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(1
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+ ,10
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+ ,dim=c(5
+ ,144)
+ ,dimnames=list(c('HOF'
+ ,'total_pageviews'
+ ,'number_logins'
+ ,'total_time'
+ ,'total_coursecompendiumviews')
+ ,1:144))
> y <- array(NA,dim=c(5,144),dimnames=list(c('HOF','total_pageviews','number_logins','total_time','total_coursecompendiumviews'),1:144))
> 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
HOF total_pageviews number_logins total_time total_coursecompendiumviews
1 1 893 63047 52 257
2 2 546 66751 25 160
3 3 186 7176 17 70
4 4 1405 78306 66 360
5 5 2047 137944 85 721
6 6 3626 261308 130 938
7 7 845 69266 36 287
8 8 663 83529 33 154
9 9 1181 73226 33 311
10 10 1836 178519 65 617
11 11 855 66476 35 262
12 12 1245 98606 46 385
13 13 993 50001 69 369
14 14 1685 91093 61 558
15 15 742 73884 25 220
16 16 868 72961 41 315
17 17 949 69388 34 229
18 18 332 15629 21 88
19 19 1602 71693 54 494
20 20 525 19920 17 155
21 21 629 39403 38 234
22 22 1279 99933 51 361
23 23 767 56088 28 280
24 24 1156 62006 32 331
25 25 1120 81665 51 378
26 26 624 65638 14 227
27 27 1203 88794 98 396
28 28 745 90642 53 179
29 29 1568 207062 62 524
30 30 1235 99340 26 504
31 31 758 56695 29 225
32 32 1088 108143 24 366
33 33 1105 58313 60 341
34 34 592 29101 40 171
35 35 1305 113060 29 437
36 36 0 0 0 0
37 37 706 65773 34 313
38 38 1188 67047 34 366
39 39 1111 41953 22 232
40 40 1095 109835 36 389
41 41 1087 86584 35 349
42 42 748 59588 26 316
43 43 404 40064 12 140
44 44 1077 70227 46 419
45 45 673 60437 29 226
46 46 537 53696 38 167
47 47 354 40295 13 103
48 48 1012 103397 55 356
49 49 891 78982 40 293
50 50 1198 67317 29 460
51 51 518 39887 21 156
52 52 697 49791 36 189
53 53 1095 129283 44 442
54 54 928 104816 44 321
55 55 1009 101395 34 367
56 56 951 72824 30 309
57 57 779 76018 27 235
58 58 439 33891 12 137
59 59 580 63694 39 198
60 60 614 28266 24 220
61 61 500 35093 22 149
62 62 824 35252 35 306
63 63 541 36977 20 178
64 64 476 42406 19 145
65 65 434 56353 13 144
66 66 818 58817 23 270
67 67 1173 76053 43 301
68 68 1720 70872 49 501
69 69 549 42372 20 153
70 70 157 19144 12 40
71 71 1594 114177 73 500
72 72 668 59414 28 209
73 73 656 51379 33 242
74 74 920 40756 39 265
75 75 847 46956 22 298
76 76 497 17799 20 141
77 77 864 71154 30 234
78 78 994 58305 38 336
79 79 443 27454 16 124
80 80 615 34323 34 241
81 81 525 44761 33 127
82 82 899 113862 27 327
83 83 556 35027 16 175
84 84 896 62396 22 331
85 85 516 29613 26 176
86 86 894 65559 28 281
87 87 1353 114480 114 293
88 88 557 31095 30 152
89 89 472 40181 25 155
90 90 639 53398 22 194
91 91 795 56435 23 300
92 92 1244 77283 46 370
93 93 559 71738 30 187
94 94 584 48503 31 212
95 95 440 25214 23 185
96 96 1319 119424 65 449
97 97 765 79201 27 234
98 98 222 19349 11 67
99 99 965 78760 54 316
100 100 822 54133 36 336
101 101 317 21623 16 116
102 102 425 25497 11 141
103 103 711 69535 38 236
104 104 364 30709 22 98
105 105 427 37043 23 97
106 106 463 24716 13 152
107 107 546 54865 17 132
108 108 369 27246 19 97
109 109 0 0 0 0
110 110 596 38814 13 165
111 111 479 27646 32 153
112 112 713 65373 18 226
113 113 639 43021 34 182
114 114 477 43116 40 172
115 115 38 3058 4 1
116 116 0 0 0 0
117 117 593 96347 25 196
118 118 724 53063 35 274
119 119 1056 73073 50 304
120 120 495 45266 22 183
121 121 778 43410 19 292
122 122 875 83842 34 257
123 123 490 39296 21 141
