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.
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Type 'contributors()' for more information and
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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(63031
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+ ,6585)
+ ,dim=c(6
+ ,144)
+ ,dimnames=list(c('time'
+ ,'comp'
+ ,'blog'
+ ,'review'
+ ,'fbm'
+ ,'charac')
+ ,1:144))
> y <- array(NA,dim=c(6,144),dimnames=list(c('time','comp','blog','review','fbm','charac'),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
time comp blog review fbm charac
1 63031 68 13 5 20 10345
2 66751 17 26 7 21 17607
3 7176 1 0 0 0 1423
4 78306 114 37 12 28 20050
5 137944 95 47 15 59 21212
6 261308 148 80 16 58 93979
7 69266 56 21 12 36 15524
8 80226 26 36 13 50 16182
9 73226 63 35 15 29 19238
10 178519 96 40 13 48 28909
11 66476 74 35 6 24 22357
12 98606 65 46 16 44 25560
13 50001 40 20 7 16 9954
14 91093 173 24 12 46 18490
15 73884 28 19 9 35 17777
16 72961 55 15 10 35 25268
17 69388 58 48 16 63 37525
18 15629 25 0 5 15 6023
19 71693 103 38 20 62 25042
20 19920 29 12 7 12 35713
21 39403 31 10 13 33 7039
22 99933 43 51 13 44 40841
23 56088 74 4 11 29 9214
24 62006 99 24 9 26 17446
25 81665 25 39 10 31 10295
26 65223 69 19 7 22 13206
27 88794 62 23 13 46 26093
28 90642 25 39 15 39 20744
29 203699 38 37 13 45 68013
30 99340 57 20 7 23 12840
31 56695 52 20 14 41 12672
32 108143 91 41 11 32 10872
33 58313 48 26 3 12 21325
34 29101 52 0 8 18 24542
35 113060 35 31 12 41 16401
36 0 0 0 0 0 0
37 65773 31 8 12 32 12821
38 67047 107 35 8 24 14662
39 41953 242 3 20 54 22190
40 109835 41 47 18 71 37929
41 86584 57 42 9 32 18009
42 59588 32 11 14 53 11076
43 40064 17 10 7 24 24981
44 70227 36 26 13 35 30691
45 60437 29 27 11 42 29164
46 47000 22 0 11 33 13985
47 40295 21 15 14 30 7588
48 103397 41 32 9 36 20023
49 78982 64 13 12 48 25524
50 60206 71 24 11 31 14717
51 39887 28 10 17 34 6832
52 49791 36 14 10 30 9624
53 129283 45 24 11 43 24300
54 104816 22 29 12 41 21790
55 101395 27 40 17 66 16493
56 72824 38 22 6 20 9269
57 76018 26 27 8 23 20105
58 33891 41 8 12 30 11216
59 62164 21 27 13 49 15569
60 28266 28 0 14 37 21799
61 35093 36 0 17 61 3772
62 35252 58 17 8 25 6057
63 36977 65 7 9 28 20828
64 42406 29 18 9 25 9976
65 56353 21 7 9 29 14055
66 58817 19 24 15 53 17455
67 76053 55 18 16 55 39553
68 70872 119 39 13 33 14818
69 42372 34 17 12 37 17065
70 19144 25 0 10 27 1536
71 114177 113 39 9 26 11938
72 53544 46 20 3 2 24589
73 51379 28 29 12 46 21332
74 40756 63 27 8 15 13229
75 46956 52 23 17 63 11331
76 17799 35 0 9 28 853
77 71154 32 31 8 24 19821
78 58305 45 19 9 31 34666
79 27454 42 12 12 25 15051
80 34323 28 23 5 7 27969
81 44761 32 33 14 35 17897
82 113862 32 21 14 42 6031
83 35027 27 17 10 10 7153
84 62396 69 27 12 33 13365
85 29613 30 14 10 28 11197
86 65559 48 12 12 25 25291
87 110811 57 21 17 62 28994
88 27883 36 14 11 29 10461
89 40181 20 14 10 30 16415
90 53398 54 22 11 36 8495
91 56435 26 25 7 17 18318
92 77283 58 36 10 34 25143
93 71738 35 10 11 37 20471
94 48503 28 16 5 20 14561
95 25214 8 12 6 7 16902
96 119424 96 20 14 46 12994
97 79201 50 38 13 43 29697
98 19349 15 13 1 0 3895
99 78760 65 12 13 45 9807
100 54133 33 11 9 26 10711
101 21623 7 8 1 1 2325
