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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> x <- array(list(9492
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+ ,1341
+ ,1244
+ ,1236
+ ,1260
+ ,1157
+ ,1235
+ ,1124
+ ,1218
+ ,1213
+ ,1302
+ ,1353
+ ,1207
+ ,1363
+ ,1220
+ ,1313
+ ,932
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+ ,1045
+ ,976
+ ,977
+ ,971
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+ ,1011
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+ ,948
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+ ,919
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+ ,977
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+ ,202
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+ ,266
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+ ,279
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+ ,241
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+ ,474
+ ,366
+ ,453
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+ ,444
+ ,458
+ ,455
+ ,415
+ ,471
+ ,403
+ ,387
+ ,434
+ ,425
+ ,423
+ ,419
+ ,473
+ ,411
+ ,469
+ ,466
+ ,409
+ ,469
+ ,477
+ ,452
+ ,504
+ ,423
+ ,454
+ ,438
+ ,446
+ ,474
+ ,468
+ ,421
+ ,430
+ ,482
+ ,483
+ ,408
+ ,435
+ ,427
+ ,447
+ ,398
+ ,440
+ ,470
+ ,401
+ ,458
+ ,438
+ ,427
+ ,453
+ ,429
+ ,453
+ ,370
+ ,392
+ ,409
+ ,427
+ ,441
+ ,428
+ ,435
+ ,404
+ ,422
+ ,445
+ ,376
+ ,427
+ ,444
+ ,429
+ ,414
+ ,505
+ ,451
+ ,461
+ ,403
+ ,440
+ ,425
+ ,432
+ ,428
+ ,429
+ ,417
+ ,371
+ ,451
+ ,454
+ ,478
+ ,394
+ ,447
+ ,455
+ ,466
+ ,436
+ ,383
+ ,467
+ ,423
+ ,407
+ ,412
+ ,463
+ ,431
+ ,436
+ ,492
+ ,449
+ ,453
+ ,418
+ ,380
+ ,440
+ ,423
+ ,493
+ ,452
+ ,450
+ ,457
+ ,470
+ ,488
+ ,440
+ ,410
+ ,423
+ ,401
+ ,437
+ ,412
+ ,441
+ ,420
+ ,506
+ ,493
+ ,457
+ ,502
+ ,445
+ ,456)
+ ,dim=c(12
+ ,150)
+ ,dimnames=list(c('Januari'
+ ,'Februari'
+ ,'Maart'
+ ,'April'
+ ,'Mei'
+ ,'Juni'
+ ,'Juli'
+ ,'Augustus'
+ ,'September'
+ ,'Oktober'
+ ,'November'
+ ,'December')
+ ,1:150))
> y <- array(NA,dim=c(12,150),dimnames=list(c('Januari','Februari','Maart','April','Mei','Juni','Juli','Augustus','September','Oktober','November','December'),1:150))
> 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'
> 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
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
Januari Februari Maart April Mei Juni Juli Augustus September Oktober
1 9492 8641 9793 9603 9238 9535 10295 9941 9984 9563
2 9215 8834 9998 9604 9507 9718 10095 9583 9883 9365
3 9769 9321 9939 9336 10195 9464 10010 10213 9563 9890
4 9928 8686 9843 9627 10074 9503 10119 10000 9313 9866
5 9659 8904 9755 9080 9435 8971 10063 9793 9454 9759
6 9676 8642 9402 9610 9294 9448 10319 9548 9801 9596
7 9829 9125 9782 9441 9162 9915 10444 10209 9985 9842
8 9849 9172 10313 9819 9955 10048 10082 10541 10208 10233
9 10158 9225 10474 9757 10490 10281 10444 10640 10695 10786
10 10411 9511 10402 9701 10540 10112 10915 11183 10384 10834
11 1073 965 1178 1115 1080 1154 1222 1196 1139 1136
12 1141 1094 1192 1108 1186 1197 1280 1189 1192 1191
13 1239 1158 1200 1138 1323 1241 1241 1306 1196 1218
14 1323 1152 1244 1267 1316 1298 1360 1352 1277 1360
15 1274 1140 1280 1188 1231 1238 1370 1345 1266 1287
16 1317 1151 1325 1325 1321 1352 1484 1352 1348 1338
17 1390 1289 1305 1289 1279 1342 1446 1420 1395 1474
18 1318 1305 1409 1362 1440 1418 1404 1386 1471 1407
19 1472 1379 1379 1379 1540 1428 1475 1491 1491 1549
20 1436 1299 1465 1328 1507 1419 1523 1623 1512 1518
21 5281 4944 5500 5379 5088 5191 5661 5449 5460 5154
22 5055 4819 5484 5276 5230 5348 5516 5207 5388 5018
23 5219 4966 5451 5062 5624 5017 5351 5562 5185 5301
24 5230 4604 5389 5151 5389 5138 5374 5243 4945 5161
25 5200 4772 5192 5022 5141 4748 5306 5240 5056 5174
26 5139 4567 5028 5101 4966 5075 5496 5200 5207 5099
27 5215 4924 5366 5160 5106 5404 5607 5429 5388 5198
28 5344 4922 5618 5265 5392 5452 5450 5776 5404 5497
29 5550 4990 5725 5338 5631 5571 5670 5846 5776 5714
30 5729 5253 5662 5382 5792 5526 5957 6038 5611 5692
