R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1418
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+ ,dim=c(7
+ ,197)
+ ,dimnames=list(c('pageviews'
+ ,'time_in_rfc'
+ ,'logins'
+ ,'compendium_views_info'
+ ,'shared_compendiums'
+ ,'feedback_messages_p1'
+ ,'totsize')
+ ,1:197))
> y <- array(NA,dim=c(7,197),dimnames=list(c('pageviews','time_in_rfc','logins','compendium_views_info','shared_compendiums','feedback_messages_p1','totsize'),1:197))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
time_in_rfc pageviews logins compendium_views_info shared_compendiums
1 210907 1418 56 396 3
2 120982 869 56 297 4
3 176508 1530 54 559 12
4 179321 2172 89 967 2
5 123185 901 40 270 1
6 52746 463 25 143 3
7 385534 3201 92 1562 0
8 33170 371 18 109 0
9 101645 1192 63 371 0
10 149061 1583 44 656 5
11 165446 1439 33 511 0
12 237213 1764 84 655 0
13 173326 1495 88 465 7
14 133131 1373 55 525 7
15 258873 2187 60 885 3
16 180083 1491 66 497 9
17 324799 4041 154 1436 0
18 230964 1706 53 612 4
19 236785 2152 119 865 3
20 135473 1036 41 385 0
21 202925 1882 61 567 7
22 215147 1929 58 639 0
23 344297 2242 75 963 1
24 153935 1220 33 398 5
25 132943 1289 40 410 7
26 174724 2515 92 966 0
27 174415 2147 100 801 0
28 225548 2352 112 892 5
29 223632 1638 73 513 0
30 124817 1222 40 469 0
31 221698 1812 45 683 0
32 210767 1677 60 643 3
33 170266 1579 62 535 4
34 260561 1731 75 625 1
35 84853 807 31 264 4
36 294424 2452 77 992 2
37 101011 829 34 238 0
38 215641 1940 46 818 0
39 325107 2662 99 937 0
40 7176 186 17 70 0
41 167542 1499 66 507 2
42 106408 865 30 260 1
43 96560 1793 76 503 0
44 265769 2527 146 927 2
45 269651 2747 67 1269 10
46 149112 1324 56 537 6
47 175824 2702 107 910 0
48 152871 1383 58 532 5
49 111665 1179 34 345 4
50 116408 2099 61 918 1
51 362301 4308 119 1635 2
52 78800 918 42 330 2
53 183167 1831 66 557 0
54 277965 3373 89 1178 8
55 150629 1713 44 740 3
56 168809 1438 66 452 0
57 24188 496 24 218 0
58 329267 2253 259 764 8
59 65029 744 17 255 5
60 101097 1161 64 454 3
61 218946 2352 41 866 1
62 244052 2144 68 574 5
63 341570 4691 168 1276 1
64 103597 1112 43 379 1
65 233328 2694 132 825 5
66 256462 1973 105 798 0
67 206161 1769 71 663 12
68 311473 3148 112 1069 8
69 235800 2474 94 921 8
70 177939 2084 82 858 8
71 207176 1954 70 711 8
72 196553 1226 57 503 2
73 174184 1389 53 382 0
74 143246 1496 103 464 5
75 187559 2269 121 717 8
76 187681 1833 62 690 2
77 119016 1268 52 462 5
78 182192 1943 52 657 12
79 73566 893 32 385 6
80 194979 1762 62 577 7
81 167488 1403 45 619 2
82 143756 1425 46 479 0
83 275541 1857 63 817 4
84 243199 1840 75 752 3
85 182999 1502 88 430 6
86 135649 1441 46 451 2
87 152299 1420 53 537 0
88 120221 1416 37 519 1
89 346485 2970 90 1000 0
90 145790 1317 63 637 5
91 193339 1644 78 465 2
92 80953 870 25 437 0
93 122774 1654 45 711 0
94 130585 1054 46 299 5
95 112611 937 41 248 0
96 286468 3004 144 1162 1
97 241066 2008 82 714 0
98 148446 2547 91 905 1
99 204713 1885 71 649 1
100 182079 1626 63 512 2
101 140344 1468 53 472 6
102 220516 2445 62 905 1
103 243060 1964 63 786 4
104 162765 1381 32 489 2
105 182613 1369 39 479 3
106 232138 1659 62 617 0
107 265318 2888 117 925 10
108 85574 1290 34 351 0
109 310839 2845 92 1144 9
110 225060 1982 93 669 7
111 232317 1904 54 707 0
112 144966 1391 144 458 0
113 43287 602 14 214 4
114 155754 1743 61 599 4
115 164709 1559 109 572 0
116 201940 2014 38 897 0
117 235454 2143 73 819 0
118 220801 2146 75 720 1
119 99466 874 50 273 0
120 92661 1590 61 508 1
121 133328 1590 55 506 0
122 61361 1210 77 451 0
123 125930 2072 75 699 4
124 100750 1281 72 407 0
125 224549 1401 50 465 4
126 82316 834 32 245 4
127 102010 1105 53 370 3
128 101523 1272 42 316 0
129 243511 1944 71 603 0
130 22938 391 10 154 0
131 41566 761 35 229 5
132 152474 1605 65 577 0
133 61857 530 25 192 4
134 99923 1988 66 617 0
135 132487 1386 41 411 0
136 317394 2395 86 975 1
137 21054 387 16 146 0
138 209641 1742 42 705 5
139 22648 620 19 184 0
140 31414 449 19 200 0
141 46698 800 45 274 0
142 131698 1684 65 502 0
143 91735 1050 35 382 0
144 244749 2699 95 964 2
145 184510 1606 49 537 7
146 79863 1502 37 438 1
147 128423 1204 64 369 8
148 97839 1138 38 417 2
149 38214 568 34 276 0
150 151101 1459 32 514 2
151 272458 2158 65 822 0
152 172494 1111 52 389 0
153 108043 1421 62 466 1
154 328107 2833 65 1255 3
155 250579 1955 83 694 0
156 351067 2922 95 1024 3
157 158015 1002 29 400 0
158 98866 1060 18 397 0
159 85439 956 33 350 0
160 229242 2186 247 719 4
161 351619 3604 139 1277 4
162 84207 1035 29 356 11
163 120445 1417 118 457 0
164 324598 3261 110 1402 0
165 131069 1587 67 600 4
166 204271 1424 42 480 0
167 165543 1701 65 595 1
168 141722 1249 94 436 0
