R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(1998
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+ ,932
+ ,835
+ ,894
+ ,912
+ ,898
+ ,956
+ ,1045
+ ,976
+ ,977
+ ,971
+ ,845
+ ,968
+ ,1999
+ ,872
+ ,902
+ ,1022
+ ,939
+ ,943
+ ,955
+ ,1011
+ ,939
+ ,987
+ ,932
+ ,891
+ ,948
+ ,2000
+ ,982
+ ,919
+ ,934
+ ,928
+ ,977
+ ,990
+ ,1033
+ ,979
+ ,953
+ ,984
+ ,965
+ ,894
+ ,2001
+ ,996
+ ,868
+ ,962
+ ,956
+ ,1030
+ ,912
+ ,1004
+ ,1033
+ ,936
+ ,1023
+ ,910
+ ,992
+ ,2002
+ ,892
+ ,872
+ ,968
+ ,925
+ ,939
+ ,861
+ ,1030
+ ,985
+ ,926
+ ,1021
+ ,879
+ ,950
+ ,2003
+ ,964
+ ,883
+ ,940
+ ,942
+ ,916
+ ,923
+ ,948
+ ,857
+ ,967
+ ,944
+ ,869
+ ,969
+ ,2004
+ ,974
+ ,861
+ ,953
+ ,902
+ ,800
+ ,957
+ ,1004
+ ,1003
+ ,949
+ ,935
+ ,892
+ ,1034
+ ,2005
+ ,964
+ ,886
+ ,1029
+ ,962
+ ,949
+ ,988
+ ,981
+ ,997
+ ,1006
+ ,973
+ ,934
+ ,926
+ ,2006
+ ,960
+ ,857
+ ,1029
+ ,898
+ ,1000
+ ,943
+ ,993
+ ,1031
+ ,1077
+ ,1065
+ ,897
+ ,1010
+ ,2007
+ ,971
+ ,852
+ ,980
+ ,881
+ ,996
+ ,942
+ ,1022
+ ,1057
+ ,991
+ ,1049
+ ,986
+ ,988
+ ,1998
+ ,247
+ ,201
+ ,234
+ ,242
+ ,238
+ ,248
+ ,262
+ ,278
+ ,289
+ ,280
+ ,252
+ ,254
+ ,1999
+ ,249
+ ,217
+ ,250
+ ,270
+ ,255
+ ,232
+ ,254
+ ,266
+ ,275
+ ,248
+ ,265
+ ,238
+ ,2000
+ ,271
+ ,257
+ ,259
+ ,245
+ ,283
+ ,249
+ ,291
+ ,269
+ ,253
+ ,299
+ ,251
+ ,283
+ ,2001
+ ,270
+ ,223
+ ,247
+ ,249
+ ,247
+ ,233
+ ,300
+ ,259
+ ,256
+ ,255
+ ,246
+ ,242
+ ,2002
+ ,276
+ ,249
+ ,275
+ ,202
+ ,266
+ ,273
+ ,267
+ ,263
+ ,266
+ ,262
+ ,238
+ ,238
+ ,2003
+ ,264
+ ,243
+ ,235
+ ,251
+ ,249
+ ,236
+ ,271
+ ,259
+ ,280
+ ,266
+ ,250
+ ,270
+ ,2004
+ ,227
+ ,232
+ ,274
+ ,232
+ ,241
+ ,246
+ ,291
+ ,281
+ ,279
+ ,247
+ ,232
+ ,273
+ ,2005
+ ,247
+ ,232
+ ,268
+ ,261
+ ,225
+ ,241
+ ,272
+ ,283
+ ,293
+ ,259
+ ,231
+ ,264
+ ,2006
+ ,253
+ ,229
+ ,286
+ ,233
+ ,276
+ ,305
+ ,239
+ ,250
+ ,258
+ ,241
+ ,281
+ ,240
+ ,2007
+ ,277
+ ,223
+ ,279
+ ,245
+ ,255
+ ,274
+ ,273
+ ,270
+ ,237
+ ,320
+ ,241
+ ,245
+ ,1998
+ ,474
+ ,366
+ ,453
+ ,455
+ ,443
+ ,460
+ ,451
+ ,444
+ ,458
+ ,455
+ ,415
+ ,471
+ ,1999
+ ,403
+ ,387
+ ,434
+ ,425
+ ,423
+ ,419
+ ,473
+ ,411
+ ,469
+ ,466
+ ,409
+ ,469
+ ,2000
+ ,477
+ ,452
+ ,504
+ ,423
+ ,454
+ ,438
+ ,446
+ ,474
+ ,468
+ ,421
+ ,430
+ ,482
+ ,2001
+ ,483
+ ,408
+ ,435
+ ,427
+ ,447
+ ,398
+ ,440
+ ,470
+ ,401
+ ,458
+ ,438
+ ,427
+ ,2002
+ ,453
+ ,429
+ ,453
+ ,370
+ ,392
+ ,409
+ ,427
+ ,441
+ ,428
+ ,435
+ ,404
+ ,422
+ ,2003
+ ,445
+ ,376
+ ,427
+ ,444
+ ,429
+ ,414
+ ,505
+ ,451
+ ,461
+ ,403
+ ,440
+ ,425
+ ,2004
+ ,432
+ ,428
+ ,429
+ ,417
+ ,371
+ ,451
+ ,454
+ ,478
+ ,394
+ ,447
+ ,455
+ ,466
+ ,2005
+ ,436
+ ,383
+ ,467
+ ,423
+ ,407
+ ,412
+ ,463
+ ,431
+ ,436
+ ,492
+ ,449
+ ,453
+ ,2006
+ ,418
+ ,380
+ ,440
+ ,423
+ ,493
+ ,452
+ ,450
+ ,457
+ ,470
+ ,488
+ ,440
+ ,410
+ ,2007
+ ,423
+ ,401
+ ,437
+ ,412
+ ,441
+ ,420
+ ,506
+ ,493
+ ,457
+ ,502
+ ,445
+ ,456)
+ ,dim=c(13
+ ,140)
+ ,dimnames=list(c('Jaar'
+ ,'Januari'
+ ,'Februari'
+ ,'Maart'
+ ,'April'
+ ,'Mei'
+ ,'Juni'
+ ,'Juli'
+ ,'Augustus'
+ ,'September'
+ ,'Oktober'
+ ,'November'
+ ,'December')
+ ,1:140))
> y <- array(NA,dim=c(13,140),dimnames=list(c('Jaar','Januari','Februari','Maart','April','Mei','Juni','Juli','Augustus','September','Oktober','November','December'),1:140))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Jaar Januari Februari Maart April Mei Juni Juli Augustus September Oktober
1 1998 1073 965 1178 1115 1080 1154 1222 1196 1139 1136
2 1999 1141 1094 1192 1108 1186 1197 1280 1189 1192 1191
3 2000 1239 1158 1200 1138 1323 1241 1241 1306 1196 1218
4 2001 1323 1152 1244 1267 1316 1298 1360 1352 1277 1360
5 2002 1274 1140 1280 1188 1231 1238 1370 1345 1266 1287
6 2003 1317 1151 1325 1325 1321 1352 1484 1352 1348 1338
7 2004 1390 1289 1305 1289 1279 1342 1446 1420 1395 1474
8 2005 1318 1305 1409 1362 1440 1418 1404 1386 1471 1407
9 2006 1472 1379 1379 1379 1540 1428 1475 1491 1491 1549
10 2007 1436 1299 1465 1328 1507 1419 1523 1623 1512 1518
11 1998 5281 4944 5500 5379 5088 5191 5661 5449 5460 5154
12 1999 5055 4819 5484 5276 5230 5348 5516 5207 5388 5018
13 2000 5219 4966 5451 5062 5624 5017 5351 5562 5185 5301
14 2001 5230 4604 5389 5151 5389 5138 5374 5243 4945 5161