124 124 713 38490 25 192
125 125 485 39841 19 129
126 126 285 19764 12 75
127 127 935 59975 21 301
128 128 554 64589 16 204
129 129 753 63339 14 257
130 130 256 11796 9 79
131 131 80 7627 8 25
132 132 618 68998 30 217
133 133 41 6836 3 11
134 134 550 35414 17 228
135 135 42 5118 3 6
136 136 347 20898 13 115
137 137 0 0 0 0
138 138 441 42690 18 167
139 139 281 14507 11 75
140 140 81 7131 4 27
141 141 61 4194 11 14
142 142 314 21416 10 96
143 143 401 37679 16 109
144 144 554 42419 10 228
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) total_pageviews
1.089e+02 -1.615e-02
number_logins total_time
-6.393e-06 -3.711e-01
total_coursecompendiumviews
-5.287e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-92.838 -24.660 0.085 30.048 62.255
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.089e+02 5.916e+00 18.406 <2e-16 ***
total_pageviews -1.615e-02 2.739e-02 -0.590 0.556
number_logins -6.393e-06 1.662e-04 -0.038 0.969
total_time -3.711e-01 2.756e-01 -1.347 0.180
total_coursecompendiumviews -5.287e-02 7.708e-02 -0.686 0.494
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 36.06 on 139 degrees of freedom
Multiple R-squared: 0.2735, Adjusted R-squared: 0.2526
F-statistic: 13.09 on 4 and 139 DF, p-value: 4.522e-09
> 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,] 1.115248e-03 2.230495e-03 9.988848e-01
[2,] 9.691250e-05 1.938250e-04 9.999031e-01
[3,] 7.906632e-06 1.581326e-05 9.999921e-01
[4,] 2.756191e-06 5.512382e-06 9.999972e-01
[5,] 5.943556e-07 1.188711e-06 9.999994e-01
[6,] 1.318574e-06 2.637147e-06 9.999987e-01
[7,] 2.432238e-07 4.864476e-07 9.999998e-01
[8,] 1.148516e-07 2.297033e-07 9.999999e-01
[9,] 4.904750e-08 9.809500e-08 1.000000e+00
[10,] 3.259817e-08 6.519635e-08 1.000000e+00
[11,] 2.237542e-08 4.475084e-08 1.000000e+00
[12,] 5.170469e-09 1.034094e-08 1.000000e+00
[13,] 1.848033e-09 3.696066e-09 1.000000e+00
[14,] 1.984699e-09 3.969397e-09 1.000000e+00
[15,] 2.576104e-09 5.152208e-09 1.000000e+00
[16,] 1.390060e-09 2.780120e-09 1.000000e+00
[17,] 4.918907e-10 9.837814e-10 1.000000e+00
[18,] 6.965573e-10 1.393115e-09 1.000000e+00
[19,] 3.976000e-10 7.952001e-10 1.000000e+00
[20,] 3.559649e-09 7.119298e-09 1.000000e+00
[21,] 6.422126e-09 1.284425e-08 1.000000e+00
[22,] 4.114702e-09 8.229403e-09 1.000000e+00
[23,] 2.288513e-09 4.577026e-09 1.000000e+00
[24,] 3.540958e-09 7.081916e-09 1.000000e+00
[25,] 2.627764e-09 5.255528e-09 1.000000e+00
[26,] 5.486612e-09 1.097322e-08 1.000000e+00
[27,] 1.182487e-08 2.364975e-08 1.000000e+00
[28,] 1.083578e-08 2.167156e-08 1.000000e+00
[29,] 2.687607e-08 5.375213e-08 1.000000e+00
[30,] 2.620910e-08 5.241819e-08 1.000000e+00
[31,] 4.100642e-08 8.201283e-08 1.000000e+00
[32,] 9.707391e-08 1.941478e-07 9.999999e-01
[33,] 1.076076e-07 2.152153e-07 9.999999e-01
[34,] 1.463520e-07 2.927040e-07 9.999999e-01
[35,] 1.605625e-07 3.211250e-07 9.999998e-01
[36,] 3.024700e-07 6.049400e-07 9.999997e-01
[37,] 3.797023e-07 7.594045e-07 9.999996e-01
[38,] 7.474638e-07 1.494928e-06 9.999993e-01
[39,] 2.172822e-06 4.345645e-06 9.999978e-01
[40,] 5.249477e-06 1.049895e-05 9.999948e-01
[41,] 9.562545e-06 1.912509e-05 9.999904e-01
[42,] 1.768833e-05 3.537666e-05 9.999823e-01
[43,] 1.943979e-05 3.887957e-05 9.999806e-01
[44,] 4.626706e-05 9.253411e-05 9.999537e-01
[45,] 1.240242e-04 2.480485e-04 9.998760e-01
[46,] 1.392307e-04 2.784613e-04 9.998608e-01
[47,] 2.289851e-04 4.579702e-04 9.997710e-01
[48,] 3.198672e-04 6.397344e-04 9.996801e-01
