R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-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.
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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(170588
+ ,95556
+ ,114468
+ ,86621
+ ,54565
+ ,88594
+ ,118522
+ ,63016
+ ,74151
+ ,152510
+ ,79774
+ ,77921
+ ,86206
+ ,31258
+ ,53212
+ ,37257
+ ,52491
+ ,34956
+ ,306055
+ ,91256
+ ,149703
+ ,32750
+ ,22807
+ ,6853
+ ,116502
+ ,77411
+ ,58907
+ ,130539
+ ,48821
+ ,67067
+ ,164604
+ ,52295
+ ,110563
+ ,128274
+ ,63262
+ ,58126
+ ,104367
+ ,50466
+ ,57113
+ ,193024
+ ,62932
+ ,77993
+ ,141574
+ ,38439
+ ,68091
+ ,254150
+ ,70817
+ ,124676
+ ,181110
+ ,105965
+ ,109522
+ ,198432
+ ,73795
+ ,75865
+ ,113853
+ ,82043
+ ,79746
+ ,159940
+ ,74349
+ ,77844
+ ,166822
+ ,82204
+ ,98681
+ ,286675
+ ,55709
+ ,105531
+ ,95297
+ ,37137
+ ,51428
+ ,108278
+ ,70780
+ ,65703
+ ,146342
+ ,55027
+ ,72562
+ ,146684
+ ,56699
+ ,81728
+ ,163569
+ ,65911
+ ,95580
+ ,162716
+ ,56316
+ ,98278
+ ,106888
+ ,26982
+ ,46629
+ ,188150
+ ,54628
+ ,115189
+ ,189401
+ ,96750
+ ,124865
+ ,129484
+ ,53009
+ ,59392
+ ,204030
+ ,64664
+ ,127818
+ ,68538
+ ,36990
+ ,17821
+ ,243625
+ ,85224
+ ,154076
+ ,167255
+ ,37048
+ ,64881
+ ,264528
+ ,59635
+ ,136506
+ ,122024
+ ,42051
+ ,66524
+ ,80964
+ ,26998
+ ,45988
+ ,209795
+ ,63717
+ ,107445
+ ,224911
+ ,55071
+ ,102772
+ ,115971
+ ,40001
+ ,46657
+ ,138191
+ ,54506
+ ,97563
+ ,81106
+ ,35838
+ ,36663
+ ,93125
+ ,50838
+ ,55369
+ ,307743
+ ,86997
+ ,77921
+ ,78800
+ ,33032
+ ,56968
+ ,158835
+ ,61704
+ ,77519
+ ,223590
+ ,117986
+ ,129805
+ ,131108
+ ,56733
+ ,72761
+ ,128734
+ ,55064
+ ,81278
+ ,24188
+ ,5950
+ ,15049
+ ,257677
+ ,84607
+ ,113935
+ ,65029
+ ,32551
+ ,25109
+ ,98066
+ ,31701
+ ,45824
+ ,173587
+ ,71170
+ ,89644
+ ,180042
+ ,101773
+ ,109011
+ ,197266
+ ,101653
+ ,134245
+ ,212120
+ ,81493
+ ,136692
+ ,141582
+ ,55901
+ ,50741
+ ,245107
+ ,109104
+ ,149510
+ ,206879
+ ,114425
+ ,147888
+ ,145696
+ ,36311
+ ,54987
+ ,173535
+ ,70027
+ ,74467
+ ,142064
+ ,73713
+ ,100033
+ ,117926
+ ,40671
+ ,85505
+ ,113461
+ ,89041
+ ,62426
+ ,145285
+ ,57231
+ ,82932
+ ,150999
+ ,68608
+ ,72002
+ ,91838
+ ,59155
+ ,65469
+ ,118807
+ ,55827
+ ,63572
+ ,69471
+ ,22618
+ ,23824
+ ,126630
+ ,58425
+ ,73831
+ ,145908
+ ,65724
+ ,63551
+ ,102896
+ ,56979
+ ,56756
+ ,190926
+ ,72369
+ ,81399
+ ,198797
+ ,79194
+ ,117881
+ ,112566
+ ,202316
+ ,70711
+ ,89318
+ ,44970
+ ,50495
+ ,120362
+ ,49319
+ ,53845
+ ,98791
+ ,36252
+ ,51390
+ ,283982
+ ,75741
+ ,104953
+ ,132798
+ ,38417
+ ,65983
+ ,137875
+ ,64102
+ ,76839
+ ,80953
+ ,56622
+ ,55792
+ ,109237
+ ,15430
+ ,25155
+ ,98724
+ ,72571
+ ,55291
+ ,226191
+ ,67271
+ ,84279
+ ,172071
+ ,43460
+ ,99692
+ ,118174
+ ,99501
+ ,59633
+ ,133561
+ ,28340
+ ,63249
+ ,152193
+ ,76013
+ ,82928
+ ,112004
+ ,37361
+ ,50000
+ ,169613
+ ,48204
+ ,69455
+ ,187483
+ ,76168
+ ,84068
+ ,130533
+ ,85168
+ ,76195
+ ,142339
+ ,125410
+ ,114634
+ ,201941
+ ,123328
+ ,139357
+ ,201744
+ ,83038
+ ,110044
+ ,247024
+ ,120087
+ ,155118
+ ,162502
+ ,91939
+ ,83061
+ ,182581
+ ,103646
+ ,127122
+ ,106351
+ ,29467
+ ,45653
+ ,43287
+ ,43750
+ ,19630
+ ,127493
+ ,34497
+ ,67229
+ ,127930
+ ,66477
+ ,86060
+ ,149006
+ ,71181
+ ,88003
+ ,187714
+ ,74482
+ ,95815
+ ,74112
+ ,174949
+ ,85499
+ ,94006
+ ,46765
+ ,27220
+ ,176625
+ ,90257
+ ,109882
+ ,141933
+ ,51370
+ ,72579
+ ,22938
+ ,1168
+ ,5841
+ ,125927
+ ,51360
+ ,68369
+ ,61857
+ ,25162
+ ,24610
+ ,91290
+ ,21067
+ ,30995
+ ,255100
+ ,58233
+ ,150662
+ ,21054
+ ,855
+ ,6622
+ ,174150
+ ,85903
+ ,93694
+ ,31414
+ ,14116
+ ,13155
+ ,189461
+ ,57637
+ ,111908
+ ,137544
+ ,94137
+ ,57550
+ ,77166
+ ,62147
+ ,16356
+ ,74567
+ ,62832
+ ,40174
+ ,38214
+ ,8773
+ ,13983
+ ,90961
+ ,63785
+ ,52316
+ ,194652
+ ,65196
+ ,99585
+ ,135261
+ ,73087
+ ,86271
+ ,248590
+ ,72631
+ ,131012
+ ,201748
+ ,86281
+ ,130274
+ ,256402
+ ,162365
+ ,159051
+ ,139144
+ ,56530
+ ,76506
+ ,76470
+ ,35606
+ ,49145
+ ,193518
+ ,70111
+ ,66398
+ ,280334
+ ,92046
+ ,127546
+ ,50999
+ ,63989
+ ,6802
+ ,254825
+ ,104911
+ ,99509
+ ,103239
+ ,43448
+ ,43106
+ ,168059
+ ,60029
+ ,108303
+ ,136709
+ ,38650
+ ,64167
+ ,78256
+ ,47261
+ ,8579
+ ,249232
+ ,73586
+ ,97811
+ ,152366
+ ,83042
+ ,84365
+ ,173260
+ ,37238
+ ,10901
+ ,197197
+ ,63958
+ ,91346
+ ,68388
+ ,78956
