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.
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(86.9,0,99.7,0,109.1,0,94.6,0,111.2,0,112.8,0,53.5,0,107.5,0,105.2,0,122.8,0,103.4,0,76.9,0,89.6,0,92.8,0,107.6,0,104.6,0,103,0,106.9,0,56.3,0,93.4,0,109.1,0,113.8,0,97.4,0,72.5,0,82.7,0,88.9,0,105.9,0,100.8,0,94,0,105,0,58.5,0,87.6,0,113.1,0,112.5,0,89.6,0,74.5,0,82.7,0,90.1,0,109.4,0,96,0,89.2,0,109.1,0,49.1,0,92.9,0,107.7,0,103.5,0,91.1,0,79.8,0,71.9,0,82.9,0,90.1,0,100.7,0,90.7,0,108.8,0,44.1,0,93.6,0,107.4,0,96.5,0,93.6,0,76.5,0,76.7,0,84,0,103.3,0,88.5,0,99,0,105.9,0,44.7,0,94,0,107.1,0,104.8,0,102.5,0,77.7,0,85.2,0,91.3,0,106.5,0,92.4,0,97.5,0,107,0,51.1,1,98.6,1,102.2,1,114.3,1,99.4,1,72.5,1,92.3,1,99.4,1,85.9,1,109.4,1,97.6,1,104.7,1,56.9,1,86.7,1,108.5,1),dim=c(2,93),dimnames=list(c('Bouwnijverheid','Wel(1)_geen(0)_financiële_crisis'),1:93))
> y <- array(NA,dim=c(2,93),dimnames=list(c('Bouwnijverheid','Wel(1)_geen(0)_financiële_crisis'),1:93))
> 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
Bouwnijverheid Wel(1)_geen(0)_financi\353le_crisis M1 M2 M3 M4 M5 M6 M7 M8
1 86.9 0 1 0 0 0 0 0 0 0
2 99.7 0 0 1 0 0 0 0 0 0
3 109.1 0 0 0 1 0 0 0 0 0
4 94.6 0 0 0 0 1 0 0 0 0
5 111.2 0 0 0 0 0 1 0 0 0
6 112.8 0 0 0 0 0 0 1 0 0
7 53.5 0 0 0 0 0 0 0 1 0
8 107.5 0 0 0 0 0 0 0 0 1
9 105.2 0 0 0 0 0 0 0 0 0
10 122.8 0 0 0 0 0 0 0 0 0
11 103.4 0 0 0 0 0 0 0 0 0
12 76.9 0 0 0 0 0 0 0 0 0
13 89.6 0 1 0 0 0 0 0 0 0
14 92.8 0 0 1 0 0 0 0 0 0
15 107.6 0 0 0 1 0 0 0 0 0
16 104.6 0 0 0 0 1 0 0 0 0
17 103.0 0 0 0 0 0 1 0 0 0
18 106.9 0 0 0 0 0 0 1 0 0
19 56.3 0 0 0 0 0 0 0 1 0
20 93.4 0 0 0 0 0 0 0 0 1
21 109.1 0 0 0 0 0 0 0 0 0
22 113.8 0 0 0 0 0 0 0 0 0
23 97.4 0 0 0 0 0 0 0 0 0
24 72.5 0 0 0 0 0 0 0 0 0
25 82.7 0 1 0 0 0 0 0 0 0
26 88.9 0 0 1 0 0 0 0 0 0
27 105.9 0 0 0 1 0 0 0 0 0
28 100.8 0 0 0 0 1 0 0 0 0
29 94.0 0 0 0 0 0 1 0 0 0
30 105.0 0 0 0 0 0 0 1 0 0
31 58.5 0 0 0 0 0 0 0 1 0
32 87.6 0 0 0 0 0 0 0 0 1
33 113.1 0 0 0 0 0 0 0 0 0
34 112.5 0 0 0 0 0 0 0 0 0
35 89.6 0 0 0 0 0 0 0 0 0
36 74.5 0 0 0 0 0 0 0 0 0
37 82.7 0 1 0 0 0 0 0 0 0
38 90.1 0 0 1 0 0 0 0 0 0
39 109.4 0 0 0 1 0 0 0 0 0
40 96.0 0 0 0 0 1 0 0 0 0
41 89.2 0 0 0 0 0 1 0 0 0
42 109.1 0 0 0 0 0 0 1 0 0
43 49.1 0 0 0 0 0 0 0 1 0
44 92.9 0 0 0 0 0 0 0 0 1
45 107.7 0 0 0 0 0 0 0 0 0
46 103.5 0 0 0 0 0 0 0 0 0
47 91.1 0 0 0 0 0 0 0 0 0
48 79.8 0 0 0 0 0 0 0 0 0
49 71.9 0 1 0 0 0 0 0 0 0
50 82.9 0 0 1 0 0 0 0 0 0
51 90.1 0 0 0 1 0 0 0 0 0
52 100.7 0 0 0 0 1 0 0 0 0
53 90.7 0 0 0 0 0 1 0 0 0
54 108.8 0 0 0 0 0 0 1 0 0
55 44.1 0 0 0 0 0 0 0 1 0
56 93.6 0 0 0 0 0 0 0 0 1
57 107.4 0 0 0 0 0 0 0 0 0
58 96.5 0 0 0 0 0 0 0 0 0
