R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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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(13
+ ,13
+ ,14
+ ,13
+ ,3
+ ,4
+ ,4
+ ,12
+ ,12
+ ,8
+ ,13
+ ,5
+ ,5
+ ,5
+ ,15
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+ ,16
+ ,6
+ ,1
+ ,9
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+ ,9
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+ ,19
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+ ,7
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+ ,5
+ ,16
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+ ,6
+ ,11
+ ,10
+ ,11
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+ ,11
+ ,12
+ ,6
+ ,1
+ ,5
+ ,15
+ ,11
+ ,16
+ ,11
+ ,5
+ ,4
+ ,4
+ ,7
+ ,14
+ ,12
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+ ,4
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+ ,4
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+ ,6
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+ ,4
+ ,18
+ ,10
+ ,11
+ ,7
+ ,9
+ ,4
+ ,5
+ ,8
+ ,6
+ ,7
+ ,9
+ ,11
+ ,4
+ ,8
+ ,3
+ ,11
+ ,9
+ ,7
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+ ,2
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+ ,11
+ ,11
+ ,15
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+ ,5
+ ,15
+ ,7
+ ,12
+ ,12
+ ,7
+ ,11
+ ,4
+ ,3
+ ,9
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+ ,12
+ ,15
+ ,13
+ ,6
+ ,6
+ ,4
+ ,15
+ ,11
+ ,17
+ ,16
+ ,6
+ ,9
+ ,6
+ ,9
+ ,11
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+ ,15
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+ ,19
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+ ,13
+ ,8
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+ ,5
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+ ,7
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+ ,8
+ ,8
+ ,13
+ ,4
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+ ,7
+ ,9
+ ,12
+ ,11
+ ,11
+ ,4
+ ,3
+ ,9
+ ,16
+ ,11
+ ,16
+ ,15
+ ,6
+ ,13
+ ,8
+ ,12
+ ,12
+ ,10
+ ,15
+ ,6
+ ,5
+ ,7
+ ,10
+ ,7
+ ,8
+ ,9
+ ,5
+ ,9
+ ,4
+ ,13
+ ,11
+ ,14
+ ,13
+ ,6
+ ,11
+ ,5
+ ,16
+ ,11
+ ,16
+ ,16
+ ,7
+ ,13
+ ,12
+ ,14
+ ,12
+ ,13
+ ,13
+ ,6
+ ,5
+ ,15
+ ,15
+ ,9
+ ,5
+ ,11
+ ,3
+ ,7
+ ,3
+ ,5
+ ,15
+ ,8
+ ,12
+ ,3
+ ,6
+ ,5
+ ,8
+ ,11
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+ ,4
+ ,13
+ ,11
+ ,11
+ ,8
+ ,12
+ ,6
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+ ,11
+ ,13
+ ,14
+ ,7
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+ ,9
+ ,17
+ ,11
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+ ,5
+ ,1
+ ,5
+ ,9
+ ,15
+ ,6
+ ,8
+ ,4
+ ,9
+ ,13
+ ,9
+ ,11
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+ ,13
+ ,5
+ ,19
+ ,4
+ ,13
+ ,12
+ ,16
+ ,16
+ ,6
+ ,13
+ ,5
+ ,10
+ ,12
+ ,5
+ ,13
+ ,6
+ ,18
