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(272545
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+ ,dim=c(8
+ ,164)
+ ,dimnames=list(c('X1'
+ ,'X2'
+ ,'X3'
+ ,'X4'
+ ,'X5'
+ ,'X6'
+ ,'X7'
+ ,'X8')
+ ,1:164))
> y <- array(NA,dim=c(8,164),dimnames=list(c('X1','X2','X3','X4','X5','X6','X7','X8'),1:164))
> 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 = '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
> 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
X1 X2 X3 X4 X5 X6 X7 X8
1 272545 1747 69 483 32033 3 116 144
2 179444 1209 64 429 20654 4 127 133
3 222373 1844 69 673 16346 16 106 162
4 218443 2683 104 1137 35926 2 133 148
5 167843 1228 51 374 10621 1 64 88
6 70849 631 28 179 10024 3 89 129
7 506574 4627 123 2251 43068 0 122 128
8 33186 381 19 111 1271 0 22 67
9 216660 2063 59 740 34416 7 117 132
10 213274 1758 44 595 20318 0 82 120
11 307153 2132 109 800 24409 0 147 169
12 237633 2128 114 660 20648 7 184 210
13 164292 1667 68 635 12347 8 113 122
14 364402 2965 79 1172 21857 4 171 191
15 244103 2098 84 674 11034 10 87 162
16 384448 4904 178 1692 33433 0 199 223
17 325587 2242 68 811 35902 6 139 140
18 323652 2977 157 1168 22355 4 92 144
19 176082 1438 55 507 31219 3 85 111
20 266736 2347 87 689 21983 8 193 199
21 278265 2522 70 837 40085 0 160 187
22 442703 2889 103 1270 18507 1 144 144
23 180393 1447 41 462 16278 5 84 89
24 189897 1717 54 601 24662 9 208 208
25 234247 3362 121 1242 31452 1 154 165
26 237452 2898 125 1025 32580 0 139 146
27 267268 2828 127 1062 22883 5 127 158
28 270787 1972 86 618 27652 0 148 154
29 155915 1495 51 559 9845 0 99 117
30 342564 2840 69 1062 20190 0 135 158
31 282172 2299 76 913 46201 3 171 183
32 216584 1909 76 643 10971 6 149 186
33 318563 2091 84 779 34811 1 178 185
34 98672 971 37 322 3029 4 137 141
35 386258 3293 95 1243 38941 4 151 156
36 273950 2764 56 1186 4958 0 127 159
37 425120 3682 120 1324 32344 0 151 161
38 227636 1918 83 640 19433 2 89 139
39 115658 947 33 284 12558 1 46 55
40 349863 3433 194 1210 36524 2 153 163
41 324178 3246 79 1490 26041 10 122 145
42 178083 1692 67 667 16637 9 111 148
43 195153 1735 73 635 28395 5 108 115
44 177694 1771 61 479 16747 6 142 174
45 153778 2496 82 1022 9105 1 45 73
46 455168 5501 151 2068 11941 2 131 147
47 78800 918 42 330 7935 2 66 82
48 208051 2228 76 648 19499 0 180 201
49 348077 4051 118 1367 22938 10 165 181
50 175523 2081 54 868 25314 3 146 164
51 224591 1875 74 588 28524 0 137 158
52 24188 496 24 218 2694 0 7 12
53 372238 2537 314 833 20867 8 157 163
54 65029 744 17 255 3597 5 61 67
55 101097 1161 64 454 5296 3 41 52
56 279012 3027 58 1108 32982 1 120 134
57 317644 2526 84 662 38975 5 228 230
58 340471 3705 185 1119 42721 5 137 145
59 358958 2667 141 1058 41455 0 150 153
60 252529 2175 83 822 23923 12 127 139
61 370628 3949 140 1302 26719 10 161 178
62 304468 3165 117 1145 53405 12 73 101
63 265870 2939 113 1185 12526 11 97 169
64 264889 2610 88 931 26584 8 142 163
65 228595 1426 66 557 37062 2 125 139
66 216027 1646 65 436 25696 0 87 116
67 198798 1971 132 596 24634 6 128 137
68 238146 2746 145 837 27269 9 148 167
69 234891 2308 81 848 25270 2 116 135
70 175816 1684 69 625 24634 5 89 102
71 239314 2537 68 865 17828 13 154 173
72 73566 893 32 385 3007 6 67 88
73 242622 2195 84 718 20065 7 171 175
74 187167 1695 53 705 24648 2 90 133
75 209049 2061 63 732 21588 2 133 148
76 360592 2329 86 988 25217 4 144 169
77 342846 2695 92 1077 30927 3 133 140
78 207650 1809 107 524 18487 6 125 154
