R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(210907
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+ ,910)
+ ,dim=c(7
+ ,156)
+ ,dimnames=list(c('time_in_rfc'
+ ,'logins'
+ ,'totblogs'
+ ,'compendiums_reviewed'
+ ,'totsize'
+ ,'totrevisions'
+ ,'Pageviews')
+ ,1:156))
> y <- array(NA,dim=c(7,156),dimnames=list(c('time_in_rfc','logins','totblogs','compendiums_reviewed','totsize','totrevisions','Pageviews'),1:156))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> 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
time_in_rfc logins totblogs compendiums_reviewed totsize totrevisions
1 210907 56 145 30 112285 24188
2 120982 56 101 28 84786 18273
3 176508 54 98 38 83123 14130
4 179321 89 132 30 101193 32287
5 123185 40 60 22 38361 8654
6 52746 25 38 26 68504 9245
7 385534 92 144 25 119182 33251
8 33170 18 5 18 22807 1271
9 149061 44 84 26 116174 27101
10 165446 33 79 25 57635 16373
11 237213 84 127 38 66198 19716
12 173326 88 78 44 71701 17753
13 133131 55 60 30 57793 9028
14 258873 60 131 40 80444 18653
15 180083 66 84 34 53855 8828
16 324799 154 133 47 97668 29498
17 230964 53 150 30 133824 27563
18 236785 119 91 31 101481 18293
19 135473 41 132 23 99645 22530
20 202925 61 136 36 114789 15977
21 215147 58 124 36 99052 35082
22 344297 75 118 30 67654 16116
23 153935 33 70 25 65553 15849
24 132943 40 107 39 97500 16026
25 174724 92 119 34 69112 26569
26 174415 100 89 31 82753 24785
27 225548 112 112 31 85323 17569
28 223632 73 108 33 72654 23825
29 124817 40 52 25 30727 7869
30 221698 45 112 33 77873 14975
31 210767 60 116 35 117478 37791
32 170266 62 123 42 74007 9605
33 260561 75 125 43 90183 27295
34 84853 31 27 30 61542 2746
35 294424 77 162 33 101494 34461
36 215641 46 64 32 55813 4787
37 325107 99 92 36 79215 24919
38 167542 66 83 28 55461 16329
39 106408 30 41 14 31081 12558
40 265769 146 120 32 83122 28522
41 269651 67 105 30 70106 22265
42 149112 56 79 35 60578 14459
43 152871 58 70 28 79892 22240
44 111665 34 55 28 49810 11802
45 116408 61 39 39 71570 7623
46 362301 119 67 34 100708 11912
47 78800 42 21 26 33032 7935
48 183167 66 127 39 82875 18220
49 277965 89 152 39 139077 19199
50 150629 44 113 33 71595 19918
51 168809 66 99 28 72260 21884
52 24188 24 7 4 5950 2694
53 329267 259 141 39 115762 15808
54 65029 17 21 18 32551 3597
55 101097 64 35 14 31701 5296
56 218946 41 109 29 80670 25239
57 244052 68 133 44 143558 29801
58 233328 132 230 28 120733 34861
59 256462 105 166 35 105195 35940
60 206161 71 68 28 73107 16688
61 311473 112 147 38 132068 24683
62 235800 94 179 23 149193 46230
63 177939 82 61 36 46821 10387
64 207176 70 101 32 87011 21436
65 196553 57 108 29 95260 30546
66 174184 53 90 25 55183 19746
67 143246 103 114 27 106671 15977
68 187559 121 103 36 73511 22583
69 187681 62 142 28 92945 17274
70 119016 52 79 23 78664 16469
71 182192 52 88 40 70054 14251
72 73566 32 25 23 22618 3007
73 194979 62 83 40 74011 16851
74 167488 45 113 28 83737 21113
75 143756 46 118 34 69094 17401
76 275541 63 110 33 93133 23958
77 243199 75 129 28 95536 23567
78 182999 88 51 34 225920 13065
79 135649 46 93 30 62133 15358
80 152299 53 76 33 61370 14587
81 120221 37 49 22 43836 12770
82 346485 90 118 38 106117 24021
83 145790 63 38 26 38692 9648
84 193339 78 141 35 84651 20537
85 80953 25 58 8 56622 7905
86 122774 45 27 24 15986 4527
87 130585 46 91 29 95364 30495
88 286468 144 63 29 89691 17719
89 241066 82 56 45 67267 27056
90 148446 91 144 37 126846 33473
91 204713 71 73 33 41140 9758
92 182079 63 168 33 102860 21115
93 140344 53 64 25 51715 7236
94 220516 62 97 32 55801 13790
95 243060 63 117 29 111813 32902
96 162765 32 100 28 120293 25131
97 182613 39 149 28 138599 30910
98 232138 62 187 31 161647 35947
99 265318 117 127 52 115929 29848
100 310839 92 245 24 162901 42705
101 225060 93 87 41 109825 31808
102 232317 54 177 33 129838 26675
103 144966 144 49 32 37510 8435
104 43287 14 49 19 43750 7409
105 155754 61 73 20 40652 14993
106 164709 109 177 31 87771 36867
