R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1
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+ ,0.00502
+ ,0.00004
+ ,0.00257
+ ,0.00312
+ ,1
+ ,112.239
+ ,126.609
+ ,104.095
+ ,0.00472
+ ,0.00004
+ ,0.00238
+ ,0.0029
+ ,1
+ ,116.15
+ ,131.731
+ ,109.815
+ ,0.00381
+ ,0.00003
+ ,0.00181
+ ,0.00232
+ ,1
+ ,170.368
+ ,268.796
+ ,79.543
+ ,0.00571
+ ,0.00003
+ ,0.00232
+ ,0.00269
+ ,1
+ ,208.083
+ ,253.792
+ ,91.802
+ ,0.00757
+ ,0.00004
+ ,0.00428
+ ,0.00428
+ ,1
+ ,198.458
+ ,219.29
+ ,148.691
+ ,0.00376
+ ,0.00002
+ ,0.00182
+ ,0.00215
+ ,1
+ ,202.805
+ ,231.508
+ ,86.232
+ ,0.0037
+ ,0.00002
+ ,0.00189
+ ,0.00211
+ ,1
+ ,202.544
+ ,241.35
+ ,164.168
+ ,0.00254
+ ,0.00001
+ ,0.001
+ ,0.00133
+ ,1
+ ,223.361
+ ,263.872
+ ,87.638
+ ,0.00352
+ ,0.00002
+ ,0.00169
+ ,0.00188
+ ,1
+ ,169.774
+ ,191.759
+ ,151.451
+ ,0.01568
+ ,0.00009
+ ,0.00863
+ ,0.00946
+ ,1
+ ,183.52
+ ,216.814
+ ,161.34
+ ,0.01466
+ ,0.00008
+ ,0.00849
+ ,0.00819
+ ,1
+ ,188.62
+ ,216.302
+ ,165.982
+ ,0.01719
+ ,0.00009
+ ,0.00996
+ ,0.01027
+ ,1
+ ,202.632
+ ,565.74
+ ,177.258
+ ,0.01627
+ ,0.00008
+ ,0.00919
+ ,0.00963
+ ,1
+ ,186.695
+ ,211.961
+ ,149.442
+ ,0.01872
+ ,0.0001
+ ,0.01075
+ ,0.01154
+ ,1
+ ,192.818
+ ,224.429
+ ,168.793
+ ,0.03107
+ ,0.00016
+ ,0.018
+ ,0.01958
+ ,1
+ ,198.116
+ ,233.099
+ ,174.478
+ ,0.02714
+ ,0.00014
+ ,0.01568
+ ,0.01699
+ ,1
+ ,121.345
+ ,139.644
+ ,98.25
+ ,0.00684
+ ,0.00006
+ ,0.00388
+ ,0.00332
+ ,1
+ ,119.1
+ ,128.442
+ ,88.833
+ ,0.00692
+ ,0.00006
+ ,0.00393
+ ,0.003
+ ,1
+ ,117.87
+ ,127.349
+ ,95.654
+ ,0.00647
+ ,0.00005
+ ,0.00356
+ ,0.003
+ ,1
+ ,122.336
+ ,142.369
+ ,94.794
+ ,0.00727
+ ,0.00006
+ ,0.00415
+ ,0.00339
+ ,1
+ ,117.963
+ ,134.209
+ ,100.757
+ ,0.01813
+ ,0.00015
+ ,0.01117
+ ,0.00718
+ ,1
+ ,126.144
+ ,154.284
+ ,97.543
+ ,0.00975
+ ,0.00008
+ ,0.00593
+ ,0.00454
+ ,1
+ ,127.93
+ ,138.752
+ ,112.173
+ ,0.00605
+ ,0.00005
+ ,0.00321
+ ,0.00318
+ ,1
+ ,114.238
+ ,124.393
+ ,77.022
+ ,0.00581
+ ,0.00005
+ ,0.00299
+ ,0.00316
+ ,1
+ ,115.322
+ ,135.738
+ ,107.802
+ ,0.00619
+ ,0.00005
+ ,0.00352
+ ,0.00329
+ ,1
+ ,114.554
+ ,126.778
+ ,91.121
+ ,0.00651
+ ,0.00006
+ ,0.00366
+ ,0.0034
+ ,1
+ ,112.15
+ ,131.669
+ ,97.527
+ ,0.00519
+ ,0.00005
+ ,0.00291
+ ,0.00284
+ ,1
+ ,102.273
+ ,142.83
+ ,85.902
+ ,0.00907
+ ,0.00009
+ ,0.00493
+ ,0.00461
+ ,0
+ ,236.2
+ ,244.663
+ ,102.137
+ ,0.00277
+ ,0.00001
+ ,0.00154
+ ,0.00153
+ ,0
+ ,237.323
+ ,243.709
+ ,229.256
+ ,0.00303
+ ,0.00001
+ ,0.00173
+ ,0.00159
+ ,0
+ ,260.105
+ ,264.919
+ ,237.303
+ ,0.00339
+ ,0.00001
+ ,0.00205
+ ,0.00186
+ ,0
+ ,197.569
+ ,217.627
+ ,90.794
+ ,0.00803
+ ,0.00004
+ ,0.0049
+ ,0.00448
+ ,0
+ ,240.301
+ ,245.135
+ ,219.783
+ ,0.00517
+ ,0.00002
+ ,0.00316
+ ,0.00283
+ ,0
+ ,244.99
+ ,272.21
+ ,239.17
+ ,0.00451
+ ,0.00002
+ ,0.00279
+ ,0.00237
+ ,0
+ ,112.547
+ ,133.374
+ ,105.715
+ ,0.00355
+ ,0.00003
+ ,0.00166
+ ,0.0019
+ ,0
+ ,110.739
+ ,113.597
+ ,100.139
+ ,0.00356
+ ,0.00003
+ ,0.0017
+ ,0.002
+ ,0
+ ,113.715
+ ,116.443
+ ,96.913
+ ,0.00349
+ ,0.00003
+ ,0.00171
+ ,0.00203
+ ,0
+ ,117.004
+ ,144.466
+ ,99.923
+ ,0.00353
+ ,0.00003
+ ,0.00176
+ ,0.00218
+ ,0
+ ,115.38
+ ,123.109
+ ,108.634
+ ,0.00332
+ ,0.00003
+ ,0.0016
+ ,0.00199
+ ,0
+ ,116.388
+ ,129.038
+ ,108.97
+ ,0.00346
+ ,0.00003
+ ,0.00169
+ ,0.00213
+ ,1
+ ,151.737
+ ,190.204
+ ,129.859
+ ,0.00314
+ ,0.00002
+ ,0.00135
+ ,0.00162
+ ,1
+ ,148.79
+ ,158.359
+ ,138.99
+ ,0.00309
+ ,0.00002
+ ,0.00152
+ ,0.00186
+ ,1
+ ,148.143
+ ,155.982
+ ,135.041
+ ,0.00392
+ ,0.00003
+ ,0.00204
+ ,0.00231
+ ,1
+ ,150.44
+ ,163.441
+ ,144.736
+ ,0.00396
+ ,0.00003
+ ,0.00206
+ ,0.00233
+ ,1
+ ,148.462
+ ,161.078
+ ,141.998
+ ,0.00397
+ ,0.00003
+ ,0.00202
+ ,0.00235
+ ,1
+ ,149.818
+ ,163.417
+ ,144.786
+ ,0.00336
+ ,0.00002
+ ,0.00174
+ ,0.00198
+ ,0
+ ,117.226
+ ,123.925
+ ,106.656
+ ,0.00417
+ ,0.00004
+ ,0.00186
+ ,0.0027
+ ,0
+ ,116.848
+ ,217.552
+ ,99.503
+ ,0.00531
+ ,0.00005
+ ,0.0026
+ ,0.00346
+ ,0
+ ,116.286
+ ,177.291
+ ,96.983
+ ,0.00314
+ ,0.00003
+ ,0.00134
+ ,0.00192
+ ,0
+ ,116.556
+ ,592.03
+ ,86.228
+ ,0.00496
+ ,0.00004
+ ,0.00254
+ ,0.00263
+ ,0
+ ,116.342
+ ,581.289
+ ,94.246
+ ,0.00267
+ ,0.00002
+ ,0.00115
+ ,0.00148
+ ,0
+ ,114.563
+ ,119.167
+ ,86.647
+ ,0.00327
+ ,0.00003
+ ,0.00146
+ ,0.00184
+ ,0
+ ,201.774
+ ,262.707
+ ,78.228
+ ,0.00694
+ ,0.00003
+ ,0.00412
+ ,0.00396
+ ,0
+ ,174.188
+ ,230.978
+ ,94.261
+ ,0.00459
+ ,0.00003
+ ,0.00263
+ ,0.00259
+ ,0
+ ,209.516
+ ,253.017
+ ,89.488
+ ,0.00564
+ ,0.00003
+ ,0.00331
+ ,0.00292
+ ,0
+ ,174.688
+ ,240.005
+ ,74.287
+ ,0.0136
+ ,0.00008
+ ,0.00624
+ ,0.00564
+ ,0
+ ,198.764
+ ,396.961
+ ,74.904
+ ,0.0074
+ ,0.00004
+ ,0.0037
+ ,0.0039
+ ,0
+ ,214.289
+ ,260.277
+ ,77.973
+ ,0.00567
+ ,0.00003
+ ,0.00295
+ ,0.00317)
+ ,dim=c(8
+ ,195)
+ ,dimnames=list(c('status'
+ ,'MDVP:Fo(Hz)'
+ ,'MDVP:Fhi(Hz)'
+ ,'MDVP:Flo(Hz)'
+ ,'MDVP:Jitter(%)'
+ ,'MDVP:Jitter(Abs)'
+ ,'MDVP:RAP'
+ ,'MDVP:PPQ')
+ ,1:195))
> y <- array(NA,dim=c(8,195),dimnames=list(c('status','MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:RAP','MDVP:PPQ'),1:195))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects 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
status MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%)
1 1 119.992 157.302 74.997 0.00784
2 1 122.400 148.650 113.819 0.00968
3 1 116.682 131.111 111.555 0.01050
4 1 116.676 137.871 111.366 0.00997
5 1 116.014 141.781 110.655 0.01284
6 1 120.552 131.162 113.787 0.00968
7 1 120.267 137.244 114.820 0.00333
8 1 107.332 113.840 104.315 0.00290
9 1 95.730 132.068 91.754 0.00551
10 1 95.056 120.103 91.226 0.00532
11 1 88.333 112.240 84.072 0.00505
12 1 91.904 115.871 86.292 0.00540
13 1 136.926 159.866 131.276 0.00293
14 1 139.173 179.139 76.556 0.00390
15 1 152.845 163.305 75.836 0.00294
16 1 142.167 217.455 83.159 0.00369
17 1 144.188 349.259 82.764 0.00544
18 1 168.778 232.181 75.603 0.00718
19 1 153.046 175.829 68.623 0.00742
20 1 156.405 189.398 142.822 0.00768
21 1 153.848 165.738 65.782 0.00840
22 1 153.880 172.860 78.128 0.00480
23 1 167.930 193.221 79.068 0.00442
24 1 173.917 192.735 86.180 0.00476
25 1 163.656 200.841 76.779 0.00742
26 1 104.400 206.002 77.968 0.00633
