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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
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(100
+ ,100
+ ,100
+ ,100
+ ,102.815
+ ,101.542
+ ,100.254
+ ,102
+ ,104.301
+ ,102.179
+ ,102.839
+ ,103.65
+ ,104.964
+ ,105.494
+ ,104.726
+ ,104.974
+ ,104.83
+ ,106.14
+ ,103.387
+ ,104.641
+ ,105.878
+ ,106.371
+ ,101.746
+ ,104.902
+ ,107.542
+ ,107.249
+ ,100.371
+ ,105.695
+ ,107.954
+ ,109.481
+ ,101.337
+ ,106.489
+ ,108.09
+ ,111.951
+ ,102.307
+ ,107.146
+ ,109.19
+ ,111.972
+ ,101.794
+ ,107.695
+ ,110.115
+ ,110.661
+ ,100.294
+ ,107.711
+ ,110.439
+ ,113.149
+ ,100.578
+ ,108.313
+ ,111.054
+ ,113.853
+ ,97.9592
+ ,108.124
+ ,112.319
+ ,115.143
+ ,100.107
+ ,109.615
+ ,113.607
+ ,116.923
+ ,102.865
+ ,111.34
+ ,112.716
+ ,116.638
+ ,102.719
+ ,110.717
+ ,113.126
+ ,116.227
+ ,103.921
+ ,111.217
+ ,112.818
+ ,115.942
+ ,105.751
+ ,111.452
+ ,112.565
+ ,116.42
+ ,106.746
+ ,111.611
+ ,112.698
+ ,113.365
+ ,108.454
+ ,111.717
+ ,113.701
+ ,112.709
+ ,107.724
+ ,112.062
+ ,113.844
+ ,115.609
+ ,108.936
+ ,112.842
+ ,114.151
+ ,115.626
+ ,109.764
+ ,113.241
+ ,114.069
+ ,116.697
+ ,108.502
+ ,113.015
+ ,114.798
+ ,119.368
+ ,109.211
+ ,113.998
+ ,114.537
+ ,120.264
+ ,113.097
+ ,114.936
+ ,114.118
+ ,118.74
+ ,112.18
+ ,114.245
+ ,113.814
+ ,116.522
+ ,114.855
+ ,114.437
+ ,115.232
+ ,116.967
+ ,114.53
+ ,115.286
+ ,115.945
+ ,118.061
+ ,115.328
+ ,116.071
+ ,117.543
+ ,118.711
+ ,117.973
+ ,117.807
+ ,118.205
+ ,119.223
+ ,117.863
+ ,118.255
+ ,119.899
+ ,119.196
+ ,116.582
+ ,118.969
+ ,121.35
+ ,120.729
+ ,117.645
+ ,120.333
+ ,122.563
+ ,121.828
+ ,120.711
+ ,121.998
+ ,124.143
+ ,122.603
+ ,121.37
+ ,123.239
+ ,126.574
+ ,123.803
+ ,120.473
+ ,124.666
+ ,128.069
+ ,127.692
+ ,122.204
+ ,126.54
+ ,128.101
+ ,128.336
+ ,124.943
+ ,127.336
+ ,128.752
+ ,128.718
+ ,125.276
+ ,127.871
+ ,129.991
+ ,130.539
+ ,130.192
+ ,130.115
+ ,133.236
+ ,132.864
+ ,131.595
+ ,132.773
+ ,134.689
+ ,134.529
+ ,133.091
+ ,134.265
+ ,135.058
+ ,135.166
+ ,133.167
+ ,134.596
+ ,135.615
+ ,133.458
+ ,131.858
+ ,134.38
+ ,136.088
+ ,135.621
+ ,132.5
+ ,135.121
+ ,136.114
+ ,137.409
+ ,131.551
+ ,135.136
+ ,136.177
+ ,138.866
+ ,131.422
+ ,135.336
+ ,136.883
+ ,135.802
+ ,131.112
+ ,135.284
+ ,139.095
+ ,139.408
+ ,131.193
+ ,137.144
+ ,141.551
+ ,142.191
+ ,136.448
+ ,140.349
+ ,144.647
+ ,146.027
+ ,138.433
+ ,143.264
+ ,147.403
+ ,145.695
+ ,136.323
+ ,144.381
+ ,148.778
+ ,148.469
+ ,137.453
+ ,145.881
+ ,149.123
+ ,152.221
+ ,137.072
+ ,146.497
+ ,150.925
+ ,157.061
+ ,139.485
+ ,148.857
+ ,152.195
+ ,160.782
+ ,142.049
+ ,150.78
+ ,155.762
+ ,164.581
+ ,141.315
+ ,153.293
+ ,159.863
+ ,171.274
+ ,145.023
+ ,157.641
+ ,164.488
+ ,177.848
+ ,148.287
+ ,162.182
+ ,172.288
+ ,185.538
+ ,147.732
+ ,167.86
+ ,181.098
+ ,193.704
+ ,151.23
+ ,175.245
+ ,186.026
+ ,203.366
+ ,150.278
+ ,179.32
+ ,191.144
+ ,213.692
+ ,154.789
+ ,184.979
+ ,196.021
+ ,220.819
+ ,153.029
+ ,188.482
+ ,200.338
+ ,225.005
+ ,157.658
+ ,192.86
+ ,202.319
+ ,229.096
+ ,161.039
+ ,195.475
+ ,204.148
+ ,233.982
+ ,165.599
+ ,198.4
+ ,205.288
+ ,234.529
+ ,171.248
+ ,200.598
+ ,206.439
+ ,238.753
+ ,172.249
+ ,202.121
+ ,210.638
+ ,238.258
+ ,177.164
+ ,205.875
+ ,212.831
+ ,241.42
+ ,174.947
+ ,207.085
+ ,214.227
+ ,242.44
+ ,179.407
+ ,209.204
+ ,216.573
+ ,248.809
+ ,181.625
+ ,212.246
+ ,217.504
+ ,254.991
+ ,188.871
+ ,215.466
+ ,219.151
+ ,255.458
+ ,189.866
+ ,216.693
+ ,220.494
+ ,261.125
+ ,192.114
+ ,219.019
+ ,220.484
+ ,258.58
+ ,189.665
+ ,217.924
+ ,220.269
+ ,257.981
+ ,191.006
+ ,217.978
+ ,222.524
+ ,257.756
+ ,186.398
+ ,218.186
+ ,221.905
+ ,257.984
+ ,189.577
+ ,218.54
+ ,222.286
+ ,252.604
+ ,190.244
+ ,217.886
+ ,219.929
+ ,251.688
+ ,190.269
+ ,216.347
+ ,222.144
+ ,255.734
+ ,196.606
+ ,219.825
+ ,224.73
+ ,257.646
+ ,197.796
+ ,221.956
+ ,228.912
+ ,263.016
+ ,205.874
+ ,227.184
+ ,231.613
+ ,265.367
+ ,206.229
+ ,229.247
+ ,235.936
+ ,271.406
+ ,208.473
+ ,233.33
+ ,239.005
+ ,278.478
+ ,211.102
+ ,236.987
+ ,242.293
+ ,284.415
+ ,211.503
+ ,240.027
+ ,248.077
+ ,287.685
+ ,218.055
+ ,245.433
+ ,248.956
+ ,287.97
+ ,221.076
+ ,246.641
+ ,252.358
+ ,290.44
+ ,226.743
+ ,250.328
+ ,254.122
+ ,292.298
+ ,223.179
+ ,250.849
+ ,255.015
+ ,296.637
+ ,219.996
+ ,251.435
+ ,253.493
+ ,299.882
+ ,223.847
+ ,252.091
+ ,255.976
+ ,292.588
+ ,227.227
+ ,252.946
+ ,255.878
+ ,292.523
+ ,226.757
+ ,252.773
+ ,254.149
+ ,290.063
+ ,223.928
+ ,250.677
+ ,252.408
+ ,296.831
+ ,220.682
+ ,250.105
+ ,252.503
+ ,296.742
+ ,227.654
+ ,251.788
+ ,253.733
+ ,296.479
+ ,218.398
+ ,250.212
+ ,252.299
+ ,295.557
+ ,213.639
