413 17.5845913 5.6450412 12.6054911 25.8659411
79 80
-17.4736090 -3.8131591
> postscript(file="/var/www/html/rcomp/tmp/6bkbx1196781536.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 -3.9516590 NA
1 -0.3912090 -3.9516590
2 11.6692409 -0.3912090
3 -0.6703092 11.6692409
4 3.8901408 -0.6703092
5 11.9505907 3.8901408
6 -15.6889594 11.9505907
7 -5.7285094 -15.6889594
8 0.9319405 -5.7285094
9 12.3923905 0.9319405
10 3.1528404 12.3923905
11 -8.5867097 3.1528404
12 -4.5262597 -8.5867097
13 -3.6658098 -4.5262597
14 5.5946401 -3.6658098
15 9.2550901 5.5946401
16 0.9155400 9.2550901
17 8.5759900 0.9155400
18 -22.6635601 8.5759900
19 -6.8031102 -22.6635601
20 8.5573398 -6.8031102
21 9.6177897 8.5573398
22 3.4782396 9.6177897
23 -1.2613104 3.4782396
24 -1.3008605 -1.2613104
25 -3.5404106 -1.3008605
26 2.8200394 -3.5404106
27 5.3804893 2.8200394
28 -3.2590607 5.3804893
29 10.6013892 -3.2590607
30 -15.5381609 10.6013892
31 -16.9777109 -15.5381609
32 11.4827390 -16.9777109
33 18.0431889 11.4827390
34 -5.9963611 18.0431889
35 -2.5359112 -5.9963611
36 -12.1754613 -2.5359112
37 -7.7150113 -12.1754613
38 12.9454386 -7.7150113
39 6.0058886 12.9454386
40 -2.4336615 6.0058886
41 17.7267884 -2.4336615
42 -18.0127616 17.7267884
43 -10.2523117 -18.0127616
44 14.2081382 -10.2523117
45 11.2685882 14.2081382
46 6.3290381 11.2685882
47 3.6894880 6.3290381
48 -10.7500620 3.6894880
49 -5.5896121 -10.7500620
50 5.1708379 -5.5896121
51 1.4312878 5.1708379
52 -3.6082623 1.4312878
53 17.4521877 -3.6082623
54 -28.7873624 17.4521877
55 -12.1269125 -28.7873624
56 -0.6035076 -12.1269125
57 -5.6430577 -0.6035076
58 -0.7826077 -5.6430577
59 -7.1221578 -0.7826077
60 -16.1617079 -7.1221578
61 -14.2012579 -16.1617079
62 4.2591920 -14.2012579
63 -11.1803580 4.2591920
64 2.3800919 -11.1803580
65 12.4405418 2.3800919
66 -30.1990082 12.4405418
67 -14.9385583 -30.1990082
68 9.4218916 -14.9385583
69 16.2823416 9.4218916
70 13.5427915 16.2823416
71 1.4032414 13.5427915
72 0.4636914 1.4032414
73 0.2241413 0.4636914
74 17.5845913 0.2241413
75 5.6450412 17.5845913
76 12.6054911 5.6450412
77 25.8659411 12.6054911
78 -17.4736090 25.8659411
79 -3.8131591 -17.4736090
80 NA -3.8131591
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.3912090 -3.9516590
[2,] 11.6692409 -0.3912090
[3,] -0.6703092 11.6692409
[4,] 3.8901408 -0.6703092
[5,] 11.9505907 3.8901408
[6,] -15.6889594 11.9505907
[7,] -5.7285094 -15.6889594
[8,] 0.9319405 -5.7285094
[9,] 12.3923905 0.9319405
[10,] 3.1528404 12.3923905
[11,] -8.5867097 3.1528404
[12,] -4.5262597 -8.5867097
[13,] -3.6658098 -4.5262597
[14,] 5.5946401 -3.6658098
[15,] 9.2550901 5.5946401
[16,] 0.9155400 9.2550901
[17,] 8.5759900 0.9155400
[18,] -22.6635601 8.5759900
[19,] -6.8031102 -22.6635601
[20,] 8.5573398 -6.8031102
[21,] 9.6177897 8.5573398
[22,] 3.4782396 9.6177897
[23,] -1.2613104 3.4782396
[24,] -1.3008605 -1.2613104
[25,] -3.5404106 -1.3008605
[26,] 2.8200394 -3.5404106
[27,] 5.3804893 2.8200394
[28,] -3.2590607 5.3804893
[29,] 10.6013892 -3.2590607
[30,] -15.5381609 10.6013892
[31,] -16.9777109 -15.5381609
[32,] 11.4827390 -16.9777109
[33,] 18.0431889 11.4827390
[34,] -5.9963611 18.0431889
[35,] -2.5359112 -5.9963611
[36,] -12.1754613 -2.5359112
[37,] -7.7150113 -12.1754613
[38,] 12.9454386 -7.7150113
[39,] 6.0058886 12.9454386
[40,] -2.4336615 6.0058886
[41,] 17.7267884 -2.4336615
[42,] -18.0127616 17.7267884
[43,] -10.2523117 -18.0127616
[44,] 14.2081382 -10.2523117
[45,] 11.2685882 14.2081382
[46,] 6.3290381 11.2685882
[47,] 3.6894880 6.3290381
[48,] -10.7500620 3.6894880
[49,] -5.5896121 -10.7500620
[50,] 5.1708379 -5.5896121
[51,] 1.4312878 5.1708379
[52,] -3.6082623 1.4312878
[53,] 17.4521877 -3.6082623
[54,] -28.7873624 17.4521877
[55,] -12.1269125 -28.7873624
[56,] -0.6035076 -12.1269125
[57,] -5.6430577 -0.6035076
