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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> x <- array(list(6.4,12.5,6.8,14.8,7.5,15.9,7.5,14.8,7.6,12.9,7.6,14.3,7.4,14.2,7.3,15.9,7.1,15.3,6.9,15.5,6.8,15.1,7.5,15,7.6,12.1,7.8,15.8,8,16.9,8.1,15.1,8.2,13.7,8.3,14.8,8.2,14.7,8,16,7.9,15.4,7.6,15,7.6,15.5,8.2,15.1,8.3,11.7,8.4,16.3,8.4,16.7,8.4,15,8.6,14.9,8.9,14.6,8.8,15.3,8.3,17.9,7.5,16.4,7.2,15.4,7.5,17.9,8.8,15.9,9.3,13.9,9.3,17.8,8.7,17.9,8.2,17.4,8.3,16.7,8.5,16,8.6,16.6,8.6,19.1,8.2,17.8,8.1,17.2,8,18.6,8.6,16.3,8.7,15.1,8.8,19.2,8.5,17.7,8.4,19.1,8.5,18,8.7,17.5,8.7,17.8,8.6,21.1,8.5,17.2,8.3,19.4,8.1,19.8,8.2,17.6,8.1,16.2,8.1,19.5,7.9,19.9,7.9,20,7.9,17.3,8,18.9,8,18.6,7.9,21.4,8,18.6,7.7,19.8,7.2,20.8,7.5,19.6,7.3,17.7,7,19.8,7,22.2,7,20.7,7.2,17.9,7.3,21.2,7.1,21.4,6.8,21.7,6.6,23.2,6.2,21.5,6.2,22.9,6.8,23.2,6.9,18.6),dim=c(2,85),dimnames=list(c('Werkloosheid','Export'),1:85))
> y <- array(NA,dim=c(2,85),dimnames=list(c('Werkloosheid','Export'),1:85))
> 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)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
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
Werkloosheid Export M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 6.4 12.5 1 0 0 0 0 0 0 0 0 0 0 1
2 6.8 14.8 0 1 0 0 0 0 0 0 0 0 0 2
3 7.5 15.9 0 0 1 0 0 0 0 0 0 0 0 3
4 7.5 14.8 0 0 0 1 0 0 0 0 0 0 0 4
5 7.6 12.9 0 0 0 0 1 0 0 0 0 0 0 5
6 7.6 14.3 0 0 0 0 0 1 0 0 0 0 0 6
7 7.4 14.2 0 0 0 0 0 0 1 0 0 0 0 7
8 7.3 15.9 0 0 0 0 0 0 0 1 0 0 0 8
9 7.1 15.3 0 0 0 0 0 0 0 0 1 0 0 9
10 6.9 15.5 0 0 0 0 0 0 0 0 0 1 0 10
11 6.8 15.1 0 0 0 0 0 0 0 0 0 0 1 11
12 7.5 15.0 0 0 0 0 0 0 0 0 0 0 0 12
13 7.6 12.1 1 0 0 0 0 0 0 0 0 0 0 13
14 7.8 15.8 0 1 0 0 0 0 0 0 0 0 0 14
15 8.0 16.9 0 0 1 0 0 0 0 0 0 0 0 15
16 8.1 15.1 0 0 0 1 0 0 0 0 0 0 0 16
17 8.2 13.7 0 0 0 0 1 0 0 0 0 0 0 17
18 8.3 14.8 0 0 0 0 0 1 0 0 0 0 0 18
19 8.2 14.7 0 0 0 0 0 0 1 0 0 0 0 19
20 8.0 16.0 0 0 0 0 0 0 0 1 0 0 0 20
21 7.9 15.4 0 0 0 0 0 0 0 0 1 0 0 21
22 7.6 15.0 0 0 0 0 0 0 0 0 0 1 0 22
23 7.6 15.5 0 0 0 0 0 0 0 0 0 0 1 23
24 8.2 15.1 0 0 0 0 0 0 0 0 0 0 0 24
25 8.3 11.7 1 0 0 0 0 0 0 0 0 0 0 25
26 8.4 16.3 0 1 0 0 0 0 0 0 0 0 0 26
27 8.4 16.7 0 0 1 0 0 0 0 0 0 0 0 27
28 8.4 15.0 0 0 0 1 0 0 0 0 0 0 0 28
29 8.6 14.9 0 0 0 0 1 0 0 0 0 0 0 29
30 8.9 14.6 0 0 0 0 0 1 0 0 0 0 0 30
31 8.8 15.3 0 0 0 0 0 0 1 0 0 0 0 31
32 8.3 17.9 0 0 0 0 0 0 0 1 0 0 0 32
33 7.5 16.4 0 0 0 0 0 0 0 0 1 0 0 33
34 7.2 15.4 0 0 0 0 0 0 0 0 0 1 0 34
35 7.5 17.9 0 0 0 0 0 0 0 0 0 0 1 35
36 8.8 15.9 0 0 0 0 0 0 0 0 0 0 0 36
37 9.3 13.9 1 0 0 0 0 0 0 0 0 0 0 37