169 116048 946 64 230 0
170 250047 1926 81 651 0
171 299775 3352 95 1367 9
172 195838 1641 67 564 1
173 173260 2035 63 716 3
174 254488 2312 83 747 10
175 104389 1369 45 467 5
176 136084 1577 30 671 0
177 199476 2201 70 861 2
178 92499 961 32 319 0
179 224330 1900 83 612 1
180 135781 1254 31 433 2
181 74408 1335 67 434 4
182 81240 1597 66 503 0
183 14688 207 10 85 0
184 181633 1645 70 564 2
185 271856 2429 103 824 1
186 7199 151 5 74 0
187 46660 474 20 259 0
188 17547 141 5 69 0
189 133368 1639 36 535 1
190 95227 872 34 239 0
191 152601 1318 48 438 2
192 98146 1018 40 459 0
193 79619 1383 43 426 3
194 59194 1314 31 288 6
195 139942 1335 42 498 0
196 118612 1403 46 454 2
197 72880 910 33 376 0
feedback_messages_p1 totsize
1 115 112285
2 109 84786
3 146 83123
4 116 101193
5 68 38361
6 101 68504
7 96 119182
8 67 22807
9 44 17140
10 100 116174
11 93 57635
12 140 66198
13 166 71701
14 99 57793
15 139 80444
16 130 53855
17 181 97668
18 116 133824
19 116 101481
20 88 99645
21 139 114789
22 135 99052
23 108 67654
24 89 65553
25 156 97500
26 129 69112
27 118 82753
28 118 85323
29 125 72654
30 95 30727
31 126 77873
32 135 117478
33 154 74007
34 165 90183
35 113 61542
36 127 101494
37 52 27570
38 121 55813
39 136 79215
40 0 1423
41 108 55461
42 46 31081
43 54 22996
44 124 83122
45 115 70106
46 128 60578
47 80 39992
48 97 79892
49 104 49810
50 59 71570
51 125 100708
52 82 33032
53 149 82875
54 149 139077
55 122 71595
56 118 72260
57 12 5950
58 144 115762
59 67 32551
60 52 31701
61 108 80670
62 166 143558
63 80 117105
64 60 23789
65 107 120733
66 127 105195
67 107 73107
68 146 132068
69 84 149193
70 141 46821
71 123 87011
72 111 95260
73 98 55183
74 105 106671
75 135 73511
76 107 92945
77 85 78664
78 155 70054
79 88 22618
80 155 74011
81 104 83737
82 132 69094
83 127 93133
84 108 95536
85 129 225920
86 116 62133
87 122 61370
88 85 43836
89 147 106117
90 99 38692
91 87 84651
92 28 56622
93 90 15986
94 109 95364
95 78 26706
96 111 89691
97 158 67267
98 141 126846
99 122 41140
100 124 102860
101 93 51715
102 124 55801
103 112 111813
104 108 120293
105 99 138599
106 117 161647
107 199 115929
108 78 24266
109 91 162901
110 158 109825
111 126 129838
112 122 37510
113 71 43750
114 75 40652
115 115 87771
116 119 85872
117 124 89275
118 72 44418
119 91 192565
120 45 35232
121 78 40909
122 39 13294
123 68 32387
124 119 140867
125 117 120662
126 39 21233
127 50 44332
128 88 61056
129 155 101338
130 0 1168
131 36 13497
132 123 65567
133 32 25162
134 99 32334
135 136 40735
136 117 91413
137 0 855
138 88 97068
139 39 44339
140 25 14116
141 52 10288
142 75 65622
143 71 16563
144 124 76643
145 151 110681
146 71 29011
147 145 92696
148 87 94785
149 27 8773
150 131 83209
151 162 93815
152 165 86687
153 54 34553
154 159 105547
155 147 103487
156 170 213688
157 119 71220
158 49 23517
159 104 56926
160 120 91721
161 150 115168
162 112 111194
163 59 51009
164 136 135777
165 107 51513
166 130 74163
167 115 51633
168 107 75345
169 75 33416
170 71 83305
171 120 98952
172 116 102372
173 79 37238
174 150 103772
175 156 123969
176 51 27142
177 118 135400
178 71 21399
179 144 130115
180 47 24874
181 28 34988
182 68 45549
183 0 6023
184 110 64466
185 147 54990
186 0 1644
187 15 6179
188 4 3926
189 64 32755
190 111 34777
191 85 73224
192 68 27114
193 40 20760
194 80 37636
195 88 65461
196 48 30080
197 76 24094
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) pageviews logins
-17515.44 15.19 198.65
compendium_views_info shared_compendiums feedback_messages_p1
133.24 -1638.69 473.37
totsize
0.35
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-120892 -17043 -1026 18379 111380
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.752e+04 6.481e+03 -2.703 0.0075 **
pageviews 1.519e+01 1.177e+01 1.290 0.1985
logins 1.986e+02 9.369e+01 2.120 0.0353 *
compendium_views_info 1.332e+02 2.686e+01 4.961 1.55e-06 ***
shared_compendiums -1.639e+03 8.077e+02 -2.029 0.0439 *
feedback_messages_p1 4.734e+02 8.230e+01 5.752 3.48e-08 ***
totsize 3.500e-01 7.790e-02 4.493 1.22e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 31770 on 190 degrees of freedom
Multiple R-squared: 0.8559, Adjusted R-squared: 0.8513
F-statistic: 188 on 6 and 190 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,] 0.9357928 0.128414493 0.064207246
[2,] 0.9138772 0.172245571 0.086122786
[3,] 0.8674096 0.265180721 0.132590361
[4,] 0.8006263 0.398747497 0.199373748
[5,] 0.7131515 0.573696952 0.286848476
[6,] 0.6281729 0.743654174 0.371827087
[7,] 0.5638789 0.872242141 0.436121070
[8,] 0.9333212 0.133357621 0.066678811
[9,] 0.9184437 0.163112594 0.081556297
[10,] 0.8927169 0.214566122 0.107283061
[11,] 0.8544818 0.291036373 0.145518186
