R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(26
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+ ,dim=c(6
+ ,162)
+ ,dimnames=list(c('1'
+ ,'2'
+ ,'3'
+ ,'4'
+ ,'5'
+ ,'6')
+ ,1:162))
> y <- array(NA,dim=c(6,162),dimnames=list(c('1','2','3','4','5','6'),1:162))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '6'
> #'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
> 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
6 1 2 3 4 5
1 23 26 21 21 23 17
2 20 20 16 15 24 17
3 20 19 19 18 22 18
4 21 19 18 11 20 21
5 24 20 16 8 24 20
6 22 25 23 19 27 28
7 23 25 17 4 28 19
8 20 22 12 20 27 22
9 25 26 19 16 24 16
10 23 22 16 14 23 18
11 27 17 19 10 24 25
12 27 22 20 13 27 17
13 22 19 13 14 27 14
14 24 24 20 8 28 11
15 25 26 27 23 27 27
16 22 21 17 11 23 20
17 28 13 8 9 24 22
18 28 26 25 24 28 22
19 27 20 26 5 27 21
20 25 22 13 15 25 23
21 16 14 19 5 19 17
22 28 21 15 19 24 24
23 21 7 5 6 20 14
24 24 23 16 13 28 17
25 27 17 14 11 26 23
26 14 25 24 17 23 24
27 14 25 24 17 23 24
28 27 19 9 5 20 8
29 20 20 19 9 11 22
30 21 23 19 15 24 23
31 22 22 25 17 25 25
32 21 22 19 17 23 21
33 12 21 18 20 18 24
34 20 15 15 12 20 15
35 24 20 12 7 20 22
36 19 22 21 16 24 21
37 28 18 12 7 23 25
38 23 20 15 14 25 16
39 27 28 28 24 28 28
40 22 22 25 15 26 23
41 27 18 19 15 26 21
42 26 23 20 10 23 21
43 22 20 24 14 22 26
44 21 25 26 18 24 22
45 19 26 25 12 21 21
46 24 15 12 9 20 18
47 19 17 12 9 22 12
48 26 23 15 8 20 25
49 22 21 17 18 25 17
50 28 13 14 10 20 24
51 21 18 16 17 22 15
52 23 19 11 14 23 13
53 28 22 20 16 25 26
54 10 16 11 10 23 16
55 24 24 22 19 23 24
56 21 18 20 10 22 21
57 21 20 19 14 24 20
58 24 24 17 10 25 14
59 24 14 21 4 21 25
60 25 22 23 19 12 25
61 25 24 18 9 17 20
62 23 18 17 12 20 22
63 21 21 27 16 23 20
64 16 23 25 11 23 26
65 17 17 19 18 20 18
66 25 22 22 11 28 22
67 24 24 24 24 24 24
68 23 21 20 17 24 17
69 25 22 19 18 24 24
70 23 16 11 9 24 20
71 28 21 22 19 28 19
72 26 23 22 18 25 20
73 22 22 16 12 21 15
74 19 24 20 23 25 23
75 26 24 24 22 25 26
76 18 16 16 14 18 22
77 18 16 16 14 17 20
78 25 21 22 16 26 24
79 27 26 24 23 28 26
80 12 15 16 7 21 21
81 15 25 27 10 27 25
82 21 18 11 12 22 13
83 23 23 21 12 21 20
84 22 20 20 12 25 22
85 21 17 20 17 22 23
86 24 25 27 21 23 28
87 27 24 20 16 26 22
88 22 17 12 11 19 20
89 28 19 8 14 25 6
90 26 20 21 13 21 21
91 10 15 18 9 13 20
92 19 27 24 19 24 18
93 22 22 16 13 25 23
94 21 23 18 19 26 20
95 24 16 20 13 25 24
96 25 19 20 13 25 22
97 21 25 19 13 22 21
98 20 19 17 14 21 18
99 21 19 16 12 23 21
100 24 26 26 22 25 23
101 23 21 15 11 24 23
102 18 20 22 5 21 15
103 24 24 17 18 21 21
104 24 22 23 19 25 24
105 19 20 21 14 22 23
106 20 18 19 15 20 21
107 18 18 14 12 20 21
108 20 24 17 19 23 20
109 27 24 12 15 28 11
110 23 22 24 17 23 22
111 26 23 18 8 28 27
112 23 22 20 10 24 25
113 17 20 16 12 18 18
114 21 18 20 12 20 20
115 25 25 22 20 28 24
116 23 18 12 12 21 10
117 27 16 16 12 21 27
118 24 20 17 14 25 21
119 20 19 22 6 19 21
120 27 15 12 10 18 18
121 21 19 14 18 21 15
122 24 19 23 18 22 24
123 21 16 15 7 24 22
124 15 17 17 18 15 14
125 25 28 28 9 28 28
126 25 23 20 17 26 18
127 22 25 23 22 23 26
128 24 20 13 11 26 17
129 21 17 18 15 20 19
130 22 23 23 17 22 22
131 23 16 19 15 20 18
132 22 23 23 22 23 24
133 20 11 12 9 22 15
134 23 18 16 13 24 18
135 25 24 23 20 23 26
136 23 23 13 14 22 11
137 22 21 22 14 26 26
138 25 16 18 12 23 21
139 26 24 23 20 27 23
140 22 23 20 20 23 23
141 24 18 10 8 21 15
142 24 20 17 17 26 22
143 25 9 18 9 23 26
144 20 24 15 18 21 16
145 26 25 23 22 27 20
146 21 20 17 10 19 18
147 26 21 17 13 23 22
148 21 25 22 15 25 16
149 22 22 20 18 23 19
150 16 21 20 18 22 20
151 26 21 19 12 22 19
152 28 22 18 12 25 23
153 18 27 22 20 25 24
154 25 24 20 12 28 25
155 23 24 22 16 28 21
156 21 21 18 16 20 21
157 20 18 16 18 25 23
158 25 16 16 16 19 27
159 22 22 16 13 25 23
160 21 20 16 17 22 18
161 16 18 17 13 18 16