79 206500 2290 62 697 18050 2 134 148
80 182357 1791 64 644 17696 0 110 134
81 153613 1678 46 622 17326 1 89 109
82 456979 4023 124 1227 39361 0 138 175
83 145943 1369 69 653 9648 5 99 99
84 280366 2308 104 656 26759 2 92 122
85 80953 870 25 437 7905 0 27 28
86 150216 1966 54 822 4527 0 77 101
87 167878 1459 59 423 41517 6 137 139
88 369718 3795 205 1489 21261 1 137 143
89 322454 2673 116 929 36099 0 122 206
90 179797 3085 104 1044 39039 1 159 171
91 262883 2367 91 792 13841 1 85 138
92 262793 2209 77 678 23841 3 138 148
93 189142 1829 63 597 8589 9 90 114
94 275997 3087 74 1099 15049 1 135 140
95 328875 2559 82 966 39038 4 147 156
96 189252 1624 36 555 30391 3 139 140
97 222504 1607 51 552 39932 5 127 127
98 287386 2109 79 778 43840 0 104 141
99 389104 4015 151 1322 43146 12 248 251
100 397681 3705 108 1415 50099 13 106 114
101 287748 2714 136 853 40312 8 176 198
102 294320 2325 65 848 32616 0 130 155
103 186856 1999 179 640 11338 0 59 138
104 43287 602 14 214 7409 4 64 71
105 185468 2146 80 716 18213 4 36 84
106 235352 2325 146 795 45873 0 98 167
107 268077 2617 48 1170 39844 0 125 155
108 305195 2688 90 1048 28317 0 124 150
109 143356 1207 72 399 24797 0 83 112
110 154165 3102 88 906 7471 0 127 161
111 307000 1869 68 609 27259 4 143 149
112 298039 2304 88 688 23201 0 115 164
113 23623 398 11 156 238 0 0 0
114 195817 2205 73 779 28830 0 103 155
115 61857 530 25 192 3913 4 30 32
116 163766 1596 48 457 9935 0 119 169
117 414506 3083 117 1195 27738 1 102 140
118 21054 387 16 146 338 0 0 0
119 252805 2137 52 866 13326 5 77 111
120 31961 492 22 200 3988 0 9 25
121 317367 3450 115 1230 24347 3 137 146
122 240153 2089 65 696 27111 7 163 183
123 175083 1658 88 491 3938 13 146 181
124 152043 1685 53 670 17416 3 84 107
125 38214 568 34 276 1888 0 21 27
126 216299 2059 42 716 18700 2 151 163
127 357602 2792 82 1021 36809 0 187 198
128 198104 1395 61 481 24959 0 171 205
129 410803 3590 80 1582 37343 4 167 187
130 316105 2387 97 820 21849 0 145 187
131 397297 3334 124 1153 49809 3 175 186
132 187992 1250 35 473 21654 0 137 151
133 102424 1121 42 401 8728 0 100 131
134 286327 2880 335 954 20920 4 150 155
135 407378 4104 170 1447 27195 4 163 172
136 143860 1759 54 546 1037 15 149 160
137 391854 4138 132 1728 42570 2 161 172
138 157429 1831 77 689 17672 4 112 143
139 258751 1787 48 590 34245 2 135 151
140 282399 2535 94 897 16786 1 124 158
141 217665 1816 113 613 20954 0 45 125
142 366774 3873 116 1548 16378 9 120 145
143 236660 2181 88 759 31852 1 126 145
144 173260 2035 63 716 2805 3 78 79
145 323545 2960 99 955 38086 11 136 174
146 168994 1915 57 720 21166 5 179 192
147 253330 2648 86 1023 34672 2 118 132
148 301703 2633 105 818 36171 1 147 159
149 1 2 0 0 0 9 0 0
150 14688 207 10 85 2065 0 0 0
151 98 5 1 0 0 0 0 0
152 455 8 2 0 0 0 0 0
153 0 0 0 0 0 1 0 0
154 0 0 0 0 0 0 0 0
155 246435 2116 84 737 19354 2 88 133
156 382374 3286 154 1080 22124 3 129 204
157 0 0 0 0 0 0 0 0
158 203 4 4 0 0 0 0 0
159 7199 151 5 74 556 0 0 0
160 46660 474 20 259 2089 0 13 15
161 17547 141 5 69 2658 0 4 4
162 116678 1047 42 285 1813 0 76 152
163 969 29 2 0 0 0 0 0
164 206501 1822 68 591 17372 2 71 125
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X2 X3 X4 X5 X6
-5384.612 27.460 197.861 98.285 1.691 -626.114
X7 X8
1.433 365.132
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-150783 -19582 -419 19640 140096
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5384.6119 8261.9450 -0.652 0.5155
X2 27.4597 15.3619 1.788 0.0758 .
