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(67.643
+ ,64.033
+ ,131.676
+ ,69.371
+ ,65.679
+ ,135.050
+ ,66.294
+ ,62.776
+ ,129.070
+ ,70.768
+ ,67.024
+ ,137.792
+ ,71.774
+ ,67.988
+ ,139.762
+ ,73.388
+ ,69.529
+ ,142.917
+ ,74.040
+ ,70.158
+ ,144.198
+ ,73.238
+ ,69.410
+ ,142.648
+ ,78.121
+ ,74.049
+ ,152.170
+ ,69.825
+ ,66.197
+ ,136.022
+ ,71.099
+ ,67.043
+ ,138.142
+ ,70.676
+ ,67.459
+ ,138.135
+ ,69.515
+ ,65.512
+ ,135.027
+ ,68.246
+ ,64.665
+ ,132.911
+ ,68.594
+ ,65.382
+ ,133.976
+ ,70.405
+ ,66.607
+ ,137.012
+ ,61.223
+ ,58.387
+ ,119.610
+ ,60.542
+ ,57.564
+ ,118.106
+ ,61.952
+ ,58.431
+ ,120.383
+ ,68.173
+ ,65.012
+ ,133.185
+ ,67.240
+ ,64.176
+ ,131.416
+ ,68.739
+ ,65.509
+ ,134.248
+ ,69.234
+ ,65.163
+ ,134.397
+ ,65.570
+ ,62.158
+ ,127.728
+ ,67.408
+ ,64.429
+ ,131.837
+ ,64.630
+ ,61.325
+ ,125.955
+ ,68.848
+ ,65.339
+ ,134.187
+ ,73.370
+ ,69.921
+ ,143.291
+ ,74.292
+ ,70.782
+ ,145.074
+ ,76.525
+ ,73.287
+ ,149.812
+ ,74.368
+ ,70.300
+ ,144.668
+ ,75.674
+ ,71.579
+ ,147.253
+ ,74.868
+ ,70.700
+ ,145.568
+ ,79.824
+ ,75.740
+ ,155.564
+ ,80.022
+ ,75.850
+ ,155.872
+ ,79.942
+ ,76.381
+ ,156.323
+ ,80.622
+ ,77.388
+ ,158.010
+ ,80.079
+ ,75.519
+ ,155.598
+ ,79.212
+ ,75.573
+ ,154.785
+ ,80.626
+ ,76.668
+ ,157.294
+ ,83.551
+ ,79.387
+ ,162.938
+ ,80.407
+ ,76.876
+ ,157.283
+ ,85.053
+ ,81.021
+ ,166.074
+ ,86.399
+ ,82.883
+ ,169.282
+ ,88.536
+ ,84.016
+ ,172.552
+ ,89.008
+ ,85.047
+ ,174.055
+ ,89.652
+ ,85.757
+ ,175.409
+ ,88.904
+ ,84.792
+ ,173.696
+ ,87.472
+ ,83.811
+ ,171.283
+ ,88.631
+ ,84.691
+ ,173.322
+ ,87.221
+ ,83.496
+ ,170.717
+ ,88.759
+ ,85.470
+ ,174.229
+ ,90.127
+ ,85.212
+ ,175.339
+ ,88.709
+ ,84.802
+ ,173.511
+ ,90.030
+ ,85.809
+ ,175.839
+ ,88.697
+ ,85.119
+ ,173.816
+ ,88.762
+ ,85.228
+ ,173.990
+ ,89.475
+ ,85.302
+ ,174.777
+ ,88.936
+ ,85.883
+ ,174.819
+ ,90.411
+ ,86.315
+ ,176.726
+ ,90.004
+ ,86.195
+ ,176.199
+ ,92.725
+ ,88.227
+ ,180.952
+ ,90.252
+ ,86.411
+ ,176.663
+ ,93.226
+ ,89.120
+ ,182.346
+ ,92.575
+ ,88.030
+ ,180.605
+ ,93.125
+ ,89.372
+ ,182.497
+ ,95.987
+ ,91.869
+ ,187.856
+ ,97.175
+ ,92.845
+ ,190.020
+ ,97.321
+ ,92.787
+ ,190.108
+ ,98.577
+ ,94.711
+ ,193.288
+ ,99.026
+ ,94.204
+ ,193.230
+ ,101.851
+ ,97.217
+ ,199.068
+ ,99.958
+ ,95.118
+ ,195.076
+ ,97.875
+ ,93.688
+ ,191.563
+ ,97.927
+ ,93.140
+ ,191.067
+ ,95.149
+ ,91.516
+ ,186.665
+ ,94.551
+ ,90.957
+ ,185.508
+ ,93.999
+ ,90.372
+ ,184.371
+ ,93.297
+ ,89.749
+ ,183.046
+ ,89.901
+ ,85.813
+ ,175.714
+ ,89.742
+ ,86.026
+ ,175.768
+ ,87.096
+ ,83.933
+ ,171.029
+ ,86.863
+ ,83.602
+ ,170.465
+ ,86.718
+ ,83.384
+ ,170.102
+ ,80.020
+ ,76.369
+ ,156.389
+ ,63.483
+ ,60.808
+ ,124.291
+ ,51.289
+ ,48.071
+ ,99.360
+ ,44.071
+ ,42.604
+ ,86.675
+ ,43.654
+ ,41.402
+ ,85.056
+ ,66.115
+ ,62.121
+ ,128.236
+ ,84.518
+ ,79.739
+ ,164.257
+ ,83.395
+ ,79.006
+ ,162.401
+ ,78.307
+ ,74.472
+ ,152.779
+ ,80.049
+ ,75.956
+ ,156.005
+ ,78.346
+ ,75.041
+ ,153.387
+ ,78.317
+ ,74.873
+ ,153.190
+ ,75.918
+ ,72.922
+ ,148.840
+ ,73.739
+ ,70.472
+ ,144.211
+ ,74.530
+ ,71.423
+ ,145.953
+ ,74.179
+ ,71.363
+ ,145.542
+ ,76.974
+ ,73.297
+ ,150.271
+ ,75.408
+ ,72.081
+ ,147.489
+ ,73.336
+ ,70.488
+ ,143.824
+ ,69.210
+ ,65.544
+ ,134.754
+ ,67.286
+ ,64.450
+ ,131.736
+ ,64.606
+ ,61.698
+ ,126.304
+ ,64.159
+ ,61.352
+ ,125.511
+ ,64.423
+ ,61.072
+ ,125.495
+ ,66.411
+ ,63.722
+ ,130.133
+ ,64.270
+ ,61.987
+ ,126.257
+ ,56.521
+ ,53.802
+ ,110.323
+ ,50.599
+ ,47.818
+ ,98.417
+ ,54.751
+ ,50.998
+ ,105.749
+ ,62.227
+ ,58.438
+ ,120.665
+ ,63.932
+ ,60.143
+ ,124.075
+ ,65.391
+ ,61.854
+ ,127.245
+ ,75.744
+ ,70.987
+ ,146.731
+ ,74.590
+ ,70.389
+ ,144.979
+ ,76.035
+ ,72.175
+ ,148.210
+ ,74.427
+ ,70.243
+ ,144.670
+ ,73.354
+ ,69.616
+ ,142.970
+ ,73.081
+ ,69.443
+ ,142.524
+ ,75.309
+ ,70.833
+ ,146.142
+ ,75.463
+ ,71.059
+ ,146.522
+ ,75.910
+ ,72.218
+ ,148.128
+ ,76.151
+ ,72.647
+ ,148.798
+ ,76.882
+ ,73.299
+ ,150.181
+ ,78.632
+ ,73.756
+ ,152.388
+ ,80.137
+ ,75.557
+ ,155.694
+ ,82.490
+ ,78.172
+ ,160.662
+ ,79.896
+ ,75.624
+ ,155.520
+ ,81.303
+ ,76.959
+ ,158.262
+ ,79.344
+ ,74.994
+ ,154.338
+ ,81.355
+ ,76.841
+ ,158.196
+ ,82.328
+ ,78.043
+ ,160.371
+ ,79.669
+ ,75.187
+ ,154.856
+ ,77.249
+ ,73.387
+ ,150.636
+ ,75.101
+ ,70.798
+ ,145.899
+ ,72.520
+ ,68.722
+ ,141.242
+ ,72.438
+ ,68.396
+ ,140.834
+ ,72.653
+ ,68.466
+ ,141.119
+ ,71.429
+ ,67.675
+ ,139.104
+ ,69.189
+ ,65.248
+ ,134.437
+ ,66.451
+ ,62.974
+ ,129.425
+ ,63.354
+ ,59.801
+ ,123.155
+ ,61.379
+ ,57.894
+ ,119.273
+ ,61.880
+ ,58.592
+ ,120.472
+ ,62.274
+ ,59.249
+ ,121.523
+ ,62.429
+ ,59.554
+ ,121.983
+ ,63.905
+ ,59.753
+ ,123.658
+ ,63.917
+ ,60.877
+ ,124.794
+ ,64.295
+ ,60.532
+ ,124.827
+ ,61.930
+ ,58.452
+ ,120.382
+ ,60.440
+ ,56.955
+ ,117.395
+ ,59.353
+ ,56.437
+ ,115.790
+ ,58.695
+ ,55.588
+ ,114.283
+ ,60.569
+ ,56.702
+ ,117.271
+ ,60.386
+ ,57.062
+ ,117.448
+ ,60.938
+ ,57.826
+ ,118.764
+ ,61.795
+ ,58.755
+ ,120.550
+ ,63.304
+ ,60.250
+ ,123.554
+ ,64.270
+ ,61.142
+ ,125.412
+ ,63.492
+ ,60.690
+ ,124.182
+ ,61.333
