R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(493,0,481,0,462,0,457,0,442,0,439,0,488,0,521,0,501,0,485,0,464,0,460,0,467,0,460,0,448,0,443,0,436,0,431,0,484,0,510,0,513,0,503,0,471,0,471,0,476,0,475,0,470,0,461,0,455,0,456,0,517,0,525,0,523,0,519,0,509,0,512,0,519,0,517,0,510,0,509,0,501,0,507,0,569,0,580,0,578,0,565,0,547,0,555,0,562,0,561,0,555,0,544,0,537,0,543,0,594,0,611,0,613,0,611,0,594,0,595,0,591,0,589,0,584,0,573,0,567,0,569,0,621,0,629,0,628,0,612,0,595,0,597,0,593,0,590,0,580,0,574,0,573,0,573,0,620,0,626,0,620,0,588,0,566,0,557,0,561,0,549,0,532,0,526,0,511,0,499,0,555,1,565,1,542,1,527,1,510,1,514,1,517,1,508,1,493,1,490,1,469,1,478,1,528,1,534,1,518,1,506,1,502,1),dim=c(2,107),dimnames=list(c('Werkloosheid','Wel(1)_geen(0)_financiële_crisis'),1:107))
> y <- array(NA,dim=c(2,107),dimnames=list(c('Werkloosheid','Wel(1)_geen(0)_financiële_crisis'),1:107))
> 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
Werkloosheid Wel(1)_geen(0)_financi\353le_crisis M1 M2 M3 M4 M5 M6 M7 M8 M9
1 493 0 1 0 0 0 0 0 0 0 0
2 481 0 0 1 0 0 0 0 0 0 0
3 462 0 0 0 1 0 0 0 0 0 0
4 457 0 0 0 0 1 0 0 0 0 0
5 442 0 0 0 0 0 1 0 0 0 0
6 439 0 0 0 0 0 0 1 0 0 0
7 488 0 0 0 0 0 0 0 1 0 0
8 521 0 0 0 0 0 0 0 0 1 0
9 501 0 0 0 0 0 0 0 0 0 1
10 485 0 0 0 0 0 0 0 0 0 0
11 464 0 0 0 0 0 0 0 0 0 0
12 460 0 0 0 0 0 0 0 0 0 0
13 467 0 1 0 0 0 0 0 0 0 0
14 460 0 0 1 0 0 0 0 0 0 0
15 448 0 0 0 1 0 0 0 0 0 0
16 443 0 0 0 0 1 0 0 0 0 0
17 436 0 0 0 0 0 1 0 0 0 0
18 431 0 0 0 0 0 0 1 0 0 0
19 484 0 0 0 0 0 0 0 1 0 0
20 510 0 0 0 0 0 0 0 0 1 0
21 513 0 0 0 0 0 0 0 0 0 1
22 503 0 0 0 0 0 0 0 0 0 0
23 471 0 0 0 0 0 0 0 0 0 0
24 471 0 0 0 0 0 0 0 0 0 0
25 476 0 1 0 0 0 0 0 0 0 0
26 475 0 0 1 0 0 0 0 0 0 0
27 470 0 0 0 1 0 0 0 0 0 0
28 461 0 0 0 0 1 0 0 0 0 0
29 455 0 0 0 0 0 1 0 0 0 0
30 456 0 0 0 0 0 0 1 0 0 0
31 517 0 0 0 0 0 0 0 1 0 0
