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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+ ,49)
+ ,dim=c(6
+ ,164)
+ ,dimnames=list(c('BlogdComputations'
+ ,'TotalTime'
+ ,'Shared'
+ ,'Caracters'
+ ,'Writing'
+ ,'Hyperlink')
+ ,1:164))
> y <- array(NA,dim=c(6,164),dimnames=list(c('BlogdComputations','TotalTime','Shared','Caracters','Writing','Hyperlink'),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'
> #'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
BlogdComputations TotalTime Shared Caracters Writing Hyperlink
1 65 146455 1 95556 114468 127
2 54 84944 4 54565 88594 90
3 58 113337 9 63016 74151 68
4 75 128655 2 79774 77921 111
5 41 74398 1 31258 53212 51
6 0 35523 2 52491 34956 33
7 111 293403 0 91256 149703 123
8 1 32750 0 22807 6853 5
9 36 106539 5 77411 58907 63
10 60 130539 0 48821 67067 66
11 63 154991 0 52295 110563 99
12 71 126683 7 63262 58126 72
13 38 100672 6 50466 57113 55
14 76 179562 3 62932 77993 116
15 61 125971 4 38439 68091 71
16 125 234509 0 70817 124676 125
17 84 158980 4 105965 109522 123
18 69 184217 3 73795 75865 74
19 77 107342 0 82043 79746 116
20 95 141371 5 74349 77844 117
21 78 154730 0 82204 98681 98
22 76 264020 1 55709 105531 101
23 40 90938 3 37137 51428 43
24 81 101324 5 70780 65703 103
25 102 130232 0 55027 72562 107
26 70 137793 0 56699 81728 77
27 75 161678 4 65911 95580 87
28 93 151503 0 56316 98278 99
29 42 105324 0 26982 46629 46
30 95 175914 0 54628 115189 96
31 87 181853 3 96750 124865 92
32 44 114928 4 53009 59392 96
33 84 190410 1 64664 127818 96
34 28 61499 4 36990 17821 15
35 87 223004 1 85224 154076 147
36 71 167131 0 37048 64881 56
37 68 233482 0 59635 136506 81
38 50 121185 2 42051 66524 69
39 30 78776 1 26998 45988 34
40 86 188967 2 63717 107445 98
41 75 199512 8 55071 102772 82
42 46 102531 5 40001 46657 64
43 52 118958 3 54506 97563 61
44 31 68948 4 35838 36663 45
45 30 93125 1 50838 55369 37
46 70 277108 2 86997 77921 64
47 20 78800 2 33032 56968 21
48 84 157250 0 61704 77519 104
49 81 210554 6 117986 129805 126
50 79 127324 3 56733 72761 104
51 70 114397 0 55064 81278 87
52 8 24188 0 5950 15049 7
53 67 246209 6 84607 113935 130
54 21 65029 5 32551 25109 21
55 30 98030 3 31701 45824 35
56 70 173587 1 71170 89644 97
57 87 172684 5 101773 109011 103
58 87 191381 5 101653 134245 210
59 112 191276 0 81493 136692 151
60 54 134043 9 55901 50741 57
61 96 233406 6 109104 149510 117
62 93 195304 6 114425 147888 152
63 49 127619 5 36311 54987 52
64 49 162810 6 70027 74467 83
65 38 129100 2 73713 100033 87
66 64 108715 0 40671 85505 80
67 62 106469 3 89041 62426 88
68 66 142069 8 57231 82932 83
69 98 143937 2 78792 79169 140
70 97 84256 5 59155 65469 76
71 56 118807 11 55827 63572 70
72 22 69471 6 22618 23824 26
73 51 122433 5 58425 73831 66
74 56 131122 1 65724 63551 89
75 94 94763 0 56979 56756 100
76 98 188780 3 72369 81399 98
77 76 191467 3 79194 117881 109
78 57 105615 6 202316 70711 51
79 75 89318 1 44970 50495 82
80 48 107335 0 49319 53845 65
81 48 98599 1 36252 51390 46
82 109 260646 0 75741 104953 104
83 27 131876 5 38417 65983 36
84 83 119291 2 64102 76839 123
85 49 80953 0 56622 55792 59
86 24 99768 0 15430 25155 27
87 43 84572 5 72571 55291 84
88 44 202373 1 67271 84279 61
89 49 166790 0 43460 99692 46
90 106 99946 1 99501 59633 125
91 42 116900 1 28340 63249 58
92 108 142146 2 76013 82928 152
93 27 99246 4 37361 50000 52
94 79 156833 1 48204 69455 85
95 49 175078 4 76168 84068 95
96 64 130533 0 85168 76195 78
97 75 142339 2 125410 114634 144
98 115 176789 0 123328 139357 149
99 92 181379 7 83038 110044 101
100 106 228548 7 120087 155118 205
101 73 142141 6 91939 83061 61
102 105 167845 0 103646 127122 145
103 30 103012 0 29467 45653 28
104 13 43287 4 43750 19630 49
105 69 125366 4 34497 67229 68
106 72 118372 0 66477 86060 142
107 80 135171 0 71181 88003 82
108 106 175568 0 74482 95815 105
109 28 74112 0 174949 85499 52
110 70 88817 0 46765 27220 56
111 51 164767 4 90257 109882 81
112 90 141933 0 51370 72579 100
113 12 22938 0 1168 5841 11
114 84 115199 0 51360 68369 87
115 23 61857 4 25162 24610 31
116 57 91185 0 21067 30995 67
