R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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.
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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
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(112.1,0,104.2,0,102.4,0,100.3,0,102.6,0,101.5,0,103.4,0,99.4,0,97.9,0,98,0,90.2,0,87.1,0,91.8,0,94.8,0,91.8,0,89.3,0,91.7,0,86.2,0,82.8,0,82.3,0,79.8,0,79.4,0,85.3,0,87.5,0,88.3,0,88.6,0,94.9,0,94.7,0,92.6,0,91.8,0,96.4,0,96.4,0,107.1,0,111.9,0,107.8,0,109.2,0,115.3,0,119.2,0,107.8,0,106.8,0,104.2,0,94.8,0,97.5,0,98.3,0,100.6,0,94.9,1,93.6,1,98,1,104.3,1,103.9,1,105.3,1,102.6,1,103.3,1,107.9,1,107.8,1,109.8,1,110.6,1,110.8,1,119.3,1,128.1,1,127.6,1,137.9,1,151.4,1,143.6,1,143.4,1,141.9,1,135.2,1,133.1,1,129.6,1,134.1,1,136.8,1,143.5,1,162.5,1,163.1,1,157.2,1,158.8,1,155.4,1,148.5,1,154.2,1,153.3,1,149.4,1,147.9,1,156,1,163,1,159.1,1,159.5,1,157.3,1,156.4,1,156.6,1,162.4,1,166.8,1,162.6,1,168.1,1),dim=c(2,93),dimnames=list(c('genotsmiddelen','uitvoersubsidie'),1:93))
> y <- array(NA,dim=c(2,93),dimnames=list(c('genotsmiddelen','uitvoersubsidie'),1:93))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> ylab = ''
> xlab = ''
> main = ''
> #'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
genotsmiddelen uitvoersubsidie M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 112.1 0 1 0 0 0 0 0 0 0 0 0 0 1
2 104.2 0 0 1 0 0 0 0 0 0 0 0 0 2
3 102.4 0 0 0 1 0 0 0 0 0 0 0 0 3
4 100.3 0 0 0 0 1 0 0 0 0 0 0 0 4
5 102.6 0 0 0 0 0 1 0 0 0 0 0 0 5
6 101.5 0 0 0 0 0 0 1 0 0 0 0 0 6
7 103.4 0 0 0 0 0 0 0 1 0 0 0 0 7
8 99.4 0 0 0 0 0 0 0 0 1 0 0 0 8
9 97.9 0 0 0 0 0 0 0 0 0 1 0 0 9
10 98.0 0 0 0 0 0 0 0 0 0 0 1 0 10
11 90.2 0 0 0 0 0 0 0 0 0 0 0 1 11
12 87.1 0 0 0 0 0 0 0 0 0 0 0 0 12
13 91.8 0 1 0 0 0 0 0 0 0 0 0 0 13
14 94.8 0 0 1 0 0 0 0 0 0 0 0 0 14
15 91.8 0 0 0 1 0 0 0 0 0 0 0 0 15
16 89.3 0 0 0 0 1 0 0 0 0 0 0 0 16
17 91.7 0 0 0 0 0 1 0 0 0 0 0 0 17
18 86.2 0 0 0 0 0 0 1 0 0 0 0 0 18
19 82.8 0 0 0 0 0 0 0 1 0 0 0 0 19
20 82.3 0 0 0 0 0 0 0 0 1 0 0 0 20
21 79.8 0 0 0 0 0 0 0 0 0 1 0 0 21
22 79.4 0 0 0 0 0 0 0 0 0 0 1 0 22
23 85.3 0 0 0 0 0 0 0 0 0 0 0 1 23
24 87.5 0 0 0 0 0 0 0 0 0 0 0 0 24
25 88.3 0 1 0 0 0 0 0 0 0 0 0 0 25
26 88.6 0 0 1 0 0 0 0 0 0 0 0 0 26
27 94.9 0 0 0 1 0 0 0 0 0 0 0 0 27
28 94.7 0 0 0 0 1 0 0 0 0 0 0 0 28
29 92.6 0 0 0 0 0 1 0 0 0 0 0 0 29
30 91.8 0 0 0 0 0 0 1 0 0 0 0 0 30
31 96.4 0 0 0 0 0 0 0 1 0 0 0 0 31
32 96.4 0 0 0 0 0 0 0 0 1 0 0 0 32
33 107.1 0 0 0 0 0 0 0 0 0 1 0 0 33
34 111.9 0 0 0 0 0 0 0 0 0 0 1 0 34
35 107.8 0 0 0 0 0 0 0 0 0 0 0 1 35
36 109.2 0 0 0 0 0 0 0 0 0 0 0 0 36
37 115.3 0 1 0 0 0 0 0 0 0 0 0 0 37