+ ,33660
+ ,139409
+ ,99518
+ ,93634
+ ,185366
+ ,111436
+ ,109348
+ ,0
+ ,0
+ ,0
+ ,14688
+ ,6023
+ ,7953
+ ,98
+ ,0
+ ,0
+ ,455
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,137885
+ ,42564
+ ,63538
+ ,185288
+ ,38885
+ ,108281
+ ,0
+ ,0
+ ,0
+ ,203
+ ,0
+ ,0
+ ,7199
+ ,1644
+ ,4245
+ ,46660
+ ,6179
+ ,21509
+ ,17547
+ ,3926
+ ,7670
+ ,73567
+ ,23238
+ ,10641
+ ,969
+ ,0
+ ,0
+ ,105477
+ ,49288
+ ,41243)
+ ,dim=c(3
+ ,164)
+ ,dimnames=list(c('TotalRFC'
+ ,'TotalCharac'
+ ,'TotalComp')
+ ,1:164))
> y <- array(NA,dim=c(3,164),dimnames=list(c('TotalRFC','TotalCharac','TotalComp'),1:164))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
TotalCharac TotalRFC TotalComp t
1 95556 170588 114468 1
2 54565 86621 88594 2
3 63016 118522 74151 3
4 79774 152510 77921 4
5 31258 86206 53212 5
6 52491 37257 34956 6
7 91256 306055 149703 7
8 22807 32750 6853 8
9 77411 116502 58907 9
10 48821 130539 67067 10
11 52295 164604 110563 11
12 63262 128274 58126 12
13 50466 104367 57113 13
14 62932 193024 77993 14
15 38439 141574 68091 15
16 70817 254150 124676 16
17 105965 181110 109522 17
18 73795 198432 75865 18
19 82043 113853 79746 19
20 74349 159940 77844 20
21 82204 166822 98681 21
22 55709 286675 105531 22
23 37137 95297 51428 23
24 70780 108278 65703 24
25 55027 146342 72562 25
26 56699 146684 81728 26
27 65911 163569 95580 27
28 56316 162716 98278 28
29 26982 106888 46629 29
30 54628 188150 115189 30
31 96750 189401 124865 31
32 53009 129484 59392 32
33 64664 204030 127818 33
34 36990 68538 17821 34
35 85224 243625 154076 35
36 37048 167255 64881 36
37 59635 264528 136506 37
38 42051 122024 66524 38
39 26998 80964 45988 39
40 63717 209795 107445 40
41 55071 224911 102772 41
42 40001 115971 46657 42
43 54506 138191 97563 43
44 35838 81106 36663 44
45 50838 93125 55369 45
46 86997 307743 77921 46
47 33032 78800 56968 47
48 61704 158835 77519 48
49 117986 223590 129805 49
50 56733 131108 72761 50
51 55064 128734 81278 51
52 5950 24188 15049 52
53 84607 257677 113935 53
54 32551 65029 25109 54
55 31701 98066 45824 55
56 71170 173587 89644 56
57 101773 180042 109011 57
58 101653 197266 134245 58
59 81493 212120 136692 59
60 55901 141582 50741 60
61 109104 245107 149510 61
62 114425 206879 147888 62
63 36311 145696 54987 63
64 70027 173535 74467 64
65 73713 142064 100033 65
66 40671 117926 85505 66
67 89041 113461 62426 67
68 57231 145285 82932 68
69 68608 150999 72002 69
70 59155 91838 65469 70
71 55827 118807 63572 71
72 22618 69471 23824 72
73 58425 126630 73831 73
74 65724 145908 63551 74
75 56979 102896 56756 75
76 72369 190926 81399 76
77 79194 198797 117881 77
78 202316 112566 70711 78
79 44970 89318 50495 79
80 49319 120362 53845 80
81 36252 98791 51390 81
82 75741 283982 104953 82
83 38417 132798 65983 83
84 64102 137875 76839 84
85 56622 80953 55792 85
86 15430 109237 25155 86
87 72571 98724 55291 87
88 67271 226191 84279 88
89 43460 172071 99692 89
90 99501 118174 59633 90
91 28340 133561 63249 91
92 76013 152193 82928 92
93 37361 112004 50000 93
94 48204 169613 69455 94
95 76168 187483 84068 95
96 85168 130533 76195 96
97 125410 142339 114634 97
98 123328 201941 139357 98
99 83038 201744 110044 99
100 120087 247024 155118 100
101 91939 162502 83061 101
102 103646 182581 127122 102
103 29467 106351 45653 103
104 43750 43287 19630 104
105 34497 127493 67229 105
106 66477 127930 86060 106
107 71181 149006 88003 107
108 74482 187714 95815 108
109 174949 74112 85499 109
110 46765 94006 27220 110
111 90257 176625 109882 111
112 51370 141933 72579 112
113 1168 22938 5841 113
114 51360 125927 68369 114
115 25162 61857 24610 115
116 21067 91290 30995 116
117 58233 255100 150662 117
118 855 21054 6622 118
119 85903 174150 93694 119
120 14116 31414 13155 120
121 57637 189461 111908 121
122 94137 137544 57550 122
123 62147 77166 16356 123
124 62832 74567 40174 124
125 8773 38214 13983 125
126 63785 90961 52316 126
127 65196 194652 99585 127
128 73087 135261 86271 128
129 72631 248590 131012 129
130 86281 201748 130274 130
131 162365 256402 159051 131
132 56530 139144 76506 132
133 35606 76470 49145 133
134 70111 193518 66398 134
135 92046 280334 127546 135
136 63989 50999 6802 136
137 104911 254825 99509 137
138 43448 103239 43106 138
139 60029 168059 108303 139
140 38650 136709 64167 140
141 47261 78256 8579 141