32 525 0 0 0 0 0 0 0 0 1 0
33 523 0 0 0 0 0 0 0 0 0 1
34 519 0 0 0 0 0 0 0 0 0 0
35 509 0 0 0 0 0 0 0 0 0 0
36 512 0 0 0 0 0 0 0 0 0 0
37 519 0 1 0 0 0 0 0 0 0 0
38 517 0 0 1 0 0 0 0 0 0 0
39 510 0 0 0 1 0 0 0 0 0 0
40 509 0 0 0 0 1 0 0 0 0 0
41 501 0 0 0 0 0 1 0 0 0 0
42 507 0 0 0 0 0 0 1 0 0 0
43 569 0 0 0 0 0 0 0 1 0 0
44 580 0 0 0 0 0 0 0 0 1 0
45 578 0 0 0 0 0 0 0 0 0 1
46 565 0 0 0 0 0 0 0 0 0 0
47 547 0 0 0 0 0 0 0 0 0 0
48 555 0 0 0 0 0 0 0 0 0 0
49 562 0 1 0 0 0 0 0 0 0 0
50 561 0 0 1 0 0 0 0 0 0 0
51 555 0 0 0 1 0 0 0 0 0 0
52 544 0 0 0 0 1 0 0 0 0 0
53 537 0 0 0 0 0 1 0 0 0 0
54 543 0 0 0 0 0 0 1 0 0 0
55 594 0 0 0 0 0 0 0 1 0 0
56 611 0 0 0 0 0 0 0 0 1 0
57 613 0 0 0 0 0 0 0 0 0 1
58 611 0 0 0 0 0 0 0 0 0 0
59 594 0 0 0 0 0 0 0 0 0 0
60 595 0 0 0 0 0 0 0 0 0 0
61 591 0 1 0 0 0 0 0 0 0 0
62 589 0 0 1 0 0 0 0 0 0 0
63 584 0 0 0 1 0 0 0 0 0 0
64 573 0 0 0 0 1 0 0 0 0 0
65 567 0 0 0 0 0 1 0 0 0 0
66 569 0 0 0 0 0 0 1 0 0 0
67 621 0 0 0 0 0 0 0 1 0 0
68 629 0 0 0 0 0 0 0 0 1 0
69 628 0 0 0 0 0 0 0 0 0 1
70 612 0 0 0 0 0 0 0 0 0 0
71 595 0 0 0 0 0 0 0 0 0 0
72 597 0 0 0 0 0 0 0 0 0 0
73 593 0 1 0 0 0 0 0 0 0 0
74 590 0 0 1 0 0 0 0 0 0 0
75 580 0 0 0 1 0 0 0 0 0 0
76 574 0 0 0 0 1 0 0 0 0 0
77 573 0 0 0 0 0 1 0 0 0 0
78 573 0 0 0 0 0 0 1 0 0 0
79 620 0 0 0 0 0 0 0 1 0 0
80 626 0 0 0 0 0 0 0 0 1 0
81 620 0 0 0 0 0 0 0 0 0 1
82 588 0 0 0 0 0 0 0 0 0 0
83 566 0 0 0 0 0 0 0 0 0 0
84 557 0 0 0 0 0 0 0 0 0 0
85 561 0 1 0 0 0 0 0 0 0 0
86 549 0 0 1 0 0 0 0 0 0 0
87 532 0 0 0 1 0 0 0 0 0 0
88 526 0 0 0 0 1 0 0 0 0 0
89 511 0 0 0 0 0 1 0 0 0 0
90 499 0 0 0 0 0 0 1 0 0 0
91 555 1 0 0 0 0 0 0 1 0 0
92 565 1 0 0 0 0 0 0 0 1 0
93 542 1 0 0 0 0 0 0 0 0 1
94 527 1 0 0 0 0 0 0 0 0 0
95 510 1 0 0 0 0 0 0 0 0 0
96 514 1 0 0 0 0 0 0 0 0 0
97 517 1 1 0 0 0 0 0 0 0 0
98 508 1 0 1 0 0 0 0 0 0 0
99 493 1 0 0 1 0 0 0 0 0 0
100 490 1 0 0 0 1 0 0 0 0 0