107 201940 38 94 31 85872 33835
108 235454 73 117 32 89275 24164
109 99466 50 55 23 192565 22609
110 100750 72 58 30 140867 6440
111 224549 50 95 31 120662 21916
112 243511 71 129 42 101338 20556
113 22938 10 11 1 1168 238
114 152474 65 101 32 65567 22392
115 61857 25 28 11 25162 3913
116 132487 41 89 36 40735 8388
117 317394 86 193 31 91413 22120
118 21054 16 4 0 855 338
119 209641 42 84 24 97068 11727
120 31414 19 39 8 14116 3988
121 244749 95 101 33 76643 20923
122 184510 49 82 40 110681 20237
123 128423 64 36 38 92696 3769
124 97839 38 75 24 94785 12252
125 38214 34 16 8 8773 1888
126 151101 32 55 35 83209 14497
127 272458 65 131 43 93815 28864
128 172494 52 131 43 86687 21721
129 328107 65 144 41 105547 33644
130 250579 83 139 38 103487 15923
131 351067 95 211 45 213688 42935
132 158015 29 78 31 71220 18864
133 85439 33 39 28 56926 7785
134 229242 247 90 31 91721 17939
135 351619 139 166 40 115168 23436
136 84207 29 12 30 111194 325
137 324598 110 133 37 135777 34538
138 131069 67 69 30 51513 12198
139 204271 42 119 35 74163 26924
140 165543 65 119 32 51633 12716
141 141722 94 65 27 75345 8172
142 299775 95 101 31 98952 14300
143 195838 67 196 31 102372 25515
144 173260 63 15 21 37238 2805
145 254488 83 136 39 103772 29402
146 104389 45 89 41 123969 16440
147 199476 70 123 32 135400 28732
148 224330 83 163 39 130115 28608
149 14688 10 5 0 6023 2065
150 181633 70 96 30 64466 14817
151 271856 103 151 37 54990 16714
152 7199 5 6 0 1644 556
153 46660 20 13 5 6179 2089
154 17547 5 3 1 3926 2658
155 95227 34 23 32 34777 1669
156 152601 48 57 24 73224 16267
Pageviews
1 3201
2 371
3 1192
4 1583
5 1439
6 1764
7 1495
8 1373
9 1491
10 4041
11 1706
12 2152
13 1036
14 1882
15 1929
16 2242
17 1220
18 1289
19 2515
20 2147
21 2352
22 1638
23 1222
24 1812
25 1677
26 1579
27 1731
28 807
29 2452
30 829
31 1940
32 2662
33 186
34 1499
35 865
36 2527
37 2747
38 2702
39 1383
40 2099
41 4308
42 918
43 3373
44 1713
45 1438
46 496
47 2253
48 744
49 1161
50 2352
51 2144
52 4691
53 1112
54 2694
55 1973
56 1769
57 3148
58 1954
59 1226
60 1389
61 1496
62 2269
63 1833
64 1268
65 1943
66 893
67 1762
68 1403
69 1425
70 1857
71 1840
72 1502
73 1441
74 1420
75 1416
76 2970
77 1317
78 1644
79 870
80 1654
81 1054
82 937
83 3004
84 2008
85 2547
86 1885
87 1626
88 2445
89 1964
90 1381
91 1369
92 1659
93 2888
94 1290
95 2845
96 1982
97 1904
98 1391
99 602
100 1559
101 2014
102 2143
103 2146
104 874
105 1590
106 1590
107 1210
108 2072
109 1401
110 391
111 761
112 1386
113 2395
114 1742
115 620
116 800
117 1684
118 1050
119 2699
120 1502
121 1459
122 2158
123 1421
124 2833
125 1955
126 2922
127 1002
128 1060
129 2186
130 3604
131 1035
132 1417
133 1587
134 1424
135 1701
136 1249
137 1926
138 3352
139 1641
140 2035
141 2312
142 2201
143 961
144 1900
145 1254
146 1335
147 207
148 2429
149 1639
150 872
151 1318
152 1018
153 1383
154 1314
155 1403
156 910
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) logins totblogs
-7.923e+03 7.266e+02 4.834e+02
compendiums_reviewed totsize totrevisions
2.093e+03 -6.233e-04 1.400e+00
Pageviews
4.893e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-116338 -26660 -2439 19373 161175
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.923e+03 1.510e+04 -0.525 0.600701
logins 7.266e+02 1.158e+02 6.276 3.58e-09 ***
totblogs 4.834e+02 1.371e+02 3.527 0.000559 ***
compendiums_reviewed 2.093e+03 4.895e+02 4.276 3.38e-05 ***
totsize -6.233e-04 1.384e-01 -0.005 0.996412
totrevisions 1.400e+00 6.549e-01 2.137 0.034213 *
Pageviews 4.893e+00 4.786e+00 1.022 0.308339
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 44580 on 149 degrees of freedom
Multiple R-squared: 0.7097, Adjusted R-squared: 0.698
F-statistic: 60.7 on 6 and 149 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,] 0.9507033 9.859338e-02 4.929669e-02
[2,] 0.9072077 1.855846e-01 9.279228e-02
[3,] 0.8774049 2.451902e-01 1.225951e-01
[4,] 0.8089465 3.821070e-01 1.910535e-01
[5,] 0.8848907 2.302186e-01 1.151093e-01