+ ,58.495
+ ,119.828
+ ,59.341
+ ,56.020
+ ,115.361
+ ,58.412
+ ,55.814
+ ,114.226
+ ,58.725
+ ,56.489
+ ,115.214
+ ,59.277
+ ,56.587
+ ,115.864
+ ,58.562
+ ,55.714
+ ,114.276
+ ,57.858
+ ,55.611
+ ,113.469
+ ,58.790
+ ,56.093
+ ,114.883
+ ,58.243
+ ,55.929
+ ,114.172
+ ,57.044
+ ,54.181
+ ,111.225
+ ,57.339
+ ,54.810
+ ,112.149
+ ,59.429
+ ,56.189
+ ,115.618
+ ,60.575
+ ,57.427
+ ,118.002
+ ,61.950
+ ,59.432
+ ,121.382
+ ,61.712
+ ,58.951
+ ,120.663
+ ,65.731
+ ,62.318
+ ,128.049
+ ,65.197
+ ,62.100
+ ,127.297)
+ ,dim=c(3
+ ,180)
+ ,dimnames=list(c('Jongens'
+ ,'Meisjes'
+ ,'Totaal')
+ ,1:180))
> y <- array(NA,dim=c(3,180),dimnames=list(c('Jongens','Meisjes','Totaal'),1:180))
> 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 = '3'
> 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
Totaal Jongens Meisjes
1 131.676 67.643 64.033
2 135.050 69.371 65.679
3 129.070 66.294 62.776
4 137.792 70.768 67.024
5 139.762 71.774 67.988
6 142.917 73.388 69.529
7 144.198 74.040 70.158
8 142.648 73.238 69.410
9 152.170 78.121 74.049
10 136.022 69.825 66.197
11 138.142 71.099 67.043
12 138.135 70.676 67.459
13 135.027 69.515 65.512
14 132.911 68.246 64.665
15 133.976 68.594 65.382
16 137.012 70.405 66.607
17 119.610 61.223 58.387
18 118.106 60.542 57.564
19 120.383 61.952 58.431
20 133.185 68.173 65.012
21 131.416 67.240 64.176
22 134.248 68.739 65.509
23 134.397 69.234 65.163
24 127.728 65.570 62.158
25 131.837 67.408 64.429
26 125.955 64.630 61.325
27 134.187 68.848 65.339
28 143.291 73.370 69.921
29 145.074 74.292 70.782
30 149.812 76.525 73.287
31 144.668 74.368 70.300
32 147.253 75.674 71.579
33 145.568 74.868 70.700
34 155.564 79.824 75.740
35 155.872 80.022 75.850
36 156.323 79.942 76.381
37 158.010 80.622 77.388
38 155.598 80.079 75.519
39 154.785 79.212 75.573
40 157.294 80.626 76.668
41 162.938 83.551 79.387
42 157.283 80.407 76.876
43 166.074 85.053 81.021
44 169.282 86.399 82.883
45 172.552 88.536 84.016
46 174.055 89.008 85.047
47 175.409 89.652 85.757
48 173.696 88.904 84.792
49 171.283 87.472 83.811
50 173.322 88.631 84.691
51 170.717 87.221 83.496
52 174.229 88.759 85.470
53 175.339 90.127 85.212
54 173.511 88.709 84.802
55 175.839 90.030 85.809
56 173.816 88.697 85.119
57 173.990 88.762 85.228
58 174.777 89.475 85.302
59 174.819 88.936 85.883
60 176.726 90.411 86.315
61 176.199 90.004 86.195
62 180.952 92.725 88.227
63 176.663 90.252 86.411
64 182.346 93.226 89.120
65 180.605 92.575 88.030
66 182.497 93.125 89.372
67 187.856 95.987 91.869
68 190.020 97.175 92.845
69 190.108 97.321 92.787
70 193.288 98.577 94.711
71 193.230 99.026 94.204
72 199.068 101.851 97.217
73 195.076 99.958 95.118
74 191.563 97.875 93.688
75 191.067 97.927 93.140
76 186.665 95.149 91.516
77 185.508 94.551 90.957
78 184.371 93.999 90.372
79 183.046 93.297 89.749
80 175.714 89.901 85.813
81 175.768 89.742 86.026
82 171.029 87.096 83.933
83 170.465 86.863 83.602
84 170.102 86.718 83.384
85 156.389 80.020 76.369
86 124.291 63.483 60.808
87 99.360 51.289 48.071
88 86.675 44.071 42.604
89 85.056 43.654 41.402
90 128.236 66.115 62.121
91 164.257 84.518 79.739
92 162.401 83.395 79.006
93 152.779 78.307 74.472
94 156.005 80.049 75.956
95 153.387 78.346 75.041
96 153.190 78.317 74.873
97 148.840 75.918 72.922
98 144.211 73.739 70.472
99 145.953 74.530 71.423
100 145.542 74.179 71.363
101 150.271 76.974 73.297
102 147.489 75.408 72.081
103 143.824 73.336 70.488
104 134.754 69.210 65.544
105 131.736 67.286 64.450
106 126.304 64.606 61.698
107 125.511 64.159 61.352
108 125.495 64.423 61.072
109 130.133 66.411 63.722
110 126.257 64.270 61.987
111 110.323 56.521 53.802
112 98.417 50.599 47.818
113 105.749 54.751 50.998
114 120.665 62.227 58.438
115 124.075 63.932 60.143
116 127.245 65.391 61.854
117 146.731 75.744 70.987
118 144.979 74.590 70.389
119 148.210 76.035 72.175
120 144.670 74.427 70.243
121 142.970 73.354 69.616
122 142.524 73.081 69.443
123 146.142 75.309 70.833
124 146.522 75.463 71.059
125 148.128 75.910 72.218
126 148.798 76.151 72.647
127 150.181 76.882 73.299
128 152.388 78.632 73.756
129 155.694 80.137 75.557
130 160.662 82.490 78.172
131 155.520 79.896 75.624
132 158.262 81.303 76.959
133 154.338 79.344 74.994
134 158.196 81.355 76.841
135 160.371 82.328 78.043
136 154.856 79.669 75.187
137 150.636 77.249 73.387
138 145.899 75.101 70.798
139 141.242 72.520 68.722
140 140.834 72.438 68.396
141 141.119 72.653 68.466
142 139.104 71.429 67.675
143 134.437 69.189 65.248
144 129.425 66.451 62.974
145 123.155 63.354 59.801
146 119.273 61.379 57.894
147 120.472 61.880 58.592
148 121.523 62.274 59.249
149 121.983 62.429 59.554
150 123.658 63.905 59.753
151 124.794 63.917 60.877
152 124.827 64.295 60.532
153 120.382 61.930 58.452
154 117.395 60.440 56.955
155 115.790 59.353 56.437
156 114.283 58.695 55.588
157 117.271 60.569 56.702
158 117.448 60.386 57.062
159 118.764 60.938 57.826
160 120.550 61.795 58.755
161 123.554 63.304 60.250
162 125.412 64.270 61.142
163 124.182 63.492 60.690
164 119.828 61.333 58.495
165 115.361 59.341 56.020
166 114.226 58.412 55.814
167 115.214 58.725 56.489
168 115.864 59.277 56.587
169 114.276 58.562 55.714