101 469 1 0 0 0 0 1 0 0 0 0
102 478 1 0 0 0 0 0 1 0 0 0
103 528 1 0 0 0 0 0 0 1 0 0
104 534 1 0 0 0 0 0 0 0 1 0
105 518 1 0 0 0 0 0 0 0 0 1
106 506 1 0 0 0 0 0 0 0 0 0
107 502 1 0 0 0 0 0 0 0 0 0
M10 M11 t
1 0 0 1
2 0 0 2
3 0 0 3
4 0 0 4
5 0 0 5
6 0 0 6
7 0 0 7
8 0 0 8
9 0 0 9
10 1 0 10
11 0 1 11
12 0 0 12
13 0 0 13
14 0 0 14
15 0 0 15
16 0 0 16
17 0 0 17
18 0 0 18
19 0 0 19
20 0 0 20
21 0 0 21
22 1 0 22
23 0 1 23
24 0 0 24
25 0 0 25
26 0 0 26
27 0 0 27
28 0 0 28
29 0 0 29
30 0 0 30
31 0 0 31
32 0 0 32
33 0 0 33
34 1 0 34
35 0 1 35
36 0 0 36
37 0 0 37
38 0 0 38
39 0 0 39
40 0 0 40
41 0 0 41
42 0 0 42
43 0 0 43
44 0 0 44
45 0 0 45
46 1 0 46
47 0 1 47
48 0 0 48
49 0 0 49
50 0 0 50
51 0 0 51
52 0 0 52
53 0 0 53
54 0 0 54
55 0 0 55
56 0 0 56
57 0 0 57
58 1 0 58
59 0 1 59
60 0 0 60
61 0 0 61
62 0 0 62
63 0 0 63
64 0 0 64
65 0 0 65
66 0 0 66
67 0 0 67
68 0 0 68
69 0 0 69
70 1 0 70
71 0 1 71
72 0 0 72
73 0 0 73
74 0 0 74
75 0 0 75
76 0 0 76
77 0 0 77
78 0 0 78
79 0 0 79
80 0 0 80
81 0 0 81
82 1 0 82
83 0 1 83
84 0 0 84
85 0 0 85
86 0 0 86
87 0 0 87
88 0 0 88
89 0 0 89
90 0 0 90
91 0 0 91
92 0 0 92
93 0 0 93
94 1 0 94
95 0 1 95
96 0 0 96
97 0 0 97
98 0 0 98
99 0 0 99
100 0 0 100
101 0 0 101
102 0 0 102
103 0 0 103
104 0 0 104
105 0 0 105
106 1 0 106
107 0 1 107
> 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`
457.194 -115.179
M1 M2
5.093 -2.015
M3 M4
-14.345 -22.342
M5 M6
-33.561 -34.780
M7 M8
29.798 42.024
M9 M10
33.138 18.141
M11 t
-1.078 1.663
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-73.127 -17.460 4.978 18.415 39.738
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 457.1943 10.4072 43.931 < 2e-16 ***
`Wel(1)_geen(0)_financi\353le_crisis` -115.1788 8.8862 -12.961 < 2e-16 ***
M1 5.0927 12.5389 0.406 0.68556
M2 -2.0152 12.5357 -0.161 0.87263
M3 -14.3454 12.5333 -1.145 0.25532
M4 -22.3422 12.5317 -1.783 0.07787 .