X3 197.8605 96.4327 2.052 0.0419 *
X4 98.2849 33.4532 2.938 0.0038 **
X5 1.6913 0.3375 5.011 1.45e-06 ***
X6 -626.1144 886.3983 -0.706 0.4810
X7 1.4329 182.5048 0.008 0.9937
X8 365.1320 173.6495 2.103 0.0371 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 39800 on 156 degrees of freedom
Multiple R-squared: 0.8848, Adjusted R-squared: 0.8796
F-statistic: 171.1 on 7 and 156 DF, p-value: < 2.2e-16
> 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,] 0.8837711 2.324579e-01 1.162289e-01
[2,] 0.9483830 1.032339e-01 5.161697e-02
[3,] 0.9168374 1.663253e-01 8.316265e-02
[4,] 0.8713602 2.572796e-01 1.286398e-01
[5,] 0.8146747 3.706507e-01 1.853253e-01
[6,] 0.9709278 5.814440e-02 2.907220e-02
[7,] 0.9761574 4.768523e-02 2.384261e-02
[8,] 0.9735934 5.281315e-02 2.640658e-02
[9,] 0.9637044 7.259115e-02 3.629558e-02
[10,] 0.9457428 1.085144e-01 5.425718e-02
[11,] 0.9267440 1.465120e-01 7.325602e-02
[12,] 0.9947377 1.052454e-02 5.262271e-03
[13,] 0.9918668 1.626635e-02 8.133175e-03
[14,] 0.9929734 1.405314e-02 7.026572e-03
[15,] 0.9993983 1.203359e-03 6.016797e-04
[16,] 0.9995530 8.939736e-04 4.469868e-04
[17,] 0.9993199 1.360230e-03 6.801150e-04
[18,] 0.9993942 1.211508e-03 6.057542e-04
[19,] 0.9991700 1.660084e-03 8.300419e-04
[20,] 0.9992444 1.511184e-03 7.555919e-04
[21,] 0.9988119 2.376134e-03 1.188067e-03
[22,] 0.9981509 3.698220e-03 1.849110e-03
[23,] 0.9984401 3.119746e-03 1.559873e-03
[24,] 0.9985306 2.938850e-03 1.469425e-03
[25,] 0.9984250 3.150032e-03 1.575016e-03
[26,] 0.9978819 4.236111e-03 2.118055e-03
[27,] 0.9987167 2.566692e-03 1.283346e-03
[28,] 0.9981664 3.667249e-03 1.833624e-03
[29,] 0.9973086 5.382743e-03 2.691372e-03
[30,] 0.9961550 7.690014e-03 3.845007e-03
[31,] 0.9952358 9.528349e-03 4.764174e-03
[32,] 0.9937138 1.257237e-02 6.286184e-03
[33,] 0.9913510 1.729808e-02 8.649038e-03
[34,] 0.9883760 2.324794e-02 1.162397e-02
[35,] 0.9945842 1.083150e-02 5.415751e-03
[36,] 0.9922512 1.549765e-02 7.748826e-03
[37,] 0.9907900 1.841991e-02 9.209956e-03
[38,] 0.9898731 2.025376e-02 1.012688e-02
[39,] 0.9863532 2.729357e-02 1.364678e-02
[40,] 0.9938768 1.224635e-02 6.123175e-03
[41,] 0.9913501 1.729978e-02 8.649891e-03
[42,] 0.9893386 2.132272e-02 1.066136e-02
[43,] 0.9946979 1.060429e-02 5.302144e-03
[44,] 0.9926819 1.463618e-02 7.318092e-03
[45,] 0.9901626 1.967477e-02 9.837383e-03
[46,] 0.9877249 2.455019e-02 1.227510e-02
[47,] 0.9852514 2.949727e-02 1.474863e-02
[48,] 0.9819104 3.617924e-02 1.808962e-02
[49,] 0.9800007 3.999857e-02 1.999929e-02
[50,] 0.9747812 5.043767e-02 2.521884e-02
[51,] 0.9672122 6.557555e-02 3.278778e-02
[52,] 0.9636763 7.264730e-02 3.632365e-02
[53,] 0.9569881 8.602383e-02 4.301192e-02
[54,] 0.9457204 1.085592e-01 5.427958e-02
[55,] 0.9342541 1.314917e-01 6.574585e-02
[56,] 0.9315894 1.368213e-01 6.841065e-02
[57,] 0.9213470 1.573060e-01 7.865300e-02
[58,] 0.9242023 1.515953e-01 7.579766e-02
[59,] 0.9079329 1.841342e-01 9.206708e-02
[60,] 0.8904988 2.190024e-01 1.095012e-01
[61,] 0.8686994 2.626012e-01 1.313006e-01
[62,] 0.8524624 2.950751e-01 1.475376e-01
[63,] 0.8252620 3.494761e-01 1.747380e-01
[64,] 0.8034872 3.930255e-01 1.965128e-01
[65,] 0.7743969 4.512062e-01 2.256031e-01
[66,] 0.8819266 2.361468e-01 1.180734e-01
[67,] 0.8942819 2.114363e-01 1.057181e-01
[68,] 0.8724921 2.550158e-01 1.275079e-01
[69,] 0.8506202 2.987597e-01 1.493798e-01
[70,] 0.8260191 3.479619e-01 1.739809e-01
[71,] 0.8069544 3.860912e-01 1.930456e-01
[72,] 0.8750112 2.499776e-01 1.249888e-01
[73,] 0.8516840 2.966320e-01 1.483160e-01
[74,] 0.8625094 2.749812e-01 1.374906e-01
[75,] 0.8361069 3.277862e-01 1.638931e-01
[76,] 0.8289763 3.420474e-01 1.710237e-01
[77,] 0.8205084 3.589831e-01 1.794916e-01
[78,] 0.7912059 4.175882e-01 2.087941e-01
[79,] 0.7561337 4.877325e-01 2.438663e-01
[80,] 0.9888683 2.226347e-02 1.113173e-02
[81,] 0.9875907 2.481861e-02 1.240931e-02
[82,] 0.9862304 2.753914e-02 1.376957e-02
[83,] 0.9830451 3.390982e-02 1.695491e-02
[84,] 0.9774252 4.514953e-02 2.257477e-02
[85,] 0.9751921 4.961587e-02 2.480794e-02
[86,] 0.9681774 6.364512e-02 3.182256e-02
[87,] 0.9590086 8.198289e-02 4.099145e-02
[88,] 0.9490514 1.018972e-01 5.094860e-02
[89,] 0.9463557 1.072886e-01 5.364432e-02
[90,] 0.9354351 1.291298e-01 6.456492e-02
[91,] 0.9293513 1.412974e-01 7.064869e-02