[6,] 0.8292600 3.414801e-01 1.707400e-01
[7,] 0.7681626 4.636748e-01 2.318374e-01
[8,] 0.6931291 6.137418e-01 3.068709e-01
[9,] 0.6238743 7.522513e-01 3.761257e-01
[10,] 0.7374242 5.251516e-01 2.625758e-01
[11,] 0.6692075 6.615850e-01 3.307925e-01
[12,] 0.5944875 8.110250e-01 4.055125e-01
[13,] 0.8845118 2.309764e-01 1.154882e-01
[14,] 0.8636510 2.726979e-01 1.363490e-01
[15,] 0.8314170 3.371659e-01 1.685830e-01
[16,] 0.9020391 1.959219e-01 9.796093e-02
[17,] 0.8937450 2.125100e-01 1.062550e-01
[18,] 0.8793888 2.412223e-01 1.206112e-01
[19,] 0.8486224 3.027553e-01 1.513776e-01
[20,] 0.8077620 3.844759e-01 1.922380e-01
[21,] 0.7945586 4.108828e-01 2.054414e-01
[22,] 0.7629738 4.740525e-01 2.370262e-01
[23,] 0.7476366 5.047268e-01 2.523634e-01
[24,] 0.7156209 5.687581e-01 2.843791e-01
[25,] 0.6835544 6.328912e-01 3.164456e-01
[26,] 0.6448879 7.102241e-01 3.551121e-01
[27,] 0.7725636 4.548728e-01 2.274364e-01
[28,] 0.9005971 1.988058e-01 9.940289e-02
[29,] 0.8780985 2.438030e-01 1.219015e-01
[30,] 0.8492378 3.015244e-01 1.507622e-01
[31,] 0.8361667 3.276666e-01 1.638333e-01
[32,] 0.8524107 2.951785e-01 1.475893e-01
[33,] 0.8225919 3.548163e-01 1.774081e-01
[34,] 0.7886211 4.227577e-01 2.113789e-01
[35,] 0.7497200 5.005601e-01 2.502800e-01
[36,] 0.7188630 5.622740e-01 2.811370e-01
[37,] 0.9707900 5.841994e-02 2.920997e-02
[38,] 0.9638618 7.227646e-02 3.613823e-02
[39,] 0.9585014 8.299711e-02 4.149856e-02
[40,] 0.9500308 9.993842e-02 4.996921e-02
[41,] 0.9431104 1.137793e-01 5.688964e-02
[42,] 0.9320220 1.359560e-01 6.797801e-02
[43,] 0.9256542 1.486916e-01 7.434578e-02
[44,] 0.9633161 7.336781e-02 3.668390e-02
[45,] 0.9526415 9.471703e-02 4.735852e-02
[46,] 0.9397509 1.204982e-01 6.024910e-02
[47,] 0.9341137 1.317726e-01 6.588632e-02
[48,] 0.9181580 1.636840e-01 8.184200e-02
[49,] 0.9654287 6.914264e-02 3.457132e-02
[50,] 0.9576704 8.465927e-02 4.232964e-02
[51,] 0.9544948 9.101044e-02 4.550522e-02
[52,] 0.9533476 9.330477e-02 4.665239e-02
[53,] 0.9503938 9.921244e-02 4.960622e-02
[54,] 0.9367369 1.265261e-01 6.326306e-02
[55,] 0.9216177 1.567646e-01 7.838230e-02
[56,] 0.9025888 1.948224e-01 9.741122e-02
[57,] 0.8834918 2.330164e-01 1.165082e-01
[58,] 0.9109764 1.780471e-01 8.902356e-02
[59,] 0.9193307 1.613386e-01 8.066930e-02
[60,] 0.9005856 1.988289e-01 9.941443e-02
[61,] 0.8889355 2.221290e-01 1.110645e-01
[62,] 0.8647166 2.705669e-01 1.352834e-01
[63,] 0.8396279 3.207442e-01 1.603721e-01
[64,] 0.8090125 3.819750e-01 1.909875e-01
[65,] 0.7757276 4.485448e-01 2.242724e-01
[66,] 0.7675659 4.648683e-01 2.324341e-01
[67,] 0.8038327 3.923345e-01 1.961673e-01
[68,] 0.7921428 4.157144e-01 2.078572e-01
[69,] 0.7618760 4.762479e-01 2.381240e-01
[70,] 0.7332745 5.334510e-01 2.667255e-01
[71,] 0.6960350 6.079300e-01 3.039650e-01
[72,] 0.6546031 6.907938e-01 3.453969e-01
[73,] 0.8566061 2.867877e-01 1.433939e-01
[74,] 0.8284715 3.430570e-01 1.715285e-01
[75,] 0.8164820 3.670361e-01 1.835180e-01
[76,] 0.7833626 4.332748e-01 2.166374e-01
[77,] 0.7527400 4.945199e-01 2.472600e-01
[78,] 0.7649641 4.700718e-01 2.350359e-01
[79,] 0.8042072 3.915856e-01 1.957928e-01
[80,] 0.7773652 4.452695e-01 2.226348e-01
[81,] 0.9132850 1.734301e-01 8.671505e-02
[82,] 0.9090693 1.818614e-01 9.093071e-02
[83,] 0.9097806 1.804388e-01 9.021942e-02
[84,] 0.8880244 2.239511e-01 1.119756e-01
[85,] 0.8898365 2.203270e-01 1.101635e-01
[86,] 0.8727663 2.544674e-01 1.272337e-01
[87,] 0.8459872 3.080256e-01 1.540128e-01
[88,] 0.8277845 3.444310e-01 1.722155e-01
[89,] 0.8052618 3.894764e-01 1.947382e-01
[90,] 0.7766005 4.467991e-01 2.233995e-01
[91,] 0.7420805 5.158391e-01 2.579195e-01
[92,] 0.7039470 5.921060e-01 2.960530e-01
[93,] 0.6647020 6.705960e-01 3.352980e-01
[94,] 0.6880489 6.239021e-01 3.119511e-01
[95,] 0.6733909 6.532181e-01 3.266091e-01
[96,] 0.6293429 7.413143e-01 3.706571e-01
[97,] 0.9034813 1.930374e-01 9.651868e-02
[98,] 0.8806243 2.387514e-01 1.193757e-01
[99,] 0.8553280 2.893440e-01 1.446720e-01
[100,] 0.8618231 2.763538e-01 1.381769e-01