M5 -33.5612 12.5311 -2.678 0.00875 **
M6 -34.7803 12.5313 -2.775 0.00666 **
M7 29.7983 12.5563 2.373 0.01969 *
M8 42.0238 12.5531 3.348 0.00118 **
M9 33.1380 12.5507 2.640 0.00971 **
M10 18.1412 12.5492 1.446 0.15165
M11 -1.0778 12.5486 -0.086 0.93174
t 1.6635 0.1045 15.915 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 25.79 on 93 degrees of freedom
Multiple R-squared: 0.787, Adjusted R-squared: 0.7572
F-statistic: 26.43 on 13 and 93 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/rcomp/tmp/123uh1228653833.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/2chhh1228653833.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/39qaa1228653833.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4d5ui1228653833.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/52dib1228653833.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 107
Frequency = 1
1 2 3 4 5 6
29.0495038 22.4939483 14.1606149 15.4939483 10.0495038 6.6050594
7 8 9 10 11 12
-10.6370283 8.4740828 -4.3036949 -6.9703616 -10.4148060 -17.1560949
13 14 15 16 17 18
-16.9122829 -18.4678385 -19.8011718 -18.4678385 -15.9122829 -21.3567274
19 20 21 22 23 24
-34.5988150 -22.4877039 -12.2654817 -8.9321484 -23.3765928 -26.1178817
25 26 27 28 29 30
-27.8740697 -23.4296252 -17.7629586 -20.4296252 -16.8740697 -16.3185141
31 32 33 34 35 36
-21.5606018 -27.4494907 -22.2272685 -12.8939351 -5.3383796 -5.0796685
37 38 39 40 41 42
-4.8358565 -1.3914120 2.2752547 7.6085880 9.1641435 14.7196991
43 44 45 46 47 48
10.4776114 7.5887226 12.8109448 13.1442781 12.6998337 17.9585448
49 50 51 52 53 54
18.2023568 22.6468012 27.3134679 22.6468012 25.2023568 30.7579123
55 56 57 58 59 60
15.5158247 18.6269358 27.8491580 39.1824914 39.7380469 37.9967580
61 62 63 64 65 66
27.2405700 30.6850145 36.3516811 31.6850145 35.2405700 36.7961256
67 68 69 70 71 72
22.5540379 16.6651490 22.8873713 20.2207046 20.7762601 20.0349712
73 74 75 76 77 78
9.2787833 11.7232277 12.3898944 12.7232277 21.2787833 20.8343388
79 80 81 82 83 84
1.5922512 -6.2966377 -5.0744155 -23.7410822 -28.1855266 -39.9268155
85 86 87 88 89 90
-42.6830035 -49.2385591 -55.5718924 -55.2385591 -60.6830035 -73.1274479
91 92 93 94 95 96
31.8092533 27.9203644 12.1425866 10.4759200 11.0314755 12.2901866
97 98 99 100 101 102
8.5339986 4.9784431 0.6451098 3.9784431 -7.4660014 1.0895542
103 104 105 106 107
-15.1525335 -23.0414223 -31.8192001 -30.4858668 -16.9303112
> postscript(file="/var/www/html/rcomp/tmp/6ac3q1228653833.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 = 107
Frequency = 1
lag(myerror, k = 1) myerror
0 29.0495038 NA
1 22.4939483 29.0495038
2 14.1606149 22.4939483
3 15.4939483 14.1606149
4 10.0495038 15.4939483
5 6.6050594 10.0495038
6 -10.6370283 6.6050594
7 8.4740828 -10.6370283
8 -4.3036949 8.4740828
9 -6.9703616 -4.3036949
10 -10.4148060 -6.9703616
11 -17.1560949 -10.4148060
12 -16.9122829 -17.1560949
13 -18.4678385 -16.9122829
14 -19.8011718 -18.4678385
15 -18.4678385 -19.8011718
16 -15.9122829 -18.4678385
17 -21.3567274 -15.9122829
18 -34.5988150 -21.3567274
19 -22.4877039 -34.5988150