+ ,7
+ ,6
+ ,9
+ ,15
+ ,11
+ ,6
+ ,6
+ ,8
+ ,12
+ ,12
+ ,12
+ ,14
+ ,5
+ ,5
+ ,9
+ ,8
+ ,12
+ ,8
+ ,13
+ ,4
+ ,3
+ ,11
+ ,14
+ ,13
+ ,13
+ ,13
+ ,5
+ ,7
+ ,4
+ ,12
+ ,11
+ ,14
+ ,13
+ ,5
+ ,8
+ ,6
+ ,11
+ ,9
+ ,12
+ ,12
+ ,4
+ ,9
+ ,8
+ ,16
+ ,9
+ ,16
+ ,16
+ ,6
+ ,13
+ ,10
+ ,8
+ ,11
+ ,10
+ ,15
+ ,2
+ ,12
+ ,4
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+ ,11
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+ ,15
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+ ,4
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+ ,3
+ ,4
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+ ,12
+ ,16
+ ,14
+ ,6
+ ,6
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+ ,14
+ ,9
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+ ,12
+ ,6
+ ,8
+ ,11
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+ ,9
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+ ,10
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+ ,14
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+ ,13
+ ,5
+ ,9
+ ,7
+ ,11
+ ,12
+ ,15
+ ,12
+ ,6
+ ,3
+ ,9
+ ,13
+ ,12
+ ,7
+ ,12
+ ,5
+ ,5
+ ,19
+ ,15
+ ,12
+ ,13
+ ,13
+ ,6
+ ,6
+ ,3
+ ,5
+ ,14
+ ,4
+ ,5
+ ,2
+ ,2
+ ,5
+ ,15
+ ,11
+ ,14
+ ,13
+ ,5
+ ,3
+ ,3
+ ,13
+ ,12
+ ,13
+ ,13
+ ,5
+ ,4
+ ,11
+ ,11
+ ,11
+ ,11
+ ,14
+ ,5
+ ,2
+ ,5
+ ,11
+ ,6
+ ,14
+ ,17
+ ,6
+ ,11
+ ,6
+ ,12
+ ,10
+ ,12
+ ,13
+ ,6
+ ,8
+ ,8
+ ,12
+ ,12
+ ,15
+ ,13
+ ,6
+ ,11
+ ,9
+ ,12
+ ,13
+ ,14
+ ,12
+ ,5
+ ,17
+ ,11
+ ,12
+ ,8
+ ,13
+ ,13
+ ,5
+ ,4
+ ,7
+ ,14
+ ,12
+ ,8
+ ,14
+ ,4
+ ,5
+ ,4
+ ,6
+ ,12
+ ,6
+ ,11
+ ,2
+ ,8
+ ,5
+ ,7
+ ,12
+ ,7
+ ,12
+ ,4
+ ,9
+ ,7
+ ,14
+ ,6
+ ,13
+ ,12
+ ,6
+ ,4
+ ,11
+ ,14
+ ,11
+ ,13
+ ,16
+ ,6
+ ,6
+ ,13
+ ,10
+ ,10
+ ,11
+ ,12
+ ,5
+ ,7
+ ,3
+ ,13
+ ,12
+ ,5
+ ,12
+ ,3
+ ,9
+ ,5
+ ,12
+ ,13
+ ,12
+ ,12
+ ,6
+ ,11
+ ,7
+ ,9
+ ,11
+ ,8
+ ,10
+ ,4
+ ,12
+ ,8
+ ,12
+ ,7
+ ,11
+ ,15
+ ,5
+ ,9
+ ,11
+ ,16
+ ,11
+ ,14
+ ,15
+ ,8
+ ,4
+ ,12
+ ,10
+ ,11
+ ,9
+ ,12
+ ,4
+ ,3
+ ,8)
+ ,dim=c(7
+ ,90)
+ ,dimnames=list(c('KansOverwinning'
+ ,'GeboekteOverwinning'
+ ,'Gevoel'
+ ,'EigenGevoel'
+ ,'Beste'
+ ,'2deBeste'
+ ,'3debeste')
+ ,1:90))
> y <- array(NA,dim=c(7,90),dimnames=list(c('KansOverwinning','GeboekteOverwinning','Gevoel','EigenGevoel','Beste','2deBeste','3debeste'),1:90))
> 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 = 'Do not include Seasonal Dummies'
> par1 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> 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
KansOverwinning GeboekteOverwinning Gevoel EigenGevoel Beste 2deBeste
1 13 13 14 13 3 4
2 12 12 8 13 5 5
3 15 10 12 16 6 1
4 12 9 7 12 6 9
5 10 10 10 11 5 19
6 12 12 7 12 3 11
7 15 13 16 18 8 3
8 9 12 11 11 4 5
9 12 12 14 14 4 8
10 11 6 6 9 4 9
11 11 5 16 14 6 11
12 11 12 11 12 6 1
13 15 11 16 11 5 4