[92,] 0.9193419 1.613161e-01 8.065807e-02
[93,] 0.9084783 1.830434e-01 9.152172e-02
[94,] 0.8975799 2.048401e-01 1.024201e-01
[95,] 0.8816996 2.366007e-01 1.183004e-01
[96,] 0.9410731 1.178537e-01 5.892687e-02
[97,] 0.9535770 9.284595e-02 4.642297e-02
[98,] 0.9409908 1.180184e-01 5.900919e-02
[99,] 0.9347584 1.304832e-01 6.524160e-02
[100,] 0.9959532 8.093684e-03 4.046842e-03
[101,] 0.9995979 8.042948e-04 4.021474e-04
[102,] 0.9996232 7.536828e-04 3.768414e-04
[103,] 0.9993905 1.219062e-03 6.095311e-04
[104,] 0.9998922 2.155338e-04 1.077669e-04
[105,] 0.9998353 3.294805e-04 1.647403e-04
[106,] 0.9997872 4.255686e-04 2.127843e-04
[107,] 0.9999951 9.868164e-06 4.934082e-06
[108,] 0.9999905 1.906916e-05 9.534581e-06
[109,] 0.9999951 9.892879e-06 4.946439e-06
[110,] 0.9999921 1.580711e-05 7.903553e-06
[111,] 0.9999913 1.749145e-05 8.745725e-06
[112,] 0.9999824 3.523847e-05 1.761924e-05
[113,] 0.9999733 5.331716e-05 2.665858e-05
[114,] 0.9999692 6.166080e-05 3.083040e-05
[115,] 0.9999444 1.112312e-04 5.561561e-05
[116,] 0.9999020 1.960032e-04 9.800158e-05
[117,] 0.9999122 1.755236e-04 8.776182e-05
[118,] 0.9998420 3.159902e-04 1.579951e-04
[119,] 0.9999711 5.786547e-05 2.893274e-05
[120,] 0.9999947 1.067575e-05 5.337875e-06
[121,] 0.9999909 1.820570e-05 9.102849e-06
[122,] 0.9999995 9.454265e-07 4.727132e-07
[123,] 0.9999989 2.151369e-06 1.075684e-06
[124,] 0.9999980 4.084006e-06 2.042003e-06
[125,] 0.9999946 1.071982e-05 5.359910e-06
[126,] 0.9999862 2.756698e-05 1.378349e-05
[127,] 0.9999695 6.109775e-05 3.054888e-05
[128,] 0.9999691 6.180612e-05 3.090306e-05
[129,] 0.9999999 1.007473e-07 5.037367e-08
[130,] 1.0000000 4.979845e-08 2.489922e-08
[131,] 1.0000000 1.679589e-09 8.397944e-10
[132,] 1.0000000 1.014547e-09 5.072734e-10
[133,] 1.0000000 3.657726e-09 1.828863e-09
[134,] 1.0000000 2.177346e-08 1.088673e-08
[135,] 0.9999999 1.085897e-07 5.429484e-08
[136,] 0.9999998 4.940209e-07 2.470105e-07
[137,] 1.0000000 3.144529e-08 1.572265e-08
[138,] 1.0000000 1.793989e-09 8.969944e-10
[139,] 1.0000000 1.645105e-08 8.225523e-09
[140,] 1.0000000 3.993695e-08 1.996848e-08
[141,] 0.9999995 9.764768e-07 4.882384e-07
[142,] 0.9999883 2.347650e-05 1.173825e-05
[143,] 0.9997595 4.809327e-04 2.404663e-04
> postscript(file="/var/wessaorg/rcomp/tmp/1hizs1324305581.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2oocp1324305581.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3bhnw1324305581.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4uhvc1324305581.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5nz6n1324305581.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 = 164
Frequency = 1
1 2 3 4 5
63790.23032 15630.99277 20392.88891 -95912.59327 43097.48591
6 7 8 9 10
-26531.04689 19575.16803 -13205.48015 -21198.32941 24902.54079
11 12 13 14 15
50598.68845 -10320.65897 -12545.33631 53100.87820 37336.49068
16 17 18 19 20
-84600.75050 67962.49262 13413.53189 -10306.04118 17632.46541
21 22 23 24 25
-18021.91386 140095.54694 35506.50580 -33940.93144 -111733.98533
26 27 28 29 30
-70826.17003 -27953.83438 41055.64332 -4297.62724 59901.11199
31 32 33 34 35
-23668.90545 8387.17493 47292.03378 -15873.64987 39720.73945
36 37 38 39 40
9166.65236 61820.51033 18533.07110 19834.95239 -16586.63163
41 42 43 44 45
-12547.18800 -18508.08171 -10997.63888 -13003.91328 -67540.52692
46 47 48 49 50
3561.38032 -23970.93693 -33098.12377 -14339.12961 -73257.07169
51 52 53 54 55
-73.44589 -19170.05084 73933.55372 -5946.85604 -8807.74470
56 57 58 59 60
-23354.42815 24886.82078 -24730.15057 33032.15350 17093.97616
61 62 63 64 65
7754.78516 -32535.00498 -24419.16049 -9982.93627 14656.55378
66 67 68 69 70
34560.97120 -22748.37496 -44501.25763 -13418.84398 -16025.09897
71 72 73 74 75
-8838.55663 -23298.70107 6848.09252 -22895.72494 -16059.58469
76 77 78 79 80
85843.57259 48434.13399 6768.40605 -15276.74864 -16411.26075
81 82 83 84 85
-25918.28191 76097.08630 -13573.84363 48638.86668 -9081.20226
86 87 88 89 90
-34504.84760 -37458.75951 -3756.69940 3735.21677 -150783.22737
91 92 93 94 95
34130.80393 32966.23611 22516.23567 -2180.97262 32131.93868
96 97 98 99 100
-12467.70427 8457.79952 16983.60699 -33033.26602 22517.03075
101 102 103 104 105
-27856.91095 27710.11703 -30619.06282 -27704.37530 -13298.02848