27 1 171.041 208.313 75.501 0.00455
28 1 146.845 208.701 81.737 0.00496
29 1 155.358 227.383 80.055 0.00310
30 1 162.568 198.346 77.630 0.00502
31 0 197.076 206.896 192.055 0.00289
32 0 199.228 209.512 192.091 0.00241
33 0 198.383 215.203 193.104 0.00212
34 0 202.266 211.604 197.079 0.00180
35 0 203.184 211.526 196.160 0.00178
36 0 201.464 210.565 195.708 0.00198
37 1 177.876 192.921 168.013 0.00411
38 1 176.170 185.604 163.564 0.00369
39 1 180.198 201.249 175.456 0.00284
40 1 187.733 202.324 173.015 0.00316
41 1 186.163 197.724 177.584 0.00298
42 1 184.055 196.537 166.977 0.00258
43 0 237.226 247.326 225.227 0.00298
44 0 241.404 248.834 232.483 0.00281
45 0 243.439 250.912 232.435 0.00210
46 0 242.852 255.034 227.911 0.00225
47 0 245.510 262.090 231.848 0.00235
48 0 252.455 261.487 182.786 0.00185
49 0 122.188 128.611 115.765 0.00524
50 0 122.964 130.049 114.676 0.00428
51 0 124.445 135.069 117.495 0.00431
52 0 126.344 134.231 112.773 0.00448
53 0 128.001 138.052 122.080 0.00436
54 0 129.336 139.867 118.604 0.00490
55 1 108.807 134.656 102.874 0.00761
56 1 109.860 126.358 104.437 0.00874
57 1 110.417 131.067 103.370 0.00784
58 1 117.274 129.916 110.402 0.00752
59 1 116.879 131.897 108.153 0.00788
60 1 114.847 271.314 104.680 0.00867
61 0 209.144 237.494 109.379 0.00282
62 0 223.365 238.987 98.664 0.00264
63 0 222.236 231.345 205.495 0.00266
64 0 228.832 234.619 223.634 0.00296
65 0 229.401 252.221 221.156 0.00205
66 0 228.969 239.541 113.201 0.00238
67 1 140.341 159.774 67.021 0.00817
68 1 136.969 166.607 66.004 0.00923
69 1 143.533 162.215 65.809 0.01101
70 1 148.090 162.824 67.343 0.00762
71 1 142.729 162.408 65.476 0.00831
72 1 136.358 176.595 65.750 0.00971
73 1 120.080 139.710 111.208 0.00405
74 1 112.014 588.518 107.024 0.00533
75 1 110.793 128.101 107.316 0.00494
76 1 110.707 122.611 105.007 0.00516
77 1 112.876 148.826 106.981 0.00500
78 1 110.568 125.394 106.821 0.00462
79 1 95.385 102.145 90.264 0.00608
80 1 100.770 115.697 85.545 0.01038
81 1 96.106 108.664 84.510 0.00694
82 1 95.605 107.715 87.549 0.00702
83 1 100.960 110.019 95.628 0.00606
84 1 98.804 102.305 87.804 0.00432
85 1 176.858 205.560 75.344 0.00747
86 1 180.978 200.125 155.495 0.00406
87 1 178.222 202.450 141.047 0.00321
88 1 176.281 227.381 125.610 0.00520
89 1 173.898 211.350 74.677 0.00448
90 1 179.711 225.930 144.878 0.00709
91 1 166.605 206.008 78.032 0.00742
92 1 151.955 163.335 147.226 0.00419
93 1 148.272 164.989 142.299 0.00459
94 1 152.125 161.469 76.596 0.00382
95 1 157.821 172.975 68.401 0.00358
96 1 157.447 163.267 149.605 0.00369
97 1 159.116 168.913 144.811 0.00342
98 1 125.036 143.946 116.187 0.01280
99 1 125.791 140.557 96.206 0.01378
100 1 126.512 141.756 99.770 0.01936
101 1 125.641 141.068 116.346 0.03316
102 1 128.451 150.449 75.632 0.01551
103 1 139.224 586.567 66.157 0.03011
104 1 150.258 154.609 75.349 0.00248
105 1 154.003 160.267 128.621 0.00183
106 1 149.689 160.368 133.608 0.00257
107 1 155.078 163.736 144.148 0.00168
108 1 151.884 157.765 133.751 0.00258
109 1 151.989 157.339 132.857 0.00174
110 1 193.030 208.900 80.297 0.00766
111 1 200.714 223.982 89.686 0.00621
112 1 208.519 220.315 199.020 0.00609
113 1 204.664 221.300 189.621 0.00841
114 1 210.141 232.706 185.258 0.00534
115 1 206.327 226.355 92.020 0.00495
116 1 151.872 492.892 69.085 0.00856
117 1 158.219 442.557 71.948 0.00476
118 1 170.756 450.247 79.032 0.00555
119 1 178.285 442.824 82.063 0.00462
120 1 217.116 233.481 93.978 0.00404
121 1 128.940 479.697 88.251 0.00581
122 1 176.824 215.293 83.961 0.00460
123 1 138.190 203.522 83.340 0.00704
124 1 182.018 197.173 79.187 0.00842
125 1 156.239 195.107 79.820 0.00694
126 1 145.174 198.109 80.637 0.00733
127 1 138.145 197.238 81.114 0.00544
128 1 166.888 198.966 79.512 0.00638
129 1 119.031 127.533 109.216 0.00440
130 1 120.078 126.632 105.667 0.00270
131 1 120.289 128.143 100.209 0.00492
132 1 120.256 125.306 104.773 0.00407
133 1 119.056 125.213 86.795 0.00346
134 1 118.747 123.723 109.836 0.00331
135 1 106.516 112.777 93.105 0.00589
136 1 110.453 127.611 105.554 0.00494
137 1 113.400 133.344 107.816 0.00451
138 1 113.166 130.270 100.673 0.00502
139 1 112.239 126.609 104.095 0.00472
140 1 116.150 131.731 109.815 0.00381
141 1 170.368 268.796 79.543 0.00571
142 1 208.083 253.792 91.802 0.00757
143 1 198.458 219.290 148.691 0.00376
144 1 202.805 231.508 86.232 0.00370
145 1 202.544 241.350 164.168 0.00254
146 1 223.361 263.872 87.638 0.00352
147 1 169.774 191.759 151.451 0.01568
148 1 183.520 216.814 161.340 0.01466
149 1 188.620 216.302 165.982 0.01719
150 1 202.632 565.740 177.258 0.01627
151 1 186.695 211.961 149.442 0.01872
152 1 192.818 224.429 168.793 0.03107
153 1 198.116 233.099 174.478 0.02714
154 1 121.345 139.644 98.250 0.00684
155 1 119.100 128.442 88.833 0.00692
156 1 117.870 127.349 95.654 0.00647
157 1 122.336 142.369 94.794 0.00727
158 1 117.963 134.209 100.757 0.01813
159 1 126.144 154.284 97.543 0.00975
160 1 127.930 138.752 112.173 0.00605
161 1 114.238 124.393 77.022 0.00581
162 1 115.322 135.738 107.802 0.00619
163 1 114.554 126.778 91.121 0.00651
164 1 112.150 131.669 97.527 0.00519
165 1 102.273 142.830 85.902 0.00907
166 0 236.200 244.663 102.137 0.00277
167 0 237.323 243.709 229.256 0.00303
168 0 260.105 264.919 237.303 0.00339
169 0 197.569 217.627 90.794 0.00803
170 0 240.301 245.135 219.783 0.00517
171 0 244.990 272.210 239.170 0.00451
172 0 112.547 133.374 105.715 0.00355
173 0 110.739 113.597 100.139 0.00356
174 0 113.715 116.443 96.913 0.00349
175 0 117.004 144.466 99.923 0.00353
176 0 115.380 123.109 108.634 0.00332
177 0 116.388 129.038 108.970 0.00346
178 1 151.737 190.204 129.859 0.00314
179 1 148.790 158.359 138.990 0.00309
180 1 148.143 155.982 135.041 0.00392
181 1 150.440 163.441 144.736 0.00396
182 1 148.462 161.078 141.998 0.00397
183 1 149.818 163.417 144.786 0.00336
184 0 117.226 123.925 106.656 0.00417
185 0 116.848 217.552 99.503 0.00531
186 0 116.286 177.291 96.983 0.00314
187 0 116.556 592.030 86.228 0.00496
188 0 116.342 581.289 94.246 0.00267
189 0 114.563 119.167 86.647 0.00327
190 0 201.774 262.707 78.228 0.00694
191 0 174.188 230.978 94.261 0.00459
192 0 209.516 253.017 89.488 0.00564