59 93.6 0 0 0 0 0 0 0 0 0
60 76.5 0 0 0 0 0 0 0 0 0
61 76.7 0 1 0 0 0 0 0 0 0
62 84.0 0 0 1 0 0 0 0 0 0
63 103.3 0 0 0 1 0 0 0 0 0
64 88.5 0 0 0 0 1 0 0 0 0
65 99.0 0 0 0 0 0 1 0 0 0
66 105.9 0 0 0 0 0 0 1 0 0
67 44.7 0 0 0 0 0 0 0 1 0
68 94.0 0 0 0 0 0 0 0 0 1
69 107.1 0 0 0 0 0 0 0 0 0
70 104.8 0 0 0 0 0 0 0 0 0
71 102.5 0 0 0 0 0 0 0 0 0
72 77.7 0 0 0 0 0 0 0 0 0
73 85.2 0 1 0 0 0 0 0 0 0
74 91.3 0 0 1 0 0 0 0 0 0
75 106.5 0 0 0 1 0 0 0 0 0
76 92.4 0 0 0 0 1 0 0 0 0
77 97.5 0 0 0 0 0 1 0 0 0
78 107.0 0 0 0 0 0 0 1 0 0
79 51.1 1 0 0 0 0 0 0 1 0
80 98.6 1 0 0 0 0 0 0 0 1
81 102.2 1 0 0 0 0 0 0 0 0
82 114.3 1 0 0 0 0 0 0 0 0
83 99.4 1 0 0 0 0 0 0 0 0
84 72.5 1 0 0 0 0 0 0 0 0
85 92.3 1 1 0 0 0 0 0 0 0
86 99.4 1 0 1 0 0 0 0 0 0
87 85.9 1 0 0 1 0 0 0 0 0
88 109.4 1 0 0 0 1 0 0 0 0
89 97.6 1 0 0 0 0 1 0 0 0
90 104.7 1 0 0 0 0 0 1 0 0
91 56.9 1 0 0 0 0 0 0 1 0
92 86.7 1 0 0 0 0 0 0 0 1
93 108.5 1 0 0 0 0 0 0 0 0
M9 M10 M11 t
1 0 0 0 1
2 0 0 0 2
3 0 0 0 3
4 0 0 0 4
5 0 0 0 5
6 0 0 0 6
7 0 0 0 7
8 0 0 0 8
9 1 0 0 9
10 0 1 0 10
11 0 0 1 11
12 0 0 0 12
13 0 0 0 13
14 0 0 0 14
15 0 0 0 15
16 0 0 0 16
17 0 0 0 17
18 0 0 0 18
19 0 0 0 19
20 0 0 0 20
21 1 0 0 21
22 0 1 0 22
23 0 0 1 23
24 0 0 0 24
25 0 0 0 25
26 0 0 0 26
27 0 0 0 27
28 0 0 0 28
29 0 0 0 29
30 0 0 0 30
31 0 0 0 31
32 0 0 0 32
33 1 0 0 33
34 0 1 0 34
35 0 0 1 35
36 0 0 0 36
37 0 0 0 37
38 0 0 0 38
39 0 0 0 39
40 0 0 0 40
41 0 0 0 41
42 0 0 0 42
43 0 0 0 43
44 0 0 0 44
45 1 0 0 45
46 0 1 0 46
47 0 0 1 47
48 0 0 0 48
49 0 0 0 49
50 0 0 0 50
51 0 0 0 51
52 0 0 0 52
53 0 0 0 53
54 0 0 0 54
55 0 0 0 55
56 0 0 0 56
57 1 0 0 57
58 0 1 0 58
59 0 0 1 59
60 0 0 0 60
61 0 0 0 61
62 0 0 0 62
63 0 0 0 63
64 0 0 0 64
65 0 0 0 65
66 0 0 0 66
67 0 0 0 67
68 0 0 0 68
69 1 0 0 69
70 0 1 0 70
71 0 0 1 71
72 0 0 0 72
73 0 0 0 73
74 0 0 0 74
75 0 0 0 75
76 0 0 0 76
77 0 0 0 77
78 0 0 0 78
79 0 0 0 79
80 0 0 0 80
81 1 0 0 81
82 0 1 0 82
83 0 0 1 83
84 0 0 0 84
85 0 0 0 85
86 0 0 0 86
87 0 0 0 87
88 0 0 0 88
89 0 0 0 89
90 0 0 0 90
91 0 0 0 91
92 0 0 0 92
93 1 0 0 93
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `Wel(1)_geen(0)_financi\353le_crisis`
80.4039 6.1396
M1 M2
7.2643 15.0166
M3 M4
26.2189 22.4836
M5 M6
21.9984 31.8632
M7 M8
-24.5395 18.0878
M9 M10
31.4526 33.7419
M11 t
20.8281 -0.1148
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-16.876 -3.536 1.009 3.431 10.474
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 80.40386 2.48594 32.343 < 2e-16 ***
`Wel(1)_geen(0)_financi\353le_crisis` 6.13955 2.11912 2.897 0.004869 **
M1 7.26430 2.97280 2.444 0.016772 *
M2 15.01658 2.97178 5.053 2.74e-06 ***
M3 26.21886 2.97103 8.825 2.14e-13 ***