162 18 20 18 17 20 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `1` `2` `3` `4` `5`
10.32715 0.06626 -0.21860 -0.02569 0.48941 0.18687
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.9723 -1.5604 0.1781 2.1234 8.1862
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.32715 2.19066 4.714 5.34e-06 ***
`1` 0.06626 0.10275 0.645 0.5200
`2` -0.21860 0.08700 -2.513 0.0130 *
`3` -0.02569 0.06696 -0.384 0.7018
`4` 0.48941 0.09165 5.340 3.23e-07 ***
`5` 0.18687 0.07568 2.469 0.0146 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.268 on 156 degrees of freedom
Multiple R-squared: 0.2188, Adjusted R-squared: 0.1938
F-statistic: 8.739 on 5 and 156 DF, p-value: 2.505e-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,] 0.15127283 0.3025456530 8.487272e-01
[2,] 0.06559059 0.1311811799 9.344094e-01
[3,] 0.32800113 0.6560022696 6.719989e-01
[4,] 0.28125739 0.5625147771 7.187426e-01
[5,] 0.19003382 0.3800676350 8.099662e-01
[6,] 0.15428768 0.3085753587 8.457123e-01
[7,] 0.09656758 0.1931351582 9.034324e-01
[8,] 0.05940772 0.1188154321 9.405923e-01
[9,] 0.17785373 0.3557074602 8.221463e-01
[10,] 0.22188634 0.4437726823 7.781137e-01
[11,] 0.17398811 0.3479762198 8.260119e-01
[12,] 0.15395873 0.3079174576 8.460413e-01
[13,] 0.29477453 0.5895490587 7.052255e-01
[14,] 0.35688327 0.7137665372 6.431167e-01
[15,] 0.28916157 0.5783231447 7.108384e-01
[16,] 0.23203542 0.4640708461 7.679646e-01
[17,] 0.19135700 0.3827139904 8.086430e-01
[18,] 0.52777155 0.9444568931 4.722284e-01
[19,] 0.72959033 0.5408193418 2.704097e-01
[20,] 0.86472791 0.2705441716 1.352721e-01
[21,] 0.88682642 0.2263471652 1.131736e-01
[22,] 0.86465253 0.2706949435 1.353475e-01
[23,] 0.82914661 0.3417067883 1.708534e-01
[24,] 0.79230565 0.4153886999 2.076943e-01
[25,] 0.91233035 0.1753393096 8.766965e-02
[26,] 0.88788136 0.2242372848 1.121186e-01
[27,] 0.86459385 0.2708122999 1.354061e-01
[28,] 0.85632585 0.2873482973 1.436741e-01
[29,] 0.85348199 0.2930360190 1.465180e-01
[30,] 0.81936039 0.3612792209 1.806396e-01
[31,] 0.83652101 0.3269579797 1.634790e-01
[32,] 0.80233315 0.3953336907 1.976668e-01
[33,] 0.80407100 0.3918579996 1.959290e-01
[34,] 0.80655079 0.3868984261 1.934492e-01
[35,] 0.76962833 0.4607433364 2.303717e-01
[36,] 0.72826395 0.5434720923 2.717360e-01
[37,] 0.69004558 0.6199088343 3.099544e-01
[38,] 0.66540173 0.6691965392 3.345983e-01
[39,] 0.65565088 0.6886982403 3.443491e-01
[40,] 0.64641490 0.7071702006 3.535851e-01
[41,] 0.59942256 0.8011548741 4.005774e-01
[42,] 0.68963128 0.6207374366 3.103687e-01
[43,] 0.64554168 0.7089166418 3.544583e-01
[44,] 0.60060084 0.7987983221 3.993992e-01
[45,] 0.61933092 0.7613381566 3.806691e-01
[46,] 0.97982784 0.0403443119 2.017216e-02
[47,] 0.97563214 0.0487357184 2.436786e-02
[48,] 0.96843314 0.0631337250 3.156686e-02
[49,] 0.96106809 0.0778638209 3.893191e-02
[50,] 0.95160054 0.0967989174 4.839946e-02
[51,] 0.94619002 0.1076199558 5.380998e-02
[52,] 0.98787979 0.0242404191 1.212021e-02
[53,] 0.99351988 0.0129602370 6.480118e-03
[54,] 0.99193369 0.0161326180 8.066309e-03
[55,] 0.98902831 0.0219433819 1.097169e-02
[56,] 0.99534387 0.0093122592 4.656130e-03
[57,] 0.99515381 0.0096923859 4.846193e-03
[58,] 0.99327448 0.0134510445 6.725522e-03
[59,] 0.99168256 0.0166348765 8.317438e-03
[60,] 0.98915185 0.0216962956 1.084815e-02
[61,] 0.98640368 0.0271926331 1.359632e-02
[62,] 0.98255473 0.0348905500 1.744527e-02
[63,] 0.98532572 0.0293485636 1.467428e-02
[64,] 0.98553558 0.0289288416 1.446442e-02
[65,] 0.98143709 0.0371258184 1.856291e-02
[66,] 0.98653755 0.0269249044 1.346245e-02
[67,] 0.98515265 0.0296946924 1.484735e-02
[68,] 0.98304867 0.0339026637 1.695133e-02
[69,] 0.97876994 0.0424601252 2.123006e-02
[70,] 0.97302122 0.0539575592 2.697878e-02
[71,] 0.96821813 0.0635637426 3.178187e-02
[72,] 0.99745250 0.0050949931 2.547497e-03
[73,] 0.99979908 0.0004018442 2.009221e-04
[74,] 0.99970817 0.0005836649 2.918325e-04
[75,] 0.99965101 0.0006979745 3.489872e-04