117 84 213765 1 58233 150662 150
118 4 21054 0 855 6622 4
119 56 167105 5 85903 93694 75
120 18 31414 0 14116 13155 39
121 86 178863 1 57637 111908 88
122 39 126681 7 94137 57550 67
123 16 64320 5 62147 16356 24
124 18 67746 2 62832 40174 58
125 16 38214 0 8773 13983 16
126 42 90961 1 63785 52316 49
127 75 181510 0 65196 99585 109
128 30 116775 0 73087 86271 124
129 104 223914 2 72631 131012 115
130 121 185139 0 86281 130274 128
131 106 242879 2 162365 159051 159
132 57 139144 0 56530 76506 75
133 28 75812 0 35606 49145 30
134 56 178218 4 70111 66398 83
135 81 246834 4 92046 127546 135
136 2 50999 8 63989 6802 8
137 88 223842 0 104911 99509 115
138 41 93577 4 43448 43106 60
139 83 155383 0 60029 108303 99
140 55 111664 1 38650 64167 98
141 3 75426 0 47261 8579 36
142 54 243551 9 73586 97811 93
143 89 136548 0 83042 84365 158
144 41 173260 3 37238 10901 16
145 94 185039 7 63958 91346 100
146 101 67507 5 78956 33660 49
147 70 139350 2 99518 93634 89
148 111 172964 1 111436 109348 153
149 0 0 9 0 0 0
150 4 14688 0 6023 7953 5
151 0 98 0 0 0 0
152 0 455 0 0 0 0
153 0 0 1 0 0 0
154 0 0 0 0 0 0
155 42 128066 2 42564 63538 80
156 97 176460 1 38885 108281 122
157 0 0 0 0 0 0
158 0 203 0 0 0 0
159 7 7199 0 1644 4245 6
160 12 46660 0 6179 21509 13
161 0 17547 0 3926 7670 3
162 37 73567 0 23238 10641 18
163 0 969 0 0 0 0
164 39 101060 2 49288 41243 49
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) TotalTime Shared Caracters Writing Hyperlink
4.731e+00 1.570e-04 -8.591e-01 3.151e-05 -2.089e-06 4.441e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-50.256 -8.848 -0.969 8.366 65.787
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.731e+00 2.901e+00 1.631 0.104891
TotalTime 1.570e-04 3.967e-05 3.959 0.000114 ***
Shared -8.591e-01 4.887e-01 -1.758 0.080703 .
Caracters 3.151e-05 5.600e-05 0.563 0.574496
Writing -2.089e-06 8.538e-05 -0.024 0.980513
Hyperlink 4.441e-01 5.614e-02 7.910 4.21e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 15.42 on 158 degrees of freedom
Multiple R-squared: 0.7784, Adjusted R-squared: 0.7714
F-statistic: 111 on 5 and 158 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,] 0.0009745993 0.0019491985 0.9990254007
[2,] 0.0005533760 0.0011067520 0.9994466240
[3,] 0.0084934621 0.0169869242 0.9915065379
[4,] 0.0025917630 0.0051835260 0.9974082370
[5,] 0.0056112936 0.0112225872 0.9943887064
[6,] 0.0896274017 0.1792548035 0.9103725983
[7,] 0.0510776454 0.1021552908 0.9489223546
[8,] 0.1232226542 0.2464453084 0.8767773458
[9,] 0.0993525845 0.1987051691 0.9006474155
[10,] 0.0632176559 0.1264353119 0.9367823441
[11,] 0.0687836543 0.1375673086 0.9312163457
[12,] 0.0559866106 0.1119732211 0.9440133894
[13,] 0.0492428628 0.0984857256 0.9507571372
[14,] 0.1460858684 0.2921717368 0.8539141316
[15,] 0.1124480034 0.2248960068 0.8875519966
[16,] 0.0995589557 0.1991179113 0.9004410443
[17,] 0.1627991319 0.3255982637 0.8372008681
[18,] 0.1474378989 0.2948757978 0.8525621011
[19,] 0.1162638747 0.2325277495 0.8837361253
[20,] 0.1264367025 0.2528734050 0.8735632975
[21,] 0.0942323615 0.1884647229 0.9057676385
[22,] 0.0980787256 0.1961574513 0.9019212744
[23,] 0.1105973042 0.2211946085 0.8894026958
[24,] 0.1914252290 0.3828504580 0.8085747710
[25,] 0.1533122055 0.3066244110 0.8466877945
[26,] 0.1505770470 0.3011540940 0.8494229530
[27,] 0.2351657851 0.4703315702 0.7648342149
[28,] 0.2160286403 0.4320572807 0.7839713597
[29,] 0.2042552718 0.4085105436 0.7957447282
[30,] 0.1721247137 0.3442494274 0.8278752863
[31,] 0.1379887588 0.2759775176 0.8620112412
[32,] 0.1124671422 0.2249342845 0.8875328578
[33,] 0.0894788289 0.1789576578 0.9105211711
[34,] 0.0709311091 0.1418622181 0.9290688909
[35,] 0.0546369228 0.1092738457 0.9453630772
[36,] 0.0416155225 0.0832310449 0.9583844775
[37,] 0.0313498905 0.0626997810 0.9686501095
[38,] 0.0248480616 0.0496961232 0.9751519384
[39,] 0.0182307628 0.0364615255 0.9817692372
[40,] 0.0134256782 0.0268513564 0.9865743218
[41,] 0.0115180397 0.0230360793 0.9884819603
[42,] 0.0084357199 0.0168714397 0.9915642801
[43,] 0.0062731054 0.0125462109 0.9937268946