31 1516 1385 1596 1501 1435 1466 1649 1567 1645 1526
32 1436 1335 1594 1556 1473 1551 1596 1521 1578 1457
33 1477 1450 1564 1461 1614 1474 1601 1612 1482 1494
34 1522 1284 1555 1455 1549 1499 1505 1473 1374 1487
35 1506 1395 1541 1454 1509 1423 1563 1559 1469 1432
36 1471 1355 1455 1512 1542 1553 1661 1511 1578 1541
37 1493 1401 1578 1503 1502 1630 1665 1593 1609 1526
38 1524 1442 1697 1515 1591 1666 1592 1686 1582 1617
39 1570 1477 1689 1583 1690 1696 1680 1741 1722 1638
40 1676 1600 1724 1535 1723 1645 1713 1837 1682 1673
41 666 701 714 687 624 683 719 688 668 643
42 695 649 684 671 688 664 713 663 677 673
43 712 632 670 632 711 641 659 722 631 660
44 687 599 664 667 696 648 728 680 627 647
45 675 611 661 648 668 675 638 637 630 648
46 707 551 625 641 571 606 707 673 629 643
47 661 633 675 644 627 642 710 710 669 669
48 663 575 667 651 701 676 717 743 665 676
49 672 623 685 695 689 729 700 706 682 758
50 760 647 679 718 746 692 748 811 718 708
51 1228 1134 1250 1272 1235 1212 1227 1267 1285 1163
52 1190 1129 1218 1191 1250 1240 1276 1188 1247 1124
53 1160 1134 1296 1227 1319 1171 1212 1323 1235 1240
54 1222 1092 1256 1164 1215 1251 1203 1237 1150 1193
55 1229 1094 1185 1141 1110 1043 1230 1202 1165 1202
56 1188 1064 1145 1146 1149 1176 1234 1269 1202 1169
57 1223 1156 1266 1210 1202 1314 1341 1272 1255 1225
58 1209 1191 1264 1260 1231 1270 1307 1408 1287 1278
59 1326 1182 1402 1180 1337 1265 1350 1360 1374 1390
60 1341 1218 1327 1266 1291 1303 1415 1402 1309 1371
61 856 809 894 918 896 870 1009 904 842 873
62 823 783 963 903 929 943 955 900 948 841
63 886 817 889 833 971 845 892 949 910 928
64 832 765 917 914 930 855 945 925 904 900
65 844 825 826 902 932 781 947 896 947 925
66 853 745 873 902 839 910 949 878 905 886
67 913 877 934 926 874 861 950 914 913 913
68 928 859 956 942 918 937 880 958 973 948
69 969 827 966 907 927 947 999 1024 1043 988
70 945 892 972 937 1008 941 1040 973 910 967
71 1015 915 1046 1001 898 960 1057 1023 1020 949
72 911 923 1025 955 890 950 976 935 938 923
73 984 933 1032 909 1009 886 987 956 927 979
74 967 864 997 951 999 885 993 928 890 934
75 946 847 979 877 922 826 928 946 845 967
76 920 852 930 900 865 830 945 869 893 860
77 925 857 913 877 901 957 941 940 942 865
78 1020 855 1034 897 951 903 954 981 897 978
79 1013 881 983 973 988 934 941 1015 955 940
80 1007 896 960 926 1024 945 1041 1015 992 973
81 3138 2732 3115 3109 3070 3190 3412 3296 3385 3273
82 3019 2921 3322 3220 3091 3173 3299 3187 3303 3156
83 3311 3197 3288 3136 3248 3206 3418 3345 3182 3371
84 3375 2930 3210 3209 3369 3067 3385 3405 3091 3345
85 3185 2992 3283 2870 3063 2985 3387 3208 3132 3298
86 3220 2924 3049 3184 3007 3021 3339 2996 3246 3159
87 3224 2912 3111 2992 2777 3169 3391 3360 3202 3170
88 3187 2945 3286 3192 3123 3178 3228 3379 3333 3329
89 3136 2856 3370 3040 3319 3282 3299 3303 3428 3523
90 3246 2959 3275 2991 3241 3167 3435 3522 3261 3624
91 63 61 54 60 51 61 66 60 55 58
92 60 55 55 61 58 45 61 41 54 62
93 51 57 50 53 49 59 55 50 58 56
94 58 51 50 48 53 51 54 44 54 44
95 50 54 47 50 53 44 56 39 54 59
96 55 51 51 54 64 54 52 47 34 48
97 60 56 62 41 43 51 51 54 41 46
98 56 40 50 72 58 58 54 55 41 42
99 44 43 43 53 63 57 38 45 61 35
100 47 37 46 28 49 54 51 62 38 46
101 295 295 312 355 352 340 354 301 356 359
102 315 305 360 341 319 329 352 325 318 296
103 289 284 339 378 332 330 333 339 321 346
104 347 310 324 308 356 343 334 338 314 340
105 344 281 361 305 315 297 358 334 331 329
106 310 314 312 335 302 306 362 310 308 341
107 363 288 316 331 321 347 326 372 324 333
108 314 299 361 339 357 357 318 339 314 349
109 328 304 365 337 337 331 386 338 354 388
110 315 326 344 329 331 318 332 349 369 390
111 1190 1035 1222 1145 1139 1186 1300 1297 1305 1208
112 1180 1110 1256 1245 1151 1238 1209 1246 1254 1214
113 1292 1285 1252 1162 1202 1199 1315 1284 1187 1321
114 1279 1121 1242 1269 1289 1181 1307 1305 1184 1269
115 1220 1161 1226 1068 1151 1145 1305 1185 1181 1251
116 1237 1108 1135 1212 1111 1142 1253 1119 1230 1205