102 25497 17 22 6 16 19000
103 69535 55 14 12 29 22418
104 30709 32 7 9 21 7872
105 37043 22 14 9 19 5650
106 24716 41 2 12 10 3979
107 54865 50 35 10 39 14956
108 27246 7 5 2 7 3738
109 0 0 0 0 0 0
110 38814 26 34 8 11 10586
111 27646 22 12 7 28 18122
112 65373 26 34 11 27 17899
113 43021 37 30 14 46 10913
114 43116 29 21 4 9 18060
115 3058 0 0 0 0 0
116 0 0 0 0 0 0
117 96347 42 28 13 49 15452
118 48626 51 16 17 27 33996
119 73073 77 12 13 31 8877
120 45266 32 14 12 46 18708
121 43410 63 7 1 3 2781
122 83842 50 41 12 41 20854
123 39296 18 21 6 15 8179
124 38490 37 28 11 21 7139
125 39841 23 1 8 23 13798
126 19764 19 10 2 4 5619
127 59975 39 31 12 41 13050
128 64589 38 7 12 46 11297
129 63339 55 26 14 54 16170
130 11796 22 1 2 1 0
131 7627 7 0 0 0 0
132 68998 21 12 9 21 20539
133 6836 5 0 1 0 0
134 33365 21 17 3 3 10056
135 5118 1 5 0 0 0
136 20898 22 4 2 3 2418
137 0 0 0 0 0 0
138 42690 31 6 12 44 11806
139 14507 25 0 14 19 15924
140 7131 0 0 0 0 0
141 4194 4 0 0 0 0
142 21416 20 15 4 12 7084
143 30591 29 0 7 24 14831
144 42419 33 12 10 26 6585
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) comp blog review fbm charac
5775.5833 173.8655 1023.2217 -1814.3373 908.9425 0.9919
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-61041 -12222 -2487 9511 68680
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5775.5833 3946.8981 1.463 0.14565
comp 173.8655 59.3196 2.931 0.00396 **
blog 1023.2217 160.1088 6.391 2.36e-09 ***
review -1814.3373 804.3030 -2.256 0.02566 *
fbm 908.9425 228.2179 3.983 0.00011 ***
charac 0.9919 0.1837 5.400 2.83e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 20010 on 138 degrees of freedom
Multiple R-squared: 0.7306, Adjusted R-squared: 0.7208
F-statistic: 74.85 on 5 and 138 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,] 0.2026228 4.052456e-01 7.973772e-01
[2,] 0.8476634 3.046732e-01 1.523366e-01
[3,] 0.8300842 3.398316e-01 1.699158e-01
[4,] 0.7425764 5.148473e-01 2.574236e-01
[5,] 0.6693197 6.613607e-01 3.306803e-01
[6,] 0.7867390 4.265221e-01 2.132610e-01
[7,] 0.7225686 5.548627e-01 2.774314e-01
[8,] 0.6770020 6.459959e-01 3.229980e-01
[9,] 0.9900005 1.999900e-02 9.999499e-03
[10,] 0.9839721 3.205578e-02 1.602789e-02
[11,] 0.9880662 2.386762e-02 1.193381e-02
[12,] 0.9927859 1.442825e-02 7.214124e-03
[13,] 0.9919997 1.600060e-02 8.000301e-03
[14,] 0.9913552 1.728966e-02 8.644828e-03
[15,] 0.9908169 1.836626e-02 9.183129e-03
[16,] 0.9882381 2.352382e-02 1.176191e-02
[17,] 0.9840944 3.181124e-02 1.590562e-02
[18,] 0.9768652 4.626963e-02 2.313482e-02
[19,] 0.9684056 6.318879e-02 3.159440e-02
[20,] 0.9633692 7.326168e-02 3.663084e-02
[21,] 0.9990254 1.949222e-03 9.746109e-04
[22,] 0.9997522 4.955758e-04 2.477879e-04
[23,] 0.9995815 8.370810e-04 4.185405e-04
[24,] 0.9996608 6.783604e-04 3.391802e-04
[25,] 0.9995604 8.791546e-04 4.395773e-04
[26,] 0.9994067 1.186560e-03 5.932798e-04
[27,] 0.9997870 4.260717e-04 2.130359e-04
[28,] 0.9996717 6.565758e-04 3.282879e-04
[29,] 0.9997372 5.256332e-04 2.628166e-04
[30,] 0.9996442 7.115774e-04 3.557887e-04
[31,] 0.9999407 1.186730e-04 5.933648e-05
[32,] 0.9999475 1.050873e-04 5.254364e-05
[33,] 0.9999149 1.701546e-04 8.507731e-05
[34,] 0.9998589 2.822911e-04 1.411456e-04
[35,] 0.9998181 3.638852e-04 1.819426e-04
