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(11
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+ ,dim=c(7
+ ,164)
+ ,dimnames=list(c('Month'
+ ,'BloggedComputations'
+ ,'TotalTime'
+ ,'Shared'
+ ,'Characters'
+ ,'Writing'
+ ,'hyperlinks')
+ ,1:164))
> y <- array(NA,dim=c(7,164),dimnames=list(c('Month','BloggedComputations','TotalTime','Shared','Characters','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 = 'No 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
BloggedComputations Month TotalTime Shared Characters Writing hyperlinks
1 65 11 146455 1 95556 114468 127
2 54 11 84944 4 54565 88594 90
3 58 10 113337 9 63016 74151 68
4 75 9 128655 2 79774 77921 111
5 41 9 74398 1 31258 53212 51
6 0 10 35523 2 52491 34956 33
7 111 10 293403 0 91256 149703 123
8 1 10 32750 0 22807 6853 5
9 36 9 106539 5 77411 58907 63
10 60 9 130539 0 48821 67067 66
11 63 11 154991 0 52295 110563 99
12 71 11 126683 7 63262 58126 72
13 38 9 100672 6 50466 57113 55
14 76 9 179562 3 62932 77993 116
15 61 9 125971 4 38439 68091 71
16 125 9 234509 0 70817 124676 125
17 84 9 158980 4 105965 109522 123
18 69 9 184217 3 73795 75865 74
19 77 11 107342 0 82043 79746 116
20 95 9 141371 5 74349 77844 117
21 78 9 154730 0 82204 98681 98
22 76 9 264020 1 55709 105531 101
23 40 9 90938 3 37137 51428 43
24 81 9 101324 5 70780 65703 103
25 102 10 130232 0 55027 72562 107
26 70 9 137793 0 56699 81728 77
27 75 9 161678 4 65911 95580 87
28 93 10 151503 0 56316 98278 99
29 42 10 105324 0 26982 46629 46
30 95 10 175914 0 54628 115189 96
31 87 10 181853 3 96750 124865 92
32 44 11 114928 4 53009 59392 96
33 84 11 190410 1 64664 127818 96
34 28 11 61499 4 36990 17821 15
35 87 9 223004 1 85224 154076 147
36 71 9 167131 0 37048 64881 56
37 68 9 233482 0 59635 136506 81
38 50 10 121185 2 42051 66524 69
39 30 9 78776 1 26998 45988 34
40 86 10 188967 2 63717 107445 98
41 75 10 199512 8 55071 102772 82
42 46 9 102531 5 40001 46657 64
43 52 10 118958 3 54506 97563 61
44 31 10 68948 4 35838 36663 45
45 30 10 93125 1 50838 55369 37
46 70 11 277108 2 86997 77921 64
47 20 11 78800 2 33032 56968 21
48 84 10 157250 0 61704 77519 104
49 81 11 210554 6 117986 129805 126
50 79 11 127324 3 56733 72761 104
51 70 10 114397 0 55064 81278 87
52 8 9 24188 0 5950 15049 7
53 67 9 246209 6 84607 113935 130
54 21 10 65029 5 32551 25109 21
55 30 10 98030 3 31701 45824 35
56 70 9 173587 1 71170 89644 97
57 87 9 172684 5 101773 109011 103
58 87 9 191381 5 101653 134245 210
59 112 9 191276 0 81493 136692 151
60 54 11 134043 9 55901 50741 57
61 96 11 233406 6 109104 149510 117
62 93 11 195304 6 114425 147888 152
63 49 11 127619 5 36311 54987 52
64 49 9 162810 6 70027 74467 83
65 38 9 129100 2 73713 100033 87
66 64 9 108715 0 40671 85505 80
67 62 9 106469 3 89041 62426 88
68 66 9 142069 8 57231 82932 83
69 98 10 143937 2 78792 79169 140
70 97 10 84256 5 59155 65469 76
71 56 10 118807 11 55827 63572 70
72 22 10 69471 6 22618 23824 26
73 51 9 122433 5 58425 73831 66
74 56 10 131122 1 65724 63551 89
75 94 10 94763 0 56979 56756 100
76 98 10 188780 3 72369 81399 98
77 76 10 191467 3 79194 117881 109
78 57 10 105615 6 202316 70711 51
79 75 10 89318 1 44970 50495 82
80 48 11 107335 0 49319 53845 65
81 48 11 98599 1 36252 51390 46
82 109 11 260646 0 75741 104953 104
83 27 11 131876 5 38417 65983 36
84 83 11 119291 2 64102 76839 123
85 49 11 80953 0 56622 55792 59
86 24 11 99768 0 15430 25155 27
87 43 10 84572 5 72571 55291 84
88 44 10 202373 1 67271 84279 61
89 49 10 166790 0 43460 99692 46
90 106 10 99946 1 99501 59633 125
91 42 10 116900 1 28340 63249 58
92 108 9 142146 2 76013 82928 152
93 27 9 99246 4 37361 50000 52
94 79 11 156833 1 48204 69455 85
95 49 11 175078 4 76168 84068 95
96 64 10 130533 0 85168 76195 78
97 75 9 142339 2 125410 114634 144
98 115 9 176789 0 123328 139357 149
99 92 9 181379 7 83038 110044 101
100 106 9 228548 7 120087 155118 205
101 73 11 142141 6 91939 83061 61
102 105 10 167845 0 103646 127122 145
103 30 10 103012 0 29467 45653 28