117 1228 1103 1139 1110 1044 1168 1316 1226 1256 1208
118 1226 1145 1161 1207 1185 1180 1194 1329 1284 1256
119 1177 1086 1250 1149 1213 1251 1231 1227 1269 1341
120 1260 1157 1235 1124 1218 1213 1302 1353 1207 1363
121 932 835 894 912 898 956 1045 976 977 971
122 872 902 1022 939 943 955 1011 939 987 932
123 982 919 934 928 977 990 1033 979 953 984
124 996 868 962 956 1030 912 1004 1033 936 1023
125 892 872 968 925 939 861 1030 985 926 1021
126 964 883 940 942 916 923 948 857 967 944
127 974 861 953 902 800 957 1004 1003 949 935
128 964 886 1029 962 949 988 981 997 1006 973
129 960 857 1029 898 1000 943 993 1031 1077 1065
130 971 852 980 881 996 942 1022 1057 991 1049
131 247 201 234 242 238 248 262 278 289 280
132 249 217 250 270 255 232 254 266 275 248
133 271 257 259 245 283 249 291 269 253 299
134 270 223 247 249 247 233 300 259 256 255
135 276 249 275 202 266 273 267 263 266 262
136 264 243 235 251 249 236 271 259 280 266
137 227 232 274 232 241 246 291 281 279 247
138 247 232 268 261 225 241 272 283 293 259
139 253 229 286 233 276 305 239 250 258 241
140 277 223 279 245 255 274 273 270 237 320
141 474 366 453 455 443 460 451 444 458 455
142 403 387 434 425 423 419 473 411 469 466
143 477 452 504 423 454 438 446 474 468 421
144 483 408 435 427 447 398 440 470 401 458
145 453 429 453 370 392 409 427 441 428 435
146 445 376 427 444 429 414 505 451 461 403
147 432 428 429 417 371 451 454 478 394 447
148 436 383 467 423 407 412 463 431 436 492
149 418 380 440 423 493 452 450 457 470 488
150 423 401 437 412 441 420 506 493 457 502
November December
1 8872 9302
2 8919 9449
3 9305 9391
4 9172 9241
5 8820 9403
6 8923 9746
7 9429 10132
8 9439 9963
9 9832 9747
10 9886 10216
11 1116 1135
12 1117 1255
13 1237 1258
14 1235 1311
15 1234 1243
16 1244 1373
17 1345 1462
18 1329 1456
19 1437 1395
20 1452 1531
21 4804 4934
22 4741 4953
23 4911 4922
24 4793 4724
25 4634 4978
26 4694 5131
27 4953 5285
28 5044 5348
29 5218 5108
30 5238 5351
31 1341 1418
32 1311 1378
33 1408 1461
34 1432 1389
35 1335 1447
36 1403 1462
37 1463 1554
38 1433 1639
39 1522 1503
40 1578 1580
41 629 576
42 607 601
43 618 622
44 623 604
45 601 590
46 564 611
47 577 652
48 627 621
49 624 626
50 651 673
51 1149 1192
52 1138 1167
53 1167 1154
54 1151 1130
55 1098 1217
56 1065 1222
57 1216 1288
58 1194 1271
59 1246 1260
60 1200 1267
61 809 855
62 790 908
63 836 832
64 756 837
65 785 857
66 845 925
67 854 891
68 918 900
69 890 859
70 912 908
71 876 893
72 895 899
73 882 853
74 831 764
75 815 867
76 817 911
77 843 900
78 872 917
79 936 860
80 897 923
81 2952 3233
82 3061 3241
83 3157 3211
84 3144 3206
85 2952 3182
86 2985 3242
87 3131 3385
88 3066 3159
89 3177 3244
90 3196 3334
91 48 49
92 50 43
93 53 46
94 43 31
95 44 42
96 29 40
97 45 35
98 45 42
99 40 52
100 44 40
101 274 326
102 299 329
103 310 297
104 311 309
105 291 304
106 296 350
107 338 340
108 298 328
109 315 348
110 304 332
111 1166 1214
112 1197 1257
113 1201 1255
114 1239 1236
115 1140 1268
116 1130 1228
117 1214 1272
118 1154 1188
119 1244 1236
120 1220 1313
121 845 968
122 891 948
123 965 894
124 910 992
125 879 950
126 869 969
127 892 1034
128 934 926
129 897 1010
130 986 988
131 252 254
132 265 238
133 251 283
134 246 242
135 238 238
136 250 270
137 232 273
138 231 264
139 281 240
140 241 245
141 415 471
142 409 469
143 430 482
144 438 427
145 404 422
146 440 425
147 455 466
148 449 453
149 440 410
150 445 456
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Februari Maart April Mei Juni
-0.55145 0.21134 -0.16118 0.40646 -0.04939 -0.12712
Juli Augustus September Oktober November December
0.34986 0.15741 -0.29397 0.43694 0.19595 -0.11475
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-129.088 -20.778 -1.504 31.280 96.348
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.55145 4.36025 -0.126 0.89954
Februari 0.21134 0.06449 3.277 0.00133 **
Maart -0.16118 0.06775 -2.379 0.01873 *
April 0.40646 0.05774 7.040 8.26e-11 ***
Mei -0.04939 0.05209 -0.948 0.34472
Juni -0.12712 0.07305 -1.740 0.08406 .