+ ,248.073
+ ,248.838
+ ,288.037
+ ,212.71
+ ,244.468
+ ,247.559
+ ,287.377
+ ,217.355
+ ,244.727
+ ,245.331
+ ,290.101
+ ,217.786
+ ,244.034
+ ,242.351
+ ,296.679
+ ,218.186
+ ,243.588
+ ,238.172
+ ,285.712
+ ,206.917
+ ,236.447
+ ,226.723
+ ,270.085
+ ,197.833
+ ,224.906
+ ,225.84
+ ,261.006
+ ,194.438
+ ,221.934
+ ,225.751
+ ,266.44
+ ,202.508
+ ,224.903
+ ,226.192
+ ,267.075
+ ,196.651
+ ,223.798
+ ,220.037
+ ,263.672
+ ,191.446
+ ,218.529
+ ,220.406
+ ,259.121
+ ,190.056
+ ,217.521
+ ,223.551
+ ,262.711
+ ,190.322
+ ,219.971
+ ,223.373
+ ,265.838
+ ,203.701
+ ,223.841
+ ,224.678
+ ,265.766
+ ,200.524
+ ,223.764
+ ,223.629
+ ,269.162
+ ,200.524
+ ,223.664
+ ,220.855
+ ,256.573
+ ,191.582
+ ,217.678
+ ,220.127
+ ,257.917
+ ,195.727
+ ,218.478
+ ,215.471
+ ,253.316
+ ,194.766
+ ,214.815
+ ,214.691
+ ,257.496
+ ,194.576
+ ,215.143
+ ,216.2
+ ,264.861
+ ,198.563
+ ,218.381
+ ,219.85
+ ,257.795
+ ,201.679
+ ,219.962
+ ,220.182
+ ,251.318
+ ,201.506
+ ,218.933
+ ,220.283
+ ,243.526
+ ,204.453
+ ,218.36
+ ,216.675
+ ,247.503
+ ,206.552
+ ,217.72
+ ,217.808
+ ,256.9
+ ,205.642
+ ,219.934
+ ,217.66
+ ,261.806
+ ,205.679
+ ,220.842
+ ,217.951
+ ,260.758
+ ,204.583
+ ,220.584
+ ,215.9
+ ,244.361
+ ,204.484
+ ,216.346
+ ,217.141
+ ,255.116
+ ,208.73
+ ,220.221
+ ,219.459
+ ,256.46
+ ,210.264
+ ,222.182
+ ,222.898
+ ,258.249
+ ,214.211
+ ,225.455
+ ,225.478
+ ,256.327
+ ,214.169
+ ,226.42
+ ,228.098
+ ,259.192
+ ,213.656
+ ,228.287
+ ,230.729
+ ,260.776
+ ,219.028
+ ,231.349
+ ,230.535
+ ,261.166
+ ,217.602
+ ,231.015
+ ,229.735
+ ,265.351
+ ,220.635
+ ,232.241
+ ,233.148
+ ,261.627
+ ,222.011
+ ,233.688
+ ,235.221
+ ,266.932
+ ,224.948
+ ,236.667
+ ,237.46
+ ,268.695
+ ,225.566
+ ,238.439
+ ,239.951
+ ,274.37
+ ,219.318
+ ,239.488
+ ,240.436
+ ,275.671
+ ,213.558
+ ,238.741
+ ,241.588
+ ,270.831
+ ,214.026
+ ,238.5
+ ,241.512
+ ,275.141
+ ,225.59
+ ,242.116
+ ,243.05
+ ,277.59
+ ,227.637
+ ,243.923
+ ,246.469
+ ,276.357
+ ,229
+ ,245.813
+ ,248.64
+ ,279.389
+ ,226.841
+ ,247.143
+ ,251.147
+ ,274.787
+ ,221.488
+ ,246.381)
+ ,dim=c(4
+ ,150)
+ ,dimnames=list(c('SMF'
+ ,'SSF'
+ ,'NS'
+ ,'TOT')
+ ,1:150))
> y <- array(NA,dim=c(4,150),dimnames=list(c('SMF','SSF','NS','TOT'),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 = '4'
> 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
TOT SMF SSF NS
1 100.000 100.000 100.000 100.0000
2 102.000 102.815 101.542 100.2540
3 103.650 104.301 102.179 102.8390
4 104.974 104.964 105.494 104.7260
5 104.641 104.830 106.140 103.3870
6 104.902 105.878 106.371 101.7460
7 105.695 107.542 107.249 100.3710
8 106.489 107.954 109.481 101.3370
9 107.146 108.090 111.951 102.3070
10 107.695 109.190 111.972 101.7940
11 107.711 110.115 110.661 100.2940
12 108.313 110.439 113.149 100.5780
13 108.124 111.054 113.853 97.9592
14 109.615 112.319 115.143 100.1070
15 111.340 113.607 116.923 102.8650
16 110.717 112.716 116.638 102.7190
17 111.217 113.126 116.227 103.9210
18 111.452 112.818 115.942 105.7510
19 111.611 112.565 116.420 106.7460
20 111.717 112.698 113.365 108.4540
21 112.062 113.701 112.709 107.7240
22 112.842 113.844 115.609 108.9360
23 113.241 114.151 115.626 109.7640
24 113.015 114.069 116.697 108.5020
25 113.998 114.798 119.368 109.2110
26 114.936 114.537 120.264 113.0970
27 114.245 114.118 118.740 112.1800
28 114.437 113.814 116.522 114.8550
29 115.286 115.232 116.967 114.5300
30 116.071 115.945 118.061 115.3280
31 117.807 117.543 118.711 117.9730
32 118.255 118.205 119.223 117.8630
33 118.969 119.899 119.196 116.5820
34 120.333 121.350 120.729 117.6450
35 121.998 122.563 121.828 120.7110
36 123.239 124.143 122.603 121.3700
37 124.666 126.574 123.803 120.4730
38 126.540 128.069 127.692 122.2040
39 127.336 128.101 128.336 124.9430
40 127.871 128.752 128.718 125.2760
41 130.115 129.991 130.539 130.1920
42 132.773 133.236 132.864 131.5950
43 134.265 134.689 134.529 133.0910
44 134.596 135.058 135.166 133.1670
45 134.380 135.615 133.458 131.8580
46 135.121 136.088 135.621 132.5000
47 135.136 136.114 137.409 131.5510
48 135.336 136.177 138.866 131.4220
49 135.284 136.883 135.802 131.1120
50 137.144 139.095 139.408 131.1930
51 140.349 141.551 142.191 136.4480
52 143.264 144.647 146.027 138.4330
53 144.381 147.403 145.695 136.3230
54 145.881 148.778 148.469 137.4530
55 146.497 149.123 152.221 137.0720
56 148.857 150.925 157.061 139.4850
57 150.780 152.195 160.782 142.0490
58 153.293 155.762 164.581 141.3150
59 157.641 159.863 171.274 145.0230
60 162.182 164.488 177.848 148.2870
61 167.860 172.288 185.538 147.7320
62 175.245 181.098 193.704 151.2300
63 179.320 186.026 203.366 150.2780
64 184.979 191.144 213.692 154.7890