193 0 174.688 240.005 74.287 0.01360
194 0 198.764 396.961 74.904 0.00740
195 0 214.289 260.277 77.973 0.00567
MDVP:Jitter(Abs) MDVP:RAP MDVP:PPQ
1 7.0e-05 0.00370 0.00554
2 8.0e-05 0.00465 0.00696
3 9.0e-05 0.00544 0.00781
4 9.0e-05 0.00502 0.00698
5 1.1e-04 0.00655 0.00908
6 8.0e-05 0.00463 0.00750
7 3.0e-05 0.00155 0.00202
8 3.0e-05 0.00144 0.00182
9 6.0e-05 0.00293 0.00332
10 6.0e-05 0.00268 0.00332
11 6.0e-05 0.00254 0.00330
12 6.0e-05 0.00281 0.00336
13 2.0e-05 0.00118 0.00153
14 3.0e-05 0.00165 0.00208
15 2.0e-05 0.00121 0.00149
16 3.0e-05 0.00157 0.00203
17 4.0e-05 0.00211 0.00292
18 4.0e-05 0.00284 0.00387
19 5.0e-05 0.00364 0.00432
20 5.0e-05 0.00372 0.00399
21 5.0e-05 0.00428 0.00450
22 3.0e-05 0.00232 0.00267
23 3.0e-05 0.00220 0.00247
24 3.0e-05 0.00221 0.00258
25 5.0e-05 0.00380 0.00390
26 6.0e-05 0.00316 0.00375
27 3.0e-05 0.00250 0.00234
28 3.0e-05 0.00250 0.00275
29 2.0e-05 0.00159 0.00176
30 3.0e-05 0.00280 0.00253
31 1.0e-05 0.00166 0.00168
32 1.0e-05 0.00134 0.00138
33 1.0e-05 0.00113 0.00135
34 9.0e-06 0.00093 0.00107
35 9.0e-06 0.00094 0.00106
36 1.0e-05 0.00105 0.00115
37 2.0e-05 0.00233 0.00241
38 2.0e-05 0.00205 0.00218
39 2.0e-05 0.00153 0.00166
40 2.0e-05 0.00168 0.00182
41 2.0e-05 0.00165 0.00175
42 1.0e-05 0.00134 0.00147
43 1.0e-05 0.00169 0.00182
44 1.0e-05 0.00157 0.00173
45 9.0e-06 0.00109 0.00137
46 9.0e-06 0.00117 0.00139
47 1.0e-05 0.00127 0.00148
48 7.0e-06 0.00092 0.00113
49 4.0e-05 0.00169 0.00203
50 3.0e-05 0.00124 0.00155
51 3.0e-05 0.00141 0.00167
52 4.0e-05 0.00131 0.00169
53 3.0e-05 0.00137 0.00166
54 4.0e-05 0.00165 0.00183
55 7.0e-05 0.00349 0.00486
56 8.0e-05 0.00398 0.00539
57 7.0e-05 0.00352 0.00514
58 6.0e-05 0.00299 0.00469
59 7.0e-05 0.00334 0.00493
60 8.0e-05 0.00373 0.00520
61 1.0e-05 0.00147 0.00152
62 1.0e-05 0.00154 0.00151
63 1.0e-05 0.00152 0.00144
64 1.0e-05 0.00175 0.00155
65 9.0e-06 0.00114 0.00113
66 1.0e-05 0.00136 0.00140
67 6.0e-05 0.00430 0.00440
68 7.0e-05 0.00507 0.00463
69 8.0e-05 0.00647 0.00467
70 5.0e-05 0.00467 0.00354
71 6.0e-05 0.00469 0.00419
72 7.0e-05 0.00534 0.00478
73 3.0e-05 0.00180 0.00220
74 5.0e-05 0.00268 0.00329
75 4.0e-05 0.00260 0.00283
76 5.0e-05 0.00277 0.00289
77 4.0e-05 0.00270 0.00289
78 4.0e-05 0.00226 0.00280
79 6.0e-05 0.00331 0.00332
80 1.0e-04 0.00622 0.00576
81 7.0e-05 0.00389 0.00415
82 7.0e-05 0.00428 0.00371
83 6.0e-05 0.00351 0.00348
84 4.0e-05 0.00247 0.00258
85 4.0e-05 0.00418 0.00420
86 2.0e-05 0.00220 0.00244
87 2.0e-05 0.00163 0.00194
88 3.0e-05 0.00287 0.00312
89 3.0e-05 0.00237 0.00254
90 4.0e-05 0.00391 0.00419
91 4.0e-05 0.00387 0.00453
92 3.0e-05 0.00224 0.00227
93 3.0e-05 0.00250 0.00256
94 3.0e-05 0.00191 0.00226
95 2.0e-05 0.00196 0.00196
96 2.0e-05 0.00201 0.00197
97 2.0e-05 0.00178 0.00184
98 1.0e-04 0.00743 0.00623
99 1.1e-04 0.00826 0.00655
100 1.5e-04 0.01159 0.00990
101 2.6e-04 0.02144 0.01522
102 1.2e-04 0.00905 0.00909
103 2.2e-04 0.01854 0.01628
104 2.0e-05 0.00105 0.00136
105 1.0e-05 0.00076 0.00100
106 2.0e-05 0.00116 0.00134
107 1.0e-05 0.00068 0.00092
108 2.0e-05 0.00115 0.00122
109 1.0e-05 0.00075 0.00096
110 4.0e-05 0.00450 0.00389
111 3.0e-05 0.00371 0.00337
112 3.0e-05 0.00368 0.00339
113 4.0e-05 0.00502 0.00485
114 3.0e-05 0.00321 0.00280
115 2.0e-05 0.00302 0.00246
116 6.0e-05 0.00404 0.00385
117 3.0e-05 0.00214 0.00207
118 3.0e-05 0.00244 0.00261
119 3.0e-05 0.00157 0.00194
120 2.0e-05 0.00127 0.00128
121 5.0e-05 0.00241 0.00314
122 3.0e-05 0.00209 0.00221
123 5.0e-05 0.00406 0.00398
124 5.0e-05 0.00506 0.00449
125 4.0e-05 0.00403 0.00395
126 5.0e-05 0.00414 0.00422
127 4.0e-05 0.00294 0.00327
128 4.0e-05 0.00368 0.00351
129 4.0e-05 0.00214 0.00192
130 2.0e-05 0.00116 0.00135
131 4.0e-05 0.00269 0.00238
132 3.0e-05 0.00224 0.00205
133 3.0e-05 0.00169 0.00170
134 3.0e-05 0.00168 0.00171
135 6.0e-05 0.00291 0.00319
136 4.0e-05 0.00244 0.00315
137 4.0e-05 0.00219 0.00283
138 4.0e-05 0.00257 0.00312
139 4.0e-05 0.00238 0.00290
140 3.0e-05 0.00181 0.00232
141 3.0e-05 0.00232 0.00269
142 4.0e-05 0.00428 0.00428
143 2.0e-05 0.00182 0.00215
144 2.0e-05 0.00189 0.00211
145 1.0e-05 0.00100 0.00133
146 2.0e-05 0.00169 0.00188
147 9.0e-05 0.00863 0.00946
148 8.0e-05 0.00849 0.00819
149 9.0e-05 0.00996 0.01027
150 8.0e-05 0.00919 0.00963
151 1.0e-04 0.01075 0.01154
152 1.6e-04 0.01800 0.01958
153 1.4e-04 0.01568 0.01699
154 6.0e-05 0.00388 0.00332
155 6.0e-05 0.00393 0.00300
156 5.0e-05 0.00356 0.00300
157 6.0e-05 0.00415 0.00339
158 1.5e-04 0.01117 0.00718
159 8.0e-05 0.00593 0.00454
160 5.0e-05 0.00321 0.00318
161 5.0e-05 0.00299 0.00316
162 5.0e-05 0.00352 0.00329
163 6.0e-05 0.00366 0.00340
164 5.0e-05 0.00291 0.00284
165 9.0e-05 0.00493 0.00461
166 1.0e-05 0.00154 0.00153
167 1.0e-05 0.00173 0.00159
168 1.0e-05 0.00205 0.00186
169 4.0e-05 0.00490 0.00448
170 2.0e-05 0.00316 0.00283
171 2.0e-05 0.00279 0.00237
172 3.0e-05 0.00166 0.00190
173 3.0e-05 0.00170 0.00200
174 3.0e-05 0.00171 0.00203
175 3.0e-05 0.00176 0.00218
176 3.0e-05 0.00160 0.00199
177 3.0e-05 0.00169 0.00213
178 2.0e-05 0.00135 0.00162
179 2.0e-05 0.00152 0.00186
180 3.0e-05 0.00204 0.00231
181 3.0e-05 0.00206 0.00233
182 3.0e-05 0.00202 0.00235
183 2.0e-05 0.00174 0.00198
184 4.0e-05 0.00186 0.00270
185 5.0e-05 0.00260 0.00346
186 3.0e-05 0.00134 0.00192
187 4.0e-05 0.00254 0.00263
188 2.0e-05 0.00115 0.00148
189 3.0e-05 0.00146 0.00184
190 3.0e-05 0.00412 0.00396
191 3.0e-05 0.00263 0.00259
192 3.0e-05 0.00331 0.00292
193 8.0e-05 0.00624 0.00564
194 4.0e-05 0.00370 0.00390
195 3.0e-05 0.00295 0.00317
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `MDVP:Fo(Hz)` `MDVP:Fhi(Hz)` `MDVP:Flo(Hz)`
1.529e+00 -2.796e-03 -3.917e-04 -2.778e-03
`MDVP:Jitter(%)` `MDVP:Jitter(Abs)` `MDVP:RAP` `MDVP:PPQ`
-8.408e+01 -3.423e+03 1.200e+02 9.662e+01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.90159 -0.16077 0.09907 0.24758 0.58709
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.529e+00 2.076e-01 7.366 5.46e-12 ***
`MDVP:Fo(Hz)` -2.796e-03 1.326e-03 -2.108 0.036320 *
`MDVP:Fhi(Hz)` -3.917e-04 3.386e-04 -1.157 0.248792
`MDVP:Flo(Hz)` -2.778e-03 8.277e-04 -3.357 0.000955 ***
`MDVP:Jitter(%)` -8.408e+01 6.251e+01 -1.345 0.180216
`MDVP:Jitter(Abs)` -3.423e+03 3.709e+03 -0.923 0.357301