38 9.3 17.8 0 1 0 0 0 0 0 0 0 0 0 38
39 8.7 17.9 0 0 1 0 0 0 0 0 0 0 0 39
40 8.2 17.4 0 0 0 1 0 0 0 0 0 0 0 40
41 8.3 16.7 0 0 0 0 1 0 0 0 0 0 0 41
42 8.5 16.0 0 0 0 0 0 1 0 0 0 0 0 42
43 8.6 16.6 0 0 0 0 0 0 1 0 0 0 0 43
44 8.6 19.1 0 0 0 0 0 0 0 1 0 0 0 44
45 8.2 17.8 0 0 0 0 0 0 0 0 1 0 0 45
46 8.1 17.2 0 0 0 0 0 0 0 0 0 1 0 46
47 8.0 18.6 0 0 0 0 0 0 0 0 0 0 1 47
48 8.6 16.3 0 0 0 0 0 0 0 0 0 0 0 48
49 8.7 15.1 1 0 0 0 0 0 0 0 0 0 0 49
50 8.8 19.2 0 1 0 0 0 0 0 0 0 0 0 50
51 8.5 17.7 0 0 1 0 0 0 0 0 0 0 0 51
52 8.4 19.1 0 0 0 1 0 0 0 0 0 0 0 52
53 8.5 18.0 0 0 0 0 1 0 0 0 0 0 0 53
54 8.7 17.5 0 0 0 0 0 1 0 0 0 0 0 54
55 8.7 17.8 0 0 0 0 0 0 1 0 0 0 0 55
56 8.6 21.1 0 0 0 0 0 0 0 1 0 0 0 56
57 8.5 17.2 0 0 0 0 0 0 0 0 1 0 0 57
58 8.3 19.4 0 0 0 0 0 0 0 0 0 1 0 58
59 8.1 19.8 0 0 0 0 0 0 0 0 0 0 1 59
60 8.2 17.6 0 0 0 0 0 0 0 0 0 0 0 60
61 8.1 16.2 1 0 0 0 0 0 0 0 0 0 0 61
62 8.1 19.5 0 1 0 0 0 0 0 0 0 0 0 62
63 7.9 19.9 0 0 1 0 0 0 0 0 0 0 0 63
64 7.9 20.0 0 0 0 1 0 0 0 0 0 0 0 64
65 7.9 17.3 0 0 0 0 1 0 0 0 0 0 0 65
66 8.0 18.9 0 0 0 0 0 1 0 0 0 0 0 66
67 8.0 18.6 0 0 0 0 0 0 1 0 0 0 0 67
68 7.9 21.4 0 0 0 0 0 0 0 1 0 0 0 68
69 8.0 18.6 0 0 0 0 0 0 0 0 1 0 0 69
70 7.7 19.8 0 0 0 0 0 0 0 0 0 1 0 70
71 7.2 20.8 0 0 0 0 0 0 0 0 0 0 1 71
72 7.5 19.6 0 0 0 0 0 0 0 0 0 0 0 72
73 7.3 17.7 1 0 0 0 0 0 0 0 0 0 0 73
74 7.0 19.8 0 1 0 0 0 0 0 0 0 0 0 74
75 7.0 22.2 0 0 1 0 0 0 0 0 0 0 0 75
76 7.0 20.7 0 0 0 1 0 0 0 0 0 0 0 76
77 7.2 17.9 0 0 0 0 1 0 0 0 0 0 0 77
78 7.3 21.2 0 0 0 0 0 1 0 0 0 0 0 78
79 7.1 21.4 0 0 0 0 0 0 1 0 0 0 0 79
80 6.8 21.7 0 0 0 0 0 0 0 1 0 0 0 80
81 6.6 23.2 0 0 0 0 0 0 0 0 1 0 0 81
82 6.2 21.5 0 0 0 0 0 0 0 0 0 1 0 82
83 6.2 22.9 0 0 0 0 0 0 0 0 0 0 1 83
84 6.8 23.2 0 0 0 0 0 0 0 0 0 0 0 84
85 6.9 18.6 1 0 0 0 0 0 0 0 0 0 0 85
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Export M1 M2 M3 M4
12.90656 -0.36323 -0.99003 0.40400 0.55375 0.18845
M5 M6 M7 M8 M9 M10
-0.28172 0.13805 0.10485 0.64231 -0.10717 -0.39874
M11 t
-0.16083 0.02923
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.28189 -0.32435 -0.02735 0.33779 1.35074
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.906556 1.181231 10.926 < 2e-16 ***
Export -0.363231 0.087128 -4.169 8.52e-05 ***
M1 -0.990033 0.384794 -2.573 0.012176 *
M2 0.403999 0.347780 1.162 0.249268
M3 0.553755 0.360113 1.538 0.128561
M4 0.188452 0.341368 0.552 0.582648
M5 -0.281722 0.347341 -0.811 0.420030
M6 0.138054 0.337087 0.410 0.683369
M7 0.104849 0.336569 0.312 0.756317
M8 0.642308 0.372795 1.723 0.089250 .
M9 -0.107173 0.338385 -0.317 0.752387