14 7 14 12 12 4 5
15 11 14 7 13 6 6
16 11 12 13 11 4 8
17 10 12 11 12 6 9
18 14 11 15 16 6 4
19 10 11 7 9 4 5
20 6 7 9 11 4 8
21 11 9 7 13 2 13
22 15 11 14 15 7 4
23 11 11 15 10 5 15
24 12 12 7 11 4 3
25 14 12 15 13 6 6
26 15 11 17 16 6 9
27 9 11 15 15 7 19
28 13 8 14 14 5 4
29 13 9 14 14 6 15
30 16 12 8 14 4 4
31 13 10 8 8 4 7
32 12 10 14 13 7 4
33 14 12 14 15 7 9
34 11 8 8 13 4 8
35 9 12 11 11 4 3
36 16 11 16 15 6 13
37 12 12 10 15 6 5
38 10 7 8 9 5 9
39 13 11 14 13 6 11
40 16 11 16 16 7 13
41 14 12 13 13 6 5
42 15 9 5 11 3 7
43 5 15 8 12 3 6
44 8 11 10 12 4 4
45 11 11 8 12 6 17
46 16 11 13 14 7 6
47 17 11 15 14 5 1
48 9 15 6 8 4 9
49 9 11 12 13 5 19
50 13 12 16 16 6 13
51 10 12 5 13 6 18
52 6 9 15 11 6 6
53 12 12 12 14 5 5
54 8 12 8 13 4 3
55 14 13 13 13 5 7
56 12 11 14 13 5 8
57 11 9 12 12 4 9
58 16 9 16 16 6 13
59 8 11 10 15 2 12
60 15 11 15 15 8 2
61 7 12 8 12 3 4
62 16 12 16 14 6 6
63 14 9 19 12 6 8
64 16 11 14 15 6 9
65 9 9 6 12 5 10
66 14 12 13 13 5 9
67 11 12 15 12 6 3
68 13 12 7 12 5 5
69 15 12 13 13 6 6
70 5 14 4 5 2 2
71 15 11 14 13 5 3
72 13 12 13 13 5 4
73 11 11 11 14 5 2
74 11 6 14 17 6 11
75 12 10 12 13 6 8
76 12 12 15 13 6 11
77 12 13 14 12 5 17
78 12 8 13 13 5 4
79 14 12 8 14 4 5
80 6 12 6 11 2 8
81 7 12 7 12 4 9
82 14 6 13 12 6 4
83 14 11 13 16 6 6
84 10 10 11 12 5 7
85 13 12 5 12 3 9
86 12 13 12 12 6 11
87 9 11 8 10 4 12
88 12 7 11 15 5 9
89 16 11 14 15 8 4
90 10 11 9 12 4 3
3debeste t
1 4 1
2 5 2
3 9 3
4 11 4
5 3 5
6 4 6
7 5 7
8 7 8
9 8 9
10 9 10
11 10 11
12 5 12
13 4 13
14 3 14
15 6 15
16 7 16
17 9 17
18 18 18
19 8 19
20 3 20
21 5 21
22 8 22
23 7 23
24 9 24
25 4 25
26 6 26
27 8 27
28 7 28
29 4 29
30 6 30
31 12 31
32 3 32
33 5 33
34 7 34
35 9 35
36 8 36
37 7 37
38 4 38
39 5 39
40 12 40
41 15 41
42 3 42
43 5 43
44 13 44
45 8 45
46 9 46
47 5 47
48 13 48
49 4 49
50 5 50
51 7 51
52 8 52
53 9 53
54 11 54
55 4 55
56 6 56
57 8 57
58 10 58
59 4 59
60 4 60
61 2 61
62 12 62
63 11 63
64 4 64
65 7 65
66 7 66
67 9 67
68 19 68
69 3 69
70 5 70
71 3 71
72 11 72
73 5 73
74 6 74
75 8 75
76 9 76
77 11 77
78 7 78
79 4 79
80 5 80
81 7 81
82 11 82
83 13 83
84 3 84
85 5 85
86 7 86
87 8 87
88 11 88
89 12 89
90 8 90
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) GeboekteOverwinning Gevoel
3.274123 -0.036585 0.141736
EigenGevoel Beste `2deBeste`
0.406011 0.519932 -0.097117
`3debeste` t
0.051275 -0.002926
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.3320 -1.4125 0.2032 1.3523 5.9694
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.274123 2.265635 1.445 0.15223
GeboekteOverwinning -0.036585 0.125769 -0.291 0.77187
Gevoel 0.141736 0.093568 1.515 0.13367
EigenGevoel 0.406011 0.145403 2.792 0.00651 **
Beste 0.519932 0.248454 2.093 0.03947 *
`2deBeste` -0.097117 0.055824 -1.740 0.08566 .
