R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(73
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+ ,242258)
+ ,dim=c(4
+ ,164)
+ ,dimnames=list(c('Numerlogins'
+ ,'TotalReceivedcompendium'
+ ,'totalwriting'
+ ,'totaltime')
+ ,1:164))
> y <- array(NA,dim=c(4,164),dimnames=list(c('Numerlogins','TotalReceivedcompendium','totalwriting','totaltime'),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 = '4'
> 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
totaltime Numerlogins TotalReceivedcompendium totalwriting
1 279055 73 42 140824
2 209884 73 38 110459
3 233939 83 46 105079
4 222117 106 42 112098
5 179751 54 30 43929
6 70849 28 35 76173
7 568125 131 40 187326
8 33186 19 18 22807
9 227332 62 38 144408
10 258874 48 37 66485
11 351915 118 46 79089
12 260484 129 60 81625
13 203988 82 37 68788
14 368577 85 55 103297
15 269455 88 44 69446
16 394578 186 63 114948
17 335567 76 40 167949
18 423110 171 43 125081
19 182016 58 32 125818
20 267365 88 52 136588
21 279428 73 49 112431
22 506616 109 41 103037
23 206722 47 25 82317
24 200004 58 57 118906
25 257139 132 45 83515
26 270815 137 42 104581
27 296850 133 45 103129
28 307100 90 43 83243
29 184160 58 36 37110
30 393860 79 45 113344
31 320558 88 50 139165
32 252512 82 50 86652
33 373013 102 51 112302
34 115602 46 42 69652
35 430118 103 44 119442
36 273950 56 42 69867
37 428077 128 44 101629
38 251349 91 40 70168
39 115658 33 17 31081
40 388812 208 43 103925
41 343783 85 41 92622
42 202289 75 41 79011
43 214344 81 40 93487
44 182398 65 49 64520
45 157164 84 52 93473
46 459440 156 42 114360
47 78800 42 26 33032
48 217932 84 59 96125
49 368086 122 50 151911
50 210554 66 50 89256
51 244640 79 47 95676
52 24188 24 4 5950
53 399093 331 51 149695
54 65029 17 18 32551
55 101097 64 14 31701
56 300488 64 41 100087
57 369627 90 61 169707
58 367127 204 40 150491
59 374158 151 44 120192
60 270099 88 40 95893
61 391871 151 51 151715
62 315924 121 29 176225
63 291391 124 43 59900
64 286417 92 42 104767
65 276201 78 41 114799
66 267432 71 30 72128
67 215924 140 39 143592
68 252767 156 51 89626
69 260919 87 40 131072
70 182961 73 29 126817
71 256967 74 47 81351
72 73566 32 23 22618
73 272362 93 48 88977
74 220707 61 38 92059
75 228835 68 42 81897
76 371391 91 46 108146
77 398210 104 40 126372
78 220401 110 45 249771
79 229333 70 42 71154
80 217623 70 41 71571
81 199011 52 37 55918
82 483074 131 47 160141
83 145943 71 26 38692
84 295224 108 48 102812
85 80953 25 8 56622
86 180759 61 27 15986
87 179344 61 38 123534
88 415550 221 41 108535
89 369093 128 61 93879
90 180679 106 45 144551
91 299505 104 41 56750
92 292260 84 42 127654
93 199481 67 35 65594
94 282361 78 36 59938
95 329281 89 40 146975
96 234577 48 40 165904
97 297995 67 38 169265
98 305984 88 43 183500
99 416463 163 65 165986
100 412530 117 33 184923
101 297080 141 51 140358
102 318283 70 45 149959
103 214250 196 36 57224
104 43287 14 19 43750
105 223456 86 25 48029
106 258249 158 44 104978
107 299566 60 45 100046
108 321797 95 44 101047
109 174736 89 35 197426
110 169545 101 46 160902
111 354041 77 44 147172
112 303273 90 45 109432
113 23668 13 1 1168
114 196743 79 40 83248
115 61857 25 11 25162
116 207339 53 51 45724
117 431443 122 38 110529
118 21054 16 0 855
119 252805 52 30 101382
120 31961 22 8 14116
121 354622 123 43 89506
122 251240 76 48 135356
123 187003 96 49 116066
124 180842 58 32 144244
125 38214 34 8 8773
126 278173 55 43 102153
127 358276 84 52 117440
128 211775 66 53 104128
129 445926 89 49 134238
130 348017 99 48 134047
131 441946 133 56 279488