[101,] 0.8517804 2.964392e-01 1.482196e-01
[102,] 0.8548474 2.903052e-01 1.451526e-01
[103,] 0.8256361 3.487278e-01 1.743639e-01
[104,] 0.7884487 4.231027e-01 2.115513e-01
[105,] 0.7994691 4.010618e-01 2.005309e-01
[106,] 0.7594734 4.810532e-01 2.405266e-01
[107,] 0.7167378 5.665244e-01 2.832622e-01
[108,] 0.7584933 4.830133e-01 2.415067e-01
[109,] 0.7124750 5.750499e-01 2.875250e-01
[110,] 0.7715107 4.569785e-01 2.284893e-01
[111,] 0.7365913 5.268174e-01 2.634087e-01
[112,] 0.7011743 5.976515e-01 2.988257e-01
[113,] 0.6488471 7.023058e-01 3.511529e-01
[114,] 0.5931046 8.137908e-01 4.068954e-01
[115,] 0.5914767 8.170467e-01 4.085233e-01
[116,] 0.5412279 9.175441e-01 4.587721e-01
[117,] 0.4828061 9.656122e-01 5.171939e-01
[118,] 0.4475866 8.951732e-01 5.524134e-01
[119,] 0.4348443 8.696886e-01 5.651557e-01
[120,] 0.4808950 9.617899e-01 5.191050e-01
[121,] 0.4143025 8.286050e-01 5.856975e-01
[122,] 0.3668251 7.336503e-01 6.331749e-01
[123,] 0.3022720 6.045441e-01 6.977280e-01
[124,] 0.2596434 5.192868e-01 7.403566e-01
[125,] 0.9121324 1.757352e-01 8.786759e-02
[126,] 0.8787293 2.425413e-01 1.212707e-01
[127,] 0.8764554 2.470891e-01 1.235446e-01
[128,] 0.8302349 3.395303e-01 1.697651e-01
[129,] 0.8469474 3.061052e-01 1.530526e-01
[130,] 0.8084610 3.830780e-01 1.915390e-01
[131,] 0.7344801 5.310398e-01 2.655199e-01
[132,] 0.9958071 8.385835e-03 4.192918e-03
[133,] 0.9994061 1.187747e-03 5.938735e-04
[134,] 0.9999709 5.810043e-05 2.905021e-05
[135,] 0.9998145 3.709290e-04 1.854645e-04
[136,] 0.9997409 5.182174e-04 2.591087e-04
[137,] 0.9975806 4.838868e-03 2.419434e-03
> postscript(file="/var/wessaorg/rcomp/tmp/1eunu1353357939.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/2eqt61353357939.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3a8w91353357939.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4mfkh1353357939.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ymkt1353357939.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 156
Frequency = 1
1 2 3 4 5
-4189.77676 -46550.03134 -7269.25779 -56894.85918 7865.11190
6 7 8 9 10
-51810.81248 150894.77605 -20558.64774 -15165.40756 16224.96005
11 12 13 14 15
7275.57985 -47819.44621 -8370.67371 40889.25657 6523.63741
16 17 18 19 20
5969.84718 20611.83251 17525.35589 -42120.90949 -7360.03964
21 22 23 24 25
-14911.89387 147365.67660 23595.78156 -52784.41593 -58235.39111
26 27 28 29 30
-40590.62575 63.41847 19987.59987 3223.14672 48746.94622
31 32 33 34 35
-16549.31222 -40645.22969 24508.17966 -16728.51826 46615.94627
36 37 38 39 40
73198.70332 93008.36601 -7256.59316 19087.22202 -7512.12567
41 42 43 44 45
63147.47749 -19788.64502 -21372.29717 -15176.01531 -38129.81532
46 47 48 49 50
161175.42261 -30471.50685 -28973.96232 33652.17208 -36453.32654
51 52 53 54 55
-18758.96329 -23801.93709 -28272.90952 -5420.68188 -748.15324
56 57 58 59 60
39759.09920 -10841.93638 -82724.42422 -21643.64605 40913.44837
61 62 63 64 65
45639.64975 -34961.06464 -2029.74839 12285.44371 -2046.11267
66 67 68 69 70
15793.79946 -66204.47208 -55999.52302 -7783.83751 -29259.31349
71 72 73 74 75
-2831.97770 -13528.79510 3421.82488 -6961.10967 -41188.29790
76 77 78 79 80
67438.96674 36294.73444 4978.80585 -23311.44271 -12565.54486
81 82 83 84 85
8524.21000 114300.51034 6973.95707 -35343.16825 2438.47456
86 87 88 89 90
19168.41879 -50181.44589 61905.65647 20717.50199 -110330.38964
91 92 93 94 95
36361.57332 -43660.94034 2269.71486 43946.77871 28050.08886
96 97 98 99 100
-4305.44208 -20926.45096 -17286.98574 -26646.91599 15949.44638
101 102 103 104 105
-16764.18091 -1368.26891 -64682.07021 -37035.33477 13467.60406
106 107 108 109 110
-116337.94858 18704.34211 22898.36052 -42046.50682 -45306.37982
111 112 113 114 115
51012.94142 14092.09850 4133.78325 -42454.40046 6562.35410
116 117 118 119 120
-23379.53941 65507.52697 9807.83938 66650.55971 -22987.17001
121 122 123 124 125
29378.39873 -5343.11909 -19260.45408 -39286.38967 -15247.57030
126 127 128 129 130