106 107 108 109 110
-68832.37300 -47052.54669 13118.77882 -20816.99138 -103691.92157
111 112 113 114 115
89544.99718 55839.16206 167.22088 -55857.11381 13030.06914
116 117 118 119 120
-7769.15634 97081.28744 -2275.30312 44057.50476 -16060.44766
121 122 123 124 125
-8433.29443 -1614.83659 4450.99078 -31945.75457 -18934.15584
126 127 128 129 130
-3645.42372 34927.67934 -11471.66908 17120.16991 50717.23032
131 132 133 134 135
22746.39866 13684.13315 -33433.07647 -37106.67444 17687.60393
136 137 138 139 140
-14401.77515 -46122.00149 -50176.66130 35586.08066 25780.76941
141 142 143 144 145
9431.08310 15529.69937 -16223.82754 8104.06591 12944.37601
146 147 148 149 150
-63278.54984 -37314.08078 14798.41950 10965.72221 563.16269
151 152 153 154 155
5147.45283 5224.21319 6010.72629 5384.61187 24489.39140
156 157 158 159 160
50696.76373 5384.61187 4686.33096 -765.53263 587.04909
161 162 163 164
5327.18159 -1684.27100 5161.55941 16441.41551
> postscript(file="/var/wessaorg/rcomp/tmp/6rrfs1324305581.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 = 164
Frequency = 1
lag(myerror, k = 1) myerror
0 63790.23032 NA
1 15630.99277 63790.23032
2 20392.88891 15630.99277
3 -95912.59327 20392.88891
4 43097.48591 -95912.59327
5 -26531.04689 43097.48591
6 19575.16803 -26531.04689
7 -13205.48015 19575.16803
8 -21198.32941 -13205.48015
9 24902.54079 -21198.32941
10 50598.68845 24902.54079
11 -10320.65897 50598.68845
12 -12545.33631 -10320.65897
13 53100.87820 -12545.33631
14 37336.49068 53100.87820
15 -84600.75050 37336.49068
16 67962.49262 -84600.75050
17 13413.53189 67962.49262
18 -10306.04118 13413.53189
19 17632.46541 -10306.04118
20 -18021.91386 17632.46541
21 140095.54694 -18021.91386
22 35506.50580 140095.54694
23 -33940.93144 35506.50580
24 -111733.98533 -33940.93144
25 -70826.17003 -111733.98533
26 -27953.83438 -70826.17003
27 41055.64332 -27953.83438
28 -4297.62724 41055.64332
29 59901.11199 -4297.62724
30 -23668.90545 59901.11199
31 8387.17493 -23668.90545
32 47292.03378 8387.17493
33 -15873.64987 47292.03378
34 39720.73945 -15873.64987
35 9166.65236 39720.73945
36 61820.51033 9166.65236
37 18533.07110 61820.51033
38 19834.95239 18533.07110
39 -16586.63163 19834.95239
40 -12547.18800 -16586.63163
41 -18508.08171 -12547.18800
42 -10997.63888 -18508.08171
43 -13003.91328 -10997.63888
44 -67540.52692 -13003.91328
45 3561.38032 -67540.52692
46 -23970.93693 3561.38032
47 -33098.12377 -23970.93693
48 -14339.12961 -33098.12377
49 -73257.07169 -14339.12961
50 -73.44589 -73257.07169
51 -19170.05084 -73.44589
52 73933.55372 -19170.05084
53 -5946.85604 73933.55372
54 -8807.74470 -5946.85604
55 -23354.42815 -8807.74470
56 24886.82078 -23354.42815
57 -24730.15057 24886.82078
58 33032.15350 -24730.15057
59 17093.97616 33032.15350
60 7754.78516 17093.97616
61 -32535.00498 7754.78516
62 -24419.16049 -32535.00498
63 -9982.93627 -24419.16049
64 14656.55378 -9982.93627
65 34560.97120 14656.55378
66 -22748.37496 34560.97120
67 -44501.25763 -22748.37496
68 -13418.84398 -44501.25763
69 -16025.09897 -13418.84398
70 -8838.55663 -16025.09897
71 -23298.70107 -8838.55663
72 6848.09252 -23298.70107
73 -22895.72494 6848.09252
74 -16059.58469 -22895.72494
75 85843.57259 -16059.58469
76 48434.13399 85843.57259
77 6768.40605 48434.13399
78 -15276.74864 6768.40605
79 -16411.26075 -15276.74864
80 -25918.28191 -16411.26075
81 76097.08630 -25918.28191
82 -13573.84363 76097.08630
83 48638.86668 -13573.84363
84 -9081.20226 48638.86668
85 -34504.84760 -9081.20226
86 -37458.75951 -34504.84760
87 -3756.69940 -37458.75951
88 3735.21677 -3756.69940
89 -150783.22737 3735.21677
90 34130.80393 -150783.22737
91 32966.23611 34130.80393
92 22516.23567 32966.23611
93 -2180.97262 22516.23567
94 32131.93868 -2180.97262
95 -12467.70427 32131.93868
96 8457.79952 -12467.70427
97 16983.60699 8457.79952
98 -33033.26602 16983.60699
99 22517.03075 -33033.26602
100 -27856.91095 22517.03075
101 27710.11703 -27856.91095
102 -30619.06282 27710.11703
103 -27704.37530 -30619.06282
104 -13298.02848 -27704.37530
105 -68832.37300 -13298.02848
106 -47052.54669 -68832.37300
107 13118.77882 -47052.54669
108 -20816.99138 13118.77882
109 -103691.92157 -20816.99138
110 89544.99718 -103691.92157
111 55839.16206 89544.99718
112 167.22088 55839.16206
113 -55857.11381 167.22088