132 208962 41 45 79756
133 105332 45 40 66089
134 316128 361 48 102070
135 466139 198 50 146760
136 160799 61 43 154771
137 412099 138 46 165933
138 173802 83 40 64593
139 292443 54 45 92280
140 283913 100 46 67150
141 234262 121 37 128692
142 386740 124 45 124089
143 246963 92 39 125386
144 173260 63 21 37238
145 346748 108 50 140015
146 176654 58 55 150047
147 264767 92 40 154451
148 314070 112 48 156349
149 1 0 0 0
150 14688 10 0 6023
151 98 1 0 0
152 455 2 0 0
153 0 0 0 0
154 0 0 0 0
155 284420 92 46 84601
156 410509 164 52 68946
157 0 0 0 0
158 203 4 0 0
159 7199 5 0 1644
160 46660 20 5 6179
161 17547 5 1 3926
162 121550 46 48 52789
163 969 2 0 0
164 242258 74 34 100350
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Numerlogins TotalReceivedcompendium
6086.6596 833.3217 2746.1082
totalwriting
0.6648
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-190453 -35458 -2941 39367 228612
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.087e+03 1.399e+04 0.435 0.664
Numerlogins 8.333e+02 1.263e+02 6.598 5.81e-10 ***
TotalReceivedcompendium 2.746e+03 5.178e+02 5.304 3.72e-07 ***
totalwriting 6.648e-01 1.435e-01 4.631 7.48e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 67680 on 160 degrees of freedom
Multiple R-squared: 0.7099, Adjusted R-squared: 0.7045
F-statistic: 130.5 on 3 and 160 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,] 0.5857370 8.285260e-01 4.142630e-01
[2,] 0.5135235 9.729531e-01 4.864765e-01
[3,] 0.4305531 8.611062e-01 5.694469e-01
[4,] 0.8060433 3.879134e-01 1.939567e-01
[5,] 0.7401901 5.196198e-01 2.598099e-01
[6,] 0.6821394 6.357212e-01 3.178606e-01
[7,] 0.6006497 7.987006e-01 3.993503e-01
[8,] 0.7913678 4.172643e-01 2.086322e-01
[9,] 0.7368275 5.263449e-01 2.631725e-01
[10,] 0.7389437 5.221127e-01 2.610563e-01
[11,] 0.6710217 6.579567e-01 3.289783e-01
[12,] 0.6093561 7.812878e-01 3.906439e-01
[13,] 0.6025910 7.948180e-01 3.974090e-01
[14,] 0.5536089 8.927821e-01 4.463911e-01
[15,] 0.4910057 9.820114e-01 5.089943e-01
[16,] 0.8973821 2.052359e-01 1.026179e-01
[17,] 0.8677357 2.645285e-01 1.322643e-01
[18,] 0.8428555 3.142889e-01 1.571445e-01
[19,] 0.8456756 3.086489e-01 1.543244e-01
[20,] 0.8609947 2.780107e-01 1.390053e-01
[21,] 0.8372307 3.255385e-01 1.627693e-01
[22,] 0.8253574 3.492852e-01 1.746426e-01
[23,] 0.7942575 4.114850e-01 2.057425e-01
[24,] 0.8726356 2.547287e-01 1.273644e-01
[25,] 0.8395827 3.208346e-01 1.604173e-01
[26,] 0.8024734 3.950532e-01 1.975266e-01
[27,] 0.7974074 4.051853e-01 2.025926e-01
[28,] 0.7932531 4.134938e-01 2.067469e-01
[29,] 0.8596487 2.807027e-01 1.403513e-01
[30,] 0.8700272 2.599455e-01 1.299728e-01
[31,] 0.8973005 2.053991e-01 1.026995e-01
[32,] 0.8713883 2.572234e-01 1.286117e-01
[33,] 0.8415898 3.168204e-01 1.584102e-01
[34,] 0.8449828 3.100344e-01 1.550172e-01
[35,] 0.8603934 2.792131e-01 1.396066e-01
[36,] 0.8364375 3.271249e-01 1.635625e-01
[37,] 0.8156598 3.686803e-01 1.843402e-01
[38,] 0.7899100 4.201801e-01 2.100900e-01
[39,] 0.8485168 3.029663e-01 1.514832e-01
[40,] 0.8776629 2.446742e-01 1.223371e-01
[41,] 0.8658535 2.682931e-01 1.341465e-01
[42,] 0.8635597 2.728806e-01 1.364403e-01
[43,] 0.8404510 3.190980e-01 1.595490e-01
[44,] 0.8181681 3.636637e-01 1.818319e-01
[45,] 0.7861879 4.276242e-01 2.138121e-01
[46,] 0.7588584 4.822833e-01 2.411416e-01
[47,] 0.9466044 1.067912e-01 5.339559e-02
[48,] 0.9350898 1.298205e-01 6.491023e-02
[49,] 0.9207067 1.585866e-01 7.929328e-02
[50,] 0.9146484 1.707031e-01 8.535157e-02
[51,] 0.8957466 2.085069e-01 1.042534e-01
[52,] 0.8895802 2.208396e-01 1.104198e-01