142 73586 249232 97811 142
143 83042 152366 84365 143
144 37238 173260 10901 144
145 63958 197197 91346 145
146 78956 68388 33660 146
147 99518 139409 93634 147
148 111436 185366 109348 148
149 0 0 0 149
150 6023 14688 7953 150
151 0 98 0 151
152 0 455 0 152
153 0 0 0 153
154 0 0 0 154
155 42564 137885 63538 155
156 38885 185288 108281 156
157 0 0 0 157
158 0 203 0 158
159 1644 7199 4245 159
160 6179 46660 21509 160
161 3926 17547 7670 161
162 23238 73567 10641 162
163 0 969 0 163
164 49288 105477 41243 164
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) TotalRFC TotalComp t
1.310e+04 -1.829e-02 6.482e-01 2.994e+01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-51368 -16197 -2445 10860 143100
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.310e+04 5.916e+03 2.215 0.0282 *
TotalRFC -1.829e-02 5.471e-02 -0.334 0.7386
TotalComp 6.482e-01 9.677e-02 6.698 3.4e-10 ***
t 2.994e+01 4.023e+01 0.744 0.4578
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 23240 on 160 degrees of freedom
Multiple R-squared: 0.5277, Adjusted R-squared: 0.5188
F-statistic: 59.59 on 3 and 160 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,] 3.503384e-01 7.006768e-01 6.496616e-01
[2,] 2.039398e-01 4.078796e-01 7.960602e-01
[3,] 3.136186e-01 6.272372e-01 6.863814e-01
[4,] 2.193098e-01 4.386196e-01 7.806902e-01
[5,] 1.505480e-01 3.010959e-01 8.494520e-01
[6,] 1.200069e-01 2.400138e-01 8.799931e-01
[7,] 7.509510e-02 1.501902e-01 9.249049e-01
[8,] 4.310048e-02 8.620096e-02 9.568995e-01
[9,] 2.899987e-02 5.799973e-02 9.710001e-01
[10,] 1.597254e-02 3.194508e-02 9.840275e-01
[11,] 1.036745e-01 2.073490e-01 8.963255e-01
[12,] 7.850221e-02 1.570044e-01 9.214978e-01
[13,] 7.192442e-02 1.438488e-01 9.280756e-01
[14,] 4.987112e-02 9.974225e-02 9.501289e-01
[15,] 3.248615e-02 6.497229e-02 9.675139e-01
[16,] 3.349063e-02 6.698126e-02 9.665094e-01
[17,] 3.027521e-02 6.055041e-02 9.697248e-01
[18,] 2.179799e-02 4.359599e-02 9.782020e-01
[19,] 1.479203e-02 2.958406e-02 9.852080e-01
[20,] 1.039882e-02 2.079765e-02 9.896012e-01
[21,] 6.669342e-03 1.333868e-02 9.933307e-01
[22,] 5.517925e-03 1.103585e-02 9.944821e-01
[23,] 4.934890e-03 9.869779e-03 9.950651e-01
[24,] 5.055758e-03 1.011152e-02 9.949442e-01
[25,] 4.715656e-03 9.431312e-03 9.952843e-01
[26,] 2.976818e-03 5.953636e-03 9.970232e-01
[27,] 2.593540e-03 5.187081e-03 9.974065e-01
[28,] 1.723880e-03 3.447761e-03 9.982761e-01
[29,] 1.135129e-03 2.270257e-03 9.988649e-01
[30,] 8.113325e-04 1.622665e-03 9.991887e-01
[31,] 8.895442e-04 1.779088e-03 9.991105e-01
[32,] 5.568109e-04 1.113622e-03 9.994432e-01
[33,] 3.887126e-04 7.774253e-04 9.996113e-01
[34,] 2.424419e-04 4.848838e-04 9.997576e-01
[35,] 1.581097e-04 3.162195e-04 9.998419e-01
[36,] 9.346920e-05 1.869384e-04 9.999065e-01
[37,] 5.897591e-05 1.179518e-04 9.999410e-01
[38,] 3.395664e-05 6.791328e-05 9.999660e-01
[39,] 2.301622e-05 4.603244e-05 9.999770e-01
[40,] 8.432491e-05 1.686498e-04 9.999157e-01
[41,] 5.455059e-05 1.091012e-04 9.999454e-01
[42,] 3.800530e-05 7.601061e-05 9.999620e-01
[43,] 3.144824e-04 6.289648e-04 9.996855e-01
[44,] 2.015787e-04 4.031574e-04 9.997984e-01
[45,] 1.272744e-04 2.545487e-04 9.998727e-01
[46,] 1.159241e-04 2.318483e-04 9.998841e-01
[47,] 8.328559e-05 1.665712e-04 9.999167e-01
[48,] 5.038326e-05 1.007665e-04 9.999496e-01
[49,] 3.243510e-05 6.487021e-05 9.999676e-01
[50,] 2.338364e-05 4.676728e-05 9.999766e-01
[51,] 5.584891e-05 1.116978e-04 9.999442e-01
[52,] 5.134221e-05 1.026844e-04 9.999487e-01
[53,] 3.677695e-05 7.355389e-05 9.999632e-01
[54,] 2.525254e-05 5.050508e-05 9.999747e-01
[55,] 2.036892e-05 4.073784e-05 9.999796e-01
[56,] 1.993924e-05 3.987849e-05 9.999801e-01
[57,] 1.435098e-05 2.870195e-05 9.999856e-01
[58,] 9.945017e-06 1.989003e-05 9.999901e-01
[59,] 6.132731e-06 1.226546e-05 9.999939e-01
[60,] 8.680022e-06 1.736004e-05 9.999913e-01
[61,] 2.890252e-05 5.780504e-05 9.999711e-01
[62,] 1.956984e-05 3.913967e-05 9.999804e-01
[63,] 1.307337e-05 2.614674e-05 9.999869e-01
[64,] 8.089024e-06 1.617805e-05 9.999919e-01
[65,] 4.807644e-06 9.615287e-06 9.999952e-01
[66,] 3.310572e-06 6.621145e-06 9.999967e-01
[67,] 2.002310e-06 4.004620e-06 9.999980e-01
[68,] 1.312059e-06 2.624118e-06 9.999987e-01
[69,] 7.762391e-07 1.552478e-06 9.999992e-01
[70,] 4.558015e-07 9.116030e-07 9.999995e-01