114 13030.06914 -55857.11381
115 -7769.15634 13030.06914
116 97081.28744 -7769.15634
117 -2275.30312 97081.28744
118 44057.50476 -2275.30312
119 -16060.44766 44057.50476
120 -8433.29443 -16060.44766
121 -1614.83659 -8433.29443
122 4450.99078 -1614.83659
123 -31945.75457 4450.99078
124 -18934.15584 -31945.75457
125 -3645.42372 -18934.15584
126 34927.67934 -3645.42372
127 -11471.66908 34927.67934
128 17120.16991 -11471.66908
129 50717.23032 17120.16991
130 22746.39866 50717.23032
131 13684.13315 22746.39866
132 -33433.07647 13684.13315
133 -37106.67444 -33433.07647
134 17687.60393 -37106.67444
135 -14401.77515 17687.60393
136 -46122.00149 -14401.77515
137 -50176.66130 -46122.00149
138 35586.08066 -50176.66130
139 25780.76941 35586.08066
140 9431.08310 25780.76941
141 15529.69937 9431.08310
142 -16223.82754 15529.69937
143 8104.06591 -16223.82754
144 12944.37601 8104.06591
145 -63278.54984 12944.37601
146 -37314.08078 -63278.54984
147 14798.41950 -37314.08078
148 10965.72221 14798.41950
149 563.16269 10965.72221
150 5147.45283 563.16269
151 5224.21319 5147.45283
152 6010.72629 5224.21319
153 5384.61187 6010.72629
154 24489.39140 5384.61187
155 50696.76373 24489.39140
156 5384.61187 50696.76373
157 4686.33096 5384.61187
158 -765.53263 4686.33096
159 587.04909 -765.53263
160 5327.18159 587.04909
161 -1684.27100 5327.18159
162 5161.55941 -1684.27100
163 16441.41551 5161.55941
164 NA 16441.41551
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 15630.99277 63790.23032
[2,] 20392.88891 15630.99277
[3,] -95912.59327 20392.88891
[4,] 43097.48591 -95912.59327
[5,] -26531.04689 43097.48591
[6,] 19575.16803 -26531.04689
[7,] -13205.48015 19575.16803
[8,] -21198.32941 -13205.48015
[9,] 24902.54079 -21198.32941
[10,] 50598.68845 24902.54079
[11,] -10320.65897 50598.68845
[12,] -12545.33631 -10320.65897
[13,] 53100.87820 -12545.33631
[14,] 37336.49068 53100.87820
[15,] -84600.75050 37336.49068
[16,] 67962.49262 -84600.75050
[17,] 13413.53189 67962.49262
[18,] -10306.04118 13413.53189
[19,] 17632.46541 -10306.04118
[20,] -18021.91386 17632.46541
[21,] 140095.54694 -18021.91386
[22,] 35506.50580 140095.54694
[23,] -33940.93144 35506.50580
[24,] -111733.98533 -33940.93144
[25,] -70826.17003 -111733.98533
[26,] -27953.83438 -70826.17003
[27,] 41055.64332 -27953.83438
[28,] -4297.62724 41055.64332
[29,] 59901.11199 -4297.62724
[30,] -23668.90545 59901.11199
[31,] 8387.17493 -23668.90545
[32,] 47292.03378 8387.17493
[33,] -15873.64987 47292.03378
[34,] 39720.73945 -15873.64987
[35,] 9166.65236 39720.73945
[36,] 61820.51033 9166.65236
[37,] 18533.07110 61820.51033
[38,] 19834.95239 18533.07110
[39,] -16586.63163 19834.95239
[40,] -12547.18800 -16586.63163
[41,] -18508.08171 -12547.18800
[42,] -10997.63888 -18508.08171
[43,] -13003.91328 -10997.63888
[44,] -67540.52692 -13003.91328
[45,] 3561.38032 -67540.52692
[46,] -23970.93693 3561.38032
[47,] -33098.12377 -23970.93693
[48,] -14339.12961 -33098.12377
[49,] -73257.07169 -14339.12961
[50,] -73.44589 -73257.07169
[51,] -19170.05084 -73.44589
[52,] 73933.55372 -19170.05084
[53,] -5946.85604 73933.55372
[54,] -8807.74470 -5946.85604
[55,] -23354.42815 -8807.74470
[56,] 24886.82078 -23354.42815
[57,] -24730.15057 24886.82078
[58,] 33032.15350 -24730.15057
[59,] 17093.97616 33032.15350
[60,] 7754.78516 17093.97616
[61,] -32535.00498 7754.78516
[62,] -24419.16049 -32535.00498
[63,] -9982.93627 -24419.16049
[64,] 14656.55378 -9982.93627
[65,] 34560.97120 14656.55378
[66,] -22748.37496 34560.97120
[67,] -44501.25763 -22748.37496
[68,] -13418.84398 -44501.25763
[69,] -16025.09897 -13418.84398
[70,] -8838.55663 -16025.09897
[71,] -23298.70107 -8838.55663
[72,] 6848.09252 -23298.70107
[73,] -22895.72494 6848.09252
[74,] -16059.58469 -22895.72494
[75,] 85843.57259 -16059.58469
[76,] 48434.13399 85843.57259
[77,] 6768.40605 48434.13399
[78,] -15276.74864 6768.40605
[79,] -16411.26075 -15276.74864
[80,] -25918.28191 -16411.26075
[81,] 76097.08630 -25918.28191
[82,] -13573.84363 76097.08630
[83,] 48638.86668 -13573.84363
[84,] -9081.20226 48638.86668
[85,] -34504.84760 -9081.20226
[86,] -37458.75951 -34504.84760
[87,] -3756.69940 -37458.75951
[88,] 3735.21677 -3756.69940
[89,] -150783.22737 3735.21677
[90,] 34130.80393 -150783.22737
[91,] 32966.23611 34130.80393
