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.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(170650
+ ,95556
+ ,128
+ ,86621
+ ,54565
+ ,89
+ ,127843
+ ,63016
+ ,68
+ ,152526
+ ,79774
+ ,108
+ ,92389
+ ,31258
+ ,51
+ ,38138
+ ,52491
+ ,33
+ ,316392
+ ,91256
+ ,119
+ ,32750
+ ,22807
+ ,5
+ ,132344
+ ,77411
+ ,63
+ ,137034
+ ,48821
+ ,66
+ ,176816
+ ,52295
+ ,98
+ ,140146
+ ,63262
+ ,71
+ ,113286
+ ,50466
+ ,55
+ ,195452
+ ,62932
+ ,116
+ ,144513
+ ,38439
+ ,71
+ ,263581
+ ,70817
+ ,120
+ ,183271
+ ,105965
+ ,122
+ ,210763
+ ,73795
+ ,74
+ ,113853
+ ,82043
+ ,111
+ ,159968
+ ,74349
+ ,103
+ ,174585
+ ,82204
+ ,98
+ ,294675
+ ,55709
+ ,100
+ ,96213
+ ,37137
+ ,42
+ ,116390
+ ,70780
+ ,100
+ ,146342
+ ,55027
+ ,105
+ ,152647
+ ,56699
+ ,77
+ ,166661
+ ,65911
+ ,83
+ ,175505
+ ,56316
+ ,98
+ ,112485
+ ,26982
+ ,46
+ ,197053
+ ,54628
+ ,95
+ ,191822
+ ,96750
+ ,91
+ ,139127
+ ,53009
+ ,91
+ ,221991
+ ,64664
+ ,94
+ ,75339
+ ,36990
+ ,15
+ ,247985
+ ,85224
+ ,137
+ ,167351
+ ,37048
+ ,56
+ ,266609
+ ,59635
+ ,78
+ ,122024
+ ,42051
+ ,68
+ ,80964
+ ,26998
+ ,34
+ ,215183
+ ,63717
+ ,94
+ ,225469
+ ,55071
+ ,82
+ ,125382
+ ,40001
+ ,63
+ ,141437
+ ,54506
+ ,58
+ ,81106
+ ,35838
+ ,43
+ ,93125
+ ,50838
+ ,36
+ ,318668
+ ,86997
+ ,64
+ ,78800
+ ,33032
+ ,21
+ ,161048
+ ,61704
+ ,104
+ ,236367
+ ,117986
+ ,124
+ ,131108
+ ,56733
+ ,101
+ ,131096
+ ,55064
+ ,85
+ ,24188
+ ,84607
+ ,7
+ ,267003
+ ,84607
+ ,124
+ ,65029
+ ,32551
+ ,21
+ ,100147
+ ,31701
+ ,35
+ ,178549
+ ,71170
+ ,95
+ ,186965
+ ,101773
+ ,102
+ ,197266
+ ,101653
+ ,212
+ ,217300
+ ,81493
+ ,141
+ ,149594
+ ,55901
+ ,54
+ ,263413
+ ,109104
+ ,117
+ ,209228
+ ,114425
+ ,145
+ ,145699
+ ,36311
+ ,50
+ ,187197
+ ,70027
+ ,80
+ ,150752
+ ,73713
+ ,87
+ ,125555
+ ,40671
+ ,78
+ ,118697
+ ,89041
+ ,86
+ ,147913
+ ,57231
+ ,82
+ ,155015
+ ,68608
+ ,119
+ ,96487
+ ,59155
+ ,75
+ ,128780
+ ,55827
+ ,70
+ ,71972
+ ,22618
+ ,25
+ ,140266
+ ,58425
+ ,66
+ ,148454
+ ,65724
+ ,89
+ ,110655
+ ,56979
+ ,99
+ ,203795
+ ,72369
+ ,98
+ ,211093
+ ,79194
+ ,104
+ ,113421
+ ,202316
+ ,48
+ ,103660
+ ,44970
+ ,81
+ ,128390
+ ,49319
+ ,64
+ ,105502
+ ,36252
+ ,44
+ ,299359
+ ,75741
+ ,104
+ ,141493
+ ,38417
+ ,36
+ ,146390
+ ,64102
+ ,120
+ ,80953
+ ,56622
+ ,58
+ ,109237
+ ,15430
+ ,27
+ ,102104
+ ,72571
+ ,84
+ ,233139
+ ,67271
+ ,56
+ ,176507
+ ,43460
+ ,46
+ ,118217
+ ,99501
+ ,119
+ ,142694
+ ,28340
+ ,57
+ ,152193
+ ,76013
+ ,139
+ ,126500
+ ,37361
+ ,51
+ ,147410
+ ,48204
+ ,85
+ ,187772
+ ,76168
+ ,91
+ ,140903
+ ,85168
+ ,79
+ ,150587
+ ,125410
+ ,142
+ ,202077
+ ,123328
+ ,149
+ ,213875
+ ,83038
+ ,96
+ ,252952
+ ,120087
+ ,198
+ ,166981
+ ,91939
+ ,61
+ ,190562
+ ,103646
+ ,145
+ ,106351
+ ,29467
+ ,26
+ ,43287
+ ,43750
+ ,49
+ ,127493
+ ,34497
+ ,68
+ ,132143
+ ,66477
+ ,145
+ ,157469
+ ,71181
+ ,82
+ ,197727
+ ,74482
+ ,102
+ ,88077
+ ,174949
+ ,52
+ ,94968
+ ,46765
+ ,56
+ ,191351
+ ,90257
+ ,80
+ ,153332
+ ,51370
+ ,99
+ ,22938
+ ,1168
+ ,11
+ ,125927
+ ,51360
+ ,87
+ ,61857
+ ,25162
+ ,28
+ ,103749
+ ,21067
+ ,67
+ ,269909
+ ,58233
+ ,150
+ ,21054
+ ,855
+ ,4
+ ,174409
+ ,85903
+ ,71
+ ,31414
+ ,14116
+ ,39
+ ,200405
+ ,57637
+ ,87
+ ,139456
+ ,94137
+ ,66
+ ,78001
+ ,62147
+ ,23
+ ,82724
+ ,62832
+ ,56
+ ,38214
+ ,8773
+ ,16
+ ,91390
+ ,63785
+ ,49
+ ,197612
+ ,65196
+ ,108
+ ,137161
+ ,73087
+ ,112
+ ,251103
+ ,72631
+ ,110
+ ,209835
+ ,86281
+ ,126
+ ,269470
+ ,162365
+ ,155
+ ,139215
+ ,56530
+ ,75
+ ,76470
+ ,35606
+ ,30
+ ,197114
+ ,70111
+ ,78
+ ,291962
+ ,92046
+ ,135
+ ,56727
+ ,63989
+ ,8
+ ,254843
+ ,104911
+ ,114
+ ,105908
+ ,43448
+ ,60
+ ,170155
+ ,60029
+ ,99
+ ,136745
+ ,38650
+ ,98
+ ,86706
+ ,47261
+ ,33
+ ,251448
+ ,73586
+ ,93
+ ,152366
+ ,83042
+ ,157
+ ,173260
+ ,37238
+ ,15
+ ,212582
+ ,63958
+ ,98
+ ,87850
+ ,78956
+ ,49
+ ,148363
+ ,99518
+ ,88
+ ,185455
+ ,111436
+ ,151
+ ,0
+ ,0
+ ,0
+ ,14688
+ ,6023
+ ,5
+ ,98
+ ,0
+ ,0
+ ,455
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,137891
+ ,42564
+ ,80
+ ,200096
+ ,38885
+ ,122
+ ,0
+ ,0
+ ,0
+ ,203
+ ,0
+ ,0
+ ,7199
+ ,1644
+ ,6
+ ,46660
+ ,6179
+ ,13
+ ,17547