[76,] 0.99952070 0.0009586018 4.793009e-04
[77,] 0.99930528 0.0013894364 6.947182e-04
[78,] 0.99917601 0.0016479703 8.239851e-04
[79,] 0.99914777 0.0017044599 8.522299e-04
[80,] 0.99875758 0.0024848330 1.242417e-03
[81,] 0.99921309 0.0015738224 7.869112e-04
[82,] 0.99964480 0.0007103903 3.551951e-04
[83,] 0.99994959 0.0001008185 5.040924e-05
[84,] 0.99993685 0.0001263042 6.315208e-05
[85,] 0.99992339 0.0001532141 7.660704e-05
[86,] 0.99991999 0.0001600104 8.000522e-05
[87,] 0.99987268 0.0002546371 1.273185e-04
[88,] 0.99981796 0.0003640727 1.820363e-04
[89,] 0.99972149 0.0005570132 2.785066e-04
[90,] 0.99959199 0.0008160254 4.080127e-04
[91,] 0.99948525 0.0010295076 5.147538e-04
[92,] 0.99932287 0.0013542513 6.771257e-04
[93,] 0.99903752 0.0019249673 9.624837e-04
[94,] 0.99878178 0.0024364399 1.218220e-03
[95,] 0.99863638 0.0027272319 1.363616e-03
[96,] 0.99802998 0.0039400332 1.970017e-03
[97,] 0.99799319 0.0040136198 2.006810e-03
[98,] 0.99711732 0.0057653679 2.882684e-03
[99,] 0.99792753 0.0041449476 2.072474e-03
[100,] 0.99760718 0.0047856497 2.392825e-03
[101,] 0.99750235 0.0049953040 2.497652e-03
[102,] 0.99658343 0.0068331348 3.416567e-03
[103,] 0.99510769 0.0097846213 4.892311e-03
[104,] 0.99321725 0.0135655000 6.782750e-03
[105,] 0.99415414 0.0116917137 5.845857e-03
[106,] 0.99154073 0.0169185306 8.459265e-03
[107,] 0.98808112 0.0238377647 1.191888e-02
[108,] 0.98644767 0.0271046699 1.355233e-02
[109,] 0.98651417 0.0269716588 1.348583e-02
[110,] 0.98087334 0.0382533158 1.912666e-02
[111,] 0.97554739 0.0489052220 2.445261e-02
[112,] 0.99119894 0.0176021247 8.801062e-03
[113,] 0.98727691 0.0254461787 1.272309e-02
[114,] 0.98510867 0.0297826651 1.489133e-02
[115,] 0.98763335 0.0247333059 1.236665e-02
[116,] 0.98510904 0.0297819192 1.489096e-02
[117,] 0.98073534 0.0385293145 1.926466e-02
[118,] 0.97775043 0.0444991304 2.224957e-02
[119,] 0.96811925 0.0637614972 3.188075e-02
[120,] 0.95507340 0.0898532040 4.492660e-02
[121,] 0.93791038 0.1241792493 6.208962e-02
[122,] 0.91578890 0.1684221998 8.421110e-02
[123,] 0.91231909 0.1753618183 8.768091e-02
[124,] 0.88504665 0.2299066968 1.149533e-01
[125,] 0.86887415 0.2622516908 1.311258e-01
[126,] 0.82900490 0.3419902042 1.709951e-01
[127,] 0.84039460 0.3192107974 1.596054e-01
[128,] 0.81168303 0.3766339304 1.883170e-01
[129,] 0.81013863 0.3797227368 1.898614e-01
[130,] 0.77245857 0.4550828521 2.275414e-01
[131,] 0.77382123 0.4523575446 2.261788e-01
[132,] 0.71899947 0.5620010679 2.810005e-01
[133,] 0.65528245 0.6894351026 3.447176e-01
[134,] 0.58229264 0.8354147208 4.177074e-01
[135,] 0.51008852 0.9798229664 4.899115e-01
[136,] 0.47368995 0.9473798981 5.263101e-01
[137,] 0.71551072 0.5689785577 2.844893e-01
[138,] 0.67506352 0.6498729666 3.249365e-01
[139,] 0.62909324 0.7418135230 3.709068e-01
[140,] 0.53332384 0.9333523270 4.666762e-01
[141,] 0.54900061 0.9019987785 4.509994e-01
[142,] 0.54138058 0.9172388492 4.586194e-01
[143,] 0.55235451 0.8952909882 4.476455e-01
[144,] 0.64844421 0.7031115741 3.515558e-01
[145,] 0.85017407 0.2996518595 1.498259e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1s5i61321810723.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2zqju1321810723.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/387y91321810723.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4sbhm1321810723.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/59mb31321810723.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 = 162
Frequency = 1
1 2 3 4 5
1.646789e+00 -2.692201e+00 -1.101130e+00 -8.133178e-02 5.673692e-01
6 7 8 9 10
-2.914404e+00 -2.418845e+00 -5.973300e+00 2.778617e+00 4.521370e-01
11 12 13 14 15
3.538942e+00 3.530070e+00 -2.215049e+00 9.009568e-01 1.183360e+00
16 17 18 19 20
-7.138143e-01 2.934306e+00 4.216806e+00 4.021195e+00 -9.117906e-02
21 22 23 24 25
-3.448651e+00 3.817564e+00 -9.483563e-01 -9.000026e-01 1.866548e+00
26 27 28 29 30