65 188.482 196.021 220.819 153.0290
66 192.860 200.338 225.005 157.6580
67 195.475 202.319 229.096 161.0390
68 198.400 204.148 233.982 165.5990
69 200.598 205.288 234.529 171.2480
70 202.121 206.439 238.753 172.2490
71 205.875 210.638 238.258 177.1640
72 207.085 212.831 241.420 174.9470
73 209.204 214.227 242.440 179.4070
74 212.246 216.573 248.809 181.6250
75 215.466 217.504 254.991 188.8710
76 216.693 219.151 255.458 189.8660
77 219.019 220.494 261.125 192.1140
78 217.924 220.484 258.580 189.6650
79 217.978 220.269 257.981 191.0060
80 218.186 222.524 257.756 186.3980
81 218.540 221.905 257.984 189.5770
82 217.886 222.286 252.604 190.2440
83 216.347 219.929 251.688 190.2690
84 219.825 222.144 255.734 196.6060
85 221.956 224.730 257.646 197.7960
86 227.184 228.912 263.016 205.8740
87 229.247 231.613 265.367 206.2290
88 233.330 235.936 271.406 208.4730
89 236.987 239.005 278.478 211.1020
90 240.027 242.293 284.415 211.5030
91 245.433 248.077 287.685 218.0550
92 246.641 248.956 287.970 221.0760
93 250.328 252.358 290.440 226.7430
94 250.849 254.122 292.298 223.1790
95 251.435 255.015 296.637 219.9960
96 252.091 253.493 299.882 223.8470
97 252.946 255.976 292.588 227.2270
98 252.773 255.878 292.523 226.7570
99 250.677 254.149 290.063 223.9280
100 250.105 252.408 296.831 220.6820
101 251.788 252.503 296.742 227.6540
102 250.212 253.733 296.479 218.3980
103 248.073 252.299 295.557 213.6390
104 244.468 248.838 288.037 212.7100
105 244.727 247.559 287.377 217.3550
106 244.034 245.331 290.101 217.7860
107 243.588 242.351 296.679 218.1860
108 236.447 238.172 285.712 206.9170
109 224.906 226.723 270.085 197.8330
110 221.934 225.840 261.006 194.4380
111 224.903 225.751 266.440 202.5080
112 223.798 226.192 267.075 196.6510
113 218.529 220.037 263.672 191.4460
114 217.521 220.406 259.121 190.0560
115 219.971 223.551 262.711 190.3220
116 223.841 223.373 265.838 203.7010
117 223.764 224.678 265.766 200.5240
118 223.664 223.629 269.162 200.5240
119 217.678 220.855 256.573 191.5820
120 218.478 220.127 257.917 195.7270
121 214.815 215.471 253.316 194.7660
122 215.143 214.691 257.496 194.5760
123 218.381 216.200 264.861 198.5630
124 219.962 219.850 257.795 201.6790
125 218.933 220.182 251.318 201.5060
126 218.360 220.283 243.526 204.4530
127 217.720 216.675 247.503 206.5520
128 219.934 217.808 256.900 205.6420
129 220.842 217.660 261.806 205.6790
130 220.584 217.951 260.758 204.5830
131 216.346 215.900 244.361 204.4840
132 220.221 217.141 255.116 208.7300
133 222.182 219.459 256.460 210.2640
134 225.455 222.898 258.249 214.2110
135 226.420 225.478 256.327 214.1690
136 228.287 228.098 259.192 213.6560
137 231.349 230.729 260.776 219.0280
138 231.015 230.535 261.166 217.6020
139 232.241 229.735 265.351 220.6350
140 233.688 233.148 261.627 222.0110
141 236.667 235.221 266.932 224.9480
142 238.439 237.460 268.695 225.5660
143 239.488 239.951 274.370 219.3180
144 238.741 240.436 275.671 213.5580
145 238.500 241.588 270.831 214.0260
146 242.116 241.512 275.141 225.5900
147 243.923 243.050 277.590 227.6370
148 245.813 246.469 276.357 229.0000
149 247.143 248.640 279.389 226.8410
150 246.381 251.147 274.787 221.4880
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) SMF SSF NS
0.5545 0.6038 0.1407 0.2509
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.75389 -0.08679 -0.01902 0.11559 0.65297
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.554471 0.120368 4.606 8.85e-06 ***
SMF 0.603776 0.004448 135.756 < 2e-16 ***
SSF 0.140660 0.002927 48.059 < 2e-16 ***
NS 0.250930 0.002144 117.014 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2048 on 146 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 3.383e+06 on 3 and 146 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,] 9.083172e-09 1.816634e-08 1.000000e+00
[2,] 1.422077e-11 2.844155e-11 1.000000e+00
[3,] 1.969604e-14 3.939209e-14 1.000000e+00
[4,] 1.286039e-16 2.572079e-16 1.000000e+00
[5,] 3.349914e-19 6.699829e-19 1.000000e+00
[6,] 6.431820e-22 1.286364e-21 1.000000e+00
[7,] 2.660228e-24 5.320456e-24 1.000000e+00
[8,] 5.569769e-27 1.113954e-26 1.000000e+00
[9,] 2.216027e-29 4.432054e-29 1.000000e+00
[10,] 3.882150e-32 7.764300e-32 1.000000e+00
[11,] 1.682117e-34 3.364233e-34 1.000000e+00
[12,] 3.699921e-37 7.399841e-37 1.000000e+00
[13,] 6.581659e-40 1.316332e-39 1.000000e+00
[14,] 1.175552e-42 2.351105e-42 1.000000e+00
[15,] 1.804254e-45 3.608508e-45 1.000000e+00
[16,] 5.101728e-48 1.020346e-47 1.000000e+00
[17,] 1.811718e-50 3.623436e-50 1.000000e+00
[18,] 3.383817e-53 6.767634e-53 1.000000e+00
[19,] 8.265620e-56 1.653124e-55 1.000000e+00
[20,] 4.316779e-58 8.633557e-58 1.000000e+00