M4 22.48364 2.97057 7.569 6.00e-11 ***
M5 21.99842 2.97038 7.406 1.24e-10 ***
M6 31.86321 2.97047 10.727 < 2e-16 ***
M7 -24.53946 2.97764 -8.241 2.95e-12 ***
M8 18.08783 2.97666 6.077 4.08e-08 ***
M9 31.45261 2.97596 10.569 < 2e-16 ***
M10 33.74187 3.06819 10.997 < 2e-16 ***
M11 20.82808 3.06778 6.789 1.88e-09 ***
t -0.11478 0.02884 -3.980 0.000152 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.739 on 79 degrees of freedom
Multiple R-squared: 0.9003, Adjusted R-squared: 0.8839
F-statistic: 54.88 on 13 and 79 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/freestat/rcomp/tmp/1ispd1228654540.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/freestat/rcomp/tmp/255f21228654540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3ce6t1228654540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/45fof1228654540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5dsew1228654540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-0.6533782 4.5091218 2.8216218 -7.8283782 9.3716218 1.2216218
7 8 9 10 11 12
-1.5609340 9.9265660 -5.6234340 9.8020885 3.4306599 -2.1264830
13 14 15 16 17 18
3.4239996 -1.0135004 2.6989996 3.5489996 2.5489996 -3.3010004
19 20 21 22 23 24
2.6164437 -2.7960563 -0.3460563 2.1794663 -1.1919623 -5.1491052
25 26 27 28 29 30
-2.0986226 -3.5361226 2.3763774 1.1263774 -5.0736226 -3.8236226
31 32 33 34 35 36
6.1938215 -7.2186785 5.0313215 2.2568441 -7.6145845 -1.7717274
37 38 39 40 41 42
-0.7212448 -0.9587448 7.2537552 -2.2962448 -8.4962448 1.6537552
43 44 45 46 47 48
-1.8288007 -0.5413007 1.0086993 -5.3657782 -4.7372067 4.9056504
49 50 51 52 53 54
-10.1438670 -6.7813670 -10.6688670 3.7811330 -5.6188670 2.7311330
55 56 57 58 59 60
-5.4514229 1.5360771 2.0860771 -10.9884004 -0.8598289 2.9830282
61 62 63 64 65 66
-3.9664892 -4.3039892 3.9085108 -7.0414892 4.0585108 1.2085108
67 68 69 70 71 72
-3.4740451 3.3134549 3.1634549 -1.3110226 9.4175489 5.5604060
73 74 75 76 77 78
5.9108886 4.3733886 8.4858886 -1.7641114 3.9358886 3.6858886
79 80 81 82 83 84
-1.8362202 3.1512798 -6.4987202 3.4268023 1.5553737 -4.4017691
85 86 87 88 89 90
8.2487135 7.7112135 -16.8762865 10.4737135 -0.7262865 -3.3762865
91 92 93
5.3411576 -7.3713424 1.1786576
> postscript(file="/var/www/html/freestat/rcomp/tmp/6rtt11228654540.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 -0.6533782 NA
1 4.5091218 -0.6533782
2 2.8216218 4.5091218
3 -7.8283782 2.8216218
4 9.3716218 -7.8283782
5 1.2216218 9.3716218
6 -1.5609340 1.2216218
7 9.9265660 -1.5609340
8 -5.6234340 9.9265660
9 9.8020885 -5.6234340
10 3.4306599 9.8020885
11 -2.1264830 3.4306599
12 3.4239996 -2.1264830
13 -1.0135004 3.4239996
14 2.6989996 -1.0135004