-8.042022e+00 -8.042022e+00 6.226518e+00 3.237490e+00 -2.356421e+00
31 32 33 34 35
-7.903522e-01 -1.375627e+00 -8.564464e+00 -3.251683e-01 1.251188e+00
36 37 38 39 40
-3.453527e+00 3.354837e+00 -2.390273e-01 2.618843e+00 -9.573899e-01
41 42 43 44 45
3.369787e+00 3.596910e+00 3.278703e-01 -6.947994e-01 -1.478659e+00
46 47 48 49 50
2.381347e+00 -2.608747e+00 3.173280e+00 -9.522128e-01 5.855501e+00
51 52 53 54 55
-1.557358e-01 4.922813e-01 3.904085e+00 -1.297232e+01 1.638408e+00
56 57 58 59 60
-5.823885e-01 -1.622711e+00 1.204147e+00 2.489037e+00 8.186203e+00
61 62 63 64 65
5.191125e+00 1.605137e+00 6.006209e-01 -6.218775e+00 -2.989787e+00
66 67 68 69 70
4.921098e-01 1.714627e+00 1.167316e+00 1.600021e+00 -1.234916e+00
71 72 73 74 75
4.324483e+00 3.447650e+00 9.402162e-01 -4.488000e+00 2.800091e+00
76 77 78 79 80
-2.450747e+00 -1.587584e+00 1.291878e+00 2.225020e+00 -9.845663e+00
81 82 83 84 85
-8.710558e+00 -1.003420e+00 2.032587e+00 -1.318647e+00 -7.100750e-01
86 87 88 89 90
1.969026e+00 3.029655e+00 4.158701e-01 5.165775e+00 5.070171e+00
91 92 93 94 95
-7.254902e+00 -2.491329e+00 -2.486751e+00 -2.890479e+00 5.983194e-01
96 97 98 99 100
1.773297e+00 -1.187731e+00 -1.151662e+00 -1.961088e+00 1.665400e+00
101 102 103 104 105
-1.201053e+00 -1.795473e+00 2.059173e+00 1.010695e+00 -2.767306e+00
106 107 108 109 110
-6.937281e-01 -3.863789e+00 -2.707094e+00 2.331960e+00 1.530500e+00
111 112 113 114 115
-4.599817e-01 -5.737451e-01 -3.019651e+00 6.346874e-01 1.507668e-01
116 117 118 119 120
2.265218e+00 4.095264e+00 2.637997e-01 1.540508e-01 6.385862e+00
121 122 123 124 125
-1.440931e-01 2.652023e+00 -2.785638e+00 -2.232418e+00 2.335459e-01
126 127 128 129 130
1.869098e+00 -5.059389e-01 -4.295761e-01 5.276784e-01 7.350562e-01
131 132 133 134 135
2.999411e+00 3.252601e-04 -1.771827e+00 2.020659e-01 2.508945e+00
136 137 138 139 140
1.527616e+00 -2.133244e+00 2.674885e+00 2.111913e+00 -5.199739e-01
141 142 143 144 145
1.790898e+00 -3.354296e-01 2.127253e+00 -1.443654e+00 2.657653e+00
146 147 148 149 150
6.581623e-01 2.963809e+00 -1.014426e+00 2.424094e-01 -5.388794e+00
151 152 153 154 155
4.425362e+00 3.924763e+00 -5.513506e+00 -6.125431e-01 -1.325098e+00
156 157 158 159 160
-8.541400e-02 -4.093289e+00 3.176838e+00 -2.486751e+00 -8.488747e-01
161 162
-3.269100e+00 -2.059097e+00
> postscript(file="/var/wessaorg/rcomp/tmp/627k91321810723.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 1.646789e+00 NA
1 -2.692201e+00 1.646789e+00
2 -1.101130e+00 -2.692201e+00
3 -8.133178e-02 -1.101130e+00
4 5.673692e-01 -8.133178e-02
5 -2.914404e+00 5.673692e-01
6 -2.418845e+00 -2.914404e+00
7 -5.973300e+00 -2.418845e+00
8 2.778617e+00 -5.973300e+00
9 4.521370e-01 2.778617e+00
10 3.538942e+00 4.521370e-01
11 3.530070e+00 3.538942e+00
12 -2.215049e+00 3.530070e+00
13 9.009568e-01 -2.215049e+00
14 1.183360e+00 9.009568e-01
15 -7.138143e-01 1.183360e+00
16 2.934306e+00 -7.138143e-01
17 4.216806e+00 2.934306e+00
18 4.021195e+00 4.216806e+00
19 -9.117906e-02 4.021195e+00
20 -3.448651e+00 -9.117906e-02
21 3.817564e+00 -3.448651e+00
22 -9.483563e-01 3.817564e+00
23 -9.000026e-01 -9.483563e-01
24 1.866548e+00 -9.000026e-01
25 -8.042022e+00 1.866548e+00
26 -8.042022e+00 -8.042022e+00
27 6.226518e+00 -8.042022e+00
28 3.237490e+00 6.226518e+00
29 -2.356421e+00 3.237490e+00
30 -7.903522e-01 -2.356421e+00
31 -1.375627e+00 -7.903522e-01
32 -8.564464e+00 -1.375627e+00
33 -3.251683e-01 -8.564464e+00
34 1.251188e+00 -3.251683e-01
35 -3.453527e+00 1.251188e+00
36 3.354837e+00 -3.453527e+00
37 -2.390273e-01 3.354837e+00
38 2.618843e+00 -2.390273e-01
39 -9.573899e-01 2.618843e+00
40 3.369787e+00 -9.573899e-01
41 3.596910e+00 3.369787e+00
42 3.278703e-01 3.596910e+00
43 -6.947994e-01 3.278703e-01
44 -1.478659e+00 -6.947994e-01
45 2.381347e+00 -1.478659e+00
46 -2.608747e+00 2.381347e+00
47 3.173280e+00 -2.608747e+00
48 -9.522128e-01 3.173280e+00
49 5.855501e+00 -9.522128e-01
