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(7.5,0,7.2,0,6.9,0,6.7,0,6.4,0,6.3,0,6.8,0,7.3,0,7.1,0,7.1,0,6.8,0,6.5,0,6.3,0,6.1,0,6.1,0,6.3,0,6.3,0,6,0,6.2,0,6.4,0,6.8,0,7.5,0,7.5,0,7.6,0,7.6,0,7.4,0,7.3,0,7.1,0,6.9,0,6.8,0,7.5,0,7.6,0,7.8,0,8.0,0,8.1,0,8.2,0,8.3,0,8.2,0,8.0,0,7.9,0,7.6,0,7.6,0,8.2,0,8.3,0,8.4,0,8.4,0,8.4,0,8.6,0,8.9,0,8.8,0,8.3,0,7.5,0,7.2,0,7.5,0,8.8,0,9.3,0,9.3,0,8.7,1,8.2,1,8.3,1,8.5,1,8.6,1,8.6,1,8.2,1,8.1,1,8.0,1,8.6,1,8.7,1,8.8,1,8.5,1,8.4,1,8.5,1,8.7,1,8.7,1,8.6,1,8.5,1,8.3,1,8.1,1,8.2,1,8.1,1,8.1,1,7.9,1,7.9,1,7.9,1,8.0,1,8.0,1,7.9,1,8.0,1,7.7,1,7.2,1,7.5,1,7.3,1,7.0,1,7.0,1,7.0,1,7.2,1,7.3,1,7.1,1,6.8,1,6.6,1,6.2,1,6.2,1,6.8,1,6.9,1,6.8,1),dim=c(2,105),dimnames=list(c('w','d'),1:105))
> y <- array(NA,dim=c(2,105),dimnames=list(c('w','d'),1:105))
> 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 = 'Do not include Seasonal 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
w d t
1 7.5 0 1
2 7.2 0 2
3 6.9 0 3
4 6.7 0 4
5 6.4 0 5
6 6.3 0 6
7 6.8 0 7
8 7.3 0 8
9 7.1 0 9
10 7.1 0 10
11 6.8 0 11
12 6.5 0 12
13 6.3 0 13
14 6.1 0 14
15 6.1 0 15
16 6.3 0 16
17 6.3 0 17
18 6.0 0 18
19 6.2 0 19
20 6.4 0 20
21 6.8 0 21
22 7.5 0 22
23 7.5 0 23
24 7.6 0 24
25 7.6 0 25
26 7.4 0 26
27 7.3 0 27
28 7.1 0 28
29 6.9 0 29
30 6.8 0 30
31 7.5 0 31
32 7.6 0 32
33 7.8 0 33
34 8.0 0 34
35 8.1 0 35
36 8.2 0 36
37 8.3 0 37
38 8.2 0 38
39 8.0 0 39
40 7.9 0 40
41 7.6 0 41
42 7.6 0 42
43 8.2 0 43
44 8.3 0 44
45 8.4 0 45
46 8.4 0 46
47 8.4 0 47
48 8.6 0 48
49 8.9 0 49
50 8.8 0 50
51 8.3 0 51
52 7.5 0 52
53 7.2 0 53
54 7.5 0 54
55 8.8 0 55
56 9.3 0 56
57 9.3 0 57
58 8.7 1 58
59 8.2 1 59
60 8.3 1 60
61 8.5 1 61
62 8.6 1 62
63 8.6 1 63
64 8.2 1 64
65 8.1 1 65
66 8.0 1 66
67 8.6 1 67
68 8.7 1 68
69 8.8 1 69
70 8.5 1 70
71 8.4 1 71
72 8.5 1 72
73 8.7 1 73
74 8.7 1 74
75 8.6 1 75
76 8.5 1 76
77 8.3 1 77
78 8.1 1 78
79 8.2 1 79
80 8.1 1 80
81 8.1 1 81
82 7.9 1 82
83 7.9 1 83
84 7.9 1 84
85 8.0 1 85
86 8.0 1 86
87 7.9 1 87
88 8.0 1 88
89 7.7 1 89
90 7.2 1 90
91 7.5 1 91
92 7.3 1 92
93 7.0 1 93
94 7.0 1 94
95 7.0 1 95
96 7.2 1 96
97 7.3 1 97
98 7.1 1 98
99 6.8 1 99
100 6.6 1 100
101 6.2 1 101
102 6.2 1 102
103 6.8 1 103
104 6.9 1 104
105 6.8 1 105
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) d t
7.232230 -0.048648 0.008024
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.80198 -0.62175 0.08323 0.65857 1.61845
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.232230 0.180285 40.115 <2e-16 ***
d -0.048648 0.307403 -0.158 0.875
t 0.008024 0.005052 1.588 0.115
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7931 on 102 degrees of freedom
Multiple R-squared: 0.07502, Adjusted R-squared: 0.05689
F-statistic: 4.136 on 2 and 102 DF, p-value: 0.01874
> 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,] 0.001123685 2.247370e-03 9.988763e-01