104 13 10 43287 4 43750 19630 49
105 69 11 125366 4 34497 67229 68
106 72 10 118372 0 66477 86060 142
107 80 10 135171 0 71181 88003 82
108 106 10 175568 0 74482 95815 105
109 28 10 74112 0 174949 85499 52
110 70 11 88817 0 46765 27220 56
111 51 9 164767 4 90257 109882 81
112 90 9 141933 0 51370 72579 100
113 12 9 22938 0 1168 5841 11
114 84 9 115199 0 51360 68369 87
115 23 10 61857 4 25162 24610 31
116 57 10 91185 0 21067 30995 67
117 84 10 213765 1 58233 150662 150
118 4 11 21054 0 855 6622 4
119 56 10 167105 5 85903 93694 75
120 18 11 31414 0 14116 13155 39
121 86 11 178863 1 57637 111908 88
122 39 11 126681 7 94137 57550 67
123 16 10 64320 5 62147 16356 24
124 18 9 67746 2 62832 40174 58
125 16 9 38214 0 8773 13983 16
126 42 9 90961 1 63785 52316 49
127 75 9 181510 0 65196 99585 109
128 30 10 116775 0 73087 86271 124
129 104 10 223914 2 72631 131012 115
130 121 10 185139 0 86281 130274 128
131 106 10 242879 2 162365 159051 159
132 57 9 139144 0 56530 76506 75
133 28 10 75812 0 35606 49145 30
134 56 10 178218 4 70111 66398 83
135 81 10 246834 4 92046 127546 135
136 2 9 50999 8 63989 6802 8
137 88 11 223842 0 104911 99509 115
138 41 11 93577 4 43448 43106 60
139 83 11 155383 0 60029 108303 99
140 55 11 111664 1 38650 64167 98
141 3 11 75426 0 47261 8579 36
142 54 11 243551 9 73586 97811 93
143 89 10 136548 0 83042 84365 158
144 41 9 173260 3 37238 10901 16
145 94 9 185039 7 63958 91346 100
146 101 9 67507 5 78956 33660 49
147 70 10 139350 2 99518 93634 89
148 111 10 172964 1 111436 109348 153
149 0 11 0 9 0 0 0
150 4 11 14688 0 6023 7953 5
151 0 11 98 0 0 0 0
152 0 10 455 0 0 0 0
153 0 9 0 1 0 0 0
154 0 11 0 0 0 0 0
155 42 10 128066 2 42564 63538 80
156 97 9 176460 1 38885 108281 122
157 0 9 0 0 0 0 0
158 0 9 203 0 0 0 0
159 7 10 7199 0 1644 4245 6
160 12 9 46660 0 6179 21509 13
161 0 11 17547 0 3926 7670 3
162 37 11 73567 0 23238 10641 18
163 0 10 969 0 0 0 0
164 39 9 101060 2 49288 41243 49
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Month TotalTime Shared Characters Writing
9.831e+00 -5.081e-01 1.576e-04 -8.600e-01 3.181e-05 -2.729e-06
hyperlinks
4.427e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-50.136 -8.548 -1.113 8.843 65.292
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.831e+00 1.580e+01 0.622 0.534621
Month -5.081e-01 1.547e+00 -0.328 0.743009
TotalTime 1.576e-04 3.981e-05 3.958 0.000114 ***
Shared -8.600e-01 4.901e-01 -1.755 0.081258 .
Characters 3.181e-05 5.617e-05 0.566 0.571995
Writing -2.729e-06 8.564e-05 -0.032 0.974618
hyperlinks 4.427e-01 5.646e-02 7.842 6.38e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 15.46 on 157 degrees of freedom
Multiple R-squared: 0.7785, Adjusted R-squared: 0.7701
F-statistic: 91.98 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,] 0.001632569 0.003265138 0.9983674308
[2,] 0.018130546 0.036261093 0.9818694536
[3,] 0.005932972 0.011865945 0.9940670276
[4,] 0.015016164 0.030032329 0.9849838355
[5,] 0.145777214 0.291554428 0.8542227861
[6,] 0.086625614 0.173251229 0.9133743857
[7,] 0.173923486 0.347846972 0.8260765142
[8,] 0.129300059 0.258600118 0.8706999408
[9,] 0.083413482 0.166826963 0.9165865184
[10,] 0.100094356 0.200188712 0.8999056440
[11,] 0.080635362 0.161270724 0.9193646378
[12,] 0.066391346 0.132782693 0.9336086537
[13,] 0.172857861 0.345715722 0.8271421391
[14,] 0.130173307 0.260346614 0.8698266928
[15,] 0.109079461 0.218158921 0.8909205393
[16,] 0.189413146 0.378826292 0.8105868540
[17,] 0.163641291 0.327282582 0.8363587091
[18,] 0.126242622 0.252485244 0.8737573779
[19,] 0.137979059 0.275958118 0.8620209409
[20,] 0.102940131 0.205880263 0.8970598685
[21,] 0.105991208 0.211982416 0.8940087919
[22,] 0.120128256 0.240256513 0.8798717437
[23,] 0.158661553 0.317323106 0.8413384469
[24,] 0.124411669 0.248823337 0.8755883313
[25,] 0.154441572 0.308883144 0.8455584282
[26,] 0.261155103 0.522310207 0.7388448966
[27,] 0.238725503 0.477451007 0.7612744966
[28,] 0.228757005 0.457514011 0.7712429946