[21,] 8.436566e-61 1.687313e-60 1.000000e+00
[22,] 2.307373e-63 4.614745e-63 1.000000e+00
[23,] 4.643987e-66 9.287974e-66 1.000000e+00
[24,] 1.173131e-68 2.346263e-68 1.000000e+00
[25,] 2.024070e-71 4.048141e-71 1.000000e+00
[26,] 7.639873e-74 1.527975e-73 1.000000e+00
[27,] 1.562911e-76 3.125821e-76 1.000000e+00
[28,] 3.119978e-79 6.239955e-79 1.000000e+00
[29,] 7.224738e-82 1.444948e-81 1.000000e+00
[30,] 1.149493e-84 2.298986e-84 1.000000e+00
[31,] 8.475554e-87 1.695111e-86 1.000000e+00
[32,] 1.606700e-89 3.213401e-89 1.000000e+00
[33,] 2.384548e-92 4.769096e-92 1.000000e+00
[34,] 3.483250e-95 6.966500e-95 1.000000e+00
[35,] 6.165492e-98 1.233098e-97 1.000000e+00
[36,] 1.068705e-100 2.137410e-100 1.000000e+00
[37,] 1.569958e-103 3.139915e-103 1.000000e+00
[38,] 3.307659e-106 6.615317e-106 1.000000e+00
[39,] 1.656634e-108 3.313268e-108 1.000000e+00
[40,] 2.376799e-111 4.753597e-111 1.000000e+00
[41,] 3.855238e-114 7.710477e-114 1.000000e+00
[42,] 5.237413e-117 1.047483e-116 1.000000e+00
[43,] 7.987538e-120 1.597508e-119 1.000000e+00
[44,] 1.034205e-122 2.068409e-122 1.000000e+00
[45,] 3.207456e-125 6.414912e-125 1.000000e+00
[46,] 5.806416e-128 1.161283e-127 1.000000e+00
[47,] 1.422914e-130 2.845828e-130 1.000000e+00
[48,] 2.116659e-133 4.233319e-133 1.000000e+00
[49,] 1.392595e-135 2.785189e-135 1.000000e+00
[50,] 1.834305e-138 3.668611e-138 1.000000e+00
[51,] 2.764439e-141 5.528878e-141 1.000000e+00
[52,] 3.448957e-144 6.897914e-144 1.000000e+00
[53,] 9.608573e-146 1.921715e-145 1.000000e+00
[54,] 1.830442e-148 3.660883e-148 1.000000e+00
[55,] 2.295846e-151 4.591691e-151 1.000000e+00
[56,] 5.251157e-154 1.050231e-153 1.000000e+00
[57,] 9.190669e-157 1.838134e-156 1.000000e+00
[58,] 1.436467e-159 2.872934e-159 1.000000e+00
[59,] 2.660980e-162 5.321960e-162 1.000000e+00
[60,] 3.667813e-165 7.335626e-165 1.000000e+00
[61,] 5.378241e-168 1.075648e-167 1.000000e+00
[62,] 7.236712e-171 1.447342e-170 1.000000e+00
[63,] 1.135655e-173 2.271310e-173 1.000000e+00
[64,] 3.082254e-176 6.164508e-176 1.000000e+00
[65,] 5.801328e-179 1.160266e-178 1.000000e+00
[66,] 1.312506e-181 2.625011e-181 1.000000e+00
[67,] 7.404291e-184 1.480858e-183 1.000000e+00
[68,] 2.921315e-57 5.842631e-57 1.000000e+00
[69,] 7.036398e-52 1.407280e-51 1.000000e+00
[70,] 2.345313e-52 4.690627e-52 1.000000e+00
[71,] 1.185389e-46 2.370777e-46 1.000000e+00
[72,] 1.041801e-46 2.083602e-46 1.000000e+00
[73,] 2.788988e-46 5.577975e-46 1.000000e+00
[74,] 1.266364e-45 2.532728e-45 1.000000e+00
[75,] 7.000723e-43 1.400145e-42 1.000000e+00
[76,] 2.496945e-26 4.993890e-26 1.000000e+00
[77,] 2.945774e-20 5.891548e-20 1.000000e+00
[78,] 1.553428e-16 3.106857e-16 1.000000e+00
[79,] 3.167532e-14 6.335064e-14 1.000000e+00
[80,] 5.135487e-12 1.027097e-11 1.000000e+00
[81,] 1.006728e-10 2.013456e-10 1.000000e+00
[82,] 3.215749e-10 6.431498e-10 1.000000e+00
[83,] 1.918831e-10 3.837663e-10 1.000000e+00
[84,] 1.223623e-10 2.447246e-10 1.000000e+00
[85,] 1.225879e-10 2.451758e-10 1.000000e+00
[86,] 4.417776e-10 8.835553e-10 1.000000e+00
[87,] 1.491791e-08 2.983582e-08 1.000000e+00
[88,] 5.299555e-08 1.059911e-07 9.999999e-01
[89,] 2.895614e-08 5.791229e-08 1.000000e+00
[90,] 2.268318e-08 4.536635e-08 1.000000e+00
[91,] 2.374710e-07 4.749421e-07 9.999998e-01
[92,] 1.545189e-06 3.090377e-06 9.999985e-01
[93,] 6.687824e-06 1.337565e-05 9.999933e-01
[94,] 4.542662e-06 9.085323e-06 9.999955e-01
[95,] 1.366902e-05 2.733804e-05 9.999863e-01
[96,] 1.024691e-05 2.049383e-05 9.999898e-01
[97,] 5.955779e-06 1.191156e-05 9.999940e-01
[98,] 8.795734e-06 1.759147e-05 9.999912e-01
[99,] 3.709362e-05 7.418723e-05 9.999629e-01
[100,] 1.359703e-04 2.719406e-04 9.998640e-01
[101,] 2.320462e-03 4.640924e-03 9.976795e-01
[102,] 6.905443e-03 1.381089e-02 9.930946e-01
[103,] 8.174897e-03 1.634979e-02 9.918251e-01
[104,] 2.152024e-02 4.304048e-02 9.784798e-01
[105,] 3.709585e-02 7.419171e-02 9.629041e-01
[106,] 4.346398e-02 8.692795e-02 9.565360e-01
[107,] 3.343213e-02 6.686427e-02 9.665679e-01
[108,] 3.128766e-02 6.257531e-02 9.687123e-01
[109,] 2.978383e-02 5.956766e-02 9.702162e-01
[110,] 3.781726e-02 7.563452e-02 9.621827e-01
[111,] 4.137027e-02 8.274054e-02 9.586297e-01
[112,] 1.139837e-01 2.279674e-01 8.860163e-01
[113,] 1.262239e-01 2.524479e-01 8.737761e-01
[114,] 1.943210e-01 3.886419e-01 8.056790e-01
[115,] 2.265697e-01 4.531394e-01 7.734303e-01
[116,] 2.596076e-01 5.192152e-01 7.403924e-01
[117,] 6.123334e-01 7.753331e-01 3.876666e-01
[118,] 8.912659e-01 2.174682e-01 1.087341e-01
[119,] 9.836828e-01 3.263434e-02 1.631717e-02
[120,] 9.942665e-01 1.146700e-02 5.733498e-03
[121,] 9.926683e-01 1.466341e-02 7.331707e-03