[12,] 0.8072939 0.385412263 0.192706131
[13,] 0.7513954 0.497209279 0.248604639
[14,] 0.9787732 0.042453627 0.021226813
[15,] 0.9714492 0.057101544 0.028550772
[16,] 0.9692751 0.061449898 0.030724949
[17,] 0.9942848 0.011430386 0.005715193
[18,] 0.9951762 0.009647686 0.004823843
[19,] 0.9926445 0.014710980 0.007355490
[20,] 0.9958461 0.008307795 0.004153898
[21,] 0.9941089 0.011782134 0.005891067
[22,] 0.9918972 0.016205681 0.008102840
[23,] 0.9881949 0.023610174 0.011805087
[24,] 0.9834031 0.033193877 0.016596938
[25,] 0.9896421 0.020715724 0.010357862
[26,] 0.9884558 0.023088402 0.011544201
[27,] 0.9871494 0.025701238 0.012850619
[28,] 0.9841179 0.031764117 0.015882059
[29,] 0.9786053 0.042789417 0.021394709
[30,] 0.9900051 0.019989743 0.009994872
[31,] 0.9866115 0.026777032 0.013388516
[32,] 0.9821010 0.035797922 0.017898961
[33,] 0.9795373 0.040925382 0.020462691
[34,] 0.9820951 0.035809888 0.017904944
[35,] 0.9791734 0.041653183 0.020826591
[36,] 0.9724646 0.055070760 0.027535380
[37,] 0.9641977 0.071604644 0.035802322
[38,] 0.9698532 0.060293597 0.030146798
[39,] 0.9606575 0.078685050 0.039342525
[40,] 0.9504132 0.099173694 0.049586847
[41,] 0.9908375 0.018324904 0.009162452
[42,] 0.9883489 0.023302240 0.011651120
[43,] 0.9859704 0.028059114 0.014029557
[44,] 0.9833434 0.033313243 0.016656622
[45,] 0.9830345 0.033931048 0.016965524
[46,] 0.9877850 0.024430080 0.012215040
[47,] 0.9839294 0.032141230 0.016070615
[48,] 0.9793236 0.041352813 0.020676407
[49,] 0.9897216 0.020556721 0.010278361
[50,] 0.9861995 0.027600955 0.013800478
[51,] 0.9817996 0.036400856 0.018200428
[52,] 0.9766841 0.046631842 0.023315921
[53,] 0.9717089 0.056582223 0.028291112
[54,] 0.9655300 0.068940059 0.034470030
[55,] 0.9570950 0.085809923 0.042904962
[56,] 0.9489657 0.102068674 0.051034337
[57,] 0.9397198 0.120560411 0.060280205
[58,] 0.9469048 0.106190404 0.053095202
[59,] 0.9361107 0.127778591 0.063889296
[60,] 0.9226355 0.154728989 0.077364494
[61,] 0.9226692 0.154661594 0.077330797
[62,] 0.9078820 0.184235916 0.092117958
[63,] 0.9070591 0.185881733 0.092940866
[64,] 0.9179298 0.164140418 0.082070209
[65,] 0.9175607 0.164878631 0.082439315
[66,] 0.9102865 0.179426907 0.089713453
[67,] 0.8934877 0.213024678 0.106512339
[68,] 0.8782107 0.243578547 0.121789274
[69,] 0.8556068 0.288786383 0.144393192
[70,] 0.8374570 0.325086014 0.162543007
[71,] 0.8127725 0.374455039 0.187227519
[72,] 0.7838376 0.432324773 0.216162387
[73,] 0.7678214 0.464357155 0.232178577
[74,] 0.8322753 0.335449406 0.167724703
[75,] 0.8408237 0.318352511 0.159176255
[76,] 0.8516721 0.296655807 0.148327903
[77,] 0.8299184 0.340163114 0.170081557
[78,] 0.8081284 0.383743163 0.191871581
[79,] 0.7844826 0.431034711 0.215517356
[80,] 0.8491152 0.301769601 0.150884800
[81,] 0.8238139 0.352372286 0.176186143
[82,] 0.8390531 0.321893749 0.160946875
[83,] 0.8161234 0.367753238 0.183876619
[84,] 0.8267862 0.346427675 0.173213838
[85,] 0.8019450 0.396110013 0.198055006
[86,] 0.7970044 0.405991134 0.202995567
[87,] 0.7687407 0.462518662 0.231259331
[88,] 0.7472732 0.505453655 0.252726828
[89,] 0.9834165 0.033167047 0.016583523
[90,] 0.9812132 0.037573693 0.018786847
[91,] 0.9761033 0.047793438 0.023896719
[92,] 0.9706160 0.058768008 0.029384004
[93,] 0.9640938 0.071812498 0.035906249
[94,] 0.9621587 0.075682637 0.037841318
[95,] 0.9527116 0.094576738 0.047288369
[96,] 0.9458487 0.108302631 0.054151316
[97,] 0.9386897 0.122620699 0.061310350
[98,] 0.9324384 0.135123261 0.067561630
[99,] 0.9216521 0.156695876 0.078347938
[100,] 0.9196525 0.160695098 0.080347549
[101,] 0.9044535 0.191093007 0.095546503
[102,] 0.8883405 0.223318924 0.111659462
[103,] 0.8748713 0.250257497 0.125128749
[104,] 0.8608248 0.278350359 0.139175180
[105,] 0.8390563 0.321887454 0.160943727
[106,] 0.8261751 0.347649887 0.173824943
[107,] 0.8202358 0.359528481 0.179764240
[108,] 0.7914313 0.417137489 0.208568744
[109,] 0.8215010 0.356998092 0.178499046
[110,] 0.8556523 0.288695475 0.144347737
[111,] 0.8482028 0.303594457 0.151797229
[112,] 0.8214386 0.357122799 0.178561399
[113,] 0.8343422 0.331315681 0.165657840
[114,] 0.8392080 0.321584012 0.160792006
[115,] 0.9326892 0.134621651 0.067310826
[116,] 0.9640420 0.071915957 0.035957979
[117,] 0.9651036 0.069792883 0.034896442
[118,] 0.9568524 0.086295242 0.043147621
[119,] 0.9476616 0.104676775 0.052338387
[120,] 0.9465557 0.106888535 0.053444268
[121,] 0.9346641 0.130671779 0.065335889
[122,] 0.9194142 0.161171559 0.080585780
[123,] 0.9129790 0.174042011 0.087021006
[124,] 0.9119237 0.176152586 0.088076293
[125,] 0.9693660 0.061268096 0.030634048
[126,] 0.9628410 0.074317900 0.037158950