[2,] 0.036842162 7.368432e-02 9.631578e-01
[3,] 0.109322892 2.186458e-01 8.906771e-01
[4,] 0.073578396 1.471568e-01 9.264216e-01
[5,] 0.042430043 8.486009e-02 9.575700e-01
[6,] 0.020602547 4.120509e-02 9.793975e-01
[7,] 0.012026066 2.405213e-02 9.879739e-01
[8,] 0.008250672 1.650134e-02 9.917493e-01
[9,] 0.006757913 1.351583e-02 9.932421e-01
[10,] 0.004668592 9.337183e-03 9.953314e-01
[11,] 0.002745018 5.490037e-03 9.972550e-01
[12,] 0.001728711 3.457422e-03 9.982713e-01
[13,] 0.001443258 2.886515e-03 9.985567e-01
[14,] 0.001220903 2.441807e-03 9.987791e-01
[15,] 0.001468485 2.936970e-03 9.985315e-01
[16,] 0.004424601 8.849201e-03 9.955754e-01
[17,] 0.049418254 9.883651e-02 9.505817e-01
[18,] 0.122949825 2.458997e-01 8.770502e-01
[19,] 0.205780666 4.115613e-01 7.942193e-01
[20,] 0.262645483 5.252910e-01 7.373545e-01
[21,] 0.275818153 5.516363e-01 7.241818e-01
[22,] 0.280238364 5.604767e-01 7.197616e-01
[23,] 0.296370133 5.927403e-01 7.036299e-01
[24,] 0.357042503 7.140850e-01 6.429575e-01
[25,] 0.481356658 9.627133e-01 5.186433e-01
[26,] 0.542548709 9.149026e-01 4.574513e-01
[27,] 0.602714764 7.945705e-01 3.972852e-01
[28,] 0.659785489 6.804290e-01 3.402145e-01
[29,] 0.711294542 5.774109e-01 2.887055e-01
[30,] 0.747594282 5.048114e-01 2.524057e-01
[31,] 0.771175064 4.576499e-01 2.288249e-01
[32,] 0.784584082 4.308318e-01 2.154159e-01
[33,] 0.778395559 4.432089e-01 2.216044e-01
[34,] 0.767920174 4.641597e-01 2.320798e-01
[35,] 0.764951951 4.700961e-01 2.350480e-01
[36,] 0.814207357 3.715853e-01 1.857926e-01
[37,] 0.868159759 2.636805e-01 1.318402e-01
[38,] 0.860491672 2.790167e-01 1.395083e-01
[39,] 0.847351876 3.052962e-01 1.526481e-01
[40,] 0.828909524 3.421810e-01 1.710905e-01
[41,] 0.804276368 3.914473e-01 1.957236e-01
[42,] 0.773567858 4.528643e-01 2.264321e-01
[43,] 0.741940075 5.161199e-01 2.580599e-01
[44,] 0.741784397 5.164312e-01 2.582156e-01
[45,] 0.722028870 5.559423e-01 2.779711e-01
[46,] 0.676108148 6.477837e-01 3.238919e-01
[47,] 0.814039204 3.719216e-01 1.859608e-01
[48,] 0.976506498 4.698700e-02 2.349350e-02
[49,] 0.999244131 1.511737e-03 7.558686e-04
[50,] 0.999302261 1.395477e-03 6.977387e-04
[51,] 0.999303550 1.392899e-03 6.964496e-04
[52,] 0.999206636 1.586728e-03 7.933642e-04
[53,] 0.998822439 2.355123e-03 1.177561e-03
[54,] 0.999435150 1.129700e-03 5.648502e-04
[55,] 0.999649691 7.006174e-04 3.503087e-04
[56,] 0.999627125 7.457508e-04 3.728754e-04
[57,] 0.999503224 9.935512e-04 4.967756e-04
[58,] 0.999314064 1.371872e-03 6.859361e-04
[59,] 0.999715036 5.699270e-04 2.849635e-04
[60,] 0.999953175 9.364969e-05 4.682484e-05
[61,] 0.999999043 1.913278e-06 9.566391e-07
[62,] 0.999998901 2.197054e-06 1.098527e-06
[63,] 0.999998022 3.956814e-06 1.978407e-06
[64,] 0.999995673 8.653923e-06 4.326961e-06
[65,] 0.999994446 1.110727e-05 5.553637e-06