+ ,3926
+ ,3
+ ,73567
+ ,23238
+ ,18
+ ,969
+ ,0
+ ,0
+ ,106662
+ ,49288
+ ,48)
+ ,dim=c(3
+ ,164)
+ ,dimnames=list(c('TijdRFC'
+ ,'Karakters'
+ ,'Blogs')
+ ,1:164))
> y <- array(NA,dim=c(3,164),dimnames=list(c('TijdRFC','Karakters','Blogs'),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 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> 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
TijdRFC Karakters Blogs
1 170650 95556 128
2 86621 54565 89
3 127843 63016 68
4 152526 79774 108
5 92389 31258 51
6 38138 52491 33
7 316392 91256 119
8 32750 22807 5
9 132344 77411 63
10 137034 48821 66
11 176816 52295 98
12 140146 63262 71
13 113286 50466 55
14 195452 62932 116
15 144513 38439 71
16 263581 70817 120
17 183271 105965 122
18 210763 73795 74
19 113853 82043 111
20 159968 74349 103
21 174585 82204 98
22 294675 55709 100
23 96213 37137 42
24 116390 70780 100
25 146342 55027 105
26 152647 56699 77
27 166661 65911 83
28 175505 56316 98
29 112485 26982 46
30 197053 54628 95
31 191822 96750 91
32 139127 53009 91
33 221991 64664 94
34 75339 36990 15
35 247985 85224 137
36 167351 37048 56
37 266609 59635 78
38 122024 42051 68
39 80964 26998 34
40 215183 63717 94
41 225469 55071 82
42 125382 40001 63
43 141437 54506 58
44 81106 35838 43
45 93125 50838 36
46 318668 86997 64
47 78800 33032 21
48 161048 61704 104
49 236367 117986 124
50 131108 56733 101
51 131096 55064 85
52 24188 84607 7
53 267003 84607 124
54 65029 32551 21
55 100147 31701 35
56 178549 71170 95
57 186965 101773 102
58 197266 101653 212
59 217300 81493 141
60 149594 55901 54
61 263413 109104 117
62 209228 114425 145
63 145699 36311 50
64 187197 70027 80
65 150752 73713 87
66 125555 40671 78
67 118697 89041 86
68 147913 57231 82
69 155015 68608 119
70 96487 59155 75
71 128780 55827 70
72 71972 22618 25
73 140266 58425 66
74 148454 65724 89
75 110655 56979 99
76 203795 72369 98
77 211093 79194 104
78 113421 202316 48
79 103660 44970 81
80 128390 49319 64
81 105502 36252 44
82 299359 75741 104
83 141493 38417 36
84 146390 64102 120
85 80953 56622 58
86 109237 15430 27
87 102104 72571 84
88 233139 67271 56
89 176507 43460 46
90 118217 99501 119
91 142694 28340 57
92 152193 76013 139
93 126500 37361 51
94 147410 48204 85
95 187772 76168 91
96 140903 85168 79
97 150587 125410 142
98 202077 123328 149
99 213875 83038 96
100 252952 120087 198
101 166981 91939 61
102 190562 103646 145
103 106351 29467 26
104 43287 43750 49
105 127493 34497 68
106 132143 66477 145
107 157469 71181 82
108 197727 74482 102
109 88077 174949 52
110 94968 46765 56
111 191351 90257 80
112 153332 51370 99
113 22938 1168 11
114 125927 51360 87
115 61857 25162 28
116 103749 21067 67
117 269909 58233 150
118 21054 855 4
119 174409 85903 71
120 31414 14116 39
121 200405 57637 87
122 139456 94137 66
123 78001 62147 23
124 82724 62832 56
125 38214 8773 16
126 91390 63785 49
127 197612 65196 108
128 137161 73087 112
129 251103 72631 110
130 209835 86281 126
131 269470 162365 155
132 139215 56530 75
133 76470 35606 30
134 197114 70111 78
135 291962 92046 135
136 56727 63989 8
137 254843 104911 114
138 105908 43448 60
139 170155 60029 99
140 136745 38650 98
141 86706 47261 33
142 251448 73586 93
143 152366 83042 157
144 173260 37238 15
145 212582 63958 98
146 87850 78956 49
147 148363 99518 88
148 185455 111436 151
149 0 0 0
150 14688 6023 5
151 98 0 0
152 455 0 0
153 0 0 0
154 0 0 0
155 137891 42564 80
156 200096 38885 122
157 0 0 0
158 203 0 0
159 7199 1644 6
160 46660 6179 13
161 17547 3926 3
162 73567 23238 18
163 969 0 0
164 106662 49288 48
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Karakters Blogs
3.786e+04 3.785e-01 1.076e+03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-107107 -31923 -2953 20910 179039
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.786e+04 7.853e+03 4.821 3.28e-06 ***
Karakters 3.785e-01 1.492e-01 2.537 0.0121 *
Blogs 1.076e+03 1.160e+02 9.273 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 46150 on 161 degrees of freedom
Multiple R-squared: 0.5939, Adjusted R-squared: 0.5889