15 2002 5200 4772 5192 5022 5141 4748 5306 5240 5056 5174
16 2003 5139 4567 5028 5101 4966 5075 5496 5200 5207 5099
17 2004 5215 4924 5366 5160 5106 5404 5607 5429 5388 5198
18 2005 5344 4922 5618 5265 5392 5452 5450 5776 5404 5497
19 2006 5550 4990 5725 5338 5631 5571 5670 5846 5776 5714
20 2007 5729 5253 5662 5382 5792 5526 5957 6038 5611 5692
21 1998 1516 1385 1596 1501 1435 1466 1649 1567 1645 1526
22 1999 1436 1335 1594 1556 1473 1551 1596 1521 1578 1457
23 2000 1477 1450 1564 1461 1614 1474 1601 1612 1482 1494
24 2001 1522 1284 1555 1455 1549 1499 1505 1473 1374 1487
25 2002 1506 1395 1541 1454 1509 1423 1563 1559 1469 1432
26 2003 1471 1355 1455 1512 1542 1553 1661 1511 1578 1541
27 2004 1493 1401 1578 1503 1502 1630 1665 1593 1609 1526
28 2005 1524 1442 1697 1515 1591 1666 1592 1686 1582 1617
29 2006 1570 1477 1689 1583 1690 1696 1680 1741 1722 1638
30 2007 1676 1600 1724 1535 1723 1645 1713 1837 1682 1673
31 1998 666 701 714 687 624 683 719 688 668 643
32 1999 695 649 684 671 688 664 713 663 677 673
33 2000 712 632 670 632 711 641 659 722 631 660
34 2001 687 599 664 667 696 648 728 680 627 647
35 2002 675 611 661 648 668 675 638 637 630 648
36 2003 707 551 625 641 571 606 707 673 629 643
37 2004 661 633 675 644 627 642 710 710 669 669
38 2005 663 575 667 651 701 676 717 743 665 676
39 2006 672 623 685 695 689 729 700 706 682 758
40 2007 760 647 679 718 746 692 748 811 718 708
41 1998 1228 1134 1250 1272 1235 1212 1227 1267 1285 1163
42 1999 1190 1129 1218 1191 1250 1240 1276 1188 1247 1124
43 2000 1160 1134 1296 1227 1319 1171 1212 1323 1235 1240
44 2001 1222 1092 1256 1164 1215 1251 1203 1237 1150 1193
45 2002 1229 1094 1185 1141 1110 1043 1230 1202 1165 1202
46 2003 1188 1064 1145 1146 1149 1176 1234 1269 1202 1169
47 2004 1223 1156 1266 1210 1202 1314 1341 1272 1255 1225
48 2005 1209 1191 1264 1260 1231 1270 1307 1408 1287 1278
49 2006 1326 1182 1402 1180 1337 1265 1350 1360 1374 1390
50 2007 1341 1218 1327 1266 1291 1303 1415 1402 1309 1371
51 1998 856 809 894 918 896 870 1009 904 842 873
52 1999 823 783 963 903 929 943 955 900 948 841
53 2000 886 817 889 833 971 845 892 949 910 928
54 2001 832 765 917 914 930 855 945 925 904 900
55 2002 844 825 826 902 932 781 947 896 947 925
56 2003 853 745 873 902 839 910 949 878 905 886
57 2004 913 877 934 926 874 861 950 914 913 913
58 2005 928 859 956 942 918 937 880 958 973 948
59 2006 969 827 966 907 927 947 999 1024 1043 988
60 2007 945 892 972 937 1008 941 1040 973 910 967
61 1998 1015 915 1046 1001 898 960 1057 1023 1020 949
62 1999 911 923 1025 955 890 950 976 935 938 923
63 2000 984 933 1032 909 1009 886 987 956 927 979
64 2001 967 864 997 951 999 885 993 928 890 934
65 2002 946 847 979 877 922 826 928 946 845 967
66 2003 920 852 930 900 865 830 945 869 893 860
67 2004 925 857 913 877 901 957 941 940 942 865
68 2005 1020 855 1034 897 951 903 954 981 897 978
69 2006 1013 881 983 973 988 934 941 1015 955 940
70 2007 1007 896 960 926 1024 945 1041 1015 992 973
71 1998 3138 2732 3115 3109 3070 3190 3412 3296 3385 3273
72 1999 3019 2921 3322 3220 3091 3173 3299 3187 3303 3156
73 2000 3311 3197 3288 3136 3248 3206 3418 3345 3182 3371
74 2001 3375 2930 3210 3209 3369 3067 3385 3405 3091 3345
75 2002 3185 2992 3283 2870 3063 2985 3387 3208 3132 3298
76 2003 3220 2924 3049 3184 3007 3021 3339 2996 3246 3159
77 2004 3224 2912 3111 2992 2777 3169 3391 3360 3202 3170
78 2005 3187 2945 3286 3192 3123 3178 3228 3379 3333 3329
79 2006 3136 2856 3370 3040 3319 3282 3299 3303 3428 3523
80 2007 3246 2959 3275 2991 3241 3167 3435 3522 3261 3624
81 1998 63 61 54 60 51 61 66 60 55 58
82 1999 60 55 55 61 58 45 61 41 54 62
83 2000 51 57 50 53 49 59 55 50 58 56
84 2001 58 51 50 48 53 51 54 44 54 44
85 2002 50 54 47 50 53 44 56 39 54 59
86 2003 55 51 51 54 64 54 52 47 34 48
87 2004 60 56 62 41 43 51 51 54 41 46
88 2005 56 40 50 72 58 58 54 55 41 42
89 2006 44 43 43 53 63 57 38 45 61 35
90 2007 47 37 46 28 49 54 51 62 38 46
91 1998 295 295 312 355 352 340 354 301 356 359
92 1999 315 305 360 341 319 329 352 325 318 296
93 2000 289 284 339 378 332 330 333 339 321 346
94 2001 347 310 324 308 356 343 334 338 314 340
95 2002 344 281 361 305 315 297 358 334 331 329