[66,] 0.999994990 1.002018e-05 5.010089e-06
[67,] 0.999992217 1.556515e-05 7.782576e-06
[68,] 0.999983046 3.390783e-05 1.695392e-05
[69,] 0.999966710 6.657979e-05 3.328989e-05
[70,] 0.999935600 1.287999e-04 6.439995e-05
[71,] 0.999879221 2.415572e-04 1.207786e-04
[72,] 0.999793111 4.137770e-04 2.068885e-04
[73,] 0.999747779 5.044420e-04 2.522210e-04
[74,] 0.999587605 8.247904e-04 4.123952e-04
[75,] 0.999383048 1.233905e-03 6.169523e-04
[76,] 0.999029647 1.940706e-03 9.703530e-04
[77,] 0.998823666 2.352668e-03 1.176334e-03
[78,] 0.998414031 3.171939e-03 1.585969e-03
[79,] 0.997674766 4.650469e-03 2.325234e-03
[80,] 0.996374136 7.251727e-03 3.625864e-03
[81,] 0.994919787 1.016043e-02 5.080213e-03
[82,] 0.993166375 1.366725e-02 6.833625e-03
[83,] 0.994792680 1.041464e-02 5.207320e-03
[84,] 0.994200847 1.159831e-02 5.799153e-03
[85,] 0.992766443 1.446711e-02 7.233557e-03
[86,] 0.990299951 1.940010e-02 9.700049e-03
[87,] 0.985296743 2.940651e-02 1.470326e-02
[88,] 0.978748781 4.250244e-02 2.125122e-02
[89,] 0.967079109 6.584178e-02 3.292089e-02
[90,] 0.946416125 1.071677e-01 5.358387e-02
[91,] 0.911977166 1.760457e-01 8.802283e-02
[92,] 0.902713952 1.945721e-01 9.728605e-02
[93,] 0.908971947 1.820561e-01 9.102805e-02
[94,] 0.899194747 2.016105e-01 1.008053e-01
> postscript(file="/var/www/html/rcomp/tmp/1j5ur1227785563.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2k2z61227785563.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3b5v71227785563.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4mvww1227785563.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5l0tu1227785563.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 = 105
Frequency = 1
1 2 3 4 5 6
0.259746810 -0.048276729 -0.356300268 -0.564323807 -0.872347346 -0.980370885
7 8 9 10 11 12
-0.488394424 0.003582037 -0.204441501 -0.212465040 -0.520488579 -0.828512118
13 14 15 16 17 18
-1.036535657 -1.244559196 -1.252582735 -1.060606274 -1.068629813 -1.376653352
19 20 21 22 23 24
-1.184676891 -0.992700430 -0.600723969 0.091252492 0.083228953 0.175205414
25 26 27 28 29 30
0.167181875 -0.040841664 -0.148865203 -0.356888742 -0.564912281 -0.672935820
31 32 33 34 35 36
0.019040641 0.111017102 0.302993563 0.494970024 0.586946486 0.678922947
37 38 39 40 41 42
0.770899408 0.662875869 0.454852330 0.346828791 0.038805252 0.030781713
43 44 45 46 47 48
0.622758174 0.714734635 0.806711096 0.798687557 0.790664018 0.982640479
49 50 51 52 53 54
1.274616940 1.166593401 0.658569862 -0.149453677 -0.457477216 -0.165500755
55 56 57 58 59 60
1.126475706 1.618452167 1.610428628 1.051053166 0.543029627 0.635006088
61 62 63 64 65 66
0.826982549 0.918959010 0.910935471 0.502911932 0.394888393 0.286864854
67 68 69 70 71 72
0.878841315 0.970817776 1.062794237 0.754770698 0.646747159 0.738723620
73 74 75 76 77 78