38 119.2 0 0 1 0 0 0 0 0 0 0 0 0 38
39 107.8 0 0 0 1 0 0 0 0 0 0 0 0 39
40 106.8 0 0 0 0 1 0 0 0 0 0 0 0 40
41 104.2 0 0 0 0 0 1 0 0 0 0 0 0 41
42 94.8 0 0 0 0 0 0 1 0 0 0 0 0 42
43 97.5 0 0 0 0 0 0 0 1 0 0 0 0 43
44 98.3 0 0 0 0 0 0 0 0 1 0 0 0 44
45 100.6 0 0 0 0 0 0 0 0 0 1 0 0 45
46 94.9 1 0 0 0 0 0 0 0 0 0 1 0 46
47 93.6 1 0 0 0 0 0 0 0 0 0 0 1 47
48 98.0 1 0 0 0 0 0 0 0 0 0 0 0 48
49 104.3 1 1 0 0 0 0 0 0 0 0 0 0 49
50 103.9 1 0 1 0 0 0 0 0 0 0 0 0 50
51 105.3 1 0 0 1 0 0 0 0 0 0 0 0 51
52 102.6 1 0 0 0 1 0 0 0 0 0 0 0 52
53 103.3 1 0 0 0 0 1 0 0 0 0 0 0 53
54 107.9 1 0 0 0 0 0 1 0 0 0 0 0 54
55 107.8 1 0 0 0 0 0 0 1 0 0 0 0 55
56 109.8 1 0 0 0 0 0 0 0 1 0 0 0 56
57 110.6 1 0 0 0 0 0 0 0 0 1 0 0 57
58 110.8 1 0 0 0 0 0 0 0 0 0 1 0 58
59 119.3 1 0 0 0 0 0 0 0 0 0 0 1 59
60 128.1 1 0 0 0 0 0 0 0 0 0 0 0 60
61 127.6 1 1 0 0 0 0 0 0 0 0 0 0 61
62 137.9 1 0 1 0 0 0 0 0 0 0 0 0 62
63 151.4 1 0 0 1 0 0 0 0 0 0 0 0 63
64 143.6 1 0 0 0 1 0 0 0 0 0 0 0 64
65 143.4 1 0 0 0 0 1 0 0 0 0 0 0 65
66 141.9 1 0 0 0 0 0 1 0 0 0 0 0 66
67 135.2 1 0 0 0 0 0 0 1 0 0 0 0 67
68 133.1 1 0 0 0 0 0 0 0 1 0 0 0 68
69 129.6 1 0 0 0 0 0 0 0 0 1 0 0 69
70 134.1 1 0 0 0 0 0 0 0 0 0 1 0 70
71 136.8 1 0 0 0 0 0 0 0 0 0 0 1 71
72 143.5 1 0 0 0 0 0 0 0 0 0 0 0 72
73 162.5 1 1 0 0 0 0 0 0 0 0 0 0 73
74 163.1 1 0 1 0 0 0 0 0 0 0 0 0 74
75 157.2 1 0 0 1 0 0 0 0 0 0 0 0 75
76 158.8 1 0 0 0 1 0 0 0 0 0 0 0 76
77 155.4 1 0 0 0 0 1 0 0 0 0 0 0 77
78 148.5 1 0 0 0 0 0 1 0 0 0 0 0 78
79 154.2 1 0 0 0 0 0 0 1 0 0 0 0 79
80 153.3 1 0 0 0 0 0 0 0 1 0 0 0 80
81 149.4 1 0 0 0 0 0 0 0 0 1 0 0 81
82 147.9 1 0 0 0 0 0 0 0 0 0 1 0 82
83 156.0 1 0 0 0 0 0 0 0 0 0 0 1 83
84 163.0 1 0 0 0 0 0 0 0 0 0 0 0 84
85 159.1 1 1 0 0 0 0 0 0 0 0 0 0 85
86 159.5 1 0 1 0 0 0 0 0 0 0 0 0 86
87 157.3 1 0 0 1 0 0 0 0 0 0 0 0 87
88 156.4 1 0 0 0 1 0 0 0 0 0 0 0 88
89 156.6 1 0 0 0 0 1 0 0 0 0 0 0 89
90 162.4 1 0 0 0 0 0 1 0 0 0 0 0 90
91 166.8 1 0 0 0 0 0 0 1 0 0 0 0 91
92 162.6 1 0 0 0 0 0 0 0 1 0 0 0 92
93 168.1 1 0 0 0 0 0 0 0 0 1 0 0 93
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) uitvoersubsidie M1 M2
73.63069 -3.15231 7.93785 8.27953
M3 M4 M5 M6
6.95871 4.07540 2.80458 0.02126
M7 M8 M9 M10
0.22545 -1.82037 -1.76619 -3.76194
M11 t
-2.98097 0.93332
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-21.5237 -10.0488 0.2519 9.7642 29.5981
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 73.63069 5.68879 12.943 < 2e-16 ***
uitvoersubsidie -3.15231 5.58111 -0.565 0.574
M1 7.93785 6.84679 1.159 0.250
M2 8.27953 6.84494 1.210 0.230
M3 6.95871 6.84468 1.017 0.312
M4 4.07540 6.84600 0.595 0.553
M5 2.80458 6.84890 0.409 0.683
M6 0.02126 6.85338 0.003 0.998
M7 0.22545 6.85944 0.033 0.974
