R version 2.6.1 (2007-11-26)
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.
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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(25.62,0,27.5,0,24.5,0,25.66,0,28.31,0,27.85,1,24.61,0,25.68,0,25.62,1,20.54,1,18.8,0,18.71,0,19.46,0,20.12,0,23.54,0,25.6,0,25.39,0,24.09,0,25.69,0,26.56,0,28.33,0,27.5,0,24.23,0,28.23,1,31.29,0,32.72,0,30.46,0,24.89,0,25.68,0,27.52,0,28.4,0,29.71,0,26.85,0,29.62,0,28.69,0,29.76,0,31.3,0,30.86,0,33.46,0,33.15,0,37.99,0,35.24,0,38.24,0,43.16,0,43.33,0,49.67,0,43.17,0,39.56,1,44.36,0,45.22,0,53.1,0,52.1,0,48.52,0,54.84,0,57.57,0,64.14,0,62.85,0,58.75,0,55.33,0,57.03,0,63.18,0,60.19,0,62.12,0,70.12,1,69.75,1,68.56,1,73.77,1,73.23,1,61.96,0,57.81,0,58.76,0,62.47,1,53.68,1,57.56,1,62.05,1,67.49,1,67.21,1,71.05,1,76.93,1,70.76,1),dim=c(2,80),dimnames=list(c('Prijs_Brentolie','Aanslagen_Nigeria'),1:80))
> y <- array(NA,dim=c(2,80),dimnames=list(c('Prijs_Brentolie','Aanslagen_Nigeria'),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 = '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
Prijs_Brentolie Aanslagen_Nigeria M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 25.62 0 1 0 0 0 0 0 0 0 0 0 0 1
2 27.50 0 0 1 0 0 0 0 0 0 0 0 0 2
3 24.50 0 0 0 1 0 0 0 0 0 0 0 0 3
4 25.66 0 0 0 0 1 0 0 0 0 0 0 0 4
5 28.31 0 0 0 0 0 1 0 0 0 0 0 0 5
6 27.85 1 0 0 0 0 0 1 0 0 0 0 0 6
7 24.61 0 0 0 0 0 0 0 1 0 0 0 0 7
8 25.68 0 0 0 0 0 0 0 0 1 0 0 0 8
9 25.62 1 0 0 0 0 0 0 0 0 1 0 0 9
10 20.54 1 0 0 0 0 0 0 0 0 0 1 0 10
11 18.80 0 0 0 0 0 0 0 0 0 0 0 1 11
12 18.71 0 0 0 0 0 0 0 0 0 0 0 0 12
13 19.46 0 1 0 0 0 0 0 0 0 0 0 0 13
14 20.12 0 0 1 0 0 0 0 0 0 0 0 0 14
15 23.54 0 0 0 1 0 0 0 0 0 0 0 0 15
16 25.60 0 0 0 0 1 0 0 0 0 0 0 0 16
17 25.39 0 0 0 0 0 1 0 0 0 0 0 0 17
18 24.09 0 0 0 0 0 0 1 0 0 0 0 0 18
19 25.69 0 0 0 0 0 0 0 1 0 0 0 0 19
20 26.56 0 0 0 0 0 0 0 0 1 0 0 0 20
21 28.33 0 0 0 0 0 0 0 0 0 1 0 0 21
22 27.50 0 0 0 0 0 0 0 0 0 0 1 0 22
23 24.23 0 0 0 0 0 0 0 0 0 0 0 1 23
24 28.23 1 0 0 0 0 0 0 0 0 0 0 0 24
25 31.29 0 1 0 0 0 0 0 0 0 0 0 0 25
26 32.72 0 0 1 0 0 0 0 0 0 0 0 0 26
27 30.46 0 0 0 1 0 0 0 0 0 0 0 0 27
28 24.89 0 0 0 0 1 0 0 0 0 0 0 0 28
29 25.68 0 0 0 0 0 1 0 0 0 0 0 0 29
30 27.52 0 0 0 0 0 0 1 0 0 0 0 0 30
31 28.40 0 0 0 0 0 0 0 1 0 0 0 0 31
32 29.71 0 0 0 0 0 0 0 0 1 0 0 0 32
33 26.85 0 0 0 0 0 0 0 0 0 1 0 0 33
34 29.62 0 0 0 0 0 0 0 0 0 0 1 0 34
35 28.69 0 0 0 0 0 0 0 0 0 0 0 1 35
36 29.76 0 0 0 0 0 0 0 0 0 0 0 0 36
37 31.30 0 1 0 0 0 0 0 0 0 0 0 0 37
38 30.86 0 0 1 0 0 0 0 0 0 0 0 0 38
39 33.46 0 0 0 1 0 0 0 0 0 0 0 0 39
40 33.15 0 0 0 0 1 0 0 0 0 0 0 0 40
41 37.99 0 0 0 0 0 1 0 0 0 0 0 0 41
42 35.24 0 0 0 0 0 0 1 0 0 0 0 0 42
43 38.24 0 0 0 0 0 0 0 1 0 0 0 0 43
44 43.16 0 0 0 0 0 0 0 0 1 0 0 0 44
45 43.33 0 0 0 0 0 0 0 0 0 1 0 0 45
46 49.67 0 0 0 0 0 0 0 0 0 0 1 0 46
47 43.17 0 0 0 0 0 0 0 0 0 0 0 1 47
48 39.56 1 0 0 0 0 0 0 0 0 0 0 0 48
49 44.36 0 1 0 0 0 0 0 0 0 0 0 0 49
50 45.22 0 0 1 0 0 0 0 0 0 0 0 0 50
51 53.10 0 0 0 1 0 0 0 0 0 0 0 0 51