[122,] 9.962567e-01 7.486601e-03 3.743301e-03
[123,] 9.994452e-01 1.109504e-03 5.547519e-04
[124,] 9.999983e-01 3.388956e-06 1.694478e-06
[125,] 9.999984e-01 3.145109e-06 1.572554e-06
[126,] 9.999961e-01 7.808528e-06 3.904264e-06
[127,] 9.999890e-01 2.208568e-05 1.104284e-05
[128,] 9.999815e-01 3.699033e-05 1.849517e-05
[129,] 9.999436e-01 1.128381e-04 5.641904e-05
[130,] 9.998829e-01 2.342714e-04 1.171357e-04
[131,] 9.998617e-01 2.766826e-04 1.383413e-04
[132,] 9.998791e-01 2.418530e-04 1.209265e-04
[133,] 9.996205e-01 7.589749e-04 3.794875e-04
[134,] 9.987450e-01 2.510058e-03 1.255029e-03
[135,] 9.955470e-01 8.905967e-03 4.452984e-03
[136,] 9.861200e-01 2.776008e-02 1.388004e-02
[137,] 9.832518e-01 3.349649e-02 1.674824e-02
> postscript(file="/var/wessaorg/rcomp/tmp/16svt1353429666.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/29z561353429666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3ykjc1353429666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4n3bx1353429666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/554fb1353429666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-0.091107978 -0.071371732 -0.056838208 -0.072935029 -0.079899740 -0.072372990
7 8 9 10 11 12
-0.062526789 -0.073634266 -0.089580330 -0.078960692 -0.060652961 -0.075502648
13 14 15 16 17 18
-0.077713387 -0.070889592 -0.065993689 -0.074305252 -0.065660399 -0.063811660
19 20 21 22 23 24
-0.068967393 -0.042142289 -0.027277692 -0.045659106 -0.042179860 -0.052643051
25 26 27 28 29 30
-0.063408208 -0.068969023 -0.062517961 -0.046224669 -0.034420558 -0.034037306
31 32 33 34 35 36
-0.018011104 -0.014126474 0.002316278 0.007866501 0.016548534 0.029207731
37 38 39 40 41 42
0.044720464 0.034688206 0.033484326 0.038134180 0.044340524 0.063997392
43 44 45 46 47 48
0.069120137 0.068655642 0.085067313 0.075136440 0.061071049 0.050461570
49 50 51 52 53 54
0.080966188 0.077868176 0.089898721 0.097939584 0.127094587 0.123160433
55 56 57 58 59 60
0.098705867 0.084412254 0.073835582 0.082981725 0.083009169 0.087809504
61 62 63 64 65 66
0.113946837 0.153295741 0.132715931 0.117188350 0.114725828 0.135865414
67 68 69 70 71 72
0.130949696 0.120136472 0.135385575 0.118110308 0.173158778 0.170623311
73 74 75 76 77 78
0.184129652 0.357244126 0.327327813 0.244544712 0.398462031 0.282007676
79 80 81 82 83 84
0.213577373 0.247997416 0.145957021 -0.148701489 -0.142029891 -0.160649410
85 86 87 88 89 90
-0.158563360 -0.237913923 -0.225485079 -0.165142388 -0.015574356 0.103488431
91 92 93 94 95 96
-0.086805926 -0.207673584 -0.344171948 -0.255263770 -0.020048432 0.132124710
97 98 99 100 101 102
-0.334221836 -0.320971649 -0.317137448 0.024569709 -0.086756148 -0.045796594
103 104 105 106 107 108
0.004884032 -0.219569555 -0.261075431 -0.100170965 0.227448425 -0.020019939
109 110 111 112 113 114
-0.170843058 -0.480748523 -0.247366075 -0.238251821 -0.006251825 -0.248108572
115 116 117 118 119 120
-0.268701244 -0.088268971 -0.145863811 -0.090183948 -0.386722021 -0.376325999
121 122 123 124 125 126
-0.339823831 -0.081160416 0.209321679 -0.201456482 -0.476444536 -0.753894887
127 128 129 130 131 132
-0.301578024 0.134908363 0.432904955 0.421637341 -0.246774074 0.300691754
133 134 135 136 137 138
0.288164658 0.242716092 -0.069138747 -0.058295789 -0.155633594 -0.069531849
139 140 141 142 143 144
0.289755519 -0.145394674 0.098794032 0.115880849 0.430441503 0.652969826
145 146 147 148 149 150
0.279778663 0.433663463 0.453925235 0.111030472 0.245509921 -0.039609839
> postscript(file="/var/wessaorg/rcomp/tmp/6ntvn1353429666.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 -0.091107978 NA
1 -0.071371732 -0.091107978
2 -0.056838208 -0.071371732
3 -0.072935029 -0.056838208
4 -0.079899740 -0.072935029
5 -0.072372990 -0.079899740
6 -0.062526789 -0.072372990
7 -0.073634266 -0.062526789
8 -0.089580330 -0.073634266
9 -0.078960692 -0.089580330
10 -0.060652961 -0.078960692
11 -0.075502648 -0.060652961
12 -0.077713387 -0.075502648
13 -0.070889592 -0.077713387
14 -0.065993689 -0.070889592
15 -0.074305252 -0.065993689
16 -0.065660399 -0.074305252
17 -0.063811660 -0.065660399
18 -0.068967393 -0.063811660
19 -0.042142289 -0.068967393
20 -0.027277692 -0.042142289
21 -0.045659106 -0.027277692
22 -0.042179860 -0.045659106
23 -0.052643051 -0.042179860
24 -0.063408208 -0.052643051
25 -0.068969023 -0.063408208
26 -0.062517961 -0.068969023
27 -0.046224669 -0.062517961
28 -0.034420558 -0.046224669
29 -0.034037306 -0.034420558
30 -0.018011104 -0.034037306
31 -0.014126474 -0.018011104
32 0.002316278 -0.014126474
33 0.007866501 0.002316278
34 0.016548534 0.007866501
35 0.029207731 0.016548534
36 0.044720464 0.029207731