170 113.469 57.858 55.611
171 114.883 58.790 56.093
172 114.172 58.243 55.929
173 111.225 57.044 54.181
174 112.149 57.339 54.810
175 115.618 59.429 56.189
176 118.002 60.575 57.427
177 121.382 61.950 59.432
178 120.663 61.712 58.951
179 128.049 65.731 62.318
180 127.297 65.197 62.100
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Jongens Meisjes
-3.3e-14 1.0e+00 1.0e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.076e-13 -4.900e-15 1.037e-15 3.135e-15 2.487e-14
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.300e-14 5.968e-15 -5.530e+00 1.13e-07 ***
Jongens 1.000e+00 1.904e-15 5.253e+14 < 2e-16 ***
Meisjes 1.000e+00 1.969e-15 5.079e+14 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.213e-14 on 177 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 3.546e+32 on 2 and 177 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,] 8.317429e-01 3.365141e-01 1.682571e-01
[2,] 8.103312e-01 3.793375e-01 1.896688e-01
[3,] 6.950480e-01 6.099040e-01 3.049520e-01
[4,] 9.414429e-01 1.171141e-01 5.855707e-02
[5,] 4.920565e-02 9.841129e-02 9.507944e-01
[6,] 1.414959e-01 2.829917e-01 8.585041e-01
[7,] 7.642019e-01 4.715961e-01 2.357981e-01
[8,] 2.782508e-01 5.565016e-01 7.217492e-01
[9,] 9.946959e-01 1.060818e-02 5.304088e-03
[10,] 9.791574e-01 4.168524e-02 2.084262e-02
[11,] 9.725155e-01 5.496892e-02 2.748446e-02
[12,] 8.374415e-02 1.674883e-01 9.162558e-01
[13,] 2.748178e-01 5.496356e-01 7.251822e-01
[14,] 3.491396e-01 6.982792e-01 6.508604e-01
[15,] 9.136549e-01 1.726902e-01 8.634510e-02
[16,] 2.081712e-02 4.163423e-02 9.791829e-01
[17,] 5.157206e-01 9.685587e-01 4.842794e-01
[18,] 8.199100e-03 1.639820e-02 9.918009e-01
[19,] 1.810242e-01 3.620484e-01 8.189758e-01
[20,] 8.963296e-01 2.073409e-01 1.036704e-01
[21,] 1.009062e-01 2.018125e-01 8.990938e-01
[22,] 9.999967e-01 6.521931e-06 3.260966e-06
[23,] 3.774231e-03 7.548462e-03 9.962258e-01
[24,] 6.938138e-01 6.123724e-01 3.061862e-01
[25,] 9.999980e-01 3.995361e-06 1.997681e-06
[26,] 3.648153e-02 7.296307e-02 9.635185e-01
[27,] 3.142294e-01 6.284588e-01 6.857706e-01
[28,] 9.897536e-01 2.049282e-02 1.024641e-02
[29,] 9.999443e-01 1.113685e-04 5.568423e-05
[30,] 7.581666e-01 4.836667e-01 2.418334e-01
[31,] 9.989168e-01 2.166483e-03 1.083242e-03
[32,] 9.991457e-01 1.708644e-03 8.543221e-04
[33,] 3.473139e-01 6.946278e-01 6.526861e-01
[34,] 3.450117e-01 6.900234e-01 6.549883e-01
[35,] 7.641013e-01 4.717973e-01 2.358987e-01
[36,] 9.999793e-01 4.131631e-05 2.065816e-05
[37,] 7.651356e-02 1.530271e-01 9.234864e-01
[38,] 9.951899e-01 9.620262e-03 4.810131e-03
[39,] 9.998983e-01 2.034306e-04 1.017153e-04
[40,] 1.317403e-01 2.634806e-01 8.682597e-01
[41,] 2.207932e-02 4.415865e-02 9.779207e-01
[42,] 4.561861e-02 9.123721e-02 9.543814e-01
[43,] 9.997322e-01 5.355177e-04 2.677589e-04
[44,] 9.712743e-01 5.745136e-02 2.872568e-02
[45,] 8.275246e-01 3.449508e-01 1.724754e-01
[46,] 9.999791e-01 4.176372e-05 2.088186e-05
[47,] 9.893775e-01 2.124510e-02 1.062255e-02
[48,] 9.987875e-01 2.424948e-03 1.212474e-03
[49,] 9.926397e-01 1.472062e-02 7.360311e-03
[50,] 9.990365e-01 1.926992e-03 9.634960e-04
[51,] 1.000000e+00 3.012855e-15 1.506428e-15
[52,] 9.908610e-01 1.827791e-02 9.138953e-03
[53,] 9.961342e-01 7.731645e-03 3.865823e-03
[54,] 1.000000e+00 5.513290e-12 2.756645e-12
[55,] 9.998876e-01 2.247549e-04 1.123774e-04
[56,] 9.976365e-01 4.726986e-03 2.363493e-03
[57,] 9.999677e-01 6.466441e-05 3.233220e-05
[58,] 1.000000e+00 2.206517e-13 1.103259e-13
[59,] 9.964275e-01 7.144909e-03 3.572454e-03
[60,] 4.098108e-02 8.196216e-02 9.590189e-01
[61,] 1.000000e+00 1.624386e-10 8.121928e-11
[62,] 9.779534e-01 4.409323e-02 2.204662e-02
[63,] 2.984771e-02 5.969542e-02 9.701523e-01
[64,] 9.949145e-01 1.017106e-02 5.085529e-03
[65,] 1.000000e+00 4.020101e-12 2.010051e-12
[66,] 1.000000e+00 6.581080e-12 3.290540e-12
[67,] 9.989951e-01 2.009850e-03 1.004925e-03
[68,] 1.000000e+00 1.214719e-12 6.073595e-13
[69,] 6.134198e-02 1.226840e-01 9.386580e-01
[70,] 2.399475e-02 4.798949e-02 9.760053e-01
[71,] 1.930766e-02 3.861531e-02 9.806923e-01
[72,] 9.996910e-01 6.180083e-04 3.090042e-04
[73,] 9.998226e-01 3.547393e-04 1.773697e-04
[74,] 1.000000e+00 9.547561e-13 4.773781e-13
[75,] 9.756368e-01 4.872643e-02 2.436322e-02
[76,] 9.474326e-01 1.051348e-01 5.256739e-02
[77,] 1.000000e+00 7.884700e-14 3.942350e-14
[78,] 9.999330e-01 1.340546e-04 6.702729e-05
[79,] 9.997229e-01 5.541696e-04 2.770848e-04
[80,] 9.995805e-01 8.389017e-04 4.194508e-04
[81,] 5.527833e-03 1.105567e-02 9.944722e-01
[82,] 9.976747e-01 4.650653e-03 2.325327e-03
[83,] 1.000000e+00 4.605174e-12 2.302587e-12
[84,] 9.977641e-01 4.471732e-03 2.235866e-03
[85,] 1.000000e+00 2.629222e-12 1.314611e-12
[86,] 9.971476e-01 5.704819e-03 2.852410e-03
[87,] 1.000000e+00 2.019643e-10 1.009821e-10
[88,] 9.986150e-01 2.770071e-03 1.385035e-03
[89,] 5.803727e-03 1.160745e-02 9.941963e-01
[90,] 1.181742e-03 2.363484e-03 9.988183e-01
[91,] 1.000000e+00 5.501517e-09 2.750759e-09
[92,] 6.608010e-04 1.321602e-03 9.993392e-01
[93,] 1.000000e+00 3.922548e-08 1.961274e-08
[94,] 9.990564e-01 1.887128e-03 9.435638e-04
[95,] 9.868322e-01 2.633569e-02 1.316784e-02