[44,] 0.0043766209 0.0087532417 0.9956233791
[45,] 0.0245165345 0.0490330689 0.9754834655
[46,] 0.0179724807 0.0359449614 0.9820275193
[47,] 0.0134243090 0.0268486180 0.9865756910
[48,] 0.0102352554 0.0204705107 0.9897647446
[49,] 0.0095738560 0.0191477120 0.9904261440
[50,] 0.0607464099 0.1214928197 0.9392535901
[51,] 0.0514648311 0.1029296622 0.9485351689
[52,] 0.0435835488 0.0871670977 0.9564164512
[53,] 0.0345122223 0.0690244446 0.9654877777
[54,] 0.0274893159 0.0549786318 0.9725106841
[55,] 0.0208786396 0.0417572791 0.9791213604
[56,] 0.0206288879 0.0412577758 0.9793711121
[57,] 0.0351635452 0.0703270904 0.9648364548
[58,] 0.0274580549 0.0549161098 0.9725419451
[59,] 0.0212985080 0.0425970160 0.9787014920
[60,] 0.0168412055 0.0336824110 0.9831587945
[61,] 0.0141346504 0.0282693008 0.9858653496
[62,] 0.1237019249 0.2474038498 0.8762980751
[63,] 0.1083062926 0.2166125853 0.8916937074
[64,] 0.0899030316 0.1798060631 0.9100969684
[65,] 0.0726622261 0.1453244521 0.9273377739
[66,] 0.0632499807 0.1264999614 0.9367500193
[67,] 0.1035892755 0.2071785510 0.8964107245
[68,] 0.1209907997 0.2419815993 0.8790092003
[69,] 0.1024944321 0.2049888642 0.8975055679
[70,] 0.0983386376 0.1966772753 0.9016613624
[71,] 0.1065042969 0.2130085938 0.8934957031
[72,] 0.0891530902 0.1783061804 0.9108469098
[73,] 0.0746195541 0.1492391081 0.9253804459
[74,] 0.0730474851 0.1460949703 0.9269525149
[75,] 0.0660562349 0.1321124697 0.9339437651
[76,] 0.0535971186 0.1071942373 0.9464028814
[77,] 0.0426107217 0.0852214433 0.9573892783
[78,] 0.0374645951 0.0749291902 0.9625354049
[79,] 0.0329185013 0.0658370026 0.9670814987
[80,] 0.0406927699 0.0813855398 0.9593072301
[81,] 0.0321381150 0.0642762301 0.9678618850
[82,] 0.0563580448 0.1127160897 0.9436419552
[83,] 0.0471750882 0.0943501764 0.9528249118
[84,] 0.0480786964 0.0961573928 0.9519213036
[85,] 0.0464069450 0.0928138900 0.9535930550
[86,] 0.0410530268 0.0821060535 0.9589469732
[87,] 0.0575609444 0.1151218887 0.9424390556
[88,] 0.0455140533 0.0910281066 0.9544859467
[89,] 0.0475555045 0.0951110089 0.9524444955
[90,] 0.0447945458 0.0895890916 0.9552054542
[91,] 0.0486486691 0.0972973382 0.9513513309
[92,] 0.0556388714 0.1112777428 0.9443611286
[93,] 0.0674539762 0.1349079524 0.9325460238
[94,] 0.0567973163 0.1135946327 0.9432026837
[95,] 0.0461650489 0.0923300977 0.9538349511
[96,] 0.0488434691 0.0976869382 0.9511565309
[97,] 0.0508787055 0.1017574110 0.9491212945
[98,] 0.0491983424 0.0983966848 0.9508016576
[99,] 0.0489665302 0.0979330604 0.9510334698
[100,] 0.0714103195 0.1428206389 0.9285896805
[101,] 0.0725958969 0.1451917938 0.9274041031
[102,] 0.1058907917 0.2117815834 0.8941092083
[103,] 0.1059071900 0.2118143801 0.8940928100
[104,] 0.1218295176 0.2436590353 0.8781704824
[105,] 0.1000752321 0.2001504642 0.8999247679
[106,] 0.1308844227 0.2617688453 0.8691155773
[107,] 0.1070979042 0.2141958085 0.8929020958
[108,] 0.1082391970 0.2164783939 0.8917608030
[109,] 0.1252513697 0.2505027394 0.8747486303
[110,] 0.1043586197 0.2087172393 0.8956413803
[111,] 0.0921015069 0.1842030138 0.9078984931
[112,] 0.0772287513 0.1544575026 0.9227712487
[113,] 0.0670600684 0.1341201369 0.9329399316
[114,] 0.0616528062 0.1233056124 0.9383471938
[115,] 0.0502954575 0.1005909151 0.9497045425
[116,] 0.0654184160 0.1308368320 0.9345815840
[117,] 0.0505674331 0.1011348663 0.9494325669
[118,] 0.0380585906 0.0761171812 0.9619414094
[119,] 0.0292714998 0.0585429996 0.9707285002
[120,] 0.2148642187 0.4297284375 0.7851357813
[121,] 0.2026404615 0.4052809230 0.7973595385
[122,] 0.3015123988 0.6030247976 0.6984876012
[123,] 0.3189344730 0.6378689461 0.6810655270
[124,] 0.2688829787 0.5377659574 0.7311170213
[125,] 0.2240016668 0.4480033336 0.7759983332
[126,] 0.1933452535 0.3866905069 0.8066547465
[127,] 0.2252789071 0.4505578142 0.7747210929
[128,] 0.2979703211 0.5959406422 0.7020296789
[129,] 0.2551849410 0.5103698821 0.7448150590
[130,] 0.2098496730 0.4196993460 0.7901503270
[131,] 0.1762185066 0.3524370132 0.8237814934
[132,] 0.1393681162 0.2787362325 0.8606318838
[133,] 0.3283529976 0.6567059953 0.6716470024