[36,] 0.9997235 5.529100e-04 2.764550e-04
[37,] 0.9997660 4.680767e-04 2.340383e-04
[38,] 0.9996988 6.024165e-04 3.012082e-04
[39,] 0.9995404 9.192726e-04 4.596363e-04
[40,] 0.9996084 7.832923e-04 3.916461e-04
[41,] 0.9993847 1.230580e-03 6.152899e-04
[42,] 0.9991012 1.797622e-03 8.988109e-04
[43,] 0.9988037 2.392607e-03 1.196304e-03
[44,] 0.9982133 3.573494e-03 1.786747e-03
[45,] 0.9998382 3.235176e-04 1.617588e-04
[46,] 0.9999458 1.083201e-04 5.416005e-05
[47,] 0.9999362 1.275156e-04 6.375781e-05
[48,] 0.9999460 1.080871e-04 5.404357e-05
[49,] 0.9999494 1.011520e-04 5.057599e-05
[50,] 0.9999226 1.547985e-04 7.739924e-05
[51,] 0.9998932 2.135825e-04 1.067912e-04
[52,] 0.9998615 2.770075e-04 1.385038e-04
[53,] 0.9998231 3.537702e-04 1.768851e-04
[54,] 0.9998102 3.795738e-04 1.897869e-04
[55,] 0.9998487 3.025530e-04 1.512765e-04
[56,] 0.9997624 4.751310e-04 2.375655e-04
[57,] 0.9997417 5.165241e-04 2.582620e-04
[58,] 0.9996424 7.152584e-04 3.576292e-04
[59,] 0.9995534 8.931011e-04 4.465505e-04
[60,] 0.9997910 4.180817e-04 2.090409e-04
[61,] 0.9997402 5.195536e-04 2.597768e-04
[62,] 0.9996210 7.579260e-04 3.789630e-04
[63,] 0.9996392 7.215501e-04 3.607751e-04
[64,] 0.9994713 1.057364e-03 5.286818e-04
[65,] 0.9995812 8.375831e-04 4.187916e-04
[66,] 0.9996160 7.679952e-04 3.839976e-04
[67,] 0.9998620 2.759959e-04 1.379979e-04
[68,] 0.9998657 2.685516e-04 1.342758e-04
[69,] 0.9998484 3.032737e-04 1.516369e-04
[70,] 0.9998183 3.633903e-04 1.816951e-04
[71,] 0.9998373 3.254141e-04 1.627070e-04
[72,] 0.9998270 3.459531e-04 1.729766e-04
[73,] 0.9998285 3.429829e-04 1.714915e-04
[74,] 0.9999999 1.033104e-07 5.165522e-08
[75,] 0.9999999 1.573940e-07 7.869699e-08
[76,] 0.9999999 2.113810e-07 1.056905e-07
[77,] 0.9999999 2.592654e-07 1.296327e-07
[78,] 0.9999998 4.444586e-07 2.222293e-07
[79,] 0.9999999 2.656087e-07 1.328044e-07
[80,] 0.9999999 2.161660e-07 1.080830e-07
[81,] 0.9999998 4.368755e-07 2.184377e-07
[82,] 0.9999997 6.203440e-07 3.101720e-07
[83,] 0.9999996 8.587502e-07 4.293751e-07
[84,] 0.9999993 1.478968e-06 7.394838e-07
[85,] 0.9999994 1.228614e-06 6.143072e-07
[86,] 0.9999988 2.376343e-06 1.188171e-06
[87,] 0.9999978 4.305985e-06 2.152993e-06
[88,] 0.9999996 8.991840e-07 4.495920e-07
[89,] 0.9999992 1.628450e-06 8.142248e-07
[90,] 0.9999983 3.358407e-06 1.679204e-06
[91,] 0.9999978 4.382983e-06 2.191492e-06
[92,] 0.9999973 5.449535e-06 2.724768e-06
[93,] 0.9999955 9.006314e-06 4.503157e-06
[94,] 0.9999960 8.012487e-06 4.006243e-06
[95,] 0.9999938 1.238664e-05 6.193321e-06
[96,] 0.9999876 2.486908e-05 1.243454e-05
[97,] 0.9999788 4.241863e-05 2.120931e-05
[98,] 0.9999625 7.506336e-05 3.753168e-05
[99,] 0.9999766 4.679917e-05 2.339958e-05
[100,] 0.9999698 6.046257e-05 3.023129e-05
[101,] 0.9999407 1.186694e-04 5.933469e-05
[102,] 0.9998894 2.212569e-04 1.106285e-04
[103,] 0.9999240 1.520959e-04 7.604795e-05
[104,] 0.9999003 1.993120e-04 9.965599e-05
[105,] 0.9999377 1.246067e-04 6.230335e-05
[106,] 0.9998727 2.545032e-04 1.272516e-04
[107,] 0.9997387 5.226501e-04 2.613251e-04
[108,] 0.9994997 1.000560e-03 5.002800e-04
[109,] 0.9998916 2.167006e-04 1.083503e-04
[110,] 0.9999280 1.440055e-04 7.200277e-05
[111,] 0.9999341 1.318657e-04 6.593287e-05
[112,] 0.9999526 9.472396e-05 4.736198e-05