`3debeste` 0.051275 0.073659 0.696 0.48833
t -0.002926 0.009271 -0.316 0.75315
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.263 on 82 degrees of freedom
Multiple R-squared: 0.4118, Adjusted R-squared: 0.3616
F-statistic: 8.201 on 7 and 82 DF, p-value: 1.515e-07
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.15637459 0.3127492 0.84362541
[2,] 0.06855737 0.1371147 0.93144263
[3,] 0.40655095 0.8131019 0.59344905
[4,] 0.56425427 0.8714915 0.43574573
[5,] 0.51433646 0.9713271 0.48566354
[6,] 0.44572126 0.8914425 0.55427874
[7,] 0.34942958 0.6988592 0.65057042
[8,] 0.29357710 0.5871542 0.70642290
[9,] 0.22116093 0.4423219 0.77883907
[10,] 0.23092463 0.4618493 0.76907537
[11,] 0.30529565 0.6105913 0.69470435
[12,] 0.35391425 0.7078285 0.64608575
[13,] 0.29981775 0.5996355 0.70018225
[14,] 0.26741128 0.5348226 0.73258872
[15,] 0.26324616 0.5264923 0.73675384
[16,] 0.22369176 0.4473835 0.77630824
[17,] 0.29809923 0.5961985 0.70190077
[18,] 0.23619945 0.4723989 0.76380055
[19,] 0.20961221 0.4192244 0.79038779
[20,] 0.31639776 0.6327955 0.68360224
[21,] 0.37933418 0.7586684 0.62066582
[22,] 0.33393505 0.6678701 0.66606495
[23,] 0.27938576 0.5587715 0.72061424
[24,] 0.23707935 0.4741587 0.76292065
[25,] 0.27235137 0.5447027 0.72764863
[26,] 0.29816280 0.5963256 0.70183720
[27,] 0.26761517 0.5352303 0.73238483
[28,] 0.21610041 0.4322008 0.78389959
[29,] 0.17333762 0.3466752 0.82666238
[30,] 0.14787776 0.2957555 0.85212224
[31,] 0.11430857 0.2286171 0.88569143
[32,] 0.41124959 0.8224992 0.58875041
[33,] 0.75102177 0.4979565 0.24897823
[34,] 0.80393673 0.3921265 0.19606327
[35,] 0.76358033 0.4728393 0.23641967
[36,] 0.76546202 0.4690760 0.23453798
[37,] 0.84867269 0.3026546 0.15132731
[38,] 0.81587101 0.3682580 0.18412899
[39,] 0.79004501 0.4199100 0.20995499
[40,] 0.75166734 0.4966653 0.24833266
[41,] 0.69584425 0.6083115 0.30415575
[42,] 0.93278260 0.1344348 0.06721740
[43,] 0.90948964 0.1810207 0.09051036
[44,] 0.93778886 0.1244223 0.06221114
[45,] 0.92946161 0.1410768 0.07053839
[46,] 0.90263282 0.1947344 0.09736718
[47,] 0.87102954 0.2579409 0.12897046
[48,] 0.86414619 0.2717076 0.13585381
[49,] 0.85497588 0.2900482 0.14502412
[50,] 0.81806028 0.3638794 0.18193972
[51,] 0.85456943 0.2908611 0.14543057
[52,] 0.83064796 0.3387041 0.16935204
[53,] 0.78818044 0.4236391 0.21181956
[54,] 0.78844250 0.4231150 0.21155750
[55,] 0.74387673 0.5122465 0.25612327
[56,] 0.73141908 0.5371618 0.26858092
[57,] 0.73377835 0.5324433 0.26622165
[58,] 0.69784339 0.6043132 0.30215661
[59,] 0.68670985 0.6265803 0.31329015
[60,] 0.62461819 0.7507636 0.37538181
[61,] 0.67047006 0.6590599 0.32952994
[62,] 0.58443767 0.8311247 0.41556233
[63,] 0.51019752 0.9796050 0.48980248
[64,] 0.51799397 0.9640121 0.48200603
[65,] 0.42544834 0.8508967 0.57455166
[66,] 0.32150725 0.6430145 0.67849275