[127,] 0.9851235 0.029752939 0.014876469
[128,] 0.9809844 0.038031110 0.019015555
[129,] 0.9854521 0.029095753 0.014547877
[130,] 0.9851933 0.029613324 0.014806662
[131,] 0.9799866 0.040026786 0.020013393
[132,] 0.9769265 0.046147095 0.023073547
[133,] 0.9740736 0.051852883 0.025926442
[134,] 0.9660538 0.067892356 0.033946178
[135,] 0.9593028 0.081394349 0.040697175
[136,] 0.9503361 0.099327849 0.049663924
[137,] 0.9663601 0.067279794 0.033639897
[138,] 0.9576742 0.084651511 0.042325756
[139,] 0.9564994 0.087001155 0.043500577
[140,] 0.9438646 0.112270732 0.056135366
[141,] 0.9321650 0.135670051 0.067835026
[142,] 0.9214753 0.157049376 0.078524688
[143,] 0.9016743 0.196651443 0.098325722
[144,] 0.8826500 0.234700039 0.117350019
[145,] 0.8797045 0.240590940 0.120295470
[146,] 0.8670322 0.265935641 0.132967821
[147,] 0.8533719 0.293256293 0.146628147
[148,] 0.8579103 0.284179413 0.142089707
[149,] 0.8257268 0.348546439 0.174273219
[150,] 0.8178767 0.364246617 0.182123309
[151,] 0.7782505 0.443499010 0.221749505
[152,] 0.7336631 0.532673730 0.266336865
[153,] 0.6982524 0.603495110 0.301747555
[154,] 0.6785975 0.642805013 0.321402506
[155,] 0.6544530 0.691093927 0.345546964
[156,] 0.6285725 0.742854958 0.371427479
[157,] 0.7058919 0.588216124 0.294108062
[158,] 0.6519591 0.696081882 0.348040941
[159,] 0.6358712 0.728257631 0.364128816
[160,] 0.5807505 0.838498994 0.419249497
[161,] 0.7699063 0.460187449 0.230093724
[162,] 0.7187252 0.562549693 0.281274847
[163,] 0.7035734 0.592853188 0.296426594
[164,] 0.6394176 0.721164886 0.360582443
[165,] 0.7831767 0.433646597 0.216823299
[166,] 0.8321646 0.335670747 0.167835373
[167,] 0.7751254 0.449749255 0.224874627
[168,] 0.8337054 0.332589170 0.166294585
[169,] 0.7730735 0.453852968 0.226926484
[170,] 0.7014730 0.597053976 0.298526988
[171,] 0.7492933 0.501413393 0.250706697
[172,] 0.6980507 0.603898674 0.301949337
[173,] 0.9989865 0.002027013 0.001013506
[174,] 0.9975048 0.004990427 0.002495214
[175,] 0.9921975 0.015605001 0.007802500
[176,] 0.9897086 0.020582899 0.010291450
[177,] 0.9739904 0.052019180 0.026009590
[178,] 0.9289442 0.142111606 0.071055803
> postscript(file="/var/wessaorg/rcomp/tmp/1pw151354461956.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/2n48c1354461956.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3zh041354461956.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4hjgf1354461956.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5b6at1354461956.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 197
Frequency = 1
1 2 3 4 5 6
54174.0081 -116.6359 7032.0955 -69732.7719 39115.4062 -27660.9217
7 8 9 10 11 12
40867.6607 -12747.4523 12277.0995 -33420.4543 22264.1285 34530.7937
13 14 15 16 17 18
-3511.0861 -6708.4090 24290.3398 29977.4748 -60864.5991 35298.9768
19 20 21 22 23 24
-2795.9530 1277.6051 9682.7973 8123.5568 111379.5806 36452.3186
25 26 27 28 29 30
-28196.2990 -78207.5251 -52096.9875 -11293.0842 48810.3856 -2392.2438
31 32 33 34 35 36
24844.3071 5109.4838 -12051.9144 45575.5826 -19699.4498 34856.5270
37 38 39 40 41 42
33202.5938 8743.7810 65566.2056 8663.8021 14363.8467 39166.1298
43 44 45 46 47 48
-28892.0202 7865.8128 455.4439 -8121.5704 -42080.6418 1286.4694
49 50 51 52 53 54
-1561.4163 -83735.3153 -18258.9688 -17043.0595 -13996.8076 -36496.4225
55 56 57 58 59 60
-43109.9514 9995.5958 -7408.7469 63739.5265 -1026.4271 -3024.2397
61 62 63 64 65 66
-519.6611 18379.4167 7215.0306 10089.3464 -10940.2535 19885.7600
67 68 69 70 71 72
37786.8308 14257.3022 -4524.7722 -36837.8452 10799.6427 34494.8017
73 74 75 76 77 78
43468.0324 -23092.8674 -25489.3846 -6804.7025 -14192.4881 -5905.3942
79 80 81 82 83 84
-19879.8654 8725.9929 -2985.1148 -20003.6433 57314.7658 38022.2537
85 86 87 88 89 90
-27376.7859 -11335.5934 -13066.2241 -14217.0660 61036.5562 -6303.2133
91 92 93 94 95 96
40895.3855 -11011.5503 -36710.0677 6332.3548 28433.5693 -7380.0456
97 98 99 100 101 102
18318.0724 -120892.2814 22503.1334 2738.9939 9848.8866 -8601.0750
103 104 105 106 107 108
27901.6921 -2157.2689 17307.4260 17967.7610 -25917.3992 -15445.7869
109 110 111 112 113 114
29090.2329 3094.6127 10893.8905 -19159.1994 -22003.7289 11685.2349
115 116 117 118 119 120
-24481.4971 -24591.2134 6845.1829 46892.5164 -53072.2830 -25776.5468
121 122 123 124 125 126
-2897.8857 -38007.7383 -33036.1645 -75357.8457 57832.8961 28822.5534
127 128 129 130 131 132
8643.0773 -13758.1363 28206.4497 11598.8797 -3516.3095 -25357.4422
133 134 135 136 137 138
23373.0069 -66264.1914 -12595.3351 65792.8434 9759.2350 30979.3582
139 140 141 142 143 144
-31525.2534 -5088.7989 -21603.3784 -14638.1791 -3958.1350 -8298.8817
145 146 147 148 149 150