M8 -1.82037 6.86707 -0.265 0.792
M9 -1.76619 6.87626 -0.257 0.798
M10 -3.76194 7.06922 -0.532 0.596
M11 -2.98097 7.06692 -0.422 0.674
t 0.93332 0.10409 8.966 1.13e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 13.22 on 79 degrees of freedom
Multiple R-squared: 0.7921, Adjusted R-squared: 0.7579
F-statistic: 23.15 on 13 and 79 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/rcomp/tmp/1akl81227545718.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/2kwij1227545718.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3ja3q1227545718.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4afg81227545718.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/52ha11227545718.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 93
Frequency = 1
1 2 3 4 5 6
29.5981481 20.4231481 19.0106481 18.8606481 21.4981481 22.2481481
7 8 9 10 11 12
23.0106481 20.1231481 17.6356481 18.7980820 9.2837963 2.2695106
13 14 15 16 17 18
-1.9016534 -0.1766534 -2.7891534 -3.3391534 -0.6016534 -4.2516534
19 20 21 22 23 24
-8.7891534 -8.1766534 -11.6641534 -11.0017196 -6.8160053 -8.5302910
25 26 27 28 29 30
-16.6014550 -17.5764550 -10.8889550 -9.1389550 -10.9014550 -9.8514550
31 32 33 34 35 36
-6.3889550 -5.2764550 4.4360450 10.2984788 4.4841931 1.9699074
37 38 39 40 41 42
-0.8012566 1.8237434 -9.1887566 -8.2387566 -10.5012566 -18.0512566
43 44 45 46 47 48
-16.4887566 -14.5762566 -13.2637566 -14.7490079 -17.7632937 -17.2775794
49 50 51 52 53 54
-19.8487434 -21.5237434 -19.7362434 -20.4862434 -19.4487434 -12.9987434
55 56 57 58 59 60
-14.2362434 -11.1237434 -11.3112434 -10.0488095 -3.2630952 1.6226190
61 62 63 64 65 66
-7.7485450 1.2764550 15.1639550 9.3139550 9.4514550 9.8014550
67 68 69 70 71 72
1.9639550 0.9764550 -3.5110450 2.0513889 3.0371032 5.8228175
73 74 75 76 77 78
15.9516534 15.2766534 9.7641534 13.3141534 10.2516534 5.2016534
79 80 81 82 83 84
9.7641534 9.9766534 5.0891534 4.6515873 11.0373016 14.1230159
85 86 87 88 89 90
1.3518519 0.4768519 -1.3356481 -0.2856481 0.2518519 7.9018519
91 92 93
11.1643519 8.0768519 12.5893519
> postscript(file="/var/www/html/rcomp/tmp/6l6hq1227545718.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 = 93
Frequency = 1
lag(myerror, k = 1) myerror
0 29.5981481 NA
1 20.4231481 29.5981481
2 19.0106481 20.4231481
3 18.8606481 19.0106481
4 21.4981481 18.8606481
5 22.2481481 21.4981481
6 23.0106481 22.2481481
7 20.1231481 23.0106481
8 17.6356481 20.1231481
9 18.7980820 17.6356481
10 9.2837963 18.7980820
11 2.2695106 9.2837963
12 -1.9016534 2.2695106
13 -0.1766534 -1.9016534
14 -2.7891534 -0.1766534
15 -3.3391534 -2.7891534
16 -0.6016534 -3.3391534
17 -4.2516534 -0.6016534