52 52.10 0 0 0 0 1 0 0 0 0 0 0 0 52
53 48.52 0 0 0 0 0 1 0 0 0 0 0 0 53
54 54.84 0 0 0 0 0 0 1 0 0 0 0 0 54
55 57.57 0 0 0 0 0 0 0 1 0 0 0 0 55
56 64.14 0 0 0 0 0 0 0 0 1 0 0 0 56
57 62.85 0 0 0 0 0 0 0 0 0 1 0 0 57
58 58.75 0 0 0 0 0 0 0 0 0 0 1 0 58
59 55.33 0 0 0 0 0 0 0 0 0 0 0 1 59
60 57.03 0 0 0 0 0 0 0 0 0 0 0 0 60
61 63.18 0 1 0 0 0 0 0 0 0 0 0 0 61
62 60.19 0 0 1 0 0 0 0 0 0 0 0 0 62
63 62.12 0 0 0 1 0 0 0 0 0 0 0 0 63
64 70.12 1 0 0 0 1 0 0 0 0 0 0 0 64
65 69.75 1 0 0 0 0 1 0 0 0 0 0 0 65
66 68.56 1 0 0 0 0 0 1 0 0 0 0 0 66
67 73.77 1 0 0 0 0 0 0 1 0 0 0 0 67
68 73.23 1 0 0 0 0 0 0 0 1 0 0 0 68
69 61.96 0 0 0 0 0 0 0 0 0 1 0 0 69
70 57.81 0 0 0 0 0 0 0 0 0 0 1 0 70
71 58.76 0 0 0 0 0 0 0 0 0 0 0 1 71
72 62.47 1 0 0 0 0 0 0 0 0 0 0 0 72
73 53.68 1 1 0 0 0 0 0 0 0 0 0 0 73
74 57.56 1 0 1 0 0 0 0 0 0 0 0 0 74
75 62.05 1 0 0 1 0 0 0 0 0 0 0 0 75
76 67.49 1 0 0 0 1 0 0 0 0 0 0 0 76
77 67.21 1 0 0 0 0 1 0 0 0 0 0 0 77
78 71.05 1 0 0 0 0 0 1 0 0 0 0 0 78
79 76.93 1 0 0 0 0 0 0 1 0 0 0 0 79
80 70.76 1 0 0 0 0 0 0 0 1 0 0 0 80
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Aanslagen_Nigeria M1 M2
9.346 4.290 3.961 4.054
M3 M4 M5 M6
5.543 5.666 5.552 5.177
M7 M8 M9 M10
7.423 7.908 5.613 4.109
M11 t
1.677 0.662
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.2405 -3.8519 -0.6457 4.6943 12.7761
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.34617 2.97856 3.138 0.00254 **
Aanslagen_Nigeria 4.29012 1.99149 2.154 0.03488 *
M1 3.96148 3.66493 1.081 0.28367
M2 4.05381 3.66643 1.106 0.27289
M3 5.54329 3.66826 1.511 0.13552
M4 5.66560 3.62849 1.561 0.12321
M5 5.55222 3.62981 1.530 0.13089
M6 5.17739 3.60911 1.435 0.15614
M7 7.42259 3.63346 2.043 0.04506 *
M8 7.90778 3.63579 2.175 0.03322 *
M9 5.61257 3.79418 1.479 0.14383
M10 4.10895 3.79608 1.082 0.28301
M11 1.67701 3.86881 0.433 0.66609
t 0.66195 0.03512 18.848 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.482 on 66 degrees of freedom
Multiple R-Squared: 0.8851, Adjusted R-squared: 0.8625
F-statistic: 39.12 on 13 and 66 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/rcomp/tmp/1coz81199373526.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/2nlcp1199373526.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/37jta1199373526.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4esz91199373526.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59sxn1199373526.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
11.650391799 12.776106085 7.624677514 8.000408769 10.101837341
6 7 8 9 10
5.064592664 3.207551626 3.130408769 0.413546700 -3.824786633
11 12 13 14 15
0.495312358 1.420371753 -2.453066477 -2.547352191 -1.278780763
16 17 18 19 20
-0.003049507 -0.761620936 -2.348746823 -3.655906650 -3.933049507
21 22 23 24 25
-0.529792787 -0.518126120 -2.018145919 -1.293205313 1.433475246
26 27 28 29 30
2.109189532 -2.302239039 -8.656507784 -8.415079212 -6.862205099
31 32 33 34 35
-8.889364926 -8.726507784 -9.953251063 -6.341584397 -5.501604195
36 37 38 39 40