[67,] 0.32212009 0.6442402 0.67787991
[68,] 0.21326357 0.4265271 0.78673643
[69,] 0.18742500 0.3748500 0.81257500
> postscript(file="/var/fisher/rcomp/tmp/1jafq1356105137.ps",horizontal=F,onefile=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/fisher/rcomp/tmp/2or2x1356105137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/36fhw1356105137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/458e41356105137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5cdp01356105137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 90
Frequency = 1
1 2 3 4 5 6
1.56552173 0.38825641 0.41953407 0.39298779 0.31460211 2.62154776
7 8 9 10 11 12
-1.47850856 -1.78999294 -0.19023378 1.80297142 -2.57501154 -1.51008576
13 14 15 16 17 18
3.01614431 -4.04191883 -0.83289648 0.24129007 -1.92362106 -1.09532858
19 20 21 22 23 24
0.53329542 -4.15788830 1.81255637 0.45693630 0.50760997 1.52697598
25 26 27 28 29 30
1.09185000 0.74548571 -4.21341682 -0.13811386 0.60357560 4.43569989
31 32 33 34 35 36
3.78522717 -1.48199656 0.16511113 0.04426769 -2.00778772 2.60840695
37 38 39 40 41 42
-1.22732773 0.37443978 0.67226885 1.48906604 0.76099087 5.96940219
43 44 45 46 47 48
-4.83905000 -3.39030031 0.37512773 2.21785542 3.69668753 0.44431614
49 50 51 52 53 54
-1.66686048 -0.56623829 -0.40314586 -6.33199160 -0.64059736 -3.44156723
55 56 57 58 59 60
2.11672171 -0.10069089 0.03304863 2.09103767 -2.28617810 -0.08269772
61 62 63 64 65 66
-2.93655482 2.24214823 0.76764297 2.79042441 -1.46467140 2.15272733
67 68 69 70 71 72
-1.92699196 1.41124098 2.55531945 -1.25615701 2.61143096 0.47959619
73 74 75 76 77 78
-1.56318781 -3.08358272 -0.42070072 -0.52973761 1.05760525 -0.44409072
79 80 81 82 83 84
2.77871653 -2.43691025 -3.02702962 1.17542179 -0.17109025 -1.16743433
85 86 87 88 89 90
3.89062653 0.46987153 -0.13569814 -0.69948509 0.92791411 -0.95473387
> postscript(file="/var/fisher/rcomp/tmp/6c65y1356105137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 90
Frequency = 1
lag(myerror, k = 1) myerror
0 1.56552173 NA
1 0.38825641 1.56552173
2 0.41953407 0.38825641
3 0.39298779 0.41953407
4 0.31460211 0.39298779
5 2.62154776 0.31460211
6 -1.47850856 2.62154776
7 -1.78999294 -1.47850856
8 -0.19023378 -1.78999294
9 1.80297142 -0.19023378
10 -2.57501154 1.80297142
11 -1.51008576 -2.57501154
12 3.01614431 -1.51008576
13 -4.04191883 3.01614431
14 -0.83289648 -4.04191883
15 0.24129007 -0.83289648
16 -1.92362106 0.24129007
17 -1.09532858 -1.92362106
18 0.53329542 -1.09532858
19 -4.15788830 0.53329542
20 1.81255637 -4.15788830
21 0.45693630 1.81255637
22 0.50760997 0.45693630
23 1.52697598 0.50760997
24 1.09185000 1.52697598
25 0.74548571 1.09185000
26 -4.21341682 0.74548571
27 -0.13811386 -4.21341682
28 0.60357560 -0.13811386
29 4.43569989 0.60357560
30 3.78522717 4.43569989