M10 -0.398739 0.337409 -1.182 0.241242
M11 -0.160833 0.352469 -0.456 0.649563
t 0.029234 0.008358 3.498 0.000814 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6287 on 71 degrees of freedom
Multiple R-squared: 0.313, Adjusted R-squared: 0.1872
F-statistic: 2.489 on 13 and 71 DF, p-value: 0.007397
> 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,] 6.589441e-02 1.317888e-01 0.934105592
[2,] 2.176675e-02 4.353350e-02 0.978233251
[3,] 6.607576e-03 1.321515e-02 0.993392424
[4,] 2.731409e-03 5.462818e-03 0.997268591
[5,] 8.102816e-04 1.620563e-03 0.999189718
[6,] 4.416888e-04 8.833776e-04 0.999558311
[7,] 1.411815e-04 2.823630e-04 0.999858819
[8,] 4.832475e-05 9.664950e-05 0.999951675
[9,] 1.598410e-05 3.196819e-05 0.999984016
[10,] 5.096493e-06 1.019299e-05 0.999994904
[11,] 6.040115e-05 1.208023e-04 0.999939599
[12,] 2.266885e-04 4.533771e-04 0.999773311
[13,] 1.151994e-04 2.303988e-04 0.999884801
[14,] 4.599230e-05 9.198461e-05 0.999954008
[15,] 1.768624e-05 3.537248e-05 0.999982314
[16,] 1.003118e-05 2.006236e-05 0.999989969
[17,] 1.768400e-03 3.536800e-03 0.998231600
[18,] 1.072531e-01 2.145061e-01 0.892746929
[19,] 2.034018e-01 4.068035e-01 0.796598231
[20,] 1.770851e-01 3.541702e-01 0.822914887
[21,] 3.546131e-01 7.092262e-01 0.645386895
[22,] 3.902763e-01 7.805525e-01 0.609723749
[23,] 3.756353e-01 7.512707e-01 0.624364663
[24,] 5.733841e-01 8.532319e-01 0.426615944
[25,] 6.817622e-01 6.364756e-01 0.318237814
[26,] 8.339204e-01 3.321592e-01 0.166079593
[27,] 8.701516e-01 2.596968e-01 0.129848423
[28,] 8.655613e-01 2.688774e-01 0.134438711
[29,] 9.412085e-01 1.175830e-01 0.058791497
[30,] 9.779631e-01 4.407376e-02 0.022036882
[31,] 9.927661e-01 1.446773e-02 0.007233866
[32,] 9.973869e-01 5.226123e-03 0.002613061
[33,] 9.973086e-01 5.382870e-03 0.002691435
[34,] 9.953636e-01 9.272886e-03 0.004636443
[35,] 9.971457e-01 5.708596e-03 0.002854298
[36,] 9.964639e-01 7.072129e-03 0.003536064
[37,] 9.952584e-01 9.483182e-03 0.004741591
[38,] 9.941794e-01 1.164120e-02 0.005820601
[39,] 9.908140e-01 1.837195e-02 0.009185974
[40,] 9.831150e-01 3.377009e-02 0.016885045
[41,] 9.736317e-01 5.273659e-02 0.026368293
[42,] 9.578845e-01 8.423101e-02 0.042115506
[43,] 9.346333e-01 1.307334e-01 0.065366717
[44,] 9.512653e-01 9.746939e-02 0.048734697
[45,] 9.596723e-01 8.065535e-02 0.040327677
[46,] 9.594484e-01 8.110320e-02 0.040551602
[47,] 9.481423e-01 1.037153e-01 0.051857667
[48,] 9.176841e-01 1.646318e-01 0.082315923
[49,] 8.903305e-01 2.193389e-01 0.109669465
[50,] 8.514530e-01 2.970940e-01 0.148546994
[51,] 7.666026e-01 4.667947e-01 0.233397351