-2400.7835 -33273.7943 -22202.5542 -36122.2211 -12279.5100 -16246.6128
151 152 153 154 155 156
25233.7879 2526.4128 -6452.2248 15165.1520 23633.6718 18538.9316
157 158 159 160 161 162
19994.6760 12379.7925 -33912.0446 -13668.2742 11864.2926 -41102.6557
163 164 165 166 167 168
-13679.0604 -27979.7737 -30903.6394 40360.6441 -5843.1713 -13522.7416
169 170 171 172 173 174
28635.1226 72708.0964 -11332.5899 10866.1289 6431.1907 29924.0766
175 176 177 178 179 180
-79094.2993 636.2680 -45038.8270 5455.6197 2885.7663 42717.5427
181 182 183 184 185 186
-18438.7308 -53767.7635 13638.8207 13749.2294 35026.2450 10992.0017
187 188 189 190 191 192
9229.0068 19466.0184 7427.4932 -3819.0415 19612.9633 -10586.0305
193 194 195 196 197
-10463.3732 -28994.1009 -2087.4933 15211.7685 -24491.8522
> postscript(file="/var/wessaorg/rcomp/tmp/66spx1354461956.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 = 197
Frequency = 1
lag(myerror, k = 1) myerror
0 54174.0081 NA
1 -116.6359 54174.0081
2 7032.0955 -116.6359
3 -69732.7719 7032.0955
4 39115.4062 -69732.7719
5 -27660.9217 39115.4062
6 40867.6607 -27660.9217
7 -12747.4523 40867.6607
8 12277.0995 -12747.4523
9 -33420.4543 12277.0995
10 22264.1285 -33420.4543
11 34530.7937 22264.1285
12 -3511.0861 34530.7937
13 -6708.4090 -3511.0861
14 24290.3398 -6708.4090
15 29977.4748 24290.3398
16 -60864.5991 29977.4748
17 35298.9768 -60864.5991
18 -2795.9530 35298.9768
19 1277.6051 -2795.9530
20 9682.7973 1277.6051
21 8123.5568 9682.7973
22 111379.5806 8123.5568
23 36452.3186 111379.5806
24 -28196.2990 36452.3186
25 -78207.5251 -28196.2990
26 -52096.9875 -78207.5251
27 -11293.0842 -52096.9875
28 48810.3856 -11293.0842
29 -2392.2438 48810.3856
30 24844.3071 -2392.2438
31 5109.4838 24844.3071
32 -12051.9144 5109.4838
33 45575.5826 -12051.9144
34 -19699.4498 45575.5826
35 34856.5270 -19699.4498
36 33202.5938 34856.5270
37 8743.7810 33202.5938
38 65566.2056 8743.7810
39 8663.8021 65566.2056
40 14363.8467 8663.8021
41 39166.1298 14363.8467
42 -28892.0202 39166.1298
43 7865.8128 -28892.0202
44 455.4439 7865.8128
45 -8121.5704 455.4439
46 -42080.6418 -8121.5704
47 1286.4694 -42080.6418
48 -1561.4163 1286.4694
49 -83735.3153 -1561.4163
50 -18258.9688 -83735.3153
51 -17043.0595 -18258.9688
52 -13996.8076 -17043.0595
53 -36496.4225 -13996.8076
54 -43109.9514 -36496.4225
55 9995.5958 -43109.9514
56 -7408.7469 9995.5958
57 63739.5265 -7408.7469
58 -1026.4271 63739.5265
59 -3024.2397 -1026.4271
60 -519.6611 -3024.2397
61 18379.4167 -519.6611
62 7215.0306 18379.4167
63 10089.3464 7215.0306
64 -10940.2535 10089.3464
65 19885.7600 -10940.2535
66 37786.8308 19885.7600
67 14257.3022 37786.8308
68 -4524.7722 14257.3022
69 -36837.8452 -4524.7722
70 10799.6427 -36837.8452
71 34494.8017 10799.6427
72 43468.0324 34494.8017
73 -23092.8674 43468.0324
74 -25489.3846 -23092.8674
75 -6804.7025 -25489.3846
76 -14192.4881 -6804.7025
77 -5905.3942 -14192.4881
78 -19879.8654 -5905.3942
79 8725.9929 -19879.8654
80 -2985.1148 8725.9929
81 -20003.6433 -2985.1148
82 57314.7658 -20003.6433
83 38022.2537 57314.7658
84 -27376.7859 38022.2537
85 -11335.5934 -27376.7859
86 -13066.2241 -11335.5934
87 -14217.0660 -13066.2241
88 61036.5562 -14217.0660
89 -6303.2133 61036.5562
90 40895.3855 -6303.2133
91 -11011.5503 40895.3855
92 -36710.0677 -11011.5503
93 6332.3548 -36710.0677
94 28433.5693 6332.3548
95 -7380.0456 28433.5693
96 18318.0724 -7380.0456
97 -120892.2814 18318.0724
98 22503.1334 -120892.2814
99 2738.9939 22503.1334
100 9848.8866 2738.9939
101 -8601.0750 9848.8866
102 27901.6921 -8601.0750
103 -2157.2689 27901.6921
104 17307.4260 -2157.2689
105 17967.7610 17307.4260
106 -25917.3992 17967.7610
107 -15445.7869 -25917.3992
108 29090.2329 -15445.7869
109 3094.6127 29090.2329
110 10893.8905 3094.6127
111 -19159.1994 10893.8905
112 -22003.7289 -19159.1994
113 11685.2349 -22003.7289
114 -24481.4971 11685.2349
115 -24591.2134 -24481.4971
116 6845.1829 -24591.2134
117 46892.5164 6845.1829
118 -53072.2830 46892.5164
119 -25776.5468 -53072.2830
120 -2897.8857 -25776.5468
121 -38007.7383 -2897.8857
122 -33036.1645 -38007.7383
123 -75357.8457 -33036.1645
124 57832.8961 -75357.8457
125 28822.5534 57832.8961
126 8643.0773 28822.5534
127 -13758.1363 8643.0773
128 28206.4497 -13758.1363
129 11598.8797 28206.4497
130 -3516.3095 11598.8797
131 -25357.4422 -3516.3095
132 23373.0069 -25357.4422
133 -66264.1914 23373.0069
134 -12595.3351 -66264.1914
135 65792.8434 -12595.3351
136 9759.2350 65792.8434
137 30979.3582 9759.2350
138 -31525.2534 30979.3582
139 -5088.7989 -31525.2534
140 -21603.3784 -5088.7989
141 -14638.1791 -21603.3784
142 -3958.1350 -14638.1791
143 -8298.8817 -3958.1350
144 -2400.7835 -8298.8817