[134,] 0.6134605813 0.7730788373 0.3865394187
[135,] 0.6166562798 0.7666874404 0.3833437202
[136,] 0.5720591833 0.8558816334 0.4279408167
[137,] 0.5124773469 0.9750453062 0.4875226531
[138,] 0.9970182025 0.0059635949 0.0029817975
[139,] 0.9946237271 0.0107525458 0.0053762729
[140,] 0.9970570139 0.0058859722 0.0029429861
[141,] 0.9972227723 0.0055544553 0.0027772277
[142,] 0.9929773772 0.0140452455 0.0070226228
[143,] 0.9828208614 0.0343582773 0.0171791386
[144,] 0.9604563863 0.0790872273 0.0395436137
[145,] 0.9995888706 0.0008222589 0.0004111294
[146,] 0.9974789991 0.0050420018 0.0025210009
[147,] 0.9995581035 0.0008837929 0.0004418965
> postscript(file="/var/wessaorg/rcomp/tmp/1z8dn1321543012.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2jzcl1321543012.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3hmf51321543012.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/40inf1321543012.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5rl891321543012.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
-21.038875989 -2.134343352 11.175817010 0.141421589 1.923546658
6 7 8 9 10
-24.826573639 3.012007910 -11.798837021 -11.458166394 4.063093064
11 12 13 14 15
-11.449583760 18.544340837 -3.279998232 -7.683012586 7.325752749
16 17 18 19 20
25.963708473 0.009674896 2.890322199 1.481951841 18.228298290
21 22 23 24 25
3.068344341 -15.717590520 3.408114707 16.820996707 27.720318912
26 27 28 29 30
7.821624551 7.805398009 18.945793784 -0.450577505 18.533235451
31 32 33 34 35
12.647386853 -19.519157014 5.826220326 9.258498918 -19.532605556
36 37 38 39 40
14.124091418 -10.958862197 -3.869861141 -2.095591425 8.011070981
41 42 43 44 45
7.877667724 -0.119810791 2.563963742 -2.157719297 -6.412546411
46 47 48 49 50
-7.526471240 -5.634484374 6.609865327 -11.039376885 9.033196739
51 52 53 54 55
7.105373001 -3.794268255 -31.396109412 0.053945415 -3.993414404
56 57 58 59 60
-6.260836770 10.729279067 -39.665895803 7.895500841 8.984391252
61 62 63 64 65
4.689794676 -8.040554823 4.403116275 -15.051619127 -26.033594891
66 67 68 69 70
5.568416497 1.373739135 7.344421088 7.897146921 47.856897955
71 72 73 74 75
9.351348129 -0.694562302 0.343115404 -9.922873489 28.304029961
76 77 78 79 80
20.572559472 -6.872994567 11.964318752 19.376744418 -3.892400915
81 82 83 84 85
7.182456533 14.988566291 -11.203601642 4.774499061 3.688793911
86 87 88 89 90
-8.821652794 -10.189239024 -20.682912618 -3.510948608 27.913782819
91 92 93 94 95
-6.746177322 12.944936638 -14.044064350 11.380309829 -24.198588998
96 97 98 99 100
1.609098033 -18.022867323 12.746066576 17.562694141 -23.100847393
101 102 103 104 105
21.291218734 6.521378927 -4.174564646 -18.189291564 16.875366809
106 107 108 109 110
-16.292349565 15.569840118 24.924937949 -16.794403602 25.036991633
111 112 113 114 115
-14.752409279 17.106655399 -1.242838405 21.069168109 -2.516031043
116 117 118 119 120
7.597878839 -21.570768549 -5.826998663 -6.492637239 -9.400496538
121 122 123 124 125
13.380186829 -12.208934679 -7.117665897 -23.303269143 -2.084593959
126 127 128 129 130
0.184125410 -8.483986881 -50.256096130 12.742482174 27.908589150
131 132 133 134 135
-10.543338477 -4.507963965 -2.977591285 -12.208884171 -21.638787501
136 137 138 139 140
-9.421352649 -6.047258459 -2.912605908 8.240472985 -11.009735689
141 142 143 144 145
-31.033341382 -24.657175127 -9.777121858 3.383125718 19.994109500
146 147 148 149 150
65.786649147 2.642320888 8.741525890 3.000572201 -5.431454587
151 152 153 154 155
-4.746869687 -4.802929674 -3.872363685 -4.731480670 -17.857576944
156 157 158 159 160
10.242320443 -4.731480670 -4.763357918 -1.569303563 -5.981214751
161 162 163 164
-8.926785691 12.013040373 -4.883643495 -3.108999578
> postscript(file="/var/wessaorg/rcomp/tmp/6l2ay1321543012.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 -21.038875989 NA
1 -2.134343352 -21.038875989
2 11.175817010 -2.134343352
3 0.141421589 11.175817010
4 1.923546658 0.141421589
5 -24.826573639 1.923546658
6 3.012007910 -24.826573639
7 -11.798837021 3.012007910
8 -11.458166394 -11.798837021
9 4.063093064 -11.458166394
10 -11.449583760 4.063093064