[53,] 0.8730200 2.539599e-01 1.269800e-01
[54,] 0.8479651 3.040699e-01 1.520349e-01
[55,] 0.8220740 3.558520e-01 1.779260e-01
[56,] 0.8147845 3.704310e-01 1.852155e-01
[57,] 0.7904990 4.190020e-01 2.095010e-01
[58,] 0.7573707 4.852587e-01 2.426293e-01
[59,] 0.7211861 5.576278e-01 2.788139e-01
[60,] 0.7227547 5.544906e-01 2.772453e-01
[61,] 0.8065267 3.869467e-01 1.934733e-01
[62,] 0.8160220 3.679561e-01 1.839780e-01
[63,] 0.7899260 4.201481e-01 2.100740e-01
[64,] 0.7856205 4.287590e-01 2.143795e-01
[65,] 0.7516491 4.967018e-01 2.483509e-01
[66,] 0.7249632 5.500737e-01 2.750368e-01
[67,] 0.6859180 6.281640e-01 3.140820e-01
[68,] 0.6445371 7.109258e-01 3.554629e-01
[69,] 0.6015671 7.968658e-01 3.984329e-01
[70,] 0.6331618 7.336765e-01 3.668382e-01
[71,] 0.6971194 6.057612e-01 3.028806e-01
[72,] 0.8838035 2.323930e-01 1.161965e-01
[73,] 0.8600319 2.799361e-01 1.399681e-01
[74,] 0.8334896 3.330208e-01 1.665104e-01
[75,] 0.8041484 3.917032e-01 1.958516e-01
[76,] 0.8818326 2.363348e-01 1.181674e-01
[77,] 0.8593625 2.812749e-01 1.406375e-01
[78,] 0.8323855 3.352291e-01 1.676145e-01
[79,] 0.8031457 3.937086e-01 1.968543e-01
[80,] 0.7810150 4.379701e-01 2.189850e-01
[81,] 0.7774756 4.450488e-01 2.225244e-01
[82,] 0.7631214 4.737571e-01 2.368786e-01
[83,] 0.7326948 5.346104e-01 2.673052e-01
[84,] 0.8257834 3.484331e-01 1.742166e-01
[85,] 0.8179999 3.640002e-01 1.820001e-01
[86,] 0.7883661 4.232678e-01 2.116339e-01
[87,] 0.7535105 4.929789e-01 2.464895e-01
[88,] 0.7610266 4.779468e-01 2.389734e-01
[89,] 0.7404144 5.191712e-01 2.595856e-01
[90,] 0.7098797 5.802406e-01 2.901203e-01
[91,] 0.6730344 6.539312e-01 3.269656e-01
[92,] 0.6312307 7.375387e-01 3.687693e-01
[93,] 0.5876906 8.246188e-01 4.123094e-01
[94,] 0.6540561 6.918878e-01 3.459439e-01
[95,] 0.6374014 7.251971e-01 3.625986e-01
[96,] 0.6051794 7.896413e-01 3.948206e-01
[97,] 0.6323113 7.353775e-01 3.676887e-01
[98,] 0.6189326 7.621349e-01 3.810674e-01
[99,] 0.5963447 8.073105e-01 4.036553e-01
[100,] 0.5905620 8.188760e-01 4.094380e-01
[101,] 0.5725930 8.548140e-01 4.274070e-01
[102,] 0.5530014 8.939972e-01 4.469986e-01
[103,] 0.6596060 6.807881e-01 3.403940e-01
[104,] 0.8173732 3.652536e-01 1.826268e-01
[105,] 0.8158717 3.682566e-01 1.841283e-01
[106,] 0.7878681 4.242637e-01 2.121319e-01
[107,] 0.7501759 4.996482e-01 2.498241e-01
[108,] 0.7213827 5.572347e-01 2.786173e-01
[109,] 0.6785799 6.428402e-01 3.214201e-01
[110,] 0.6331899 7.336202e-01 3.668101e-01
[111,] 0.8115333 3.769334e-01 1.884667e-01
[112,] 0.7749315 4.501369e-01 2.250685e-01
[113,] 0.7653131 4.693739e-01 2.346869e-01
[114,] 0.7270075 5.459850e-01 2.729925e-01
[115,] 0.7457168 5.085665e-01 2.542832e-01
[116,] 0.7132605 5.734790e-01 2.867395e-01
[117,] 0.7778326 4.443348e-01 2.221674e-01
[118,] 0.7651892 4.696216e-01 2.348108e-01
[119,] 0.7243318 5.513365e-01 2.756682e-01
[120,] 0.6935477 6.129047e-01 3.064523e-01
[121,] 0.6918109 6.163782e-01 3.081891e-01
[122,] 0.6845615 6.308770e-01 3.154385e-01
[123,] 0.8587178 2.825643e-01 1.412822e-01
[124,] 0.8462731 3.074538e-01 1.537269e-01
[125,] 0.8087798 3.824404e-01 1.912202e-01
[126,] 0.7646731 4.706538e-01 2.353269e-01
[127,] 0.8027970 3.944060e-01 1.972030e-01
[128,] 0.9998352 3.296552e-04 1.648276e-04
[129,] 0.9997564 4.871524e-04 2.435762e-04
[130,] 0.9997871 4.258315e-04 2.129158e-04
[131,] 0.9997280 5.440004e-04 2.720002e-04
[132,] 0.9998310 3.380357e-04 1.690179e-04
[133,] 0.9999965 7.062496e-06 3.531248e-06
[134,] 0.9999911 1.781867e-05 8.909333e-06
[135,] 1.0000000 9.271215e-09 4.635607e-09