0.930700081 0.922676542 0.814653003 0.706629464 0.498605925 0.290582386
79 80 81 82 83 84
0.382558847 0.274535308 0.266511769 0.058488231 0.050464692 0.042441153
85 86 87 88 89 90
0.134417614 0.126394075 0.018370536 0.110346997 -0.197676542 -0.705700081
91 92 93 94 95 96
-0.413723620 -0.621747159 -0.929770698 -0.937794237 -0.945817776 -0.753841315
97 98 99 100 101 102
-0.661864854 -0.869888393 -1.177911932 -1.385935471 -1.793959010 -1.801982549
103 104 105
-1.210006088 -1.118029627 -1.226053166
> postscript(file="/var/www/html/rcomp/tmp/6lroo1227785563.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 = 105
Frequency = 1
lag(myerror, k = 1) myerror
0 0.259746810 NA
1 -0.048276729 0.259746810
2 -0.356300268 -0.048276729
3 -0.564323807 -0.356300268
4 -0.872347346 -0.564323807
5 -0.980370885 -0.872347346
6 -0.488394424 -0.980370885
7 0.003582037 -0.488394424
8 -0.204441501 0.003582037
9 -0.212465040 -0.204441501
10 -0.520488579 -0.212465040
11 -0.828512118 -0.520488579
12 -1.036535657 -0.828512118
13 -1.244559196 -1.036535657
14 -1.252582735 -1.244559196
15 -1.060606274 -1.252582735
16 -1.068629813 -1.060606274
17 -1.376653352 -1.068629813
18 -1.184676891 -1.376653352
19 -0.992700430 -1.184676891
20 -0.600723969 -0.992700430
21 0.091252492 -0.600723969
22 0.083228953 0.091252492
23 0.175205414 0.083228953
24 0.167181875 0.175205414
25 -0.040841664 0.167181875
26 -0.148865203 -0.040841664
27 -0.356888742 -0.148865203
28 -0.564912281 -0.356888742
29 -0.672935820 -0.564912281
30 0.019040641 -0.672935820
31 0.111017102 0.019040641
32 0.302993563 0.111017102
33 0.494970024 0.302993563
34 0.586946486 0.494970024
35 0.678922947 0.586946486
36 0.770899408 0.678922947
37 0.662875869 0.770899408
38 0.454852330 0.662875869
39 0.346828791 0.454852330
40 0.038805252 0.346828791
41 0.030781713 0.038805252
42 0.622758174 0.030781713
43 0.714734635 0.622758174
44 0.806711096 0.714734635
45 0.798687557 0.806711096
46 0.790664018 0.798687557
47 0.982640479 0.790664018
48 1.274616940 0.982640479
49 1.166593401 1.274616940
50 0.658569862 1.166593401
51 -0.149453677 0.658569862
52 -0.457477216 -0.149453677
53 -0.165500755 -0.457477216
54 1.126475706 -0.165500755
55 1.618452167 1.126475706
56 1.610428628 1.618452167
57 1.051053166 1.610428628
58 0.543029627 1.051053166
59 0.635006088 0.543029627
60 0.826982549 0.635006088
61 0.918959010 0.826982549
62 0.910935471 0.918959010
63 0.502911932 0.910935471
64 0.394888393 0.502911932
65 0.286864854 0.394888393
66 0.878841315 0.286864854
67 0.970817776 0.878841315
68 1.062794237 0.970817776
69 0.754770698 1.062794237
70 0.646747159 0.754770698
71 0.738723620 0.646747159
72 0.930700081 0.738723620
73 0.922676542 0.930700081
74 0.814653003 0.922676542
75 0.706629464 0.814653003
76 0.498605925 0.706629464
77 0.290582386 0.498605925