[29,] 0.193182615 0.386365230 0.8068173849
[30,] 0.156346633 0.312693265 0.8436533673
[31,] 0.128497176 0.256994352 0.8715028241
[32,] 0.102716427 0.205432854 0.8972835728
[33,] 0.082338487 0.164676974 0.9176615128
[34,] 0.063748973 0.127497946 0.9362510268
[35,] 0.048745161 0.097490321 0.9512548394
[36,] 0.036848400 0.073696801 0.9631515995
[37,] 0.028284606 0.056569213 0.9717153936
[38,] 0.020662851 0.041325703 0.9793371487
[39,] 0.015348479 0.030696958 0.9846515212
[40,] 0.012663711 0.025327422 0.9873362889
[41,] 0.009476393 0.018952787 0.9905236066
[42,] 0.007078385 0.014156770 0.9929216150
[43,] 0.004993079 0.009986158 0.9950069212
[44,] 0.028158675 0.056317350 0.9718413248
[45,] 0.020730830 0.041461660 0.9792691701
[46,] 0.015507291 0.031014582 0.9844927088
[47,] 0.011931563 0.023863125 0.9880684374
[48,] 0.010873337 0.021746674 0.9891266628
[49,] 0.067270760 0.134541520 0.9327292401
[50,] 0.056794655 0.113589310 0.9432053452
[51,] 0.048461395 0.096922789 0.9515386054
[52,] 0.038584471 0.077168942 0.9614155291
[53,] 0.030646038 0.061292076 0.9693539622
[54,] 0.023393018 0.046786036 0.9766069822
[55,] 0.023133824 0.046267649 0.9768661755
[56,] 0.038388960 0.076777921 0.9616110396
[57,] 0.030028126 0.060056252 0.9699718742
[58,] 0.023507826 0.047015652 0.9764921742
[59,] 0.018676544 0.037353089 0.9813234557
[60,] 0.015560100 0.031120200 0.9844399000
[61,] 0.130729914 0.261459828 0.8692700859
[62,] 0.114804695 0.229609390 0.8851953050
[63,] 0.095516352 0.191032704 0.9044836480
[64,] 0.077093517 0.154187034 0.9229064828
[65,] 0.067371324 0.134742649 0.9326286755
[66,] 0.107971964 0.215943927 0.8920280365
[67,] 0.124356264 0.248712528 0.8756437359
[68,] 0.105487449 0.210974897 0.8945125514
[69,] 0.102273524 0.204547047 0.8977264765
[70,] 0.109783863 0.219567727 0.8902161365
[71,] 0.092595161 0.185190322 0.9074048391
[72,] 0.077892348 0.155784697 0.9221076515
[73,] 0.075544678 0.151089356 0.9244553218
[74,] 0.068766082 0.137532164 0.9312339182
[75,] 0.056374447 0.112748894 0.9436255532
[76,] 0.045346587 0.090693175 0.9546534126
[77,] 0.040096519 0.080193038 0.9599034808
[78,] 0.035169342 0.070338685 0.9648306576
[79,] 0.043563297 0.087126594 0.9564367032
[80,] 0.034488107 0.068976213 0.9655118935
[81,] 0.060797498 0.121594997 0.9392025015
[82,] 0.051022287 0.102044574 0.9489777131
[83,] 0.050522718 0.101045435 0.9494772824
[84,] 0.049422681 0.098845363 0.9505773185
[85,] 0.044563689 0.089127379 0.9554363106
[86,] 0.060535649 0.121071297 0.9394643514
[87,] 0.047930633 0.095861266 0.9520693671
[88,] 0.050329157 0.100658315 0.9496708425
[89,] 0.046517959 0.093035919 0.9534820406
[90,] 0.049350519 0.098701038 0.9506494810
[91,] 0.057766889 0.115533778 0.9422331109
[92,] 0.072788825 0.145577650 0.9272111748
[93,] 0.061392132 0.122784264 0.9386078678
[94,] 0.049908958 0.099817915 0.9500910423
[95,] 0.052797462 0.105594924 0.9472025380
[96,] 0.057525246 0.115050492 0.9424747539
[97,] 0.056111747 0.112223494 0.9438882528
[98,] 0.056122879 0.112245759 0.9438771205
[99,] 0.081557808 0.163115616 0.9184421920
[100,] 0.081216890 0.162433781 0.9187831096
[101,] 0.131307102 0.262614205 0.8686928975
[102,] 0.141590571 0.283181143 0.8584094285
[103,] 0.149460293 0.298920586 0.8505397072
[104,] 0.123187096 0.246374192 0.8768129038
[105,] 0.145906833 0.291813665 0.8540931673
[106,] 0.119902844 0.239805687 0.8800971565
[107,] 0.120310988 0.240621976 0.8796890119
[108,] 0.142175200 0.284350400 0.8578247998
[109,] 0.119276395 0.238552791 0.8807236047
[110,] 0.105344242 0.210688484 0.8946557581
[111,] 0.090217340 0.180434679 0.9097826603
[112,] 0.082487660 0.164975321 0.9175123396
[113,] 0.071661138 0.143322275 0.9283388624
[114,] 0.058140379 0.116280758 0.9418596209
[115,] 0.084082007 0.168164015 0.9159179927
[116,] 0.065389719 0.130779437 0.9346102813
[117,] 0.051092577 0.102185154 0.9489074232
[118,] 0.041338816 0.082677632 0.9586611839
[119,] 0.292004724 0.584009449 0.7079952755
[120,] 0.277470848 0.554941697 0.7225291516