Juli 0.34986 0.05206 6.720 4.38e-10 ***
Augustus 0.15741 0.05377 2.928 0.00399 **
September -0.29397 0.05643 -5.209 6.76e-07 ***
Oktober 0.43694 0.07697 5.677 7.78e-08 ***
November 0.19595 0.09237 2.121 0.03567 *
December -0.11475 0.05470 -2.098 0.03775 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 42.48 on 138 degrees of freedom
Multiple R-squared: 0.9997, Adjusted R-squared: 0.9997
F-statistic: 4.642e+04 on 11 and 138 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,] 0.8456345 3.087310e-01 1.543655e-01
[2,] 0.7370775 5.258450e-01 2.629225e-01
[3,] 0.8441224 3.117552e-01 1.558776e-01
[4,] 0.7618612 4.762775e-01 2.381388e-01
[5,] 0.6799508 6.400983e-01 3.200492e-01
[6,] 0.5808451 8.383098e-01 4.191549e-01
[7,] 0.6820835 6.358330e-01 3.179165e-01
[8,] 0.8925414 2.149171e-01 1.074586e-01
[9,] 0.8989696 2.020609e-01 1.010304e-01
[10,] 0.8901957 2.196087e-01 1.098043e-01
[11,] 0.8737403 2.525194e-01 1.262597e-01
[12,] 0.8656366 2.687268e-01 1.343634e-01
[13,] 0.8492214 3.015572e-01 1.507786e-01
[14,] 0.8246020 3.507960e-01 1.753980e-01
[15,] 0.9594863 8.102742e-02 4.051371e-02
[16,] 0.9661132 6.777366e-02 3.388683e-02
[17,] 0.9533743 9.325136e-02 4.662568e-02
[18,] 0.9486172 1.027656e-01 5.138279e-02
[19,] 0.9470959 1.058082e-01 5.290410e-02
[20,] 0.9584006 8.319881e-02 4.159940e-02
[21,] 0.9662412 6.751767e-02 3.375883e-02
[22,] 0.9846868 3.062633e-02 1.531317e-02
[23,] 0.9790468 4.190641e-02 2.095320e-02
[24,] 0.9702877 5.942451e-02 2.971225e-02
[25,] 0.9600611 7.987786e-02 3.993893e-02
[26,] 0.9606396 7.872087e-02 3.936043e-02
[27,] 0.9653214 6.935723e-02 3.467862e-02
[28,] 0.9534700 9.306009e-02 4.653004e-02
[29,] 0.9558828 8.823440e-02 4.411720e-02
[30,] 0.9412118 1.175765e-01 5.878824e-02
[31,] 0.9317940 1.364121e-01 6.820604e-02
[32,] 0.9362268 1.275464e-01 6.377318e-02
[33,] 0.9225903 1.548195e-01 7.740975e-02
[34,] 0.9029152 1.941696e-01 9.708481e-02
[35,] 0.9389295 1.221411e-01 6.107053e-02
[36,] 0.9302257 1.395487e-01 6.977433e-02
[37,] 0.9309834 1.380331e-01 6.901655e-02
[38,] 0.9296312 1.407377e-01 7.036883e-02
[39,] 0.9546093 9.078131e-02 4.539066e-02
[40,] 0.9571220 8.575594e-02 4.287797e-02
[41,] 0.9497455 1.005090e-01 5.025450e-02
[42,] 0.9410635 1.178729e-01 5.893647e-02
[43,] 0.9270075 1.459849e-01 7.299247e-02
[44,] 0.9690297 6.194052e-02 3.097026e-02
[45,] 0.9737339 5.253214e-02 2.626607e-02
[46,] 0.9681225 6.375508e-02 3.187754e-02
[47,] 0.9845668 3.086640e-02 1.543320e-02
[48,] 0.9821011 3.579786e-02 1.789893e-02
[49,] 0.9758979 4.820414e-02 2.410207e-02
[50,] 0.9807694 3.846115e-02 1.923058e-02
[51,] 0.9923941 1.521173e-02 7.605867e-03
[52,] 0.9901111 1.977781e-02 9.888905e-03
[53,] 0.9873416 2.531690e-02 1.265845e-02
[54,] 0.9835449 3.291020e-02 1.645510e-02
[55,] 0.9829264 3.414723e-02 1.707361e-02
[56,] 0.9845969 3.080626e-02 1.540313e-02
[57,] 0.9843572 3.128556e-02 1.564278e-02
[58,] 0.9822308 3.553835e-02 1.776917e-02
[59,] 0.9773283 4.534331e-02 2.267166e-02
[60,] 0.9757086 4.858287e-02 2.429143e-02
[61,] 0.9745056 5.098875e-02 2.549438e-02
[62,] 0.9698599 6.028010e-02 3.014005e-02
[63,] 0.9709490 5.810208e-02 2.905104e-02
[64,] 0.9961004 7.799208e-03 3.899604e-03
[65,] 0.9980422 3.915555e-03 1.957777e-03
[66,] 0.9975536 4.892719e-03 2.446360e-03
[67,] 0.9965059 6.988251e-03 3.494126e-03
[68,] 0.9999947 1.057597e-05 5.287983e-06
[69,] 0.9999975 4.960698e-06 2.480349e-06
[70,] 0.9999986 2.897415e-06 1.448708e-06
[71,] 0.9999989 2.170139e-06 1.085069e-06
[72,] 0.9999990 1.922862e-06 9.614308e-07
[73,] 0.9999985 2.902506e-06 1.451253e-06
[74,] 0.9999983 3.309838e-06 1.654919e-06
[75,] 0.9999976 4.779457e-06 2.389728e-06
[76,] 0.9999994 1.151157e-06 5.755783e-07
[77,] 0.9999988 2.333692e-06 1.166846e-06
[78,] 0.9999977 4.579719e-06 2.289860e-06
[79,] 0.9999956 8.742093e-06 4.371046e-06
[80,] 0.9999919 1.629613e-05 8.148065e-06
[81,] 0.9999846 3.086800e-05 1.543400e-05
[82,] 0.9999712 5.768483e-05 2.884242e-05
[83,] 0.9999501 9.984005e-05 4.992003e-05
[84,] 0.9999094 1.811337e-04 9.056685e-05
[85,] 0.9998532 2.936267e-04 1.468134e-04
[86,] 0.9997416 5.167192e-04 2.583596e-04
[87,] 0.9997106 5.788684e-04 2.894342e-04
[88,] 0.9995276 9.448903e-04 4.724452e-04
[89,] 0.9995131 9.738610e-04 4.869305e-04
[90,] 0.9992042 1.591621e-03 7.958106e-04
[91,] 0.9994591 1.081787e-03 5.408935e-04
[92,] 0.9994124 1.175245e-03 5.876227e-04
[93,] 0.9990883 1.823470e-03 9.117350e-04
[94,] 0.9985539 2.892281e-03 1.446140e-03
[95,] 0.9976678 4.664413e-03 2.332206e-03