31 -1.48199656 3.78522717
32 0.16511113 -1.48199656
33 0.04426769 0.16511113
34 -2.00778772 0.04426769
35 2.60840695 -2.00778772
36 -1.22732773 2.60840695
37 0.37443978 -1.22732773
38 0.67226885 0.37443978
39 1.48906604 0.67226885
40 0.76099087 1.48906604
41 5.96940219 0.76099087
42 -4.83905000 5.96940219
43 -3.39030031 -4.83905000
44 0.37512773 -3.39030031
45 2.21785542 0.37512773
46 3.69668753 2.21785542
47 0.44431614 3.69668753
48 -1.66686048 0.44431614
49 -0.56623829 -1.66686048
50 -0.40314586 -0.56623829
51 -6.33199160 -0.40314586
52 -0.64059736 -6.33199160
53 -3.44156723 -0.64059736
54 2.11672171 -3.44156723
55 -0.10069089 2.11672171
56 0.03304863 -0.10069089
57 2.09103767 0.03304863
58 -2.28617810 2.09103767
59 -0.08269772 -2.28617810
60 -2.93655482 -0.08269772
61 2.24214823 -2.93655482
62 0.76764297 2.24214823
63 2.79042441 0.76764297
64 -1.46467140 2.79042441
65 2.15272733 -1.46467140
66 -1.92699196 2.15272733
67 1.41124098 -1.92699196
68 2.55531945 1.41124098
69 -1.25615701 2.55531945
70 2.61143096 -1.25615701
71 0.47959619 2.61143096
72 -1.56318781 0.47959619
73 -3.08358272 -1.56318781
74 -0.42070072 -3.08358272
75 -0.52973761 -0.42070072
76 1.05760525 -0.52973761
77 -0.44409072 1.05760525
78 2.77871653 -0.44409072
79 -2.43691025 2.77871653
80 -3.02702962 -2.43691025
81 1.17542179 -3.02702962
82 -0.17109025 1.17542179
83 -1.16743433 -0.17109025
84 3.89062653 -1.16743433
85 0.46987153 3.89062653
86 -0.13569814 0.46987153
87 -0.69948509 -0.13569814
88 0.92791411 -0.69948509
89 -0.95473387 0.92791411
90 NA -0.95473387
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.38825641 1.56552173
[2,] 0.41953407 0.38825641
[3,] 0.39298779 0.41953407
[4,] 0.31460211 0.39298779
[5,] 2.62154776 0.31460211
[6,] -1.47850856 2.62154776
[7,] -1.78999294 -1.47850856
[8,] -0.19023378 -1.78999294
[9,] 1.80297142 -0.19023378
[10,] -2.57501154 1.80297142
[11,] -1.51008576 -2.57501154
[12,] 3.01614431 -1.51008576
[13,] -4.04191883 3.01614431
[14,] -0.83289648 -4.04191883
[15,] 0.24129007 -0.83289648
[16,] -1.92362106 0.24129007
[17,] -1.09532858 -1.92362106
[18,] 0.53329542 -1.09532858
[19,] -4.15788830 0.53329542
[20,] 1.81255637 -4.15788830
[21,] 0.45693630 1.81255637
[22,] 0.50760997 0.45693630
[23,] 1.52697598 0.50760997
[24,] 1.09185000 1.52697598
[25,] 0.74548571 1.09185000
[26,] -4.21341682 0.74548571
[27,] -0.13811386 -4.21341682
[28,] 0.60357560 -0.13811386
[29,] 4.43569989 0.60357560
[30,] 3.78522717 4.43569989
[31,] -1.48199656 3.78522717
[32,] 0.16511113 -1.48199656
[33,] 0.04426769 0.16511113
[34,] -2.00778772 0.04426769
[35,] 2.60840695 -2.00778772
[36,] -1.22732773 2.60840695
[37,] 0.37443978 -1.22732773
[38,] 0.67226885 0.37443978
[39,] 1.48906604 0.67226885
[40,] 0.76099087 1.48906604
[41,] 5.96940219 0.76099087
[42,] -4.83905000 5.96940219