F-statistic: 117.7 on 2 and 161 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,] 0.2675644 5.351289e-01 7.324356e-01
[2,] 0.9396000 1.208000e-01 6.040001e-02
[3,] 0.9063154 1.873692e-01 9.368458e-02
[4,] 0.8472052 3.055896e-01 1.527948e-01
[5,] 0.8009158 3.981685e-01 1.990842e-01
[6,] 0.7400883 5.198234e-01 2.599117e-01
[7,] 0.6566811 6.866377e-01 3.433189e-01
[8,] 0.5699060 8.601879e-01 4.300940e-01
[9,] 0.4791557 9.583113e-01 5.208443e-01
[10,] 0.4173842 8.347684e-01 5.826158e-01
[11,] 0.4576366 9.152732e-01 5.423634e-01
[12,] 0.4238169 8.476339e-01 5.761831e-01
[13,] 0.5330140 9.339720e-01 4.669860e-01
[14,] 0.6756763 6.486474e-01 3.243237e-01
[15,] 0.6177197 7.645606e-01 3.822803e-01
[16,] 0.5471773 9.056454e-01 4.528227e-01
[17,] 0.8350220 3.299560e-01 1.649780e-01
[18,] 0.7902755 4.194490e-01 2.097245e-01
[19,] 0.8184044 3.631913e-01 1.815956e-01
[20,] 0.8040414 3.919172e-01 1.959586e-01
[21,] 0.7595789 4.808423e-01 2.404211e-01
[22,] 0.7140692 5.718616e-01 2.859308e-01
[23,] 0.6598529 6.802942e-01 3.401471e-01
[24,] 0.6065523 7.868954e-01 3.934477e-01
[25,] 0.5670422 8.659155e-01 4.329578e-01
[26,] 0.5321116 9.357769e-01 4.678884e-01
[27,] 0.4898982 9.797964e-01 5.101018e-01
[28,] 0.5086455 9.827090e-01 4.913545e-01
[29,] 0.4604478 9.208956e-01 5.395522e-01
[30,] 0.4141714 8.283427e-01 5.858286e-01
[31,] 0.4264924 8.529848e-01 5.735076e-01
[32,] 0.7091281 5.817437e-01 2.908719e-01
[33,] 0.6662557 6.674886e-01 3.337443e-01
[34,] 0.6175171 7.649658e-01 3.824829e-01
[35,] 0.6117023 7.765953e-01 3.882977e-01
[36,] 0.6734873 6.530254e-01 3.265127e-01
[37,] 0.6267958 7.464084e-01 3.732042e-01
[38,] 0.5845810 8.308380e-01 4.154190e-01
[39,] 0.5443377 9.113246e-01 4.556623e-01
[40,] 0.4930872 9.861743e-01 5.069128e-01
[41,] 0.9456125 1.087750e-01 5.438750e-02
[42,] 0.9308363 1.383275e-01 6.916375e-02
[43,] 0.9166820 1.666360e-01 8.331802e-02
[44,] 0.8999033 2.001934e-01 1.000967e-01
[45,] 0.8954787 2.090425e-01 1.045213e-01
[46,] 0.8784809 2.430382e-01 1.215191e-01
[47,] 0.8964897 2.070206e-01 1.035103e-01
[48,] 0.9054068 1.891863e-01 9.459316e-02
[49,] 0.8841208 2.317585e-01 1.158792e-01
[50,] 0.8613078 2.773844e-01 1.386922e-01
[51,] 0.8353514 3.292972e-01 1.646486e-01
[52,] 0.8075284 3.849433e-01 1.924716e-01
[53,] 0.9271820 1.456360e-01 7.281801e-02
[54,] 0.9099486 1.801029e-01 9.005144e-02
[55,] 0.8979031 2.041937e-01 1.020969e-01
[56,] 0.9044574 1.910853e-01 9.554264e-02
[57,] 0.8946465 2.107070e-01 1.053535e-01
[58,] 0.8877111 2.245777e-01 1.122889e-01
[59,] 0.8775762 2.448475e-01 1.224238e-01
[60,] 0.8554741 2.890518e-01 1.445259e-01
[61,] 0.8302431 3.395138e-01 1.697569e-01
[62,] 0.8357362 3.285276e-01 1.642638e-01
[63,] 0.8065995 3.868010e-01 1.934005e-01
[64,] 0.7946082 4.107835e-01 2.053918e-01
[65,] 0.7952251 4.095498e-01 2.047749e-01
[66,] 0.7630187 4.739625e-01 2.369813e-01
[67,] 0.7276944 5.446112e-01 2.723056e-01
[68,] 0.6907473 6.185054e-01 3.092527e-01
[69,] 0.6526633 6.946733e-01 3.473367e-01
[70,] 0.6686563 6.626874e-01 3.313437e-01
[71,] 0.6483377 7.033245e-01 3.516623e-01
[72,] 0.6255358 7.489283e-01 3.744642e-01
[73,] 0.6465587 7.068826e-01 3.534413e-01
[74,] 0.6355593 7.288814e-01 3.644407e-01
[75,] 0.5932231 8.135539e-01 4.067769e-01
[76,] 0.5504459 8.991082e-01 4.495541e-01
[77,] 0.7889273 4.221454e-01 2.110727e-01
[78,] 0.7953354 4.093291e-01 2.046646e-01
[79,] 0.7919060 4.161879e-01 2.080940e-01
[80,] 0.7851867 4.296267e-01 2.148133e-01
[81,] 0.7732371 4.535258e-01 2.267629e-01
[82,] 0.7833779 4.332442e-01 2.166221e-01
[83,] 0.9100959 1.798082e-01 8.990409e-02
[84,] 0.9382643 1.234715e-01 6.173574e-02
[85,] 0.9641316 7.173673e-02 3.586836e-02
[86,] 0.9609527 7.809458e-02 3.904729e-02
[87,] 0.9683432 6.331362e-02 3.165681e-02
[88,] 0.9621261 7.574776e-02 3.787388e-02
[89,] 0.9518950 9.621000e-02 4.810500e-02
[90,] 0.9441345 1.117310e-01 5.586552e-02
[91,] 0.9308761 1.382477e-01 6.912386e-02
[92,] 0.9625993 7.480135e-02 3.740068e-02
[93,] 0.9617083 7.658331e-02 3.829166e-02
[94,] 0.9614304 7.713910e-02 3.856955e-02
[95,] 0.9629432 7.411368e-02 3.705684e-02
[96,] 0.9581994 8.360111e-02 4.180056e-02