[96,] 0.9966167 6.766690e-03 3.383345e-03
[97,] 0.9960162 7.967539e-03 3.983770e-03
[98,] 0.9973496 5.300816e-03 2.650408e-03
[99,] 0.9956313 8.737480e-03 4.368740e-03
[100,] 0.9933724 1.325529e-02 6.627644e-03
[101,] 0.9908220 1.835595e-02 9.177974e-03
[102,] 0.9906812 1.863763e-02 9.318816e-03
[103,] 0.9911827 1.763463e-02 8.817315e-03
[104,] 0.9882960 2.340801e-02 1.170401e-02
[105,] 0.9872193 2.556148e-02 1.278074e-02
[106,] 0.9810379 3.792425e-02 1.896213e-02
[107,] 0.9695742 6.085165e-02 3.042582e-02
[108,] 0.9929989 1.400220e-02 7.001098e-03
[109,] 0.9880449 2.391025e-02 1.195513e-02
[110,] 0.9793936 4.121284e-02 2.060642e-02
[111,] 0.9934101 1.317984e-02 6.589922e-03
[112,] 0.9896513 2.069746e-02 1.034873e-02
[113,] 0.9895817 2.083667e-02 1.041834e-02
[114,] 0.9839873 3.202538e-02 1.601269e-02
[115,] 0.9693247 6.135063e-02 3.067532e-02
[116,] 0.9553807 8.923862e-02 4.461931e-02
[117,] 0.9646685 7.066305e-02 3.533152e-02
[118,] 0.9291522 1.416956e-01 7.084780e-02
[119,] 0.8761440 2.477120e-01 1.238560e-01
[120,] 0.7712799 4.574403e-01 2.287201e-01
[121,] 0.7069743 5.860513e-01 2.930257e-01
> postscript(file="/var/wessaorg/rcomp/tmp/17kqr1353336440.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/21dcu1353336440.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/34j4v1353336440.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/462jd1353336440.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5j1kb1353336440.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 = 150
Frequency = 1
1 2 3 4 5 6
-71.2903672 -129.0880571 -52.0867451 47.5816629 69.4128791 64.5451547
7 8 9 10 11 12
42.2118010 -11.9552649 50.5452100 -84.1772674 -59.4569505 -16.9839188
13 14 15 16 17 18
30.3813165 -3.4261758 -5.1437282 -20.1697526 -20.8668037 -17.4345135
19 20 21 22 23 24
-10.3052633 -2.7688584 -12.3926194 -4.1108773 -30.4859323 47.8155505
25 26 27 28 29 30
52.6813434 32.1700747 13.9443890 25.2941205 96.3477058 48.3978226
31 32 33 34 35 36
21.3916769 -20.5117096 -42.8071523 52.0430133 42.8898045 -56.8542420
37 38 39 40 41 42
-13.1212282 11.3219688 -10.8105531 33.6459024 -29.6995417 15.4555618
43 44 45 46 47 48
49.8725512 0.7329943 36.8909062 46.7954269 -6.6710199 -13.8038850
49 50 51 52 53 54
-41.6844272 28.2502905 40.9170881 36.4885633 -62.2390328 45.4461358
55 56 57 58 59 60
35.2103892 31.2942679 -8.3104565 -75.4170450 60.5421629 -6.8923436
61 62 63 64 65 66
-78.2609675 -3.1688716 6.1957766 -54.0225337 -71.6056772 -23.4450144
67 68 69 70 71 72
-16.8924783 19.1894345 36.3656767 -44.2868409 35.4649311 -30.1686150
73 74 75 76 77 78
20.6977313 5.8217472 11.5017270 36.5854426 61.1453782 86.2094535
79 80 81 82 83 84
54.1406528 39.8866204 1.6621058 -106.8903579 -50.8792262 4.2951913
85 86 87 88 89 90
22.9781671 53.6477357 50.2371189 -18.7119687 -34.3943294 -128.7033286
91 92 93 94 95 96
-0.2436359 -2.2910010 -3.6964395 11.3845528 -4.8362190 3.4080178
97 98 99 100 101 102
11.4602695 -0.7178665 11.5994829 4.4869852 -39.9153010 -2.9065826
103 104 105 106 107 108
-64.4717619 18.4580531 26.2794684 -39.6115710 28.9608643 -10.7893282
109 110 111 112 113 114
-30.6323740 -29.3477780 18.0482759 -14.6542055 -19.5147016 -29.2430833
115 116 117 118 119 120
20.6736433 33.7763004 23.9689163 1.5474759 -37.2217401 -25.0413107
121 122 123 124 125 126
-15.4367098 -51.4535988 -16.0365377 -3.7937169 -108.4006197 55.2716534
127 128 129 130 131 132
45.8853493 18.2850953 31.2377680 -10.5114671 -5.3383104 -6.5449743
133 134 135 136 137 138
-12.1038927 1.5175251 43.5677391 3.8477987 -14.5238715 -4.3359848
139 140 141 142 143 144
28.9989533 -4.0398527 46.5099573 -29.1251905 63.4534739 19.5622197
145 146 147 148 149 150
43.0637423 2.5693605 -31.8483333 -21.2134206 -24.5711302 -57.5571138
> postscript(file="/var/wessaorg/rcomp/tmp/64ljx1353336440.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 = 150
Frequency = 1
lag(myerror, k = 1) myerror
0 -71.2903672 NA
1 -129.0880571 -71.2903672
2 -52.0867451 -129.0880571
3 47.5816629 -52.0867451
4 69.4128791 47.5816629
5 64.5451547 69.4128791
6 42.2118010 64.5451547
7 -11.9552649 42.2118010
8 50.5452100 -11.9552649
9 -84.1772674 50.5452100
10 -59.4569505 -84.1772674
11 -16.9839188 -59.4569505
12 30.3813165 -16.9839188
13 -3.4261758 30.3813165
14 -5.1437282 -3.4261758
15 -20.1697526 -5.1437282
16 -20.8668037 -20.1697526
17 -17.4345135 -20.8668037
18 -10.3052633 -17.4345135
19 -2.7688584 -10.3052633
20 -12.3926194 -2.7688584
21 -4.1108773 -12.3926194