145 -33273.7943 -2400.7835
146 -22202.5542 -33273.7943
147 -36122.2211 -22202.5542
148 -12279.5100 -36122.2211
149 -16246.6128 -12279.5100
150 25233.7879 -16246.6128
151 2526.4128 25233.7879
152 -6452.2248 2526.4128
153 15165.1520 -6452.2248
154 23633.6718 15165.1520
155 18538.9316 23633.6718
156 19994.6760 18538.9316
157 12379.7925 19994.6760
158 -33912.0446 12379.7925
159 -13668.2742 -33912.0446
160 11864.2926 -13668.2742
161 -41102.6557 11864.2926
162 -13679.0604 -41102.6557
163 -27979.7737 -13679.0604
164 -30903.6394 -27979.7737
165 40360.6441 -30903.6394
166 -5843.1713 40360.6441
167 -13522.7416 -5843.1713
168 28635.1226 -13522.7416
169 72708.0964 28635.1226
170 -11332.5899 72708.0964
171 10866.1289 -11332.5899
172 6431.1907 10866.1289
173 29924.0766 6431.1907
174 -79094.2993 29924.0766
175 636.2680 -79094.2993
176 -45038.8270 636.2680
177 5455.6197 -45038.8270
178 2885.7663 5455.6197
179 42717.5427 2885.7663
180 -18438.7308 42717.5427
181 -53767.7635 -18438.7308
182 13638.8207 -53767.7635
183 13749.2294 13638.8207
184 35026.2450 13749.2294
185 10992.0017 35026.2450
186 9229.0068 10992.0017
187 19466.0184 9229.0068
188 7427.4932 19466.0184
189 -3819.0415 7427.4932
190 19612.9633 -3819.0415
191 -10586.0305 19612.9633
192 -10463.3732 -10586.0305
193 -28994.1009 -10463.3732
194 -2087.4933 -28994.1009
195 15211.7685 -2087.4933
196 -24491.8522 15211.7685
197 NA -24491.8522
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -116.6359 54174.0081
[2,] 7032.0955 -116.6359
[3,] -69732.7719 7032.0955
[4,] 39115.4062 -69732.7719
[5,] -27660.9217 39115.4062
[6,] 40867.6607 -27660.9217
[7,] -12747.4523 40867.6607
[8,] 12277.0995 -12747.4523
[9,] -33420.4543 12277.0995
[10,] 22264.1285 -33420.4543
[11,] 34530.7937 22264.1285
[12,] -3511.0861 34530.7937
[13,] -6708.4090 -3511.0861
[14,] 24290.3398 -6708.4090
[15,] 29977.4748 24290.3398
[16,] -60864.5991 29977.4748
[17,] 35298.9768 -60864.5991
[18,] -2795.9530 35298.9768
[19,] 1277.6051 -2795.9530
[20,] 9682.7973 1277.6051
[21,] 8123.5568 9682.7973
[22,] 111379.5806 8123.5568
[23,] 36452.3186 111379.5806
[24,] -28196.2990 36452.3186
[25,] -78207.5251 -28196.2990
[26,] -52096.9875 -78207.5251
[27,] -11293.0842 -52096.9875
[28,] 48810.3856 -11293.0842
[29,] -2392.2438 48810.3856
[30,] 24844.3071 -2392.2438
[31,] 5109.4838 24844.3071
[32,] -12051.9144 5109.4838
[33,] 45575.5826 -12051.9144
[34,] -19699.4498 45575.5826
[35,] 34856.5270 -19699.4498
[36,] 33202.5938 34856.5270
[37,] 8743.7810 33202.5938
[38,] 65566.2056 8743.7810
[39,] 8663.8021 65566.2056
[40,] 14363.8467 8663.8021
[41,] 39166.1298 14363.8467
[42,] -28892.0202 39166.1298
[43,] 7865.8128 -28892.0202
[44,] 455.4439 7865.8128
[45,] -8121.5704 455.4439
[46,] -42080.6418 -8121.5704
[47,] 1286.4694 -42080.6418
[48,] -1561.4163 1286.4694
[49,] -83735.3153 -1561.4163
[50,] -18258.9688 -83735.3153
[51,] -17043.0595 -18258.9688
[52,] -13996.8076 -17043.0595
[53,] -36496.4225 -13996.8076
[54,] -43109.9514 -36496.4225
[55,] 9995.5958 -43109.9514
[56,] -7408.7469 9995.5958
[57,] 63739.5265 -7408.7469
[58,] -1026.4271 63739.5265
[59,] -3024.2397 -1026.4271
[60,] -519.6611 -3024.2397
[61,] 18379.4167 -519.6611
[62,] 7215.0306 18379.4167
[63,] 10089.3464 7215.0306
[64,] -10940.2535 10089.3464
[65,] 19885.7600 -10940.2535
[66,] 37786.8308 19885.7600
[67,] 14257.3022 37786.8308
[68,] -4524.7722 14257.3022
[69,] -36837.8452 -4524.7722
[70,] 10799.6427 -36837.8452
[71,] 34494.8017 10799.6427
[72,] 43468.0324 34494.8017
[73,] -23092.8674 43468.0324
[74,] -25489.3846 -23092.8674
[75,] -6804.7025 -25489.3846
[76,] -14192.4881 -6804.7025
[77,] -5905.3942 -14192.4881
[78,] -19879.8654 -5905.3942
[79,] 8725.9929 -19879.8654
[80,] -2985.1148 8725.9929
[81,] -20003.6433 -2985.1148
[82,] 57314.7658 -20003.6433
[83,] 38022.2537 57314.7658
[84,] -27376.7859 38022.2537
[85,] -11335.5934 -27376.7859
[86,] -13066.2241 -11335.5934
[87,] -14217.0660 -13066.2241
[88,] 61036.5562 -14217.0660
[89,] -6303.2133 61036.5562
[90,] 40895.3855 -6303.2133
[91,] -11011.5503 40895.3855
[92,] -36710.0677 -11011.5503
[93,] 6332.3548 -36710.0677
[94,] 28433.5693 6332.3548
[95,] -7380.0456 28433.5693
[96,] 18318.0724 -7380.0456
[97,] -120892.2814 18318.0724
[98,] 22503.1334 -120892.2814
[99,] 2738.9939 22503.1334
[100,] 9848.8866 2738.9939
[101,] -8601.0750 9848.8866
[102,] 27901.6921 -8601.0750
[103,] -2157.2689 27901.6921
[104,] 17307.4260 -2157.2689
[105,] 17967.7610 17307.4260
[106,] -25917.3992 17967.7610
[107,] -15445.7869 -25917.3992
[108,] 29090.2329 -15445.7869
[109,] 3094.6127 29090.2329
[110,] 10893.8905 3094.6127
[111,] -19159.1994 10893.8905
[112,] -22003.7289 -19159.1994
[113,] 11685.2349 -22003.7289