[58,] -0.7826077 -5.6430577
[59,] -7.1221578 -0.7826077
[60,] -16.1617079 -7.1221578
[61,] -14.2012579 -16.1617079
[62,] 4.2591920 -14.2012579
[63,] -11.1803580 4.2591920
[64,] 2.3800919 -11.1803580
[65,] 12.4405418 2.3800919
[66,] -30.1990082 12.4405418
[67,] -14.9385583 -30.1990082
[68,] 9.4218916 -14.9385583
[69,] 16.2823416 9.4218916
[70,] 13.5427915 16.2823416
[71,] 1.4032414 13.5427915
[72,] 0.4636914 1.4032414
[73,] 0.2241413 0.4636914
[74,] 17.5845913 0.2241413
[75,] 5.6450412 17.5845913
[76,] 12.6054911 5.6450412
[77,] 25.8659411 12.6054911
[78,] -17.4736090 25.8659411
[79,] -3.8131591 -17.4736090
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.3912090 -3.9516590
2 11.6692409 -0.3912090
3 -0.6703092 11.6692409
4 3.8901408 -0.6703092
5 11.9505907 3.8901408
6 -15.6889594 11.9505907
7 -5.7285094 -15.6889594
8 0.9319405 -5.7285094
9 12.3923905 0.9319405
10 3.1528404 12.3923905
11 -8.5867097 3.1528404
12 -4.5262597 -8.5867097
13 -3.6658098 -4.5262597
14 5.5946401 -3.6658098
15 9.2550901 5.5946401
16 0.9155400 9.2550901
17 8.5759900 0.9155400
18 -22.6635601 8.5759900
19 -6.8031102 -22.6635601
20 8.5573398 -6.8031102
21 9.6177897 8.5573398
22 3.4782396 9.6177897
23 -1.2613104 3.4782396
24 -1.3008605 -1.2613104
25 -3.5404106 -1.3008605
26 2.8200394 -3.5404106
27 5.3804893 2.8200394
28 -3.2590607 5.3804893
29 10.6013892 -3.2590607
30 -15.5381609 10.6013892
31 -16.9777109 -15.5381609
32 11.4827390 -16.9777109
33 18.0431889 11.4827390
34 -5.9963611 18.0431889
35 -2.5359112 -5.9963611
36 -12.1754613 -2.5359112
37 -7.7150113 -12.1754613
38 12.9454386 -7.7150113
39 6.0058886 12.9454386
40 -2.4336615 6.0058886
41 17.7267884 -2.4336615
42 -18.0127616 17.7267884
43 -10.2523117 -18.0127616
44 14.2081382 -10.2523117
45 11.2685882 14.2081382
46 6.3290381 11.2685882
47 3.6894880 6.3290381
48 -10.7500620 3.6894880
49 -5.5896121 -10.7500620
50 5.1708379 -5.5896121
51 1.4312878 5.1708379
52 -3.6082623 1.4312878
53 17.4521877 -3.6082623
54 -28.7873624 17.4521877
55 -12.1269125 -28.7873624
56 -0.6035076 -12.1269125
57 -5.6430577 -0.6035076
58 -0.7826077 -5.6430577
59 -7.1221578 -0.7826077
60 -16.1617079 -7.1221578
61 -14.2012579 -16.1617079
62 4.2591920 -14.2012579
63 -11.1803580 4.2591920
64 2.3800919 -11.1803580
65 12.4405418 2.3800919
66 -30.1990082 12.4405418
67 -14.9385583 -30.1990082
68 9.4218916 -14.9385583
69 16.2823416 9.4218916
70 13.5427915 16.2823416
71 1.4032414 13.5427915
72 0.4636914 1.4032414
73 0.2241413 0.4636914
74 17.5845913 0.2241413
75 5.6450412 17.5845913
76 12.6054911 5.6450412
77 25.8659411 12.6054911
78 -17.4736090 25.8659411
79 -3.8131591 -17.4736090
> 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/7je9u1196781536.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kueg1196781536.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9hjc91196781536.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10ugo21196781536.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/11wluv1196781537.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/126j3s1196781537.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/13o5hb1196781537.tab")
>
> system("convert tmp/1ukqc1196781536.ps tmp/1ukqc1196781536.png")
> system("convert tmp/2pv2f1196781536.ps tmp/2pv2f1196781536.png")
> system("convert tmp/39y801196781536.ps tmp/39y801196781536.png")
> system("convert tmp/4anbf1196781536.ps tmp/4anbf1196781536.png")
> system("convert tmp/5nqzq1196781536.ps tmp/5nqzq1196781536.png")
> system("convert tmp/6bkbx1196781536.ps tmp/6bkbx1196781536.png")
> system("convert tmp/7je9u1196781536.ps tmp/7je9u1196781536.png")
> system("convert tmp/8kueg1196781536.ps tmp/8kueg1196781536.png")
> system("convert tmp/9hjc91196781536.ps tmp/9hjc91196781536.png")
>
>
> proc.time()
user system elapsed
2.366 1.480 2.772