[52,] 6.559432e-01 6.881136e-01 0.344056793
> postscript(file="/var/www/html/rcomp/tmp/1t9521228903818.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/22eka1228903818.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/36ms51228903818.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lzzp1228903818.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5grar1228903818.ps",horizontal=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 = 85
Frequency = 1
1 2 3 4 5 6
-1.00536331 -1.19319668 -0.27263146 -0.33611744 -0.48531772 -0.42580302
7 8 9 10 11 12
-0.65815488 -0.70735515 -0.40504694 -0.27006876 -0.78250053 -0.30889099
13 14 15 16 17 18
-0.30146356 -0.18077283 0.23979239 0.02204436 0.05445984 0.10500508
19 20 21 22 23 24
-0.02734678 -0.32183965 0.08046856 -0.10249216 -0.18801558 0.07662451
25 26 27 28 29 30
-0.09756381 0.25003527 0.21633844 -0.06508644 0.53952999 0.28155113
31 32 33 34 35 36
0.43978447 0.31749255 -0.30710759 -0.70800721 0.23293237 0.61640206
37 38 39 40 41 42
1.35073784 1.34407487 0.60140859 0.25586151 0.54253904 0.03926758
43 44 45 46 47 48
0.36117777 0.70256270 0.55060886 0.49500184 0.63638677 0.21088701
49 50 51 52 53 54
0.83580799 1.00179132 -0.02204536 0.72254741 0.86393234 0.43330718
55 56 57 58 59 60
0.54624792 1.07821805 0.28186232 1.14330349 0.82145692 -0.06771968
61 62 63 64 65 66
0.28455499 0.05995312 -0.17374371 0.19864811 -0.34113736 -0.10897637
67 68 69 70 71 72
-0.21397453 0.13637985 -0.06042123 0.33778844 -0.06611923 -0.39206434
73 74 75 76 77 78
-0.32140541 -1.28188508 -0.58911891 -0.79789749 -1.17400611 -0.32435157
79 80 81 82 83 84
-0.44773398 -1.20545835 -0.14036399 -0.89552566 -0.65414073 -0.13523859
85
-0.74530471
> postscript(file="/var/www/html/rcomp/tmp/6sojf1228903818.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 = 85
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.00536331 NA
1 -1.19319668 -1.00536331
2 -0.27263146 -1.19319668
3 -0.33611744 -0.27263146
4 -0.48531772 -0.33611744
5 -0.42580302 -0.48531772
6 -0.65815488 -0.42580302
7 -0.70735515 -0.65815488
8 -0.40504694 -0.70735515
9 -0.27006876 -0.40504694
10 -0.78250053 -0.27006876
11 -0.30889099 -0.78250053
12 -0.30146356 -0.30889099
13 -0.18077283 -0.30146356
14 0.23979239 -0.18077283
15 0.02204436 0.23979239
16 0.05445984 0.02204436
17 0.10500508 0.05445984
18 -0.02734678 0.10500508
19 -0.32183965 -0.02734678
20 0.08046856 -0.32183965
21 -0.10249216 0.08046856
22 -0.18801558 -0.10249216
23 0.07662451 -0.18801558
24 -0.09756381 0.07662451
25 0.25003527 -0.09756381
26 0.21633844 0.25003527