[136,] 1.0000000 3.898604e-09 1.949302e-09
[137,] 1.0000000 7.257507e-09 3.628754e-09
[138,] 1.0000000 3.158289e-08 1.579145e-08
[139,] 1.0000000 1.023861e-08 5.119306e-09
[140,] 1.0000000 2.150285e-08 1.075142e-08
[141,] 1.0000000 5.625705e-08 2.812852e-08
[142,] 1.0000000 2.080223e-10 1.040112e-10
[143,] 1.0000000 1.853822e-09 9.269112e-10
[144,] 1.0000000 5.629616e-09 2.814808e-09
[145,] 1.0000000 5.776771e-08 2.888386e-08
[146,] 0.9999997 5.531529e-07 2.765765e-07
[147,] 0.9999976 4.837390e-06 2.418695e-06
[148,] 0.9999802 3.955856e-05 1.977928e-05
[149,] 0.9999973 5.403732e-06 2.701866e-06
[150,] 0.9999561 8.771027e-05 4.385513e-05
[151,] 0.9994345 1.131054e-03 5.655270e-04
> postscript(file="/var/wessaorg/rcomp/tmp/1avvr1324578994.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/2zknm1324578994.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/333nt1324578994.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4sjn71324578994.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5vqmf1324578994.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
3185.164 -34815.969 -37486.644 -62156.560 17079.484 -105321.210
7 8 9 10 11 12
218502.057 -53324.899 -30769.364 66985.333 68600.206 -72128.679
13 14 15 16 17 18
-17764.547 71954.341 23042.353 -15924.134 44657.803 73293.844
19 20 21 22 23 24
-43917.542 -45649.821 3209.934 228611.973 38095.475 -89987.427
25 26 27 28 29 30
-38038.417 -34294.532 -12199.339 52595.126 6211.546 123019.511
31 32 33 34 35 36
11322.309 -16815.224 67222.142 -90455.859 137970.192 59416.000
37 38 39 40 41 42
126937.518 12940.864 14726.482 22226.601 92702.168 -31410.568
43 44 45 46 47 48
-31232.454 -55304.181 -123856.412 131996.668 -55643.334 -84074.112
49 50 51 52 53 54
22044.342 -47171.112 -19947.728 -16838.135 -122385.894 -26292.676
55 56 57 58 59 60
-17841.317 61944.491 8214.379 -18841.997 41512.181 17089.881
61 62 63 64 65 66
19047.197 12220.964 24070.678 18683.299 16211.040 71848.451
67 68 69 70 71 72
-109380.121 -82949.135 -14642.385 -47898.138 6068.566 -37382.982
73 74 75 76 77 78
-2185.114 -1761.527 -3695.922 91259.964 111606.517 -166963.660
79 80 81 82 83 84
2276.951 -6964.146 10813.564 132299.797 -16429.204 -1019.892
85 86 87 88 89 90
-5575.603 39067.943 -64047.844 40559.092 26421.567 -133406.337
91 92 93 94 95 96
56437.322 15978.513 -2156.262 72570.973 41481.295 -31639.755
97 98 99 100 101 102
19203.091 -13501.063 -14292.967 95393.736 -59860.908 30602.223
103 104 105 106 107 108
-92067.829 -55725.465 45123.216 -70116.414 53398.600 48544.024
109 110 111 112 113 114
-132870.965 -153989.326 65125.765 25866.513 3225.610 -40360.334
115 116 117 118 119 120
-11996.581 -13360.711 145863.734 1065.823 53607.678 -23811.354
121 122 123 124 125 126
68454.119 -39971.544 -110797.868 -57340.407 -24006.402 40263.776
127 128 129 130 131 132
61323.288 -64074.746 141878.368 38509.228 -14546.919 -7884.309
133 134 135 136 137 138
-92031.782 -190453.031 60189.071 -117088.490 54387.364 -54233.526
139 140 141 142 143 144
56438.056 23534.565 -59811.870 71257.189 -26239.061 32251.472
145 146 147 148 149 150
20280.829 -128546.498 -30502.415 -21096.429 -6085.660 -3735.725
151 152 153 154 155 156
-6821.981 -7298.303 -6086.660 -6086.660 19107.414 79127.418
157 158 159 160 161 162
-6086.660 -9216.946 -4147.133 6068.814 1937.776 -89774.663
163 164
-6784.303 14429.200
> postscript(file="/var/wessaorg/rcomp/tmp/6v8je1324578994.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 3185.164 NA
1 -34815.969 3185.164
2 -37486.644 -34815.969
3 -62156.560 -37486.644