[43,] -3.39030031 -4.83905000
[44,] 0.37512773 -3.39030031
[45,] 2.21785542 0.37512773
[46,] 3.69668753 2.21785542
[47,] 0.44431614 3.69668753
[48,] -1.66686048 0.44431614
[49,] -0.56623829 -1.66686048
[50,] -0.40314586 -0.56623829
[51,] -6.33199160 -0.40314586
[52,] -0.64059736 -6.33199160
[53,] -3.44156723 -0.64059736
[54,] 2.11672171 -3.44156723
[55,] -0.10069089 2.11672171
[56,] 0.03304863 -0.10069089
[57,] 2.09103767 0.03304863
[58,] -2.28617810 2.09103767
[59,] -0.08269772 -2.28617810
[60,] -2.93655482 -0.08269772
[61,] 2.24214823 -2.93655482
[62,] 0.76764297 2.24214823
[63,] 2.79042441 0.76764297
[64,] -1.46467140 2.79042441
[65,] 2.15272733 -1.46467140
[66,] -1.92699196 2.15272733
[67,] 1.41124098 -1.92699196
[68,] 2.55531945 1.41124098
[69,] -1.25615701 2.55531945
[70,] 2.61143096 -1.25615701
[71,] 0.47959619 2.61143096
[72,] -1.56318781 0.47959619
[73,] -3.08358272 -1.56318781
[74,] -0.42070072 -3.08358272
[75,] -0.52973761 -0.42070072
[76,] 1.05760525 -0.52973761
[77,] -0.44409072 1.05760525
[78,] 2.77871653 -0.44409072
[79,] -2.43691025 2.77871653
[80,] -3.02702962 -2.43691025
[81,] 1.17542179 -3.02702962
[82,] -0.17109025 1.17542179
[83,] -1.16743433 -0.17109025
[84,] 3.89062653 -1.16743433
[85,] 0.46987153 3.89062653
[86,] -0.13569814 0.46987153
[87,] -0.69948509 -0.13569814
[88,] 0.92791411 -0.69948509
[89,] -0.95473387 0.92791411
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.38825641 1.56552173
2 0.41953407 0.38825641
3 0.39298779 0.41953407
4 0.31460211 0.39298779
5 2.62154776 0.31460211
6 -1.47850856 2.62154776
7 -1.78999294 -1.47850856
8 -0.19023378 -1.78999294
9 1.80297142 -0.19023378
10 -2.57501154 1.80297142
11 -1.51008576 -2.57501154
12 3.01614431 -1.51008576
13 -4.04191883 3.01614431
14 -0.83289648 -4.04191883
15 0.24129007 -0.83289648
16 -1.92362106 0.24129007
17 -1.09532858 -1.92362106
18 0.53329542 -1.09532858
19 -4.15788830 0.53329542
20 1.81255637 -4.15788830
21 0.45693630 1.81255637
22 0.50760997 0.45693630
23 1.52697598 0.50760997
24 1.09185000 1.52697598
25 0.74548571 1.09185000
26 -4.21341682 0.74548571
27 -0.13811386 -4.21341682
28 0.60357560 -0.13811386
29 4.43569989 0.60357560
30 3.78522717 4.43569989
31 -1.48199656 3.78522717
32 0.16511113 -1.48199656
33 0.04426769 0.16511113
34 -2.00778772 0.04426769
35 2.60840695 -2.00778772
36 -1.22732773 2.60840695
37 0.37443978 -1.22732773
38 0.67226885 0.37443978
39 1.48906604 0.67226885
40 0.76099087 1.48906604
41 5.96940219 0.76099087
42 -4.83905000 5.96940219
43 -3.39030031 -4.83905000
44 0.37512773 -3.39030031
45 2.21785542 0.37512773
46 3.69668753 2.21785542
47 0.44431614 3.69668753
48 -1.66686048 0.44431614
49 -0.56623829 -1.66686048
50 -0.40314586 -0.56623829
51 -6.33199160 -0.40314586
52 -0.64059736 -6.33199160
53 -3.44156723 -0.64059736
54 2.11672171 -3.44156723