78 0.382558847 0.290582386
79 0.274535308 0.382558847
80 0.266511769 0.274535308
81 0.058488231 0.266511769
82 0.050464692 0.058488231
83 0.042441153 0.050464692
84 0.134417614 0.042441153
85 0.126394075 0.134417614
86 0.018370536 0.126394075
87 0.110346997 0.018370536
88 -0.197676542 0.110346997
89 -0.705700081 -0.197676542
90 -0.413723620 -0.705700081
91 -0.621747159 -0.413723620
92 -0.929770698 -0.621747159
93 -0.937794237 -0.929770698
94 -0.945817776 -0.937794237
95 -0.753841315 -0.945817776
96 -0.661864854 -0.753841315
97 -0.869888393 -0.661864854
98 -1.177911932 -0.869888393
99 -1.385935471 -1.177911932
100 -1.793959010 -1.385935471
101 -1.801982549 -1.793959010
102 -1.210006088 -1.801982549
103 -1.118029627 -1.210006088
104 -1.226053166 -1.118029627
105 NA -1.226053166
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.048276729 0.259746810
[2,] -0.356300268 -0.048276729
[3,] -0.564323807 -0.356300268
[4,] -0.872347346 -0.564323807
[5,] -0.980370885 -0.872347346
[6,] -0.488394424 -0.980370885
[7,] 0.003582037 -0.488394424
[8,] -0.204441501 0.003582037
[9,] -0.212465040 -0.204441501
[10,] -0.520488579 -0.212465040
[11,] -0.828512118 -0.520488579
[12,] -1.036535657 -0.828512118
[13,] -1.244559196 -1.036535657
[14,] -1.252582735 -1.244559196
[15,] -1.060606274 -1.252582735
[16,] -1.068629813 -1.060606274
[17,] -1.376653352 -1.068629813
[18,] -1.184676891 -1.376653352
[19,] -0.992700430 -1.184676891
[20,] -0.600723969 -0.992700430
[21,] 0.091252492 -0.600723969
[22,] 0.083228953 0.091252492
[23,] 0.175205414 0.083228953
[24,] 0.167181875 0.175205414
[25,] -0.040841664 0.167181875
[26,] -0.148865203 -0.040841664
[27,] -0.356888742 -0.148865203
[28,] -0.564912281 -0.356888742
[29,] -0.672935820 -0.564912281
[30,] 0.019040641 -0.672935820
[31,] 0.111017102 0.019040641
[32,] 0.302993563 0.111017102
[33,] 0.494970024 0.302993563
[34,] 0.586946486 0.494970024
[35,] 0.678922947 0.586946486
[36,] 0.770899408 0.678922947
[37,] 0.662875869 0.770899408
[38,] 0.454852330 0.662875869
[39,] 0.346828791 0.454852330
[40,] 0.038805252 0.346828791
[41,] 0.030781713 0.038805252
[42,] 0.622758174 0.030781713
[43,] 0.714734635 0.622758174
[44,] 0.806711096 0.714734635
[45,] 0.798687557 0.806711096
[46,] 0.790664018 0.798687557
[47,] 0.982640479 0.790664018
[48,] 1.274616940 0.982640479
[49,] 1.166593401 1.274616940
[50,] 0.658569862 1.166593401
[51,] -0.149453677 0.658569862
[52,] -0.457477216 -0.149453677
[53,] -0.165500755 -0.457477216
[54,] 1.126475706 -0.165500755
[55,] 1.618452167 1.126475706
[56,] 1.610428628 1.618452167
[57,] 1.051053166 1.610428628
[58,] 0.543029627 1.051053166
[59,] 0.635006088 0.543029627
[60,] 0.826982549 0.635006088
[61,] 0.918959010 0.826982549
[62,] 0.910935471 0.918959010
[63,] 0.502911932 0.910935471
[64,] 0.394888393 0.502911932