4 17079.484 -62156.560
5 -105321.210 17079.484
6 218502.057 -105321.210
7 -53324.899 218502.057
8 -30769.364 -53324.899
9 66985.333 -30769.364
10 68600.206 66985.333
11 -72128.679 68600.206
12 -17764.547 -72128.679
13 71954.341 -17764.547
14 23042.353 71954.341
15 -15924.134 23042.353
16 44657.803 -15924.134
17 73293.844 44657.803
18 -43917.542 73293.844
19 -45649.821 -43917.542
20 3209.934 -45649.821
21 228611.973 3209.934
22 38095.475 228611.973
23 -89987.427 38095.475
24 -38038.417 -89987.427
25 -34294.532 -38038.417
26 -12199.339 -34294.532
27 52595.126 -12199.339
28 6211.546 52595.126
29 123019.511 6211.546
30 11322.309 123019.511
31 -16815.224 11322.309
32 67222.142 -16815.224
33 -90455.859 67222.142
34 137970.192 -90455.859
35 59416.000 137970.192
36 126937.518 59416.000
37 12940.864 126937.518
38 14726.482 12940.864
39 22226.601 14726.482
40 92702.168 22226.601
41 -31410.568 92702.168
42 -31232.454 -31410.568
43 -55304.181 -31232.454
44 -123856.412 -55304.181
45 131996.668 -123856.412
46 -55643.334 131996.668
47 -84074.112 -55643.334
48 22044.342 -84074.112
49 -47171.112 22044.342
50 -19947.728 -47171.112
51 -16838.135 -19947.728
52 -122385.894 -16838.135
53 -26292.676 -122385.894
54 -17841.317 -26292.676
55 61944.491 -17841.317
56 8214.379 61944.491
57 -18841.997 8214.379
58 41512.181 -18841.997
59 17089.881 41512.181
60 19047.197 17089.881
61 12220.964 19047.197
62 24070.678 12220.964
63 18683.299 24070.678
64 16211.040 18683.299
65 71848.451 16211.040
66 -109380.121 71848.451
67 -82949.135 -109380.121
68 -14642.385 -82949.135
69 -47898.138 -14642.385
70 6068.566 -47898.138
71 -37382.982 6068.566
72 -2185.114 -37382.982
73 -1761.527 -2185.114
74 -3695.922 -1761.527
75 91259.964 -3695.922
76 111606.517 91259.964
77 -166963.660 111606.517
78 2276.951 -166963.660
79 -6964.146 2276.951
80 10813.564 -6964.146
81 132299.797 10813.564
82 -16429.204 132299.797
83 -1019.892 -16429.204
84 -5575.603 -1019.892
85 39067.943 -5575.603
86 -64047.844 39067.943
87 40559.092 -64047.844
88 26421.567 40559.092
89 -133406.337 26421.567
90 56437.322 -133406.337
91 15978.513 56437.322
92 -2156.262 15978.513
93 72570.973 -2156.262
94 41481.295 72570.973
95 -31639.755 41481.295
96 19203.091 -31639.755
97 -13501.063 19203.091
98 -14292.967 -13501.063
99 95393.736 -14292.967
100 -59860.908 95393.736
101 30602.223 -59860.908
102 -92067.829 30602.223
103 -55725.465 -92067.829
104 45123.216 -55725.465
105 -70116.414 45123.216
106 53398.600 -70116.414
107 48544.024 53398.600
108 -132870.965 48544.024
109 -153989.326 -132870.965
110 65125.765 -153989.326
111 25866.513 65125.765
112 3225.610 25866.513
113 -40360.334 3225.610
114 -11996.581 -40360.334
115 -13360.711 -11996.581
116 145863.734 -13360.711
117 1065.823 145863.734
118 53607.678 1065.823
119 -23811.354 53607.678
120 68454.119 -23811.354
121 -39971.544 68454.119
122 -110797.868 -39971.544
123 -57340.407 -110797.868
124 -24006.402 -57340.407
125 40263.776 -24006.402
126 61323.288 40263.776
127 -64074.746 61323.288
128 141878.368 -64074.746
129 38509.228 141878.368
130 -14546.919 38509.228
131 -7884.309 -14546.919
132 -92031.782 -7884.309
133 -190453.031 -92031.782
134 60189.071 -190453.031
135 -117088.490 60189.071
136 54387.364 -117088.490
137 -54233.526 54387.364
138 56438.056 -54233.526
139 23534.565 56438.056
140 -59811.870 23534.565
141 71257.189 -59811.870
142 -26239.061 71257.189
143 32251.472 -26239.061
144 20280.829 32251.472
145 -128546.498 20280.829
146 -30502.415 -128546.498
147 -21096.429 -30502.415
148 -6085.660 -21096.429