[121,] 0.392917057 0.785834114 0.6070829431
[122,] 0.409206896 0.818413792 0.5907931042
[123,] 0.362689001 0.725378002 0.6373109989
[124,] 0.310334028 0.620668056 0.6896659722
[125,] 0.272857258 0.545714516 0.7271427421
[126,] 0.321915422 0.643830845 0.6780845777
[127,] 0.502191520 0.995616960 0.4978084801
[128,] 0.436883877 0.873767754 0.5631161229
[129,] 0.371268002 0.742536005 0.6287319977
[130,] 0.390313793 0.780627586 0.6096862068
[131,] 0.341148132 0.682296265 0.6588518676
[132,] 0.486925281 0.973850562 0.5130747189
[133,] 0.626262494 0.747475012 0.3737375058
[134,] 0.631366146 0.737267708 0.3686338540
[135,] 0.597225562 0.805548877 0.4027744383
[136,] 0.527907572 0.944184856 0.4720924282
[137,] 0.994040249 0.011919502 0.0059597511
[138,] 0.989101635 0.021796730 0.0108983652
[139,] 0.993412208 0.013175584 0.0065877922
[140,] 0.995916451 0.008167099 0.0040835493
[141,] 0.989360113 0.021279774 0.0106398870
[142,] 0.974627150 0.050745701 0.0253728505
[143,] 0.940413686 0.119172629 0.0595863145
[144,] 0.999202142 0.001595716 0.0007978582
[145,] 0.997372768 0.005254465 0.0026272323
> postscript(file="/var/wessaorg/rcomp/tmp/19xb11321893932.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2bvrw1321893932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3l00g1321893932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4vu971321893932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/558i81321893932.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
-20.41038749 -1.52469689 11.22471962 -0.27621330 1.45118260 -24.81163458
7 8 9 10 11 12
3.07057972 -11.83148315 -11.93816097 3.58358147 -10.85430872 19.08743244
13 14 15 16 17 18
-3.75989183 -8.11530465 6.86289589 25.53886912 -0.39380349 2.39355247
19 20 21 22 23 24
2.09747740 17.81625701 2.62965610 -16.19794935 2.91477835 16.40476868
25 26 27 28 29 30
27.80665863 7.36023139 7.35444076 19.02580894 -0.44222816 18.60731183
31 32 33 34 35 36
12.70900821 -18.93574779 6.40649905 9.73838474 -19.90580862 13.61331017
37 38 39 40 41 42
-11.43226879 -3.82868007 -2.59687111 8.07492578 7.91896991 -0.59286709
43 44 45 46 47 48
2.61245574 -2.13655499 -6.41057819 -7.07447552 -5.13127899 6.67868544
49 50 51 52 53 54
-10.43931464 9.62737876 7.17853413 -4.31730511 -31.82609502 0.03894905
55 56 57 58 59 60
-3.99538964 -6.71264612 10.29292848 -39.95008319 7.53398456 9.50230844
61 62 63 64 65 66
5.28052348 -7.38411217 4.92270713 -15.52161718 -26.46824580 5.13406264
67 68 69 70 71 72
0.92487237 6.89668681 8.02000637 47.92444330 9.39720563 -0.70207103
73 74 75 76 77 78
-0.12609670 -9.86967935 28.38925777 20.61812604 -6.79255041 11.94738649
79 80 81 82 83 84
19.44084013 -3.35323420 7.70365931 15.52296294 -10.70174155 5.39845452
85 86 87 88 89 90
4.23306724 -8.33845228 -10.12149885 -20.69360743 -3.50676402 28.02028413
91 92 93 94 95 96
-6.71656643 12.58032229 -14.52968787 11.93127669 -23.64019036 1.64894732
97 98 99 100 101 102
-18.39306016 12.37876450 17.12700751 -23.40231674 21.81739411 6.65959698
103 104 105 106 107 108
-4.19091066 -18.16209899 17.42550615 -16.14662722 15.62441583 24.99310495
109 110 111 112 113 114
-16.78062833 25.55758846 -15.21138928 16.67016643 -1.76419890 20.62667702
115 116 117 118 119 120
-2.51465623 7.63429620 -21.42088808 -5.34017198 -6.46116729 -8.87129252
121 122 123 124 125 126
13.94770468 -11.68232493 -7.14269426 -23.77943927 -2.60445174 -0.31027286
127 128 129 130 131 132
-8.91642559 -50.13593600 12.82308323 28.02150484 -10.42195391 -4.97611061
133 134 135 136 137 138
-2.97612867 -12.18612259 -21.54953188 -9.97307964 -5.49023336 -2.37436341
139 140 141 142 143 144
8.83176207 -10.41714293 -30.54490285 -24.12442602 -9.62544322 2.78248885
145 146 147 148 149 150
19.54884566 65.29198695 2.70106836 8.87506826 3.49738672 -4.94052730
151 152 153 154 155 156
-4.25817047 -4.82248866 -4.39884656 -4.24272853 -17.80716293 9.84473585
157 158 159 160 161 162
-5.25885936 -5.29084624 -1.58208985 -6.50412813 -8.43968286 12.48645960