96 2003 310 314 312 335 302 306 362 310 308 341
97 2004 363 288 316 331 321 347 326 372 324 333
98 2005 314 299 361 339 357 357 318 339 314 349
99 2006 328 304 365 337 337 331 386 338 354 388
100 2007 315 326 344 329 331 318 332 349 369 390
101 1998 1190 1035 1222 1145 1139 1186 1300 1297 1305 1208
102 1999 1180 1110 1256 1245 1151 1238 1209 1246 1254 1214
103 2000 1292 1285 1252 1162 1202 1199 1315 1284 1187 1321
104 2001 1279 1121 1242 1269 1289 1181 1307 1305 1184 1269
105 2002 1220 1161 1226 1068 1151 1145 1305 1185 1181 1251
106 2003 1237 1108 1135 1212 1111 1142 1253 1119 1230 1205
107 2004 1228 1103 1139 1110 1044 1168 1316 1226 1256 1208
108 2005 1226 1145 1161 1207 1185 1180 1194 1329 1284 1256
109 2006 1177 1086 1250 1149 1213 1251 1231 1227 1269 1341
110 2007 1260 1157 1235 1124 1218 1213 1302 1353 1207 1363
111 1998 932 835 894 912 898 956 1045 976 977 971
112 1999 872 902 1022 939 943 955 1011 939 987 932
113 2000 982 919 934 928 977 990 1033 979 953 984
114 2001 996 868 962 956 1030 912 1004 1033 936 1023
115 2002 892 872 968 925 939 861 1030 985 926 1021
116 2003 964 883 940 942 916 923 948 857 967 944
117 2004 974 861 953 902 800 957 1004 1003 949 935
118 2005 964 886 1029 962 949 988 981 997 1006 973
119 2006 960 857 1029 898 1000 943 993 1031 1077 1065
120 2007 971 852 980 881 996 942 1022 1057 991 1049
121 1998 247 201 234 242 238 248 262 278 289 280
122 1999 249 217 250 270 255 232 254 266 275 248
123 2000 271 257 259 245 283 249 291 269 253 299
124 2001 270 223 247 249 247 233 300 259 256 255
125 2002 276 249 275 202 266 273 267 263 266 262
126 2003 264 243 235 251 249 236 271 259 280 266
127 2004 227 232 274 232 241 246 291 281 279 247
128 2005 247 232 268 261 225 241 272 283 293 259
129 2006 253 229 286 233 276 305 239 250 258 241
130 2007 277 223 279 245 255 274 273 270 237 320
131 1998 474 366 453 455 443 460 451 444 458 455
132 1999 403 387 434 425 423 419 473 411 469 466
133 2000 477 452 504 423 454 438 446 474 468 421
134 2001 483 408 435 427 447 398 440 470 401 458
135 2002 453 429 453 370 392 409 427 441 428 435
136 2003 445 376 427 444 429 414 505 451 461 403
137 2004 432 428 429 417 371 451 454 478 394 447
138 2005 436 383 467 423 407 412 463 431 436 492
139 2006 418 380 440 423 493 452 450 457 470 488
140 2007 423 401 437 412 441 420 506 493 457 502
November December
1 1116 1135
2 1117 1255
3 1237 1258
4 1235 1311
5 1234 1243
6 1244 1373
7 1345 1462
8 1329 1456
9 1437 1395
10 1452 1531
11 4804 4934
12 4741 4953
13 4911 4922
14 4793 4724
15 4634 4978
16 4694 5131
17 4953 5285
18 5044 5348
19 5218 5108
20 5238 5351
21 1341 1418
22 1311 1378
23 1408 1461
24 1432 1389
25 1335 1447
26 1403 1462
27 1463 1554
28 1433 1639
29 1522 1503
30 1578 1580
31 629 576
32 607 601
33 618 622
34 623 604
35 601 590
36 564 611
37 577 652
38 627 621
39 624 626
40 651 673
41 1149 1192
42 1138 1167
43 1167 1154
44 1151 1130
45 1098 1217
46 1065 1222
47 1216 1288
48 1194 1271
49 1246 1260
50 1200 1267
51 809 855
52 790 908
53 836 832
54 756 837
55 785 857
56 845 925
57 854 891
58 918 900
59 890 859
60 912 908
61 876 893
62 895 899
63 882 853
64 831 764
65 815 867
66 817 911
67 843 900
68 872 917
69 936 860
70 897 923
71 2952 3233
72 3061 3241
73 3157 3211
74 3144 3206
75 2952 3182
76 2985 3242
77 3131 3385
78 3066 3159
79 3177 3244
80 3196 3334
81 48 49
82 50 43
83 53 46
84 43 31
85 44 42
86 29 40
87 45 35
88 45 42
89 40 52
90 44 40
91 274 326
92 299 329
93 310 297
94 311 309
95 291 304
96 296 350
97 338 340
98 298 328
99 315 348
100 304 332
101 1166 1214
102 1197 1257
103 1201 1255
104 1239 1236
105 1140 1268
106 1130 1228
107 1214 1272
108 1154 1188
109 1244 1236
110 1220 1313
111 845 968
112 891 948
113 965 894
114 910 992
115 879 950
116 869 969
117 892 1034
118 934 926
119 897 1010
120 986 988
121 252 254
122 265 238
123 251 283
124 246 242
125 238 238
126 250 270
127 232 273
128 231 264
129 281 240
130 241 245
131 415 471
132 409 469
133 430 482
134 438 427
135 404 422
136 440 425
137 455 466
138 449 453
139 440 410
140 445 456
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Januari Februari Maart April Mei
2.002e+03 8.996e-03 -1.543e-04 -8.605e-03 -1.161e-02 -1.488e-03