-3.416544800 -6.499983030 -7.694268744 -7.245697316 -8.339966060
41 42 43 44 45
-4.048537489 -7.085663376 -6.992823203 -3.219966060 -1.416709340
46 47 48 49 50
5.764957327 1.034937528 -5.850121866 -1.383441307 -1.277727021
51 52 53 54 55
4.450844408 2.666575663 -1.461995765 4.570878348 4.393718521
56 57 58 59 60
9.816575663 10.159832384 6.901499050 5.251479252 7.966538647
61 62 63 64 65
9.493100417 5.748814703 5.527386131 8.452998597 7.534427169
66 67 68 69 70
6.057301282 8.360141454 6.672998597 1.326374107 -1.981959226
71 72 73 74 75
0.738020975 1.172961581 -12.240476649 -9.114762363 -6.776190935
76 77 78 79 80
-2.120459679 -2.949031108 0.603843005 3.576683178 -3.740459679
> postscript(file="/var/www/html/rcomp/tmp/6bvnf1199373526.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 11.650391799 NA
1 12.776106085 11.650391799
2 7.624677514 12.776106085
3 8.000408769 7.624677514
4 10.101837341 8.000408769
5 5.064592664 10.101837341
6 3.207551626 5.064592664
7 3.130408769 3.207551626
8 0.413546700 3.130408769
9 -3.824786633 0.413546700
10 0.495312358 -3.824786633
11 1.420371753 0.495312358
12 -2.453066477 1.420371753
13 -2.547352191 -2.453066477
14 -1.278780763 -2.547352191
15 -0.003049507 -1.278780763
16 -0.761620936 -0.003049507
17 -2.348746823 -0.761620936
18 -3.655906650 -2.348746823
19 -3.933049507 -3.655906650
20 -0.529792787 -3.933049507
21 -0.518126120 -0.529792787
22 -2.018145919 -0.518126120
23 -1.293205313 -2.018145919
24 1.433475246 -1.293205313
25 2.109189532 1.433475246
26 -2.302239039 2.109189532
27 -8.656507784 -2.302239039
28 -8.415079212 -8.656507784
29 -6.862205099 -8.415079212
30 -8.889364926 -6.862205099
31 -8.726507784 -8.889364926
32 -9.953251063 -8.726507784
33 -6.341584397 -9.953251063
34 -5.501604195 -6.341584397
35 -3.416544800 -5.501604195
36 -6.499983030 -3.416544800
37 -7.694268744 -6.499983030
38 -7.245697316 -7.694268744
39 -8.339966060 -7.245697316
40 -4.048537489 -8.339966060
41 -7.085663376 -4.048537489
42 -6.992823203 -7.085663376
43 -3.219966060 -6.992823203
44 -1.416709340 -3.219966060
45 5.764957327 -1.416709340
46 1.034937528 5.764957327
47 -5.850121866 1.034937528
48 -1.383441307 -5.850121866
49 -1.277727021 -1.383441307
50 4.450844408 -1.277727021
51 2.666575663 4.450844408
52 -1.461995765 2.666575663
53 4.570878348 -1.461995765
54 4.393718521 4.570878348
55 9.816575663 4.393718521
56 10.159832384 9.816575663
57 6.901499050 10.159832384
58 5.251479252 6.901499050
59 7.966538647 5.251479252
60 9.493100417 7.966538647
61 5.748814703 9.493100417
62 5.527386131 5.748814703
63 8.452998597 5.527386131
64 7.534427169 8.452998597
65 6.057301282 7.534427169
66 8.360141454 6.057301282
67 6.672998597 8.360141454
68 1.326374107 6.672998597
69 -1.981959226 1.326374107
70 0.738020975 -1.981959226
71 1.172961581 0.738020975
72 -12.240476649 1.172961581
73 -9.114762363 -12.240476649
74 -6.776190935 -9.114762363
75 -2.120459679 -6.776190935
76 -2.949031108 -2.120459679
77 0.603843005 -2.949031108
78 3.576683178 0.603843005