27 -0.06508644 0.21633844
28 0.53952999 -0.06508644
29 0.28155113 0.53952999
30 0.43978447 0.28155113
31 0.31749255 0.43978447
32 -0.30710759 0.31749255
33 -0.70800721 -0.30710759
34 0.23293237 -0.70800721
35 0.61640206 0.23293237
36 1.35073784 0.61640206
37 1.34407487 1.35073784
38 0.60140859 1.34407487
39 0.25586151 0.60140859
40 0.54253904 0.25586151
41 0.03926758 0.54253904
42 0.36117777 0.03926758
43 0.70256270 0.36117777
44 0.55060886 0.70256270
45 0.49500184 0.55060886
46 0.63638677 0.49500184
47 0.21088701 0.63638677
48 0.83580799 0.21088701
49 1.00179132 0.83580799
50 -0.02204536 1.00179132
51 0.72254741 -0.02204536
52 0.86393234 0.72254741
53 0.43330718 0.86393234
54 0.54624792 0.43330718
55 1.07821805 0.54624792
56 0.28186232 1.07821805
57 1.14330349 0.28186232
58 0.82145692 1.14330349
59 -0.06771968 0.82145692
60 0.28455499 -0.06771968
61 0.05995312 0.28455499
62 -0.17374371 0.05995312
63 0.19864811 -0.17374371
64 -0.34113736 0.19864811
65 -0.10897637 -0.34113736
66 -0.21397453 -0.10897637
67 0.13637985 -0.21397453
68 -0.06042123 0.13637985
69 0.33778844 -0.06042123
70 -0.06611923 0.33778844
71 -0.39206434 -0.06611923
72 -0.32140541 -0.39206434
73 -1.28188508 -0.32140541
74 -0.58911891 -1.28188508
75 -0.79789749 -0.58911891
76 -1.17400611 -0.79789749
77 -0.32435157 -1.17400611
78 -0.44773398 -0.32435157
79 -1.20545835 -0.44773398
80 -0.14036399 -1.20545835
81 -0.89552566 -0.14036399
82 -0.65414073 -0.89552566
83 -0.13523859 -0.65414073
84 -0.74530471 -0.13523859
85 NA -0.74530471
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.19319668 -1.00536331
[2,] -0.27263146 -1.19319668
[3,] -0.33611744 -0.27263146
[4,] -0.48531772 -0.33611744
[5,] -0.42580302 -0.48531772
[6,] -0.65815488 -0.42580302
[7,] -0.70735515 -0.65815488
[8,] -0.40504694 -0.70735515
[9,] -0.27006876 -0.40504694
[10,] -0.78250053 -0.27006876
[11,] -0.30889099 -0.78250053
[12,] -0.30146356 -0.30889099
[13,] -0.18077283 -0.30146356
[14,] 0.23979239 -0.18077283
[15,] 0.02204436 0.23979239
[16,] 0.05445984 0.02204436
[17,] 0.10500508 0.05445984
[18,] -0.02734678 0.10500508
[19,] -0.32183965 -0.02734678
[20,] 0.08046856 -0.32183965
[21,] -0.10249216 0.08046856
[22,] -0.18801558 -0.10249216
[23,] 0.07662451 -0.18801558
[24,] -0.09756381 0.07662451
[25,] 0.25003527 -0.09756381
[26,] 0.21633844 0.25003527
[27,] -0.06508644 0.21633844
[28,] 0.53952999 -0.06508644
[29,] 0.28155113 0.53952999
[30,] 0.43978447 0.28155113
[31,] 0.31749255 0.43978447
[32,] -0.30710759 0.31749255
[33,] -0.70800721 -0.30710759
[34,] 0.23293237 -0.70800721