20 -12.2654817 -22.4877039
21 -8.9321484 -12.2654817
22 -23.3765928 -8.9321484
23 -26.1178817 -23.3765928
24 -27.8740697 -26.1178817
25 -23.4296252 -27.8740697
26 -17.7629586 -23.4296252
27 -20.4296252 -17.7629586
28 -16.8740697 -20.4296252
29 -16.3185141 -16.8740697
30 -21.5606018 -16.3185141
31 -27.4494907 -21.5606018
32 -22.2272685 -27.4494907
33 -12.8939351 -22.2272685
34 -5.3383796 -12.8939351
35 -5.0796685 -5.3383796
36 -4.8358565 -5.0796685
37 -1.3914120 -4.8358565
38 2.2752547 -1.3914120
39 7.6085880 2.2752547
40 9.1641435 7.6085880
41 14.7196991 9.1641435
42 10.4776114 14.7196991
43 7.5887226 10.4776114
44 12.8109448 7.5887226
45 13.1442781 12.8109448
46 12.6998337 13.1442781
47 17.9585448 12.6998337
48 18.2023568 17.9585448
49 22.6468012 18.2023568
50 27.3134679 22.6468012
51 22.6468012 27.3134679
52 25.2023568 22.6468012
53 30.7579123 25.2023568
54 15.5158247 30.7579123
55 18.6269358 15.5158247
56 27.8491580 18.6269358
57 39.1824914 27.8491580
58 39.7380469 39.1824914
59 37.9967580 39.7380469
60 27.2405700 37.9967580
61 30.6850145 27.2405700
62 36.3516811 30.6850145
63 31.6850145 36.3516811
64 35.2405700 31.6850145
65 36.7961256 35.2405700
66 22.5540379 36.7961256
67 16.6651490 22.5540379
68 22.8873713 16.6651490
69 20.2207046 22.8873713
70 20.7762601 20.2207046
71 20.0349712 20.7762601
72 9.2787833 20.0349712
73 11.7232277 9.2787833
74 12.3898944 11.7232277
75 12.7232277 12.3898944
76 21.2787833 12.7232277
77 20.8343388 21.2787833
78 1.5922512 20.8343388
79 -6.2966377 1.5922512
80 -5.0744155 -6.2966377
81 -23.7410822 -5.0744155
82 -28.1855266 -23.7410822
83 -39.9268155 -28.1855266
84 -42.6830035 -39.9268155
85 -49.2385591 -42.6830035
86 -55.5718924 -49.2385591
87 -55.2385591 -55.5718924
88 -60.6830035 -55.2385591
89 -73.1274479 -60.6830035
90 31.8092533 -73.1274479
91 27.9203644 31.8092533
92 12.1425866 27.9203644
93 10.4759200 12.1425866
94 11.0314755 10.4759200
95 12.2901866 11.0314755
96 8.5339986 12.2901866
97 4.9784431 8.5339986
98 0.6451098 4.9784431
99 3.9784431 0.6451098
100 -7.4660014 3.9784431
101 1.0895542 -7.4660014
102 -15.1525335 1.0895542
103 -23.0414223 -15.1525335
104 -31.8192001 -23.0414223
105 -30.4858668 -31.8192001
106 -16.9303112 -30.4858668
107 NA -16.9303112
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 22.4939483 29.0495038
[2,] 14.1606149 22.4939483
[3,] 15.4939483 14.1606149
[4,] 10.0495038 15.4939483
[5,] 6.6050594 10.0495038
[6,] -10.6370283 6.6050594
[7,] 8.4740828 -10.6370283
[8,] -4.3036949 8.4740828
[9,] -6.9703616 -4.3036949
[10,] -10.4148060 -6.9703616
[11,] -17.1560949 -10.4148060
[12,] -16.9122829 -17.1560949
[13,] -18.4678385 -16.9122829
[14,] -19.8011718 -18.4678385
[15,] -18.4678385 -19.8011718
[16,] -15.9122829 -18.4678385
[17,] -21.3567274 -15.9122829
[18,] -34.5988150 -21.3567274
[19,] -22.4877039 -34.5988150
[20,] -12.2654817 -22.4877039
[21,] -8.9321484 -12.2654817
[22,] -23.3765928 -8.9321484
[23,] -26.1178817 -23.3765928
[24,] -27.8740697 -26.1178817
[25,] -23.4296252 -27.8740697
[26,] -17.7629586 -23.4296252
[27,] -20.4296252 -17.7629586
