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)
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> x <- array(list(65
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+ ,dim=c(6
+ ,164)
+ ,dimnames=list(c('BloggedComputation'
+ ,'TotalTime'
+ ,'Shared'
+ ,'Charachters'
+ ,'Writing'
+ ,'Hyperlinks')
+ ,1:164))
> y <- array(NA,dim=c(6,164),dimnames=list(c('BloggedComputation','TotalTime','Shared','Charachters','Writing','Hyperlinks'),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 = '6'
> #'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
Hyperlinks BloggedComputation TotalTime Shared Charachters Writing t
1 127 65 146455 1 95556 114468 1
2 90 54 84944 4 54565 88594 2
3 68 58 113337 9 63016 74151 3
4 111 75 128655 2 79774 77921 4
5 51 41 74398 1 31258 53212 5
6 33 0 35523 2 52491 34956 6
7 123 111 293403 0 91256 149703 7
8 5 1 32750 0 22807 6853 8
9 63 36 106539 5 77411 58907 9
10 66 60 130539 0 48821 67067 10
11 99 63 154991 0 52295 110563 11
12 72 71 126683 7 63262 58126 12
13 55 38 100672 6 50466 57113 13
14 116 76 179562 3 62932 77993 14
15 71 61 125971 4 38439 68091 15
16 125 125 234509 0 70817 124676 16
17 123 84 158980 4 105965 109522 17
18 74 69 184217 3 73795 75865 18
19 116 77 107342 0 82043 79746 19
20 117 95 141371 5 74349 77844 20
21 98 78 154730 0 82204 98681 21
22 101 76 264020 1 55709 105531 22
23 43 40 90938 3 37137 51428 23
24 103 81 101324 5 70780 65703 24
25 107 102 130232 0 55027 72562 25
26 77 70 137793 0 56699 81728 26
27 87 75 161678 4 65911 95580 27
28 99 93 151503 0 56316 98278 28
29 46 42 105324 0 26982 46629 29
30 96 95 175914 0 54628 115189 30
31 92 87 181853 3 96750 124865 31
32 96 44 114928 4 53009 59392 32
33 96 84 190410 1 64664 127818 33
34 15 28 61499 4 36990 17821 34
35 147 87 223004 1 85224 154076 35
36 56 71 167131 0 37048 64881 36
37 81 68 233482 0 59635 136506 37
38 69 50 121185 2 42051 66524 38
39 34 30 78776 1 26998 45988 39
40 98 86 188967 2 63717 107445 40
41 82 75 199512 8 55071 102772 41
42 64 46 102531 5 40001 46657 42
43 61 52 118958 3 54506 97563 43
44 45 31 68948 4 35838 36663 44
45 37 30 93125 1 50838 55369 45
46 64 70 277108 2 86997 77921 46
47 21 20 78800 2 33032 56968 47
48 104 84 157250 0 61704 77519 48
49 126 81 210554 6 117986 129805 49
50 104 79 127324 3 56733 72761 50
51 87 70 114397 0 55064 81278 51
52 7 8 24188 0 5950 15049 52
53 130 67 246209 6 84607 113935 53
54 21 21 65029 5 32551 25109 54
55 35 30 98030 3 31701 45824 55
56 97 70 173587 1 71170 89644 56
57 103 87 172684 5 101773 109011 57
58 210 87 191381 5 101653 134245 58
59 151 112 191276 0 81493 136692 59
60 57 54 134043 9 55901 50741 60
61 117 96 233406 6 109104 149510 61
62 152 93 195304 6 114425 147888 62
63 52 49 127619 5 36311 54987 63
64 83 49 162810 6 70027 74467 64
65 87 38 129100 2 73713 100033 65
66 80 64 108715 0 40671 85505 66
67 88 62 106469 3 89041 62426 67
68 83 66 142069 8 57231 82932 68
69 140 98 143937 2 78792 79169 69
70 76 97 84256 5 59155 65469 70
71 70 56 118807 11 55827 63572 71
72 26 22 69471 6 22618 23824 72
73 66 51 122433 5 58425 73831 73
74 89 56 131122 1 65724 63551 74
75 100 94 94763 0 56979 56756 75
76 98 98 188780 3 72369 81399 76
77 109 76 191467 3 79194 117881 77
78 51 57 105615 6 202316 70711 78
79 82 75 89318 1 44970 50495 79
80 65 48 107335 0 49319 53845 80
81 46 48 98599 1 36252 51390 81
82 104 109 260646 0 75741 104953 82
83 36 27 131876 5 38417 65983 83
84 123 83 119291 2 64102 76839 84
85 59 49 80953 0 56622 55792 85
86 27 24 99768 0 15430 25155 86
87 84 43 84572 5 72571 55291 87
88 61 44 202373 1 67271 84279 88
89 46 49 166790 0 43460 99692 89
90 125 106 99946 1 99501 59633 90
91 58 42 116900 1 28340 63249 91
92 152 108 142146 2 76013 82928 92
93 52 27 99246 4 37361 50000 93
94 85 79 156833 1 48204 69455 94
95 95 49 175078 4 76168 84068 95
96 78 64 130533 0 85168 76195 96
97 144 75 142339 2 125410 114634 97