11 18.544340837 -11.449583760
12 -3.279998232 18.544340837
13 -7.683012586 -3.279998232
14 7.325752749 -7.683012586
15 25.963708473 7.325752749
16 0.009674896 25.963708473
17 2.890322199 0.009674896
18 1.481951841 2.890322199
19 18.228298290 1.481951841
20 3.068344341 18.228298290
21 -15.717590520 3.068344341
22 3.408114707 -15.717590520
23 16.820996707 3.408114707
24 27.720318912 16.820996707
25 7.821624551 27.720318912
26 7.805398009 7.821624551
27 18.945793784 7.805398009
28 -0.450577505 18.945793784
29 18.533235451 -0.450577505
30 12.647386853 18.533235451
31 -19.519157014 12.647386853
32 5.826220326 -19.519157014
33 9.258498918 5.826220326
34 -19.532605556 9.258498918
35 14.124091418 -19.532605556
36 -10.958862197 14.124091418
37 -3.869861141 -10.958862197
38 -2.095591425 -3.869861141
39 8.011070981 -2.095591425
40 7.877667724 8.011070981
41 -0.119810791 7.877667724
42 2.563963742 -0.119810791
43 -2.157719297 2.563963742
44 -6.412546411 -2.157719297
45 -7.526471240 -6.412546411
46 -5.634484374 -7.526471240
47 6.609865327 -5.634484374
48 -11.039376885 6.609865327
49 9.033196739 -11.039376885
50 7.105373001 9.033196739
51 -3.794268255 7.105373001
52 -31.396109412 -3.794268255
53 0.053945415 -31.396109412
54 -3.993414404 0.053945415
55 -6.260836770 -3.993414404
56 10.729279067 -6.260836770
57 -39.665895803 10.729279067
58 7.895500841 -39.665895803
59 8.984391252 7.895500841
60 4.689794676 8.984391252
61 -8.040554823 4.689794676
62 4.403116275 -8.040554823
63 -15.051619127 4.403116275
64 -26.033594891 -15.051619127
65 5.568416497 -26.033594891
66 1.373739135 5.568416497
67 7.344421088 1.373739135
68 7.897146921 7.344421088
69 47.856897955 7.897146921
70 9.351348129 47.856897955
71 -0.694562302 9.351348129
72 0.343115404 -0.694562302
73 -9.922873489 0.343115404
74 28.304029961 -9.922873489
75 20.572559472 28.304029961
76 -6.872994567 20.572559472
77 11.964318752 -6.872994567
78 19.376744418 11.964318752
79 -3.892400915 19.376744418
80 7.182456533 -3.892400915
81 14.988566291 7.182456533
82 -11.203601642 14.988566291
83 4.774499061 -11.203601642
84 3.688793911 4.774499061
85 -8.821652794 3.688793911
86 -10.189239024 -8.821652794
87 -20.682912618 -10.189239024
88 -3.510948608 -20.682912618
89 27.913782819 -3.510948608
90 -6.746177322 27.913782819
91 12.944936638 -6.746177322
92 -14.044064350 12.944936638
93 11.380309829 -14.044064350
94 -24.198588998 11.380309829
95 1.609098033 -24.198588998
96 -18.022867323 1.609098033
97 12.746066576 -18.022867323
98 17.562694141 12.746066576
99 -23.100847393 17.562694141
100 21.291218734 -23.100847393
101 6.521378927 21.291218734
102 -4.174564646 6.521378927
103 -18.189291564 -4.174564646
104 16.875366809 -18.189291564
105 -16.292349565 16.875366809
106 15.569840118 -16.292349565
107 24.924937949 15.569840118
108 -16.794403602 24.924937949
109 25.036991633 -16.794403602
110 -14.752409279 25.036991633
111 17.106655399 -14.752409279
112 -1.242838405 17.106655399
113 21.069168109 -1.242838405
114 -2.516031043 21.069168109
115 7.597878839 -2.516031043
116 -21.570768549 7.597878839
117 -5.826998663 -21.570768549
118 -6.492637239 -5.826998663
119 -9.400496538 -6.492637239
120 13.380186829 -9.400496538
121 -12.208934679 13.380186829
122 -7.117665897 -12.208934679
123 -23.303269143 -7.117665897
124 -2.084593959 -23.303269143
125 0.184125410 -2.084593959
126 -8.483986881 0.184125410
127 -50.256096130 -8.483986881
128 12.742482174 -50.256096130
129 27.908589150 12.742482174
130 -10.543338477 27.908589150
131 -4.507963965 -10.543338477
132 -2.977591285 -4.507963965
133 -12.208884171 -2.977591285
134 -21.638787501 -12.208884171
135 -9.421352649 -21.638787501
136 -6.047258459 -9.421352649
137 -2.912605908 -6.047258459
138 8.240472985 -2.912605908
139 -11.009735689 8.240472985
140 -31.033341382 -11.009735689
141 -24.657175127 -31.033341382
142 -9.777121858 -24.657175127
143 3.383125718 -9.777121858
144 19.994109500 3.383125718
145 65.786649147 19.994109500
146 2.642320888 65.786649147
147 8.741525890 2.642320888
148 3.000572201 8.741525890
149 -5.431454587 3.000572201