[28,] -16.8740697 -20.4296252
[29,] -16.3185141 -16.8740697
[30,] -21.5606018 -16.3185141
[31,] -27.4494907 -21.5606018
[32,] -22.2272685 -27.4494907
[33,] -12.8939351 -22.2272685
[34,] -5.3383796 -12.8939351
[35,] -5.0796685 -5.3383796
[36,] -4.8358565 -5.0796685
[37,] -1.3914120 -4.8358565
[38,] 2.2752547 -1.3914120
[39,] 7.6085880 2.2752547
[40,] 9.1641435 7.6085880
[41,] 14.7196991 9.1641435
[42,] 10.4776114 14.7196991
[43,] 7.5887226 10.4776114
[44,] 12.8109448 7.5887226
[45,] 13.1442781 12.8109448
[46,] 12.6998337 13.1442781
[47,] 17.9585448 12.6998337
[48,] 18.2023568 17.9585448
[49,] 22.6468012 18.2023568
[50,] 27.3134679 22.6468012
[51,] 22.6468012 27.3134679
[52,] 25.2023568 22.6468012
[53,] 30.7579123 25.2023568
[54,] 15.5158247 30.7579123
[55,] 18.6269358 15.5158247
[56,] 27.8491580 18.6269358
[57,] 39.1824914 27.8491580
[58,] 39.7380469 39.1824914
[59,] 37.9967580 39.7380469
[60,] 27.2405700 37.9967580
[61,] 30.6850145 27.2405700
[62,] 36.3516811 30.6850145
[63,] 31.6850145 36.3516811
[64,] 35.2405700 31.6850145
[65,] 36.7961256 35.2405700
[66,] 22.5540379 36.7961256
[67,] 16.6651490 22.5540379
[68,] 22.8873713 16.6651490
[69,] 20.2207046 22.8873713
[70,] 20.7762601 20.2207046
[71,] 20.0349712 20.7762601
[72,] 9.2787833 20.0349712
[73,] 11.7232277 9.2787833
[74,] 12.3898944 11.7232277
[75,] 12.7232277 12.3898944
[76,] 21.2787833 12.7232277
[77,] 20.8343388 21.2787833
[78,] 1.5922512 20.8343388
[79,] -6.2966377 1.5922512
[80,] -5.0744155 -6.2966377
[81,] -23.7410822 -5.0744155
[82,] -28.1855266 -23.7410822
[83,] -39.9268155 -28.1855266
[84,] -42.6830035 -39.9268155
[85,] -49.2385591 -42.6830035
[86,] -55.5718924 -49.2385591
[87,] -55.2385591 -55.5718924
[88,] -60.6830035 -55.2385591
[89,] -73.1274479 -60.6830035
[90,] 31.8092533 -73.1274479
[91,] 27.9203644 31.8092533
[92,] 12.1425866 27.9203644
[93,] 10.4759200 12.1425866
[94,] 11.0314755 10.4759200
[95,] 12.2901866 11.0314755
[96,] 8.5339986 12.2901866
[97,] 4.9784431 8.5339986
[98,] 0.6451098 4.9784431
[99,] 3.9784431 0.6451098
[100,] -7.4660014 3.9784431
[101,] 1.0895542 -7.4660014
[102,] -15.1525335 1.0895542
[103,] -23.0414223 -15.1525335
[104,] -31.8192001 -23.0414223
[105,] -30.4858668 -31.8192001
[106,] -16.9303112 -30.4858668
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 22.4939483 29.0495038
2 14.1606149 22.4939483
3 15.4939483 14.1606149
4 10.0495038 15.4939483
5 6.6050594 10.0495038
6 -10.6370283 6.6050594
7 8.4740828 -10.6370283
8 -4.3036949 8.4740828
9 -6.9703616 -4.3036949
10 -10.4148060 -6.9703616
11 -17.1560949 -10.4148060
12 -16.9122829 -17.1560949
13 -18.4678385 -16.9122829
14 -19.8011718 -18.4678385
15 -18.4678385 -19.8011718
16 -15.9122829 -18.4678385
17 -21.3567274 -15.9122829
18 -34.5988150 -21.3567274
19 -22.4877039 -34.5988150
20 -12.2654817 -22.4877039
21 -8.9321484 -12.2654817
22 -23.3765928 -8.9321484
23 -26.1178817 -23.3765928
24 -27.8740697 -26.1178817
25 -23.4296252 -27.8740697
26 -17.7629586 -23.4296252
27 -20.4296252 -17.7629586
28 -16.8740697 -20.4296252
29 -16.3185141 -16.8740697
30 -21.5606018 -16.3185141