149 -3735.725 -6085.660
150 -6821.981 -3735.725
151 -7298.303 -6821.981
152 -6086.660 -7298.303
153 -6086.660 -6086.660
154 19107.414 -6086.660
155 79127.418 19107.414
156 -6086.660 79127.418
157 -9216.946 -6086.660
158 -4147.133 -9216.946
159 6068.814 -4147.133
160 1937.776 6068.814
161 -89774.663 1937.776
162 -6784.303 -89774.663
163 14429.200 -6784.303
164 NA 14429.200
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -34815.969 3185.164
[2,] -37486.644 -34815.969
[3,] -62156.560 -37486.644
[4,] 17079.484 -62156.560
[5,] -105321.210 17079.484
[6,] 218502.057 -105321.210
[7,] -53324.899 218502.057
[8,] -30769.364 -53324.899
[9,] 66985.333 -30769.364
[10,] 68600.206 66985.333
[11,] -72128.679 68600.206
[12,] -17764.547 -72128.679
[13,] 71954.341 -17764.547
[14,] 23042.353 71954.341
[15,] -15924.134 23042.353
[16,] 44657.803 -15924.134
[17,] 73293.844 44657.803
[18,] -43917.542 73293.844
[19,] -45649.821 -43917.542
[20,] 3209.934 -45649.821
[21,] 228611.973 3209.934
[22,] 38095.475 228611.973
[23,] -89987.427 38095.475
[24,] -38038.417 -89987.427
[25,] -34294.532 -38038.417
[26,] -12199.339 -34294.532
[27,] 52595.126 -12199.339
[28,] 6211.546 52595.126
[29,] 123019.511 6211.546
[30,] 11322.309 123019.511
[31,] -16815.224 11322.309
[32,] 67222.142 -16815.224
[33,] -90455.859 67222.142
[34,] 137970.192 -90455.859
[35,] 59416.000 137970.192
[36,] 126937.518 59416.000
[37,] 12940.864 126937.518
[38,] 14726.482 12940.864
[39,] 22226.601 14726.482
[40,] 92702.168 22226.601
[41,] -31410.568 92702.168
[42,] -31232.454 -31410.568
[43,] -55304.181 -31232.454
[44,] -123856.412 -55304.181
[45,] 131996.668 -123856.412
[46,] -55643.334 131996.668
[47,] -84074.112 -55643.334
[48,] 22044.342 -84074.112
[49,] -47171.112 22044.342
[50,] -19947.728 -47171.112
[51,] -16838.135 -19947.728
[52,] -122385.894 -16838.135
[53,] -26292.676 -122385.894
[54,] -17841.317 -26292.676
[55,] 61944.491 -17841.317
[56,] 8214.379 61944.491
[57,] -18841.997 8214.379
[58,] 41512.181 -18841.997
[59,] 17089.881 41512.181
[60,] 19047.197 17089.881
[61,] 12220.964 19047.197
[62,] 24070.678 12220.964
[63,] 18683.299 24070.678
[64,] 16211.040 18683.299
[65,] 71848.451 16211.040
[66,] -109380.121 71848.451
[67,] -82949.135 -109380.121
[68,] -14642.385 -82949.135
[69,] -47898.138 -14642.385
[70,] 6068.566 -47898.138
[71,] -37382.982 6068.566
[72,] -2185.114 -37382.982
[73,] -1761.527 -2185.114
[74,] -3695.922 -1761.527
[75,] 91259.964 -3695.922
[76,] 111606.517 91259.964
[77,] -166963.660 111606.517
[78,] 2276.951 -166963.660
[79,] -6964.146 2276.951
[80,] 10813.564 -6964.146
[81,] 132299.797 10813.564
[82,] -16429.204 132299.797
[83,] -1019.892 -16429.204
[84,] -5575.603 -1019.892
[85,] 39067.943 -5575.603
[86,] -64047.844 39067.943
[87,] 40559.092 -64047.844
[88,] 26421.567 40559.092
[89,] -133406.337 26421.567
[90,] 56437.322 -133406.337
[91,] 15978.513 56437.322
[92,] -2156.262 15978.513
[93,] 72570.973 -2156.262
[94,] 41481.295 72570.973
[95,] -31639.755 41481.295
[96,] 19203.091 -31639.755
[97,] -13501.063 19203.091
[98,] -14292.967 -13501.063
[99,] 95393.736 -14292.967
[100,] -59860.908 95393.736
[101,] 30602.223 -59860.908
[102,] -92067.829 30602.223
[103,] -55725.465 -92067.829
[104,] 45123.216 -55725.465
[105,] -70116.414 45123.216
[106,] 53398.600 -70116.414
[107,] 48544.024 53398.600
[108,] -132870.965 48544.024
[109,] -153989.326 -132870.965
[110,] 65125.765 -153989.326
[111,] 25866.513 65125.765
[112,] 3225.610 25866.513
[113,] -40360.334 3225.610
[114,] -11996.581 -40360.334
[115,] -13360.711 -11996.581