[114,] -24481.4971 11685.2349
[115,] -24591.2134 -24481.4971
[116,] 6845.1829 -24591.2134
[117,] 46892.5164 6845.1829
[118,] -53072.2830 46892.5164
[119,] -25776.5468 -53072.2830
[120,] -2897.8857 -25776.5468
[121,] -38007.7383 -2897.8857
[122,] -33036.1645 -38007.7383
[123,] -75357.8457 -33036.1645
[124,] 57832.8961 -75357.8457
[125,] 28822.5534 57832.8961
[126,] 8643.0773 28822.5534
[127,] -13758.1363 8643.0773
[128,] 28206.4497 -13758.1363
[129,] 11598.8797 28206.4497
[130,] -3516.3095 11598.8797
[131,] -25357.4422 -3516.3095
[132,] 23373.0069 -25357.4422
[133,] -66264.1914 23373.0069
[134,] -12595.3351 -66264.1914
[135,] 65792.8434 -12595.3351
[136,] 9759.2350 65792.8434
[137,] 30979.3582 9759.2350
[138,] -31525.2534 30979.3582
[139,] -5088.7989 -31525.2534
[140,] -21603.3784 -5088.7989
[141,] -14638.1791 -21603.3784
[142,] -3958.1350 -14638.1791
[143,] -8298.8817 -3958.1350
[144,] -2400.7835 -8298.8817
[145,] -33273.7943 -2400.7835
[146,] -22202.5542 -33273.7943
[147,] -36122.2211 -22202.5542
[148,] -12279.5100 -36122.2211
[149,] -16246.6128 -12279.5100
[150,] 25233.7879 -16246.6128
[151,] 2526.4128 25233.7879
[152,] -6452.2248 2526.4128
[153,] 15165.1520 -6452.2248
[154,] 23633.6718 15165.1520
[155,] 18538.9316 23633.6718
[156,] 19994.6760 18538.9316
[157,] 12379.7925 19994.6760
[158,] -33912.0446 12379.7925
[159,] -13668.2742 -33912.0446
[160,] 11864.2926 -13668.2742
[161,] -41102.6557 11864.2926
[162,] -13679.0604 -41102.6557
[163,] -27979.7737 -13679.0604
[164,] -30903.6394 -27979.7737
[165,] 40360.6441 -30903.6394
[166,] -5843.1713 40360.6441
[167,] -13522.7416 -5843.1713
[168,] 28635.1226 -13522.7416
[169,] 72708.0964 28635.1226
[170,] -11332.5899 72708.0964
[171,] 10866.1289 -11332.5899
[172,] 6431.1907 10866.1289
[173,] 29924.0766 6431.1907
[174,] -79094.2993 29924.0766
[175,] 636.2680 -79094.2993
[176,] -45038.8270 636.2680
[177,] 5455.6197 -45038.8270
[178,] 2885.7663 5455.6197
[179,] 42717.5427 2885.7663
[180,] -18438.7308 42717.5427
[181,] -53767.7635 -18438.7308
[182,] 13638.8207 -53767.7635
[183,] 13749.2294 13638.8207
[184,] 35026.2450 13749.2294
[185,] 10992.0017 35026.2450
[186,] 9229.0068 10992.0017
[187,] 19466.0184 9229.0068
[188,] 7427.4932 19466.0184
[189,] -3819.0415 7427.4932
[190,] 19612.9633 -3819.0415
[191,] -10586.0305 19612.9633
[192,] -10463.3732 -10586.0305
[193,] -28994.1009 -10463.3732
[194,] -2087.4933 -28994.1009
[195,] 15211.7685 -2087.4933
[196,] -24491.8522 15211.7685
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -116.6359 54174.0081
2 7032.0955 -116.6359
3 -69732.7719 7032.0955
4 39115.4062 -69732.7719
5 -27660.9217 39115.4062
6 40867.6607 -27660.9217
7 -12747.4523 40867.6607
8 12277.0995 -12747.4523
9 -33420.4543 12277.0995
10 22264.1285 -33420.4543
11 34530.7937 22264.1285
12 -3511.0861 34530.7937
13 -6708.4090 -3511.0861
14 24290.3398 -6708.4090
15 29977.4748 24290.3398
16 -60864.5991 29977.4748
17 35298.9768 -60864.5991
18 -2795.9530 35298.9768
19 1277.6051 -2795.9530
20 9682.7973 1277.6051
21 8123.5568 9682.7973
22 111379.5806 8123.5568
23 36452.3186 111379.5806
24 -28196.2990 36452.3186
25 -78207.5251 -28196.2990
26 -52096.9875 -78207.5251
27 -11293.0842 -52096.9875
28 48810.3856 -11293.0842
29 -2392.2438 48810.3856
30 24844.3071 -2392.2438
31 5109.4838 24844.3071
32 -12051.9144 5109.4838
33 45575.5826 -12051.9144
34 -19699.4498 45575.5826
35 34856.5270 -19699.4498
36 33202.5938 34856.5270
37 8743.7810 33202.5938
38 65566.2056 8743.7810
39 8663.8021 65566.2056
40 14363.8467 8663.8021
41 39166.1298 14363.8467
42 -28892.0202 39166.1298
43 7865.8128 -28892.0202
44 455.4439 7865.8128
45 -8121.5704 455.4439
46 -42080.6418 -8121.5704
47 1286.4694 -42080.6418
48 -1561.4163 1286.4694
49 -83735.3153 -1561.4163
50 -18258.9688 -83735.3153
51 -17043.0595 -18258.9688
52 -13996.8076 -17043.0595
53 -36496.4225 -13996.8076
54 -43109.9514 -36496.4225
55 9995.5958 -43109.9514
56 -7408.7469 9995.5958
57 63739.5265 -7408.7469
58 -1026.4271 63739.5265
59 -3024.2397 -1026.4271
60 -519.6611 -3024.2397
61 18379.4167 -519.6611
62 7215.0306 18379.4167
63 10089.3464 7215.0306
64 -10940.2535 10089.3464
65 19885.7600 -10940.2535
66 37786.8308 19885.7600
67 14257.3022 37786.8308
68 -4524.7722 14257.3022
69 -36837.8452 -4524.7722
70 10799.6427 -36837.8452
71 34494.8017 10799.6427
72 43468.0324 34494.8017
73 -23092.8674 43468.0324
74 -25489.3846 -23092.8674
75 -6804.7025 -25489.3846
76 -14192.4881 -6804.7025
77 -5905.3942 -14192.4881
78 -19879.8654 -5905.3942
79 8725.9929 -19879.8654
80 -2985.1148 8725.9929
81 -20003.6433 -2985.1148
82 57314.7658 -20003.6433
83 38022.2537 57314.7658
84 -27376.7859 38022.2537
85 -11335.5934 -27376.7859