[97,] 0.9591447 8.171063e-02 4.085531e-02
[98,] 0.9566996 8.660084e-02 4.330042e-02
[99,] 0.9667103 6.657949e-02 3.328975e-02
[100,] 0.9572905 8.541902e-02 4.270951e-02
[101,] 0.9857575 2.848502e-02 1.424251e-02
[102,] 0.9807251 3.854972e-02 1.927486e-02
[103,] 0.9756118 4.877638e-02 2.438819e-02
[104,] 0.9875563 2.488745e-02 1.244372e-02
[105,] 0.9841021 3.179582e-02 1.589791e-02
[106,] 0.9810634 3.787328e-02 1.893664e-02
[107,] 0.9748927 5.021468e-02 2.510734e-02
[108,] 0.9698242 6.035169e-02 3.017584e-02
[109,] 0.9636734 7.265327e-02 3.632663e-02
[110,] 0.9538519 9.229610e-02 4.614805e-02
[111,] 0.9412202 1.175596e-01 5.877981e-02
[112,] 0.9438195 1.123610e-01 5.618051e-02
[113,] 0.9312094 1.375812e-01 6.879060e-02
[114,] 0.9177721 1.644558e-01 8.222788e-02
[115,] 0.9196955 1.606090e-01 8.030449e-02
[116,] 0.9245062 1.509876e-01 7.549378e-02
[117,] 0.9048453 1.903094e-01 9.515472e-02
[118,] 0.8807729 2.384542e-01 1.192271e-01
[119,] 0.8758482 2.483035e-01 1.241518e-01
[120,] 0.8491257 3.017487e-01 1.508743e-01
[121,] 0.8261600 3.476801e-01 1.738400e-01
[122,] 0.7952558 4.094883e-01 2.047442e-01
[123,] 0.8099630 3.800740e-01 1.900370e-01
[124,] 0.8535607 2.928787e-01 1.464393e-01
[125,] 0.8176486 3.647029e-01 1.823514e-01
[126,] 0.8054031 3.891938e-01 1.945969e-01
[127,] 0.7616162 4.767676e-01 2.383838e-01
[128,] 0.7133682 5.732636e-01 2.866318e-01
[129,] 0.7119035 5.761930e-01 2.880965e-01
[130,] 0.7868788 4.262425e-01 2.131212e-01
[131,] 0.7545609 4.908782e-01 2.454391e-01
[132,] 0.7603910 4.792180e-01 2.396090e-01
[133,] 0.7077353 5.845294e-01 2.922647e-01
[134,] 0.6555633 6.888733e-01 3.444367e-01
[135,] 0.5945088 8.109824e-01 4.054912e-01
[136,] 0.5274321 9.451357e-01 4.725679e-01
[137,] 0.7604511 4.790977e-01 2.395489e-01
[138,] 0.8471720 3.056560e-01 1.528280e-01
[139,] 0.9993272 1.345531e-03 6.727657e-04
[140,] 0.9999271 1.457513e-04 7.287566e-05
[141,] 0.9998220 3.560829e-04 1.780414e-04
[142,] 0.9996592 6.816730e-04 3.408365e-04
[143,] 0.9999998 3.554416e-07 1.777208e-07
[144,] 0.9999992 1.520391e-06 7.601953e-07
[145,] 0.9999966 6.779395e-06 3.389697e-06
[146,] 0.9999863 2.732198e-05 1.366099e-05
[147,] 0.9999465 1.070432e-04 5.352160e-05
[148,] 0.9998034 3.932178e-04 1.966089e-04
[149,] 0.9993185 1.363062e-03 6.815309e-04
[150,] 0.9981123 3.775325e-03 1.887663e-03
[151,] 0.9929600 1.408000e-02 7.040000e-03
[152,] 0.9785297 4.294056e-02 2.147028e-02
[153,] 0.9404837 1.190326e-01 5.951630e-02
> postscript(file="/var/wessaorg/rcomp/tmp/1rjf51321994336.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/2huwp1321994336.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3e8pq1321994336.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4pxf91321994336.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5rc7b1321994336.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-41060.0658 -67623.9238 -7011.8226 -31697.6790 -12159.6453 -55085.3578
7 8 9 10 11 12
115990.3182 -19119.9829 -2580.9146 9703.1439 13749.3689 1971.1094
13 14 15 16 17 18
-2835.2865 8997.6175 15733.3571 69839.6142 -25924.8451 65374.5188
19 20 21 22 23 24
-74456.4315 -16824.1069 198.1231 128164.8990 -879.9070 -55824.3179
25 26 27 28 29 30
-25288.2370 10502.2176 14575.6522 10916.4622 14933.0423 36330.3122
31 32 33 34 35 36
19459.1855 -16680.3003 58545.4437 7344.3671 30504.6101 55232.6340
37 38 39 40 41 42
122277.3189 -4895.7878 -3686.1753 52095.8733 78562.1328 4616.3818
43 44 45 46 47 48
20559.6559 -16570.9022 -2699.6740 179039.2308 5849.5098 -12033.7590
49 50 51 52 53 54
20470.0249 -36865.3276 -19035.1774 -53223.9472 63739.6297 -7739.4367
55 56 57 58 59 60
12641.1349 11565.3371 868.8472 -107106.5849 -3070.8579 32491.2757
61 62 63 64 65 66
58407.3420 -27909.8905 40313.4999 36780.7485 -8588.9365 -11599.0041
67 68 69 70 71 72
-45369.7721 188.5952 -36814.6491 -44436.0465 -5505.1671 -1339.5121
73 74 75 76 77 78
9300.1296 -10014.4894 -55260.1296 33130.5684 31391.4577 -52644.3617
79 80 81 82 83 84
-38348.0905 3021.9624 6592.7499 120964.3823 50369.5454 -44809.8281
85 86 87 88 89 90
-40725.2281 36494.7649 -53577.7418 109581.5424 72718.2904 -85305.3287