98 149 115 176789 0 123328 139357 98
99 101 92 181379 7 83038 110044 99
100 205 106 228548 7 120087 155118 100
101 61 73 142141 6 91939 83061 101
102 145 105 167845 0 103646 127122 102
103 28 30 103012 0 29467 45653 103
104 49 13 43287 4 43750 19630 104
105 68 69 125366 4 34497 67229 105
106 142 72 118372 0 66477 86060 106
107 82 80 135171 0 71181 88003 107
108 105 106 175568 0 74482 95815 108
109 52 28 74112 0 174949 85499 109
110 56 70 88817 0 46765 27220 110
111 81 51 164767 4 90257 109882 111
112 100 90 141933 0 51370 72579 112
113 11 12 22938 0 1168 5841 113
114 87 84 115199 0 51360 68369 114
115 31 23 61857 4 25162 24610 115
116 67 57 91185 0 21067 30995 116
117 150 84 213765 1 58233 150662 117
118 4 4 21054 0 855 6622 118
119 75 56 167105 5 85903 93694 119
120 39 18 31414 0 14116 13155 120
121 88 86 178863 1 57637 111908 121
122 67 39 126681 7 94137 57550 122
123 24 16 64320 5 62147 16356 123
124 58 18 67746 2 62832 40174 124
125 16 16 38214 0 8773 13983 125
126 49 42 90961 1 63785 52316 126
127 109 75 181510 0 65196 99585 127
128 124 30 116775 0 73087 86271 128
129 115 104 223914 2 72631 131012 129
130 128 121 185139 0 86281 130274 130
131 159 106 242879 2 162365 159051 131
132 75 57 139144 0 56530 76506 132
133 30 28 75812 0 35606 49145 133
134 83 56 178218 4 70111 66398 134
135 135 81 246834 4 92046 127546 135
136 8 2 50999 8 63989 6802 136
137 115 88 223842 0 104911 99509 137
138 60 41 93577 4 43448 43106 138
139 99 83 155383 0 60029 108303 139
140 98 55 111664 1 38650 64167 140
141 36 3 75426 0 47261 8579 141
142 93 54 243551 9 73586 97811 142
143 158 89 136548 0 83042 84365 143
144 16 41 173260 3 37238 10901 144
145 100 94 185039 7 63958 91346 145
146 49 101 67507 5 78956 33660 146
147 89 70 139350 2 99518 93634 147
148 153 111 172964 1 111436 109348 148
149 0 0 0 9 0 0 149
150 5 4 14688 0 6023 7953 150
151 0 0 98 0 0 0 151
152 0 0 455 0 0 0 152
153 0 0 0 1 0 0 153
154 0 0 0 0 0 0 154
155 80 42 128066 2 42564 63538 155
156 122 97 176460 1 38885 108281 156
157 0 0 0 0 0 0 157
158 0 0 203 0 0 0 158
159 6 7 7199 0 1644 4245 159
160 13 12 46660 0 6179 21509 160
161 3 0 17547 0 3926 7670 161
162 18 37 73567 0 23238 10641 162
163 0 0 969 0 0 0 163
164 49 39 101060 2 49288 41243 164
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) BloggedComputation TotalTime Shared
-2.7209106 0.6431806 -0.0001005 0.1717732
Charachters Writing t
0.0001467 0.0005940 0.0379900
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-47.998 -10.459 -3.000 8.224 78.286
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.721e+00 5.025e+00 -0.541 0.5890
BloggedComputation 6.432e-01 8.075e-02 7.965 3.14e-13 ***
TotalTime -1.005e-04 4.922e-05 -2.042 0.0428 *
Shared 1.718e-01 5.990e-01 0.287 0.7747
Charachters 1.467e-04 6.638e-05 2.210 0.0286 *
Writing 5.940e-04 9.269e-05 6.408 1.64e-09 ***
t 3.799e-02 3.246e-02 1.170 0.2437
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 18.47 on 157 degrees of freedom
Multiple R-squared: 0.8299, Adjusted R-squared: 0.8234
F-statistic: 127.7 on 6 and 157 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,] 0.001061716 2.123433e-03 9.989383e-01
[2,] 0.005289433 1.057887e-02 9.947106e-01
[3,] 0.001043034 2.086069e-03 9.989570e-01
[4,] 0.000537576 1.075152e-03 9.994624e-01
[5,] 0.141199752 2.823995e-01 8.588002e-01
[6,] 0.084088234 1.681765e-01 9.159118e-01
[7,] 0.129726587 2.594532e-01 8.702734e-01
[8,] 0.109718896 2.194378e-01 8.902811e-01
[9,] 0.076282400 1.525648e-01 9.237176e-01
[10,] 0.048638823 9.727765e-02 9.513612e-01
[11,] 0.032498313 6.499663e-02 9.675017e-01
[12,] 0.030153004 6.030601e-02 9.698470e-01
[13,] 0.043449876 8.689975e-02 9.565501e-01
[14,] 0.033066450 6.613290e-02 9.669336e-01
[15,] 0.022177352 4.435470e-02 9.778226e-01
[16,] 0.013494005 2.698801e-02 9.865060e-01
[17,] 0.010662790 2.132558e-02 9.893372e-01
[18,] 0.006959717 1.391943e-02 9.930403e-01