163 164
-4.90348006 -3.61068650
> postscript(file="/var/wessaorg/rcomp/tmp/67k3t1321893932.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 -20.41038749 NA
1 -1.52469689 -20.41038749
2 11.22471962 -1.52469689
3 -0.27621330 11.22471962
4 1.45118260 -0.27621330
5 -24.81163458 1.45118260
6 3.07057972 -24.81163458
7 -11.83148315 3.07057972
8 -11.93816097 -11.83148315
9 3.58358147 -11.93816097
10 -10.85430872 3.58358147
11 19.08743244 -10.85430872
12 -3.75989183 19.08743244
13 -8.11530465 -3.75989183
14 6.86289589 -8.11530465
15 25.53886912 6.86289589
16 -0.39380349 25.53886912
17 2.39355247 -0.39380349
18 2.09747740 2.39355247
19 17.81625701 2.09747740
20 2.62965610 17.81625701
21 -16.19794935 2.62965610
22 2.91477835 -16.19794935
23 16.40476868 2.91477835
24 27.80665863 16.40476868
25 7.36023139 27.80665863
26 7.35444076 7.36023139
27 19.02580894 7.35444076
28 -0.44222816 19.02580894
29 18.60731183 -0.44222816
30 12.70900821 18.60731183
31 -18.93574779 12.70900821
32 6.40649905 -18.93574779
33 9.73838474 6.40649905
34 -19.90580862 9.73838474
35 13.61331017 -19.90580862
36 -11.43226879 13.61331017
37 -3.82868007 -11.43226879
38 -2.59687111 -3.82868007
39 8.07492578 -2.59687111
40 7.91896991 8.07492578
41 -0.59286709 7.91896991
42 2.61245574 -0.59286709
43 -2.13655499 2.61245574
44 -6.41057819 -2.13655499
45 -7.07447552 -6.41057819
46 -5.13127899 -7.07447552
47 6.67868544 -5.13127899
48 -10.43931464 6.67868544
49 9.62737876 -10.43931464
50 7.17853413 9.62737876
51 -4.31730511 7.17853413
52 -31.82609502 -4.31730511
53 0.03894905 -31.82609502
54 -3.99538964 0.03894905
55 -6.71264612 -3.99538964
56 10.29292848 -6.71264612
57 -39.95008319 10.29292848
58 7.53398456 -39.95008319
59 9.50230844 7.53398456
60 5.28052348 9.50230844
61 -7.38411217 5.28052348
62 4.92270713 -7.38411217
63 -15.52161718 4.92270713
64 -26.46824580 -15.52161718
65 5.13406264 -26.46824580
66 0.92487237 5.13406264
67 6.89668681 0.92487237
68 8.02000637 6.89668681
69 47.92444330 8.02000637
70 9.39720563 47.92444330
71 -0.70207103 9.39720563
72 -0.12609670 -0.70207103
73 -9.86967935 -0.12609670
74 28.38925777 -9.86967935
75 20.61812604 28.38925777
76 -6.79255041 20.61812604
77 11.94738649 -6.79255041
78 19.44084013 11.94738649
79 -3.35323420 19.44084013
80 7.70365931 -3.35323420
81 15.52296294 7.70365931
82 -10.70174155 15.52296294
83 5.39845452 -10.70174155
84 4.23306724 5.39845452
85 -8.33845228 4.23306724
86 -10.12149885 -8.33845228
87 -20.69360743 -10.12149885
88 -3.50676402 -20.69360743
89 28.02028413 -3.50676402
90 -6.71656643 28.02028413
91 12.58032229 -6.71656643
92 -14.52968787 12.58032229
93 11.93127669 -14.52968787
94 -23.64019036 11.93127669
95 1.64894732 -23.64019036
96 -18.39306016 1.64894732
97 12.37876450 -18.39306016
98 17.12700751 12.37876450
99 -23.40231674 17.12700751
100 21.81739411 -23.40231674
101 6.65959698 21.81739411
102 -4.19091066 6.65959698
103 -18.16209899 -4.19091066
104 17.42550615 -18.16209899
105 -16.14662722 17.42550615
106 15.62441583 -16.14662722
107 24.99310495 15.62441583
108 -16.78062833 24.99310495
109 25.55758846 -16.78062833
110 -15.21138928 25.55758846
111 16.67016643 -15.21138928
112 -1.76419890 16.67016643
113 20.62667702 -1.76419890
114 -2.51465623 20.62667702
115 7.63429620 -2.51465623
116 -21.42088808 7.63429620
117 -5.34017198 -21.42088808
118 -6.46116729 -5.34017198
119 -8.87129252 -6.46116729
120 13.94770468 -8.87129252
121 -11.68232493 13.94770468
122 -7.14269426 -11.68232493
123 -23.77943927 -7.14269426
124 -2.60445174 -23.77943927
125 -0.31027286 -2.60445174
126 -8.91642559 -0.31027286
127 -50.13593600 -8.91642559
128 12.82308323 -50.13593600
129 28.02150484 12.82308323
130 -10.42195391 28.02150484
131 -4.97611061 -10.42195391
132 -2.97612867 -4.97611061
133 -12.18612259 -2.97612867
134 -21.54953188 -12.18612259
135 -9.97307964 -21.54953188
136 -5.49023336 -9.97307964
137 -2.37436341 -5.49023336
138 8.83176207 -2.37436341
139 -10.41714293 8.83176207