79 -3.740459679 3.576683178
80 NA -3.740459679
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 12.776106085 11.650391799
[2,] 7.624677514 12.776106085
[3,] 8.000408769 7.624677514
[4,] 10.101837341 8.000408769
[5,] 5.064592664 10.101837341
[6,] 3.207551626 5.064592664
[7,] 3.130408769 3.207551626
[8,] 0.413546700 3.130408769
[9,] -3.824786633 0.413546700
[10,] 0.495312358 -3.824786633
[11,] 1.420371753 0.495312358
[12,] -2.453066477 1.420371753
[13,] -2.547352191 -2.453066477
[14,] -1.278780763 -2.547352191
[15,] -0.003049507 -1.278780763
[16,] -0.761620936 -0.003049507
[17,] -2.348746823 -0.761620936
[18,] -3.655906650 -2.348746823
[19,] -3.933049507 -3.655906650
[20,] -0.529792787 -3.933049507
[21,] -0.518126120 -0.529792787
[22,] -2.018145919 -0.518126120
[23,] -1.293205313 -2.018145919
[24,] 1.433475246 -1.293205313
[25,] 2.109189532 1.433475246
[26,] -2.302239039 2.109189532
[27,] -8.656507784 -2.302239039
[28,] -8.415079212 -8.656507784
[29,] -6.862205099 -8.415079212
[30,] -8.889364926 -6.862205099
[31,] -8.726507784 -8.889364926
[32,] -9.953251063 -8.726507784
[33,] -6.341584397 -9.953251063
[34,] -5.501604195 -6.341584397
[35,] -3.416544800 -5.501604195
[36,] -6.499983030 -3.416544800
[37,] -7.694268744 -6.499983030
[38,] -7.245697316 -7.694268744
[39,] -8.339966060 -7.245697316
[40,] -4.048537489 -8.339966060
[41,] -7.085663376 -4.048537489
[42,] -6.992823203 -7.085663376
[43,] -3.219966060 -6.992823203
[44,] -1.416709340 -3.219966060
[45,] 5.764957327 -1.416709340
[46,] 1.034937528 5.764957327
[47,] -5.850121866 1.034937528
[48,] -1.383441307 -5.850121866
[49,] -1.277727021 -1.383441307
[50,] 4.450844408 -1.277727021
[51,] 2.666575663 4.450844408
[52,] -1.461995765 2.666575663
[53,] 4.570878348 -1.461995765
[54,] 4.393718521 4.570878348
[55,] 9.816575663 4.393718521
[56,] 10.159832384 9.816575663
[57,] 6.901499050 10.159832384
[58,] 5.251479252 6.901499050
[59,] 7.966538647 5.251479252
[60,] 9.493100417 7.966538647
[61,] 5.748814703 9.493100417
[62,] 5.527386131 5.748814703
[63,] 8.452998597 5.527386131
[64,] 7.534427169 8.452998597
[65,] 6.057301282 7.534427169
[66,] 8.360141454 6.057301282
[67,] 6.672998597 8.360141454
[68,] 1.326374107 6.672998597
[69,] -1.981959226 1.326374107
[70,] 0.738020975 -1.981959226
[71,] 1.172961581 0.738020975
[72,] -12.240476649 1.172961581
[73,] -9.114762363 -12.240476649
[74,] -6.776190935 -9.114762363
[75,] -2.120459679 -6.776190935
[76,] -2.949031108 -2.120459679
[77,] 0.603843005 -2.949031108
[78,] 3.576683178 0.603843005
[79,] -3.740459679 3.576683178
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 12.776106085 11.650391799
2 7.624677514 12.776106085
3 8.000408769 7.624677514
4 10.101837341 8.000408769
5 5.064592664 10.101837341
6 3.207551626 5.064592664
7 3.130408769 3.207551626
8 0.413546700 3.130408769
9 -3.824786633 0.413546700
10 0.495312358 -3.824786633
11 1.420371753 0.495312358
12 -2.453066477 1.420371753
13 -2.547352191 -2.453066477
14 -1.278780763 -2.547352191