1395.58665 34583.97660 -46224.68701 75658.16910 11613.11093
131 132 133 134 135
28755.03017 8986.27436 -26697.28168 -82705.81938 53529.53580
136 137 138 139 140
-4027.36350 53182.66830 -39274.86104 5229.34610 -25985.36726
141 142 143 144 145
-29288.00473 94244.13374 -44901.68609 71006.21179 7510.75887
146 147 148 149 150
-78684.91444 -11040.54349 -40320.53789 2021.88563 4533.65355
151 152 153 154 155
24698.30395 2830.16883 13615.18316 8146.38687 -8825.97834
156
20686.83106
> postscript(file="/var/wessaorg/rcomp/tmp/6hevs1353357939.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 -4189.77676 NA
1 -46550.03134 -4189.77676
2 -7269.25779 -46550.03134
3 -56894.85918 -7269.25779
4 7865.11190 -56894.85918
5 -51810.81248 7865.11190
6 150894.77605 -51810.81248
7 -20558.64774 150894.77605
8 -15165.40756 -20558.64774
9 16224.96005 -15165.40756
10 7275.57985 16224.96005
11 -47819.44621 7275.57985
12 -8370.67371 -47819.44621
13 40889.25657 -8370.67371
14 6523.63741 40889.25657
15 5969.84718 6523.63741
16 20611.83251 5969.84718
17 17525.35589 20611.83251
18 -42120.90949 17525.35589
19 -7360.03964 -42120.90949
20 -14911.89387 -7360.03964
21 147365.67660 -14911.89387
22 23595.78156 147365.67660
23 -52784.41593 23595.78156
24 -58235.39111 -52784.41593
25 -40590.62575 -58235.39111
26 63.41847 -40590.62575
27 19987.59987 63.41847
28 3223.14672 19987.59987
29 48746.94622 3223.14672
30 -16549.31222 48746.94622
31 -40645.22969 -16549.31222
32 24508.17966 -40645.22969
33 -16728.51826 24508.17966
34 46615.94627 -16728.51826
35 73198.70332 46615.94627
36 93008.36601 73198.70332
37 -7256.59316 93008.36601
38 19087.22202 -7256.59316
39 -7512.12567 19087.22202
40 63147.47749 -7512.12567
41 -19788.64502 63147.47749
42 -21372.29717 -19788.64502
43 -15176.01531 -21372.29717
44 -38129.81532 -15176.01531
45 161175.42261 -38129.81532
46 -30471.50685 161175.42261
47 -28973.96232 -30471.50685
48 33652.17208 -28973.96232
49 -36453.32654 33652.17208
50 -18758.96329 -36453.32654
51 -23801.93709 -18758.96329
52 -28272.90952 -23801.93709
53 -5420.68188 -28272.90952
54 -748.15324 -5420.68188
55 39759.09920 -748.15324
56 -10841.93638 39759.09920
57 -82724.42422 -10841.93638
58 -21643.64605 -82724.42422
59 40913.44837 -21643.64605
60 45639.64975 40913.44837
61 -34961.06464 45639.64975
62 -2029.74839 -34961.06464
63 12285.44371 -2029.74839
64 -2046.11267 12285.44371
65 15793.79946 -2046.11267
66 -66204.47208 15793.79946
67 -55999.52302 -66204.47208
68 -7783.83751 -55999.52302
69 -29259.31349 -7783.83751
70 -2831.97770 -29259.31349
71 -13528.79510 -2831.97770
72 3421.82488 -13528.79510
73 -6961.10967 3421.82488
74 -41188.29790 -6961.10967
75 67438.96674 -41188.29790
76 36294.73444 67438.96674
77 4978.80585 36294.73444
78 -23311.44271 4978.80585
79 -12565.54486 -23311.44271
80 8524.21000 -12565.54486
81 114300.51034 8524.21000
82 6973.95707 114300.51034
83 -35343.16825 6973.95707
84 2438.47456 -35343.16825
85 19168.41879 2438.47456
86 -50181.44589 19168.41879
87 61905.65647 -50181.44589
88 20717.50199 61905.65647
89 -110330.38964 20717.50199
90 36361.57332 -110330.38964
91 -43660.94034 36361.57332
92 2269.71486 -43660.94034
93 43946.77871 2269.71486
94 28050.08886 43946.77871
95 -4305.44208 28050.08886
96 -20926.45096 -4305.44208
97 -17286.98574 -20926.45096
98 -26646.91599 -17286.98574
99 15949.44638 -26646.91599
100 -16764.18091 15949.44638
101 -1368.26891 -16764.18091
102 -64682.07021 -1368.26891
103 -37035.33477 -64682.07021
104 13467.60406 -37035.33477
105 -116337.94858 13467.60406
106 18704.34211 -116337.94858
107 22898.36052 18704.34211
108 -42046.50682 22898.36052
109 -45306.37982 -42046.50682
110 51012.94142 -45306.37982
111 14092.09850 51012.94142
112 4133.78325 14092.09850
113 -42454.40046 4133.78325
114 6562.35410 -42454.40046
115 -23379.53941 6562.35410
116 65507.52697 -23379.53941
117 9807.83938 65507.52697
118 66650.55971 9807.83938
119 -22987.17001 66650.55971
120 29378.39873 -22987.17001
121 -5343.11909 29378.39873