[92,] 22516.23567 32966.23611
[93,] -2180.97262 22516.23567
[94,] 32131.93868 -2180.97262
[95,] -12467.70427 32131.93868
[96,] 8457.79952 -12467.70427
[97,] 16983.60699 8457.79952
[98,] -33033.26602 16983.60699
[99,] 22517.03075 -33033.26602
[100,] -27856.91095 22517.03075
[101,] 27710.11703 -27856.91095
[102,] -30619.06282 27710.11703
[103,] -27704.37530 -30619.06282
[104,] -13298.02848 -27704.37530
[105,] -68832.37300 -13298.02848
[106,] -47052.54669 -68832.37300
[107,] 13118.77882 -47052.54669
[108,] -20816.99138 13118.77882
[109,] -103691.92157 -20816.99138
[110,] 89544.99718 -103691.92157
[111,] 55839.16206 89544.99718
[112,] 167.22088 55839.16206
[113,] -55857.11381 167.22088
[114,] 13030.06914 -55857.11381
[115,] -7769.15634 13030.06914
[116,] 97081.28744 -7769.15634
[117,] -2275.30312 97081.28744
[118,] 44057.50476 -2275.30312
[119,] -16060.44766 44057.50476
[120,] -8433.29443 -16060.44766
[121,] -1614.83659 -8433.29443
[122,] 4450.99078 -1614.83659
[123,] -31945.75457 4450.99078
[124,] -18934.15584 -31945.75457
[125,] -3645.42372 -18934.15584
[126,] 34927.67934 -3645.42372
[127,] -11471.66908 34927.67934
[128,] 17120.16991 -11471.66908
[129,] 50717.23032 17120.16991
[130,] 22746.39866 50717.23032
[131,] 13684.13315 22746.39866
[132,] -33433.07647 13684.13315
[133,] -37106.67444 -33433.07647
[134,] 17687.60393 -37106.67444
[135,] -14401.77515 17687.60393
[136,] -46122.00149 -14401.77515
[137,] -50176.66130 -46122.00149
[138,] 35586.08066 -50176.66130
[139,] 25780.76941 35586.08066
[140,] 9431.08310 25780.76941
[141,] 15529.69937 9431.08310
[142,] -16223.82754 15529.69937
[143,] 8104.06591 -16223.82754
[144,] 12944.37601 8104.06591
[145,] -63278.54984 12944.37601
[146,] -37314.08078 -63278.54984
[147,] 14798.41950 -37314.08078
[148,] 10965.72221 14798.41950
[149,] 563.16269 10965.72221
[150,] 5147.45283 563.16269
[151,] 5224.21319 5147.45283
[152,] 6010.72629 5224.21319
[153,] 5384.61187 6010.72629
[154,] 24489.39140 5384.61187
[155,] 50696.76373 24489.39140
[156,] 5384.61187 50696.76373
[157,] 4686.33096 5384.61187
[158,] -765.53263 4686.33096
[159,] 587.04909 -765.53263
[160,] 5327.18159 587.04909
[161,] -1684.27100 5327.18159
[162,] 5161.55941 -1684.27100
[163,] 16441.41551 5161.55941
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 15630.99277 63790.23032
2 20392.88891 15630.99277
3 -95912.59327 20392.88891
4 43097.48591 -95912.59327
5 -26531.04689 43097.48591
6 19575.16803 -26531.04689
7 -13205.48015 19575.16803
8 -21198.32941 -13205.48015
9 24902.54079 -21198.32941
10 50598.68845 24902.54079
11 -10320.65897 50598.68845
12 -12545.33631 -10320.65897
13 53100.87820 -12545.33631
14 37336.49068 53100.87820
15 -84600.75050 37336.49068
16 67962.49262 -84600.75050
17 13413.53189 67962.49262
18 -10306.04118 13413.53189
19 17632.46541 -10306.04118
20 -18021.91386 17632.46541
21 140095.54694 -18021.91386
22 35506.50580 140095.54694
23 -33940.93144 35506.50580
24 -111733.98533 -33940.93144
25 -70826.17003 -111733.98533
26 -27953.83438 -70826.17003
27 41055.64332 -27953.83438
28 -4297.62724 41055.64332
29 59901.11199 -4297.62724
30 -23668.90545 59901.11199
31 8387.17493 -23668.90545
32 47292.03378 8387.17493
33 -15873.64987 47292.03378
34 39720.73945 -15873.64987
35 9166.65236 39720.73945
36 61820.51033 9166.65236
37 18533.07110 61820.51033
38 19834.95239 18533.07110
39 -16586.63163 19834.95239
40 -12547.18800 -16586.63163
41 -18508.08171 -12547.18800
42 -10997.63888 -18508.08171
43 -13003.91328 -10997.63888
44 -67540.52692 -13003.91328
45 3561.38032 -67540.52692
46 -23970.93693 3561.38032
47 -33098.12377 -23970.93693
48 -14339.12961 -33098.12377
49 -73257.07169 -14339.12961
50 -73.44589 -73257.07169
51 -19170.05084 -73.44589
52 73933.55372 -19170.05084
53 -5946.85604 73933.55372
54 -8807.74470 -5946.85604
55 -23354.42815 -8807.74470
56 24886.82078 -23354.42815
57 -24730.15057 24886.82078
58 33032.15350 -24730.15057
59 17093.97616 33032.15350
60 7754.78516 17093.97616
61 -32535.00498 7754.78516
62 -24419.16049 -32535.00498
63 -9982.93627 -24419.16049
64 14656.55378 -9982.93627
65 34560.97120 14656.55378
66 -22748.37496 34560.97120
67 -44501.25763 -22748.37496
68 -13418.84398 -44501.25763
69 -16025.09897 -13418.84398
70 -8838.55663 -16025.09897
71 -23298.70107 -8838.55663
72 6848.09252 -23298.70107