22 -30.4859323 -4.1108773
23 47.8155505 -30.4859323
24 52.6813434 47.8155505
25 32.1700747 52.6813434
26 13.9443890 32.1700747
27 25.2941205 13.9443890
28 96.3477058 25.2941205
29 48.3978226 96.3477058
30 21.3916769 48.3978226
31 -20.5117096 21.3916769
32 -42.8071523 -20.5117096
33 52.0430133 -42.8071523
34 42.8898045 52.0430133
35 -56.8542420 42.8898045
36 -13.1212282 -56.8542420
37 11.3219688 -13.1212282
38 -10.8105531 11.3219688
39 33.6459024 -10.8105531
40 -29.6995417 33.6459024
41 15.4555618 -29.6995417
42 49.8725512 15.4555618
43 0.7329943 49.8725512
44 36.8909062 0.7329943
45 46.7954269 36.8909062
46 -6.6710199 46.7954269
47 -13.8038850 -6.6710199
48 -41.6844272 -13.8038850
49 28.2502905 -41.6844272
50 40.9170881 28.2502905
51 36.4885633 40.9170881
52 -62.2390328 36.4885633
53 45.4461358 -62.2390328
54 35.2103892 45.4461358
55 31.2942679 35.2103892
56 -8.3104565 31.2942679
57 -75.4170450 -8.3104565
58 60.5421629 -75.4170450
59 -6.8923436 60.5421629
60 -78.2609675 -6.8923436
61 -3.1688716 -78.2609675
62 6.1957766 -3.1688716
63 -54.0225337 6.1957766
64 -71.6056772 -54.0225337
65 -23.4450144 -71.6056772
66 -16.8924783 -23.4450144
67 19.1894345 -16.8924783
68 36.3656767 19.1894345
69 -44.2868409 36.3656767
70 35.4649311 -44.2868409
71 -30.1686150 35.4649311
72 20.6977313 -30.1686150
73 5.8217472 20.6977313
74 11.5017270 5.8217472
75 36.5854426 11.5017270
76 61.1453782 36.5854426
77 86.2094535 61.1453782
78 54.1406528 86.2094535
79 39.8866204 54.1406528
80 1.6621058 39.8866204
81 -106.8903579 1.6621058
82 -50.8792262 -106.8903579
83 4.2951913 -50.8792262
84 22.9781671 4.2951913
85 53.6477357 22.9781671
86 50.2371189 53.6477357
87 -18.7119687 50.2371189
88 -34.3943294 -18.7119687
89 -128.7033286 -34.3943294
90 -0.2436359 -128.7033286
91 -2.2910010 -0.2436359
92 -3.6964395 -2.2910010
93 11.3845528 -3.6964395
94 -4.8362190 11.3845528
95 3.4080178 -4.8362190
96 11.4602695 3.4080178
97 -0.7178665 11.4602695
98 11.5994829 -0.7178665
99 4.4869852 11.5994829
100 -39.9153010 4.4869852
101 -2.9065826 -39.9153010
102 -64.4717619 -2.9065826
103 18.4580531 -64.4717619
104 26.2794684 18.4580531
105 -39.6115710 26.2794684
106 28.9608643 -39.6115710
107 -10.7893282 28.9608643
108 -30.6323740 -10.7893282
109 -29.3477780 -30.6323740
110 18.0482759 -29.3477780
111 -14.6542055 18.0482759
112 -19.5147016 -14.6542055
113 -29.2430833 -19.5147016
114 20.6736433 -29.2430833
115 33.7763004 20.6736433
116 23.9689163 33.7763004
117 1.5474759 23.9689163
118 -37.2217401 1.5474759
119 -25.0413107 -37.2217401
120 -15.4367098 -25.0413107
121 -51.4535988 -15.4367098
122 -16.0365377 -51.4535988
123 -3.7937169 -16.0365377
124 -108.4006197 -3.7937169
125 55.2716534 -108.4006197
126 45.8853493 55.2716534
127 18.2850953 45.8853493
128 31.2377680 18.2850953
129 -10.5114671 31.2377680
130 -5.3383104 -10.5114671
131 -6.5449743 -5.3383104
132 -12.1038927 -6.5449743
133 1.5175251 -12.1038927
134 43.5677391 1.5175251
135 3.8477987 43.5677391
136 -14.5238715 3.8477987
137 -4.3359848 -14.5238715
138 28.9989533 -4.3359848
139 -4.0398527 28.9989533
140 46.5099573 -4.0398527
141 -29.1251905 46.5099573
142 63.4534739 -29.1251905
143 19.5622197 63.4534739
144 43.0637423 19.5622197
145 2.5693605 43.0637423
146 -31.8483333 2.5693605
147 -21.2134206 -31.8483333
148 -24.5711302 -21.2134206
149 -57.5571138 -24.5711302
150 NA -57.5571138
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -129.0880571 -71.2903672
[2,] -52.0867451 -129.0880571
[3,] 47.5816629 -52.0867451
[4,] 69.4128791 47.5816629
[5,] 64.5451547 69.4128791
[6,] 42.2118010 64.5451547
[7,] -11.9552649 42.2118010
[8,] 50.5452100 -11.9552649
[9,] -84.1772674 50.5452100
[10,] -59.4569505 -84.1772674
[11,] -16.9839188 -59.4569505
[12,] 30.3813165 -16.9839188
[13,] -3.4261758 30.3813165
[14,] -5.1437282 -3.4261758
[15,] -20.1697526 -5.1437282
[16,] -20.8668037 -20.1697526
[17,] -17.4345135 -20.8668037
[18,] -10.3052633 -17.4345135
[19,] -2.7688584 -10.3052633
[20,] -12.3926194 -2.7688584
[21,] -4.1108773 -12.3926194
[22,] -30.4859323 -4.1108773
[23,] 47.8155505 -30.4859323
[24,] 52.6813434 47.8155505
[25,] 32.1700747 52.6813434
[26,] 13.9443890 32.1700747
[27,] 25.2941205 13.9443890
[28,] 96.3477058 25.2941205
[29,] 48.3978226 96.3477058
[30,] 21.3916769 48.3978226
[31,] -20.5117096 21.3916769
[32,] -42.8071523 -20.5117096
[33,] 52.0430133 -42.8071523
[34,] 42.8898045 52.0430133
[35,] -56.8542420 42.8898045
[36,] -13.1212282 -56.8542420
[37,] 11.3219688 -13.1212282