[19,] 0.004429182 8.858363e-03 9.955708e-01
[20,] 0.002590166 5.180331e-03 9.974098e-01
[21,] 0.002030559 4.061117e-03 9.979694e-01
[22,] 0.004790914 9.581827e-03 9.952091e-01
[23,] 0.050360959 1.007219e-01 9.496390e-01
[24,] 0.039210331 7.842066e-02 9.607897e-01
[25,] 0.041418579 8.283716e-02 9.585814e-01
[26,] 0.064002923 1.280058e-01 9.359971e-01
[27,] 0.052338115 1.046762e-01 9.476619e-01
[28,] 0.049010241 9.802048e-02 9.509898e-01
[29,] 0.040815707 8.163141e-02 9.591843e-01
[30,] 0.029561157 5.912231e-02 9.704388e-01
[31,] 0.021483157 4.296631e-02 9.785168e-01
[32,] 0.015848403 3.169681e-02 9.841516e-01
[33,] 0.014751323 2.950265e-02 9.852487e-01
[34,] 0.014972893 2.994579e-02 9.850271e-01
[35,] 0.011105411 2.221082e-02 9.888946e-01
[36,] 0.009064181 1.812836e-02 9.909358e-01
[37,] 0.007611128 1.522226e-02 9.923889e-01
[38,] 0.006995480 1.399096e-02 9.930045e-01
[39,] 0.006880778 1.376156e-02 9.931192e-01
[40,] 0.004878708 9.757416e-03 9.951213e-01
[41,] 0.005234912 1.046982e-02 9.947651e-01
[42,] 0.003584101 7.168201e-03 9.964159e-01
[43,] 0.002421707 4.843414e-03 9.975783e-01
[44,] 0.011476501 2.295300e-02 9.885235e-01
[45,] 0.008495672 1.699134e-02 9.915043e-01
[46,] 0.006056543 1.211309e-02 9.939435e-01
[47,] 0.004499156 8.998312e-03 9.955008e-01
[48,] 0.004732087 9.464174e-03 9.952679e-01
[49,] 0.371922126 7.438443e-01 6.280779e-01
[50,] 0.330756254 6.615125e-01 6.692437e-01
[51,] 0.295253975 5.905079e-01 7.047460e-01
[52,] 0.369644423 7.392888e-01 6.303556e-01
[53,] 0.326778751 6.535575e-01 6.732212e-01
[54,] 0.286528615 5.730572e-01 7.134714e-01
[55,] 0.260176913 5.203538e-01 7.398231e-01
[56,] 0.224264184 4.485284e-01 7.757358e-01
[57,] 0.193961452 3.879229e-01 8.060385e-01
[58,] 0.168986720 3.379734e-01 8.310133e-01
[59,] 0.141117535 2.822351e-01 8.588825e-01
[60,] 0.180408398 3.608168e-01 8.195916e-01
[61,] 0.228596264 4.571925e-01 7.714037e-01
[62,] 0.194445406 3.888908e-01 8.055546e-01
[63,] 0.164668620 3.293372e-01 8.353314e-01
[64,] 0.142111995 2.842240e-01 8.578880e-01
[65,] 0.133839547 2.676791e-01 8.661605e-01
[66,] 0.112115984 2.242320e-01 8.878840e-01
[67,] 0.094646624 1.892932e-01 9.053534e-01
[68,] 0.077078213 1.541564e-01 9.229218e-01
[69,] 0.365296948 7.305939e-01 6.347031e-01
[70,] 0.326812234 6.536245e-01 6.731878e-01
[71,] 0.290574500 5.811490e-01 7.094255e-01
[72,] 0.264907597 5.298152e-01 7.350924e-01
[73,] 0.247453020 4.949060e-01 7.525470e-01
[74,] 0.233216795 4.664336e-01 7.667832e-01
[75,] 0.265665566 5.313311e-01 7.343344e-01
[76,] 0.233075223 4.661504e-01 7.669248e-01
[77,] 0.201793267 4.035865e-01 7.982067e-01
[78,] 0.206708871 4.134177e-01 7.932911e-01
[79,] 0.185581265 3.711625e-01 8.144187e-01
[80,] 0.297252204 5.945044e-01 7.027478e-01
[81,] 0.300041150 6.000823e-01 6.999588e-01
[82,] 0.267716749 5.354335e-01 7.322833e-01
[83,] 0.401143231 8.022865e-01 5.988568e-01
[84,] 0.364103093 7.282062e-01 6.358969e-01
[85,] 0.321096387 6.421928e-01 6.789036e-01
[86,] 0.312623218 6.252464e-01 6.873768e-01
[87,] 0.282593161 5.651863e-01 7.174068e-01
[88,] 0.305154132 6.103083e-01 6.948459e-01
[89,] 0.272989533 5.459791e-01 7.270105e-01
[90,] 0.273219095 5.464382e-01 7.267809e-01
[91,] 0.554101806 8.917964e-01 4.458982e-01
[92,] 0.656085401 6.878292e-01 3.439146e-01
[93,] 0.615444265 7.691115e-01 3.845557e-01
[94,] 0.623881624 7.522368e-01 3.761184e-01
[95,] 0.670703121 6.585938e-01 3.292969e-01
[96,] 0.637141006 7.257180e-01 3.628590e-01
[97,] 0.860776808 2.784464e-01 1.392232e-01
[98,] 0.859501737 2.809965e-01 1.404983e-01
[99,] 0.845692798 3.086144e-01 1.543072e-01
[100,] 0.921683645 1.566327e-01 7.831635e-02
[101,] 0.901998065 1.960039e-01 9.800193e-02
[102,] 0.914753153 1.704937e-01 8.524685e-02
[103,] 0.897730003 2.045400e-01 1.022700e-01
[104,] 0.872789670 2.544207e-01 1.272103e-01
[105,] 0.844801270 3.103975e-01 1.551987e-01
[106,] 0.812714574 3.745709e-01 1.872854e-01