Juni Juli Augustus September Oktober November
1.142e-02 -1.107e-02 5.285e-03 3.282e-03 1.074e-02 -6.442e-03
December
-5.000e-04
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.4121 -1.8860 0.3924 2.0248 5.6085
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.002e+03 3.155e-01 6346.250 <2e-16 ***
Januari 8.996e-03 6.047e-03 1.488 0.1393
Februari -1.543e-04 4.983e-03 -0.031 0.9753
Maart -8.606e-03 5.097e-03 -1.688 0.0938 .
April -1.161e-02 4.954e-03 -2.344 0.0207 *
Mei -1.488e-03 3.898e-03 -0.382 0.7033
Juni 1.142e-02 5.138e-03 2.222 0.0280 *
Juli -1.107e-02 5.046e-03 -2.193 0.0301 *
Augustus 5.285e-03 4.464e-03 1.184 0.2387
September 3.282e-03 4.679e-03 0.702 0.4842
Oktober 1.074e-02 5.607e-03 1.916 0.0576 .
November -6.442e-03 6.440e-03 -1.000 0.3191
December -5.000e-04 4.150e-03 -0.120 0.9043
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.605 on 127 degrees of freedom
Multiple R-squared: 0.2541, Adjusted R-squared: 0.1836
F-statistic: 3.605 on 12 and 127 DF, p-value: 0.0001204
> 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,] 0.45262323 0.9052465 0.5473768
[2,] 0.50591365 0.9881727 0.4940864
[3,] 0.39420942 0.7884188 0.6057906
[4,] 0.70018913 0.5996217 0.2998109
[5,] 0.73629986 0.5274003 0.2637001
[6,] 0.70081717 0.5983657 0.2991828
[7,] 0.60834428 0.7833114 0.3916557
[8,] 0.51235378 0.9752924 0.4876462
[9,] 0.42055683 0.8411137 0.5794432
[10,] 0.37873527 0.7574705 0.6212647
[11,] 0.31215185 0.6243037 0.6878481
[12,] 0.29516670 0.5903334 0.7048333
[13,] 0.27676582 0.5535316 0.7232342
[14,] 0.32361280 0.6472256 0.6763872
[15,] 0.37916159 0.7583232 0.6208384
[16,] 0.36153744 0.7230749 0.6384626
[17,] 0.35169458 0.7033892 0.6483054
[18,] 0.33402218 0.6680444 0.6659778
[19,] 0.27530620 0.5506124 0.7246938
[20,] 0.22345533 0.4469107 0.7765447
[21,] 0.19236553 0.3847311 0.8076345
[22,] 0.18810233 0.3762047 0.8118977
[23,] 0.20057591 0.4011518 0.7994241
[24,] 0.22293464 0.4458693 0.7770654
[25,] 0.28093360 0.5618672 0.7190664
[26,] 0.30302578 0.6060516 0.6969742
[27,] 0.27113557 0.5422711 0.7288644
[28,] 0.22932375 0.4586475 0.7706762
[29,] 0.19363180 0.3872636 0.8063682
[30,] 0.16711846 0.3342369 0.8328815
[31,] 0.13618491 0.2723698 0.8638151
[32,] 0.13424685 0.2684937 0.8657532
[33,] 0.12952213 0.2590443 0.8704779
[34,] 0.13496707 0.2699341 0.8650329
[35,] 0.16941261 0.3388252 0.8305874
[36,] 0.16626944 0.3325389 0.8337306
[37,] 0.14956344 0.2991269 0.8504366
[38,] 0.16453082 0.3290616 0.8354692
[39,] 0.13451457 0.2690291 0.8654854
[40,] 0.10780224 0.2156045 0.8921978
[41,] 0.09431642 0.1886328 0.9056836
[42,] 0.10786442 0.2157288 0.8921356
[43,] 0.11043324 0.2208665 0.8895668
[44,] 0.10986473 0.2197295 0.8901353
[45,] 0.26345471 0.5269094 0.7365453
[46,] 0.26987333 0.5397467 0.7301267
[47,] 0.24339558 0.4867912 0.7566044
[48,] 0.22203475 0.4440695 0.7779652
[49,] 0.18563382 0.3712676 0.8143662
[50,] 0.15399874 0.3079975 0.8460013
[51,] 0.15257522 0.3051504 0.8474248
[52,] 0.14204381 0.2840876 0.8579562
[53,] 0.13226637 0.2645327 0.8677336
[54,] 0.16858776 0.3371755 0.8314122
[55,] 0.29818952 0.5963790 0.7018105
[56,] 0.41498899 0.8299780 0.5850110
[57,] 0.37700515 0.7540103 0.6229948
[58,] 0.38298826 0.7659765 0.6170117
[59,] 0.33796250 0.6759250 0.6620375
[60,] 0.29113160 0.5822632 0.7088684
[61,] 0.34857682 0.6971536 0.6514232
[62,] 0.30391515 0.6078303 0.6960848
[63,] 0.27457774 0.5491555 0.7254223
[64,] 0.23921064 0.4784213 0.7607894
[65,] 0.23389663 0.4677933 0.7661034
[66,] 0.31524411 0.6304882 0.6847559
[67,] 0.35330871 0.7066174 0.6466913
[68,] 0.36135545 0.7227109 0.6386446
[69,] 0.32999830 0.6599966 0.6700017
[70,] 0.28943216 0.5788643 0.7105678
[71,] 0.24689145 0.4937829 0.7531086
[72,] 0.21434944 0.4286989 0.7856506
[73,] 0.22027480 0.4405496 0.7797252
[74,] 0.26257003 0.5251401 0.7374300
[75,] 0.34001065 0.6800213 0.6599893
[76,] 0.38213956 0.7642791 0.6178604
[77,] 0.34848505 0.6969701 0.6515149
[78,] 0.33383031 0.6676606 0.6661697
[79,] 0.30082436 0.6016487 0.6991756
[80,] 0.25375373 0.5075075 0.7462463
[81,] 0.21767703 0.4353541 0.7823230
[82,] 0.18214982 0.3642996 0.8178502
[83,] 0.17867186 0.3573437 0.8213281
[84,] 0.19529374 0.3905875 0.8047063
[85,] 0.21440779 0.4288156 0.7855922