`MDVP:RAP` 1.200e+02 7.444e+01 1.612 0.108634
`MDVP:PPQ` 9.662e+01 4.793e+01 2.016 0.045236 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3771 on 187 degrees of freedom
Multiple R-squared: 0.2651, Adjusted R-squared: 0.2375
F-statistic: 9.635 on 7 and 187 DF, p-value: 3.24e-10
> 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,] 3.666438e-54 7.332876e-54 1.000000000
[2,] 1.946725e-66 3.893451e-66 1.000000000
[3,] 3.097209e-93 6.194418e-93 1.000000000
[4,] 1.718351e-92 3.436703e-92 1.000000000
[5,] 5.742669e-108 1.148534e-107 1.000000000
[6,] 0.000000e+00 0.000000e+00 1.000000000
[7,] 1.170841e-148 2.341682e-148 1.000000000
[8,] 6.221932e-155 1.244386e-154 1.000000000
[9,] 2.747854e-169 5.495709e-169 1.000000000
[10,] 4.904526e-193 9.809052e-193 1.000000000
[11,] 1.045233e-225 2.090467e-225 1.000000000
[12,] 7.432430e-218 1.486486e-217 1.000000000
[13,] 1.216391e-229 2.432783e-229 1.000000000
[14,] 5.583323e-248 1.116665e-247 1.000000000
[15,] 9.954098e-267 1.990820e-266 1.000000000
[16,] 5.801695e-308 1.160339e-307 1.000000000
[17,] 1.087341e-296 2.174681e-296 1.000000000
[18,] 2.417276e-307 4.834552e-307 1.000000000
[19,] 0.000000e+00 0.000000e+00 1.000000000
[20,] 0.000000e+00 0.000000e+00 1.000000000
[21,] 2.649017e-09 5.298034e-09 0.999999997
[22,] 3.225501e-09 6.451001e-09 0.999999997
[23,] 1.439791e-09 2.879582e-09 0.999999999
[24,] 4.549528e-10 9.099055e-10 1.000000000
[25,] 1.405319e-10 2.810637e-10 1.000000000
[26,] 4.440812e-11 8.881624e-11 1.000000000
[27,] 2.079537e-08 4.159075e-08 0.999999979
[28,] 3.326613e-07 6.653227e-07 0.999999667
[29,] 2.261219e-05 4.522439e-05 0.999977388
[30,] 2.114749e-04 4.229499e-04 0.999788525
[31,] 8.886790e-04 1.777358e-03 0.999111321
[32,] 1.291579e-03 2.583157e-03 0.998708421
[33,] 8.449718e-04 1.689944e-03 0.999155028
[34,] 5.194886e-04 1.038977e-03 0.999480511
[35,] 3.301697e-04 6.603394e-04 0.999669830
[36,] 2.005303e-04 4.010606e-04 0.999799470
[37,] 1.224469e-04 2.448939e-04 0.999877553
[38,] 7.438331e-05 1.487666e-04 0.999925617
[39,] 4.274603e-04 8.549205e-04 0.999572540
[40,] 6.801330e-04 1.360266e-03 0.999319867
[41,] 1.019005e-03 2.038011e-03 0.998980995
[42,] 8.835737e-04 1.767147e-03 0.999116426
[43,] 1.019247e-03 2.038493e-03 0.998980753
[44,] 1.073522e-03 2.147045e-03 0.998926478
[45,] 9.450983e-04 1.890197e-03 0.999054902
[46,] 1.147929e-03 2.295859e-03 0.998852071
[47,] 9.352684e-04 1.870537e-03 0.999064732
[48,] 1.287790e-03 2.575581e-03 0.998712210
[49,] 1.527090e-03 3.054180e-03 0.998472910
[50,] 1.144677e-03 2.289353e-03 0.998855323
[51,] 4.390732e-03 8.781464e-03 0.995609268
[52,] 9.030038e-03 1.806008e-02 0.990969962
[53,] 8.140840e-03 1.628168e-02 0.991859160
[54,] 7.100689e-03 1.420138e-02 0.992899311
[55,] 5.876693e-03 1.175339e-02 0.994123307
[56,] 6.878407e-03 1.375681e-02 0.993121593
[57,] 4.997473e-03 9.994946e-03 0.995002527
[58,] 3.640471e-03 7.280943e-03 0.996359529
[59,] 2.575348e-03 5.150695e-03 0.997424652
[60,] 2.062975e-03 4.125951e-03 0.997937025
[61,] 1.456185e-03 2.912370e-03 0.998543815
[62,] 1.028314e-03 2.056628e-03 0.998971686
[63,] 7.218494e-04 1.443699e-03 0.999278151
[64,] 1.817115e-03 3.634231e-03 0.998182885
[65,] 1.297795e-03 2.595589e-03 0.998702205
[66,] 9.108017e-04 1.821603e-03 0.999089198
[67,] 6.565540e-04 1.313108e-03 0.999343446
[68,] 4.483875e-04 8.967749e-04 0.999551613
[69,] 3.019029e-04 6.038059e-04 0.999698097
[70,] 2.117367e-04 4.234733e-04 0.999788263
[71,] 1.456569e-04 2.913137e-04 0.999854343
[72,] 9.875713e-05 1.975143e-04 0.999901243
[73,] 6.557390e-05 1.311478e-04 0.999934426
[74,] 4.853944e-05 9.707888e-05 0.999951461
[75,] 3.362745e-05 6.725489e-05 0.999966373
[76,] 3.181460e-05 6.362919e-05 0.999968185
[77,] 3.861446e-05 7.722892e-05 0.999961386
[78,] 2.814815e-05 5.629631e-05 0.999971852
[79,] 2.095819e-05 4.191638e-05 0.999979042
[80,] 1.465651e-05 2.931303e-05 0.999985343
[81,] 1.119931e-05 2.239861e-05 0.999988801
[82,] 1.029234e-05 2.058468e-05 0.999989708
[83,] 7.342651e-06 1.468530e-05 0.999992657
[84,] 5.225000e-06 1.045000e-05 0.999994775
[85,] 3.458397e-06 6.916794e-06 0.999996542
[86,] 2.659998e-06 5.319997e-06 0.999997340
[87,] 2.282512e-06 4.565023e-06 0.999997717
[88,] 1.392122e-06 2.784244e-06 0.999998608
[89,] 8.205410e-07 1.641082e-06 0.999999179
[90,] 5.392218e-07 1.078444e-06 0.999999461
[91,] 3.891032e-07 7.782065e-07 0.999999611
[92,] 3.923618e-07 7.847235e-07 0.999999608
[93,] 1.542378e-06 3.084755e-06 0.999998458
[94,] 1.102479e-06 2.204957e-06 0.999998898
[95,] 8.645672e-07 1.729134e-06 0.999999135
[96,] 7.809042e-07 1.561808e-06 0.999999219
[97,] 6.995690e-07 1.399138e-06 0.999999300
[98,] 6.943920e-07 1.388784e-06 0.999999306
[99,] 5.506889e-07 1.101378e-06 0.999999449
[100,] 3.605753e-07 7.211507e-07 0.999999639
[101,] 2.617085e-07 5.234170e-07 0.999999738
[102,] 3.864805e-07 7.729609e-07 0.999999614
[103,] 3.117356e-07 6.234713e-07 0.999999688
[104,] 7.243598e-07 1.448720e-06 0.999999276
[105,] 6.423072e-07 1.284614e-06 0.999999358
[106,] 6.991646e-07 1.398329e-06 0.999999301
[107,] 6.472863e-07 1.294573e-06 0.999999353
[108,] 6.580608e-07 1.316122e-06 0.999999342
[109,] 1.379033e-06 2.758065e-06 0.999998621
[110,] 7.341414e-06 1.468283e-05 0.999992659
[111,] 8.522791e-06 1.704558e-05 0.999991477
[112,] 9.371027e-06 1.874205e-05 0.999990629
[113,] 7.027404e-06 1.405481e-05 0.999992973
[114,] 5.169601e-06 1.033920e-05 0.999994830
[115,] 4.366307e-06 8.732615e-06 0.999995634
[116,] 3.369872e-06 6.739743e-06 0.999996630
[117,] 2.728548e-06 5.457095e-06 0.999997271
[118,] 2.370065e-06 4.740130e-06 0.999997630
[119,] 1.763168e-06 3.526336e-06 0.999998237
[120,] 1.351842e-06 2.703684e-06 0.999998648
[121,] 9.487842e-07 1.897568e-06 0.999999051
[122,] 7.389465e-07 1.477893e-06 0.999999261
[123,] 5.995940e-07 1.199188e-06 0.999999400
[124,] 4.760559e-07 9.521117e-07 0.999999524
[125,] 2.959384e-07 5.918768e-07 0.999999704
[126,] 2.118008e-07 4.236016e-07 0.999999788
[127,] 1.490426e-07 2.980851e-07 0.999999851
[128,] 1.157770e-07 2.315540e-07 0.999999884
[129,] 8.811213e-08 1.762243e-07 0.999999912
[130,] 8.162246e-08 1.632449e-07 0.999999918
[131,] 1.706443e-07 3.412886e-07 0.999999829
[132,] 2.429968e-07 4.859936e-07 0.999999757