[96,] 7.769941e-01 4.460119e-01 2.230059e-01
[97,] 6.609910e-01 6.780180e-01 3.390090e-01
[98,] 1.000000e+00 1.987851e-10 9.939255e-11
[99,] 9.988911e-01 2.217800e-03 1.108900e-03
[100,] 4.753101e-01 9.506202e-01 5.246899e-01
[101,] 9.976168e-01 4.766419e-03 2.383210e-03
[102,] 9.995709e-01 8.582201e-04 4.291100e-04
[103,] 9.983935e-01 3.212910e-03 1.606455e-03
[104,] 1.000000e+00 5.995667e-09 2.997833e-09
[105,] 1.000000e+00 5.549387e-09 2.774694e-09
[106,] 1.605744e-04 3.211489e-04 9.998394e-01
[107,] 9.960489e-01 7.902144e-03 3.951072e-03
[108,] 8.721971e-05 1.744394e-04 9.999128e-01
[109,] 9.994301e-01 1.139888e-03 5.699438e-04
[110,] 1.675941e-05 3.351882e-05 9.999832e-01
[111,] 1.522133e-01 3.044265e-01 8.477867e-01
[112,] 1.079643e-01 2.159286e-01 8.920357e-01
[113,] 1.717592e-01 3.435183e-01 8.282408e-01
[114,] 1.000000e+00 4.090196e-09 2.045098e-09
[115,] 2.328609e-02 4.657219e-02 9.767139e-01
[116,] 3.138176e-01 6.276352e-01 6.861824e-01
[117,] 2.996802e-01 5.993604e-01 7.003198e-01
[118,] 9.999975e-01 5.055851e-06 2.527925e-06
[119,] 9.991938e-01 1.612371e-03 8.061857e-04
[120,] 9.999282e-01 1.436315e-04 7.181577e-05
[121,] 9.927574e-01 1.448515e-02 7.242573e-03
[122,] 9.999938e-01 1.243068e-05 6.215339e-06
[123,] 9.512029e-02 1.902406e-01 9.048797e-01
[124,] 8.732311e-02 1.746462e-01 9.126769e-01
[125,] 9.945599e-01 1.088014e-02 5.440072e-03
[126,] 1.000000e+00 1.548126e-08 7.740631e-09
[127,] 7.969860e-01 4.060280e-01 2.030140e-01
[128,] 9.999999e-01 2.473199e-07 1.236600e-07
[129,] 9.999997e-01 5.601983e-07 2.800991e-07
[130,] 9.991384e-01 1.723178e-03 8.615888e-04
[131,] 9.999994e-01 1.102484e-06 5.512418e-07
[132,] 9.999989e-01 2.203003e-06 1.101501e-06
[133,] 8.711703e-02 1.742341e-01 9.128830e-01
[134,] 1.315679e-01 2.631358e-01 8.684321e-01
[135,] 9.958225e-01 8.354971e-03 4.177485e-03
[136,] 9.976638e-01 4.672301e-03 2.336150e-03
[137,] 9.999989e-01 2.255021e-06 1.127510e-06
[138,] 1.718159e-01 3.436317e-01 8.281841e-01
[139,] 9.073214e-01 1.853573e-01 9.267863e-02
[140,] 7.751682e-01 4.496636e-01 2.248318e-01
[141,] 9.996809e-01 6.381518e-04 3.190759e-04
[142,] 8.328068e-01 3.343863e-01 1.671932e-01
[143,] 8.743158e-02 1.748632e-01 9.125684e-01
[144,] 3.444023e-01 6.888046e-01 6.555977e-01
[145,] 9.999996e-01 8.279291e-07 4.139646e-07
[146,] 9.995401e-01 9.197995e-04 4.598998e-04
[147,] 6.085291e-02 1.217058e-01 9.391471e-01
[148,] 1.437084e-01 2.874169e-01 8.562916e-01
[149,] 7.474145e-02 1.494829e-01 9.252586e-01
[150,] 1.295353e-02 2.590705e-02 9.870465e-01
[151,] 9.419984e-01 1.160032e-01 5.800161e-02
[152,] 9.999659e-01 6.823300e-05 3.411650e-05
[153,] 4.408148e-01 8.816296e-01 5.591852e-01
[154,] 9.938192e-01 1.236159e-02 6.180794e-03
[155,] 9.999605e-01 7.897738e-05 3.948869e-05
[156,] 6.774150e-01 6.451700e-01 3.225850e-01
[157,] 9.869823e-01 2.603536e-02 1.301768e-02
[158,] 8.066886e-01 3.866228e-01 1.933114e-01
[159,] 8.340087e-01 3.319827e-01 1.659913e-01
[160,] 5.424081e-01 9.151837e-01 4.575919e-01
[161,] 9.352002e-01 1.295996e-01 6.479980e-02
[162,] 8.824092e-01 2.351816e-01 1.175908e-01
[163,] 3.348632e-01 6.697264e-01 6.651368e-01
[164,] 2.269128e-02 4.538256e-02 9.773087e-01
[165,] 6.153722e-01 7.692555e-01 3.846278e-01
[166,] 3.077896e-03 6.155792e-03 9.969221e-01
[167,] 8.081824e-01 3.836351e-01 1.918176e-01
[168,] 5.145128e-01 9.709744e-01 4.854872e-01
[169,] 2.131119e-01 4.262237e-01 7.868881e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1hl761353254712.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/26na21353254712.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3v5yw1353254712.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/43nfb1353254712.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/58jj81353254712.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 180
Frequency = 1
1 2 3 4 5
-1.075944e-13 2.486699e-14 -4.694580e-15 1.394902e-15 1.611370e-15
6 7 8 9 10
9.912799e-16 9.927660e-16 9.957423e-16 -1.411576e-14 -1.359014e-14
11 12 13 14 15
-1.287559e-14 -1.287219e-14 -1.334124e-14 9.177927e-16 3.836833e-16
16 17 18 19 20
1.507065e-15 3.075193e-15 -5.843337e-15 1.296288e-15 1.031949e-15
21 22 23 24 25
1.330399e-15 -1.356742e-14 1.042987e-15 1.638450e-15 -1.275162e-14
26 27 28 29 30
1.976995e-15 1.492639e-14 -1.319135e-14 1.519790e-14 1.184283e-15
31 32 33 34 35
1.515132e-14 -1.290619e-14 1.530723e-14 3.136320e-16 1.440757e-14
36 37 38 39 40
1.471488e-14 -1.372702e-14 1.378436e-14 4.242271e-16 1.704620e-16
41 42 43 44 45
-1.344684e-14 -1.368711e-14 1.512646e-14 1.428160e-14 -1.322807e-14
46 47 48 49 50
1.410122e-14 -1.334374e-14 1.288739e-15 -1.276370e-14 -3.651177e-16
51 52 53 54 55
1.601297e-14 1.403184e-14 5.497194e-16 -1.265516e-14 4.664253e-17
56 57 58 59 60
7.118550e-16 1.488690e-14 -1.354923e-14 -1.422305e-14 -5.290280e-15
61 62 63 64 65
1.184905e-14 -4.589008e-15 8.864586e-15 -5.472642e-15 -1.834309e-14
66 67 68 69 70
8.670758e-15 -5.951823e-15 8.429223e-15 -3.164710e-15 7.418733e-15
71 72 73 74 75
-3.926084e-15 9.084509e-15 -4.255113e-15 -1.917618e-14 -6.584261e-15
76 77 78 79 80
-1.959927e-14 6.958520e-15 8.373477e-15 -4.031100e-15 8.879817e-16