50 -1.557358e-01 5.855501e+00
51 4.922813e-01 -1.557358e-01
52 3.904085e+00 4.922813e-01
53 -1.297232e+01 3.904085e+00
54 1.638408e+00 -1.297232e+01
55 -5.823885e-01 1.638408e+00
56 -1.622711e+00 -5.823885e-01
57 1.204147e+00 -1.622711e+00
58 2.489037e+00 1.204147e+00
59 8.186203e+00 2.489037e+00
60 5.191125e+00 8.186203e+00
61 1.605137e+00 5.191125e+00
62 6.006209e-01 1.605137e+00
63 -6.218775e+00 6.006209e-01
64 -2.989787e+00 -6.218775e+00
65 4.921098e-01 -2.989787e+00
66 1.714627e+00 4.921098e-01
67 1.167316e+00 1.714627e+00
68 1.600021e+00 1.167316e+00
69 -1.234916e+00 1.600021e+00
70 4.324483e+00 -1.234916e+00
71 3.447650e+00 4.324483e+00
72 9.402162e-01 3.447650e+00
73 -4.488000e+00 9.402162e-01
74 2.800091e+00 -4.488000e+00
75 -2.450747e+00 2.800091e+00
76 -1.587584e+00 -2.450747e+00
77 1.291878e+00 -1.587584e+00
78 2.225020e+00 1.291878e+00
79 -9.845663e+00 2.225020e+00
80 -8.710558e+00 -9.845663e+00
81 -1.003420e+00 -8.710558e+00
82 2.032587e+00 -1.003420e+00
83 -1.318647e+00 2.032587e+00
84 -7.100750e-01 -1.318647e+00
85 1.969026e+00 -7.100750e-01
86 3.029655e+00 1.969026e+00
87 4.158701e-01 3.029655e+00
88 5.165775e+00 4.158701e-01
89 5.070171e+00 5.165775e+00
90 -7.254902e+00 5.070171e+00
91 -2.491329e+00 -7.254902e+00
92 -2.486751e+00 -2.491329e+00
93 -2.890479e+00 -2.486751e+00
94 5.983194e-01 -2.890479e+00
95 1.773297e+00 5.983194e-01
96 -1.187731e+00 1.773297e+00
97 -1.151662e+00 -1.187731e+00
98 -1.961088e+00 -1.151662e+00
99 1.665400e+00 -1.961088e+00
100 -1.201053e+00 1.665400e+00
101 -1.795473e+00 -1.201053e+00
102 2.059173e+00 -1.795473e+00
103 1.010695e+00 2.059173e+00
104 -2.767306e+00 1.010695e+00
105 -6.937281e-01 -2.767306e+00
106 -3.863789e+00 -6.937281e-01
107 -2.707094e+00 -3.863789e+00
108 2.331960e+00 -2.707094e+00
109 1.530500e+00 2.331960e+00
110 -4.599817e-01 1.530500e+00
111 -5.737451e-01 -4.599817e-01
112 -3.019651e+00 -5.737451e-01
113 6.346874e-01 -3.019651e+00
114 1.507668e-01 6.346874e-01
115 2.265218e+00 1.507668e-01
116 4.095264e+00 2.265218e+00
117 2.637997e-01 4.095264e+00
118 1.540508e-01 2.637997e-01
119 6.385862e+00 1.540508e-01
120 -1.440931e-01 6.385862e+00
121 2.652023e+00 -1.440931e-01
122 -2.785638e+00 2.652023e+00
123 -2.232418e+00 -2.785638e+00
124 2.335459e-01 -2.232418e+00
125 1.869098e+00 2.335459e-01
126 -5.059389e-01 1.869098e+00
127 -4.295761e-01 -5.059389e-01
128 5.276784e-01 -4.295761e-01
129 7.350562e-01 5.276784e-01
130 2.999411e+00 7.350562e-01
131 3.252601e-04 2.999411e+00
132 -1.771827e+00 3.252601e-04
133 2.020659e-01 -1.771827e+00
134 2.508945e+00 2.020659e-01
135 1.527616e+00 2.508945e+00
136 -2.133244e+00 1.527616e+00
137 2.674885e+00 -2.133244e+00
138 2.111913e+00 2.674885e+00
139 -5.199739e-01 2.111913e+00
140 1.790898e+00 -5.199739e-01
141 -3.354296e-01 1.790898e+00
142 2.127253e+00 -3.354296e-01
143 -1.443654e+00 2.127253e+00
144 2.657653e+00 -1.443654e+00
145 6.581623e-01 2.657653e+00
146 2.963809e+00 6.581623e-01
147 -1.014426e+00 2.963809e+00
148 2.424094e-01 -1.014426e+00
149 -5.388794e+00 2.424094e-01
150 4.425362e+00 -5.388794e+00
151 3.924763e+00 4.425362e+00
152 -5.513506e+00 3.924763e+00
153 -6.125431e-01 -5.513506e+00
154 -1.325098e+00 -6.125431e-01
155 -8.541400e-02 -1.325098e+00
156 -4.093289e+00 -8.541400e-02
157 3.176838e+00 -4.093289e+00
158 -2.486751e+00 3.176838e+00
159 -8.488747e-01 -2.486751e+00
160 -3.269100e+00 -8.488747e-01
161 -2.059097e+00 -3.269100e+00
162 NA -2.059097e+00
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.692201e+00 1.646789e+00
[2,] -1.101130e+00 -2.692201e+00
[3,] -8.133178e-02 -1.101130e+00
[4,] 5.673692e-01 -8.133178e-02
[5,] -2.914404e+00 5.673692e-01
[6,] -2.418845e+00 -2.914404e+00
[7,] -5.973300e+00 -2.418845e+00
[8,] 2.778617e+00 -5.973300e+00
[9,] 4.521370e-01 2.778617e+00
[10,] 3.538942e+00 4.521370e-01
[11,] 3.530070e+00 3.538942e+00
[12,] -2.215049e+00 3.530070e+00
[13,] 9.009568e-01 -2.215049e+00