[49,] 5.581019e-04 1.116204e-03 9.994419e-01
[50,] 1.088755e-03 2.177510e-03 9.989112e-01
[51,] 2.312997e-03 4.625994e-03 9.976870e-01
[52,] 4.897944e-03 9.795889e-03 9.951021e-01
[53,] 8.715429e-03 1.743086e-02 9.912846e-01
[54,] 1.696932e-02 3.393864e-02 9.830307e-01
[55,] 2.638525e-02 5.277050e-02 9.736148e-01
[56,] 4.464956e-02 8.929912e-02 9.553504e-01
[57,] 7.650695e-02 1.530139e-01 9.234931e-01
[58,] 1.312934e-01 2.625867e-01 8.687066e-01
[59,] 1.808661e-01 3.617321e-01 8.191339e-01
[60,] 2.562052e-01 5.124105e-01 7.437948e-01
[61,] 3.204101e-01 6.408203e-01 6.795899e-01
[62,] 4.184928e-01 8.369856e-01 5.815072e-01
[63,] 5.811261e-01 8.377478e-01 4.188739e-01
[64,] 6.750604e-01 6.498792e-01 3.249396e-01
[65,] 7.602592e-01 4.794816e-01 2.397408e-01
[66,] 8.224551e-01 3.550898e-01 1.775449e-01
[67,] 8.530980e-01 2.938041e-01 1.469020e-01
[68,] 8.791492e-01 2.417017e-01 1.208508e-01
[69,] 9.171863e-01 1.656273e-01 8.281366e-02
[70,] 9.449746e-01 1.100509e-01 5.502545e-02
[71,] 9.570191e-01 8.596186e-02 4.298093e-02
[72,] 9.769612e-01 4.607760e-02 2.303880e-02
[73,] 9.843896e-01 3.122071e-02 1.561036e-02
[74,] 9.919600e-01 1.608004e-02 8.040019e-03
[75,] 9.956222e-01 8.755508e-03 4.377754e-03
[76,] 9.978360e-01 4.327931e-03 2.163966e-03
[77,] 9.986808e-01 2.638445e-03 1.319223e-03
[78,] 9.993164e-01 1.367283e-03 6.836414e-04
[79,] 9.996388e-01 7.224846e-04 3.612423e-04
[80,] 9.999020e-01 1.960045e-04 9.800227e-05
[81,] 9.999363e-01 1.273080e-04 6.365402e-05
[82,] 9.999675e-01 6.501532e-05 3.250766e-05
[83,] 9.999862e-01 2.769421e-05 1.384711e-05
[84,] 9.999949e-01 1.017021e-05 5.085104e-06
[85,] 9.999964e-01 7.250847e-06 3.625424e-06
[86,] 9.999978e-01 4.394999e-06 2.197499e-06
[87,] 9.999986e-01 2.794364e-06 1.397182e-06
[88,] 9.999994e-01 1.227038e-06 6.135191e-07
[89,] 9.999994e-01 1.146536e-06 5.732681e-07
[90,] 9.999997e-01 6.247586e-07 3.123793e-07
[91,] 9.999999e-01 2.244207e-07 1.122104e-07
[92,] 9.999999e-01 2.627623e-07 1.313811e-07
[93,] 9.999999e-01 1.340909e-07 6.704547e-08
[94,] 1.000000e+00 5.029554e-08 2.514777e-08
[95,] 1.000000e+00 1.440896e-08 7.204481e-09
[96,] 1.000000e+00 1.256920e-08 6.284602e-09
[97,] 1.000000e+00 1.032876e-08 5.164378e-09
[98,] 1.000000e+00 1.158800e-08 5.794000e-09
[99,] 1.000000e+00 4.646207e-09 2.323104e-09
[100,] 1.000000e+00 4.622670e-09 2.311335e-09
[101,] 1.000000e+00 3.864771e-09 1.932385e-09
[102,] 1.000000e+00 7.133501e-10 3.566751e-10
[103,] 1.000000e+00 3.157061e-10 1.578531e-10
[104,] 1.000000e+00 4.168097e-10 2.084049e-10
[105,] 1.000000e+00 2.262563e-10 1.131281e-10
[106,] 1.000000e+00 3.915257e-10 1.957629e-10
[107,] 1.000000e+00 8.346030e-10 4.173015e-10
[108,] 1.000000e+00 2.396230e-10 1.198115e-10
[109,] 1.000000e+00 1.184812e-11 5.924062e-12
[110,] 1.000000e+00 1.474690e-11 7.373448e-12
[111,] 1.000000e+00 3.280093e-11 1.640047e-11
[112,] 1.000000e+00 9.651015e-11 4.825508e-11
[113,] 1.000000e+00 8.393153e-11 4.196576e-11
[114,] 1.000000e+00 6.618949e-11 3.309474e-11
[115,] 1.000000e+00 2.973584e-10 1.486792e-10
[116,] 1.000000e+00 7.512578e-10 3.756289e-10
[117,] 1.000000e+00 3.199315e-09 1.599657e-09
[118,] 1.000000e+00 1.027228e-08 5.136142e-09
[119,] 1.000000e+00 1.698373e-08 8.491866e-09
[120,] 1.000000e+00 4.314433e-08 2.157217e-08
[121,] 9.999999e-01 1.691094e-07 8.455472e-08
[122,] 9.999999e-01 1.276880e-07 6.384399e-08
[123,] 9.999999e-01 1.555384e-07 7.776920e-08
[124,] 9.999996e-01 7.514181e-07 3.757091e-07