[116,] 145863.734 -13360.711
[117,] 1065.823 145863.734
[118,] 53607.678 1065.823
[119,] -23811.354 53607.678
[120,] 68454.119 -23811.354
[121,] -39971.544 68454.119
[122,] -110797.868 -39971.544
[123,] -57340.407 -110797.868
[124,] -24006.402 -57340.407
[125,] 40263.776 -24006.402
[126,] 61323.288 40263.776
[127,] -64074.746 61323.288
[128,] 141878.368 -64074.746
[129,] 38509.228 141878.368
[130,] -14546.919 38509.228
[131,] -7884.309 -14546.919
[132,] -92031.782 -7884.309
[133,] -190453.031 -92031.782
[134,] 60189.071 -190453.031
[135,] -117088.490 60189.071
[136,] 54387.364 -117088.490
[137,] -54233.526 54387.364
[138,] 56438.056 -54233.526
[139,] 23534.565 56438.056
[140,] -59811.870 23534.565
[141,] 71257.189 -59811.870
[142,] -26239.061 71257.189
[143,] 32251.472 -26239.061
[144,] 20280.829 32251.472
[145,] -128546.498 20280.829
[146,] -30502.415 -128546.498
[147,] -21096.429 -30502.415
[148,] -6085.660 -21096.429
[149,] -3735.725 -6085.660
[150,] -6821.981 -3735.725
[151,] -7298.303 -6821.981
[152,] -6086.660 -7298.303
[153,] -6086.660 -6086.660
[154,] 19107.414 -6086.660
[155,] 79127.418 19107.414
[156,] -6086.660 79127.418
[157,] -9216.946 -6086.660
[158,] -4147.133 -9216.946
[159,] 6068.814 -4147.133
[160,] 1937.776 6068.814
[161,] -89774.663 1937.776
[162,] -6784.303 -89774.663
[163,] 14429.200 -6784.303
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -34815.969 3185.164
2 -37486.644 -34815.969
3 -62156.560 -37486.644
4 17079.484 -62156.560
5 -105321.210 17079.484
6 218502.057 -105321.210
7 -53324.899 218502.057
8 -30769.364 -53324.899
9 66985.333 -30769.364
10 68600.206 66985.333
11 -72128.679 68600.206
12 -17764.547 -72128.679
13 71954.341 -17764.547
14 23042.353 71954.341
15 -15924.134 23042.353
16 44657.803 -15924.134
17 73293.844 44657.803
18 -43917.542 73293.844
19 -45649.821 -43917.542
20 3209.934 -45649.821
21 228611.973 3209.934
22 38095.475 228611.973
23 -89987.427 38095.475
24 -38038.417 -89987.427
25 -34294.532 -38038.417
26 -12199.339 -34294.532
27 52595.126 -12199.339
28 6211.546 52595.126
29 123019.511 6211.546
30 11322.309 123019.511
31 -16815.224 11322.309
32 67222.142 -16815.224
33 -90455.859 67222.142
34 137970.192 -90455.859
35 59416.000 137970.192
36 126937.518 59416.000
37 12940.864 126937.518
38 14726.482 12940.864
39 22226.601 14726.482
40 92702.168 22226.601
41 -31410.568 92702.168
42 -31232.454 -31410.568
43 -55304.181 -31232.454
44 -123856.412 -55304.181
45 131996.668 -123856.412
46 -55643.334 131996.668
47 -84074.112 -55643.334
48 22044.342 -84074.112
49 -47171.112 22044.342
50 -19947.728 -47171.112
51 -16838.135 -19947.728
52 -122385.894 -16838.135
53 -26292.676 -122385.894
54 -17841.317 -26292.676
55 61944.491 -17841.317
56 8214.379 61944.491
57 -18841.997 8214.379
58 41512.181 -18841.997
59 17089.881 41512.181
60 19047.197 17089.881
61 12220.964 19047.197
62 24070.678 12220.964
63 18683.299 24070.678
64 16211.040 18683.299
65 71848.451 16211.040
66 -109380.121 71848.451
67 -82949.135 -109380.121
68 -14642.385 -82949.135
69 -47898.138 -14642.385
70 6068.566 -47898.138
71 -37382.982 6068.566
72 -2185.114 -37382.982
73 -1761.527 -2185.114
74 -3695.922 -1761.527
75 91259.964 -3695.922
76 111606.517 91259.964
77 -166963.660 111606.517
78 2276.951 -166963.660
79 -6964.146 2276.951
80 10813.564 -6964.146
81 132299.797 10813.564
82 -16429.204 132299.797
83 -1019.892 -16429.204
84 -5575.603 -1019.892
85 39067.943 -5575.603
86 -64047.844 39067.943
87 40559.092 -64047.844
88 26421.567 40559.092
89 -133406.337 26421.567
90 56437.322 -133406.337