18 -8.7891534 -4.2516534
19 -8.1766534 -8.7891534
20 -11.6641534 -8.1766534
21 -11.0017196 -11.6641534
22 -6.8160053 -11.0017196
23 -8.5302910 -6.8160053
24 -16.6014550 -8.5302910
25 -17.5764550 -16.6014550
26 -10.8889550 -17.5764550
27 -9.1389550 -10.8889550
28 -10.9014550 -9.1389550
29 -9.8514550 -10.9014550
30 -6.3889550 -9.8514550
31 -5.2764550 -6.3889550
32 4.4360450 -5.2764550
33 10.2984788 4.4360450
34 4.4841931 10.2984788
35 1.9699074 4.4841931
36 -0.8012566 1.9699074
37 1.8237434 -0.8012566
38 -9.1887566 1.8237434
39 -8.2387566 -9.1887566
40 -10.5012566 -8.2387566
41 -18.0512566 -10.5012566
42 -16.4887566 -18.0512566
43 -14.5762566 -16.4887566
44 -13.2637566 -14.5762566
45 -14.7490079 -13.2637566
46 -17.7632937 -14.7490079
47 -17.2775794 -17.7632937
48 -19.8487434 -17.2775794
49 -21.5237434 -19.8487434
50 -19.7362434 -21.5237434
51 -20.4862434 -19.7362434
52 -19.4487434 -20.4862434
53 -12.9987434 -19.4487434
54 -14.2362434 -12.9987434
55 -11.1237434 -14.2362434
56 -11.3112434 -11.1237434
57 -10.0488095 -11.3112434
58 -3.2630952 -10.0488095
59 1.6226190 -3.2630952
60 -7.7485450 1.6226190
61 1.2764550 -7.7485450
62 15.1639550 1.2764550
63 9.3139550 15.1639550
64 9.4514550 9.3139550
65 9.8014550 9.4514550
66 1.9639550 9.8014550
67 0.9764550 1.9639550
68 -3.5110450 0.9764550
69 2.0513889 -3.5110450
70 3.0371032 2.0513889
71 5.8228175 3.0371032
72 15.9516534 5.8228175
73 15.2766534 15.9516534
74 9.7641534 15.2766534
75 13.3141534 9.7641534
76 10.2516534 13.3141534
77 5.2016534 10.2516534
78 9.7641534 5.2016534
79 9.9766534 9.7641534
80 5.0891534 9.9766534
81 4.6515873 5.0891534
82 11.0373016 4.6515873
83 14.1230159 11.0373016
84 1.3518519 14.1230159
85 0.4768519 1.3518519
86 -1.3356481 0.4768519
87 -0.2856481 -1.3356481
88 0.2518519 -0.2856481
89 7.9018519 0.2518519
90 11.1643519 7.9018519
91 8.0768519 11.1643519
92 12.5893519 8.0768519
93 NA 12.5893519
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 20.4231481 29.5981481
[2,] 19.0106481 20.4231481
[3,] 18.8606481 19.0106481
[4,] 21.4981481 18.8606481
[5,] 22.2481481 21.4981481
[6,] 23.0106481 22.2481481
[7,] 20.1231481 23.0106481
[8,] 17.6356481 20.1231481
[9,] 18.7980820 17.6356481
[10,] 9.2837963 18.7980820
[11,] 2.2695106 9.2837963
[12,] -1.9016534 2.2695106
[13,] -0.1766534 -1.9016534
[14,] -2.7891534 -0.1766534
[15,] -3.3391534 -2.7891534
[16,] -0.6016534 -3.3391534
[17,] -4.2516534 -0.6016534
[18,] -8.7891534 -4.2516534
[19,] -8.1766534 -8.7891534
[20,] -11.6641534 -8.1766534
[21,] -11.0017196 -11.6641534
[22,] -6.8160053 -11.0017196
[23,] -8.5302910 -6.8160053
[24,] -16.6014550 -8.5302910
[25,] -17.5764550 -16.6014550
[26,] -10.8889550 -17.5764550