[125,] 9.999986e-01 2.886231e-06 1.443115e-06
[126,] 9.999947e-01 1.053826e-05 5.269131e-06
[127,] 9.999710e-01 5.798816e-05 2.899408e-05
[128,] 9.998319e-01 3.361430e-04 1.680715e-04
[129,] 9.991224e-01 1.755143e-03 8.775713e-04
> postscript(file="/var/wessaorg/rcomp/tmp/1yv901322149810.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/29gio1322149810.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jfny1322149810.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/49pug1322149810.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5kk3q1322149810.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 = 144
Frequency = 1
1 2 3 4 5 6
-60.18494651 -79.91442160 -92.83760286 -38.17588980 -0.28718353 55.17982881
7 8 9 10 11 12
-59.27192192 -69.26568440 -51.66342666 -11.35650994 -56.82107142 -38.73075255
13 14 15 16 17 18
-34.42297396 -14.95832443 -60.53052425 -46.54094268 -51.39983645 -72.98912106
19 20 21 22 23 24
-17.40442886 -65.78613044 -51.01192960 -27.58647503 -47.95497076 -36.45268275
25 26 27 28 29 30
-26.37287505 -55.20127464 -4.59356649 -39.15202702 -2.53336906 -22.01788169
31 32 33 34 35 36
-42.63324100 -30.37443792 -17.38083909 -41.26410982 -18.22845183 -72.89750824
37 38 39 40 41 42
-30.90714576 -19.31090183 -31.25286337 -16.58142618 -18.34520185 -28.07836744
43 44 45 46 47 48
-47.26043164 -7.82838147 -29.92949348 -30.94897669 -45.65173461 -4.65729119
49 50 51 52 53 54
-14.66515621 -4.03330148 -33.23431405 -21.96842456 2.31376598 -5.93758230
55 56 57 58 59 60
-4.92991264 -9.60031407 -16.38396507 -31.89313539 -15.18038957 -18.26090484
61 62 63 64 65 66
-23.55472487 -4.19515986 -20.08933551 -22.22041244 -24.08911616 -6.49785897
67 68 69 70 71 72
9.40763788 32.01106406 -15.24737189 -29.67110742 42.10592137 -4.28668680
73 74 75 76 77 78
0.06827996 8.70745197 4.00403660 -9.87889443 6.01839062 17.39769597
79 80 81 82 83 84
-10.07261853 6.61516750 -0.17021031 15.66045469 -1.50247901 15.63898447
85 86 87 88 89 90
3.58056049 17.20994109 58.48551209 7.46781121 5.45600363 10.18679522
91 92 93 94 95 96
19.70148883 40.32371956 14.61041257 17.55855205 11.68732804 57.03214133
97 98 99 100 101 102
23.35736540 0.43658305 42.94008647 35.85044079 9.43180269 11.66744048
103 104 105 106 107 108
32.61103479 14.52399333 16.90037876 17.60003625 20.56048657 17.41647470
109 110 111 112 113 114
0.10249176 24.52589266 29.98084413 33.66618919 36.93911619 37.02071015
115 116 117 118 119 120
8.27311294 7.10249176 37.93733144 48.61151374 62.25488440 37.22713250
121 122 123 124 125 126
47.43627869 53.97757856 37.51655695 46.29437956 38.06264706 30.25095931
127 128 129 130 131 132
57.29629393 45.18744461 51.45393290 32.82977224 27.73402297 56.13205706
133 134 135 136 137 138
26.50333673 52.57628760 28.24415571 43.74562555 28.10249176 52.00808394
139 140 141 142 143 144
42.78164447 35.36837745 37.93686313 47.09806199 52.52125210 60.08804082
> postscript(file="/var/wessaorg/rcomp/tmp/65a981322149810.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 = 144
Frequency = 1
lag(myerror, k = 1) myerror
0 -60.18494651 NA
1 -79.91442160 -60.18494651
2 -92.83760286 -79.91442160
3 -38.17588980 -92.83760286
4 -0.28718353 -38.17588980
5 55.17982881 -0.28718353
6 -59.27192192 55.17982881
7 -69.26568440 -59.27192192
8 -51.66342666 -69.26568440
9 -11.35650994 -51.66342666
10 -56.82107142 -11.35650994
11 -38.73075255 -56.82107142
12 -34.42297396 -38.73075255
13 -14.95832443 -34.42297396
14 -60.53052425 -14.95832443