15 3.5489996 2.6989996
16 2.5489996 3.5489996
17 -3.3010004 2.5489996
18 2.6164437 -3.3010004
19 -2.7960563 2.6164437
20 -0.3460563 -2.7960563
21 2.1794663 -0.3460563
22 -1.1919623 2.1794663
23 -5.1491052 -1.1919623
24 -2.0986226 -5.1491052
25 -3.5361226 -2.0986226
26 2.3763774 -3.5361226
27 1.1263774 2.3763774
28 -5.0736226 1.1263774
29 -3.8236226 -5.0736226
30 6.1938215 -3.8236226
31 -7.2186785 6.1938215
32 5.0313215 -7.2186785
33 2.2568441 5.0313215
34 -7.6145845 2.2568441
35 -1.7717274 -7.6145845
36 -0.7212448 -1.7717274
37 -0.9587448 -0.7212448
38 7.2537552 -0.9587448
39 -2.2962448 7.2537552
40 -8.4962448 -2.2962448
41 1.6537552 -8.4962448
42 -1.8288007 1.6537552
43 -0.5413007 -1.8288007
44 1.0086993 -0.5413007
45 -5.3657782 1.0086993
46 -4.7372067 -5.3657782
47 4.9056504 -4.7372067
48 -10.1438670 4.9056504
49 -6.7813670 -10.1438670
50 -10.6688670 -6.7813670
51 3.7811330 -10.6688670
52 -5.6188670 3.7811330
53 2.7311330 -5.6188670
54 -5.4514229 2.7311330
55 1.5360771 -5.4514229
56 2.0860771 1.5360771
57 -10.9884004 2.0860771
58 -0.8598289 -10.9884004
59 2.9830282 -0.8598289
60 -3.9664892 2.9830282
61 -4.3039892 -3.9664892
62 3.9085108 -4.3039892
63 -7.0414892 3.9085108
64 4.0585108 -7.0414892
65 1.2085108 4.0585108
66 -3.4740451 1.2085108
67 3.3134549 -3.4740451
68 3.1634549 3.3134549
69 -1.3110226 3.1634549
70 9.4175489 -1.3110226
71 5.5604060 9.4175489
72 5.9108886 5.5604060
73 4.3733886 5.9108886
74 8.4858886 4.3733886
75 -1.7641114 8.4858886
76 3.9358886 -1.7641114
77 3.6858886 3.9358886
78 -1.8362202 3.6858886
79 3.1512798 -1.8362202
80 -6.4987202 3.1512798
81 3.4268023 -6.4987202
82 1.5553737 3.4268023
83 -4.4017691 1.5553737
84 8.2487135 -4.4017691
85 7.7112135 8.2487135
86 -16.8762865 7.7112135
87 10.4737135 -16.8762865
88 -0.7262865 10.4737135
89 -3.3762865 -0.7262865
90 5.3411576 -3.3762865
91 -7.3713424 5.3411576
92 1.1786576 -7.3713424
93 NA 1.1786576
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.5091218 -0.6533782
[2,] 2.8216218 4.5091218
[3,] -7.8283782 2.8216218
[4,] 9.3716218 -7.8283782
[5,] 1.2216218 9.3716218
[6,] -1.5609340 1.2216218
[7,] 9.9265660 -1.5609340
[8,] -5.6234340 9.9265660
[9,] 9.8020885 -5.6234340
[10,] 3.4306599 9.8020885
[11,] -2.1264830 3.4306599
[12,] 3.4239996 -2.1264830
[13,] -1.0135004 3.4239996
[14,] 2.6989996 -1.0135004
[15,] 3.5489996 2.6989996
[16,] 2.5489996 3.5489996
[17,] -3.3010004 2.5489996
[18,] 2.6164437 -3.3010004
[19,] -2.7960563 2.6164437
[20,] -0.3460563 -2.7960563
[21,] 2.1794663 -0.3460563
[22,] -1.1919623 2.1794663
[23,] -5.1491052 -1.1919623
[24,] -2.0986226 -5.1491052
[25,] -3.5361226 -2.0986226
[26,] 2.3763774 -3.5361226