122 -19260.45408 -5343.11909
123 -39286.38967 -19260.45408
124 -15247.57030 -39286.38967
125 1395.58665 -15247.57030
126 34583.97660 1395.58665
127 -46224.68701 34583.97660
128 75658.16910 -46224.68701
129 11613.11093 75658.16910
130 28755.03017 11613.11093
131 8986.27436 28755.03017
132 -26697.28168 8986.27436
133 -82705.81938 -26697.28168
134 53529.53580 -82705.81938
135 -4027.36350 53529.53580
136 53182.66830 -4027.36350
137 -39274.86104 53182.66830
138 5229.34610 -39274.86104
139 -25985.36726 5229.34610
140 -29288.00473 -25985.36726
141 94244.13374 -29288.00473
142 -44901.68609 94244.13374
143 71006.21179 -44901.68609
144 7510.75887 71006.21179
145 -78684.91444 7510.75887
146 -11040.54349 -78684.91444
147 -40320.53789 -11040.54349
148 2021.88563 -40320.53789
149 4533.65355 2021.88563
150 24698.30395 4533.65355
151 2830.16883 24698.30395
152 13615.18316 2830.16883
153 8146.38687 13615.18316
154 -8825.97834 8146.38687
155 20686.83106 -8825.97834
156 NA 20686.83106
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -46550.03134 -4189.77676
[2,] -7269.25779 -46550.03134
[3,] -56894.85918 -7269.25779
[4,] 7865.11190 -56894.85918
[5,] -51810.81248 7865.11190
[6,] 150894.77605 -51810.81248
[7,] -20558.64774 150894.77605
[8,] -15165.40756 -20558.64774
[9,] 16224.96005 -15165.40756
[10,] 7275.57985 16224.96005
[11,] -47819.44621 7275.57985
[12,] -8370.67371 -47819.44621
[13,] 40889.25657 -8370.67371
[14,] 6523.63741 40889.25657
[15,] 5969.84718 6523.63741
[16,] 20611.83251 5969.84718
[17,] 17525.35589 20611.83251
[18,] -42120.90949 17525.35589
[19,] -7360.03964 -42120.90949
[20,] -14911.89387 -7360.03964
[21,] 147365.67660 -14911.89387
[22,] 23595.78156 147365.67660
[23,] -52784.41593 23595.78156
[24,] -58235.39111 -52784.41593
[25,] -40590.62575 -58235.39111
[26,] 63.41847 -40590.62575
[27,] 19987.59987 63.41847
[28,] 3223.14672 19987.59987
[29,] 48746.94622 3223.14672
[30,] -16549.31222 48746.94622
[31,] -40645.22969 -16549.31222
[32,] 24508.17966 -40645.22969
[33,] -16728.51826 24508.17966
[34,] 46615.94627 -16728.51826
[35,] 73198.70332 46615.94627
[36,] 93008.36601 73198.70332
[37,] -7256.59316 93008.36601
[38,] 19087.22202 -7256.59316
[39,] -7512.12567 19087.22202
[40,] 63147.47749 -7512.12567
[41,] -19788.64502 63147.47749
[42,] -21372.29717 -19788.64502
[43,] -15176.01531 -21372.29717
[44,] -38129.81532 -15176.01531
[45,] 161175.42261 -38129.81532
[46,] -30471.50685 161175.42261
[47,] -28973.96232 -30471.50685
[48,] 33652.17208 -28973.96232
[49,] -36453.32654 33652.17208
[50,] -18758.96329 -36453.32654
[51,] -23801.93709 -18758.96329
[52,] -28272.90952 -23801.93709
[53,] -5420.68188 -28272.90952
[54,] -748.15324 -5420.68188
[55,] 39759.09920 -748.15324
[56,] -10841.93638 39759.09920
[57,] -82724.42422 -10841.93638
[58,] -21643.64605 -82724.42422
[59,] 40913.44837 -21643.64605
[60,] 45639.64975 40913.44837
[61,] -34961.06464 45639.64975
[62,] -2029.74839 -34961.06464
[63,] 12285.44371 -2029.74839
[64,] -2046.11267 12285.44371
[65,] 15793.79946 -2046.11267
[66,] -66204.47208 15793.79946
[67,] -55999.52302 -66204.47208
[68,] -7783.83751 -55999.52302
[69,] -29259.31349 -7783.83751
[70,] -2831.97770 -29259.31349
[71,] -13528.79510 -2831.97770
[72,] 3421.82488 -13528.79510
[73,] -6961.10967 3421.82488
[74,] -41188.29790 -6961.10967
[75,] 67438.96674 -41188.29790
[76,] 36294.73444 67438.96674
[77,] 4978.80585 36294.73444
[78,] -23311.44271 4978.80585
[79,] -12565.54486 -23311.44271
[80,] 8524.21000 -12565.54486
[81,] 114300.51034 8524.21000
[82,] 6973.95707 114300.51034
[83,] -35343.16825 6973.95707
[84,] 2438.47456 -35343.16825
[85,] 19168.41879 2438.47456
[86,] -50181.44589 19168.41879
[87,] 61905.65647 -50181.44589
[88,] 20717.50199 61905.65647
[89,] -110330.38964 20717.50199
[90,] 36361.57332 -110330.38964
[91,] -43660.94034 36361.57332
[92,] 2269.71486 -43660.94034
[93,] 43946.77871 2269.71486
[94,] 28050.08886 43946.77871
[95,] -4305.44208 28050.08886
[96,] -20926.45096 -4305.44208