15 -46.54094268 -60.53052425
16 -51.39983645 -46.54094268
17 -72.98912106 -51.39983645
18 -17.40442886 -72.98912106
19 -65.78613044 -17.40442886
20 -51.01192960 -65.78613044
21 -27.58647503 -51.01192960
22 -47.95497076 -27.58647503
23 -36.45268275 -47.95497076
24 -26.37287505 -36.45268275
25 -55.20127464 -26.37287505
26 -4.59356649 -55.20127464
27 -39.15202702 -4.59356649
28 -2.53336906 -39.15202702
29 -22.01788169 -2.53336906
30 -42.63324100 -22.01788169
31 -30.37443792 -42.63324100
32 -17.38083909 -30.37443792
33 -41.26410982 -17.38083909
34 -18.22845183 -41.26410982
35 -72.89750824 -18.22845183
36 -30.90714576 -72.89750824
37 -19.31090183 -30.90714576
38 -31.25286337 -19.31090183
39 -16.58142618 -31.25286337
40 -18.34520185 -16.58142618
41 -28.07836744 -18.34520185
42 -47.26043164 -28.07836744
43 -7.82838147 -47.26043164
44 -29.92949348 -7.82838147
45 -30.94897669 -29.92949348
46 -45.65173461 -30.94897669
47 -4.65729119 -45.65173461
48 -14.66515621 -4.65729119
49 -4.03330148 -14.66515621
50 -33.23431405 -4.03330148
51 -21.96842456 -33.23431405
52 2.31376598 -21.96842456
53 -5.93758230 2.31376598
54 -4.92991264 -5.93758230
55 -9.60031407 -4.92991264
56 -16.38396507 -9.60031407
57 -31.89313539 -16.38396507
58 -15.18038957 -31.89313539
59 -18.26090484 -15.18038957
60 -23.55472487 -18.26090484
61 -4.19515986 -23.55472487
62 -20.08933551 -4.19515986
63 -22.22041244 -20.08933551
64 -24.08911616 -22.22041244
65 -6.49785897 -24.08911616
66 9.40763788 -6.49785897
67 32.01106406 9.40763788
68 -15.24737189 32.01106406
69 -29.67110742 -15.24737189
70 42.10592137 -29.67110742
71 -4.28668680 42.10592137
72 0.06827996 -4.28668680
73 8.70745197 0.06827996
74 4.00403660 8.70745197
75 -9.87889443 4.00403660
76 6.01839062 -9.87889443
77 17.39769597 6.01839062
78 -10.07261853 17.39769597
79 6.61516750 -10.07261853
80 -0.17021031 6.61516750
81 15.66045469 -0.17021031
82 -1.50247901 15.66045469
83 15.63898447 -1.50247901
84 3.58056049 15.63898447
85 17.20994109 3.58056049
86 58.48551209 17.20994109
87 7.46781121 58.48551209
88 5.45600363 7.46781121
89 10.18679522 5.45600363
90 19.70148883 10.18679522
91 40.32371956 19.70148883
92 14.61041257 40.32371956
93 17.55855205 14.61041257
94 11.68732804 17.55855205
95 57.03214133 11.68732804
96 23.35736540 57.03214133
97 0.43658305 23.35736540
98 42.94008647 0.43658305
99 35.85044079 42.94008647
100 9.43180269 35.85044079
101 11.66744048 9.43180269
102 32.61103479 11.66744048
103 14.52399333 32.61103479
104 16.90037876 14.52399333
105 17.60003625 16.90037876
106 20.56048657 17.60003625
107 17.41647470 20.56048657
108 0.10249176 17.41647470
109 24.52589266 0.10249176
110 29.98084413 24.52589266
111 33.66618919 29.98084413
112 36.93911619 33.66618919
113 37.02071015 36.93911619
114 8.27311294 37.02071015
115 7.10249176 8.27311294
116 37.93733144 7.10249176
117 48.61151374 37.93733144
118 62.25488440 48.61151374
119 37.22713250 62.25488440
120 47.43627869 37.22713250
121 53.97757856 47.43627869
122 37.51655695 53.97757856
123 46.29437956 37.51655695
124 38.06264706 46.29437956
125 30.25095931 38.06264706
126 57.29629393 30.25095931
127 45.18744461 57.29629393
128 51.45393290 45.18744461
129 32.82977224 51.45393290
130 27.73402297 32.82977224
131 56.13205706 27.73402297
132 26.50333673 56.13205706
133 52.57628760 26.50333673
134 28.24415571 52.57628760
135 43.74562555 28.24415571
136 28.10249176 43.74562555
137 52.00808394 28.10249176
138 42.78164447 52.00808394
139 35.36837745 42.78164447