[107,] 0.835494456 3.290111e-01 1.645055e-01
[108,] 0.822763867 3.544723e-01 1.772361e-01
[109,] 0.785742547 4.285149e-01 2.142575e-01
[110,] 0.791913464 4.161731e-01 2.080865e-01
[111,] 0.818421981 3.631560e-01 1.815780e-01
[112,] 0.847800709 3.043986e-01 1.521993e-01
[113,] 0.813200180 3.735996e-01 1.867998e-01
[114,] 0.773197762 4.536045e-01 2.268022e-01
[115,] 0.753324018 4.933520e-01 2.466760e-01
[116,] 0.706519597 5.869608e-01 2.934804e-01
[117,] 0.682039783 6.359204e-01 3.179602e-01
[118,] 0.632532746 7.349345e-01 3.674673e-01
[119,] 0.844143482 3.117130e-01 1.558565e-01
[120,] 0.854580986 2.908380e-01 1.454190e-01
[121,] 0.870614238 2.587715e-01 1.293858e-01
[122,] 0.878874560 2.422509e-01 1.211254e-01
[123,] 0.861611685 2.767766e-01 1.383883e-01
[124,] 0.916159286 1.676814e-01 8.384071e-02
[125,] 0.889725610 2.205488e-01 1.102744e-01
[126,] 0.862239834 2.755203e-01 1.377602e-01
[127,] 0.823729528 3.525409e-01 1.762705e-01
[128,] 0.816981504 3.660370e-01 1.830185e-01
[129,] 0.768362225 4.632755e-01 2.316378e-01
[130,] 0.934358498 1.312830e-01 6.564150e-02
[131,] 0.915741229 1.685175e-01 8.425877e-02
[132,] 0.905969226 1.880615e-01 9.403077e-02
[133,] 0.868164303 2.636714e-01 1.318357e-01
[134,] 0.998918632 2.162735e-03 1.081368e-03
[135,] 0.997608589 4.782822e-03 2.391411e-03
[136,] 0.996170356 7.659289e-03 3.829644e-03
[137,] 0.997785894 4.428213e-03 2.214106e-03
[138,] 0.999997264 5.471054e-06 2.735527e-06
[139,] 0.999986343 2.731438e-05 1.365719e-05
[140,] 0.999958946 8.210851e-05 4.105426e-05
[141,] 0.999880118 2.397647e-04 1.198823e-04
[142,] 0.999458495 1.083011e-03 5.415054e-04
[143,] 0.997803745 4.392509e-03 2.196255e-03
[144,] 0.998468472 3.063056e-03 1.531528e-03
[145,] 0.991173254 1.765349e-02 8.826746e-03
> postscript(file="/var/wessaorg/rcomp/tmp/1dmc81321619737.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/280of1321619737.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ddju1321619737.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4jnze1321619737.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5jw321321619737.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
20.4144968 5.1356000 -10.1408447 19.9315388 -1.7261610 10.2563539
7 8 9 10 11 12
-18.7566640 2.6493334 5.7282026 -4.1280838 3.0162124 -3.6765137
13 14 15 16 17 18
0.5466141 31.2811753 -0.1927441 -14.1585373 5.7407796 -6.2301313
19 20 21 22 23 24
19.8598153 14.0643946 -5.3671950 9.5119351 -8.2506160 12.6270040
25 26 27 28 29 30
5.0832797 -9.3027647 -10.4223394 -10.5682739 -0.4634431 -22.2744269
31 32 33 34 35 36
-33.0117271 37.0154044 -23.0018888 -12.0972091 10.6556426 -15.4874720
37 38 39 40 41 42
-27.7848847 4.2719115 -7.5869040 -10.6306855 -15.5204342 11.4039741
43 44 45 46 47 48
-25.8639447 5.3190523 -12.4419582 -11.5865867 -22.0355380 11.5784845
49 50 51 52 53 54
0.4835290 14.7506184 -2.0969264 -4.7806533 31.2428361 -5.8495668
55 56 57 58 59 60
-6.1955317 6.1586395 -15.5842826 78.2857957 5.5203915 -3.7033372
61 62 63 64 65 66
-26.7248871 6.5199225 -5.2084089 12.6021185 5.2113065 -6.7782777
67 68 69 70 71 72
8.3423273 -4.0632317 32.6077851 -26.2826685 -1.8931644 0.3184846
73 74 75 76 77 78
-7.8330123 18.5090613 6.8665006 -5.7051281 -2.9942354 -47.9984250
79 80 81 82 83 84
5.6965070 5.3792354 -11.3335320 -13.7549565 -14.2308249 25.7473451
85 86 87 88 89 90
-6.3333498 3.8398818 19.9126551 -7.6823378 -35.0032647 15.9811610
91 92 93 94 95 96
0.1015673 35.2970335 7.9300582 0.6034311 18.3972977 -8.7222741
97 98 99 100 101 102
22.2722654 -9.0666519 -19.7307168 47.7594337 -36.6363497 2.4688790
103 104 105 106 107 108
-13.5737108 24.9945374 -10.7278260 45.4121415 -19.9269740 -14.7518192
109 110 111 112 113 114
-36.4290882 -4.5820221 -15.9332906 4.1987372 0.3745422 -5.2030389
115 116 117 118 119 120
1.7799324 16.3167339 17.5285990 -2.2773187 -15.1356676 18.8576514
121 122 123 124 125 126
-26.3107695 3.5392219 -1.4684896 17.8186557 -2.0705221 -11.5405718