37 0.034688206 0.044720464
38 0.033484326 0.034688206
39 0.038134180 0.033484326
40 0.044340524 0.038134180
41 0.063997392 0.044340524
42 0.069120137 0.063997392
43 0.068655642 0.069120137
44 0.085067313 0.068655642
45 0.075136440 0.085067313
46 0.061071049 0.075136440
47 0.050461570 0.061071049
48 0.080966188 0.050461570
49 0.077868176 0.080966188
50 0.089898721 0.077868176
51 0.097939584 0.089898721
52 0.127094587 0.097939584
53 0.123160433 0.127094587
54 0.098705867 0.123160433
55 0.084412254 0.098705867
56 0.073835582 0.084412254
57 0.082981725 0.073835582
58 0.083009169 0.082981725
59 0.087809504 0.083009169
60 0.113946837 0.087809504
61 0.153295741 0.113946837
62 0.132715931 0.153295741
63 0.117188350 0.132715931
64 0.114725828 0.117188350
65 0.135865414 0.114725828
66 0.130949696 0.135865414
67 0.120136472 0.130949696
68 0.135385575 0.120136472
69 0.118110308 0.135385575
70 0.173158778 0.118110308
71 0.170623311 0.173158778
72 0.184129652 0.170623311
73 0.357244126 0.184129652
74 0.327327813 0.357244126
75 0.244544712 0.327327813
76 0.398462031 0.244544712
77 0.282007676 0.398462031
78 0.213577373 0.282007676
79 0.247997416 0.213577373
80 0.145957021 0.247997416
81 -0.148701489 0.145957021
82 -0.142029891 -0.148701489
83 -0.160649410 -0.142029891
84 -0.158563360 -0.160649410
85 -0.237913923 -0.158563360
86 -0.225485079 -0.237913923
87 -0.165142388 -0.225485079
88 -0.015574356 -0.165142388
89 0.103488431 -0.015574356
90 -0.086805926 0.103488431
91 -0.207673584 -0.086805926
92 -0.344171948 -0.207673584
93 -0.255263770 -0.344171948
94 -0.020048432 -0.255263770
95 0.132124710 -0.020048432
96 -0.334221836 0.132124710
97 -0.320971649 -0.334221836
98 -0.317137448 -0.320971649
99 0.024569709 -0.317137448
100 -0.086756148 0.024569709
101 -0.045796594 -0.086756148
102 0.004884032 -0.045796594
103 -0.219569555 0.004884032
104 -0.261075431 -0.219569555
105 -0.100170965 -0.261075431
106 0.227448425 -0.100170965
107 -0.020019939 0.227448425
108 -0.170843058 -0.020019939
109 -0.480748523 -0.170843058
110 -0.247366075 -0.480748523
111 -0.238251821 -0.247366075
112 -0.006251825 -0.238251821
113 -0.248108572 -0.006251825
114 -0.268701244 -0.248108572
115 -0.088268971 -0.268701244
116 -0.145863811 -0.088268971
117 -0.090183948 -0.145863811
118 -0.386722021 -0.090183948
119 -0.376325999 -0.386722021
120 -0.339823831 -0.376325999
121 -0.081160416 -0.339823831
122 0.209321679 -0.081160416
123 -0.201456482 0.209321679
124 -0.476444536 -0.201456482
125 -0.753894887 -0.476444536
126 -0.301578024 -0.753894887
127 0.134908363 -0.301578024
128 0.432904955 0.134908363
129 0.421637341 0.432904955
130 -0.246774074 0.421637341
131 0.300691754 -0.246774074
132 0.288164658 0.300691754
133 0.242716092 0.288164658
134 -0.069138747 0.242716092
135 -0.058295789 -0.069138747
136 -0.155633594 -0.058295789
137 -0.069531849 -0.155633594
138 0.289755519 -0.069531849
139 -0.145394674 0.289755519
140 0.098794032 -0.145394674
141 0.115880849 0.098794032
142 0.430441503 0.115880849
143 0.652969826 0.430441503
144 0.279778663 0.652969826
145 0.433663463 0.279778663
146 0.453925235 0.433663463
147 0.111030472 0.453925235
148 0.245509921 0.111030472
149 -0.039609839 0.245509921
150 NA -0.039609839
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.071371732 -0.091107978
[2,] -0.056838208 -0.071371732
[3,] -0.072935029 -0.056838208
[4,] -0.079899740 -0.072935029
[5,] -0.072372990 -0.079899740
[6,] -0.062526789 -0.072372990
[7,] -0.073634266 -0.062526789
[8,] -0.089580330 -0.073634266
[9,] -0.078960692 -0.089580330
[10,] -0.060652961 -0.078960692
[11,] -0.075502648 -0.060652961
[12,] -0.077713387 -0.075502648
[13,] -0.070889592 -0.077713387
[14,] -0.065993689 -0.070889592
[15,] -0.074305252 -0.065993689
[16,] -0.065660399 -0.074305252
[17,] -0.063811660 -0.065660399
[18,] -0.068967393 -0.063811660
[19,] -0.042142289 -0.068967393
[20,] -0.027277692 -0.042142289
[21,] -0.045659106 -0.027277692
[22,] -0.042179860 -0.045659106
[23,] -0.052643051 -0.042179860
[24,] -0.063408208 -0.052643051
[25,] -0.068969023 -0.063408208
[26,] -0.062517961 -0.068969023
[27,] -0.046224669 -0.062517961
[28,] -0.034420558 -0.046224669
[29,] -0.034037306 -0.034420558
[30,] -0.018011104 -0.034037306
[31,] -0.014126474 -0.018011104
[32,] 0.002316278 -0.014126474
[33,] 0.007866501 0.002316278
[34,] 0.016548534 0.007866501
[35,] 0.029207731 0.016548534
[36,] 0.044720464 0.029207731
[37,] 0.034688206 0.044720464
[38,] 0.033484326 0.034688206
[39,] 0.038134180 0.033484326
[40,] 0.044340524 0.038134180
[41,] 0.063997392 0.044340524
[42,] 0.069120137 0.063997392
[43,] 0.068655642 0.069120137
[44,] 0.085067313 0.068655642