140 -30.54490285 -10.41714293
141 -24.12442602 -30.54490285
142 -9.62544322 -24.12442602
143 2.78248885 -9.62544322
144 19.54884566 2.78248885
145 65.29198695 19.54884566
146 2.70106836 65.29198695
147 8.87506826 2.70106836
148 3.49738672 8.87506826
149 -4.94052730 3.49738672
150 -4.25817047 -4.94052730
151 -4.82248866 -4.25817047
152 -4.39884656 -4.82248866
153 -4.24272853 -4.39884656
154 -17.80716293 -4.24272853
155 9.84473585 -17.80716293
156 -5.25885936 9.84473585
157 -5.29084624 -5.25885936
158 -1.58208985 -5.29084624
159 -6.50412813 -1.58208985
160 -8.43968286 -6.50412813
161 12.48645960 -8.43968286
162 -4.90348006 12.48645960
163 -3.61068650 -4.90348006
164 NA -3.61068650
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.52469689 -20.41038749
[2,] 11.22471962 -1.52469689
[3,] -0.27621330 11.22471962
[4,] 1.45118260 -0.27621330
[5,] -24.81163458 1.45118260
[6,] 3.07057972 -24.81163458
[7,] -11.83148315 3.07057972
[8,] -11.93816097 -11.83148315
[9,] 3.58358147 -11.93816097
[10,] -10.85430872 3.58358147
[11,] 19.08743244 -10.85430872
[12,] -3.75989183 19.08743244
[13,] -8.11530465 -3.75989183
[14,] 6.86289589 -8.11530465
[15,] 25.53886912 6.86289589
[16,] -0.39380349 25.53886912
[17,] 2.39355247 -0.39380349
[18,] 2.09747740 2.39355247
[19,] 17.81625701 2.09747740
[20,] 2.62965610 17.81625701
[21,] -16.19794935 2.62965610
[22,] 2.91477835 -16.19794935
[23,] 16.40476868 2.91477835
[24,] 27.80665863 16.40476868
[25,] 7.36023139 27.80665863
[26,] 7.35444076 7.36023139
[27,] 19.02580894 7.35444076
[28,] -0.44222816 19.02580894
[29,] 18.60731183 -0.44222816
[30,] 12.70900821 18.60731183
[31,] -18.93574779 12.70900821
[32,] 6.40649905 -18.93574779
[33,] 9.73838474 6.40649905
[34,] -19.90580862 9.73838474
[35,] 13.61331017 -19.90580862
[36,] -11.43226879 13.61331017
[37,] -3.82868007 -11.43226879
[38,] -2.59687111 -3.82868007
[39,] 8.07492578 -2.59687111
[40,] 7.91896991 8.07492578
[41,] -0.59286709 7.91896991
[42,] 2.61245574 -0.59286709
[43,] -2.13655499 2.61245574
[44,] -6.41057819 -2.13655499
[45,] -7.07447552 -6.41057819
[46,] -5.13127899 -7.07447552
[47,] 6.67868544 -5.13127899
[48,] -10.43931464 6.67868544
[49,] 9.62737876 -10.43931464
[50,] 7.17853413 9.62737876
[51,] -4.31730511 7.17853413
[52,] -31.82609502 -4.31730511
[53,] 0.03894905 -31.82609502
[54,] -3.99538964 0.03894905
[55,] -6.71264612 -3.99538964
[56,] 10.29292848 -6.71264612
[57,] -39.95008319 10.29292848
[58,] 7.53398456 -39.95008319
[59,] 9.50230844 7.53398456
[60,] 5.28052348 9.50230844
[61,] -7.38411217 5.28052348
[62,] 4.92270713 -7.38411217
[63,] -15.52161718 4.92270713
[64,] -26.46824580 -15.52161718
[65,] 5.13406264 -26.46824580
[66,] 0.92487237 5.13406264
[67,] 6.89668681 0.92487237
[68,] 8.02000637 6.89668681
[69,] 47.92444330 8.02000637
[70,] 9.39720563 47.92444330
[71,] -0.70207103 9.39720563
[72,] -0.12609670 -0.70207103
[73,] -9.86967935 -0.12609670
[74,] 28.38925777 -9.86967935
[75,] 20.61812604 28.38925777
[76,] -6.79255041 20.61812604
[77,] 11.94738649 -6.79255041
[78,] 19.44084013 11.94738649
[79,] -3.35323420 19.44084013
[80,] 7.70365931 -3.35323420
[81,] 15.52296294 7.70365931
[82,] -10.70174155 15.52296294
[83,] 5.39845452 -10.70174155
[84,] 4.23306724 5.39845452
[85,] -8.33845228 4.23306724
[86,] -10.12149885 -8.33845228
[87,] -20.69360743 -10.12149885
[88,] -3.50676402 -20.69360743
[89,] 28.02028413 -3.50676402
[90,] -6.71656643 28.02028413
[91,] 12.58032229 -6.71656643
[92,] -14.52968787 12.58032229
[93,] 11.93127669 -14.52968787
[94,] -23.64019036 11.93127669
[95,] 1.64894732 -23.64019036
[96,] -18.39306016 1.64894732
[97,] 12.37876450 -18.39306016
[98,] 17.12700751 12.37876450
[99,] -23.40231674 17.12700751
[100,] 21.81739411 -23.40231674
[101,] 6.65959698 21.81739411
[102,] -4.19091066 6.65959698
[103,] -18.16209899 -4.19091066
[104,] 17.42550615 -18.16209899
[105,] -16.14662722 17.42550615
[106,] 15.62441583 -16.14662722
[107,] 24.99310495 15.62441583
[108,] -16.78062833 24.99310495