150 -4.746869687 -5.431454587
151 -4.802929674 -4.746869687
152 -3.872363685 -4.802929674
153 -4.731480670 -3.872363685
154 -17.857576944 -4.731480670
155 10.242320443 -17.857576944
156 -4.731480670 10.242320443
157 -4.763357918 -4.731480670
158 -1.569303563 -4.763357918
159 -5.981214751 -1.569303563
160 -8.926785691 -5.981214751
161 12.013040373 -8.926785691
162 -4.883643495 12.013040373
163 -3.108999578 -4.883643495
164 NA -3.108999578
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.134343352 -21.038875989
[2,] 11.175817010 -2.134343352
[3,] 0.141421589 11.175817010
[4,] 1.923546658 0.141421589
[5,] -24.826573639 1.923546658
[6,] 3.012007910 -24.826573639
[7,] -11.798837021 3.012007910
[8,] -11.458166394 -11.798837021
[9,] 4.063093064 -11.458166394
[10,] -11.449583760 4.063093064
[11,] 18.544340837 -11.449583760
[12,] -3.279998232 18.544340837
[13,] -7.683012586 -3.279998232
[14,] 7.325752749 -7.683012586
[15,] 25.963708473 7.325752749
[16,] 0.009674896 25.963708473
[17,] 2.890322199 0.009674896
[18,] 1.481951841 2.890322199
[19,] 18.228298290 1.481951841
[20,] 3.068344341 18.228298290
[21,] -15.717590520 3.068344341
[22,] 3.408114707 -15.717590520
[23,] 16.820996707 3.408114707
[24,] 27.720318912 16.820996707
[25,] 7.821624551 27.720318912
[26,] 7.805398009 7.821624551
[27,] 18.945793784 7.805398009
[28,] -0.450577505 18.945793784
[29,] 18.533235451 -0.450577505
[30,] 12.647386853 18.533235451
[31,] -19.519157014 12.647386853
[32,] 5.826220326 -19.519157014
[33,] 9.258498918 5.826220326
[34,] -19.532605556 9.258498918
[35,] 14.124091418 -19.532605556
[36,] -10.958862197 14.124091418
[37,] -3.869861141 -10.958862197
[38,] -2.095591425 -3.869861141
[39,] 8.011070981 -2.095591425
[40,] 7.877667724 8.011070981
[41,] -0.119810791 7.877667724
[42,] 2.563963742 -0.119810791
[43,] -2.157719297 2.563963742
[44,] -6.412546411 -2.157719297
[45,] -7.526471240 -6.412546411
[46,] -5.634484374 -7.526471240
[47,] 6.609865327 -5.634484374
[48,] -11.039376885 6.609865327
[49,] 9.033196739 -11.039376885
[50,] 7.105373001 9.033196739
[51,] -3.794268255 7.105373001
[52,] -31.396109412 -3.794268255
[53,] 0.053945415 -31.396109412
[54,] -3.993414404 0.053945415
[55,] -6.260836770 -3.993414404
[56,] 10.729279067 -6.260836770
[57,] -39.665895803 10.729279067
[58,] 7.895500841 -39.665895803
[59,] 8.984391252 7.895500841
[60,] 4.689794676 8.984391252
[61,] -8.040554823 4.689794676
[62,] 4.403116275 -8.040554823
[63,] -15.051619127 4.403116275
[64,] -26.033594891 -15.051619127
[65,] 5.568416497 -26.033594891
[66,] 1.373739135 5.568416497
[67,] 7.344421088 1.373739135
[68,] 7.897146921 7.344421088
[69,] 47.856897955 7.897146921
[70,] 9.351348129 47.856897955
[71,] -0.694562302 9.351348129
[72,] 0.343115404 -0.694562302
[73,] -9.922873489 0.343115404
[74,] 28.304029961 -9.922873489
[75,] 20.572559472 28.304029961
[76,] -6.872994567 20.572559472
[77,] 11.964318752 -6.872994567
[78,] 19.376744418 11.964318752
[79,] -3.892400915 19.376744418
[80,] 7.182456533 -3.892400915
[81,] 14.988566291 7.182456533
[82,] -11.203601642 14.988566291
[83,] 4.774499061 -11.203601642
[84,] 3.688793911 4.774499061
[85,] -8.821652794 3.688793911
[86,] -10.189239024 -8.821652794
[87,] -20.682912618 -10.189239024
[88,] -3.510948608 -20.682912618
[89,] 27.913782819 -3.510948608
[90,] -6.746177322 27.913782819
[91,] 12.944936638 -6.746177322
[92,] -14.044064350 12.944936638
[93,] 11.380309829 -14.044064350
[94,] -24.198588998 11.380309829
[95,] 1.609098033 -24.198588998
[96,] -18.022867323 1.609098033
[97,] 12.746066576 -18.022867323
[98,] 17.562694141 12.746066576
[99,] -23.100847393 17.562694141
[100,] 21.291218734 -23.100847393
[101,] 6.521378927 21.291218734
[102,] -4.174564646 6.521378927
[103,] -18.189291564 -4.174564646
[104,] 16.875366809 -18.189291564
[105,] -16.292349565 16.875366809
[106,] 15.569840118 -16.292349565
[107,] 24.924937949 15.569840118
[108,] -16.794403602 24.924937949
[109,] 25.036991633 -16.794403602
[110,] -14.752409279 25.036991633
[111,] 17.106655399 -14.752409279
[112,] -1.242838405 17.106655399
[113,] 21.069168109 -1.242838405