[86,] 0.35964271 0.7192854 0.6403573
[87,] 0.40642421 0.8128484 0.5935758
[88,] 0.41979573 0.8395915 0.5802043
[89,] 0.35752490 0.7150498 0.6424751
[90,] 0.30266266 0.6053253 0.6973373
[91,] 0.30671815 0.6134363 0.6932819
[92,] 0.25290145 0.5058029 0.7470985
[93,] 0.22354863 0.4470973 0.7764514
[94,] 0.22390063 0.4478013 0.7760994
[95,] 0.18110248 0.3622050 0.8188975
[96,] 0.18395305 0.3679061 0.8160469
[97,] 0.21101190 0.4220238 0.7889881
[98,] 0.30074435 0.6014887 0.6992556
[99,] 0.25111313 0.5022263 0.7488869
[100,] 0.29190894 0.5838179 0.7080911
[101,] 0.41015514 0.8203103 0.5898449
[102,] 0.37256754 0.7451351 0.6274325
[103,] 0.40352828 0.8070566 0.5964717
[104,] 0.32213553 0.6442711 0.6778645
[105,] 0.26144803 0.5228961 0.7385520
[106,] 0.39400255 0.7880051 0.6059974
[107,] 0.83354061 0.3329188 0.1664594
[108,] 0.71670735 0.5665853 0.2832926
[109,] 0.66520210 0.6695958 0.3347979
> postscript(file="/var/www/rcomp/tmp/1vrai1322065678.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2qa8f1322065678.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3j2il1322065678.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4654b1322065678.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5jqrh1322065678.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 = 140
Frequency = 1
1 2 3 4 5 6
-3.32164116 -3.22708763 -3.55884837 -2.80438128 -0.48655350 1.47504593
7 8 9 10 11 12
-0.39858306 2.44473465 1.34957776 3.12506698 0.95377884 2.02768792
13 14 15 16 17 18
-1.82330734 1.16299920 1.21275971 1.32815055 1.85044903 -1.26328307
19 20 21 22 23 24
-1.82756885 1.51435569 -3.69321880 -1.86966966 -1.33428707 -1.07348817
25 26 27 28 29 30
0.63435047 0.69587564 1.62307017 0.89622503 2.51647308 2.96976737
31 32 33 34 35 36
-3.41906040 -3.23499698 -3.20428645 -0.57055311 -0.99513549 0.36277956
37 38 39 40 41 42
1.45786961 2.31276406 2.33576106 3.86318657 -3.52746846 -2.27951710
43 44 45 46 47 48
-1.47716125 -2.16155931 0.15675753 -0.51284966 1.87212081 2.19276132
49 50 51 52 53 54
2.18280341 3.52597560 -2.28679058 -2.03533315 -3.37228040 -0.36748428
55 56 57 58 59 60
0.42195601 1.21620460 2.65750999 1.92916047 2.24208336 5.60850211
61 62 63 64 65 66
-3.50168011 -1.93479238 -1.81728937 -0.04591313 -0.38716601 2.18414320
67 68 69 70 71 72
0.85788128 2.02380885 3.51331305 4.16492684 -6.41209062 0.54058624
73 74 75 76 77 78
-3.28169574 -1.14560576 -1.06826986 2.69428661 -0.38110709 0.62268114
79 80 81 82 83 84
-0.32094820 -0.50551454 -4.41886955 -3.16419157 -2.43389257 -1.36777110
85 86 87 88 89 90
-0.31863721 0.61858485 1.57215905 2.87904795 3.58363625 4.33585975
91 92 93 94 95 96
-4.27667214 -2.31292572 -1.60186583 -2.05785442 -0.06971169 1.21861924
97 98 99 100 101 102
0.85883611 2.40394077 3.88418993 4.06519357 -4.54136508 -2.97565042
103 104 105 106 107 108
-3.36544960 -0.17850445 -0.71340266 1.01109588 1.12225962 0.60443788
109 110 111 112 113 114
2.04796203 2.30523247 -5.20474568 -1.66961954 -2.85583515 -1.72880744
115 116 117 118 119 120
0.72112829 0.69598027 0.87386686 2.58435600 1.99808216 3.48479405
121 122 123 124 125 126
-4.96714851 -2.87159662 -2.57831892 -0.93970630 -1.27072891 0.52794011
127 128 129 130 131 132
2.04601340 2.77780349 3.32577492 3.74646740 -4.80415641 -3.01161866
133 134 135 136 137 138
-2.26222344 -1.62273034 -1.11755209 1.85865391 1.33591973 2.87014581
139 140
3.04104557 4.51080079
> postscript(file="/var/www/rcomp/tmp/6gs9o1322065678.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 = 140
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.32164116 NA
1 -3.22708763 -3.32164116
2 -3.55884837 -3.22708763
3 -2.80438128 -3.55884837
4 -0.48655350 -2.80438128
5 1.47504593 -0.48655350
6 -0.39858306 1.47504593
7 2.44473465 -0.39858306
8 1.34957776 2.44473465
9 3.12506698 1.34957776
10 0.95377884 3.12506698
11 2.02768792 0.95377884
12 -1.82330734 2.02768792
13 1.16299920 -1.82330734
14 1.21275971 1.16299920
15 1.32815055 1.21275971
16 1.85044903 1.32815055
17 -1.26328307 1.85044903
18 -1.82756885 -1.26328307
19 1.51435569 -1.82756885
20 -3.69321880 1.51435569
21 -1.86966966 -3.69321880
22 -1.33428707 -1.86966966
23 -1.07348817 -1.33428707
24 0.63435047 -1.07348817