15 -0.003049507 -1.278780763
16 -0.761620936 -0.003049507
17 -2.348746823 -0.761620936
18 -3.655906650 -2.348746823
19 -3.933049507 -3.655906650
20 -0.529792787 -3.933049507
21 -0.518126120 -0.529792787
22 -2.018145919 -0.518126120
23 -1.293205313 -2.018145919
24 1.433475246 -1.293205313
25 2.109189532 1.433475246
26 -2.302239039 2.109189532
27 -8.656507784 -2.302239039
28 -8.415079212 -8.656507784
29 -6.862205099 -8.415079212
30 -8.889364926 -6.862205099
31 -8.726507784 -8.889364926
32 -9.953251063 -8.726507784
33 -6.341584397 -9.953251063
34 -5.501604195 -6.341584397
35 -3.416544800 -5.501604195
36 -6.499983030 -3.416544800
37 -7.694268744 -6.499983030
38 -7.245697316 -7.694268744
39 -8.339966060 -7.245697316
40 -4.048537489 -8.339966060
41 -7.085663376 -4.048537489
42 -6.992823203 -7.085663376
43 -3.219966060 -6.992823203
44 -1.416709340 -3.219966060
45 5.764957327 -1.416709340
46 1.034937528 5.764957327
47 -5.850121866 1.034937528
48 -1.383441307 -5.850121866
49 -1.277727021 -1.383441307
50 4.450844408 -1.277727021
51 2.666575663 4.450844408
52 -1.461995765 2.666575663
53 4.570878348 -1.461995765
54 4.393718521 4.570878348
55 9.816575663 4.393718521
56 10.159832384 9.816575663
57 6.901499050 10.159832384
58 5.251479252 6.901499050
59 7.966538647 5.251479252
60 9.493100417 7.966538647
61 5.748814703 9.493100417
62 5.527386131 5.748814703
63 8.452998597 5.527386131
64 7.534427169 8.452998597
65 6.057301282 7.534427169
66 8.360141454 6.057301282
67 6.672998597 8.360141454
68 1.326374107 6.672998597
69 -1.981959226 1.326374107
70 0.738020975 -1.981959226
71 1.172961581 0.738020975
72 -12.240476649 1.172961581
73 -9.114762363 -12.240476649
74 -6.776190935 -9.114762363
75 -2.120459679 -6.776190935
76 -2.949031108 -2.120459679
77 0.603843005 -2.949031108
78 3.576683178 0.603843005
79 -3.740459679 3.576683178
> 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/7vzby1199373526.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8qe421199373526.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9777e1199373526.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/108b9w1199373526.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/11a7vh1199373526.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/123oig1199373526.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/131ppd1199373526.tab")
>
> system("convert tmp/1coz81199373526.ps tmp/1coz81199373526.png")
> system("convert tmp/2nlcp1199373526.ps tmp/2nlcp1199373526.png")
> system("convert tmp/37jta1199373526.ps tmp/37jta1199373526.png")
> system("convert tmp/4esz91199373526.ps tmp/4esz91199373526.png")
> system("convert tmp/59sxn1199373526.ps tmp/59sxn1199373526.png")
> system("convert tmp/6bvnf1199373526.ps tmp/6bvnf1199373526.png")
> system("convert tmp/7vzby1199373526.ps tmp/7vzby1199373526.png")
> system("convert tmp/8qe421199373526.ps tmp/8qe421199373526.png")
> system("convert tmp/9777e1199373526.ps tmp/9777e1199373526.png")
>
>
> proc.time()
user system elapsed
4.089 2.463 4.420