[114,] -2.516031043 21.069168109
[115,] 7.597878839 -2.516031043
[116,] -21.570768549 7.597878839
[117,] -5.826998663 -21.570768549
[118,] -6.492637239 -5.826998663
[119,] -9.400496538 -6.492637239
[120,] 13.380186829 -9.400496538
[121,] -12.208934679 13.380186829
[122,] -7.117665897 -12.208934679
[123,] -23.303269143 -7.117665897
[124,] -2.084593959 -23.303269143
[125,] 0.184125410 -2.084593959
[126,] -8.483986881 0.184125410
[127,] -50.256096130 -8.483986881
[128,] 12.742482174 -50.256096130
[129,] 27.908589150 12.742482174
[130,] -10.543338477 27.908589150
[131,] -4.507963965 -10.543338477
[132,] -2.977591285 -4.507963965
[133,] -12.208884171 -2.977591285
[134,] -21.638787501 -12.208884171
[135,] -9.421352649 -21.638787501
[136,] -6.047258459 -9.421352649
[137,] -2.912605908 -6.047258459
[138,] 8.240472985 -2.912605908
[139,] -11.009735689 8.240472985
[140,] -31.033341382 -11.009735689
[141,] -24.657175127 -31.033341382
[142,] -9.777121858 -24.657175127
[143,] 3.383125718 -9.777121858
[144,] 19.994109500 3.383125718
[145,] 65.786649147 19.994109500
[146,] 2.642320888 65.786649147
[147,] 8.741525890 2.642320888
[148,] 3.000572201 8.741525890
[149,] -5.431454587 3.000572201
[150,] -4.746869687 -5.431454587
[151,] -4.802929674 -4.746869687
[152,] -3.872363685 -4.802929674
[153,] -4.731480670 -3.872363685
[154,] -17.857576944 -4.731480670
[155,] 10.242320443 -17.857576944
[156,] -4.731480670 10.242320443
[157,] -4.763357918 -4.731480670
[158,] -1.569303563 -4.763357918
[159,] -5.981214751 -1.569303563
[160,] -8.926785691 -5.981214751
[161,] 12.013040373 -8.926785691
[162,] -4.883643495 12.013040373
[163,] -3.108999578 -4.883643495
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.134343352 -21.038875989
2 11.175817010 -2.134343352
3 0.141421589 11.175817010
4 1.923546658 0.141421589
5 -24.826573639 1.923546658
6 3.012007910 -24.826573639
7 -11.798837021 3.012007910
8 -11.458166394 -11.798837021
9 4.063093064 -11.458166394
10 -11.449583760 4.063093064
11 18.544340837 -11.449583760
12 -3.279998232 18.544340837
13 -7.683012586 -3.279998232
14 7.325752749 -7.683012586
15 25.963708473 7.325752749
16 0.009674896 25.963708473
17 2.890322199 0.009674896
18 1.481951841 2.890322199
19 18.228298290 1.481951841
20 3.068344341 18.228298290
21 -15.717590520 3.068344341
22 3.408114707 -15.717590520
23 16.820996707 3.408114707
24 27.720318912 16.820996707
25 7.821624551 27.720318912
26 7.805398009 7.821624551
27 18.945793784 7.805398009
28 -0.450577505 18.945793784
29 18.533235451 -0.450577505
30 12.647386853 18.533235451
31 -19.519157014 12.647386853
32 5.826220326 -19.519157014
33 9.258498918 5.826220326
34 -19.532605556 9.258498918
35 14.124091418 -19.532605556
36 -10.958862197 14.124091418
37 -3.869861141 -10.958862197
38 -2.095591425 -3.869861141
39 8.011070981 -2.095591425
40 7.877667724 8.011070981
41 -0.119810791 7.877667724
42 2.563963742 -0.119810791
43 -2.157719297 2.563963742
44 -6.412546411 -2.157719297
45 -7.526471240 -6.412546411
46 -5.634484374 -7.526471240
47 6.609865327 -5.634484374
48 -11.039376885 6.609865327
49 9.033196739 -11.039376885
50 7.105373001 9.033196739
51 -3.794268255 7.105373001
52 -31.396109412 -3.794268255
53 0.053945415 -31.396109412
54 -3.993414404 0.053945415
55 -6.260836770 -3.993414404
56 10.729279067 -6.260836770
57 -39.665895803 10.729279067
58 7.895500841 -39.665895803
59 8.984391252 7.895500841
60 4.689794676 8.984391252
61 -8.040554823 4.689794676
62 4.403116275 -8.040554823
63 -15.051619127 4.403116275
64 -26.033594891 -15.051619127
65 5.568416497 -26.033594891
66 1.373739135 5.568416497
67 7.344421088 1.373739135
68 7.897146921 7.344421088
69 47.856897955 7.897146921
70 9.351348129 47.856897955
71 -0.694562302 9.351348129
72 0.343115404 -0.694562302
73 -9.922873489 0.343115404
74 28.304029961 -9.922873489
75 20.572559472 28.304029961
76 -6.872994567 20.572559472
77 11.964318752 -6.872994567
78 19.376744418 11.964318752
79 -3.892400915 19.376744418
80 7.182456533 -3.892400915
81 14.988566291 7.182456533
82 -11.203601642 14.988566291
83 4.774499061 -11.203601642
84 3.688793911 4.774499061