[14,] 1.183360e+00 9.009568e-01
[15,] -7.138143e-01 1.183360e+00
[16,] 2.934306e+00 -7.138143e-01
[17,] 4.216806e+00 2.934306e+00
[18,] 4.021195e+00 4.216806e+00
[19,] -9.117906e-02 4.021195e+00
[20,] -3.448651e+00 -9.117906e-02
[21,] 3.817564e+00 -3.448651e+00
[22,] -9.483563e-01 3.817564e+00
[23,] -9.000026e-01 -9.483563e-01
[24,] 1.866548e+00 -9.000026e-01
[25,] -8.042022e+00 1.866548e+00
[26,] -8.042022e+00 -8.042022e+00
[27,] 6.226518e+00 -8.042022e+00
[28,] 3.237490e+00 6.226518e+00
[29,] -2.356421e+00 3.237490e+00
[30,] -7.903522e-01 -2.356421e+00
[31,] -1.375627e+00 -7.903522e-01
[32,] -8.564464e+00 -1.375627e+00
[33,] -3.251683e-01 -8.564464e+00
[34,] 1.251188e+00 -3.251683e-01
[35,] -3.453527e+00 1.251188e+00
[36,] 3.354837e+00 -3.453527e+00
[37,] -2.390273e-01 3.354837e+00
[38,] 2.618843e+00 -2.390273e-01
[39,] -9.573899e-01 2.618843e+00
[40,] 3.369787e+00 -9.573899e-01
[41,] 3.596910e+00 3.369787e+00
[42,] 3.278703e-01 3.596910e+00
[43,] -6.947994e-01 3.278703e-01
[44,] -1.478659e+00 -6.947994e-01
[45,] 2.381347e+00 -1.478659e+00
[46,] -2.608747e+00 2.381347e+00
[47,] 3.173280e+00 -2.608747e+00
[48,] -9.522128e-01 3.173280e+00
[49,] 5.855501e+00 -9.522128e-01
[50,] -1.557358e-01 5.855501e+00
[51,] 4.922813e-01 -1.557358e-01
[52,] 3.904085e+00 4.922813e-01
[53,] -1.297232e+01 3.904085e+00
[54,] 1.638408e+00 -1.297232e+01
[55,] -5.823885e-01 1.638408e+00
[56,] -1.622711e+00 -5.823885e-01
[57,] 1.204147e+00 -1.622711e+00
[58,] 2.489037e+00 1.204147e+00
[59,] 8.186203e+00 2.489037e+00
[60,] 5.191125e+00 8.186203e+00
[61,] 1.605137e+00 5.191125e+00
[62,] 6.006209e-01 1.605137e+00
[63,] -6.218775e+00 6.006209e-01
[64,] -2.989787e+00 -6.218775e+00
[65,] 4.921098e-01 -2.989787e+00
[66,] 1.714627e+00 4.921098e-01
[67,] 1.167316e+00 1.714627e+00
[68,] 1.600021e+00 1.167316e+00
[69,] -1.234916e+00 1.600021e+00
[70,] 4.324483e+00 -1.234916e+00
[71,] 3.447650e+00 4.324483e+00
[72,] 9.402162e-01 3.447650e+00
[73,] -4.488000e+00 9.402162e-01
[74,] 2.800091e+00 -4.488000e+00
[75,] -2.450747e+00 2.800091e+00
[76,] -1.587584e+00 -2.450747e+00
[77,] 1.291878e+00 -1.587584e+00
[78,] 2.225020e+00 1.291878e+00
[79,] -9.845663e+00 2.225020e+00
[80,] -8.710558e+00 -9.845663e+00
[81,] -1.003420e+00 -8.710558e+00
[82,] 2.032587e+00 -1.003420e+00
[83,] -1.318647e+00 2.032587e+00
[84,] -7.100750e-01 -1.318647e+00
[85,] 1.969026e+00 -7.100750e-01
[86,] 3.029655e+00 1.969026e+00
[87,] 4.158701e-01 3.029655e+00
[88,] 5.165775e+00 4.158701e-01
[89,] 5.070171e+00 5.165775e+00
[90,] -7.254902e+00 5.070171e+00
[91,] -2.491329e+00 -7.254902e+00
[92,] -2.486751e+00 -2.491329e+00
[93,] -2.890479e+00 -2.486751e+00
[94,] 5.983194e-01 -2.890479e+00
[95,] 1.773297e+00 5.983194e-01
[96,] -1.187731e+00 1.773297e+00
[97,] -1.151662e+00 -1.187731e+00
[98,] -1.961088e+00 -1.151662e+00
[99,] 1.665400e+00 -1.961088e+00
[100,] -1.201053e+00 1.665400e+00
[101,] -1.795473e+00 -1.201053e+00
[102,] 2.059173e+00 -1.795473e+00
[103,] 1.010695e+00 2.059173e+00
[104,] -2.767306e+00 1.010695e+00
[105,] -6.937281e-01 -2.767306e+00
[106,] -3.863789e+00 -6.937281e-01
[107,] -2.707094e+00 -3.863789e+00
[108,] 2.331960e+00 -2.707094e+00
[109,] 1.530500e+00 2.331960e+00
[110,] -4.599817e-01 1.530500e+00
[111,] -5.737451e-01 -4.599817e-01
[112,] -3.019651e+00 -5.737451e-01
[113,] 6.346874e-01 -3.019651e+00
[114,] 1.507668e-01 6.346874e-01
[115,] 2.265218e+00 1.507668e-01
[116,] 4.095264e+00 2.265218e+00
[117,] 2.637997e-01 4.095264e+00
[118,] 1.540508e-01 2.637997e-01
[119,] 6.385862e+00 1.540508e-01
[120,] -1.440931e-01 6.385862e+00
[121,] 2.652023e+00 -1.440931e-01
[122,] -2.785638e+00 2.652023e+00
[123,] -2.232418e+00 -2.785638e+00
[124,] 2.335459e-01 -2.232418e+00
[125,] 1.869098e+00 2.335459e-01
[126,] -5.059389e-01 1.869098e+00
[127,] -4.295761e-01 -5.059389e-01
[128,] 5.276784e-01 -4.295761e-01
[129,] 7.350562e-01 5.276784e-01
[130,] 2.999411e+00 7.350562e-01