127 128 129 130 131 132
8.1850232 52.3344707 -20.3827037 -23.4724779 -5.6569323 -3.7059579
133 134 135 136 137 138
-17.1356919 12.1133606 15.3534399 -5.4072632 3.9179124 7.8482549
139 140 141 142 143 144
-14.4628092 27.2947691 26.9872832 9.6347898 49.4762052 -8.1584807
145 146 147 148 149 150
-9.4914396 -44.4364334 -15.4398232 14.6198784 -4.4855645 -4.6815508
151 152 153 154 155 156
-3.0057359 -3.0078436 -3.2633392 -3.1295560 18.3625406 3.9482171
157 158 159 160 161 162
-3.2435262 -3.2611126 -3.8608477 -7.0684031 -3.7636492 -11.5663555
163 164
-3.3740717 -1.5075255
> postscript(file="/var/wessaorg/rcomp/tmp/628641321619737.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 20.4144968 NA
1 5.1356000 20.4144968
2 -10.1408447 5.1356000
3 19.9315388 -10.1408447
4 -1.7261610 19.9315388
5 10.2563539 -1.7261610
6 -18.7566640 10.2563539
7 2.6493334 -18.7566640
8 5.7282026 2.6493334
9 -4.1280838 5.7282026
10 3.0162124 -4.1280838
11 -3.6765137 3.0162124
12 0.5466141 -3.6765137
13 31.2811753 0.5466141
14 -0.1927441 31.2811753
15 -14.1585373 -0.1927441
16 5.7407796 -14.1585373
17 -6.2301313 5.7407796
18 19.8598153 -6.2301313
19 14.0643946 19.8598153
20 -5.3671950 14.0643946
21 9.5119351 -5.3671950
22 -8.2506160 9.5119351
23 12.6270040 -8.2506160
24 5.0832797 12.6270040
25 -9.3027647 5.0832797
26 -10.4223394 -9.3027647
27 -10.5682739 -10.4223394
28 -0.4634431 -10.5682739
29 -22.2744269 -0.4634431
30 -33.0117271 -22.2744269
31 37.0154044 -33.0117271
32 -23.0018888 37.0154044
33 -12.0972091 -23.0018888
34 10.6556426 -12.0972091
35 -15.4874720 10.6556426
36 -27.7848847 -15.4874720
37 4.2719115 -27.7848847
38 -7.5869040 4.2719115
39 -10.6306855 -7.5869040
40 -15.5204342 -10.6306855
41 11.4039741 -15.5204342
42 -25.8639447 11.4039741
43 5.3190523 -25.8639447
44 -12.4419582 5.3190523
45 -11.5865867 -12.4419582
46 -22.0355380 -11.5865867
47 11.5784845 -22.0355380
48 0.4835290 11.5784845
49 14.7506184 0.4835290
50 -2.0969264 14.7506184
51 -4.7806533 -2.0969264
52 31.2428361 -4.7806533
53 -5.8495668 31.2428361
54 -6.1955317 -5.8495668
55 6.1586395 -6.1955317
56 -15.5842826 6.1586395
57 78.2857957 -15.5842826
58 5.5203915 78.2857957
59 -3.7033372 5.5203915
60 -26.7248871 -3.7033372
61 6.5199225 -26.7248871
62 -5.2084089 6.5199225
63 12.6021185 -5.2084089
64 5.2113065 12.6021185
65 -6.7782777 5.2113065
66 8.3423273 -6.7782777
67 -4.0632317 8.3423273
68 32.6077851 -4.0632317
69 -26.2826685 32.6077851
70 -1.8931644 -26.2826685
71 0.3184846 -1.8931644
72 -7.8330123 0.3184846
73 18.5090613 -7.8330123
74 6.8665006 18.5090613
75 -5.7051281 6.8665006
76 -2.9942354 -5.7051281
77 -47.9984250 -2.9942354
78 5.6965070 -47.9984250
79 5.3792354 5.6965070
80 -11.3335320 5.3792354
81 -13.7549565 -11.3335320
82 -14.2308249 -13.7549565
83 25.7473451 -14.2308249
84 -6.3333498 25.7473451
85 3.8398818 -6.3333498
86 19.9126551 3.8398818
87 -7.6823378 19.9126551
88 -35.0032647 -7.6823378
89 15.9811610 -35.0032647
90 0.1015673 15.9811610
91 35.2970335 0.1015673
92 7.9300582 35.2970335
93 0.6034311 7.9300582
94 18.3972977 0.6034311
95 -8.7222741 18.3972977
96 22.2722654 -8.7222741
97 -9.0666519 22.2722654
98 -19.7307168 -9.0666519
99 47.7594337 -19.7307168
100 -36.6363497 47.7594337
101 2.4688790 -36.6363497
102 -13.5737108 2.4688790
103 24.9945374 -13.5737108
104 -10.7278260 24.9945374
105 45.4121415 -10.7278260
106 -19.9269740 45.4121415
107 -14.7518192 -19.9269740
108 -36.4290882 -14.7518192
109 -4.5820221 -36.4290882
110 -15.9332906 -4.5820221
111 4.1987372 -15.9332906
112 0.3745422 4.1987372
113 -5.2030389 0.3745422
114 1.7799324 -5.2030389
115 16.3167339 1.7799324
116 17.5285990 16.3167339
117 -2.2773187 17.5285990
118 -15.1356676 -2.2773187
119 18.8576514 -15.1356676
120 -26.3107695 18.8576514
121 3.5392219 -26.3107695
122 -1.4684896 3.5392219
123 17.8186557 -1.4684896
124 -2.0705221 17.8186557
125 -11.5405718 -2.0705221