81 82 83 84 85
1.351619e-15 -1.401361e-14 5.685234e-17 8.840969e-16 1.434890e-14
86 87 88 89 90
1.573007e-15 -2.620715e-15 -3.738934e-15 -1.062630e-14 -5.676666e-15
91 92 93 94 95
1.688041e-15 1.567537e-14 2.957177e-16 -1.384408e-14 1.593492e-16
96 97 98 99 100
4.707473e-16 1.424307e-15 1.521728e-14 9.671568e-16 1.122593e-15
101 102 103 104 105
-1.293184e-14 1.350516e-15 1.537838e-14 5.106145e-16 -1.371284e-14
106 107 108 109 110
8.591982e-15 -5.042708e-15 1.393396e-15 7.850608e-15 8.887697e-15
111 112 113 114 115
-8.936163e-15 2.384668e-15 -3.740809e-15 9.903985e-15 2.256654e-15
116 117 118 119 120
2.422324e-15 1.244800e-15 1.497590e-14 1.561058e-14 -1.330336e-14
121 122 123 124 125
1.011302e-15 9.631281e-16 1.135139e-15 1.247840e-15 -1.284561e-14
126 127 128 129 130
1.428315e-15 1.211778e-15 4.011641e-16 -1.355488e-14 1.494474e-14
131 132 133 134 135
1.484006e-14 4.313775e-17 -1.936493e-16 -3.814308e-16 4.080680e-16
136 137 138 139 140
-7.414551e-17 1.394264e-15 9.356691e-16 1.349273e-15 1.348890e-15
141 142 143 144 145
1.533852e-15 1.574832e-14 1.499925e-14 2.248898e-14 2.531892e-15
146 147 148 149 150
2.062872e-15 -5.900312e-15 -4.099916e-15 1.088299e-15 2.095979e-15
151 152 153 154 155
-5.125759e-15 2.254972e-15 8.919427e-15 2.256162e-15 9.528878e-15
156 157 158 159 160
3.334716e-15 1.274892e-15 -4.932495e-15 -4.265757e-15 -5.759862e-15
161 162 163 164 165
1.981182e-15 8.836275e-15 8.643495e-15 1.039957e-14 1.568190e-15
166 167 168 169 170
2.787860e-15 2.104958e-15 2.631735e-15 2.476328e-15 2.366991e-15
171 172 173 174 175
-5.252459e-15 -5.059130e-15 -3.498536e-15 3.312967e-15 -4.888802e-15
176 177 178 179 180
-4.155943e-15 1.871086e-15 -5.729799e-15 1.553311e-14 -4.880117e-15
> postscript(file="/var/wessaorg/rcomp/tmp/679mv1353254712.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 = 180
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.075944e-13 NA
1 2.486699e-14 -1.075944e-13
2 -4.694580e-15 2.486699e-14
3 1.394902e-15 -4.694580e-15
4 1.611370e-15 1.394902e-15
5 9.912799e-16 1.611370e-15
6 9.927660e-16 9.912799e-16
7 9.957423e-16 9.927660e-16
8 -1.411576e-14 9.957423e-16
9 -1.359014e-14 -1.411576e-14
10 -1.287559e-14 -1.359014e-14
11 -1.287219e-14 -1.287559e-14
12 -1.334124e-14 -1.287219e-14
13 9.177927e-16 -1.334124e-14
14 3.836833e-16 9.177927e-16
15 1.507065e-15 3.836833e-16
16 3.075193e-15 1.507065e-15
17 -5.843337e-15 3.075193e-15
18 1.296288e-15 -5.843337e-15
19 1.031949e-15 1.296288e-15
20 1.330399e-15 1.031949e-15
21 -1.356742e-14 1.330399e-15
22 1.042987e-15 -1.356742e-14
23 1.638450e-15 1.042987e-15
24 -1.275162e-14 1.638450e-15
25 1.976995e-15 -1.275162e-14
26 1.492639e-14 1.976995e-15
27 -1.319135e-14 1.492639e-14
28 1.519790e-14 -1.319135e-14
29 1.184283e-15 1.519790e-14
30 1.515132e-14 1.184283e-15
31 -1.290619e-14 1.515132e-14
32 1.530723e-14 -1.290619e-14
33 3.136320e-16 1.530723e-14
34 1.440757e-14 3.136320e-16
35 1.471488e-14 1.440757e-14
36 -1.372702e-14 1.471488e-14
37 1.378436e-14 -1.372702e-14
38 4.242271e-16 1.378436e-14
39 1.704620e-16 4.242271e-16
40 -1.344684e-14 1.704620e-16
41 -1.368711e-14 -1.344684e-14
42 1.512646e-14 -1.368711e-14
43 1.428160e-14 1.512646e-14
44 -1.322807e-14 1.428160e-14
45 1.410122e-14 -1.322807e-14
46 -1.334374e-14 1.410122e-14
47 1.288739e-15 -1.334374e-14
48 -1.276370e-14 1.288739e-15
49 -3.651177e-16 -1.276370e-14
50 1.601297e-14 -3.651177e-16
51 1.403184e-14 1.601297e-14
52 5.497194e-16 1.403184e-14
53 -1.265516e-14 5.497194e-16
54 4.664253e-17 -1.265516e-14
55 7.118550e-16 4.664253e-17
56 1.488690e-14 7.118550e-16
57 -1.354923e-14 1.488690e-14
58 -1.422305e-14 -1.354923e-14
59 -5.290280e-15 -1.422305e-14
60 1.184905e-14 -5.290280e-15
61 -4.589008e-15 1.184905e-14
62 8.864586e-15 -4.589008e-15
63 -5.472642e-15 8.864586e-15
64 -1.834309e-14 -5.472642e-15
65 8.670758e-15 -1.834309e-14
66 -5.951823e-15 8.670758e-15
67 8.429223e-15 -5.951823e-15
68 -3.164710e-15 8.429223e-15
69 7.418733e-15 -3.164710e-15
70 -3.926084e-15 7.418733e-15
71 9.084509e-15 -3.926084e-15
72 -4.255113e-15 9.084509e-15
73 -1.917618e-14 -4.255113e-15
74 -6.584261e-15 -1.917618e-14
75 -1.959927e-14 -6.584261e-15
76 6.958520e-15 -1.959927e-14
77 8.373477e-15 6.958520e-15
78 -4.031100e-15 8.373477e-15
79 8.879817e-16 -4.031100e-15
80 1.351619e-15 8.879817e-16
81 -1.401361e-14 1.351619e-15
82 5.685234e-17 -1.401361e-14
83 8.840969e-16 5.685234e-17
84 1.434890e-14 8.840969e-16
85 1.573007e-15 1.434890e-14
86 -2.620715e-15 1.573007e-15
87 -3.738934e-15 -2.620715e-15
88 -1.062630e-14 -3.738934e-15
89 -5.676666e-15 -1.062630e-14
90 1.688041e-15 -5.676666e-15
91 1.567537e-14 1.688041e-15
92 2.957177e-16 1.567537e-14
93 -1.384408e-14 2.957177e-16
94 1.593492e-16 -1.384408e-14
95 4.707473e-16 1.593492e-16
96 1.424307e-15 4.707473e-16
97 1.521728e-14 1.424307e-15
98 9.671568e-16 1.521728e-14
99 1.122593e-15 9.671568e-16
100 -1.293184e-14 1.122593e-15
101 1.350516e-15 -1.293184e-14
102 1.537838e-14 1.350516e-15
103 5.106145e-16 1.537838e-14
104 -1.371284e-14 5.106145e-16
105 8.591982e-15 -1.371284e-14
106 -5.042708e-15 8.591982e-15
107 1.393396e-15 -5.042708e-15