[133,] 5.977927e-07 1.195585e-06 0.999999402
[134,] 1.993504e-06 3.987008e-06 0.999998006
[135,] 1.075130e-05 2.150260e-05 0.999989249
[136,] 1.494835e-04 2.989669e-04 0.999850517
[137,] 1.079775e-04 2.159550e-04 0.999892023
[138,] 7.547038e-05 1.509408e-04 0.999924530
[139,] 5.222123e-05 1.044425e-04 0.999947779
[140,] 1.026615e-04 2.053230e-04 0.999897339
[141,] 8.578030e-05 1.715606e-04 0.999914220
[142,] 8.057238e-05 1.611448e-04 0.999919428
[143,] 5.740354e-05 1.148071e-04 0.999942596
[144,] 4.388767e-05 8.777535e-05 0.999956112
[145,] 3.270820e-05 6.541639e-05 0.999967292
[146,] 3.058412e-05 6.116824e-05 0.999969416
[147,] 2.703001e-05 5.406002e-05 0.999972970
[148,] 6.089221e-05 1.217844e-04 0.999939108
[149,] 4.044362e-05 8.088724e-05 0.999959556
[150,] 3.801857e-05 7.603714e-05 0.999961981
[151,] 5.114830e-05 1.022966e-04 0.999948852
[152,] 4.283728e-05 8.567456e-05 0.999957163
[153,] 3.566894e-05 7.133788e-05 0.999964331
[154,] 4.535965e-05 9.071930e-05 0.999954640
[155,] 5.052614e-04 1.010523e-03 0.999494739
[156,] 5.369153e-04 1.073831e-03 0.999463085
[157,] 6.091668e-04 1.218334e-03 0.999390833
[158,] 9.530511e-04 1.906102e-03 0.999046949
[159,] 1.762463e-03 3.524926e-03 0.998237537
[160,] 5.684359e-03 1.136872e-02 0.994315641
[161,] 9.986182e-01 2.763697e-03 0.001381848
[162,] 9.987306e-01 2.538709e-03 0.001269354
[163,] 9.982066e-01 3.586786e-03 0.001793393
[164,] 9.975366e-01 4.926804e-03 0.002463402
[165,] 9.960598e-01 7.880365e-03 0.003940183
[166,] 9.960864e-01 7.827224e-03 0.003913612
[167,] 9.976926e-01 4.614759e-03 0.002307379
[168,] 9.966311e-01 6.737841e-03 0.003368921
[169,] 9.920251e-01 1.594987e-02 0.007974936
[170,] 9.926949e-01 1.461018e-02 0.007305088
[171,] 9.824290e-01 3.514202e-02 0.017571009
[172,] 9.754829e-01 4.903422e-02 0.024517112
[173,] 1.000000e+00 0.000000e+00 0.000000000
[174,] 1.000000e+00 0.000000e+00 0.000000000
> postscript(file="/var/wessaorg/rcomp/tmp/1cucy1386681599.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2m9211386681599.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3tm0s1386681599.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4k5t61386681599.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5tcya1386681599.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 = 195
Frequency = 1
1 2 3 4 5 6
-0.004208007 0.044725916 -0.058181860 0.029958720 -0.049081369 -0.017155064
7 8 9 10 11 12
0.181347198 0.103194254 0.041387429 0.047375504 0.001651995 0.010456604
13 14 15 16 17 18
0.306397157 0.174441442 0.199332110 0.212941860 0.299698215 0.269607794
19 20 21 22 23 24
0.099073534 0.364075030 0.077673248 0.155735603 0.207383569 0.260452888
25 26 27 28 29 30
0.182577811 0.056087522 0.199572799 0.144252550 0.184943483 0.163049532
31 32 33 34 35 36
-0.447812235 -0.413640680 -0.407243162 -0.366025085 -0.367957501 -0.376056850
37 38 39 40 41 42
0.412098902 0.412612235 0.504220707 0.512375011 0.514106272 0.474673179
43 44 45 46 47 48
-0.237104729 -0.195869057 -0.160231787 -0.171748510 -0.159479691 -0.253095675
49 50 51 52 53 54
-0.636982287 -0.651839211 -0.667372758 -0.616920676 -0.633551796 -0.609161587
55 56 57 58 59 60
0.104661426 0.127924920 0.097820450 0.182029841 0.174758132 0.241799308
61 62 63 64 65 66
-0.599407980 -0.611396964 -0.309890914 -0.252774192 -0.217324660 -0.544753056
67 68 69 70 71 72
0.063161642 0.062308893 0.090417496 0.045136866 0.051836777 0.057271052
73 74 75 76 77 78
0.184899924 0.291680078 0.095754131 0.113474835 0.096013002 0.109484559
79 80 81 82 83 84
0.026885278 -0.052375058 -0.027793099 -0.018686207 0.019319116 -0.014459221
85 86 87 88 89 90
0.112742786 0.397313876 0.395622200 0.295799733 0.196853220 0.323304968
91 92 93 94 95 96
0.092830681 0.335557578 0.286628998 0.148527571 0.114772028 0.337817001
97 98 99 100 101 102
0.348836567 0.124532487 0.055904766 -0.048926247 -0.164847627 -0.150475951
103 104 105 106 107 108
-0.239554045 0.180423423 0.321823569 0.339249546 0.374045400 0.358400151
109 110 111 112 113 114
0.324311628 0.180557515 0.222940184 0.538671703 0.429585455 0.560173830
115 116 117 118 119 120
0.276608911 0.348759026 0.332551045 0.368548206 0.486060620 0.562519103
121 122 123 124 125 126
0.331478930 0.307949747 0.059765966 0.115041200 0.061022067 0.061258750
127 128 129 130 131 132
0.085245293 0.128884648 0.221569194 0.175571471 0.133574369 0.125242600
133 134 135 136 137 138
0.120433550 0.170622978 0.114655918 0.077998990 0.119534961 0.067089750
139 140 141 142 143 144
0.091404474 0.133941266 0.317931266 0.253350782 0.483193227 0.317022226
145 146 147 148 149 150
0.587087711 0.422174129 0.118168513 0.213412193 0.109941928 0.359989264
151 152 153 154 155 156
0.002263911 -0.325078232 -0.161304202 0.131848604 0.126664104 0.114086446
157 158 159 160 161 162
0.123074625 0.136743835 0.098277385 0.181875157 0.048458594 0.097236738
163 164 165 166 167 168
0.078938461 0.090828353 0.084996424 -0.554637010 -0.205430160 -0.145282126
169 170 171 172 173 174
-0.848066580 -0.300121721 -0.189187072 -0.850160725 -0.892076504 -0.901587404
175 176 177 178 179 180
-0.890181128 -0.858984004 -0.865465541 0.344318572 0.301178989 0.285599123
181 182 183 184 185 186
0.320910337 0.310555493 0.306843809 -0.853103641 -0.869516033 -0.844766571
187 188 189 190 191 192
-0.736789320 -0.702395947 -0.896809709 -0.835590430 -0.767020714 -0.698068780
193 194 195
-0.616790168 -0.671548528 -0.692300844
> postscript(file="/var/wessaorg/rcomp/tmp/6j6041386681599.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 = 195
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.004208007 NA
1 0.044725916 -0.004208007
2 -0.058181860 0.044725916
3 0.029958720 -0.058181860
4 -0.049081369 0.029958720
5 -0.017155064 -0.049081369
6 0.181347198 -0.017155064
7 0.103194254 0.181347198
8 0.041387429 0.103194254
9 0.047375504 0.041387429
10 0.001651995 0.047375504
11 0.010456604 0.001651995
12 0.306397157 0.010456604
13 0.174441442 0.306397157
14 0.199332110 0.174441442
15 0.212941860 0.199332110
16 0.299698215 0.212941860
17 0.269607794 0.299698215
18 0.099073534 0.269607794
19 0.364075030 0.099073534
20 0.077673248 0.364075030
21 0.155735603 0.077673248
22 0.207383569 0.155735603