126 8.1850232 -11.5405718
127 52.3344707 8.1850232
128 -20.3827037 52.3344707
129 -23.4724779 -20.3827037
130 -5.6569323 -23.4724779
131 -3.7059579 -5.6569323
132 -17.1356919 -3.7059579
133 12.1133606 -17.1356919
134 15.3534399 12.1133606
135 -5.4072632 15.3534399
136 3.9179124 -5.4072632
137 7.8482549 3.9179124
138 -14.4628092 7.8482549
139 27.2947691 -14.4628092
140 26.9872832 27.2947691
141 9.6347898 26.9872832
142 49.4762052 9.6347898
143 -8.1584807 49.4762052
144 -9.4914396 -8.1584807
145 -44.4364334 -9.4914396
146 -15.4398232 -44.4364334
147 14.6198784 -15.4398232
148 -4.4855645 14.6198784
149 -4.6815508 -4.4855645
150 -3.0057359 -4.6815508
151 -3.0078436 -3.0057359
152 -3.2633392 -3.0078436
153 -3.1295560 -3.2633392
154 18.3625406 -3.1295560
155 3.9482171 18.3625406
156 -3.2435262 3.9482171
157 -3.2611126 -3.2435262
158 -3.8608477 -3.2611126
159 -7.0684031 -3.8608477
160 -3.7636492 -7.0684031
161 -11.5663555 -3.7636492
162 -3.3740717 -11.5663555
163 -1.5075255 -3.3740717
164 NA -1.5075255
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 5.1356000 20.4144968
[2,] -10.1408447 5.1356000
[3,] 19.9315388 -10.1408447
[4,] -1.7261610 19.9315388
[5,] 10.2563539 -1.7261610
[6,] -18.7566640 10.2563539
[7,] 2.6493334 -18.7566640
[8,] 5.7282026 2.6493334
[9,] -4.1280838 5.7282026
[10,] 3.0162124 -4.1280838
[11,] -3.6765137 3.0162124
[12,] 0.5466141 -3.6765137
[13,] 31.2811753 0.5466141
[14,] -0.1927441 31.2811753
[15,] -14.1585373 -0.1927441
[16,] 5.7407796 -14.1585373
[17,] -6.2301313 5.7407796
[18,] 19.8598153 -6.2301313
[19,] 14.0643946 19.8598153
[20,] -5.3671950 14.0643946
[21,] 9.5119351 -5.3671950
[22,] -8.2506160 9.5119351
[23,] 12.6270040 -8.2506160
[24,] 5.0832797 12.6270040
[25,] -9.3027647 5.0832797
[26,] -10.4223394 -9.3027647
[27,] -10.5682739 -10.4223394
[28,] -0.4634431 -10.5682739
[29,] -22.2744269 -0.4634431
[30,] -33.0117271 -22.2744269
[31,] 37.0154044 -33.0117271
[32,] -23.0018888 37.0154044
[33,] -12.0972091 -23.0018888
[34,] 10.6556426 -12.0972091
[35,] -15.4874720 10.6556426
[36,] -27.7848847 -15.4874720
[37,] 4.2719115 -27.7848847
[38,] -7.5869040 4.2719115
[39,] -10.6306855 -7.5869040
[40,] -15.5204342 -10.6306855
[41,] 11.4039741 -15.5204342
[42,] -25.8639447 11.4039741
[43,] 5.3190523 -25.8639447
[44,] -12.4419582 5.3190523
[45,] -11.5865867 -12.4419582
[46,] -22.0355380 -11.5865867
[47,] 11.5784845 -22.0355380
[48,] 0.4835290 11.5784845
[49,] 14.7506184 0.4835290
[50,] -2.0969264 14.7506184
[51,] -4.7806533 -2.0969264
[52,] 31.2428361 -4.7806533
[53,] -5.8495668 31.2428361
[54,] -6.1955317 -5.8495668
[55,] 6.1586395 -6.1955317
[56,] -15.5842826 6.1586395
[57,] 78.2857957 -15.5842826
[58,] 5.5203915 78.2857957
[59,] -3.7033372 5.5203915
[60,] -26.7248871 -3.7033372
[61,] 6.5199225 -26.7248871
[62,] -5.2084089 6.5199225
[63,] 12.6021185 -5.2084089
[64,] 5.2113065 12.6021185
[65,] -6.7782777 5.2113065
[66,] 8.3423273 -6.7782777
[67,] -4.0632317 8.3423273
[68,] 32.6077851 -4.0632317
[69,] -26.2826685 32.6077851
[70,] -1.8931644 -26.2826685
[71,] 0.3184846 -1.8931644
[72,] -7.8330123 0.3184846
[73,] 18.5090613 -7.8330123
[74,] 6.8665006 18.5090613
[75,] -5.7051281 6.8665006
[76,] -2.9942354 -5.7051281
[77,] -47.9984250 -2.9942354
[78,] 5.6965070 -47.9984250
[79,] 5.3792354 5.6965070
[80,] -11.3335320 5.3792354
[81,] -13.7549565 -11.3335320
[82,] -14.2308249 -13.7549565
[83,] 25.7473451 -14.2308249
[84,] -6.3333498 25.7473451
[85,] 3.8398818 -6.3333498
[86,] 19.9126551 3.8398818
[87,] -7.6823378 19.9126551
[88,] -35.0032647 -7.6823378
[89,] 15.9811610 -35.0032647
[90,] 0.1015673 15.9811610
[91,] 35.2970335 0.1015673
[92,] 7.9300582 35.2970335
[93,] 0.6034311 7.9300582
[94,] 18.3972977 0.6034311
[95,] -8.7222741 18.3972977
[96,] 22.2722654 -8.7222741
[97,] -9.0666519 22.2722654
[98,] -19.7307168 -9.0666519
[99,] 47.7594337 -19.7307168
[100,] -36.6363497 47.7594337