[38,] -10.8105531 11.3219688
[39,] 33.6459024 -10.8105531
[40,] -29.6995417 33.6459024
[41,] 15.4555618 -29.6995417
[42,] 49.8725512 15.4555618
[43,] 0.7329943 49.8725512
[44,] 36.8909062 0.7329943
[45,] 46.7954269 36.8909062
[46,] -6.6710199 46.7954269
[47,] -13.8038850 -6.6710199
[48,] -41.6844272 -13.8038850
[49,] 28.2502905 -41.6844272
[50,] 40.9170881 28.2502905
[51,] 36.4885633 40.9170881
[52,] -62.2390328 36.4885633
[53,] 45.4461358 -62.2390328
[54,] 35.2103892 45.4461358
[55,] 31.2942679 35.2103892
[56,] -8.3104565 31.2942679
[57,] -75.4170450 -8.3104565
[58,] 60.5421629 -75.4170450
[59,] -6.8923436 60.5421629
[60,] -78.2609675 -6.8923436
[61,] -3.1688716 -78.2609675
[62,] 6.1957766 -3.1688716
[63,] -54.0225337 6.1957766
[64,] -71.6056772 -54.0225337
[65,] -23.4450144 -71.6056772
[66,] -16.8924783 -23.4450144
[67,] 19.1894345 -16.8924783
[68,] 36.3656767 19.1894345
[69,] -44.2868409 36.3656767
[70,] 35.4649311 -44.2868409
[71,] -30.1686150 35.4649311
[72,] 20.6977313 -30.1686150
[73,] 5.8217472 20.6977313
[74,] 11.5017270 5.8217472
[75,] 36.5854426 11.5017270
[76,] 61.1453782 36.5854426
[77,] 86.2094535 61.1453782
[78,] 54.1406528 86.2094535
[79,] 39.8866204 54.1406528
[80,] 1.6621058 39.8866204
[81,] -106.8903579 1.6621058
[82,] -50.8792262 -106.8903579
[83,] 4.2951913 -50.8792262
[84,] 22.9781671 4.2951913
[85,] 53.6477357 22.9781671
[86,] 50.2371189 53.6477357
[87,] -18.7119687 50.2371189
[88,] -34.3943294 -18.7119687
[89,] -128.7033286 -34.3943294
[90,] -0.2436359 -128.7033286
[91,] -2.2910010 -0.2436359
[92,] -3.6964395 -2.2910010
[93,] 11.3845528 -3.6964395
[94,] -4.8362190 11.3845528
[95,] 3.4080178 -4.8362190
[96,] 11.4602695 3.4080178
[97,] -0.7178665 11.4602695
[98,] 11.5994829 -0.7178665
[99,] 4.4869852 11.5994829
[100,] -39.9153010 4.4869852
[101,] -2.9065826 -39.9153010
[102,] -64.4717619 -2.9065826
[103,] 18.4580531 -64.4717619
[104,] 26.2794684 18.4580531
[105,] -39.6115710 26.2794684
[106,] 28.9608643 -39.6115710
[107,] -10.7893282 28.9608643
[108,] -30.6323740 -10.7893282
[109,] -29.3477780 -30.6323740
[110,] 18.0482759 -29.3477780
[111,] -14.6542055 18.0482759
[112,] -19.5147016 -14.6542055
[113,] -29.2430833 -19.5147016
[114,] 20.6736433 -29.2430833
[115,] 33.7763004 20.6736433
[116,] 23.9689163 33.7763004
[117,] 1.5474759 23.9689163
[118,] -37.2217401 1.5474759
[119,] -25.0413107 -37.2217401
[120,] -15.4367098 -25.0413107
[121,] -51.4535988 -15.4367098
[122,] -16.0365377 -51.4535988
[123,] -3.7937169 -16.0365377
[124,] -108.4006197 -3.7937169
[125,] 55.2716534 -108.4006197
[126,] 45.8853493 55.2716534
[127,] 18.2850953 45.8853493
[128,] 31.2377680 18.2850953
[129,] -10.5114671 31.2377680
[130,] -5.3383104 -10.5114671
[131,] -6.5449743 -5.3383104
[132,] -12.1038927 -6.5449743
[133,] 1.5175251 -12.1038927
[134,] 43.5677391 1.5175251
[135,] 3.8477987 43.5677391
[136,] -14.5238715 3.8477987
[137,] -4.3359848 -14.5238715
[138,] 28.9989533 -4.3359848
[139,] -4.0398527 28.9989533
[140,] 46.5099573 -4.0398527
[141,] -29.1251905 46.5099573
[142,] 63.4534739 -29.1251905
[143,] 19.5622197 63.4534739
[144,] 43.0637423 19.5622197
[145,] 2.5693605 43.0637423
[146,] -31.8483333 2.5693605
[147,] -21.2134206 -31.8483333
[148,] -24.5711302 -21.2134206
[149,] -57.5571138 -24.5711302
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -129.0880571 -71.2903672
2 -52.0867451 -129.0880571
3 47.5816629 -52.0867451
4 69.4128791 47.5816629
5 64.5451547 69.4128791
6 42.2118010 64.5451547
7 -11.9552649 42.2118010
8 50.5452100 -11.9552649
9 -84.1772674 50.5452100
10 -59.4569505 -84.1772674
11 -16.9839188 -59.4569505
12 30.3813165 -16.9839188
13 -3.4261758 30.3813165
14 -5.1437282 -3.4261758
15 -20.1697526 -5.1437282
16 -20.8668037 -20.1697526
17 -17.4345135 -20.8668037
18 -10.3052633 -17.4345135
19 -2.7688584 -10.3052633
20 -12.3926194 -2.7688584
21 -4.1108773 -12.3926194
22 -30.4859323 -4.1108773
23 47.8155505 -30.4859323
24 52.6813434 47.8155505
25 32.1700747 52.6813434
26 13.9443890 32.1700747
27 25.2941205 13.9443890
28 96.3477058 25.2941205
29 48.3978226 96.3477058
30 21.3916769 48.3978226
31 -20.5117096 21.3916769
32 -42.8071523 -20.5117096
33 52.0430133 -42.8071523
34 42.8898045 52.0430133
35 -56.8542420 42.8898045
36 -13.1212282 -56.8542420
37 11.3219688 -13.1212282
38 -10.8105531 11.3219688
39 33.6459024 -10.8105531
40 -29.6995417 33.6459024
41 15.4555618 -29.6995417
42 49.8725512 15.4555618
43 0.7329943 49.8725512
44 36.8909062 0.7329943