[45,] 0.075136440 0.085067313
[46,] 0.061071049 0.075136440
[47,] 0.050461570 0.061071049
[48,] 0.080966188 0.050461570
[49,] 0.077868176 0.080966188
[50,] 0.089898721 0.077868176
[51,] 0.097939584 0.089898721
[52,] 0.127094587 0.097939584
[53,] 0.123160433 0.127094587
[54,] 0.098705867 0.123160433
[55,] 0.084412254 0.098705867
[56,] 0.073835582 0.084412254
[57,] 0.082981725 0.073835582
[58,] 0.083009169 0.082981725
[59,] 0.087809504 0.083009169
[60,] 0.113946837 0.087809504
[61,] 0.153295741 0.113946837
[62,] 0.132715931 0.153295741
[63,] 0.117188350 0.132715931
[64,] 0.114725828 0.117188350
[65,] 0.135865414 0.114725828
[66,] 0.130949696 0.135865414
[67,] 0.120136472 0.130949696
[68,] 0.135385575 0.120136472
[69,] 0.118110308 0.135385575
[70,] 0.173158778 0.118110308
[71,] 0.170623311 0.173158778
[72,] 0.184129652 0.170623311
[73,] 0.357244126 0.184129652
[74,] 0.327327813 0.357244126
[75,] 0.244544712 0.327327813
[76,] 0.398462031 0.244544712
[77,] 0.282007676 0.398462031
[78,] 0.213577373 0.282007676
[79,] 0.247997416 0.213577373
[80,] 0.145957021 0.247997416
[81,] -0.148701489 0.145957021
[82,] -0.142029891 -0.148701489
[83,] -0.160649410 -0.142029891
[84,] -0.158563360 -0.160649410
[85,] -0.237913923 -0.158563360
[86,] -0.225485079 -0.237913923
[87,] -0.165142388 -0.225485079
[88,] -0.015574356 -0.165142388
[89,] 0.103488431 -0.015574356
[90,] -0.086805926 0.103488431
[91,] -0.207673584 -0.086805926
[92,] -0.344171948 -0.207673584
[93,] -0.255263770 -0.344171948
[94,] -0.020048432 -0.255263770
[95,] 0.132124710 -0.020048432
[96,] -0.334221836 0.132124710
[97,] -0.320971649 -0.334221836
[98,] -0.317137448 -0.320971649
[99,] 0.024569709 -0.317137448
[100,] -0.086756148 0.024569709
[101,] -0.045796594 -0.086756148
[102,] 0.004884032 -0.045796594
[103,] -0.219569555 0.004884032
[104,] -0.261075431 -0.219569555
[105,] -0.100170965 -0.261075431
[106,] 0.227448425 -0.100170965
[107,] -0.020019939 0.227448425
[108,] -0.170843058 -0.020019939
[109,] -0.480748523 -0.170843058
[110,] -0.247366075 -0.480748523
[111,] -0.238251821 -0.247366075
[112,] -0.006251825 -0.238251821
[113,] -0.248108572 -0.006251825
[114,] -0.268701244 -0.248108572
[115,] -0.088268971 -0.268701244
[116,] -0.145863811 -0.088268971
[117,] -0.090183948 -0.145863811
[118,] -0.386722021 -0.090183948
[119,] -0.376325999 -0.386722021
[120,] -0.339823831 -0.376325999
[121,] -0.081160416 -0.339823831
[122,] 0.209321679 -0.081160416
[123,] -0.201456482 0.209321679
[124,] -0.476444536 -0.201456482
[125,] -0.753894887 -0.476444536
[126,] -0.301578024 -0.753894887
[127,] 0.134908363 -0.301578024
[128,] 0.432904955 0.134908363
[129,] 0.421637341 0.432904955
[130,] -0.246774074 0.421637341
[131,] 0.300691754 -0.246774074
[132,] 0.288164658 0.300691754
[133,] 0.242716092 0.288164658
[134,] -0.069138747 0.242716092
[135,] -0.058295789 -0.069138747
[136,] -0.155633594 -0.058295789
[137,] -0.069531849 -0.155633594
[138,] 0.289755519 -0.069531849
[139,] -0.145394674 0.289755519
[140,] 0.098794032 -0.145394674
[141,] 0.115880849 0.098794032
[142,] 0.430441503 0.115880849
[143,] 0.652969826 0.430441503
[144,] 0.279778663 0.652969826
[145,] 0.433663463 0.279778663
[146,] 0.453925235 0.433663463
[147,] 0.111030472 0.453925235
[148,] 0.245509921 0.111030472
[149,] -0.039609839 0.245509921
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.071371732 -0.091107978
2 -0.056838208 -0.071371732
3 -0.072935029 -0.056838208
4 -0.079899740 -0.072935029
5 -0.072372990 -0.079899740
6 -0.062526789 -0.072372990
7 -0.073634266 -0.062526789
8 -0.089580330 -0.073634266
9 -0.078960692 -0.089580330
10 -0.060652961 -0.078960692
11 -0.075502648 -0.060652961
12 -0.077713387 -0.075502648
13 -0.070889592 -0.077713387
14 -0.065993689 -0.070889592
15 -0.074305252 -0.065993689
16 -0.065660399 -0.074305252
17 -0.063811660 -0.065660399
18 -0.068967393 -0.063811660
19 -0.042142289 -0.068967393
20 -0.027277692 -0.042142289
21 -0.045659106 -0.027277692
22 -0.042179860 -0.045659106
23 -0.052643051 -0.042179860
24 -0.063408208 -0.052643051
25 -0.068969023 -0.063408208
26 -0.062517961 -0.068969023
27 -0.046224669 -0.062517961
28 -0.034420558 -0.046224669
29 -0.034037306 -0.034420558
30 -0.018011104 -0.034037306
31 -0.014126474 -0.018011104
32 0.002316278 -0.014126474
33 0.007866501 0.002316278
34 0.016548534 0.007866501
35 0.029207731 0.016548534
36 0.044720464 0.029207731
37 0.034688206 0.044720464
38 0.033484326 0.034688206
39 0.038134180 0.033484326
40 0.044340524 0.038134180
41 0.063997392 0.044340524
42 0.069120137 0.063997392
43 0.068655642 0.069120137
44 0.085067313 0.068655642
45 0.075136440 0.085067313
46 0.061071049 0.075136440