23 0.260452888 0.207383569
24 0.182577811 0.260452888
25 0.056087522 0.182577811
26 0.199572799 0.056087522
27 0.144252550 0.199572799
28 0.184943483 0.144252550
29 0.163049532 0.184943483
30 -0.447812235 0.163049532
31 -0.413640680 -0.447812235
32 -0.407243162 -0.413640680
33 -0.366025085 -0.407243162
34 -0.367957501 -0.366025085
35 -0.376056850 -0.367957501
36 0.412098902 -0.376056850
37 0.412612235 0.412098902
38 0.504220707 0.412612235
39 0.512375011 0.504220707
40 0.514106272 0.512375011
41 0.474673179 0.514106272
42 -0.237104729 0.474673179
43 -0.195869057 -0.237104729
44 -0.160231787 -0.195869057
45 -0.171748510 -0.160231787
46 -0.159479691 -0.171748510
47 -0.253095675 -0.159479691
48 -0.636982287 -0.253095675
49 -0.651839211 -0.636982287
50 -0.667372758 -0.651839211
51 -0.616920676 -0.667372758
52 -0.633551796 -0.616920676
53 -0.609161587 -0.633551796
54 0.104661426 -0.609161587
55 0.127924920 0.104661426
56 0.097820450 0.127924920
57 0.182029841 0.097820450
58 0.174758132 0.182029841
59 0.241799308 0.174758132
60 -0.599407980 0.241799308
61 -0.611396964 -0.599407980
62 -0.309890914 -0.611396964
63 -0.252774192 -0.309890914
64 -0.217324660 -0.252774192
65 -0.544753056 -0.217324660
66 0.063161642 -0.544753056
67 0.062308893 0.063161642
68 0.090417496 0.062308893
69 0.045136866 0.090417496
70 0.051836777 0.045136866
71 0.057271052 0.051836777
72 0.184899924 0.057271052
73 0.291680078 0.184899924
74 0.095754131 0.291680078
75 0.113474835 0.095754131
76 0.096013002 0.113474835
77 0.109484559 0.096013002
78 0.026885278 0.109484559
79 -0.052375058 0.026885278
80 -0.027793099 -0.052375058
81 -0.018686207 -0.027793099
82 0.019319116 -0.018686207
83 -0.014459221 0.019319116
84 0.112742786 -0.014459221
85 0.397313876 0.112742786
86 0.395622200 0.397313876
87 0.295799733 0.395622200
88 0.196853220 0.295799733
89 0.323304968 0.196853220
90 0.092830681 0.323304968
91 0.335557578 0.092830681
92 0.286628998 0.335557578
93 0.148527571 0.286628998
94 0.114772028 0.148527571
95 0.337817001 0.114772028
96 0.348836567 0.337817001
97 0.124532487 0.348836567
98 0.055904766 0.124532487
99 -0.048926247 0.055904766
100 -0.164847627 -0.048926247
101 -0.150475951 -0.164847627
102 -0.239554045 -0.150475951
103 0.180423423 -0.239554045
104 0.321823569 0.180423423
105 0.339249546 0.321823569
106 0.374045400 0.339249546
107 0.358400151 0.374045400
108 0.324311628 0.358400151
109 0.180557515 0.324311628
110 0.222940184 0.180557515
111 0.538671703 0.222940184
112 0.429585455 0.538671703
113 0.560173830 0.429585455
114 0.276608911 0.560173830
115 0.348759026 0.276608911
116 0.332551045 0.348759026
117 0.368548206 0.332551045
118 0.486060620 0.368548206
119 0.562519103 0.486060620
120 0.331478930 0.562519103
121 0.307949747 0.331478930
122 0.059765966 0.307949747
123 0.115041200 0.059765966
124 0.061022067 0.115041200
125 0.061258750 0.061022067
126 0.085245293 0.061258750
127 0.128884648 0.085245293
128 0.221569194 0.128884648
129 0.175571471 0.221569194
130 0.133574369 0.175571471
131 0.125242600 0.133574369
132 0.120433550 0.125242600
133 0.170622978 0.120433550
134 0.114655918 0.170622978
135 0.077998990 0.114655918
136 0.119534961 0.077998990
137 0.067089750 0.119534961
138 0.091404474 0.067089750
139 0.133941266 0.091404474
140 0.317931266 0.133941266
141 0.253350782 0.317931266
142 0.483193227 0.253350782
143 0.317022226 0.483193227
144 0.587087711 0.317022226
145 0.422174129 0.587087711
146 0.118168513 0.422174129
147 0.213412193 0.118168513
148 0.109941928 0.213412193
149 0.359989264 0.109941928
150 0.002263911 0.359989264
151 -0.325078232 0.002263911
152 -0.161304202 -0.325078232
153 0.131848604 -0.161304202
154 0.126664104 0.131848604
155 0.114086446 0.126664104
156 0.123074625 0.114086446
157 0.136743835 0.123074625
158 0.098277385 0.136743835
159 0.181875157 0.098277385
160 0.048458594 0.181875157
161 0.097236738 0.048458594
162 0.078938461 0.097236738
163 0.090828353 0.078938461
164 0.084996424 0.090828353
165 -0.554637010 0.084996424
166 -0.205430160 -0.554637010
167 -0.145282126 -0.205430160
168 -0.848066580 -0.145282126
169 -0.300121721 -0.848066580
170 -0.189187072 -0.300121721
171 -0.850160725 -0.189187072
172 -0.892076504 -0.850160725
173 -0.901587404 -0.892076504
174 -0.890181128 -0.901587404
175 -0.858984004 -0.890181128
176 -0.865465541 -0.858984004
177 0.344318572 -0.865465541
178 0.301178989 0.344318572
179 0.285599123 0.301178989
180 0.320910337 0.285599123
181 0.310555493 0.320910337
182 0.306843809 0.310555493
183 -0.853103641 0.306843809
184 -0.869516033 -0.853103641
185 -0.844766571 -0.869516033
186 -0.736789320 -0.844766571
187 -0.702395947 -0.736789320
188 -0.896809709 -0.702395947
189 -0.835590430 -0.896809709
190 -0.767020714 -0.835590430
191 -0.698068780 -0.767020714
192 -0.616790168 -0.698068780
193 -0.671548528 -0.616790168
194 -0.692300844 -0.671548528
195 NA -0.692300844
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.044725916 -0.004208007
[2,] -0.058181860 0.044725916
[3,] 0.029958720 -0.058181860
[4,] -0.049081369 0.029958720
[5,] -0.017155064 -0.049081369
[6,] 0.181347198 -0.017155064
[7,] 0.103194254 0.181347198
[8,] 0.041387429 0.103194254
[9,] 0.047375504 0.041387429
[10,] 0.001651995 0.047375504
[11,] 0.010456604 0.001651995
[12,] 0.306397157 0.010456604
[13,] 0.174441442 0.306397157
[14,] 0.199332110 0.174441442
[15,] 0.212941860 0.199332110
[16,] 0.299698215 0.212941860
[17,] 0.269607794 0.299698215
[18,] 0.099073534 0.269607794
[19,] 0.364075030 0.099073534
[20,] 0.077673248 0.364075030
[21,] 0.155735603 0.077673248
[22,] 0.207383569 0.155735603
[23,] 0.260452888 0.207383569
[24,] 0.182577811 0.260452888
[25,] 0.056087522 0.182577811
[26,] 0.199572799 0.056087522
[27,] 0.144252550 0.199572799
[28,] 0.184943483 0.144252550
[29,] 0.163049532 0.184943483
[30,] -0.447812235 0.163049532
[31,] -0.413640680 -0.447812235
[32,] -0.407243162 -0.413640680
[33,] -0.366025085 -0.407243162
[34,] -0.367957501 -0.366025085
[35,] -0.376056850 -0.367957501
[36,] 0.412098902 -0.376056850
[37,] 0.412612235 0.412098902
[38,] 0.504220707 0.412612235
[39,] 0.512375011 0.504220707
[40,] 0.514106272 0.512375011
[41,] 0.474673179 0.514106272
[42,] -0.237104729 0.474673179