[27,] -9.1389550 -10.8889550
[28,] -10.9014550 -9.1389550
[29,] -9.8514550 -10.9014550
[30,] -6.3889550 -9.8514550
[31,] -5.2764550 -6.3889550
[32,] 4.4360450 -5.2764550
[33,] 10.2984788 4.4360450
[34,] 4.4841931 10.2984788
[35,] 1.9699074 4.4841931
[36,] -0.8012566 1.9699074
[37,] 1.8237434 -0.8012566
[38,] -9.1887566 1.8237434
[39,] -8.2387566 -9.1887566
[40,] -10.5012566 -8.2387566
[41,] -18.0512566 -10.5012566
[42,] -16.4887566 -18.0512566
[43,] -14.5762566 -16.4887566
[44,] -13.2637566 -14.5762566
[45,] -14.7490079 -13.2637566
[46,] -17.7632937 -14.7490079
[47,] -17.2775794 -17.7632937
[48,] -19.8487434 -17.2775794
[49,] -21.5237434 -19.8487434
[50,] -19.7362434 -21.5237434
[51,] -20.4862434 -19.7362434
[52,] -19.4487434 -20.4862434
[53,] -12.9987434 -19.4487434
[54,] -14.2362434 -12.9987434
[55,] -11.1237434 -14.2362434
[56,] -11.3112434 -11.1237434
[57,] -10.0488095 -11.3112434
[58,] -3.2630952 -10.0488095
[59,] 1.6226190 -3.2630952
[60,] -7.7485450 1.6226190
[61,] 1.2764550 -7.7485450
[62,] 15.1639550 1.2764550
[63,] 9.3139550 15.1639550
[64,] 9.4514550 9.3139550
[65,] 9.8014550 9.4514550
[66,] 1.9639550 9.8014550
[67,] 0.9764550 1.9639550
[68,] -3.5110450 0.9764550
[69,] 2.0513889 -3.5110450
[70,] 3.0371032 2.0513889
[71,] 5.8228175 3.0371032
[72,] 15.9516534 5.8228175
[73,] 15.2766534 15.9516534
[74,] 9.7641534 15.2766534
[75,] 13.3141534 9.7641534
[76,] 10.2516534 13.3141534
[77,] 5.2016534 10.2516534
[78,] 9.7641534 5.2016534
[79,] 9.9766534 9.7641534
[80,] 5.0891534 9.9766534
[81,] 4.6515873 5.0891534
[82,] 11.0373016 4.6515873
[83,] 14.1230159 11.0373016
[84,] 1.3518519 14.1230159
[85,] 0.4768519 1.3518519
[86,] -1.3356481 0.4768519
[87,] -0.2856481 -1.3356481
[88,] 0.2518519 -0.2856481
[89,] 7.9018519 0.2518519
[90,] 11.1643519 7.9018519
[91,] 8.0768519 11.1643519
[92,] 12.5893519 8.0768519
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 20.4231481 29.5981481
2 19.0106481 20.4231481
3 18.8606481 19.0106481
4 21.4981481 18.8606481
5 22.2481481 21.4981481
6 23.0106481 22.2481481
7 20.1231481 23.0106481
8 17.6356481 20.1231481
9 18.7980820 17.6356481
10 9.2837963 18.7980820
11 2.2695106 9.2837963
12 -1.9016534 2.2695106
13 -0.1766534 -1.9016534
14 -2.7891534 -0.1766534
15 -3.3391534 -2.7891534
16 -0.6016534 -3.3391534
17 -4.2516534 -0.6016534
18 -8.7891534 -4.2516534
19 -8.1766534 -8.7891534
20 -11.6641534 -8.1766534
21 -11.0017196 -11.6641534
22 -6.8160053 -11.0017196
23 -8.5302910 -6.8160053
24 -16.6014550 -8.5302910
25 -17.5764550 -16.6014550
26 -10.8889550 -17.5764550
27 -9.1389550 -10.8889550
28 -10.9014550 -9.1389550
29 -9.8514550 -10.9014550
30 -6.3889550 -9.8514550
31 -5.2764550 -6.3889550