25 0.69587564 0.63435047
26 1.62307017 0.69587564
27 0.89622503 1.62307017
28 2.51647308 0.89622503
29 2.96976737 2.51647308
30 -3.41906040 2.96976737
31 -3.23499698 -3.41906040
32 -3.20428645 -3.23499698
33 -0.57055311 -3.20428645
34 -0.99513549 -0.57055311
35 0.36277956 -0.99513549
36 1.45786961 0.36277956
37 2.31276406 1.45786961
38 2.33576106 2.31276406
39 3.86318657 2.33576106
40 -3.52746846 3.86318657
41 -2.27951710 -3.52746846
42 -1.47716125 -2.27951710
43 -2.16155931 -1.47716125
44 0.15675753 -2.16155931
45 -0.51284966 0.15675753
46 1.87212081 -0.51284966
47 2.19276132 1.87212081
48 2.18280341 2.19276132
49 3.52597560 2.18280341
50 -2.28679058 3.52597560
51 -2.03533315 -2.28679058
52 -3.37228040 -2.03533315
53 -0.36748428 -3.37228040
54 0.42195601 -0.36748428
55 1.21620460 0.42195601
56 2.65750999 1.21620460
57 1.92916047 2.65750999
58 2.24208336 1.92916047
59 5.60850211 2.24208336
60 -3.50168011 5.60850211
61 -1.93479238 -3.50168011
62 -1.81728937 -1.93479238
63 -0.04591313 -1.81728937
64 -0.38716601 -0.04591313
65 2.18414320 -0.38716601
66 0.85788128 2.18414320
67 2.02380885 0.85788128
68 3.51331305 2.02380885
69 4.16492684 3.51331305
70 -6.41209062 4.16492684
71 0.54058624 -6.41209062
72 -3.28169574 0.54058624
73 -1.14560576 -3.28169574
74 -1.06826986 -1.14560576
75 2.69428661 -1.06826986
76 -0.38110709 2.69428661
77 0.62268114 -0.38110709
78 -0.32094820 0.62268114
79 -0.50551454 -0.32094820
80 -4.41886955 -0.50551454
81 -3.16419157 -4.41886955
82 -2.43389257 -3.16419157
83 -1.36777110 -2.43389257
84 -0.31863721 -1.36777110
85 0.61858485 -0.31863721
86 1.57215905 0.61858485
87 2.87904795 1.57215905
88 3.58363625 2.87904795
89 4.33585975 3.58363625
90 -4.27667214 4.33585975
91 -2.31292572 -4.27667214
92 -1.60186583 -2.31292572
93 -2.05785442 -1.60186583
94 -0.06971169 -2.05785442
95 1.21861924 -0.06971169
96 0.85883611 1.21861924
97 2.40394077 0.85883611
98 3.88418993 2.40394077
99 4.06519357 3.88418993
100 -4.54136508 4.06519357
101 -2.97565042 -4.54136508
102 -3.36544960 -2.97565042
103 -0.17850445 -3.36544960
104 -0.71340266 -0.17850445
105 1.01109588 -0.71340266
106 1.12225962 1.01109588
107 0.60443788 1.12225962
108 2.04796203 0.60443788
109 2.30523247 2.04796203
110 -5.20474568 2.30523247
111 -1.66961954 -5.20474568
112 -2.85583515 -1.66961954
113 -1.72880744 -2.85583515
114 0.72112829 -1.72880744
115 0.69598027 0.72112829
116 0.87386686 0.69598027
117 2.58435600 0.87386686
118 1.99808216 2.58435600
119 3.48479405 1.99808216
120 -4.96714851 3.48479405
121 -2.87159662 -4.96714851
122 -2.57831892 -2.87159662
123 -0.93970630 -2.57831892
124 -1.27072891 -0.93970630
125 0.52794011 -1.27072891
126 2.04601340 0.52794011
127 2.77780349 2.04601340
128 3.32577492 2.77780349
129 3.74646740 3.32577492
130 -4.80415641 3.74646740
131 -3.01161866 -4.80415641
132 -2.26222344 -3.01161866
133 -1.62273034 -2.26222344
134 -1.11755209 -1.62273034
135 1.85865391 -1.11755209
136 1.33591973 1.85865391
137 2.87014581 1.33591973
138 3.04104557 2.87014581
139 4.51080079 3.04104557
140 NA 4.51080079
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.22708763 -3.32164116
[2,] -3.55884837 -3.22708763
[3,] -2.80438128 -3.55884837
[4,] -0.48655350 -2.80438128
[5,] 1.47504593 -0.48655350
[6,] -0.39858306 1.47504593
[7,] 2.44473465 -0.39858306
[8,] 1.34957776 2.44473465
[9,] 3.12506698 1.34957776
[10,] 0.95377884 3.12506698
[11,] 2.02768792 0.95377884
[12,] -1.82330734 2.02768792
[13,] 1.16299920 -1.82330734
[14,] 1.21275971 1.16299920
[15,] 1.32815055 1.21275971
[16,] 1.85044903 1.32815055
[17,] -1.26328307 1.85044903
[18,] -1.82756885 -1.26328307
[19,] 1.51435569 -1.82756885
[20,] -3.69321880 1.51435569
[21,] -1.86966966 -3.69321880
[22,] -1.33428707 -1.86966966
[23,] -1.07348817 -1.33428707
[24,] 0.63435047 -1.07348817
[25,] 0.69587564 0.63435047
[26,] 1.62307017 0.69587564
[27,] 0.89622503 1.62307017
[28,] 2.51647308 0.89622503
[29,] 2.96976737 2.51647308
[30,] -3.41906040 2.96976737
[31,] -3.23499698 -3.41906040
[32,] -3.20428645 -3.23499698
[33,] -0.57055311 -3.20428645
[34,] -0.99513549 -0.57055311
[35,] 0.36277956 -0.99513549
[36,] 1.45786961 0.36277956
[37,] 2.31276406 1.45786961
[38,] 2.33576106 2.31276406