[43,] -0.195869057 -0.237104729
[44,] -0.160231787 -0.195869057
[45,] -0.171748510 -0.160231787
[46,] -0.159479691 -0.171748510
[47,] -0.253095675 -0.159479691
[48,] -0.636982287 -0.253095675
[49,] -0.651839211 -0.636982287
[50,] -0.667372758 -0.651839211
[51,] -0.616920676 -0.667372758
[52,] -0.633551796 -0.616920676
[53,] -0.609161587 -0.633551796
[54,] 0.104661426 -0.609161587
[55,] 0.127924920 0.104661426
[56,] 0.097820450 0.127924920
[57,] 0.182029841 0.097820450
[58,] 0.174758132 0.182029841
[59,] 0.241799308 0.174758132
[60,] -0.599407980 0.241799308
[61,] -0.611396964 -0.599407980
[62,] -0.309890914 -0.611396964
[63,] -0.252774192 -0.309890914
[64,] -0.217324660 -0.252774192
[65,] -0.544753056 -0.217324660
[66,] 0.063161642 -0.544753056
[67,] 0.062308893 0.063161642
[68,] 0.090417496 0.062308893
[69,] 0.045136866 0.090417496
[70,] 0.051836777 0.045136866
[71,] 0.057271052 0.051836777
[72,] 0.184899924 0.057271052
[73,] 0.291680078 0.184899924
[74,] 0.095754131 0.291680078
[75,] 0.113474835 0.095754131
[76,] 0.096013002 0.113474835
[77,] 0.109484559 0.096013002
[78,] 0.026885278 0.109484559
[79,] -0.052375058 0.026885278
[80,] -0.027793099 -0.052375058
[81,] -0.018686207 -0.027793099
[82,] 0.019319116 -0.018686207
[83,] -0.014459221 0.019319116
[84,] 0.112742786 -0.014459221
[85,] 0.397313876 0.112742786
[86,] 0.395622200 0.397313876
[87,] 0.295799733 0.395622200
[88,] 0.196853220 0.295799733
[89,] 0.323304968 0.196853220
[90,] 0.092830681 0.323304968
[91,] 0.335557578 0.092830681
[92,] 0.286628998 0.335557578
[93,] 0.148527571 0.286628998
[94,] 0.114772028 0.148527571
[95,] 0.337817001 0.114772028
[96,] 0.348836567 0.337817001
[97,] 0.124532487 0.348836567
[98,] 0.055904766 0.124532487
[99,] -0.048926247 0.055904766
[100,] -0.164847627 -0.048926247
[101,] -0.150475951 -0.164847627
[102,] -0.239554045 -0.150475951
[103,] 0.180423423 -0.239554045
[104,] 0.321823569 0.180423423
[105,] 0.339249546 0.321823569
[106,] 0.374045400 0.339249546
[107,] 0.358400151 0.374045400
[108,] 0.324311628 0.358400151
[109,] 0.180557515 0.324311628
[110,] 0.222940184 0.180557515
[111,] 0.538671703 0.222940184
[112,] 0.429585455 0.538671703
[113,] 0.560173830 0.429585455
[114,] 0.276608911 0.560173830
[115,] 0.348759026 0.276608911
[116,] 0.332551045 0.348759026
[117,] 0.368548206 0.332551045
[118,] 0.486060620 0.368548206
[119,] 0.562519103 0.486060620
[120,] 0.331478930 0.562519103
[121,] 0.307949747 0.331478930
[122,] 0.059765966 0.307949747
[123,] 0.115041200 0.059765966
[124,] 0.061022067 0.115041200
[125,] 0.061258750 0.061022067
[126,] 0.085245293 0.061258750
[127,] 0.128884648 0.085245293
[128,] 0.221569194 0.128884648
[129,] 0.175571471 0.221569194
[130,] 0.133574369 0.175571471
[131,] 0.125242600 0.133574369
[132,] 0.120433550 0.125242600
[133,] 0.170622978 0.120433550
[134,] 0.114655918 0.170622978
[135,] 0.077998990 0.114655918
[136,] 0.119534961 0.077998990
[137,] 0.067089750 0.119534961
[138,] 0.091404474 0.067089750
[139,] 0.133941266 0.091404474
[140,] 0.317931266 0.133941266
[141,] 0.253350782 0.317931266
[142,] 0.483193227 0.253350782
[143,] 0.317022226 0.483193227
[144,] 0.587087711 0.317022226
[145,] 0.422174129 0.587087711
[146,] 0.118168513 0.422174129
[147,] 0.213412193 0.118168513
[148,] 0.109941928 0.213412193
[149,] 0.359989264 0.109941928
[150,] 0.002263911 0.359989264
[151,] -0.325078232 0.002263911
[152,] -0.161304202 -0.325078232
[153,] 0.131848604 -0.161304202
[154,] 0.126664104 0.131848604
[155,] 0.114086446 0.126664104
[156,] 0.123074625 0.114086446
[157,] 0.136743835 0.123074625
[158,] 0.098277385 0.136743835
[159,] 0.181875157 0.098277385
[160,] 0.048458594 0.181875157
[161,] 0.097236738 0.048458594
[162,] 0.078938461 0.097236738
[163,] 0.090828353 0.078938461
[164,] 0.084996424 0.090828353
[165,] -0.554637010 0.084996424
[166,] -0.205430160 -0.554637010
[167,] -0.145282126 -0.205430160
[168,] -0.848066580 -0.145282126
[169,] -0.300121721 -0.848066580
[170,] -0.189187072 -0.300121721
[171,] -0.850160725 -0.189187072
[172,] -0.892076504 -0.850160725
[173,] -0.901587404 -0.892076504
[174,] -0.890181128 -0.901587404
[175,] -0.858984004 -0.890181128
[176,] -0.865465541 -0.858984004
[177,] 0.344318572 -0.865465541
[178,] 0.301178989 0.344318572
[179,] 0.285599123 0.301178989
[180,] 0.320910337 0.285599123
[181,] 0.310555493 0.320910337
[182,] 0.306843809 0.310555493
[183,] -0.853103641 0.306843809
[184,] -0.869516033 -0.853103641
[185,] -0.844766571 -0.869516033
[186,] -0.736789320 -0.844766571
[187,] -0.702395947 -0.736789320
[188,] -0.896809709 -0.702395947
[189,] -0.835590430 -0.896809709
[190,] -0.767020714 -0.835590430
[191,] -0.698068780 -0.767020714
[192,] -0.616790168 -0.698068780
[193,] -0.671548528 -0.616790168
[194,] -0.692300844 -0.671548528
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.044725916 -0.004208007
2 -0.058181860 0.044725916
3 0.029958720 -0.058181860
4 -0.049081369 0.029958720
5 -0.017155064 -0.049081369
6 0.181347198 -0.017155064
7 0.103194254 0.181347198
8 0.041387429 0.103194254
9 0.047375504 0.041387429
10 0.001651995 0.047375504
11 0.010456604 0.001651995
12 0.306397157 0.010456604
13 0.174441442 0.306397157
14 0.199332110 0.174441442
15 0.212941860 0.199332110
16 0.299698215 0.212941860
17 0.269607794 0.299698215
18 0.099073534 0.269607794
19 0.364075030 0.099073534
20 0.077673248 0.364075030
21 0.155735603 0.077673248
22 0.207383569 0.155735603
23 0.260452888 0.207383569
24 0.182577811 0.260452888
25 0.056087522 0.182577811
26 0.199572799 0.056087522
27 0.144252550 0.199572799
28 0.184943483 0.144252550
29 0.163049532 0.184943483
30 -0.447812235 0.163049532
31 -0.413640680 -0.447812235
32 -0.407243162 -0.413640680
33 -0.366025085 -0.407243162
34 -0.367957501 -0.366025085
35 -0.376056850 -0.367957501
36 0.412098902 -0.376056850
37 0.412612235 0.412098902
38 0.504220707 0.412612235
39 0.512375011 0.504220707
40 0.514106272 0.512375011
41 0.474673179 0.514106272
42 -0.237104729 0.474673179
43 -0.195869057 -0.237104729
44 -0.160231787 -0.195869057
45 -0.171748510 -0.160231787
46 -0.159479691 -0.171748510
47 -0.253095675 -0.159479691
48 -0.636982287 -0.253095675
49 -0.651839211 -0.636982287