73 -22895.72494 6848.09252
74 -16059.58469 -22895.72494
75 85843.57259 -16059.58469
76 48434.13399 85843.57259
77 6768.40605 48434.13399
78 -15276.74864 6768.40605
79 -16411.26075 -15276.74864
80 -25918.28191 -16411.26075
81 76097.08630 -25918.28191
82 -13573.84363 76097.08630
83 48638.86668 -13573.84363
84 -9081.20226 48638.86668
85 -34504.84760 -9081.20226
86 -37458.75951 -34504.84760
87 -3756.69940 -37458.75951
88 3735.21677 -3756.69940
89 -150783.22737 3735.21677
90 34130.80393 -150783.22737
91 32966.23611 34130.80393
92 22516.23567 32966.23611
93 -2180.97262 22516.23567
94 32131.93868 -2180.97262
95 -12467.70427 32131.93868
96 8457.79952 -12467.70427
97 16983.60699 8457.79952
98 -33033.26602 16983.60699
99 22517.03075 -33033.26602
100 -27856.91095 22517.03075
101 27710.11703 -27856.91095
102 -30619.06282 27710.11703
103 -27704.37530 -30619.06282
104 -13298.02848 -27704.37530
105 -68832.37300 -13298.02848
106 -47052.54669 -68832.37300
107 13118.77882 -47052.54669
108 -20816.99138 13118.77882
109 -103691.92157 -20816.99138
110 89544.99718 -103691.92157
111 55839.16206 89544.99718
112 167.22088 55839.16206
113 -55857.11381 167.22088
114 13030.06914 -55857.11381
115 -7769.15634 13030.06914
116 97081.28744 -7769.15634
117 -2275.30312 97081.28744
118 44057.50476 -2275.30312
119 -16060.44766 44057.50476
120 -8433.29443 -16060.44766
121 -1614.83659 -8433.29443
122 4450.99078 -1614.83659
123 -31945.75457 4450.99078
124 -18934.15584 -31945.75457
125 -3645.42372 -18934.15584
126 34927.67934 -3645.42372
127 -11471.66908 34927.67934
128 17120.16991 -11471.66908
129 50717.23032 17120.16991
130 22746.39866 50717.23032
131 13684.13315 22746.39866
132 -33433.07647 13684.13315
133 -37106.67444 -33433.07647
134 17687.60393 -37106.67444
135 -14401.77515 17687.60393
136 -46122.00149 -14401.77515
137 -50176.66130 -46122.00149
138 35586.08066 -50176.66130
139 25780.76941 35586.08066
140 9431.08310 25780.76941
141 15529.69937 9431.08310
142 -16223.82754 15529.69937
143 8104.06591 -16223.82754
144 12944.37601 8104.06591
145 -63278.54984 12944.37601
146 -37314.08078 -63278.54984
147 14798.41950 -37314.08078
148 10965.72221 14798.41950
149 563.16269 10965.72221
150 5147.45283 563.16269
151 5224.21319 5147.45283
152 6010.72629 5224.21319
153 5384.61187 6010.72629
154 24489.39140 5384.61187
155 50696.76373 24489.39140
156 5384.61187 50696.76373
157 4686.33096 5384.61187
158 -765.53263 4686.33096
159 587.04909 -765.53263
160 5327.18159 587.04909
161 -1684.27100 5327.18159
162 5161.55941 -1684.27100
163 16441.41551 5161.55941
> 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/7ddh81324305581.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8wwge1324305581.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9fg731324305581.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/105jtg1324305581.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/110gdu1324305582.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/12y87m1324305582.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/138d9z1324305582.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/145pri1324305582.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/1528qz1324305582.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/1657ek1324305582.tab")
+ }
>
> try(system("convert tmp/1hizs1324305581.ps tmp/1hizs1324305581.png",intern=TRUE))
character(0)
> try(system("convert tmp/2oocp1324305581.ps tmp/2oocp1324305581.png",intern=TRUE))
character(0)
> try(system("convert tmp/3bhnw1324305581.ps tmp/3bhnw1324305581.png",intern=TRUE))
character(0)
> try(system("convert tmp/4uhvc1324305581.ps tmp/4uhvc1324305581.png",intern=TRUE))
character(0)
> try(system("convert tmp/5nz6n1324305581.ps tmp/5nz6n1324305581.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rrfs1324305581.ps tmp/6rrfs1324305581.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ddh81324305581.ps tmp/7ddh81324305581.png",intern=TRUE))
character(0)
> try(system("convert tmp/8wwge1324305581.ps tmp/8wwge1324305581.png",intern=TRUE))
character(0)
> try(system("convert tmp/9fg731324305581.ps tmp/9fg731324305581.png",intern=TRUE))
character(0)
> try(system("convert tmp/105jtg1324305581.ps tmp/105jtg1324305581.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.326 0.645 6.000