[27,] 1.1263774 2.3763774
[28,] -5.0736226 1.1263774
[29,] -3.8236226 -5.0736226
[30,] 6.1938215 -3.8236226
[31,] -7.2186785 6.1938215
[32,] 5.0313215 -7.2186785
[33,] 2.2568441 5.0313215
[34,] -7.6145845 2.2568441
[35,] -1.7717274 -7.6145845
[36,] -0.7212448 -1.7717274
[37,] -0.9587448 -0.7212448
[38,] 7.2537552 -0.9587448
[39,] -2.2962448 7.2537552
[40,] -8.4962448 -2.2962448
[41,] 1.6537552 -8.4962448
[42,] -1.8288007 1.6537552
[43,] -0.5413007 -1.8288007
[44,] 1.0086993 -0.5413007
[45,] -5.3657782 1.0086993
[46,] -4.7372067 -5.3657782
[47,] 4.9056504 -4.7372067
[48,] -10.1438670 4.9056504
[49,] -6.7813670 -10.1438670
[50,] -10.6688670 -6.7813670
[51,] 3.7811330 -10.6688670
[52,] -5.6188670 3.7811330
[53,] 2.7311330 -5.6188670
[54,] -5.4514229 2.7311330
[55,] 1.5360771 -5.4514229
[56,] 2.0860771 1.5360771
[57,] -10.9884004 2.0860771
[58,] -0.8598289 -10.9884004
[59,] 2.9830282 -0.8598289
[60,] -3.9664892 2.9830282
[61,] -4.3039892 -3.9664892
[62,] 3.9085108 -4.3039892
[63,] -7.0414892 3.9085108
[64,] 4.0585108 -7.0414892
[65,] 1.2085108 4.0585108
[66,] -3.4740451 1.2085108
[67,] 3.3134549 -3.4740451
[68,] 3.1634549 3.3134549
[69,] -1.3110226 3.1634549
[70,] 9.4175489 -1.3110226
[71,] 5.5604060 9.4175489
[72,] 5.9108886 5.5604060
[73,] 4.3733886 5.9108886
[74,] 8.4858886 4.3733886
[75,] -1.7641114 8.4858886
[76,] 3.9358886 -1.7641114
[77,] 3.6858886 3.9358886
[78,] -1.8362202 3.6858886
[79,] 3.1512798 -1.8362202
[80,] -6.4987202 3.1512798
[81,] 3.4268023 -6.4987202
[82,] 1.5553737 3.4268023
[83,] -4.4017691 1.5553737
[84,] 8.2487135 -4.4017691
[85,] 7.7112135 8.2487135
[86,] -16.8762865 7.7112135
[87,] 10.4737135 -16.8762865
[88,] -0.7262865 10.4737135
[89,] -3.3762865 -0.7262865
[90,] 5.3411576 -3.3762865
[91,] -7.3713424 5.3411576
[92,] 1.1786576 -7.3713424
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.5091218 -0.6533782
2 2.8216218 4.5091218
3 -7.8283782 2.8216218
4 9.3716218 -7.8283782
5 1.2216218 9.3716218
6 -1.5609340 1.2216218
7 9.9265660 -1.5609340
8 -5.6234340 9.9265660
9 9.8020885 -5.6234340
10 3.4306599 9.8020885
11 -2.1264830 3.4306599
12 3.4239996 -2.1264830
13 -1.0135004 3.4239996
14 2.6989996 -1.0135004
15 3.5489996 2.6989996
16 2.5489996 3.5489996
17 -3.3010004 2.5489996
18 2.6164437 -3.3010004
19 -2.7960563 2.6164437
20 -0.3460563 -2.7960563
21 2.1794663 -0.3460563
22 -1.1919623 2.1794663
23 -5.1491052 -1.1919623
24 -2.0986226 -5.1491052
25 -3.5361226 -2.0986226
26 2.3763774 -3.5361226
27 1.1263774 2.3763774
28 -5.0736226 1.1263774
29 -3.8236226 -5.0736226
30 6.1938215 -3.8236226
31 -7.2186785 6.1938215
32 5.0313215 -7.2186785
33 2.2568441 5.0313215
34 -7.6145845 2.2568441