[109,] 25.55758846 -16.78062833
[110,] -15.21138928 25.55758846
[111,] 16.67016643 -15.21138928
[112,] -1.76419890 16.67016643
[113,] 20.62667702 -1.76419890
[114,] -2.51465623 20.62667702
[115,] 7.63429620 -2.51465623
[116,] -21.42088808 7.63429620
[117,] -5.34017198 -21.42088808
[118,] -6.46116729 -5.34017198
[119,] -8.87129252 -6.46116729
[120,] 13.94770468 -8.87129252
[121,] -11.68232493 13.94770468
[122,] -7.14269426 -11.68232493
[123,] -23.77943927 -7.14269426
[124,] -2.60445174 -23.77943927
[125,] -0.31027286 -2.60445174
[126,] -8.91642559 -0.31027286
[127,] -50.13593600 -8.91642559
[128,] 12.82308323 -50.13593600
[129,] 28.02150484 12.82308323
[130,] -10.42195391 28.02150484
[131,] -4.97611061 -10.42195391
[132,] -2.97612867 -4.97611061
[133,] -12.18612259 -2.97612867
[134,] -21.54953188 -12.18612259
[135,] -9.97307964 -21.54953188
[136,] -5.49023336 -9.97307964
[137,] -2.37436341 -5.49023336
[138,] 8.83176207 -2.37436341
[139,] -10.41714293 8.83176207
[140,] -30.54490285 -10.41714293
[141,] -24.12442602 -30.54490285
[142,] -9.62544322 -24.12442602
[143,] 2.78248885 -9.62544322
[144,] 19.54884566 2.78248885
[145,] 65.29198695 19.54884566
[146,] 2.70106836 65.29198695
[147,] 8.87506826 2.70106836
[148,] 3.49738672 8.87506826
[149,] -4.94052730 3.49738672
[150,] -4.25817047 -4.94052730
[151,] -4.82248866 -4.25817047
[152,] -4.39884656 -4.82248866
[153,] -4.24272853 -4.39884656
[154,] -17.80716293 -4.24272853
[155,] 9.84473585 -17.80716293
[156,] -5.25885936 9.84473585
[157,] -5.29084624 -5.25885936
[158,] -1.58208985 -5.29084624
[159,] -6.50412813 -1.58208985
[160,] -8.43968286 -6.50412813
[161,] 12.48645960 -8.43968286
[162,] -4.90348006 12.48645960
[163,] -3.61068650 -4.90348006
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.52469689 -20.41038749
2 11.22471962 -1.52469689
3 -0.27621330 11.22471962
4 1.45118260 -0.27621330
5 -24.81163458 1.45118260
6 3.07057972 -24.81163458
7 -11.83148315 3.07057972
8 -11.93816097 -11.83148315
9 3.58358147 -11.93816097
10 -10.85430872 3.58358147
11 19.08743244 -10.85430872
12 -3.75989183 19.08743244
13 -8.11530465 -3.75989183
14 6.86289589 -8.11530465
15 25.53886912 6.86289589
16 -0.39380349 25.53886912
17 2.39355247 -0.39380349
18 2.09747740 2.39355247
19 17.81625701 2.09747740
20 2.62965610 17.81625701
21 -16.19794935 2.62965610
22 2.91477835 -16.19794935
23 16.40476868 2.91477835
24 27.80665863 16.40476868
25 7.36023139 27.80665863
26 7.35444076 7.36023139
27 19.02580894 7.35444076
28 -0.44222816 19.02580894
29 18.60731183 -0.44222816
30 12.70900821 18.60731183
31 -18.93574779 12.70900821
32 6.40649905 -18.93574779
33 9.73838474 6.40649905
34 -19.90580862 9.73838474
35 13.61331017 -19.90580862
36 -11.43226879 13.61331017
37 -3.82868007 -11.43226879
38 -2.59687111 -3.82868007
39 8.07492578 -2.59687111
40 7.91896991 8.07492578
41 -0.59286709 7.91896991
42 2.61245574 -0.59286709
43 -2.13655499 2.61245574
44 -6.41057819 -2.13655499
45 -7.07447552 -6.41057819
46 -5.13127899 -7.07447552
47 6.67868544 -5.13127899
48 -10.43931464 6.67868544
49 9.62737876 -10.43931464
50 7.17853413 9.62737876
51 -4.31730511 7.17853413
52 -31.82609502 -4.31730511
53 0.03894905 -31.82609502
54 -3.99538964 0.03894905
55 -6.71264612 -3.99538964
56 10.29292848 -6.71264612
57 -39.95008319 10.29292848
58 7.53398456 -39.95008319
59 9.50230844 7.53398456
60 5.28052348 9.50230844
61 -7.38411217 5.28052348
62 4.92270713 -7.38411217
63 -15.52161718 4.92270713
64 -26.46824580 -15.52161718
65 5.13406264 -26.46824580
66 0.92487237 5.13406264
67 6.89668681 0.92487237
68 8.02000637 6.89668681
69 47.92444330 8.02000637
70 9.39720563 47.92444330
71 -0.70207103 9.39720563
72 -0.12609670 -0.70207103
73 -9.86967935 -0.12609670
74 28.38925777 -9.86967935
75 20.61812604 28.38925777
76 -6.79255041 20.61812604
77 11.94738649 -6.79255041
78 19.44084013 11.94738649
79 -3.35323420 19.44084013
80 7.70365931 -3.35323420
81 15.52296294 7.70365931
82 -10.70174155 15.52296294
83 5.39845452 -10.70174155