91 92 93 94 95 96
32795.8683 -63952.4284 19641.4326 -124.7386 23199.2587 -14168.3095
97 98 99 100 101 102
-87481.6394 -42733.1963 41323.7692 -43338.5177 28708.6947 -42496.1510
103 104 105 106 107 108
29371.5594 -63838.4311 3432.3227 -86847.0706 4464.6651 21960.2071
109 110 111 112 113 114
-71932.8492 -20828.1496 33277.9037 -10460.1814 -27195.7653 -24953.5582
115 116 117 118 119 120
-15644.3492 -14152.9086 48660.9318 -21431.7257 27664.7261 -53738.7381
121 122 123 124 125 126
47148.6625 -5026.4914 -8120.5216 -39153.3422 -20176.4447 -23318.4705
127 128 129 130 131 132
18905.9425 -48834.3317 67431.5659 3786.7317 3430.7856 -714.5113
133 134 135 136 137 138
-7135.6011 48817.2618 74050.8604 -13956.9016 54651.3085 -12935.3123
139 140 141 142 143 144
3085.4771 -21157.1403 -4537.8571 85701.2125 -85801.5893 105171.5017
145 146 147 148 149 150
45101.0446 -32600.5364 -21820.5141 -57005.5042 -37859.5043 -30829.4132
151 152 153 154 155 156
-37761.5043 -37404.5043 -37859.5043 -37859.5043 -2130.7913 16289.2381
157 158 159 160 161 162
-37859.5043 -37656.5043 -37736.6604 -7521.6831 -25025.4141 7550.3967
163 164
-36890.5043 -1483.8534
> postscript(file="/var/wessaorg/rcomp/tmp/6obi61321994336.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 -41060.0658 NA
1 -67623.9238 -41060.0658
2 -7011.8226 -67623.9238
3 -31697.6790 -7011.8226
4 -12159.6453 -31697.6790
5 -55085.3578 -12159.6453
6 115990.3182 -55085.3578
7 -19119.9829 115990.3182
8 -2580.9146 -19119.9829
9 9703.1439 -2580.9146
10 13749.3689 9703.1439
11 1971.1094 13749.3689
12 -2835.2865 1971.1094
13 8997.6175 -2835.2865
14 15733.3571 8997.6175
15 69839.6142 15733.3571
16 -25924.8451 69839.6142
17 65374.5188 -25924.8451
18 -74456.4315 65374.5188
19 -16824.1069 -74456.4315
20 198.1231 -16824.1069
21 128164.8990 198.1231
22 -879.9070 128164.8990
23 -55824.3179 -879.9070
24 -25288.2370 -55824.3179
25 10502.2176 -25288.2370
26 14575.6522 10502.2176
27 10916.4622 14575.6522
28 14933.0423 10916.4622
29 36330.3122 14933.0423
30 19459.1855 36330.3122
31 -16680.3003 19459.1855
32 58545.4437 -16680.3003
33 7344.3671 58545.4437
34 30504.6101 7344.3671
35 55232.6340 30504.6101
36 122277.3189 55232.6340
37 -4895.7878 122277.3189
38 -3686.1753 -4895.7878
39 52095.8733 -3686.1753
40 78562.1328 52095.8733
41 4616.3818 78562.1328
42 20559.6559 4616.3818
43 -16570.9022 20559.6559
44 -2699.6740 -16570.9022
45 179039.2308 -2699.6740
46 5849.5098 179039.2308
47 -12033.7590 5849.5098
48 20470.0249 -12033.7590
49 -36865.3276 20470.0249
50 -19035.1774 -36865.3276
51 -53223.9472 -19035.1774
52 63739.6297 -53223.9472
53 -7739.4367 63739.6297
54 12641.1349 -7739.4367
55 11565.3371 12641.1349
56 868.8472 11565.3371
57 -107106.5849 868.8472
58 -3070.8579 -107106.5849
59 32491.2757 -3070.8579
60 58407.3420 32491.2757
61 -27909.8905 58407.3420
62 40313.4999 -27909.8905
63 36780.7485 40313.4999
64 -8588.9365 36780.7485
65 -11599.0041 -8588.9365
66 -45369.7721 -11599.0041
67 188.5952 -45369.7721
68 -36814.6491 188.5952
69 -44436.0465 -36814.6491
70 -5505.1671 -44436.0465
71 -1339.5121 -5505.1671
72 9300.1296 -1339.5121
73 -10014.4894 9300.1296
74 -55260.1296 -10014.4894
75 33130.5684 -55260.1296
76 31391.4577 33130.5684
77 -52644.3617 31391.4577
78 -38348.0905 -52644.3617
79 3021.9624 -38348.0905
80 6592.7499 3021.9624
81 120964.3823 6592.7499
82 50369.5454 120964.3823
83 -44809.8281 50369.5454
84 -40725.2281 -44809.8281
85 36494.7649 -40725.2281
86 -53577.7418 36494.7649
87 109581.5424 -53577.7418
88 72718.2904 109581.5424
89 -85305.3287 72718.2904
90 32795.8683 -85305.3287
91 -63952.4284 32795.8683
92 19641.4326 -63952.4284
93 -124.7386 19641.4326
94 23199.2587 -124.7386
95 -14168.3095 23199.2587
96 -87481.6394 -14168.3095
97 -42733.1963 -87481.6394
98 41323.7692 -42733.1963
99 -43338.5177 41323.7692
100 28708.6947 -43338.5177
101 -42496.1510 28708.6947
102 29371.5594 -42496.1510
103 -63838.4311 29371.5594
104 3432.3227 -63838.4311
105 -86847.0706 3432.3227
106 4464.6651 -86847.0706
107 21960.2071 4464.6651
108 -71932.8492 21960.2071
109 -20828.1496 -71932.8492
110 33277.9037 -20828.1496