35 -1.7717274 -7.6145845
36 -0.7212448 -1.7717274
37 -0.9587448 -0.7212448
38 7.2537552 -0.9587448
39 -2.2962448 7.2537552
40 -8.4962448 -2.2962448
41 1.6537552 -8.4962448
42 -1.8288007 1.6537552
43 -0.5413007 -1.8288007
44 1.0086993 -0.5413007
45 -5.3657782 1.0086993
46 -4.7372067 -5.3657782
47 4.9056504 -4.7372067
48 -10.1438670 4.9056504
49 -6.7813670 -10.1438670
50 -10.6688670 -6.7813670
51 3.7811330 -10.6688670
52 -5.6188670 3.7811330
53 2.7311330 -5.6188670
54 -5.4514229 2.7311330
55 1.5360771 -5.4514229
56 2.0860771 1.5360771
57 -10.9884004 2.0860771
58 -0.8598289 -10.9884004
59 2.9830282 -0.8598289
60 -3.9664892 2.9830282
61 -4.3039892 -3.9664892
62 3.9085108 -4.3039892
63 -7.0414892 3.9085108
64 4.0585108 -7.0414892
65 1.2085108 4.0585108
66 -3.4740451 1.2085108
67 3.3134549 -3.4740451
68 3.1634549 3.3134549
69 -1.3110226 3.1634549
70 9.4175489 -1.3110226
71 5.5604060 9.4175489
72 5.9108886 5.5604060
73 4.3733886 5.9108886
74 8.4858886 4.3733886
75 -1.7641114 8.4858886
76 3.9358886 -1.7641114
77 3.6858886 3.9358886
78 -1.8362202 3.6858886
79 3.1512798 -1.8362202
80 -6.4987202 3.1512798
81 3.4268023 -6.4987202
82 1.5553737 3.4268023
83 -4.4017691 1.5553737
84 8.2487135 -4.4017691
85 7.7112135 8.2487135
86 -16.8762865 7.7112135
87 10.4737135 -16.8762865
88 -0.7262865 10.4737135
89 -3.3762865 -0.7262865
90 5.3411576 -3.3762865
91 -7.3713424 5.3411576
92 1.1786576 -7.3713424
> 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/freestat/rcomp/tmp/7olh01228654540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/801cs1228654540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9lzd61228654540.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/10irb31228654540.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/freestat/rcomp/tmp/11lfi61228654540.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/freestat/rcomp/tmp/129j9h1228654540.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/freestat/rcomp/tmp/13elvn1228654540.tab")
>
> system("convert tmp/1ispd1228654540.ps tmp/1ispd1228654540.png")
> system("convert tmp/255f21228654540.ps tmp/255f21228654540.png")
> system("convert tmp/3ce6t1228654540.ps tmp/3ce6t1228654540.png")
> system("convert tmp/45fof1228654540.ps tmp/45fof1228654540.png")
> system("convert tmp/5dsew1228654540.ps tmp/5dsew1228654540.png")
> system("convert tmp/6rtt11228654540.ps tmp/6rtt11228654540.png")
> system("convert tmp/7olh01228654540.ps tmp/7olh01228654540.png")
> system("convert tmp/801cs1228654540.ps tmp/801cs1228654540.png")
> system("convert tmp/9lzd61228654540.ps tmp/9lzd61228654540.png")
>
>
> proc.time()
user system elapsed
3.180 2.345 5.587