[101,] 2.4688790 -36.6363497
[102,] -13.5737108 2.4688790
[103,] 24.9945374 -13.5737108
[104,] -10.7278260 24.9945374
[105,] 45.4121415 -10.7278260
[106,] -19.9269740 45.4121415
[107,] -14.7518192 -19.9269740
[108,] -36.4290882 -14.7518192
[109,] -4.5820221 -36.4290882
[110,] -15.9332906 -4.5820221
[111,] 4.1987372 -15.9332906
[112,] 0.3745422 4.1987372
[113,] -5.2030389 0.3745422
[114,] 1.7799324 -5.2030389
[115,] 16.3167339 1.7799324
[116,] 17.5285990 16.3167339
[117,] -2.2773187 17.5285990
[118,] -15.1356676 -2.2773187
[119,] 18.8576514 -15.1356676
[120,] -26.3107695 18.8576514
[121,] 3.5392219 -26.3107695
[122,] -1.4684896 3.5392219
[123,] 17.8186557 -1.4684896
[124,] -2.0705221 17.8186557
[125,] -11.5405718 -2.0705221
[126,] 8.1850232 -11.5405718
[127,] 52.3344707 8.1850232
[128,] -20.3827037 52.3344707
[129,] -23.4724779 -20.3827037
[130,] -5.6569323 -23.4724779
[131,] -3.7059579 -5.6569323
[132,] -17.1356919 -3.7059579
[133,] 12.1133606 -17.1356919
[134,] 15.3534399 12.1133606
[135,] -5.4072632 15.3534399
[136,] 3.9179124 -5.4072632
[137,] 7.8482549 3.9179124
[138,] -14.4628092 7.8482549
[139,] 27.2947691 -14.4628092
[140,] 26.9872832 27.2947691
[141,] 9.6347898 26.9872832
[142,] 49.4762052 9.6347898
[143,] -8.1584807 49.4762052
[144,] -9.4914396 -8.1584807
[145,] -44.4364334 -9.4914396
[146,] -15.4398232 -44.4364334
[147,] 14.6198784 -15.4398232
[148,] -4.4855645 14.6198784
[149,] -4.6815508 -4.4855645
[150,] -3.0057359 -4.6815508
[151,] -3.0078436 -3.0057359
[152,] -3.2633392 -3.0078436
[153,] -3.1295560 -3.2633392
[154,] 18.3625406 -3.1295560
[155,] 3.9482171 18.3625406
[156,] -3.2435262 3.9482171
[157,] -3.2611126 -3.2435262
[158,] -3.8608477 -3.2611126
[159,] -7.0684031 -3.8608477
[160,] -3.7636492 -7.0684031
[161,] -11.5663555 -3.7636492
[162,] -3.3740717 -11.5663555
[163,] -1.5075255 -3.3740717
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 5.1356000 20.4144968
2 -10.1408447 5.1356000
3 19.9315388 -10.1408447
4 -1.7261610 19.9315388
5 10.2563539 -1.7261610
6 -18.7566640 10.2563539
7 2.6493334 -18.7566640
8 5.7282026 2.6493334
9 -4.1280838 5.7282026
10 3.0162124 -4.1280838
11 -3.6765137 3.0162124
12 0.5466141 -3.6765137
13 31.2811753 0.5466141
14 -0.1927441 31.2811753
15 -14.1585373 -0.1927441
16 5.7407796 -14.1585373
17 -6.2301313 5.7407796
18 19.8598153 -6.2301313
19 14.0643946 19.8598153
20 -5.3671950 14.0643946
21 9.5119351 -5.3671950
22 -8.2506160 9.5119351
23 12.6270040 -8.2506160
24 5.0832797 12.6270040
25 -9.3027647 5.0832797
26 -10.4223394 -9.3027647
27 -10.5682739 -10.4223394
28 -0.4634431 -10.5682739
29 -22.2744269 -0.4634431
30 -33.0117271 -22.2744269
31 37.0154044 -33.0117271
32 -23.0018888 37.0154044
33 -12.0972091 -23.0018888
34 10.6556426 -12.0972091
35 -15.4874720 10.6556426
36 -27.7848847 -15.4874720
37 4.2719115 -27.7848847
38 -7.5869040 4.2719115
39 -10.6306855 -7.5869040
40 -15.5204342 -10.6306855
41 11.4039741 -15.5204342
42 -25.8639447 11.4039741
43 5.3190523 -25.8639447
44 -12.4419582 5.3190523
45 -11.5865867 -12.4419582
46 -22.0355380 -11.5865867
47 11.5784845 -22.0355380
48 0.4835290 11.5784845
49 14.7506184 0.4835290
50 -2.0969264 14.7506184
51 -4.7806533 -2.0969264
52 31.2428361 -4.7806533
53 -5.8495668 31.2428361
54 -6.1955317 -5.8495668
55 6.1586395 -6.1955317
56 -15.5842826 6.1586395
57 78.2857957 -15.5842826
58 5.5203915 78.2857957
59 -3.7033372 5.5203915
60 -26.7248871 -3.7033372
61 6.5199225 -26.7248871
62 -5.2084089 6.5199225
63 12.6021185 -5.2084089
64 5.2113065 12.6021185
65 -6.7782777 5.2113065
66 8.3423273 -6.7782777
67 -4.0632317 8.3423273
68 32.6077851 -4.0632317
69 -26.2826685 32.6077851
70 -1.8931644 -26.2826685
71 0.3184846 -1.8931644
72 -7.8330123 0.3184846
73 18.5090613 -7.8330123
74 6.8665006 18.5090613
75 -5.7051281 6.8665006
76 -2.9942354 -5.7051281
77 -47.9984250 -2.9942354
78 5.6965070 -47.9984250
79 5.3792354 5.6965070
80 -11.3335320 5.3792354