[65,] 0.286864854 0.394888393
[66,] 0.878841315 0.286864854
[67,] 0.970817776 0.878841315
[68,] 1.062794237 0.970817776
[69,] 0.754770698 1.062794237
[70,] 0.646747159 0.754770698
[71,] 0.738723620 0.646747159
[72,] 0.930700081 0.738723620
[73,] 0.922676542 0.930700081
[74,] 0.814653003 0.922676542
[75,] 0.706629464 0.814653003
[76,] 0.498605925 0.706629464
[77,] 0.290582386 0.498605925
[78,] 0.382558847 0.290582386
[79,] 0.274535308 0.382558847
[80,] 0.266511769 0.274535308
[81,] 0.058488231 0.266511769
[82,] 0.050464692 0.058488231
[83,] 0.042441153 0.050464692
[84,] 0.134417614 0.042441153
[85,] 0.126394075 0.134417614
[86,] 0.018370536 0.126394075
[87,] 0.110346997 0.018370536
[88,] -0.197676542 0.110346997
[89,] -0.705700081 -0.197676542
[90,] -0.413723620 -0.705700081
[91,] -0.621747159 -0.413723620
[92,] -0.929770698 -0.621747159
[93,] -0.937794237 -0.929770698
[94,] -0.945817776 -0.937794237
[95,] -0.753841315 -0.945817776
[96,] -0.661864854 -0.753841315
[97,] -0.869888393 -0.661864854
[98,] -1.177911932 -0.869888393
[99,] -1.385935471 -1.177911932
[100,] -1.793959010 -1.385935471
[101,] -1.801982549 -1.793959010
[102,] -1.210006088 -1.801982549
[103,] -1.118029627 -1.210006088
[104,] -1.226053166 -1.118029627
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.048276729 0.259746810
2 -0.356300268 -0.048276729
3 -0.564323807 -0.356300268
4 -0.872347346 -0.564323807
5 -0.980370885 -0.872347346
6 -0.488394424 -0.980370885
7 0.003582037 -0.488394424
8 -0.204441501 0.003582037
9 -0.212465040 -0.204441501
10 -0.520488579 -0.212465040
11 -0.828512118 -0.520488579
12 -1.036535657 -0.828512118
13 -1.244559196 -1.036535657
14 -1.252582735 -1.244559196
15 -1.060606274 -1.252582735
16 -1.068629813 -1.060606274
17 -1.376653352 -1.068629813
18 -1.184676891 -1.376653352
19 -0.992700430 -1.184676891
20 -0.600723969 -0.992700430
21 0.091252492 -0.600723969
22 0.083228953 0.091252492
23 0.175205414 0.083228953
24 0.167181875 0.175205414
25 -0.040841664 0.167181875
26 -0.148865203 -0.040841664
27 -0.356888742 -0.148865203
28 -0.564912281 -0.356888742
29 -0.672935820 -0.564912281
30 0.019040641 -0.672935820
31 0.111017102 0.019040641
32 0.302993563 0.111017102
33 0.494970024 0.302993563
34 0.586946486 0.494970024
35 0.678922947 0.586946486
36 0.770899408 0.678922947
37 0.662875869 0.770899408
38 0.454852330 0.662875869
39 0.346828791 0.454852330
40 0.038805252 0.346828791
41 0.030781713 0.038805252
42 0.622758174 0.030781713
43 0.714734635 0.622758174
44 0.806711096 0.714734635
45 0.798687557 0.806711096
46 0.790664018 0.798687557
47 0.982640479 0.790664018
48 1.274616940 0.982640479
49 1.166593401 1.274616940
50 0.658569862 1.166593401
51 -0.149453677 0.658569862
52 -0.457477216 -0.149453677
53 -0.165500755 -0.457477216
54 1.126475706 -0.165500755
55 1.618452167 1.126475706