111 -10460.1814 33277.9037
112 -27195.7653 -10460.1814
113 -24953.5582 -27195.7653
114 -15644.3492 -24953.5582
115 -14152.9086 -15644.3492
116 48660.9318 -14152.9086
117 -21431.7257 48660.9318
118 27664.7261 -21431.7257
119 -53738.7381 27664.7261
120 47148.6625 -53738.7381
121 -5026.4914 47148.6625
122 -8120.5216 -5026.4914
123 -39153.3422 -8120.5216
124 -20176.4447 -39153.3422
125 -23318.4705 -20176.4447
126 18905.9425 -23318.4705
127 -48834.3317 18905.9425
128 67431.5659 -48834.3317
129 3786.7317 67431.5659
130 3430.7856 3786.7317
131 -714.5113 3430.7856
132 -7135.6011 -714.5113
133 48817.2618 -7135.6011
134 74050.8604 48817.2618
135 -13956.9016 74050.8604
136 54651.3085 -13956.9016
137 -12935.3123 54651.3085
138 3085.4771 -12935.3123
139 -21157.1403 3085.4771
140 -4537.8571 -21157.1403
141 85701.2125 -4537.8571
142 -85801.5893 85701.2125
143 105171.5017 -85801.5893
144 45101.0446 105171.5017
145 -32600.5364 45101.0446
146 -21820.5141 -32600.5364
147 -57005.5042 -21820.5141
148 -37859.5043 -57005.5042
149 -30829.4132 -37859.5043
150 -37761.5043 -30829.4132
151 -37404.5043 -37761.5043
152 -37859.5043 -37404.5043
153 -37859.5043 -37859.5043
154 -2130.7913 -37859.5043
155 16289.2381 -2130.7913
156 -37859.5043 16289.2381
157 -37656.5043 -37859.5043
158 -37736.6604 -37656.5043
159 -7521.6831 -37736.6604
160 -25025.4141 -7521.6831
161 7550.3967 -25025.4141
162 -36890.5043 7550.3967
163 -1483.8534 -36890.5043
164 NA -1483.8534
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -67623.9238 -41060.0658
[2,] -7011.8226 -67623.9238
[3,] -31697.6790 -7011.8226
[4,] -12159.6453 -31697.6790
[5,] -55085.3578 -12159.6453
[6,] 115990.3182 -55085.3578
[7,] -19119.9829 115990.3182
[8,] -2580.9146 -19119.9829
[9,] 9703.1439 -2580.9146
[10,] 13749.3689 9703.1439
[11,] 1971.1094 13749.3689
[12,] -2835.2865 1971.1094
[13,] 8997.6175 -2835.2865
[14,] 15733.3571 8997.6175
[15,] 69839.6142 15733.3571
[16,] -25924.8451 69839.6142
[17,] 65374.5188 -25924.8451
[18,] -74456.4315 65374.5188
[19,] -16824.1069 -74456.4315
[20,] 198.1231 -16824.1069
[21,] 128164.8990 198.1231
[22,] -879.9070 128164.8990
[23,] -55824.3179 -879.9070
[24,] -25288.2370 -55824.3179
[25,] 10502.2176 -25288.2370
[26,] 14575.6522 10502.2176
[27,] 10916.4622 14575.6522
[28,] 14933.0423 10916.4622
[29,] 36330.3122 14933.0423
[30,] 19459.1855 36330.3122
[31,] -16680.3003 19459.1855
[32,] 58545.4437 -16680.3003
[33,] 7344.3671 58545.4437
[34,] 30504.6101 7344.3671
[35,] 55232.6340 30504.6101
[36,] 122277.3189 55232.6340
[37,] -4895.7878 122277.3189
[38,] -3686.1753 -4895.7878
[39,] 52095.8733 -3686.1753
[40,] 78562.1328 52095.8733
[41,] 4616.3818 78562.1328
[42,] 20559.6559 4616.3818
[43,] -16570.9022 20559.6559
[44,] -2699.6740 -16570.9022
[45,] 179039.2308 -2699.6740
[46,] 5849.5098 179039.2308
[47,] -12033.7590 5849.5098
[48,] 20470.0249 -12033.7590
[49,] -36865.3276 20470.0249
[50,] -19035.1774 -36865.3276
[51,] -53223.9472 -19035.1774
[52,] 63739.6297 -53223.9472
[53,] -7739.4367 63739.6297
[54,] 12641.1349 -7739.4367
[55,] 11565.3371 12641.1349
[56,] 868.8472 11565.3371
[57,] -107106.5849 868.8472
[58,] -3070.8579 -107106.5849
[59,] 32491.2757 -3070.8579
[60,] 58407.3420 32491.2757
[61,] -27909.8905 58407.3420
[62,] 40313.4999 -27909.8905
[63,] 36780.7485 40313.4999
[64,] -8588.9365 36780.7485
[65,] -11599.0041 -8588.9365
[66,] -45369.7721 -11599.0041
[67,] 188.5952 -45369.7721
[68,] -36814.6491 188.5952
[69,] -44436.0465 -36814.6491
[70,] -5505.1671 -44436.0465
[71,] -1339.5121 -5505.1671
[72,] 9300.1296 -1339.5121
[73,] -10014.4894 9300.1296
[74,] -55260.1296 -10014.4894
[75,] 33130.5684 -55260.1296
[76,] 31391.4577 33130.5684
[77,] -52644.3617 31391.4577
[78,] -38348.0905 -52644.3617
[79,] 3021.9624 -38348.0905
[80,] 6592.7499 3021.9624
[81,] 120964.3823 6592.7499
[82,] 50369.5454 120964.3823
[83,] -44809.8281 50369.5454
[84,] -40725.2281 -44809.8281
[85,] 36494.7649 -40725.2281
[86,] -53577.7418 36494.7649
[87,] 109581.5424 -53577.7418
[88,] 72718.2904 109581.5424
[89,] -85305.3287 72718.2904
[90,] 32795.8683 -85305.3287
[91,] -63952.4284 32795.8683