140 37.93686313 35.36837745
141 47.09806199 37.93686313
142 52.52125210 47.09806199
143 60.08804082 52.52125210
144 NA 60.08804082
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -79.91442160 -60.18494651
[2,] -92.83760286 -79.91442160
[3,] -38.17588980 -92.83760286
[4,] -0.28718353 -38.17588980
[5,] 55.17982881 -0.28718353
[6,] -59.27192192 55.17982881
[7,] -69.26568440 -59.27192192
[8,] -51.66342666 -69.26568440
[9,] -11.35650994 -51.66342666
[10,] -56.82107142 -11.35650994
[11,] -38.73075255 -56.82107142
[12,] -34.42297396 -38.73075255
[13,] -14.95832443 -34.42297396
[14,] -60.53052425 -14.95832443
[15,] -46.54094268 -60.53052425
[16,] -51.39983645 -46.54094268
[17,] -72.98912106 -51.39983645
[18,] -17.40442886 -72.98912106
[19,] -65.78613044 -17.40442886
[20,] -51.01192960 -65.78613044
[21,] -27.58647503 -51.01192960
[22,] -47.95497076 -27.58647503
[23,] -36.45268275 -47.95497076
[24,] -26.37287505 -36.45268275
[25,] -55.20127464 -26.37287505
[26,] -4.59356649 -55.20127464
[27,] -39.15202702 -4.59356649
[28,] -2.53336906 -39.15202702
[29,] -22.01788169 -2.53336906
[30,] -42.63324100 -22.01788169
[31,] -30.37443792 -42.63324100
[32,] -17.38083909 -30.37443792
[33,] -41.26410982 -17.38083909
[34,] -18.22845183 -41.26410982
[35,] -72.89750824 -18.22845183
[36,] -30.90714576 -72.89750824
[37,] -19.31090183 -30.90714576
[38,] -31.25286337 -19.31090183
[39,] -16.58142618 -31.25286337
[40,] -18.34520185 -16.58142618
[41,] -28.07836744 -18.34520185
[42,] -47.26043164 -28.07836744
[43,] -7.82838147 -47.26043164
[44,] -29.92949348 -7.82838147
[45,] -30.94897669 -29.92949348
[46,] -45.65173461 -30.94897669
[47,] -4.65729119 -45.65173461
[48,] -14.66515621 -4.65729119
[49,] -4.03330148 -14.66515621
[50,] -33.23431405 -4.03330148
[51,] -21.96842456 -33.23431405
[52,] 2.31376598 -21.96842456
[53,] -5.93758230 2.31376598
[54,] -4.92991264 -5.93758230
[55,] -9.60031407 -4.92991264
[56,] -16.38396507 -9.60031407
[57,] -31.89313539 -16.38396507
[58,] -15.18038957 -31.89313539
[59,] -18.26090484 -15.18038957
[60,] -23.55472487 -18.26090484
[61,] -4.19515986 -23.55472487
[62,] -20.08933551 -4.19515986
[63,] -22.22041244 -20.08933551
[64,] -24.08911616 -22.22041244
[65,] -6.49785897 -24.08911616
[66,] 9.40763788 -6.49785897
[67,] 32.01106406 9.40763788
[68,] -15.24737189 32.01106406
[69,] -29.67110742 -15.24737189
[70,] 42.10592137 -29.67110742
[71,] -4.28668680 42.10592137
[72,] 0.06827996 -4.28668680
[73,] 8.70745197 0.06827996
[74,] 4.00403660 8.70745197
[75,] -9.87889443 4.00403660
[76,] 6.01839062 -9.87889443
[77,] 17.39769597 6.01839062
[78,] -10.07261853 17.39769597
[79,] 6.61516750 -10.07261853
[80,] -0.17021031 6.61516750
[81,] 15.66045469 -0.17021031
[82,] -1.50247901 15.66045469
[83,] 15.63898447 -1.50247901
[84,] 3.58056049 15.63898447
[85,] 17.20994109 3.58056049
[86,] 58.48551209 17.20994109
[87,] 7.46781121 58.48551209
[88,] 5.45600363 7.46781121
[89,] 10.18679522 5.45600363
[90,] 19.70148883 10.18679522
[91,] 40.32371956 19.70148883
[92,] 14.61041257 40.32371956
[93,] 17.55855205 14.61041257
[94,] 11.68732804 17.55855205
[95,] 57.03214133 11.68732804
[96,] 23.35736540 57.03214133
[97,] 0.43658305 23.35736540
[98,] 42.94008647 0.43658305
[99,] 35.85044079 42.94008647
[100,] 9.43180269 35.85044079
[101,] 11.66744048 9.43180269
[102,] 32.61103479 11.66744048
[103,] 14.52399333 32.61103479
[104,] 16.90037876 14.52399333
[105,] 17.60003625 16.90037876
[106,] 20.56048657 17.60003625