[35,] 0.61640206 0.23293237
[36,] 1.35073784 0.61640206
[37,] 1.34407487 1.35073784
[38,] 0.60140859 1.34407487
[39,] 0.25586151 0.60140859
[40,] 0.54253904 0.25586151
[41,] 0.03926758 0.54253904
[42,] 0.36117777 0.03926758
[43,] 0.70256270 0.36117777
[44,] 0.55060886 0.70256270
[45,] 0.49500184 0.55060886
[46,] 0.63638677 0.49500184
[47,] 0.21088701 0.63638677
[48,] 0.83580799 0.21088701
[49,] 1.00179132 0.83580799
[50,] -0.02204536 1.00179132
[51,] 0.72254741 -0.02204536
[52,] 0.86393234 0.72254741
[53,] 0.43330718 0.86393234
[54,] 0.54624792 0.43330718
[55,] 1.07821805 0.54624792
[56,] 0.28186232 1.07821805
[57,] 1.14330349 0.28186232
[58,] 0.82145692 1.14330349
[59,] -0.06771968 0.82145692
[60,] 0.28455499 -0.06771968
[61,] 0.05995312 0.28455499
[62,] -0.17374371 0.05995312
[63,] 0.19864811 -0.17374371
[64,] -0.34113736 0.19864811
[65,] -0.10897637 -0.34113736
[66,] -0.21397453 -0.10897637
[67,] 0.13637985 -0.21397453
[68,] -0.06042123 0.13637985
[69,] 0.33778844 -0.06042123
[70,] -0.06611923 0.33778844
[71,] -0.39206434 -0.06611923
[72,] -0.32140541 -0.39206434
[73,] -1.28188508 -0.32140541
[74,] -0.58911891 -1.28188508
[75,] -0.79789749 -0.58911891
[76,] -1.17400611 -0.79789749
[77,] -0.32435157 -1.17400611
[78,] -0.44773398 -0.32435157
[79,] -1.20545835 -0.44773398
[80,] -0.14036399 -1.20545835
[81,] -0.89552566 -0.14036399
[82,] -0.65414073 -0.89552566
[83,] -0.13523859 -0.65414073
[84,] -0.74530471 -0.13523859
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.19319668 -1.00536331
2 -0.27263146 -1.19319668
3 -0.33611744 -0.27263146
4 -0.48531772 -0.33611744
5 -0.42580302 -0.48531772
6 -0.65815488 -0.42580302
7 -0.70735515 -0.65815488
8 -0.40504694 -0.70735515
9 -0.27006876 -0.40504694
10 -0.78250053 -0.27006876
11 -0.30889099 -0.78250053
12 -0.30146356 -0.30889099
13 -0.18077283 -0.30146356
14 0.23979239 -0.18077283
15 0.02204436 0.23979239
16 0.05445984 0.02204436
17 0.10500508 0.05445984
18 -0.02734678 0.10500508
19 -0.32183965 -0.02734678
20 0.08046856 -0.32183965
21 -0.10249216 0.08046856
22 -0.18801558 -0.10249216
23 0.07662451 -0.18801558
24 -0.09756381 0.07662451
25 0.25003527 -0.09756381
26 0.21633844 0.25003527
27 -0.06508644 0.21633844
28 0.53952999 -0.06508644
29 0.28155113 0.53952999
30 0.43978447 0.28155113
31 0.31749255 0.43978447
32 -0.30710759 0.31749255
33 -0.70800721 -0.30710759
34 0.23293237 -0.70800721
35 0.61640206 0.23293237
36 1.35073784 0.61640206
37 1.34407487 1.35073784
38 0.60140859 1.34407487
39 0.25586151 0.60140859
40 0.54253904 0.25586151
41 0.03926758 0.54253904
42 0.36117777 0.03926758