[71,] 2.966862e-07 5.933724e-07 9.999997e-01
[72,] 3.084458e-01 6.168915e-01 6.915542e-01
[73,] 2.754292e-01 5.508585e-01 7.245708e-01
[74,] 2.411782e-01 4.823563e-01 7.588218e-01
[75,] 2.271606e-01 4.543211e-01 7.728394e-01
[76,] 1.973475e-01 3.946949e-01 8.026525e-01
[77,] 1.996289e-01 3.992578e-01 8.003711e-01
[78,] 1.709215e-01 3.418431e-01 8.290785e-01
[79,] 1.437060e-01 2.874120e-01 8.562940e-01
[80,] 1.427498e-01 2.854996e-01 8.572502e-01
[81,] 1.297221e-01 2.594441e-01 8.702779e-01
[82,] 1.084447e-01 2.168894e-01 8.915553e-01
[83,] 1.549069e-01 3.098138e-01 8.450931e-01
[84,] 2.159877e-01 4.319753e-01 7.840123e-01
[85,] 2.534730e-01 5.069460e-01 7.465270e-01
[86,] 2.197652e-01 4.395305e-01 7.802348e-01
[87,] 2.049086e-01 4.098172e-01 7.950914e-01
[88,] 1.927254e-01 3.854508e-01 8.072746e-01
[89,] 1.648453e-01 3.296906e-01 8.351547e-01
[90,] 1.490800e-01 2.981601e-01 8.509200e-01
[91,] 1.755268e-01 3.510536e-01 8.244732e-01
[92,] 1.601557e-01 3.203114e-01 8.398443e-01
[93,] 1.365083e-01 2.730166e-01 8.634917e-01
[94,] 1.136353e-01 2.272706e-01 8.863647e-01
[95,] 1.040948e-01 2.081896e-01 8.959052e-01
[96,] 8.489528e-02 1.697906e-01 9.151047e-01
[97,] 8.406730e-02 1.681346e-01 9.159327e-01
[98,] 6.883969e-02 1.376794e-01 9.311603e-01
[99,] 8.066933e-02 1.613387e-01 9.193307e-01
[100,] 6.736069e-02 1.347214e-01 9.326393e-01
[101,] 5.445238e-02 1.089048e-01 9.455476e-01
[102,] 4.415942e-02 8.831885e-02 9.558406e-01
[103,] 7.403621e-01 5.192758e-01 2.596379e-01
[104,] 7.023413e-01 5.953175e-01 2.976587e-01
[105,] 6.658435e-01 6.683130e-01 3.341565e-01
[106,] 6.362586e-01 7.274828e-01 3.637414e-01
[107,] 6.405404e-01 7.189193e-01 3.594596e-01
[108,] 6.037676e-01 7.924648e-01 3.962324e-01
[109,] 5.684576e-01 8.630847e-01 4.315424e-01
[110,] 5.693578e-01 8.612844e-01 4.306422e-01
[111,] 7.871210e-01 4.257580e-01 2.128790e-01
[112,] 8.107090e-01 3.785820e-01 1.892910e-01
[113,] 7.747326e-01 4.505349e-01 2.252674e-01
[114,] 7.707938e-01 4.584124e-01 2.292062e-01
[115,] 8.343635e-01 3.312730e-01 1.656365e-01
[116,] 8.582238e-01 2.835524e-01 1.417762e-01
[117,] 8.607547e-01 2.784906e-01 1.392453e-01
[118,] 8.446824e-01 3.106352e-01 1.553176e-01
[119,] 8.512404e-01 2.975192e-01 1.487596e-01
[120,] 8.213251e-01 3.573498e-01 1.786749e-01
[121,] 8.190643e-01 3.618714e-01 1.809357e-01
[122,] 7.790158e-01 4.419684e-01 2.209842e-01
[123,] 8.401856e-01 3.196287e-01 1.598144e-01
[124,] 8.320393e-01 3.359214e-01 1.679607e-01
[125,] 9.209105e-01 1.581790e-01 7.908952e-02
[126,] 9.067162e-01 1.865676e-01 9.328382e-02
[127,] 9.015381e-01 1.969238e-01 9.846191e-02
[128,] 8.751711e-01 2.496578e-01 1.248289e-01
[129,] 8.626044e-01 2.747912e-01 1.373956e-01
[130,] 9.027004e-01 1.945992e-01 9.729962e-02
[131,] 8.867555e-01 2.264889e-01 1.132445e-01
[132,] 8.528027e-01 2.943947e-01 1.471973e-01
[133,] 8.769211e-01 2.461579e-01 1.230789e-01
[134,] 9.031139e-01 1.937721e-01 9.688605e-02
[135,] 8.884463e-01 2.231074e-01 1.115537e-01
[136,] 8.831008e-01 2.337983e-01 1.168992e-01
[137,] 8.431325e-01 3.137351e-01 1.568675e-01
[138,] 7.967663e-01 4.064674e-01 2.032337e-01
[139,] 8.607378e-01 2.785245e-01 1.392622e-01
[140,] 9.300732e-01 1.398536e-01 6.992682e-02
[141,] 9.703011e-01 5.939781e-02 2.969891e-02
[142,] 9.999967e-01 6.568842e-06 3.284421e-06
[143,] 9.999874e-01 2.517711e-05 1.258856e-05
[144,] 9.999585e-01 8.308640e-05 4.154320e-05
[145,] 9.998516e-01 2.967321e-04 1.483661e-04
[146,] 9.994913e-01 1.017417e-03 5.087084e-04
[147,] 9.983846e-01 3.230731e-03 1.615365e-03
[148,] 9.954188e-01 9.162373e-03 4.581187e-03
[149,] 9.906827e-01 1.863470e-02 9.317349e-03
[150,] 9.850808e-01 2.983832e-02 1.491916e-02
[151,] 9.571170e-01 8.576603e-02 4.288301e-02
> postscript(file="/var/wessaorg/rcomp/tmp/1lrlh1321911448.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2b8nb1321911448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39xkr1321911448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4beu41321911448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5j41e1321911448.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 = 164
Frequency = 1
1 2 3 4 5 6
11343.5566 -14441.5508 3924.8670 18830.8145 -14911.4097 17229.9427
7 8 9 10 11 12
-13497.8490 5619.9520 27984.4059 -5668.1170 -29795.0940 14467.1165
13 14 15 16 17 18