91 15978.513 56437.322
92 -2156.262 15978.513
93 72570.973 -2156.262
94 41481.295 72570.973
95 -31639.755 41481.295
96 19203.091 -31639.755
97 -13501.063 19203.091
98 -14292.967 -13501.063
99 95393.736 -14292.967
100 -59860.908 95393.736
101 30602.223 -59860.908
102 -92067.829 30602.223
103 -55725.465 -92067.829
104 45123.216 -55725.465
105 -70116.414 45123.216
106 53398.600 -70116.414
107 48544.024 53398.600
108 -132870.965 48544.024
109 -153989.326 -132870.965
110 65125.765 -153989.326
111 25866.513 65125.765
112 3225.610 25866.513
113 -40360.334 3225.610
114 -11996.581 -40360.334
115 -13360.711 -11996.581
116 145863.734 -13360.711
117 1065.823 145863.734
118 53607.678 1065.823
119 -23811.354 53607.678
120 68454.119 -23811.354
121 -39971.544 68454.119
122 -110797.868 -39971.544
123 -57340.407 -110797.868
124 -24006.402 -57340.407
125 40263.776 -24006.402
126 61323.288 40263.776
127 -64074.746 61323.288
128 141878.368 -64074.746
129 38509.228 141878.368
130 -14546.919 38509.228
131 -7884.309 -14546.919
132 -92031.782 -7884.309
133 -190453.031 -92031.782
134 60189.071 -190453.031
135 -117088.490 60189.071
136 54387.364 -117088.490
137 -54233.526 54387.364
138 56438.056 -54233.526
139 23534.565 56438.056
140 -59811.870 23534.565
141 71257.189 -59811.870
142 -26239.061 71257.189
143 32251.472 -26239.061
144 20280.829 32251.472
145 -128546.498 20280.829
146 -30502.415 -128546.498
147 -21096.429 -30502.415
148 -6085.660 -21096.429
149 -3735.725 -6085.660
150 -6821.981 -3735.725
151 -7298.303 -6821.981
152 -6086.660 -7298.303
153 -6086.660 -6086.660
154 19107.414 -6086.660
155 79127.418 19107.414
156 -6086.660 79127.418
157 -9216.946 -6086.660
158 -4147.133 -9216.946
159 6068.814 -4147.133
160 1937.776 6068.814
161 -89774.663 1937.776
162 -6784.303 -89774.663
163 14429.200 -6784.303
> 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/7ncoy1324578994.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/874pi1324578994.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/90k901324578994.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/105wm81324578994.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11092t1324578994.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12tq0p1324578994.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13gn4c1324578994.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14wnrj1324578994.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/150fi71324578994.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/16k7ql1324578994.tab")
+ }
>
> try(system("convert tmp/1avvr1324578994.ps tmp/1avvr1324578994.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zknm1324578994.ps tmp/2zknm1324578994.png",intern=TRUE))
character(0)
> try(system("convert tmp/333nt1324578994.ps tmp/333nt1324578994.png",intern=TRUE))
character(0)
> try(system("convert tmp/4sjn71324578994.ps tmp/4sjn71324578994.png",intern=TRUE))
character(0)
> try(system("convert tmp/5vqmf1324578994.ps tmp/5vqmf1324578994.png",intern=TRUE))
character(0)
> try(system("convert tmp/6v8je1324578994.ps tmp/6v8je1324578994.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ncoy1324578994.ps tmp/7ncoy1324578994.png",intern=TRUE))
character(0)
> try(system("convert tmp/874pi1324578994.ps tmp/874pi1324578994.png",intern=TRUE))
character(0)
> try(system("convert tmp/90k901324578994.ps tmp/90k901324578994.png",intern=TRUE))
character(0)
> try(system("convert tmp/105wm81324578994.ps tmp/105wm81324578994.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.734 0.546 5.288