[97,] -17286.98574 -20926.45096
[98,] -26646.91599 -17286.98574
[99,] 15949.44638 -26646.91599
[100,] -16764.18091 15949.44638
[101,] -1368.26891 -16764.18091
[102,] -64682.07021 -1368.26891
[103,] -37035.33477 -64682.07021
[104,] 13467.60406 -37035.33477
[105,] -116337.94858 13467.60406
[106,] 18704.34211 -116337.94858
[107,] 22898.36052 18704.34211
[108,] -42046.50682 22898.36052
[109,] -45306.37982 -42046.50682
[110,] 51012.94142 -45306.37982
[111,] 14092.09850 51012.94142
[112,] 4133.78325 14092.09850
[113,] -42454.40046 4133.78325
[114,] 6562.35410 -42454.40046
[115,] -23379.53941 6562.35410
[116,] 65507.52697 -23379.53941
[117,] 9807.83938 65507.52697
[118,] 66650.55971 9807.83938
[119,] -22987.17001 66650.55971
[120,] 29378.39873 -22987.17001
[121,] -5343.11909 29378.39873
[122,] -19260.45408 -5343.11909
[123,] -39286.38967 -19260.45408
[124,] -15247.57030 -39286.38967
[125,] 1395.58665 -15247.57030
[126,] 34583.97660 1395.58665
[127,] -46224.68701 34583.97660
[128,] 75658.16910 -46224.68701
[129,] 11613.11093 75658.16910
[130,] 28755.03017 11613.11093
[131,] 8986.27436 28755.03017
[132,] -26697.28168 8986.27436
[133,] -82705.81938 -26697.28168
[134,] 53529.53580 -82705.81938
[135,] -4027.36350 53529.53580
[136,] 53182.66830 -4027.36350
[137,] -39274.86104 53182.66830
[138,] 5229.34610 -39274.86104
[139,] -25985.36726 5229.34610
[140,] -29288.00473 -25985.36726
[141,] 94244.13374 -29288.00473
[142,] -44901.68609 94244.13374
[143,] 71006.21179 -44901.68609
[144,] 7510.75887 71006.21179
[145,] -78684.91444 7510.75887
[146,] -11040.54349 -78684.91444
[147,] -40320.53789 -11040.54349
[148,] 2021.88563 -40320.53789
[149,] 4533.65355 2021.88563
[150,] 24698.30395 4533.65355
[151,] 2830.16883 24698.30395
[152,] 13615.18316 2830.16883
[153,] 8146.38687 13615.18316
[154,] -8825.97834 8146.38687
[155,] 20686.83106 -8825.97834
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -46550.03134 -4189.77676
2 -7269.25779 -46550.03134
3 -56894.85918 -7269.25779
4 7865.11190 -56894.85918
5 -51810.81248 7865.11190
6 150894.77605 -51810.81248
7 -20558.64774 150894.77605
8 -15165.40756 -20558.64774
9 16224.96005 -15165.40756
10 7275.57985 16224.96005
11 -47819.44621 7275.57985
12 -8370.67371 -47819.44621
13 40889.25657 -8370.67371
14 6523.63741 40889.25657
15 5969.84718 6523.63741
16 20611.83251 5969.84718
17 17525.35589 20611.83251
18 -42120.90949 17525.35589
19 -7360.03964 -42120.90949
20 -14911.89387 -7360.03964
21 147365.67660 -14911.89387
22 23595.78156 147365.67660
23 -52784.41593 23595.78156
24 -58235.39111 -52784.41593
25 -40590.62575 -58235.39111
26 63.41847 -40590.62575
27 19987.59987 63.41847
28 3223.14672 19987.59987
29 48746.94622 3223.14672
30 -16549.31222 48746.94622
31 -40645.22969 -16549.31222
32 24508.17966 -40645.22969
33 -16728.51826 24508.17966
34 46615.94627 -16728.51826
35 73198.70332 46615.94627
36 93008.36601 73198.70332
37 -7256.59316 93008.36601
38 19087.22202 -7256.59316
39 -7512.12567 19087.22202
40 63147.47749 -7512.12567
41 -19788.64502 63147.47749
42 -21372.29717 -19788.64502
43 -15176.01531 -21372.29717
44 -38129.81532 -15176.01531
45 161175.42261 -38129.81532
46 -30471.50685 161175.42261
47 -28973.96232 -30471.50685
48 33652.17208 -28973.96232
49 -36453.32654 33652.17208
50 -18758.96329 -36453.32654
51 -23801.93709 -18758.96329
52 -28272.90952 -23801.93709
53 -5420.68188 -28272.90952
54 -748.15324 -5420.68188
55 39759.09920 -748.15324
56 -10841.93638 39759.09920
57 -82724.42422 -10841.93638
58 -21643.64605 -82724.42422
59 40913.44837 -21643.64605
60 45639.64975 40913.44837
61 -34961.06464 45639.64975
62 -2029.74839 -34961.06464
63 12285.44371 -2029.74839
64 -2046.11267 12285.44371
65 15793.79946 -2046.11267
66 -66204.47208 15793.79946
67 -55999.52302 -66204.47208
68 -7783.83751 -55999.52302
69 -29259.31349 -7783.83751
70 -2831.97770 -29259.31349
71 -13528.79510 -2831.97770
72 3421.82488 -13528.79510
73 -6961.10967 3421.82488
74 -41188.29790 -6961.10967
75 67438.96674 -41188.29790