1860.5696 2383.6693 -16661.7686 -18933.1468 24671.8994 14605.1709
19 20 21 22 23 24
18760.7251 13112.5301 7556.9390 -21216.2005 -8248.7269 16348.7036
25 26 27 28 29 30
-3184.0890 -7477.1641 -6965.1463 -18354.5287 -15260.6928 -30598.9420
31 32 33 34 35 36
5244.0261 2816.7751 -28548.4429 12569.5222 -24344.5803 -16131.1720
37 38 39 40 41 42
-38222.3282 -13080.2661 -15602.7411 -16393.8586 -21764.3168 -2482.9791
43 44 45 46 47 48
-20598.7372 -865.4062 2199.2631 27635.2453 -16965.0779 -180.4066
49 50 51 52 53 54
23364.2336 -2634.2558 -9897.3224 -18023.7444 775.7822 2743.4249
55 56 57 58 59 60
-10959.7434 1456.4420 19593.8934 3402.3159 -18102.1071 10699.2054
61 62 63 64 65 66
1743.6909 7386.9836 -11657.6331 9910.6587 -3580.6918 -27677.0609
67 68 69 70 71 72
35541.1099 -9008.7692 9527.5996 3197.3564 1562.2759 -6814.3677
73 74 75 76 77 78
-2406.4045 11878.7040 6721.6339 7718.0957 -8990.4730 143100.0487
79 80 81 82 83 84
-1597.0904 1118.2583 -10781.8638 -2655.3480 -17513.9636 1197.1032
85 86 87 88 89 90
6288.7613 -14557.0452 22827.6331 1038.9262 -33782.4872 47209.0439
91 92 93 94 95 96
-26044.3752 9183.5405 -8889.5295 -9633.5764 9155.1768 22186.9560
97 98 99 100 101 102
37698.8292 20651.5222 -671.3812 7958.8929 24942.3857 8426.3899
103 104 105 106 107 108
-14368.6228 15599.1414 -22997.3774 -3245.5534 554.5083 -530.2395
109 110 111 112 113 114
104515.9938 14442.2470 5833.9207 -9537.7476 -18686.4909 -7171.4471
115 116 117 118 119 120
-6206.6332 -13932.0300 -51368.0584 -19689.9029 11688.1524 -10533.9971
121 122 123 124 125 126
-28164.0007 42591.3322 36169.0391 21337.7726 -16439.0548 14660.2875
127 128 129 130 131 132
-12701.9693 2703.0149 -24711.3290 -11469.5832 46930.8204 -7573.0819
133 134 135 136 137 138
-11937.8956 13494.4561 -2648.7702 43336.1119 27863.3575 158.4339
139 140 141 142 143 144
-24365.6276 -17739.0346 25805.0471 -2613.0034 13757.1718 15924.6077
145 146 147 148 149 150
-9091.8835 40912.3946 23868.2824 26411.0454 -17565.8039 -16459.2437
151 152 153 154 155 156
-17623.8968 -17647.3103 -17685.5742 -17715.5168 -13844.9489 -45689.2940
157 158 159 160 161 162
-17805.3445 -17831.5745 -18841.1715 -24804.9199 -18649.8843 -269.0883
163 164
-17967.2782 6468.4510
> postscript(file="/var/wessaorg/rcomp/tmp/6a0361321911448.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 = 164
Frequency = 1
lag(myerror, k = 1) myerror
0 11343.5566 NA
1 -14441.5508 11343.5566
2 3924.8670 -14441.5508
3 18830.8145 3924.8670
4 -14911.4097 18830.8145
5 17229.9427 -14911.4097
6 -13497.8490 17229.9427
7 5619.9520 -13497.8490
8 27984.4059 5619.9520
9 -5668.1170 27984.4059
10 -29795.0940 -5668.1170
11 14467.1165 -29795.0940
12 1860.5696 14467.1165
13 2383.6693 1860.5696
14 -16661.7686 2383.6693
15 -18933.1468 -16661.7686
16 24671.8994 -18933.1468
17 14605.1709 24671.8994
18 18760.7251 14605.1709
19 13112.5301 18760.7251
20 7556.9390 13112.5301
21 -21216.2005 7556.9390
22 -8248.7269 -21216.2005
23 16348.7036 -8248.7269
24 -3184.0890 16348.7036
25 -7477.1641 -3184.0890
26 -6965.1463 -7477.1641
27 -18354.5287 -6965.1463
28 -15260.6928 -18354.5287
29 -30598.9420 -15260.6928
30 5244.0261 -30598.9420
31 2816.7751 5244.0261
32 -28548.4429 2816.7751
33 12569.5222 -28548.4429
34 -24344.5803 12569.5222
35 -16131.1720 -24344.5803
36 -38222.3282 -16131.1720
37 -13080.2661 -38222.3282
38 -15602.7411 -13080.2661
39 -16393.8586 -15602.7411
40 -21764.3168 -16393.8586
41 -2482.9791 -21764.3168
42 -20598.7372 -2482.9791
43 -865.4062 -20598.7372
44 2199.2631 -865.4062
45 27635.2453 2199.2631
46 -16965.0779 27635.2453
47 -180.4066 -16965.0779
48 23364.2336 -180.4066
49 -2634.2558 23364.2336
50 -9897.3224 -2634.2558
51 -18023.7444 -9897.3224
52 775.7822 -18023.7444
53 2743.4249 775.7822
54 -10959.7434 2743.4249
55 1456.4420 -10959.7434
56 19593.8934 1456.4420
57 3402.3159 19593.8934
58 -18102.1071 3402.3159
59 10699.2054 -18102.1071
60 1743.6909 10699.2054
61 7386.9836 1743.6909
62 -11657.6331 7386.9836
63 9910.6587 -11657.6331
64 -3580.6918 9910.6587
65 -27677.0609 -3580.6918
66 35541.1099 -27677.0609
67 -9008.7692 35541.1099
68 9527.5996 -9008.7692
69 3197.3564 9527.5996
70 1562.2759 3197.3564
71 -6814.3677 1562.2759
72 -2406.4045 -6814.3677
73 11878.7040 -2406.4045
74 6721.6339 11878.7040
75 7718.0957 6721.6339
76 -8990.4730 7718.0957
77 143100.0487 -8990.4730
78 -1597.0904 143100.0487