32 4.4360450 -5.2764550
33 10.2984788 4.4360450
34 4.4841931 10.2984788
35 1.9699074 4.4841931
36 -0.8012566 1.9699074
37 1.8237434 -0.8012566
38 -9.1887566 1.8237434
39 -8.2387566 -9.1887566
40 -10.5012566 -8.2387566
41 -18.0512566 -10.5012566
42 -16.4887566 -18.0512566
43 -14.5762566 -16.4887566
44 -13.2637566 -14.5762566
45 -14.7490079 -13.2637566
46 -17.7632937 -14.7490079
47 -17.2775794 -17.7632937
48 -19.8487434 -17.2775794
49 -21.5237434 -19.8487434
50 -19.7362434 -21.5237434
51 -20.4862434 -19.7362434
52 -19.4487434 -20.4862434
53 -12.9987434 -19.4487434
54 -14.2362434 -12.9987434
55 -11.1237434 -14.2362434
56 -11.3112434 -11.1237434
57 -10.0488095 -11.3112434
58 -3.2630952 -10.0488095
59 1.6226190 -3.2630952
60 -7.7485450 1.6226190
61 1.2764550 -7.7485450
62 15.1639550 1.2764550
63 9.3139550 15.1639550
64 9.4514550 9.3139550
65 9.8014550 9.4514550
66 1.9639550 9.8014550
67 0.9764550 1.9639550
68 -3.5110450 0.9764550
69 2.0513889 -3.5110450
70 3.0371032 2.0513889
71 5.8228175 3.0371032
72 15.9516534 5.8228175
73 15.2766534 15.9516534
74 9.7641534 15.2766534
75 13.3141534 9.7641534
76 10.2516534 13.3141534
77 5.2016534 10.2516534
78 9.7641534 5.2016534
79 9.9766534 9.7641534
80 5.0891534 9.9766534
81 4.6515873 5.0891534
82 11.0373016 4.6515873
83 14.1230159 11.0373016
84 1.3518519 14.1230159
85 0.4768519 1.3518519
86 -1.3356481 0.4768519
87 -0.2856481 -1.3356481
88 0.2518519 -0.2856481
89 7.9018519 0.2518519
90 11.1643519 7.9018519
91 8.0768519 11.1643519
92 12.5893519 8.0768519
> 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/7cjrd1227545718.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8tajv1227545718.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9swk11227545718.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/100cc71227545718.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/114sua1227545718.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/12yau51227545718.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/13l1xa1227545718.tab")
>
> system("convert tmp/1akl81227545718.ps tmp/1akl81227545718.png")
> system("convert tmp/2kwij1227545718.ps tmp/2kwij1227545718.png")
> system("convert tmp/3ja3q1227545718.ps tmp/3ja3q1227545718.png")
> system("convert tmp/4afg81227545718.ps tmp/4afg81227545718.png")
> system("convert tmp/52ha11227545718.ps tmp/52ha11227545718.png")
> system("convert tmp/6l6hq1227545718.ps tmp/6l6hq1227545718.png")
> system("convert tmp/7cjrd1227545718.ps tmp/7cjrd1227545718.png")
> system("convert tmp/8tajv1227545718.ps tmp/8tajv1227545718.png")
> system("convert tmp/9swk11227545718.ps tmp/9swk11227545718.png")
>
>
> proc.time()
user system elapsed
2.031 1.449 2.411