76 36294.73444 67438.96674
77 4978.80585 36294.73444
78 -23311.44271 4978.80585
79 -12565.54486 -23311.44271
80 8524.21000 -12565.54486
81 114300.51034 8524.21000
82 6973.95707 114300.51034
83 -35343.16825 6973.95707
84 2438.47456 -35343.16825
85 19168.41879 2438.47456
86 -50181.44589 19168.41879
87 61905.65647 -50181.44589
88 20717.50199 61905.65647
89 -110330.38964 20717.50199
90 36361.57332 -110330.38964
91 -43660.94034 36361.57332
92 2269.71486 -43660.94034
93 43946.77871 2269.71486
94 28050.08886 43946.77871
95 -4305.44208 28050.08886
96 -20926.45096 -4305.44208
97 -17286.98574 -20926.45096
98 -26646.91599 -17286.98574
99 15949.44638 -26646.91599
100 -16764.18091 15949.44638
101 -1368.26891 -16764.18091
102 -64682.07021 -1368.26891
103 -37035.33477 -64682.07021
104 13467.60406 -37035.33477
105 -116337.94858 13467.60406
106 18704.34211 -116337.94858
107 22898.36052 18704.34211
108 -42046.50682 22898.36052
109 -45306.37982 -42046.50682
110 51012.94142 -45306.37982
111 14092.09850 51012.94142
112 4133.78325 14092.09850
113 -42454.40046 4133.78325
114 6562.35410 -42454.40046
115 -23379.53941 6562.35410
116 65507.52697 -23379.53941
117 9807.83938 65507.52697
118 66650.55971 9807.83938
119 -22987.17001 66650.55971
120 29378.39873 -22987.17001
121 -5343.11909 29378.39873
122 -19260.45408 -5343.11909
123 -39286.38967 -19260.45408
124 -15247.57030 -39286.38967
125 1395.58665 -15247.57030
126 34583.97660 1395.58665
127 -46224.68701 34583.97660
128 75658.16910 -46224.68701
129 11613.11093 75658.16910
130 28755.03017 11613.11093
131 8986.27436 28755.03017
132 -26697.28168 8986.27436
133 -82705.81938 -26697.28168
134 53529.53580 -82705.81938
135 -4027.36350 53529.53580
136 53182.66830 -4027.36350
137 -39274.86104 53182.66830
138 5229.34610 -39274.86104
139 -25985.36726 5229.34610
140 -29288.00473 -25985.36726
141 94244.13374 -29288.00473
142 -44901.68609 94244.13374
143 71006.21179 -44901.68609
144 7510.75887 71006.21179
145 -78684.91444 7510.75887
146 -11040.54349 -78684.91444
147 -40320.53789 -11040.54349
148 2021.88563 -40320.53789
149 4533.65355 2021.88563
150 24698.30395 4533.65355
151 2830.16883 24698.30395
152 13615.18316 2830.16883
153 8146.38687 13615.18316
154 -8825.97834 8146.38687
155 20686.83106 -8825.97834
> 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/7tkl61353357939.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/857y71353357939.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/9tffg1353357939.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/10bykg1353357939.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11bbmt1353357939.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/121qr71353357939.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13bi8u1353357939.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14290z1353357939.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/15hurp1353357939.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/16hf971353357939.tab")
+ }
>
> try(system("convert tmp/1eunu1353357939.ps tmp/1eunu1353357939.png",intern=TRUE))
character(0)
> try(system("convert tmp/2eqt61353357939.ps tmp/2eqt61353357939.png",intern=TRUE))
character(0)
> try(system("convert tmp/3a8w91353357939.ps tmp/3a8w91353357939.png",intern=TRUE))
character(0)
> try(system("convert tmp/4mfkh1353357939.ps tmp/4mfkh1353357939.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ymkt1353357939.ps tmp/5ymkt1353357939.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hevs1353357939.ps tmp/6hevs1353357939.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tkl61353357939.ps tmp/7tkl61353357939.png",intern=TRUE))
character(0)
> try(system("convert tmp/857y71353357939.ps tmp/857y71353357939.png",intern=TRUE))
character(0)
> try(system("convert tmp/9tffg1353357939.ps tmp/9tffg1353357939.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bykg1353357939.ps tmp/10bykg1353357939.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.721 0.931 9.700