79 1118.2583 -1597.0904
80 -10781.8638 1118.2583
81 -2655.3480 -10781.8638
82 -17513.9636 -2655.3480
83 1197.1032 -17513.9636
84 6288.7613 1197.1032
85 -14557.0452 6288.7613
86 22827.6331 -14557.0452
87 1038.9262 22827.6331
88 -33782.4872 1038.9262
89 47209.0439 -33782.4872
90 -26044.3752 47209.0439
91 9183.5405 -26044.3752
92 -8889.5295 9183.5405
93 -9633.5764 -8889.5295
94 9155.1768 -9633.5764
95 22186.9560 9155.1768
96 37698.8292 22186.9560
97 20651.5222 37698.8292
98 -671.3812 20651.5222
99 7958.8929 -671.3812
100 24942.3857 7958.8929
101 8426.3899 24942.3857
102 -14368.6228 8426.3899
103 15599.1414 -14368.6228
104 -22997.3774 15599.1414
105 -3245.5534 -22997.3774
106 554.5083 -3245.5534
107 -530.2395 554.5083
108 104515.9938 -530.2395
109 14442.2470 104515.9938
110 5833.9207 14442.2470
111 -9537.7476 5833.9207
112 -18686.4909 -9537.7476
113 -7171.4471 -18686.4909
114 -6206.6332 -7171.4471
115 -13932.0300 -6206.6332
116 -51368.0584 -13932.0300
117 -19689.9029 -51368.0584
118 11688.1524 -19689.9029
119 -10533.9971 11688.1524
120 -28164.0007 -10533.9971
121 42591.3322 -28164.0007
122 36169.0391 42591.3322
123 21337.7726 36169.0391
124 -16439.0548 21337.7726
125 14660.2875 -16439.0548
126 -12701.9693 14660.2875
127 2703.0149 -12701.9693
128 -24711.3290 2703.0149
129 -11469.5832 -24711.3290
130 46930.8204 -11469.5832
131 -7573.0819 46930.8204
132 -11937.8956 -7573.0819
133 13494.4561 -11937.8956
134 -2648.7702 13494.4561
135 43336.1119 -2648.7702
136 27863.3575 43336.1119
137 158.4339 27863.3575
138 -24365.6276 158.4339
139 -17739.0346 -24365.6276
140 25805.0471 -17739.0346
141 -2613.0034 25805.0471
142 13757.1718 -2613.0034
143 15924.6077 13757.1718
144 -9091.8835 15924.6077
145 40912.3946 -9091.8835
146 23868.2824 40912.3946
147 26411.0454 23868.2824
148 -17565.8039 26411.0454
149 -16459.2437 -17565.8039
150 -17623.8968 -16459.2437
151 -17647.3103 -17623.8968
152 -17685.5742 -17647.3103
153 -17715.5168 -17685.5742
154 -13844.9489 -17715.5168
155 -45689.2940 -13844.9489
156 -17805.3445 -45689.2940
157 -17831.5745 -17805.3445
158 -18841.1715 -17831.5745
159 -24804.9199 -18841.1715
160 -18649.8843 -24804.9199
161 -269.0883 -18649.8843
162 -17967.2782 -269.0883
163 6468.4510 -17967.2782
164 NA 6468.4510
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -14441.5508 11343.5566
[2,] 3924.8670 -14441.5508
[3,] 18830.8145 3924.8670
[4,] -14911.4097 18830.8145
[5,] 17229.9427 -14911.4097
[6,] -13497.8490 17229.9427
[7,] 5619.9520 -13497.8490
[8,] 27984.4059 5619.9520
[9,] -5668.1170 27984.4059
[10,] -29795.0940 -5668.1170
[11,] 14467.1165 -29795.0940
[12,] 1860.5696 14467.1165
[13,] 2383.6693 1860.5696
[14,] -16661.7686 2383.6693
[15,] -18933.1468 -16661.7686
[16,] 24671.8994 -18933.1468
[17,] 14605.1709 24671.8994
[18,] 18760.7251 14605.1709
[19,] 13112.5301 18760.7251
[20,] 7556.9390 13112.5301
[21,] -21216.2005 7556.9390
[22,] -8248.7269 -21216.2005
[23,] 16348.7036 -8248.7269
[24,] -3184.0890 16348.7036
[25,] -7477.1641 -3184.0890
[26,] -6965.1463 -7477.1641
[27,] -18354.5287 -6965.1463
[28,] -15260.6928 -18354.5287
[29,] -30598.9420 -15260.6928
[30,] 5244.0261 -30598.9420
[31,] 2816.7751 5244.0261
[32,] -28548.4429 2816.7751
[33,] 12569.5222 -28548.4429
[34,] -24344.5803 12569.5222
[35,] -16131.1720 -24344.5803
[36,] -38222.3282 -16131.1720
[37,] -13080.2661 -38222.3282
[38,] -15602.7411 -13080.2661
[39,] -16393.8586 -15602.7411
[40,] -21764.3168 -16393.8586
[41,] -2482.9791 -21764.3168
[42,] -20598.7372 -2482.9791
[43,] -865.4062 -20598.7372
[44,] 2199.2631 -865.4062
[45,] 27635.2453 2199.2631
[46,] -16965.0779 27635.2453
[47,] -180.4066 -16965.0779
[48,] 23364.2336 -180.4066
[49,] -2634.2558 23364.2336
[50,] -9897.3224 -2634.2558
[51,] -18023.7444 -9897.3224
[52,] 775.7822 -18023.7444
[53,] 2743.4249 775.7822
[54,] -10959.7434 2743.4249
[55,] 1456.4420 -10959.7434
[56,] 19593.8934 1456.4420
[57,] 3402.3159 19593.8934
[58,] -18102.1071 3402.3159
[59,] 10699.2054 -18102.1071
[60,] 1743.6909 10699.2054
[61,] 7386.9836 1743.6909
[62,] -11657.6331 7386.9836
[63,] 9910.6587 -11657.6331
[64,] -3580.6918 9910.6587
[65,] -27677.0609 -3580.6918
[66,] 35541.1099 -27677.0609
[67,] -9008.7692 35541.1099
[68,] 9527.5996 -9008.7692
[69,] 3197.3564 9527.5996
[70,] 1562.2759 3197.3564
[71,] -6814.3677 1562.2759