81 -13.7549565 -11.3335320
82 -14.2308249 -13.7549565
83 25.7473451 -14.2308249
84 -6.3333498 25.7473451
85 3.8398818 -6.3333498
86 19.9126551 3.8398818
87 -7.6823378 19.9126551
88 -35.0032647 -7.6823378
89 15.9811610 -35.0032647
90 0.1015673 15.9811610
91 35.2970335 0.1015673
92 7.9300582 35.2970335
93 0.6034311 7.9300582
94 18.3972977 0.6034311
95 -8.7222741 18.3972977
96 22.2722654 -8.7222741
97 -9.0666519 22.2722654
98 -19.7307168 -9.0666519
99 47.7594337 -19.7307168
100 -36.6363497 47.7594337
101 2.4688790 -36.6363497
102 -13.5737108 2.4688790
103 24.9945374 -13.5737108
104 -10.7278260 24.9945374
105 45.4121415 -10.7278260
106 -19.9269740 45.4121415
107 -14.7518192 -19.9269740
108 -36.4290882 -14.7518192
109 -4.5820221 -36.4290882
110 -15.9332906 -4.5820221
111 4.1987372 -15.9332906
112 0.3745422 4.1987372
113 -5.2030389 0.3745422
114 1.7799324 -5.2030389
115 16.3167339 1.7799324
116 17.5285990 16.3167339
117 -2.2773187 17.5285990
118 -15.1356676 -2.2773187
119 18.8576514 -15.1356676
120 -26.3107695 18.8576514
121 3.5392219 -26.3107695
122 -1.4684896 3.5392219
123 17.8186557 -1.4684896
124 -2.0705221 17.8186557
125 -11.5405718 -2.0705221
126 8.1850232 -11.5405718
127 52.3344707 8.1850232
128 -20.3827037 52.3344707
129 -23.4724779 -20.3827037
130 -5.6569323 -23.4724779
131 -3.7059579 -5.6569323
132 -17.1356919 -3.7059579
133 12.1133606 -17.1356919
134 15.3534399 12.1133606
135 -5.4072632 15.3534399
136 3.9179124 -5.4072632
137 7.8482549 3.9179124
138 -14.4628092 7.8482549
139 27.2947691 -14.4628092
140 26.9872832 27.2947691
141 9.6347898 26.9872832
142 49.4762052 9.6347898
143 -8.1584807 49.4762052
144 -9.4914396 -8.1584807
145 -44.4364334 -9.4914396
146 -15.4398232 -44.4364334
147 14.6198784 -15.4398232
148 -4.4855645 14.6198784
149 -4.6815508 -4.4855645
150 -3.0057359 -4.6815508
151 -3.0078436 -3.0057359
152 -3.2633392 -3.0078436
153 -3.1295560 -3.2633392
154 18.3625406 -3.1295560
155 3.9482171 18.3625406
156 -3.2435262 3.9482171
157 -3.2611126 -3.2435262
158 -3.8608477 -3.2611126
159 -7.0684031 -3.8608477
160 -3.7636492 -7.0684031
161 -11.5663555 -3.7636492
162 -3.3740717 -11.5663555
163 -1.5075255 -3.3740717
> 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/7nfot1321619737.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8at8n1321619737.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9flwo1321619737.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10lk3z1321619737.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ymth1321619737.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/12atwt1321619737.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/13p0vs1321619737.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/1482ww1321619738.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/15hlqr1321619738.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/16fyfv1321619738.tab")
+ }
>
> try(system("convert tmp/1dmc81321619737.ps tmp/1dmc81321619737.png",intern=TRUE))
character(0)
> try(system("convert tmp/280of1321619737.ps tmp/280of1321619737.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ddju1321619737.ps tmp/3ddju1321619737.png",intern=TRUE))
character(0)
> try(system("convert tmp/4jnze1321619737.ps tmp/4jnze1321619737.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jw321321619737.ps tmp/5jw321321619737.png",intern=TRUE))
character(0)
> try(system("convert tmp/628641321619737.ps tmp/628641321619737.png",intern=TRUE))
character(0)
> try(system("convert tmp/7nfot1321619737.ps tmp/7nfot1321619737.png",intern=TRUE))
character(0)
> try(system("convert tmp/8at8n1321619737.ps tmp/8at8n1321619737.png",intern=TRUE))
character(0)
> try(system("convert tmp/9flwo1321619737.ps tmp/9flwo1321619737.png",intern=TRUE))
character(0)
> try(system("convert tmp/10lk3z1321619737.ps tmp/10lk3z1321619737.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.017 0.476 5.575