56 1.610428628 1.618452167
57 1.051053166 1.610428628
58 0.543029627 1.051053166
59 0.635006088 0.543029627
60 0.826982549 0.635006088
61 0.918959010 0.826982549
62 0.910935471 0.918959010
63 0.502911932 0.910935471
64 0.394888393 0.502911932
65 0.286864854 0.394888393
66 0.878841315 0.286864854
67 0.970817776 0.878841315
68 1.062794237 0.970817776
69 0.754770698 1.062794237
70 0.646747159 0.754770698
71 0.738723620 0.646747159
72 0.930700081 0.738723620
73 0.922676542 0.930700081
74 0.814653003 0.922676542
75 0.706629464 0.814653003
76 0.498605925 0.706629464
77 0.290582386 0.498605925
78 0.382558847 0.290582386
79 0.274535308 0.382558847
80 0.266511769 0.274535308
81 0.058488231 0.266511769
82 0.050464692 0.058488231
83 0.042441153 0.050464692
84 0.134417614 0.042441153
85 0.126394075 0.134417614
86 0.018370536 0.126394075
87 0.110346997 0.018370536
88 -0.197676542 0.110346997
89 -0.705700081 -0.197676542
90 -0.413723620 -0.705700081
91 -0.621747159 -0.413723620
92 -0.929770698 -0.621747159
93 -0.937794237 -0.929770698
94 -0.945817776 -0.937794237
95 -0.753841315 -0.945817776
96 -0.661864854 -0.753841315
97 -0.869888393 -0.661864854
98 -1.177911932 -0.869888393
99 -1.385935471 -1.177911932
100 -1.793959010 -1.385935471
101 -1.801982549 -1.793959010
102 -1.210006088 -1.801982549
103 -1.118029627 -1.210006088
104 -1.226053166 -1.118029627
> 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/7fxg01227785563.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8z1xt1227785563.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rzyj1227785563.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10p5951227785563.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11gda71227785563.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/12215b1227785563.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/13438r1227785564.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/14a0nk1227785564.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/153r2k1227785564.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/16oc9f1227785564.tab")
+ }
>
> system("convert tmp/1j5ur1227785563.ps tmp/1j5ur1227785563.png")
> system("convert tmp/2k2z61227785563.ps tmp/2k2z61227785563.png")
> system("convert tmp/3b5v71227785563.ps tmp/3b5v71227785563.png")
> system("convert tmp/4mvww1227785563.ps tmp/4mvww1227785563.png")
> system("convert tmp/5l0tu1227785563.ps tmp/5l0tu1227785563.png")
> system("convert tmp/6lroo1227785563.ps tmp/6lroo1227785563.png")
> system("convert tmp/7fxg01227785563.ps tmp/7fxg01227785563.png")
> system("convert tmp/8z1xt1227785563.ps tmp/8z1xt1227785563.png")
> system("convert tmp/9rzyj1227785563.ps tmp/9rzyj1227785563.png")
> system("convert tmp/10p5951227785563.ps tmp/10p5951227785563.png")
>
>
> proc.time()
user system elapsed
2.947 1.578 3.588