45 46.7954269 36.8909062
46 -6.6710199 46.7954269
47 -13.8038850 -6.6710199
48 -41.6844272 -13.8038850
49 28.2502905 -41.6844272
50 40.9170881 28.2502905
51 36.4885633 40.9170881
52 -62.2390328 36.4885633
53 45.4461358 -62.2390328
54 35.2103892 45.4461358
55 31.2942679 35.2103892
56 -8.3104565 31.2942679
57 -75.4170450 -8.3104565
58 60.5421629 -75.4170450
59 -6.8923436 60.5421629
60 -78.2609675 -6.8923436
61 -3.1688716 -78.2609675
62 6.1957766 -3.1688716
63 -54.0225337 6.1957766
64 -71.6056772 -54.0225337
65 -23.4450144 -71.6056772
66 -16.8924783 -23.4450144
67 19.1894345 -16.8924783
68 36.3656767 19.1894345
69 -44.2868409 36.3656767
70 35.4649311 -44.2868409
71 -30.1686150 35.4649311
72 20.6977313 -30.1686150
73 5.8217472 20.6977313
74 11.5017270 5.8217472
75 36.5854426 11.5017270
76 61.1453782 36.5854426
77 86.2094535 61.1453782
78 54.1406528 86.2094535
79 39.8866204 54.1406528
80 1.6621058 39.8866204
81 -106.8903579 1.6621058
82 -50.8792262 -106.8903579
83 4.2951913 -50.8792262
84 22.9781671 4.2951913
85 53.6477357 22.9781671
86 50.2371189 53.6477357
87 -18.7119687 50.2371189
88 -34.3943294 -18.7119687
89 -128.7033286 -34.3943294
90 -0.2436359 -128.7033286
91 -2.2910010 -0.2436359
92 -3.6964395 -2.2910010
93 11.3845528 -3.6964395
94 -4.8362190 11.3845528
95 3.4080178 -4.8362190
96 11.4602695 3.4080178
97 -0.7178665 11.4602695
98 11.5994829 -0.7178665
99 4.4869852 11.5994829
100 -39.9153010 4.4869852
101 -2.9065826 -39.9153010
102 -64.4717619 -2.9065826
103 18.4580531 -64.4717619
104 26.2794684 18.4580531
105 -39.6115710 26.2794684
106 28.9608643 -39.6115710
107 -10.7893282 28.9608643
108 -30.6323740 -10.7893282
109 -29.3477780 -30.6323740
110 18.0482759 -29.3477780
111 -14.6542055 18.0482759
112 -19.5147016 -14.6542055
113 -29.2430833 -19.5147016
114 20.6736433 -29.2430833
115 33.7763004 20.6736433
116 23.9689163 33.7763004
117 1.5474759 23.9689163
118 -37.2217401 1.5474759
119 -25.0413107 -37.2217401
120 -15.4367098 -25.0413107
121 -51.4535988 -15.4367098
122 -16.0365377 -51.4535988
123 -3.7937169 -16.0365377
124 -108.4006197 -3.7937169
125 55.2716534 -108.4006197
126 45.8853493 55.2716534
127 18.2850953 45.8853493
128 31.2377680 18.2850953
129 -10.5114671 31.2377680
130 -5.3383104 -10.5114671
131 -6.5449743 -5.3383104
132 -12.1038927 -6.5449743
133 1.5175251 -12.1038927
134 43.5677391 1.5175251
135 3.8477987 43.5677391
136 -14.5238715 3.8477987
137 -4.3359848 -14.5238715
138 28.9989533 -4.3359848
139 -4.0398527 28.9989533
140 46.5099573 -4.0398527
141 -29.1251905 46.5099573
142 63.4534739 -29.1251905
143 19.5622197 63.4534739
144 43.0637423 19.5622197
145 2.5693605 43.0637423
146 -31.8483333 2.5693605
147 -21.2134206 -31.8483333
148 -24.5711302 -21.2134206
149 -57.5571138 -24.5711302
> 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/7k1kb1353336440.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8ytdl1353336440.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9t6h41353336440.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/107tvt1353336440.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11d0yq1353336440.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/125kal1353336440.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/13m4731353336440.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/1459od1353336440.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/155lwa1353336440.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/16m7ib1353336440.tab")
+ }
>
> try(system("convert tmp/17kqr1353336440.ps tmp/17kqr1353336440.png",intern=TRUE))
character(0)
> try(system("convert tmp/21dcu1353336440.ps tmp/21dcu1353336440.png",intern=TRUE))
character(0)
> try(system("convert tmp/34j4v1353336440.ps tmp/34j4v1353336440.png",intern=TRUE))
character(0)
> try(system("convert tmp/462jd1353336440.ps tmp/462jd1353336440.png",intern=TRUE))
character(0)
> try(system("convert tmp/5j1kb1353336440.ps tmp/5j1kb1353336440.png",intern=TRUE))
character(0)
> try(system("convert tmp/64ljx1353336440.ps tmp/64ljx1353336440.png",intern=TRUE))
character(0)
> try(system("convert tmp/7k1kb1353336440.ps tmp/7k1kb1353336440.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ytdl1353336440.ps tmp/8ytdl1353336440.png",intern=TRUE))
character(0)
> try(system("convert tmp/9t6h41353336440.ps tmp/9t6h41353336440.png",intern=TRUE))
character(0)
> try(system("convert tmp/107tvt1353336440.ps tmp/107tvt1353336440.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.560 1.165 10.355