108 7.850608e-15 1.393396e-15
109 8.887697e-15 7.850608e-15
110 -8.936163e-15 8.887697e-15
111 2.384668e-15 -8.936163e-15
112 -3.740809e-15 2.384668e-15
113 9.903985e-15 -3.740809e-15
114 2.256654e-15 9.903985e-15
115 2.422324e-15 2.256654e-15
116 1.244800e-15 2.422324e-15
117 1.497590e-14 1.244800e-15
118 1.561058e-14 1.497590e-14
119 -1.330336e-14 1.561058e-14
120 1.011302e-15 -1.330336e-14
121 9.631281e-16 1.011302e-15
122 1.135139e-15 9.631281e-16
123 1.247840e-15 1.135139e-15
124 -1.284561e-14 1.247840e-15
125 1.428315e-15 -1.284561e-14
126 1.211778e-15 1.428315e-15
127 4.011641e-16 1.211778e-15
128 -1.355488e-14 4.011641e-16
129 1.494474e-14 -1.355488e-14
130 1.484006e-14 1.494474e-14
131 4.313775e-17 1.484006e-14
132 -1.936493e-16 4.313775e-17
133 -3.814308e-16 -1.936493e-16
134 4.080680e-16 -3.814308e-16
135 -7.414551e-17 4.080680e-16
136 1.394264e-15 -7.414551e-17
137 9.356691e-16 1.394264e-15
138 1.349273e-15 9.356691e-16
139 1.348890e-15 1.349273e-15
140 1.533852e-15 1.348890e-15
141 1.574832e-14 1.533852e-15
142 1.499925e-14 1.574832e-14
143 2.248898e-14 1.499925e-14
144 2.531892e-15 2.248898e-14
145 2.062872e-15 2.531892e-15
146 -5.900312e-15 2.062872e-15
147 -4.099916e-15 -5.900312e-15
148 1.088299e-15 -4.099916e-15
149 2.095979e-15 1.088299e-15
150 -5.125759e-15 2.095979e-15
151 2.254972e-15 -5.125759e-15
152 8.919427e-15 2.254972e-15
153 2.256162e-15 8.919427e-15
154 9.528878e-15 2.256162e-15
155 3.334716e-15 9.528878e-15
156 1.274892e-15 3.334716e-15
157 -4.932495e-15 1.274892e-15
158 -4.265757e-15 -4.932495e-15
159 -5.759862e-15 -4.265757e-15
160 1.981182e-15 -5.759862e-15
161 8.836275e-15 1.981182e-15
162 8.643495e-15 8.836275e-15
163 1.039957e-14 8.643495e-15
164 1.568190e-15 1.039957e-14
165 2.787860e-15 1.568190e-15
166 2.104958e-15 2.787860e-15
167 2.631735e-15 2.104958e-15
168 2.476328e-15 2.631735e-15
169 2.366991e-15 2.476328e-15
170 -5.252459e-15 2.366991e-15
171 -5.059130e-15 -5.252459e-15
172 -3.498536e-15 -5.059130e-15
173 3.312967e-15 -3.498536e-15
174 -4.888802e-15 3.312967e-15
175 -4.155943e-15 -4.888802e-15
176 1.871086e-15 -4.155943e-15
177 -5.729799e-15 1.871086e-15
178 1.553311e-14 -5.729799e-15
179 -4.880117e-15 1.553311e-14
180 NA -4.880117e-15
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.486699e-14 -1.075944e-13
[2,] -4.694580e-15 2.486699e-14
[3,] 1.394902e-15 -4.694580e-15
[4,] 1.611370e-15 1.394902e-15
[5,] 9.912799e-16 1.611370e-15
[6,] 9.927660e-16 9.912799e-16
[7,] 9.957423e-16 9.927660e-16
[8,] -1.411576e-14 9.957423e-16
[9,] -1.359014e-14 -1.411576e-14
[10,] -1.287559e-14 -1.359014e-14
[11,] -1.287219e-14 -1.287559e-14
[12,] -1.334124e-14 -1.287219e-14
[13,] 9.177927e-16 -1.334124e-14
[14,] 3.836833e-16 9.177927e-16
[15,] 1.507065e-15 3.836833e-16
[16,] 3.075193e-15 1.507065e-15
[17,] -5.843337e-15 3.075193e-15
[18,] 1.296288e-15 -5.843337e-15
[19,] 1.031949e-15 1.296288e-15
[20,] 1.330399e-15 1.031949e-15
[21,] -1.356742e-14 1.330399e-15
[22,] 1.042987e-15 -1.356742e-14
[23,] 1.638450e-15 1.042987e-15
[24,] -1.275162e-14 1.638450e-15
[25,] 1.976995e-15 -1.275162e-14
[26,] 1.492639e-14 1.976995e-15
[27,] -1.319135e-14 1.492639e-14
[28,] 1.519790e-14 -1.319135e-14
[29,] 1.184283e-15 1.519790e-14
[30,] 1.515132e-14 1.184283e-15
[31,] -1.290619e-14 1.515132e-14
[32,] 1.530723e-14 -1.290619e-14
[33,] 3.136320e-16 1.530723e-14
[34,] 1.440757e-14 3.136320e-16
[35,] 1.471488e-14 1.440757e-14
[36,] -1.372702e-14 1.471488e-14
[37,] 1.378436e-14 -1.372702e-14
[38,] 4.242271e-16 1.378436e-14
[39,] 1.704620e-16 4.242271e-16
[40,] -1.344684e-14 1.704620e-16
[41,] -1.368711e-14 -1.344684e-14
[42,] 1.512646e-14 -1.368711e-14
[43,] 1.428160e-14 1.512646e-14
[44,] -1.322807e-14 1.428160e-14
[45,] 1.410122e-14 -1.322807e-14
[46,] -1.334374e-14 1.410122e-14
[47,] 1.288739e-15 -1.334374e-14
[48,] -1.276370e-14 1.288739e-15
[49,] -3.651177e-16 -1.276370e-14
[50,] 1.601297e-14 -3.651177e-16
[51,] 1.403184e-14 1.601297e-14
[52,] 5.497194e-16 1.403184e-14
[53,] -1.265516e-14 5.497194e-16
[54,] 4.664253e-17 -1.265516e-14
[55,] 7.118550e-16 4.664253e-17
[56,] 1.488690e-14 7.118550e-16
[57,] -1.354923e-14 1.488690e-14
[58,] -1.422305e-14 -1.354923e-14
[59,] -5.290280e-15 -1.422305e-14
[60,] 1.184905e-14 -5.290280e-15
[61,] -4.589008e-15 1.184905e-14
[62,] 8.864586e-15 -4.589008e-15
[63,] -5.472642e-15 8.864586e-15
[64,] -1.834309e-14 -5.472642e-15
[65,] 8.670758e-15 -1.834309e-14
[66,] -5.951823e-15 8.670758e-15
[67,] 8.429223e-15 -5.951823e-15
[68,] -3.164710e-15 8.429223e-15
[69,] 7.418733e-15 -3.164710e-15
[70,] -3.926084e-15 7.418733e-15
[71,] 9.084509e-15 -3.926084e-15
[72,] -4.255113e-15 9.084509e-15
[73,] -1.917618e-14 -4.255113e-15
[74,] -6.584261e-15 -1.917618e-14
[75,] -1.959927e-14 -6.584261e-15
[76,] 6.958520e-15 -1.959927e-14
[77,] 8.373477e-15 6.958520e-15
[78,] -4.031100e-15 8.373477e-15
[79,] 8.879817e-16 -4.031100e-15
[80,] 1.351619e-15 8.879817e-16
[81,] -1.401361e-14 1.351619e-15
[82,] 5.685234e-17 -1.401361e-14
[83,] 8.840969e-16 5.685234e-17
[84,] 1.434890e-14 8.840969e-16
[85,] 1.573007e-15 1.434890e-14
[86,] -2.620715e-15 1.573007e-15
[87,] -3.738934e-15 -2.620715e-15