85 -8.821652794 3.688793911
86 -10.189239024 -8.821652794
87 -20.682912618 -10.189239024
88 -3.510948608 -20.682912618
89 27.913782819 -3.510948608
90 -6.746177322 27.913782819
91 12.944936638 -6.746177322
92 -14.044064350 12.944936638
93 11.380309829 -14.044064350
94 -24.198588998 11.380309829
95 1.609098033 -24.198588998
96 -18.022867323 1.609098033
97 12.746066576 -18.022867323
98 17.562694141 12.746066576
99 -23.100847393 17.562694141
100 21.291218734 -23.100847393
101 6.521378927 21.291218734
102 -4.174564646 6.521378927
103 -18.189291564 -4.174564646
104 16.875366809 -18.189291564
105 -16.292349565 16.875366809
106 15.569840118 -16.292349565
107 24.924937949 15.569840118
108 -16.794403602 24.924937949
109 25.036991633 -16.794403602
110 -14.752409279 25.036991633
111 17.106655399 -14.752409279
112 -1.242838405 17.106655399
113 21.069168109 -1.242838405
114 -2.516031043 21.069168109
115 7.597878839 -2.516031043
116 -21.570768549 7.597878839
117 -5.826998663 -21.570768549
118 -6.492637239 -5.826998663
119 -9.400496538 -6.492637239
120 13.380186829 -9.400496538
121 -12.208934679 13.380186829
122 -7.117665897 -12.208934679
123 -23.303269143 -7.117665897
124 -2.084593959 -23.303269143
125 0.184125410 -2.084593959
126 -8.483986881 0.184125410
127 -50.256096130 -8.483986881
128 12.742482174 -50.256096130
129 27.908589150 12.742482174
130 -10.543338477 27.908589150
131 -4.507963965 -10.543338477
132 -2.977591285 -4.507963965
133 -12.208884171 -2.977591285
134 -21.638787501 -12.208884171
135 -9.421352649 -21.638787501
136 -6.047258459 -9.421352649
137 -2.912605908 -6.047258459
138 8.240472985 -2.912605908
139 -11.009735689 8.240472985
140 -31.033341382 -11.009735689
141 -24.657175127 -31.033341382
142 -9.777121858 -24.657175127
143 3.383125718 -9.777121858
144 19.994109500 3.383125718
145 65.786649147 19.994109500
146 2.642320888 65.786649147
147 8.741525890 2.642320888
148 3.000572201 8.741525890
149 -5.431454587 3.000572201
150 -4.746869687 -5.431454587
151 -4.802929674 -4.746869687
152 -3.872363685 -4.802929674
153 -4.731480670 -3.872363685
154 -17.857576944 -4.731480670
155 10.242320443 -17.857576944
156 -4.731480670 10.242320443
157 -4.763357918 -4.731480670
158 -1.569303563 -4.763357918
159 -5.981214751 -1.569303563
160 -8.926785691 -5.981214751
161 12.013040373 -8.926785691
162 -4.883643495 12.013040373
163 -3.108999578 -4.883643495
> 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/7ku8m1321543012.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8u2lu1321543012.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9azht1321543012.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10yufg1321543012.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11hulh1321543013.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/12d9x41321543013.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/13u5ef1321543013.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/148pes1321543013.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/15jmed1321543013.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/16o07z1321543013.tab")
+ }
>
> try(system("convert tmp/1z8dn1321543012.ps tmp/1z8dn1321543012.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jzcl1321543012.ps tmp/2jzcl1321543012.png",intern=TRUE))
character(0)
> try(system("convert tmp/3hmf51321543012.ps tmp/3hmf51321543012.png",intern=TRUE))
character(0)
> try(system("convert tmp/40inf1321543012.ps tmp/40inf1321543012.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rl891321543012.ps tmp/5rl891321543012.png",intern=TRUE))
character(0)
> try(system("convert tmp/6l2ay1321543012.ps tmp/6l2ay1321543012.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ku8m1321543012.ps tmp/7ku8m1321543012.png",intern=TRUE))
character(0)
> try(system("convert tmp/8u2lu1321543012.ps tmp/8u2lu1321543012.png",intern=TRUE))
character(0)
> try(system("convert tmp/9azht1321543012.ps tmp/9azht1321543012.png",intern=TRUE))
character(0)
> try(system("convert tmp/10yufg1321543012.ps tmp/10yufg1321543012.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.414 0.596 6.203