86 -13066.2241 -11335.5934
87 -14217.0660 -13066.2241
88 61036.5562 -14217.0660
89 -6303.2133 61036.5562
90 40895.3855 -6303.2133
91 -11011.5503 40895.3855
92 -36710.0677 -11011.5503
93 6332.3548 -36710.0677
94 28433.5693 6332.3548
95 -7380.0456 28433.5693
96 18318.0724 -7380.0456
97 -120892.2814 18318.0724
98 22503.1334 -120892.2814
99 2738.9939 22503.1334
100 9848.8866 2738.9939
101 -8601.0750 9848.8866
102 27901.6921 -8601.0750
103 -2157.2689 27901.6921
104 17307.4260 -2157.2689
105 17967.7610 17307.4260
106 -25917.3992 17967.7610
107 -15445.7869 -25917.3992
108 29090.2329 -15445.7869
109 3094.6127 29090.2329
110 10893.8905 3094.6127
111 -19159.1994 10893.8905
112 -22003.7289 -19159.1994
113 11685.2349 -22003.7289
114 -24481.4971 11685.2349
115 -24591.2134 -24481.4971
116 6845.1829 -24591.2134
117 46892.5164 6845.1829
118 -53072.2830 46892.5164
119 -25776.5468 -53072.2830
120 -2897.8857 -25776.5468
121 -38007.7383 -2897.8857
122 -33036.1645 -38007.7383
123 -75357.8457 -33036.1645
124 57832.8961 -75357.8457
125 28822.5534 57832.8961
126 8643.0773 28822.5534
127 -13758.1363 8643.0773
128 28206.4497 -13758.1363
129 11598.8797 28206.4497
130 -3516.3095 11598.8797
131 -25357.4422 -3516.3095
132 23373.0069 -25357.4422
133 -66264.1914 23373.0069
134 -12595.3351 -66264.1914
135 65792.8434 -12595.3351
136 9759.2350 65792.8434
137 30979.3582 9759.2350
138 -31525.2534 30979.3582
139 -5088.7989 -31525.2534
140 -21603.3784 -5088.7989
141 -14638.1791 -21603.3784
142 -3958.1350 -14638.1791
143 -8298.8817 -3958.1350
144 -2400.7835 -8298.8817
145 -33273.7943 -2400.7835
146 -22202.5542 -33273.7943
147 -36122.2211 -22202.5542
148 -12279.5100 -36122.2211
149 -16246.6128 -12279.5100
150 25233.7879 -16246.6128
151 2526.4128 25233.7879
152 -6452.2248 2526.4128
153 15165.1520 -6452.2248
154 23633.6718 15165.1520
155 18538.9316 23633.6718
156 19994.6760 18538.9316
157 12379.7925 19994.6760
158 -33912.0446 12379.7925
159 -13668.2742 -33912.0446
160 11864.2926 -13668.2742
161 -41102.6557 11864.2926
162 -13679.0604 -41102.6557
163 -27979.7737 -13679.0604
164 -30903.6394 -27979.7737
165 40360.6441 -30903.6394
166 -5843.1713 40360.6441
167 -13522.7416 -5843.1713
168 28635.1226 -13522.7416
169 72708.0964 28635.1226
170 -11332.5899 72708.0964
171 10866.1289 -11332.5899
172 6431.1907 10866.1289
173 29924.0766 6431.1907
174 -79094.2993 29924.0766
175 636.2680 -79094.2993
176 -45038.8270 636.2680
177 5455.6197 -45038.8270
178 2885.7663 5455.6197
179 42717.5427 2885.7663
180 -18438.7308 42717.5427
181 -53767.7635 -18438.7308
182 13638.8207 -53767.7635
183 13749.2294 13638.8207
184 35026.2450 13749.2294
185 10992.0017 35026.2450
186 9229.0068 10992.0017
187 19466.0184 9229.0068
188 7427.4932 19466.0184
189 -3819.0415 7427.4932
190 19612.9633 -3819.0415
191 -10586.0305 19612.9633
192 -10463.3732 -10586.0305
193 -28994.1009 -10463.3732
194 -2087.4933 -28994.1009
195 15211.7685 -2087.4933
196 -24491.8522 15211.7685
> 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/75mp91354461956.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/8xa7y1354461956.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/9xfv41354461956.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/10t98s1354461956.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/112y2t1354461956.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1250sr1354461956.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13bjwf1354461956.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14rf2n1354461956.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/15tv091354461957.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/16j0h31354461957.tab")
+ }
>
> try(system("convert tmp/1pw151354461956.ps tmp/1pw151354461956.png",intern=TRUE))
character(0)
> try(system("convert tmp/2n48c1354461956.ps tmp/2n48c1354461956.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zh041354461956.ps tmp/3zh041354461956.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hjgf1354461956.ps tmp/4hjgf1354461956.png",intern=TRUE))
character(0)
> try(system("convert tmp/5b6at1354461956.ps tmp/5b6at1354461956.png",intern=TRUE))
character(0)
> try(system("convert tmp/66spx1354461956.ps tmp/66spx1354461956.png",intern=TRUE))
character(0)
> try(system("convert tmp/75mp91354461956.ps tmp/75mp91354461956.png",intern=TRUE))
character(0)
> try(system("convert tmp/8xa7y1354461956.ps tmp/8xa7y1354461956.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xfv41354461956.ps tmp/9xfv41354461956.png",intern=TRUE))
character(0)
> try(system("convert tmp/10t98s1354461956.ps tmp/10t98s1354461956.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.890 0.865 10.048