[107,] 17.41647470 20.56048657
[108,] 0.10249176 17.41647470
[109,] 24.52589266 0.10249176
[110,] 29.98084413 24.52589266
[111,] 33.66618919 29.98084413
[112,] 36.93911619 33.66618919
[113,] 37.02071015 36.93911619
[114,] 8.27311294 37.02071015
[115,] 7.10249176 8.27311294
[116,] 37.93733144 7.10249176
[117,] 48.61151374 37.93733144
[118,] 62.25488440 48.61151374
[119,] 37.22713250 62.25488440
[120,] 47.43627869 37.22713250
[121,] 53.97757856 47.43627869
[122,] 37.51655695 53.97757856
[123,] 46.29437956 37.51655695
[124,] 38.06264706 46.29437956
[125,] 30.25095931 38.06264706
[126,] 57.29629393 30.25095931
[127,] 45.18744461 57.29629393
[128,] 51.45393290 45.18744461
[129,] 32.82977224 51.45393290
[130,] 27.73402297 32.82977224
[131,] 56.13205706 27.73402297
[132,] 26.50333673 56.13205706
[133,] 52.57628760 26.50333673
[134,] 28.24415571 52.57628760
[135,] 43.74562555 28.24415571
[136,] 28.10249176 43.74562555
[137,] 52.00808394 28.10249176
[138,] 42.78164447 52.00808394
[139,] 35.36837745 42.78164447
[140,] 37.93686313 35.36837745
[141,] 47.09806199 37.93686313
[142,] 52.52125210 47.09806199
[143,] 60.08804082 52.52125210
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -79.91442160 -60.18494651
2 -92.83760286 -79.91442160
3 -38.17588980 -92.83760286
4 -0.28718353 -38.17588980
5 55.17982881 -0.28718353
6 -59.27192192 55.17982881
7 -69.26568440 -59.27192192
8 -51.66342666 -69.26568440
9 -11.35650994 -51.66342666
10 -56.82107142 -11.35650994
11 -38.73075255 -56.82107142
12 -34.42297396 -38.73075255
13 -14.95832443 -34.42297396
14 -60.53052425 -14.95832443
15 -46.54094268 -60.53052425
16 -51.39983645 -46.54094268
17 -72.98912106 -51.39983645
18 -17.40442886 -72.98912106
19 -65.78613044 -17.40442886
20 -51.01192960 -65.78613044
21 -27.58647503 -51.01192960
22 -47.95497076 -27.58647503
23 -36.45268275 -47.95497076
24 -26.37287505 -36.45268275
25 -55.20127464 -26.37287505
26 -4.59356649 -55.20127464
27 -39.15202702 -4.59356649
28 -2.53336906 -39.15202702
29 -22.01788169 -2.53336906
30 -42.63324100 -22.01788169
31 -30.37443792 -42.63324100
32 -17.38083909 -30.37443792
33 -41.26410982 -17.38083909
34 -18.22845183 -41.26410982
35 -72.89750824 -18.22845183
36 -30.90714576 -72.89750824
37 -19.31090183 -30.90714576
38 -31.25286337 -19.31090183
39 -16.58142618 -31.25286337
40 -18.34520185 -16.58142618
41 -28.07836744 -18.34520185
42 -47.26043164 -28.07836744
43 -7.82838147 -47.26043164
44 -29.92949348 -7.82838147
45 -30.94897669 -29.92949348
46 -45.65173461 -30.94897669
47 -4.65729119 -45.65173461
48 -14.66515621 -4.65729119
49 -4.03330148 -14.66515621
50 -33.23431405 -4.03330148
51 -21.96842456 -33.23431405
52 2.31376598 -21.96842456
53 -5.93758230 2.31376598
54 -4.92991264 -5.93758230
55 -9.60031407 -4.92991264
56 -16.38396507 -9.60031407
57 -31.89313539 -16.38396507
58 -15.18038957 -31.89313539
59 -18.26090484 -15.18038957
60 -23.55472487 -18.26090484
61 -4.19515986 -23.55472487
62 -20.08933551 -4.19515986
63 -22.22041244 -20.08933551
64 -24.08911616 -22.22041244
65 -6.49785897 -24.08911616
66 9.40763788 -6.49785897
67 32.01106406 9.40763788
68 -15.24737189 32.01106406
69 -29.67110742 -15.24737189
70 42.10592137 -29.67110742
71 -4.28668680 42.10592137
72 0.06827996 -4.28668680
73 8.70745197 0.06827996
74 4.00403660 8.70745197
75 -9.87889443 4.00403660
76 6.01839062 -9.87889443
77 17.39769597 6.01839062
78 -10.07261853 17.39769597
79 6.61516750 -10.07261853
80 -0.17021031 6.61516750