[92,] 19641.4326 -63952.4284
[93,] -124.7386 19641.4326
[94,] 23199.2587 -124.7386
[95,] -14168.3095 23199.2587
[96,] -87481.6394 -14168.3095
[97,] -42733.1963 -87481.6394
[98,] 41323.7692 -42733.1963
[99,] -43338.5177 41323.7692
[100,] 28708.6947 -43338.5177
[101,] -42496.1510 28708.6947
[102,] 29371.5594 -42496.1510
[103,] -63838.4311 29371.5594
[104,] 3432.3227 -63838.4311
[105,] -86847.0706 3432.3227
[106,] 4464.6651 -86847.0706
[107,] 21960.2071 4464.6651
[108,] -71932.8492 21960.2071
[109,] -20828.1496 -71932.8492
[110,] 33277.9037 -20828.1496
[111,] -10460.1814 33277.9037
[112,] -27195.7653 -10460.1814
[113,] -24953.5582 -27195.7653
[114,] -15644.3492 -24953.5582
[115,] -14152.9086 -15644.3492
[116,] 48660.9318 -14152.9086
[117,] -21431.7257 48660.9318
[118,] 27664.7261 -21431.7257
[119,] -53738.7381 27664.7261
[120,] 47148.6625 -53738.7381
[121,] -5026.4914 47148.6625
[122,] -8120.5216 -5026.4914
[123,] -39153.3422 -8120.5216
[124,] -20176.4447 -39153.3422
[125,] -23318.4705 -20176.4447
[126,] 18905.9425 -23318.4705
[127,] -48834.3317 18905.9425
[128,] 67431.5659 -48834.3317
[129,] 3786.7317 67431.5659
[130,] 3430.7856 3786.7317
[131,] -714.5113 3430.7856
[132,] -7135.6011 -714.5113
[133,] 48817.2618 -7135.6011
[134,] 74050.8604 48817.2618
[135,] -13956.9016 74050.8604
[136,] 54651.3085 -13956.9016
[137,] -12935.3123 54651.3085
[138,] 3085.4771 -12935.3123
[139,] -21157.1403 3085.4771
[140,] -4537.8571 -21157.1403
[141,] 85701.2125 -4537.8571
[142,] -85801.5893 85701.2125
[143,] 105171.5017 -85801.5893
[144,] 45101.0446 105171.5017
[145,] -32600.5364 45101.0446
[146,] -21820.5141 -32600.5364
[147,] -57005.5042 -21820.5141
[148,] -37859.5043 -57005.5042
[149,] -30829.4132 -37859.5043
[150,] -37761.5043 -30829.4132
[151,] -37404.5043 -37761.5043
[152,] -37859.5043 -37404.5043
[153,] -37859.5043 -37859.5043
[154,] -2130.7913 -37859.5043
[155,] 16289.2381 -2130.7913
[156,] -37859.5043 16289.2381
[157,] -37656.5043 -37859.5043
[158,] -37736.6604 -37656.5043
[159,] -7521.6831 -37736.6604
[160,] -25025.4141 -7521.6831
[161,] 7550.3967 -25025.4141
[162,] -36890.5043 7550.3967
[163,] -1483.8534 -36890.5043
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -67623.9238 -41060.0658
2 -7011.8226 -67623.9238
3 -31697.6790 -7011.8226
4 -12159.6453 -31697.6790
5 -55085.3578 -12159.6453
6 115990.3182 -55085.3578
7 -19119.9829 115990.3182
8 -2580.9146 -19119.9829
9 9703.1439 -2580.9146
10 13749.3689 9703.1439
11 1971.1094 13749.3689
12 -2835.2865 1971.1094
13 8997.6175 -2835.2865
14 15733.3571 8997.6175
15 69839.6142 15733.3571
16 -25924.8451 69839.6142
17 65374.5188 -25924.8451
18 -74456.4315 65374.5188
19 -16824.1069 -74456.4315
20 198.1231 -16824.1069
21 128164.8990 198.1231
22 -879.9070 128164.8990
23 -55824.3179 -879.9070
24 -25288.2370 -55824.3179
25 10502.2176 -25288.2370
26 14575.6522 10502.2176
27 10916.4622 14575.6522
28 14933.0423 10916.4622
29 36330.3122 14933.0423
30 19459.1855 36330.3122
31 -16680.3003 19459.1855
32 58545.4437 -16680.3003
33 7344.3671 58545.4437
34 30504.6101 7344.3671
35 55232.6340 30504.6101
36 122277.3189 55232.6340
37 -4895.7878 122277.3189
38 -3686.1753 -4895.7878
39 52095.8733 -3686.1753
40 78562.1328 52095.8733
41 4616.3818 78562.1328
42 20559.6559 4616.3818
43 -16570.9022 20559.6559
44 -2699.6740 -16570.9022
45 179039.2308 -2699.6740
46 5849.5098 179039.2308
47 -12033.7590 5849.5098
48 20470.0249 -12033.7590
49 -36865.3276 20470.0249
50 -19035.1774 -36865.3276
51 -53223.9472 -19035.1774
52 63739.6297 -53223.9472
53 -7739.4367 63739.6297
54 12641.1349 -7739.4367
55 11565.3371 12641.1349
56 868.8472 11565.3371
57 -107106.5849 868.8472
58 -3070.8579 -107106.5849
59 32491.2757 -3070.8579
60 58407.3420 32491.2757
61 -27909.8905 58407.3420
62 40313.4999 -27909.8905
63 36780.7485 40313.4999
64 -8588.9365 36780.7485
65 -11599.0041 -8588.9365
66 -45369.7721 -11599.0041
67 188.5952 -45369.7721
68 -36814.6491 188.5952
69 -44436.0465 -36814.6491
70 -5505.1671 -44436.0465
71 -1339.5121 -5505.1671
72 9300.1296 -1339.5121
73 -10014.4894 9300.1296
74 -55260.1296 -10014.4894