[39,] 3.86318657 2.33576106
[40,] -3.52746846 3.86318657
[41,] -2.27951710 -3.52746846
[42,] -1.47716125 -2.27951710
[43,] -2.16155931 -1.47716125
[44,] 0.15675753 -2.16155931
[45,] -0.51284966 0.15675753
[46,] 1.87212081 -0.51284966
[47,] 2.19276132 1.87212081
[48,] 2.18280341 2.19276132
[49,] 3.52597560 2.18280341
[50,] -2.28679058 3.52597560
[51,] -2.03533315 -2.28679058
[52,] -3.37228040 -2.03533315
[53,] -0.36748428 -3.37228040
[54,] 0.42195601 -0.36748428
[55,] 1.21620460 0.42195601
[56,] 2.65750999 1.21620460
[57,] 1.92916047 2.65750999
[58,] 2.24208336 1.92916047
[59,] 5.60850211 2.24208336
[60,] -3.50168011 5.60850211
[61,] -1.93479238 -3.50168011
[62,] -1.81728937 -1.93479238
[63,] -0.04591313 -1.81728937
[64,] -0.38716601 -0.04591313
[65,] 2.18414320 -0.38716601
[66,] 0.85788128 2.18414320
[67,] 2.02380885 0.85788128
[68,] 3.51331305 2.02380885
[69,] 4.16492684 3.51331305
[70,] -6.41209062 4.16492684
[71,] 0.54058624 -6.41209062
[72,] -3.28169574 0.54058624
[73,] -1.14560576 -3.28169574
[74,] -1.06826986 -1.14560576
[75,] 2.69428661 -1.06826986
[76,] -0.38110709 2.69428661
[77,] 0.62268114 -0.38110709
[78,] -0.32094820 0.62268114
[79,] -0.50551454 -0.32094820
[80,] -4.41886955 -0.50551454
[81,] -3.16419157 -4.41886955
[82,] -2.43389257 -3.16419157
[83,] -1.36777110 -2.43389257
[84,] -0.31863721 -1.36777110
[85,] 0.61858485 -0.31863721
[86,] 1.57215905 0.61858485
[87,] 2.87904795 1.57215905
[88,] 3.58363625 2.87904795
[89,] 4.33585975 3.58363625
[90,] -4.27667214 4.33585975
[91,] -2.31292572 -4.27667214
[92,] -1.60186583 -2.31292572
[93,] -2.05785442 -1.60186583
[94,] -0.06971169 -2.05785442
[95,] 1.21861924 -0.06971169
[96,] 0.85883611 1.21861924
[97,] 2.40394077 0.85883611
[98,] 3.88418993 2.40394077
[99,] 4.06519357 3.88418993
[100,] -4.54136508 4.06519357
[101,] -2.97565042 -4.54136508
[102,] -3.36544960 -2.97565042
[103,] -0.17850445 -3.36544960
[104,] -0.71340266 -0.17850445
[105,] 1.01109588 -0.71340266
[106,] 1.12225962 1.01109588
[107,] 0.60443788 1.12225962
[108,] 2.04796203 0.60443788
[109,] 2.30523247 2.04796203
[110,] -5.20474568 2.30523247
[111,] -1.66961954 -5.20474568
[112,] -2.85583515 -1.66961954
[113,] -1.72880744 -2.85583515
[114,] 0.72112829 -1.72880744
[115,] 0.69598027 0.72112829
[116,] 0.87386686 0.69598027
[117,] 2.58435600 0.87386686
[118,] 1.99808216 2.58435600
[119,] 3.48479405 1.99808216
[120,] -4.96714851 3.48479405
[121,] -2.87159662 -4.96714851
[122,] -2.57831892 -2.87159662
[123,] -0.93970630 -2.57831892
[124,] -1.27072891 -0.93970630
[125,] 0.52794011 -1.27072891
[126,] 2.04601340 0.52794011
[127,] 2.77780349 2.04601340
[128,] 3.32577492 2.77780349
[129,] 3.74646740 3.32577492
[130,] -4.80415641 3.74646740
[131,] -3.01161866 -4.80415641
[132,] -2.26222344 -3.01161866
[133,] -1.62273034 -2.26222344
[134,] -1.11755209 -1.62273034
[135,] 1.85865391 -1.11755209
[136,] 1.33591973 1.85865391
[137,] 2.87014581 1.33591973
[138,] 3.04104557 2.87014581
[139,] 4.51080079 3.04104557
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.22708763 -3.32164116
2 -3.55884837 -3.22708763
3 -2.80438128 -3.55884837
4 -0.48655350 -2.80438128
5 1.47504593 -0.48655350
6 -0.39858306 1.47504593
7 2.44473465 -0.39858306
8 1.34957776 2.44473465
9 3.12506698 1.34957776
10 0.95377884 3.12506698
11 2.02768792 0.95377884
12 -1.82330734 2.02768792
13 1.16299920 -1.82330734
14 1.21275971 1.16299920
15 1.32815055 1.21275971
16 1.85044903 1.32815055
17 -1.26328307 1.85044903
18 -1.82756885 -1.26328307
19 1.51435569 -1.82756885
20 -3.69321880 1.51435569
21 -1.86966966 -3.69321880
22 -1.33428707 -1.86966966
23 -1.07348817 -1.33428707
24 0.63435047 -1.07348817
25 0.69587564 0.63435047
26 1.62307017 0.69587564
27 0.89622503 1.62307017
28 2.51647308 0.89622503
29 2.96976737 2.51647308
30 -3.41906040 2.96976737
31 -3.23499698 -3.41906040
32 -3.20428645 -3.23499698
33 -0.57055311 -3.20428645
34 -0.99513549 -0.57055311
35 0.36277956 -0.99513549
36 1.45786961 0.36277956
37 2.31276406 1.45786961
38 2.33576106 2.31276406
39 3.86318657 2.33576106
40 -3.52746846 3.86318657
41 -2.27951710 -3.52746846
42 -1.47716125 -2.27951710
43 -2.16155931 -1.47716125
44 0.15675753 -2.16155931
45 -0.51284966 0.15675753