55 -0.10069089 2.11672171
56 0.03304863 -0.10069089
57 2.09103767 0.03304863
58 -2.28617810 2.09103767
59 -0.08269772 -2.28617810
60 -2.93655482 -0.08269772
61 2.24214823 -2.93655482
62 0.76764297 2.24214823
63 2.79042441 0.76764297
64 -1.46467140 2.79042441
65 2.15272733 -1.46467140
66 -1.92699196 2.15272733
67 1.41124098 -1.92699196
68 2.55531945 1.41124098
69 -1.25615701 2.55531945
70 2.61143096 -1.25615701
71 0.47959619 2.61143096
72 -1.56318781 0.47959619
73 -3.08358272 -1.56318781
74 -0.42070072 -3.08358272
75 -0.52973761 -0.42070072
76 1.05760525 -0.52973761
77 -0.44409072 1.05760525
78 2.77871653 -0.44409072
79 -2.43691025 2.77871653
80 -3.02702962 -2.43691025
81 1.17542179 -3.02702962
82 -0.17109025 1.17542179
83 -1.16743433 -0.17109025
84 3.89062653 -1.16743433
85 0.46987153 3.89062653
86 -0.13569814 0.46987153
87 -0.69948509 -0.13569814
88 0.92791411 -0.69948509
89 -0.95473387 0.92791411
> 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/fisher/rcomp/tmp/7f37l1356105137.ps",horizontal=F,onefile=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/fisher/rcomp/tmp/88yq01356105137.ps",horizontal=F,onefile=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/fisher/rcomp/tmp/9ev0k1356105137.ps",horizontal=F,onefile=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
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10z3jo1356105137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11qws41356105137.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12pm3v1356105137.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/131hwg1356105137.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14n0we1356105137.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15fsqx1356105137.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/16bj811356105137.tab")
+ }
>
> try(system("convert tmp/1jafq1356105137.ps tmp/1jafq1356105137.png",intern=TRUE))
character(0)
> try(system("convert tmp/2or2x1356105137.ps tmp/2or2x1356105137.png",intern=TRUE))
character(0)
> try(system("convert tmp/36fhw1356105137.ps tmp/36fhw1356105137.png",intern=TRUE))
character(0)
> try(system("convert tmp/458e41356105137.ps tmp/458e41356105137.png",intern=TRUE))
character(0)
> try(system("convert tmp/5cdp01356105137.ps tmp/5cdp01356105137.png",intern=TRUE))
character(0)
> try(system("convert tmp/6c65y1356105137.ps tmp/6c65y1356105137.png",intern=TRUE))
character(0)
> try(system("convert tmp/7f37l1356105137.ps tmp/7f37l1356105137.png",intern=TRUE))
character(0)
> try(system("convert tmp/88yq01356105137.ps tmp/88yq01356105137.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ev0k1356105137.ps tmp/9ev0k1356105137.png",intern=TRUE))
character(0)
> try(system("convert tmp/10z3jo1356105137.ps tmp/10z3jo1356105137.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.106 1.853 8.976