50 -0.667372758 -0.651839211
51 -0.616920676 -0.667372758
52 -0.633551796 -0.616920676
53 -0.609161587 -0.633551796
54 0.104661426 -0.609161587
55 0.127924920 0.104661426
56 0.097820450 0.127924920
57 0.182029841 0.097820450
58 0.174758132 0.182029841
59 0.241799308 0.174758132
60 -0.599407980 0.241799308
61 -0.611396964 -0.599407980
62 -0.309890914 -0.611396964
63 -0.252774192 -0.309890914
64 -0.217324660 -0.252774192
65 -0.544753056 -0.217324660
66 0.063161642 -0.544753056
67 0.062308893 0.063161642
68 0.090417496 0.062308893
69 0.045136866 0.090417496
70 0.051836777 0.045136866
71 0.057271052 0.051836777
72 0.184899924 0.057271052
73 0.291680078 0.184899924
74 0.095754131 0.291680078
75 0.113474835 0.095754131
76 0.096013002 0.113474835
77 0.109484559 0.096013002
78 0.026885278 0.109484559
79 -0.052375058 0.026885278
80 -0.027793099 -0.052375058
81 -0.018686207 -0.027793099
82 0.019319116 -0.018686207
83 -0.014459221 0.019319116
84 0.112742786 -0.014459221
85 0.397313876 0.112742786
86 0.395622200 0.397313876
87 0.295799733 0.395622200
88 0.196853220 0.295799733
89 0.323304968 0.196853220
90 0.092830681 0.323304968
91 0.335557578 0.092830681
92 0.286628998 0.335557578
93 0.148527571 0.286628998
94 0.114772028 0.148527571
95 0.337817001 0.114772028
96 0.348836567 0.337817001
97 0.124532487 0.348836567
98 0.055904766 0.124532487
99 -0.048926247 0.055904766
100 -0.164847627 -0.048926247
101 -0.150475951 -0.164847627
102 -0.239554045 -0.150475951
103 0.180423423 -0.239554045
104 0.321823569 0.180423423
105 0.339249546 0.321823569
106 0.374045400 0.339249546
107 0.358400151 0.374045400
108 0.324311628 0.358400151
109 0.180557515 0.324311628
110 0.222940184 0.180557515
111 0.538671703 0.222940184
112 0.429585455 0.538671703
113 0.560173830 0.429585455
114 0.276608911 0.560173830
115 0.348759026 0.276608911
116 0.332551045 0.348759026
117 0.368548206 0.332551045
118 0.486060620 0.368548206
119 0.562519103 0.486060620
120 0.331478930 0.562519103
121 0.307949747 0.331478930
122 0.059765966 0.307949747
123 0.115041200 0.059765966
124 0.061022067 0.115041200
125 0.061258750 0.061022067
126 0.085245293 0.061258750
127 0.128884648 0.085245293
128 0.221569194 0.128884648
129 0.175571471 0.221569194
130 0.133574369 0.175571471
131 0.125242600 0.133574369
132 0.120433550 0.125242600
133 0.170622978 0.120433550
134 0.114655918 0.170622978
135 0.077998990 0.114655918
136 0.119534961 0.077998990
137 0.067089750 0.119534961
138 0.091404474 0.067089750
139 0.133941266 0.091404474
140 0.317931266 0.133941266
141 0.253350782 0.317931266
142 0.483193227 0.253350782
143 0.317022226 0.483193227
144 0.587087711 0.317022226
145 0.422174129 0.587087711
146 0.118168513 0.422174129
147 0.213412193 0.118168513
148 0.109941928 0.213412193
149 0.359989264 0.109941928
150 0.002263911 0.359989264
151 -0.325078232 0.002263911
152 -0.161304202 -0.325078232
153 0.131848604 -0.161304202
154 0.126664104 0.131848604
155 0.114086446 0.126664104
156 0.123074625 0.114086446
157 0.136743835 0.123074625
158 0.098277385 0.136743835
159 0.181875157 0.098277385
160 0.048458594 0.181875157
161 0.097236738 0.048458594
162 0.078938461 0.097236738
163 0.090828353 0.078938461
164 0.084996424 0.090828353
165 -0.554637010 0.084996424
166 -0.205430160 -0.554637010
167 -0.145282126 -0.205430160
168 -0.848066580 -0.145282126
169 -0.300121721 -0.848066580
170 -0.189187072 -0.300121721
171 -0.850160725 -0.189187072
172 -0.892076504 -0.850160725
173 -0.901587404 -0.892076504
174 -0.890181128 -0.901587404
175 -0.858984004 -0.890181128
176 -0.865465541 -0.858984004
177 0.344318572 -0.865465541
178 0.301178989 0.344318572
179 0.285599123 0.301178989
180 0.320910337 0.285599123
181 0.310555493 0.320910337
182 0.306843809 0.310555493
183 -0.853103641 0.306843809
184 -0.869516033 -0.853103641
185 -0.844766571 -0.869516033
186 -0.736789320 -0.844766571
187 -0.702395947 -0.736789320
188 -0.896809709 -0.702395947
189 -0.835590430 -0.896809709
190 -0.767020714 -0.835590430
191 -0.698068780 -0.767020714
192 -0.616790168 -0.698068780
193 -0.671548528 -0.616790168
194 -0.692300844 -0.671548528
> 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/7hzv21386681599.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8hvl01386681599.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9ik5i1386681599.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10htsl1386681599.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$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/11ufae1386681599.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$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/128gwz1386681599.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, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1346es1386681599.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,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14d7qu1386681599.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,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/150p7c1386681599.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,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ 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/164u2l1386681599.tab")
+ }
>
> try(system("convert tmp/1cucy1386681599.ps tmp/1cucy1386681599.png",intern=TRUE))
character(0)
> try(system("convert tmp/2m9211386681599.ps tmp/2m9211386681599.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tm0s1386681599.ps tmp/3tm0s1386681599.png",intern=TRUE))
character(0)
> try(system("convert tmp/4k5t61386681599.ps tmp/4k5t61386681599.png",intern=TRUE))
character(0)
> try(system("convert tmp/5tcya1386681599.ps tmp/5tcya1386681599.png",intern=TRUE))
character(0)
> try(system("convert tmp/6j6041386681599.ps tmp/6j6041386681599.png",intern=TRUE))
character(0)
> try(system("convert tmp/7hzv21386681599.ps tmp/7hzv21386681599.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hvl01386681599.ps tmp/8hvl01386681599.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ik5i1386681599.ps tmp/9ik5i1386681599.png",intern=TRUE))
character(0)
> try(system("convert tmp/10htsl1386681599.ps tmp/10htsl1386681599.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
14.690 2.695 17.395