[88,] -1.062630e-14 -3.738934e-15
[89,] -5.676666e-15 -1.062630e-14
[90,] 1.688041e-15 -5.676666e-15
[91,] 1.567537e-14 1.688041e-15
[92,] 2.957177e-16 1.567537e-14
[93,] -1.384408e-14 2.957177e-16
[94,] 1.593492e-16 -1.384408e-14
[95,] 4.707473e-16 1.593492e-16
[96,] 1.424307e-15 4.707473e-16
[97,] 1.521728e-14 1.424307e-15
[98,] 9.671568e-16 1.521728e-14
[99,] 1.122593e-15 9.671568e-16
[100,] -1.293184e-14 1.122593e-15
[101,] 1.350516e-15 -1.293184e-14
[102,] 1.537838e-14 1.350516e-15
[103,] 5.106145e-16 1.537838e-14
[104,] -1.371284e-14 5.106145e-16
[105,] 8.591982e-15 -1.371284e-14
[106,] -5.042708e-15 8.591982e-15
[107,] 1.393396e-15 -5.042708e-15
[108,] 7.850608e-15 1.393396e-15
[109,] 8.887697e-15 7.850608e-15
[110,] -8.936163e-15 8.887697e-15
[111,] 2.384668e-15 -8.936163e-15
[112,] -3.740809e-15 2.384668e-15
[113,] 9.903985e-15 -3.740809e-15
[114,] 2.256654e-15 9.903985e-15
[115,] 2.422324e-15 2.256654e-15
[116,] 1.244800e-15 2.422324e-15
[117,] 1.497590e-14 1.244800e-15
[118,] 1.561058e-14 1.497590e-14
[119,] -1.330336e-14 1.561058e-14
[120,] 1.011302e-15 -1.330336e-14
[121,] 9.631281e-16 1.011302e-15
[122,] 1.135139e-15 9.631281e-16
[123,] 1.247840e-15 1.135139e-15
[124,] -1.284561e-14 1.247840e-15
[125,] 1.428315e-15 -1.284561e-14
[126,] 1.211778e-15 1.428315e-15
[127,] 4.011641e-16 1.211778e-15
[128,] -1.355488e-14 4.011641e-16
[129,] 1.494474e-14 -1.355488e-14
[130,] 1.484006e-14 1.494474e-14
[131,] 4.313775e-17 1.484006e-14
[132,] -1.936493e-16 4.313775e-17
[133,] -3.814308e-16 -1.936493e-16
[134,] 4.080680e-16 -3.814308e-16
[135,] -7.414551e-17 4.080680e-16
[136,] 1.394264e-15 -7.414551e-17
[137,] 9.356691e-16 1.394264e-15
[138,] 1.349273e-15 9.356691e-16
[139,] 1.348890e-15 1.349273e-15
[140,] 1.533852e-15 1.348890e-15
[141,] 1.574832e-14 1.533852e-15
[142,] 1.499925e-14 1.574832e-14
[143,] 2.248898e-14 1.499925e-14
[144,] 2.531892e-15 2.248898e-14
[145,] 2.062872e-15 2.531892e-15
[146,] -5.900312e-15 2.062872e-15
[147,] -4.099916e-15 -5.900312e-15
[148,] 1.088299e-15 -4.099916e-15
[149,] 2.095979e-15 1.088299e-15
[150,] -5.125759e-15 2.095979e-15
[151,] 2.254972e-15 -5.125759e-15
[152,] 8.919427e-15 2.254972e-15
[153,] 2.256162e-15 8.919427e-15
[154,] 9.528878e-15 2.256162e-15
[155,] 3.334716e-15 9.528878e-15
[156,] 1.274892e-15 3.334716e-15
[157,] -4.932495e-15 1.274892e-15
[158,] -4.265757e-15 -4.932495e-15
[159,] -5.759862e-15 -4.265757e-15
[160,] 1.981182e-15 -5.759862e-15
[161,] 8.836275e-15 1.981182e-15
[162,] 8.643495e-15 8.836275e-15
[163,] 1.039957e-14 8.643495e-15
[164,] 1.568190e-15 1.039957e-14
[165,] 2.787860e-15 1.568190e-15
[166,] 2.104958e-15 2.787860e-15
[167,] 2.631735e-15 2.104958e-15
[168,] 2.476328e-15 2.631735e-15
[169,] 2.366991e-15 2.476328e-15
[170,] -5.252459e-15 2.366991e-15
[171,] -5.059130e-15 -5.252459e-15
[172,] -3.498536e-15 -5.059130e-15
[173,] 3.312967e-15 -3.498536e-15
[174,] -4.888802e-15 3.312967e-15
[175,] -4.155943e-15 -4.888802e-15
[176,] 1.871086e-15 -4.155943e-15
[177,] -5.729799e-15 1.871086e-15
[178,] 1.553311e-14 -5.729799e-15
[179,] -4.880117e-15 1.553311e-14
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.486699e-14 -1.075944e-13
2 -4.694580e-15 2.486699e-14
3 1.394902e-15 -4.694580e-15
4 1.611370e-15 1.394902e-15
5 9.912799e-16 1.611370e-15
6 9.927660e-16 9.912799e-16
7 9.957423e-16 9.927660e-16
8 -1.411576e-14 9.957423e-16
9 -1.359014e-14 -1.411576e-14
10 -1.287559e-14 -1.359014e-14
11 -1.287219e-14 -1.287559e-14
12 -1.334124e-14 -1.287219e-14
13 9.177927e-16 -1.334124e-14
14 3.836833e-16 9.177927e-16
15 1.507065e-15 3.836833e-16
16 3.075193e-15 1.507065e-15
17 -5.843337e-15 3.075193e-15
18 1.296288e-15 -5.843337e-15
19 1.031949e-15 1.296288e-15
20 1.330399e-15 1.031949e-15
21 -1.356742e-14 1.330399e-15
22 1.042987e-15 -1.356742e-14
23 1.638450e-15 1.042987e-15
24 -1.275162e-14 1.638450e-15
25 1.976995e-15 -1.275162e-14
26 1.492639e-14 1.976995e-15
27 -1.319135e-14 1.492639e-14
28 1.519790e-14 -1.319135e-14
29 1.184283e-15 1.519790e-14
30 1.515132e-14 1.184283e-15
31 -1.290619e-14 1.515132e-14
32 1.530723e-14 -1.290619e-14
33 3.136320e-16 1.530723e-14
34 1.440757e-14 3.136320e-16
35 1.471488e-14 1.440757e-14
36 -1.372702e-14 1.471488e-14
37 1.378436e-14 -1.372702e-14
38 4.242271e-16 1.378436e-14
39 1.704620e-16 4.242271e-16
40 -1.344684e-14 1.704620e-16
41 -1.368711e-14 -1.344684e-14
42 1.512646e-14 -1.368711e-14
43 1.428160e-14 1.512646e-14
44 -1.322807e-14 1.428160e-14
45 1.410122e-14 -1.322807e-14
46 -1.334374e-14 1.410122e-14
47 1.288739e-15 -1.334374e-14
48 -1.276370e-14 1.288739e-15
49 -3.651177e-16 -1.276370e-14
50 1.601297e-14 -3.651177e-16
51 1.403184e-14 1.601297e-14
52 5.497194e-16 1.403184e-14
53 -1.265516e-14 5.497194e-16
54 4.664253e-17 -1.265516e-14
55 7.118550e-16 4.664253e-17
56 1.488690e-14 7.118550e-16
57 -1.354923e-14 1.488690e-14
58 -1.422305e-14 -1.354923e-14
59 -5.290280e-15 -1.422305e-14
60 1.184905e-14 -5.290280e-15
61 -4.589008e-15 1.184905e-14
62 8.864586e-15 -4.589008e-15
63 -5.472642e-15 8.864586e-15
64 -1.834309e-14 -5.472642e-15
65 8.670758e-15 -1.834309e-14
66 -5.951823e-15 8.670758e-15
67 8.429223e-15 -5.951823e-15