31 -27.4494907 -21.5606018
32 -22.2272685 -27.4494907
33 -12.8939351 -22.2272685
34 -5.3383796 -12.8939351
35 -5.0796685 -5.3383796
36 -4.8358565 -5.0796685
37 -1.3914120 -4.8358565
38 2.2752547 -1.3914120
39 7.6085880 2.2752547
40 9.1641435 7.6085880
41 14.7196991 9.1641435
42 10.4776114 14.7196991
43 7.5887226 10.4776114
44 12.8109448 7.5887226
45 13.1442781 12.8109448
46 12.6998337 13.1442781
47 17.9585448 12.6998337
48 18.2023568 17.9585448
49 22.6468012 18.2023568
50 27.3134679 22.6468012
51 22.6468012 27.3134679
52 25.2023568 22.6468012
53 30.7579123 25.2023568
54 15.5158247 30.7579123
55 18.6269358 15.5158247
56 27.8491580 18.6269358
57 39.1824914 27.8491580
58 39.7380469 39.1824914
59 37.9967580 39.7380469
60 27.2405700 37.9967580
61 30.6850145 27.2405700
62 36.3516811 30.6850145
63 31.6850145 36.3516811
64 35.2405700 31.6850145
65 36.7961256 35.2405700
66 22.5540379 36.7961256
67 16.6651490 22.5540379
68 22.8873713 16.6651490
69 20.2207046 22.8873713
70 20.7762601 20.2207046
71 20.0349712 20.7762601
72 9.2787833 20.0349712
73 11.7232277 9.2787833
74 12.3898944 11.7232277
75 12.7232277 12.3898944
76 21.2787833 12.7232277
77 20.8343388 21.2787833
78 1.5922512 20.8343388
79 -6.2966377 1.5922512
80 -5.0744155 -6.2966377
81 -23.7410822 -5.0744155
82 -28.1855266 -23.7410822
83 -39.9268155 -28.1855266
84 -42.6830035 -39.9268155
85 -49.2385591 -42.6830035
86 -55.5718924 -49.2385591
87 -55.2385591 -55.5718924
88 -60.6830035 -55.2385591
89 -73.1274479 -60.6830035
90 31.8092533 -73.1274479
91 27.9203644 31.8092533
92 12.1425866 27.9203644
93 10.4759200 12.1425866
94 11.0314755 10.4759200
95 12.2901866 11.0314755
96 8.5339986 12.2901866
97 4.9784431 8.5339986
98 0.6451098 4.9784431
99 3.9784431 0.6451098
100 -7.4660014 3.9784431
101 1.0895542 -7.4660014
102 -15.1525335 1.0895542
103 -23.0414223 -15.1525335
104 -31.8192001 -23.0414223
105 -30.4858668 -31.8192001
106 -16.9303112 -30.4858668
> 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/7ud4j1228653833.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/840ip1228653833.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9g08r1228653833.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10kqht1228653833.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/11dme11228653833.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/12q2h81228653833.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/13q6oz1228653833.tab")
>
> system("convert tmp/123uh1228653833.ps tmp/123uh1228653833.png")
> system("convert tmp/2chhh1228653833.ps tmp/2chhh1228653833.png")
> system("convert tmp/39qaa1228653833.ps tmp/39qaa1228653833.png")
> system("convert tmp/4d5ui1228653833.ps tmp/4d5ui1228653833.png")
> system("convert tmp/52dib1228653833.ps tmp/52dib1228653833.png")
> system("convert tmp/6ac3q1228653833.ps tmp/6ac3q1228653833.png")
> system("convert tmp/7ud4j1228653833.ps tmp/7ud4j1228653833.png")
> system("convert tmp/840ip1228653833.ps tmp/840ip1228653833.png")
> system("convert tmp/9g08r1228653833.ps tmp/9g08r1228653833.png")
>
>
> proc.time()
user system elapsed
2.097 1.487 2.508