75 33130.5684 -55260.1296
76 31391.4577 33130.5684
77 -52644.3617 31391.4577
78 -38348.0905 -52644.3617
79 3021.9624 -38348.0905
80 6592.7499 3021.9624
81 120964.3823 6592.7499
82 50369.5454 120964.3823
83 -44809.8281 50369.5454
84 -40725.2281 -44809.8281
85 36494.7649 -40725.2281
86 -53577.7418 36494.7649
87 109581.5424 -53577.7418
88 72718.2904 109581.5424
89 -85305.3287 72718.2904
90 32795.8683 -85305.3287
91 -63952.4284 32795.8683
92 19641.4326 -63952.4284
93 -124.7386 19641.4326
94 23199.2587 -124.7386
95 -14168.3095 23199.2587
96 -87481.6394 -14168.3095
97 -42733.1963 -87481.6394
98 41323.7692 -42733.1963
99 -43338.5177 41323.7692
100 28708.6947 -43338.5177
101 -42496.1510 28708.6947
102 29371.5594 -42496.1510
103 -63838.4311 29371.5594
104 3432.3227 -63838.4311
105 -86847.0706 3432.3227
106 4464.6651 -86847.0706
107 21960.2071 4464.6651
108 -71932.8492 21960.2071
109 -20828.1496 -71932.8492
110 33277.9037 -20828.1496
111 -10460.1814 33277.9037
112 -27195.7653 -10460.1814
113 -24953.5582 -27195.7653
114 -15644.3492 -24953.5582
115 -14152.9086 -15644.3492
116 48660.9318 -14152.9086
117 -21431.7257 48660.9318
118 27664.7261 -21431.7257
119 -53738.7381 27664.7261
120 47148.6625 -53738.7381
121 -5026.4914 47148.6625
122 -8120.5216 -5026.4914
123 -39153.3422 -8120.5216
124 -20176.4447 -39153.3422
125 -23318.4705 -20176.4447
126 18905.9425 -23318.4705
127 -48834.3317 18905.9425
128 67431.5659 -48834.3317
129 3786.7317 67431.5659
130 3430.7856 3786.7317
131 -714.5113 3430.7856
132 -7135.6011 -714.5113
133 48817.2618 -7135.6011
134 74050.8604 48817.2618
135 -13956.9016 74050.8604
136 54651.3085 -13956.9016
137 -12935.3123 54651.3085
138 3085.4771 -12935.3123
139 -21157.1403 3085.4771
140 -4537.8571 -21157.1403
141 85701.2125 -4537.8571
142 -85801.5893 85701.2125
143 105171.5017 -85801.5893
144 45101.0446 105171.5017
145 -32600.5364 45101.0446
146 -21820.5141 -32600.5364
147 -57005.5042 -21820.5141
148 -37859.5043 -57005.5042
149 -30829.4132 -37859.5043
150 -37761.5043 -30829.4132
151 -37404.5043 -37761.5043
152 -37859.5043 -37404.5043
153 -37859.5043 -37859.5043
154 -2130.7913 -37859.5043
155 16289.2381 -2130.7913
156 -37859.5043 16289.2381
157 -37656.5043 -37859.5043
158 -37736.6604 -37656.5043
159 -7521.6831 -37736.6604
160 -25025.4141 -7521.6831
161 7550.3967 -25025.4141
162 -36890.5043 7550.3967
163 -1483.8534 -36890.5043
> 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/7ohau1321994336.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/8hn091321994336.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/97fep1321994336.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/109aeo1321994336.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11i4e81321994336.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/121uit1321994337.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/136p4u1321994337.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/141n1p1321994337.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/154cjl1321994337.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/160ju71321994337.tab")
+ }
>
> try(system("convert tmp/1rjf51321994336.ps tmp/1rjf51321994336.png",intern=TRUE))
character(0)
> try(system("convert tmp/2huwp1321994336.ps tmp/2huwp1321994336.png",intern=TRUE))
character(0)
> try(system("convert tmp/3e8pq1321994336.ps tmp/3e8pq1321994336.png",intern=TRUE))
character(0)
> try(system("convert tmp/4pxf91321994336.ps tmp/4pxf91321994336.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rc7b1321994336.ps tmp/5rc7b1321994336.png",intern=TRUE))
character(0)
> try(system("convert tmp/6obi61321994336.ps tmp/6obi61321994336.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ohau1321994336.ps tmp/7ohau1321994336.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hn091321994336.ps tmp/8hn091321994336.png",intern=TRUE))
character(0)
> try(system("convert tmp/97fep1321994336.ps tmp/97fep1321994336.png",intern=TRUE))
character(0)
> try(system("convert tmp/109aeo1321994336.ps tmp/109aeo1321994336.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.655 0.502 5.219