[131,] 3.252601e-04 2.999411e+00
[132,] -1.771827e+00 3.252601e-04
[133,] 2.020659e-01 -1.771827e+00
[134,] 2.508945e+00 2.020659e-01
[135,] 1.527616e+00 2.508945e+00
[136,] -2.133244e+00 1.527616e+00
[137,] 2.674885e+00 -2.133244e+00
[138,] 2.111913e+00 2.674885e+00
[139,] -5.199739e-01 2.111913e+00
[140,] 1.790898e+00 -5.199739e-01
[141,] -3.354296e-01 1.790898e+00
[142,] 2.127253e+00 -3.354296e-01
[143,] -1.443654e+00 2.127253e+00
[144,] 2.657653e+00 -1.443654e+00
[145,] 6.581623e-01 2.657653e+00
[146,] 2.963809e+00 6.581623e-01
[147,] -1.014426e+00 2.963809e+00
[148,] 2.424094e-01 -1.014426e+00
[149,] -5.388794e+00 2.424094e-01
[150,] 4.425362e+00 -5.388794e+00
[151,] 3.924763e+00 4.425362e+00
[152,] -5.513506e+00 3.924763e+00
[153,] -6.125431e-01 -5.513506e+00
[154,] -1.325098e+00 -6.125431e-01
[155,] -8.541400e-02 -1.325098e+00
[156,] -4.093289e+00 -8.541400e-02
[157,] 3.176838e+00 -4.093289e+00
[158,] -2.486751e+00 3.176838e+00
[159,] -8.488747e-01 -2.486751e+00
[160,] -3.269100e+00 -8.488747e-01
[161,] -2.059097e+00 -3.269100e+00
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.692201e+00 1.646789e+00
2 -1.101130e+00 -2.692201e+00
3 -8.133178e-02 -1.101130e+00
4 5.673692e-01 -8.133178e-02
5 -2.914404e+00 5.673692e-01
6 -2.418845e+00 -2.914404e+00
7 -5.973300e+00 -2.418845e+00
8 2.778617e+00 -5.973300e+00
9 4.521370e-01 2.778617e+00
10 3.538942e+00 4.521370e-01
11 3.530070e+00 3.538942e+00
12 -2.215049e+00 3.530070e+00
13 9.009568e-01 -2.215049e+00
14 1.183360e+00 9.009568e-01
15 -7.138143e-01 1.183360e+00
16 2.934306e+00 -7.138143e-01
17 4.216806e+00 2.934306e+00
18 4.021195e+00 4.216806e+00
19 -9.117906e-02 4.021195e+00
20 -3.448651e+00 -9.117906e-02
21 3.817564e+00 -3.448651e+00
22 -9.483563e-01 3.817564e+00
23 -9.000026e-01 -9.483563e-01
24 1.866548e+00 -9.000026e-01
25 -8.042022e+00 1.866548e+00
26 -8.042022e+00 -8.042022e+00
27 6.226518e+00 -8.042022e+00
28 3.237490e+00 6.226518e+00
29 -2.356421e+00 3.237490e+00
30 -7.903522e-01 -2.356421e+00
31 -1.375627e+00 -7.903522e-01
32 -8.564464e+00 -1.375627e+00
33 -3.251683e-01 -8.564464e+00
34 1.251188e+00 -3.251683e-01
35 -3.453527e+00 1.251188e+00
36 3.354837e+00 -3.453527e+00
37 -2.390273e-01 3.354837e+00
38 2.618843e+00 -2.390273e-01
39 -9.573899e-01 2.618843e+00
40 3.369787e+00 -9.573899e-01
41 3.596910e+00 3.369787e+00
42 3.278703e-01 3.596910e+00
43 -6.947994e-01 3.278703e-01
44 -1.478659e+00 -6.947994e-01
45 2.381347e+00 -1.478659e+00
46 -2.608747e+00 2.381347e+00
47 3.173280e+00 -2.608747e+00
48 -9.522128e-01 3.173280e+00
49 5.855501e+00 -9.522128e-01
50 -1.557358e-01 5.855501e+00
51 4.922813e-01 -1.557358e-01
52 3.904085e+00 4.922813e-01
53 -1.297232e+01 3.904085e+00
54 1.638408e+00 -1.297232e+01
55 -5.823885e-01 1.638408e+00
56 -1.622711e+00 -5.823885e-01
57 1.204147e+00 -1.622711e+00
58 2.489037e+00 1.204147e+00
59 8.186203e+00 2.489037e+00
60 5.191125e+00 8.186203e+00
61 1.605137e+00 5.191125e+00
62 6.006209e-01 1.605137e+00
63 -6.218775e+00 6.006209e-01
64 -2.989787e+00 -6.218775e+00
65 4.921098e-01 -2.989787e+00
66 1.714627e+00 4.921098e-01
67 1.167316e+00 1.714627e+00
68 1.600021e+00 1.167316e+00
69 -1.234916e+00 1.600021e+00
70 4.324483e+00 -1.234916e+00
71 3.447650e+00 4.324483e+00
72 9.402162e-01 3.447650e+00
73 -4.488000e+00 9.402162e-01
74 2.800091e+00 -4.488000e+00
75 -2.450747e+00 2.800091e+00
76 -1.587584e+00 -2.450747e+00
77 1.291878e+00 -1.587584e+00
78 2.225020e+00 1.291878e+00
79 -9.845663e+00 2.225020e+00
80 -8.710558e+00 -9.845663e+00
81 -1.003420e+00 -8.710558e+00
82 2.032587e+00 -1.003420e+00
83 -1.318647e+00 2.032587e+00
84 -7.100750e-01 -1.318647e+00
85 1.969026e+00 -7.100750e-01
86 3.029655e+00 1.969026e+00
87 4.158701e-01 3.029655e+00
88 5.165775e+00 4.158701e-01
89 5.070171e+00 5.165775e+00
90 -7.254902e+00 5.070171e+00
91 -2.491329e+00 -7.254902e+00
92 -2.486751e+00 -2.491329e+00
93 -2.890479e+00 -2.486751e+00
94 5.983194e-01 -2.890479e+00