[72,] -2406.4045 -6814.3677
[73,] 11878.7040 -2406.4045
[74,] 6721.6339 11878.7040
[75,] 7718.0957 6721.6339
[76,] -8990.4730 7718.0957
[77,] 143100.0487 -8990.4730
[78,] -1597.0904 143100.0487
[79,] 1118.2583 -1597.0904
[80,] -10781.8638 1118.2583
[81,] -2655.3480 -10781.8638
[82,] -17513.9636 -2655.3480
[83,] 1197.1032 -17513.9636
[84,] 6288.7613 1197.1032
[85,] -14557.0452 6288.7613
[86,] 22827.6331 -14557.0452
[87,] 1038.9262 22827.6331
[88,] -33782.4872 1038.9262
[89,] 47209.0439 -33782.4872
[90,] -26044.3752 47209.0439
[91,] 9183.5405 -26044.3752
[92,] -8889.5295 9183.5405
[93,] -9633.5764 -8889.5295
[94,] 9155.1768 -9633.5764
[95,] 22186.9560 9155.1768
[96,] 37698.8292 22186.9560
[97,] 20651.5222 37698.8292
[98,] -671.3812 20651.5222
[99,] 7958.8929 -671.3812
[100,] 24942.3857 7958.8929
[101,] 8426.3899 24942.3857
[102,] -14368.6228 8426.3899
[103,] 15599.1414 -14368.6228
[104,] -22997.3774 15599.1414
[105,] -3245.5534 -22997.3774
[106,] 554.5083 -3245.5534
[107,] -530.2395 554.5083
[108,] 104515.9938 -530.2395
[109,] 14442.2470 104515.9938
[110,] 5833.9207 14442.2470
[111,] -9537.7476 5833.9207
[112,] -18686.4909 -9537.7476
[113,] -7171.4471 -18686.4909
[114,] -6206.6332 -7171.4471
[115,] -13932.0300 -6206.6332
[116,] -51368.0584 -13932.0300
[117,] -19689.9029 -51368.0584
[118,] 11688.1524 -19689.9029
[119,] -10533.9971 11688.1524
[120,] -28164.0007 -10533.9971
[121,] 42591.3322 -28164.0007
[122,] 36169.0391 42591.3322
[123,] 21337.7726 36169.0391
[124,] -16439.0548 21337.7726
[125,] 14660.2875 -16439.0548
[126,] -12701.9693 14660.2875
[127,] 2703.0149 -12701.9693
[128,] -24711.3290 2703.0149
[129,] -11469.5832 -24711.3290
[130,] 46930.8204 -11469.5832
[131,] -7573.0819 46930.8204
[132,] -11937.8956 -7573.0819
[133,] 13494.4561 -11937.8956
[134,] -2648.7702 13494.4561
[135,] 43336.1119 -2648.7702
[136,] 27863.3575 43336.1119
[137,] 158.4339 27863.3575
[138,] -24365.6276 158.4339
[139,] -17739.0346 -24365.6276
[140,] 25805.0471 -17739.0346
[141,] -2613.0034 25805.0471
[142,] 13757.1718 -2613.0034
[143,] 15924.6077 13757.1718
[144,] -9091.8835 15924.6077
[145,] 40912.3946 -9091.8835
[146,] 23868.2824 40912.3946
[147,] 26411.0454 23868.2824
[148,] -17565.8039 26411.0454
[149,] -16459.2437 -17565.8039
[150,] -17623.8968 -16459.2437
[151,] -17647.3103 -17623.8968
[152,] -17685.5742 -17647.3103
[153,] -17715.5168 -17685.5742
[154,] -13844.9489 -17715.5168
[155,] -45689.2940 -13844.9489
[156,] -17805.3445 -45689.2940
[157,] -17831.5745 -17805.3445
[158,] -18841.1715 -17831.5745
[159,] -24804.9199 -18841.1715
[160,] -18649.8843 -24804.9199
[161,] -269.0883 -18649.8843
[162,] -17967.2782 -269.0883
[163,] 6468.4510 -17967.2782
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -14441.5508 11343.5566
2 3924.8670 -14441.5508
3 18830.8145 3924.8670
4 -14911.4097 18830.8145
5 17229.9427 -14911.4097
6 -13497.8490 17229.9427
7 5619.9520 -13497.8490
8 27984.4059 5619.9520
9 -5668.1170 27984.4059
10 -29795.0940 -5668.1170
11 14467.1165 -29795.0940
12 1860.5696 14467.1165
13 2383.6693 1860.5696
14 -16661.7686 2383.6693
15 -18933.1468 -16661.7686
16 24671.8994 -18933.1468
17 14605.1709 24671.8994
18 18760.7251 14605.1709
19 13112.5301 18760.7251
20 7556.9390 13112.5301
21 -21216.2005 7556.9390
22 -8248.7269 -21216.2005
23 16348.7036 -8248.7269
24 -3184.0890 16348.7036
25 -7477.1641 -3184.0890
26 -6965.1463 -7477.1641
27 -18354.5287 -6965.1463
28 -15260.6928 -18354.5287
29 -30598.9420 -15260.6928
30 5244.0261 -30598.9420
31 2816.7751 5244.0261
32 -28548.4429 2816.7751
33 12569.5222 -28548.4429
34 -24344.5803 12569.5222
35 -16131.1720 -24344.5803
36 -38222.3282 -16131.1720
37 -13080.2661 -38222.3282
38 -15602.7411 -13080.2661
39 -16393.8586 -15602.7411
40 -21764.3168 -16393.8586
41 -2482.9791 -21764.3168
42 -20598.7372 -2482.9791
43 -865.4062 -20598.7372
44 2199.2631 -865.4062
45 27635.2453 2199.2631
46 -16965.0779 27635.2453
47 -180.4066 -16965.0779
48 23364.2336 -180.4066
49 -2634.2558 23364.2336
50 -9897.3224 -2634.2558
51 -18023.7444 -9897.3224
52 775.7822 -18023.7444
53 2743.4249 775.7822
54 -10959.7434 2743.4249
55 1456.4420 -10959.7434
56 19593.8934 1456.4420
57 3402.3159 19593.8934
58 -18102.1071 3402.3159
59 10699.2054 -18102.1071
60 1743.6909 10699.2054
61 7386.9836 1743.6909
62 -11657.6331 7386.9836
63 9910.6587 -11657.6331