43 0.70256270 0.36117777
44 0.55060886 0.70256270
45 0.49500184 0.55060886
46 0.63638677 0.49500184
47 0.21088701 0.63638677
48 0.83580799 0.21088701
49 1.00179132 0.83580799
50 -0.02204536 1.00179132
51 0.72254741 -0.02204536
52 0.86393234 0.72254741
53 0.43330718 0.86393234
54 0.54624792 0.43330718
55 1.07821805 0.54624792
56 0.28186232 1.07821805
57 1.14330349 0.28186232
58 0.82145692 1.14330349
59 -0.06771968 0.82145692
60 0.28455499 -0.06771968
61 0.05995312 0.28455499
62 -0.17374371 0.05995312
63 0.19864811 -0.17374371
64 -0.34113736 0.19864811
65 -0.10897637 -0.34113736
66 -0.21397453 -0.10897637
67 0.13637985 -0.21397453
68 -0.06042123 0.13637985
69 0.33778844 -0.06042123
70 -0.06611923 0.33778844
71 -0.39206434 -0.06611923
72 -0.32140541 -0.39206434
73 -1.28188508 -0.32140541
74 -0.58911891 -1.28188508
75 -0.79789749 -0.58911891
76 -1.17400611 -0.79789749
77 -0.32435157 -1.17400611
78 -0.44773398 -0.32435157
79 -1.20545835 -0.44773398
80 -0.14036399 -1.20545835
81 -0.89552566 -0.14036399
82 -0.65414073 -0.89552566
83 -0.13523859 -0.65414073
84 -0.74530471 -0.13523859
> 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/75rn61228903818.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8fxtl1228903818.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9i3l81228903818.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/100f121228903818.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11oe6g1228903818.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/120y0c1228903818.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/13o3wy1228903818.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/14tff71228903818.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/www/html/rcomp/tmp/15osne1228903818.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/www/html/rcomp/tmp/168mwe1228903818.tab")
+ }
>
> system("convert tmp/1t9521228903818.ps tmp/1t9521228903818.png")
> system("convert tmp/22eka1228903818.ps tmp/22eka1228903818.png")
> system("convert tmp/36ms51228903818.ps tmp/36ms51228903818.png")
> system("convert tmp/4lzzp1228903818.ps tmp/4lzzp1228903818.png")
> system("convert tmp/5grar1228903818.ps tmp/5grar1228903818.png")
> system("convert tmp/6sojf1228903818.ps tmp/6sojf1228903818.png")
> system("convert tmp/75rn61228903818.ps tmp/75rn61228903818.png")
> system("convert tmp/8fxtl1228903818.ps tmp/8fxtl1228903818.png")
> system("convert tmp/9i3l81228903818.ps tmp/9i3l81228903818.png")
> system("convert tmp/100f121228903818.ps tmp/100f121228903818.png")
>
>
> proc.time()
user system elapsed
3.017 1.729 4.815