47 0.050461570 0.061071049
48 0.080966188 0.050461570
49 0.077868176 0.080966188
50 0.089898721 0.077868176
51 0.097939584 0.089898721
52 0.127094587 0.097939584
53 0.123160433 0.127094587
54 0.098705867 0.123160433
55 0.084412254 0.098705867
56 0.073835582 0.084412254
57 0.082981725 0.073835582
58 0.083009169 0.082981725
59 0.087809504 0.083009169
60 0.113946837 0.087809504
61 0.153295741 0.113946837
62 0.132715931 0.153295741
63 0.117188350 0.132715931
64 0.114725828 0.117188350
65 0.135865414 0.114725828
66 0.130949696 0.135865414
67 0.120136472 0.130949696
68 0.135385575 0.120136472
69 0.118110308 0.135385575
70 0.173158778 0.118110308
71 0.170623311 0.173158778
72 0.184129652 0.170623311
73 0.357244126 0.184129652
74 0.327327813 0.357244126
75 0.244544712 0.327327813
76 0.398462031 0.244544712
77 0.282007676 0.398462031
78 0.213577373 0.282007676
79 0.247997416 0.213577373
80 0.145957021 0.247997416
81 -0.148701489 0.145957021
82 -0.142029891 -0.148701489
83 -0.160649410 -0.142029891
84 -0.158563360 -0.160649410
85 -0.237913923 -0.158563360
86 -0.225485079 -0.237913923
87 -0.165142388 -0.225485079
88 -0.015574356 -0.165142388
89 0.103488431 -0.015574356
90 -0.086805926 0.103488431
91 -0.207673584 -0.086805926
92 -0.344171948 -0.207673584
93 -0.255263770 -0.344171948
94 -0.020048432 -0.255263770
95 0.132124710 -0.020048432
96 -0.334221836 0.132124710
97 -0.320971649 -0.334221836
98 -0.317137448 -0.320971649
99 0.024569709 -0.317137448
100 -0.086756148 0.024569709
101 -0.045796594 -0.086756148
102 0.004884032 -0.045796594
103 -0.219569555 0.004884032
104 -0.261075431 -0.219569555
105 -0.100170965 -0.261075431
106 0.227448425 -0.100170965
107 -0.020019939 0.227448425
108 -0.170843058 -0.020019939
109 -0.480748523 -0.170843058
110 -0.247366075 -0.480748523
111 -0.238251821 -0.247366075
112 -0.006251825 -0.238251821
113 -0.248108572 -0.006251825
114 -0.268701244 -0.248108572
115 -0.088268971 -0.268701244
116 -0.145863811 -0.088268971
117 -0.090183948 -0.145863811
118 -0.386722021 -0.090183948
119 -0.376325999 -0.386722021
120 -0.339823831 -0.376325999
121 -0.081160416 -0.339823831
122 0.209321679 -0.081160416
123 -0.201456482 0.209321679
124 -0.476444536 -0.201456482
125 -0.753894887 -0.476444536
126 -0.301578024 -0.753894887
127 0.134908363 -0.301578024
128 0.432904955 0.134908363
129 0.421637341 0.432904955
130 -0.246774074 0.421637341
131 0.300691754 -0.246774074
132 0.288164658 0.300691754
133 0.242716092 0.288164658
134 -0.069138747 0.242716092
135 -0.058295789 -0.069138747
136 -0.155633594 -0.058295789
137 -0.069531849 -0.155633594
138 0.289755519 -0.069531849
139 -0.145394674 0.289755519
140 0.098794032 -0.145394674
141 0.115880849 0.098794032
142 0.430441503 0.115880849
143 0.652969826 0.430441503
144 0.279778663 0.652969826
145 0.433663463 0.279778663
146 0.453925235 0.433663463
147 0.111030472 0.453925235
148 0.245509921 0.111030472
149 -0.039609839 0.245509921
> 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/7fa1d1353429666.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/875201353429666.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/995jd1353429666.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/10fi2m1353429666.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11l8wf1353429666.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/128uxn1353429666.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1378vi1353429666.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14fw901353429666.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/15oa6h1353429667.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/16ijsd1353429667.tab")
+ }
>
> try(system("convert tmp/16svt1353429666.ps tmp/16svt1353429666.png",intern=TRUE))
character(0)
> try(system("convert tmp/29z561353429666.ps tmp/29z561353429666.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ykjc1353429666.ps tmp/3ykjc1353429666.png",intern=TRUE))
character(0)
> try(system("convert tmp/4n3bx1353429666.ps tmp/4n3bx1353429666.png",intern=TRUE))
character(0)
> try(system("convert tmp/554fb1353429666.ps tmp/554fb1353429666.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ntvn1353429666.ps tmp/6ntvn1353429666.png",intern=TRUE))
character(0)
> try(system("convert tmp/7fa1d1353429666.ps tmp/7fa1d1353429666.png",intern=TRUE))
character(0)
> try(system("convert tmp/875201353429666.ps tmp/875201353429666.png",intern=TRUE))
character(0)
> try(system("convert tmp/995jd1353429666.ps tmp/995jd1353429666.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fi2m1353429666.ps tmp/10fi2m1353429666.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.693 1.467 10.167