64 -3580.6918 9910.6587
65 -27677.0609 -3580.6918
66 35541.1099 -27677.0609
67 -9008.7692 35541.1099
68 9527.5996 -9008.7692
69 3197.3564 9527.5996
70 1562.2759 3197.3564
71 -6814.3677 1562.2759
72 -2406.4045 -6814.3677
73 11878.7040 -2406.4045
74 6721.6339 11878.7040
75 7718.0957 6721.6339
76 -8990.4730 7718.0957
77 143100.0487 -8990.4730
78 -1597.0904 143100.0487
79 1118.2583 -1597.0904
80 -10781.8638 1118.2583
81 -2655.3480 -10781.8638
82 -17513.9636 -2655.3480
83 1197.1032 -17513.9636
84 6288.7613 1197.1032
85 -14557.0452 6288.7613
86 22827.6331 -14557.0452
87 1038.9262 22827.6331
88 -33782.4872 1038.9262
89 47209.0439 -33782.4872
90 -26044.3752 47209.0439
91 9183.5405 -26044.3752
92 -8889.5295 9183.5405
93 -9633.5764 -8889.5295
94 9155.1768 -9633.5764
95 22186.9560 9155.1768
96 37698.8292 22186.9560
97 20651.5222 37698.8292
98 -671.3812 20651.5222
99 7958.8929 -671.3812
100 24942.3857 7958.8929
101 8426.3899 24942.3857
102 -14368.6228 8426.3899
103 15599.1414 -14368.6228
104 -22997.3774 15599.1414
105 -3245.5534 -22997.3774
106 554.5083 -3245.5534
107 -530.2395 554.5083
108 104515.9938 -530.2395
109 14442.2470 104515.9938
110 5833.9207 14442.2470
111 -9537.7476 5833.9207
112 -18686.4909 -9537.7476
113 -7171.4471 -18686.4909
114 -6206.6332 -7171.4471
115 -13932.0300 -6206.6332
116 -51368.0584 -13932.0300
117 -19689.9029 -51368.0584
118 11688.1524 -19689.9029
119 -10533.9971 11688.1524
120 -28164.0007 -10533.9971
121 42591.3322 -28164.0007
122 36169.0391 42591.3322
123 21337.7726 36169.0391
124 -16439.0548 21337.7726
125 14660.2875 -16439.0548
126 -12701.9693 14660.2875
127 2703.0149 -12701.9693
128 -24711.3290 2703.0149
129 -11469.5832 -24711.3290
130 46930.8204 -11469.5832
131 -7573.0819 46930.8204
132 -11937.8956 -7573.0819
133 13494.4561 -11937.8956
134 -2648.7702 13494.4561
135 43336.1119 -2648.7702
136 27863.3575 43336.1119
137 158.4339 27863.3575
138 -24365.6276 158.4339
139 -17739.0346 -24365.6276
140 25805.0471 -17739.0346
141 -2613.0034 25805.0471
142 13757.1718 -2613.0034
143 15924.6077 13757.1718
144 -9091.8835 15924.6077
145 40912.3946 -9091.8835
146 23868.2824 40912.3946
147 26411.0454 23868.2824
148 -17565.8039 26411.0454
149 -16459.2437 -17565.8039
150 -17623.8968 -16459.2437
151 -17647.3103 -17623.8968
152 -17685.5742 -17647.3103
153 -17715.5168 -17685.5742
154 -13844.9489 -17715.5168
155 -45689.2940 -13844.9489
156 -17805.3445 -45689.2940
157 -17831.5745 -17805.3445
158 -18841.1715 -17831.5745
159 -24804.9199 -18841.1715
160 -18649.8843 -24804.9199
161 -269.0883 -18649.8843
162 -17967.2782 -269.0883
163 6468.4510 -17967.2782
> 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/7zisa1321911448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8cz2y1321911448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9qnf21321911448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10v2d41321911448.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11zywb1321911448.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/12drgw1321911448.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/13vhb61321911448.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/14ifch1321911448.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/15x4sa1321911448.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/164u8v1321911448.tab")
+ }
>
> try(system("convert tmp/1lrlh1321911448.ps tmp/1lrlh1321911448.png",intern=TRUE))
character(0)
> try(system("convert tmp/2b8nb1321911448.ps tmp/2b8nb1321911448.png",intern=TRUE))
character(0)
> try(system("convert tmp/39xkr1321911448.ps tmp/39xkr1321911448.png",intern=TRUE))
character(0)
> try(system("convert tmp/4beu41321911448.ps tmp/4beu41321911448.png",intern=TRUE))
character(0)
> try(system("convert tmp/5j41e1321911448.ps tmp/5j41e1321911448.png",intern=TRUE))
character(0)
> try(system("convert tmp/6a0361321911448.ps tmp/6a0361321911448.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zisa1321911448.ps tmp/7zisa1321911448.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cz2y1321911448.ps tmp/8cz2y1321911448.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qnf21321911448.ps tmp/9qnf21321911448.png",intern=TRUE))
character(0)
> try(system("convert tmp/10v2d41321911448.ps tmp/10v2d41321911448.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.732 0.536 5.288