84 4.23306724 5.39845452
85 -8.33845228 4.23306724
86 -10.12149885 -8.33845228
87 -20.69360743 -10.12149885
88 -3.50676402 -20.69360743
89 28.02028413 -3.50676402
90 -6.71656643 28.02028413
91 12.58032229 -6.71656643
92 -14.52968787 12.58032229
93 11.93127669 -14.52968787
94 -23.64019036 11.93127669
95 1.64894732 -23.64019036
96 -18.39306016 1.64894732
97 12.37876450 -18.39306016
98 17.12700751 12.37876450
99 -23.40231674 17.12700751
100 21.81739411 -23.40231674
101 6.65959698 21.81739411
102 -4.19091066 6.65959698
103 -18.16209899 -4.19091066
104 17.42550615 -18.16209899
105 -16.14662722 17.42550615
106 15.62441583 -16.14662722
107 24.99310495 15.62441583
108 -16.78062833 24.99310495
109 25.55758846 -16.78062833
110 -15.21138928 25.55758846
111 16.67016643 -15.21138928
112 -1.76419890 16.67016643
113 20.62667702 -1.76419890
114 -2.51465623 20.62667702
115 7.63429620 -2.51465623
116 -21.42088808 7.63429620
117 -5.34017198 -21.42088808
118 -6.46116729 -5.34017198
119 -8.87129252 -6.46116729
120 13.94770468 -8.87129252
121 -11.68232493 13.94770468
122 -7.14269426 -11.68232493
123 -23.77943927 -7.14269426
124 -2.60445174 -23.77943927
125 -0.31027286 -2.60445174
126 -8.91642559 -0.31027286
127 -50.13593600 -8.91642559
128 12.82308323 -50.13593600
129 28.02150484 12.82308323
130 -10.42195391 28.02150484
131 -4.97611061 -10.42195391
132 -2.97612867 -4.97611061
133 -12.18612259 -2.97612867
134 -21.54953188 -12.18612259
135 -9.97307964 -21.54953188
136 -5.49023336 -9.97307964
137 -2.37436341 -5.49023336
138 8.83176207 -2.37436341
139 -10.41714293 8.83176207
140 -30.54490285 -10.41714293
141 -24.12442602 -30.54490285
142 -9.62544322 -24.12442602
143 2.78248885 -9.62544322
144 19.54884566 2.78248885
145 65.29198695 19.54884566
146 2.70106836 65.29198695
147 8.87506826 2.70106836
148 3.49738672 8.87506826
149 -4.94052730 3.49738672
150 -4.25817047 -4.94052730
151 -4.82248866 -4.25817047
152 -4.39884656 -4.82248866
153 -4.24272853 -4.39884656
154 -17.80716293 -4.24272853
155 9.84473585 -17.80716293
156 -5.25885936 9.84473585
157 -5.29084624 -5.25885936
158 -1.58208985 -5.29084624
159 -6.50412813 -1.58208985
160 -8.43968286 -6.50412813
161 12.48645960 -8.43968286
162 -4.90348006 12.48645960
163 -3.61068650 -4.90348006
> 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/7z3aq1321893932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/85z6f1321893932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9xhg81321893932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10d84z1321893932.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1184f41321893932.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/12zpc51321893932.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/13xud51321893932.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/14ehze1321893932.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/15k94q1321893932.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/168b561321893932.tab")
+ }
>
> try(system("convert tmp/19xb11321893932.ps tmp/19xb11321893932.png",intern=TRUE))
character(0)
> try(system("convert tmp/2bvrw1321893932.ps tmp/2bvrw1321893932.png",intern=TRUE))
character(0)
> try(system("convert tmp/3l00g1321893932.ps tmp/3l00g1321893932.png",intern=TRUE))
character(0)
> try(system("convert tmp/4vu971321893932.ps tmp/4vu971321893932.png",intern=TRUE))
character(0)
> try(system("convert tmp/558i81321893932.ps tmp/558i81321893932.png",intern=TRUE))
character(0)
> try(system("convert tmp/67k3t1321893932.ps tmp/67k3t1321893932.png",intern=TRUE))
character(0)
> try(system("convert tmp/7z3aq1321893932.ps tmp/7z3aq1321893932.png",intern=TRUE))
character(0)
> try(system("convert tmp/85z6f1321893932.ps tmp/85z6f1321893932.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xhg81321893932.ps tmp/9xhg81321893932.png",intern=TRUE))
character(0)
> try(system("convert tmp/10d84z1321893932.ps tmp/10d84z1321893932.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.978 0.652 5.747