68 -3.164710e-15 8.429223e-15
69 7.418733e-15 -3.164710e-15
70 -3.926084e-15 7.418733e-15
71 9.084509e-15 -3.926084e-15
72 -4.255113e-15 9.084509e-15
73 -1.917618e-14 -4.255113e-15
74 -6.584261e-15 -1.917618e-14
75 -1.959927e-14 -6.584261e-15
76 6.958520e-15 -1.959927e-14
77 8.373477e-15 6.958520e-15
78 -4.031100e-15 8.373477e-15
79 8.879817e-16 -4.031100e-15
80 1.351619e-15 8.879817e-16
81 -1.401361e-14 1.351619e-15
82 5.685234e-17 -1.401361e-14
83 8.840969e-16 5.685234e-17
84 1.434890e-14 8.840969e-16
85 1.573007e-15 1.434890e-14
86 -2.620715e-15 1.573007e-15
87 -3.738934e-15 -2.620715e-15
88 -1.062630e-14 -3.738934e-15
89 -5.676666e-15 -1.062630e-14
90 1.688041e-15 -5.676666e-15
91 1.567537e-14 1.688041e-15
92 2.957177e-16 1.567537e-14
93 -1.384408e-14 2.957177e-16
94 1.593492e-16 -1.384408e-14
95 4.707473e-16 1.593492e-16
96 1.424307e-15 4.707473e-16
97 1.521728e-14 1.424307e-15
98 9.671568e-16 1.521728e-14
99 1.122593e-15 9.671568e-16
100 -1.293184e-14 1.122593e-15
101 1.350516e-15 -1.293184e-14
102 1.537838e-14 1.350516e-15
103 5.106145e-16 1.537838e-14
104 -1.371284e-14 5.106145e-16
105 8.591982e-15 -1.371284e-14
106 -5.042708e-15 8.591982e-15
107 1.393396e-15 -5.042708e-15
108 7.850608e-15 1.393396e-15
109 8.887697e-15 7.850608e-15
110 -8.936163e-15 8.887697e-15
111 2.384668e-15 -8.936163e-15
112 -3.740809e-15 2.384668e-15
113 9.903985e-15 -3.740809e-15
114 2.256654e-15 9.903985e-15
115 2.422324e-15 2.256654e-15
116 1.244800e-15 2.422324e-15
117 1.497590e-14 1.244800e-15
118 1.561058e-14 1.497590e-14
119 -1.330336e-14 1.561058e-14
120 1.011302e-15 -1.330336e-14
121 9.631281e-16 1.011302e-15
122 1.135139e-15 9.631281e-16
123 1.247840e-15 1.135139e-15
124 -1.284561e-14 1.247840e-15
125 1.428315e-15 -1.284561e-14
126 1.211778e-15 1.428315e-15
127 4.011641e-16 1.211778e-15
128 -1.355488e-14 4.011641e-16
129 1.494474e-14 -1.355488e-14
130 1.484006e-14 1.494474e-14
131 4.313775e-17 1.484006e-14
132 -1.936493e-16 4.313775e-17
133 -3.814308e-16 -1.936493e-16
134 4.080680e-16 -3.814308e-16
135 -7.414551e-17 4.080680e-16
136 1.394264e-15 -7.414551e-17
137 9.356691e-16 1.394264e-15
138 1.349273e-15 9.356691e-16
139 1.348890e-15 1.349273e-15
140 1.533852e-15 1.348890e-15
141 1.574832e-14 1.533852e-15
142 1.499925e-14 1.574832e-14
143 2.248898e-14 1.499925e-14
144 2.531892e-15 2.248898e-14
145 2.062872e-15 2.531892e-15
146 -5.900312e-15 2.062872e-15
147 -4.099916e-15 -5.900312e-15
148 1.088299e-15 -4.099916e-15
149 2.095979e-15 1.088299e-15
150 -5.125759e-15 2.095979e-15
151 2.254972e-15 -5.125759e-15
152 8.919427e-15 2.254972e-15
153 2.256162e-15 8.919427e-15
154 9.528878e-15 2.256162e-15
155 3.334716e-15 9.528878e-15
156 1.274892e-15 3.334716e-15
157 -4.932495e-15 1.274892e-15
158 -4.265757e-15 -4.932495e-15
159 -5.759862e-15 -4.265757e-15
160 1.981182e-15 -5.759862e-15
161 8.836275e-15 1.981182e-15
162 8.643495e-15 8.836275e-15
163 1.039957e-14 8.643495e-15
164 1.568190e-15 1.039957e-14
165 2.787860e-15 1.568190e-15
166 2.104958e-15 2.787860e-15
167 2.631735e-15 2.104958e-15
168 2.476328e-15 2.631735e-15
169 2.366991e-15 2.476328e-15
170 -5.252459e-15 2.366991e-15
171 -5.059130e-15 -5.252459e-15
172 -3.498536e-15 -5.059130e-15
173 3.312967e-15 -3.498536e-15
174 -4.888802e-15 3.312967e-15
175 -4.155943e-15 -4.888802e-15
176 1.871086e-15 -4.155943e-15
177 -5.729799e-15 1.871086e-15
178 1.553311e-14 -5.729799e-15
179 -4.880117e-15 1.553311e-14
> 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/7s6df1353254712.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/8tq0v1353254712.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/9v1rx1353254712.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/10bk6n1353254712.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11x9rb1353254712.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12q7ms1353254712.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13kstb1353254712.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14ijk71353254712.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/15zox51353254712.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/16s5jd1353254712.tab")
+ }
>
> try(system("convert tmp/1hl761353254712.ps tmp/1hl761353254712.png",intern=TRUE))
character(0)
> try(system("convert tmp/26na21353254712.ps tmp/26na21353254712.png",intern=TRUE))
character(0)
> try(system("convert tmp/3v5yw1353254712.ps tmp/3v5yw1353254712.png",intern=TRUE))
character(0)
> try(system("convert tmp/43nfb1353254712.ps tmp/43nfb1353254712.png",intern=TRUE))
character(0)
> try(system("convert tmp/58jj81353254712.ps tmp/58jj81353254712.png",intern=TRUE))
character(0)
> try(system("convert tmp/679mv1353254712.ps tmp/679mv1353254712.png",intern=TRUE))
character(0)
> try(system("convert tmp/7s6df1353254712.ps tmp/7s6df1353254712.png",intern=TRUE))
character(0)
> try(system("convert tmp/8tq0v1353254712.ps tmp/8tq0v1353254712.png",intern=TRUE))
character(0)
> try(system("convert tmp/9v1rx1353254712.ps tmp/9v1rx1353254712.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bk6n1353254712.ps tmp/10bk6n1353254712.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.056 0.909 8.984