46 1.87212081 -0.51284966
47 2.19276132 1.87212081
48 2.18280341 2.19276132
49 3.52597560 2.18280341
50 -2.28679058 3.52597560
51 -2.03533315 -2.28679058
52 -3.37228040 -2.03533315
53 -0.36748428 -3.37228040
54 0.42195601 -0.36748428
55 1.21620460 0.42195601
56 2.65750999 1.21620460
57 1.92916047 2.65750999
58 2.24208336 1.92916047
59 5.60850211 2.24208336
60 -3.50168011 5.60850211
61 -1.93479238 -3.50168011
62 -1.81728937 -1.93479238
63 -0.04591313 -1.81728937
64 -0.38716601 -0.04591313
65 2.18414320 -0.38716601
66 0.85788128 2.18414320
67 2.02380885 0.85788128
68 3.51331305 2.02380885
69 4.16492684 3.51331305
70 -6.41209062 4.16492684
71 0.54058624 -6.41209062
72 -3.28169574 0.54058624
73 -1.14560576 -3.28169574
74 -1.06826986 -1.14560576
75 2.69428661 -1.06826986
76 -0.38110709 2.69428661
77 0.62268114 -0.38110709
78 -0.32094820 0.62268114
79 -0.50551454 -0.32094820
80 -4.41886955 -0.50551454
81 -3.16419157 -4.41886955
82 -2.43389257 -3.16419157
83 -1.36777110 -2.43389257
84 -0.31863721 -1.36777110
85 0.61858485 -0.31863721
86 1.57215905 0.61858485
87 2.87904795 1.57215905
88 3.58363625 2.87904795
89 4.33585975 3.58363625
90 -4.27667214 4.33585975
91 -2.31292572 -4.27667214
92 -1.60186583 -2.31292572
93 -2.05785442 -1.60186583
94 -0.06971169 -2.05785442
95 1.21861924 -0.06971169
96 0.85883611 1.21861924
97 2.40394077 0.85883611
98 3.88418993 2.40394077
99 4.06519357 3.88418993
100 -4.54136508 4.06519357
101 -2.97565042 -4.54136508
102 -3.36544960 -2.97565042
103 -0.17850445 -3.36544960
104 -0.71340266 -0.17850445
105 1.01109588 -0.71340266
106 1.12225962 1.01109588
107 0.60443788 1.12225962
108 2.04796203 0.60443788
109 2.30523247 2.04796203
110 -5.20474568 2.30523247
111 -1.66961954 -5.20474568
112 -2.85583515 -1.66961954
113 -1.72880744 -2.85583515
114 0.72112829 -1.72880744
115 0.69598027 0.72112829
116 0.87386686 0.69598027
117 2.58435600 0.87386686
118 1.99808216 2.58435600
119 3.48479405 1.99808216
120 -4.96714851 3.48479405
121 -2.87159662 -4.96714851
122 -2.57831892 -2.87159662
123 -0.93970630 -2.57831892
124 -1.27072891 -0.93970630
125 0.52794011 -1.27072891
126 2.04601340 0.52794011
127 2.77780349 2.04601340
128 3.32577492 2.77780349
129 3.74646740 3.32577492
130 -4.80415641 3.74646740
131 -3.01161866 -4.80415641
132 -2.26222344 -3.01161866
133 -1.62273034 -2.26222344
134 -1.11755209 -1.62273034
135 1.85865391 -1.11755209
136 1.33591973 1.85865391
137 2.87014581 1.33591973
138 3.04104557 2.87014581
139 4.51080079 3.04104557
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/73uc51322065679.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/862111322065679.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9uckl1322065679.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10ms061322065679.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11uir11322065679.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12ovc71322065679.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13m4o21322065679.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14wyr01322065679.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/15s35y1322065679.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/1657001322065679.tab")
+ }
>
> try(system("convert tmp/1vrai1322065678.ps tmp/1vrai1322065678.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qa8f1322065678.ps tmp/2qa8f1322065678.png",intern=TRUE))
character(0)
> try(system("convert tmp/3j2il1322065678.ps tmp/3j2il1322065678.png",intern=TRUE))
character(0)
> try(system("convert tmp/4654b1322065678.ps tmp/4654b1322065678.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jqrh1322065678.ps tmp/5jqrh1322065678.png",intern=TRUE))
character(0)
> try(system("convert tmp/6gs9o1322065678.ps tmp/6gs9o1322065678.png",intern=TRUE))
character(0)
> try(system("convert tmp/73uc51322065679.ps tmp/73uc51322065679.png",intern=TRUE))
character(0)
> try(system("convert tmp/862111322065679.ps tmp/862111322065679.png",intern=TRUE))
character(0)
> try(system("convert tmp/9uckl1322065679.ps tmp/9uckl1322065679.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ms061322065679.ps tmp/10ms061322065679.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.760 0.310 6.062