81 15.66045469 -0.17021031
82 -1.50247901 15.66045469
83 15.63898447 -1.50247901
84 3.58056049 15.63898447
85 17.20994109 3.58056049
86 58.48551209 17.20994109
87 7.46781121 58.48551209
88 5.45600363 7.46781121
89 10.18679522 5.45600363
90 19.70148883 10.18679522
91 40.32371956 19.70148883
92 14.61041257 40.32371956
93 17.55855205 14.61041257
94 11.68732804 17.55855205
95 57.03214133 11.68732804
96 23.35736540 57.03214133
97 0.43658305 23.35736540
98 42.94008647 0.43658305
99 35.85044079 42.94008647
100 9.43180269 35.85044079
101 11.66744048 9.43180269
102 32.61103479 11.66744048
103 14.52399333 32.61103479
104 16.90037876 14.52399333
105 17.60003625 16.90037876
106 20.56048657 17.60003625
107 17.41647470 20.56048657
108 0.10249176 17.41647470
109 24.52589266 0.10249176
110 29.98084413 24.52589266
111 33.66618919 29.98084413
112 36.93911619 33.66618919
113 37.02071015 36.93911619
114 8.27311294 37.02071015
115 7.10249176 8.27311294
116 37.93733144 7.10249176
117 48.61151374 37.93733144
118 62.25488440 48.61151374
119 37.22713250 62.25488440
120 47.43627869 37.22713250
121 53.97757856 47.43627869
122 37.51655695 53.97757856
123 46.29437956 37.51655695
124 38.06264706 46.29437956
125 30.25095931 38.06264706
126 57.29629393 30.25095931
127 45.18744461 57.29629393
128 51.45393290 45.18744461
129 32.82977224 51.45393290
130 27.73402297 32.82977224
131 56.13205706 27.73402297
132 26.50333673 56.13205706
133 52.57628760 26.50333673
134 28.24415571 52.57628760
135 43.74562555 28.24415571
136 28.10249176 43.74562555
137 52.00808394 28.10249176
138 42.78164447 52.00808394
139 35.36837745 42.78164447
140 37.93686313 35.36837745
141 47.09806199 37.93686313
142 52.52125210 47.09806199
143 60.08804082 52.52125210
> 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/7fh0f1322149810.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/850v41322149810.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9w4551322149810.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10knu41322149810.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113i2c1322149810.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/127xtr1322149810.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/13hwhm1322149810.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/146rvl1322149810.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/158v1e1322149810.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/16wwit1322149810.tab")
+ }
>
> try(system("convert tmp/1yv901322149810.ps tmp/1yv901322149810.png",intern=TRUE))
character(0)
> try(system("convert tmp/29gio1322149810.ps tmp/29gio1322149810.png",intern=TRUE))
character(0)
> try(system("convert tmp/3jfny1322149810.ps tmp/3jfny1322149810.png",intern=TRUE))
character(0)
> try(system("convert tmp/49pug1322149810.ps tmp/49pug1322149810.png",intern=TRUE))
character(0)
> try(system("convert tmp/5kk3q1322149810.ps tmp/5kk3q1322149810.png",intern=TRUE))
character(0)
> try(system("convert tmp/65a981322149810.ps tmp/65a981322149810.png",intern=TRUE))
character(0)
> try(system("convert tmp/7fh0f1322149810.ps tmp/7fh0f1322149810.png",intern=TRUE))
character(0)
> try(system("convert tmp/850v41322149810.ps tmp/850v41322149810.png",intern=TRUE))
character(0)
> try(system("convert tmp/9w4551322149810.ps tmp/9w4551322149810.png",intern=TRUE))
character(0)
> try(system("convert tmp/10knu41322149810.ps tmp/10knu41322149810.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.746 0.793 8.246