95 1.773297e+00 5.983194e-01
96 -1.187731e+00 1.773297e+00
97 -1.151662e+00 -1.187731e+00
98 -1.961088e+00 -1.151662e+00
99 1.665400e+00 -1.961088e+00
100 -1.201053e+00 1.665400e+00
101 -1.795473e+00 -1.201053e+00
102 2.059173e+00 -1.795473e+00
103 1.010695e+00 2.059173e+00
104 -2.767306e+00 1.010695e+00
105 -6.937281e-01 -2.767306e+00
106 -3.863789e+00 -6.937281e-01
107 -2.707094e+00 -3.863789e+00
108 2.331960e+00 -2.707094e+00
109 1.530500e+00 2.331960e+00
110 -4.599817e-01 1.530500e+00
111 -5.737451e-01 -4.599817e-01
112 -3.019651e+00 -5.737451e-01
113 6.346874e-01 -3.019651e+00
114 1.507668e-01 6.346874e-01
115 2.265218e+00 1.507668e-01
116 4.095264e+00 2.265218e+00
117 2.637997e-01 4.095264e+00
118 1.540508e-01 2.637997e-01
119 6.385862e+00 1.540508e-01
120 -1.440931e-01 6.385862e+00
121 2.652023e+00 -1.440931e-01
122 -2.785638e+00 2.652023e+00
123 -2.232418e+00 -2.785638e+00
124 2.335459e-01 -2.232418e+00
125 1.869098e+00 2.335459e-01
126 -5.059389e-01 1.869098e+00
127 -4.295761e-01 -5.059389e-01
128 5.276784e-01 -4.295761e-01
129 7.350562e-01 5.276784e-01
130 2.999411e+00 7.350562e-01
131 3.252601e-04 2.999411e+00
132 -1.771827e+00 3.252601e-04
133 2.020659e-01 -1.771827e+00
134 2.508945e+00 2.020659e-01
135 1.527616e+00 2.508945e+00
136 -2.133244e+00 1.527616e+00
137 2.674885e+00 -2.133244e+00
138 2.111913e+00 2.674885e+00
139 -5.199739e-01 2.111913e+00
140 1.790898e+00 -5.199739e-01
141 -3.354296e-01 1.790898e+00
142 2.127253e+00 -3.354296e-01
143 -1.443654e+00 2.127253e+00
144 2.657653e+00 -1.443654e+00
145 6.581623e-01 2.657653e+00
146 2.963809e+00 6.581623e-01
147 -1.014426e+00 2.963809e+00
148 2.424094e-01 -1.014426e+00
149 -5.388794e+00 2.424094e-01
150 4.425362e+00 -5.388794e+00
151 3.924763e+00 4.425362e+00
152 -5.513506e+00 3.924763e+00
153 -6.125431e-01 -5.513506e+00
154 -1.325098e+00 -6.125431e-01
155 -8.541400e-02 -1.325098e+00
156 -4.093289e+00 -8.541400e-02
157 3.176838e+00 -4.093289e+00
158 -2.486751e+00 3.176838e+00
159 -8.488747e-01 -2.486751e+00
160 -3.269100e+00 -8.488747e-01
161 -2.059097e+00 -3.269100e+00
> 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/wessaorg/rcomp/tmp/757cz1321810723.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8mmxf1321810723.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9rflr1321810723.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/1065lf1321810723.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11cko51321810723.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/wessaorg/rcomp/tmp/12v6r51321810723.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/wessaorg/rcomp/tmp/13t28v1321810723.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/wessaorg/rcomp/tmp/14at5f1321810723.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/wessaorg/rcomp/tmp/151hfn1321810723.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/wessaorg/rcomp/tmp/164c3w1321810723.tab")
+ }
>
> try(system("convert tmp/1s5i61321810723.ps tmp/1s5i61321810723.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zqju1321810723.ps tmp/2zqju1321810723.png",intern=TRUE))
character(0)
> try(system("convert tmp/387y91321810723.ps tmp/387y91321810723.png",intern=TRUE))
character(0)
> try(system("convert tmp/4sbhm1321810723.ps tmp/4sbhm1321810723.png",intern=TRUE))
character(0)
> try(system("convert tmp/59mb31321810723.ps tmp/59mb31321810723.png",intern=TRUE))
character(0)
> try(system("convert tmp/627k91321810723.ps tmp/627k91321810723.png",intern=TRUE))
character(0)
> try(system("convert tmp/757cz1321810723.ps tmp/757cz1321810723.png",intern=TRUE))
character(0)
> try(system("convert tmp/8mmxf1321810723.ps tmp/8mmxf1321810723.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rflr1321810723.ps tmp/9rflr1321810723.png",intern=TRUE))
character(0)
> try(system("convert tmp/1065lf1321810723.ps tmp/1065lf1321810723.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.884 0.489 5.414