[113,] 0.9999206 1.587494e-04 7.937470e-05
[114,] 0.9998054 3.892663e-04 1.946331e-04
[115,] 0.9995648 8.703537e-04 4.351768e-04
[116,] 0.9990219 1.956266e-03 9.781329e-04
[117,] 0.9978610 4.277928e-03 2.138964e-03
[118,] 0.9955486 8.902817e-03 4.451409e-03
[119,] 0.9908085 1.838300e-02 9.191500e-03
[120,] 0.9944462 1.110767e-02 5.553836e-03
[121,] 0.9920464 1.590711e-02 7.953554e-03
[122,] 0.9840904 3.181914e-02 1.590957e-02
[123,] 0.9665012 6.699759e-02 3.349880e-02
[124,] 0.9992298 1.540460e-03 7.702302e-04
[125,] 0.9967773 6.445447e-03 3.222724e-03
[126,] 0.9984968 3.006313e-03 1.503157e-03
[127,] 0.9907549 1.849020e-02 9.245099e-03
> postscript(file="/var/wessaorg/rcomp/tmp/1st4b1322147198.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2yzxa1322147198.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3q9u51322147198.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4b7041322147198.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5y9lk1322147198.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
12762.3594 7564.2045 -184.9160 -8715.2989 20107.1409 31035.9780
7 8 9 10 11 12
5918.2266 -4817.6593 2457.7733 66405.9012 -21082.7923 -1855.9595
13 14 15 16 17 18
5090.3166 -7698.0879 10682.0941 3541.6615 -61041.2802 -5029.8573
19 20 21 22 23 24
-35779.8877 -36806.8531 4614.7005 -22420.1932 17812.5375 -10147.6650
25 26 27 28 29 30
11391.7243 7614.1403 4598.1601 11804.5558 68679.5141 42248.3958
31 32 33 34 35 36
-3021.2417 24681.2294 -9028.3599 -11904.9523 37716.5798 -5775.5833
37 38 39 40 41 42
26390.6098 -14988.0437 -43774.0046 -20658.9359 -2697.3977 3233.8193
43 44 45 46 47 48
-12792.3118 -7080.3703 -25153.1812 13490.3272 6125.8608 21496.2204
49 50 51 52 53 54
1602.8128 -5288.5864 12174.0261 4760.2519 47896.2730 28433.9503
55 56 57 58 59 60
4490.3784 21443.9453 11761.8909 -3820.1680 -11284.3617 -12230.2945
61 62 63 64 65 66
-5284.9272 -12219.3255 -17042.9438 -3119.3488 15792.3031 -13091.7756
67 68 69 70 71 72
-17898.0554 -16605.8481 -15495.2989 1100.1543 29704.2518 -1458.4204
73 74 75 76 77 78
-30136.6669 -15841.3197 -29053.4977 -4029.3158 1134.5668 -20968.9711
79 80 81 82 83 84
-13783.1270 -24888.1510 -24508.8133 62278.0814 9121.1991 -4483.0257
85 86 87 88 89 90
-14116.9217 13121.6709 19367.7121 -15254.6848 -8803.8620 -5467.5686
91 92 93 94 95 96
-362.8310 -13112.6623 15666.6548 -2062.5172 -6472.7580 47193.5739
97 98 99 100 101 102
-19104.7439 -4385.5438 22360.9540 13436.7559 5043.8222 -28248.2438
103 104 105 106 107 108
13048.1146 1640.2118 6572.1910 18501.3590 -27556.7903 8695.6217
109 110 111 112 113 114
-5775.5833 -12255.4992 -24958.4419 -2050.2980 -27119.4451 -8026.1036
115 116 117 118 119 120
-2717.5833 -5775.5833 18340.2929 -9806.4606 28235.2255 -18994.0690
121 122 123 124 125 126
15847.3871 -8758.5303 -1957.6448 -8580.0384 8966.1372 -5127.8008
127 128 129 130 131 132
-12740.0406 13799.2272 -18324.0710 3891.8862 634.3582 24161.2842
133 134 135 136 137 138
2005.4265 -714.8442 -5947.5575 5707.9329 -5775.5833 -4546.4825
139 140 141 142 143 144
-3279.3480 1355.4167 -2277.0453 -13861.7670 -4051.7417 6606.4319
> postscript(file="/var/wessaorg/rcomp/tmp/6wutn1322147198.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 12762.3594 NA
1 7564.2045 12762.3594
2 -184.9160 7564.2045
3 -8715.2989 -184.9160
4 20107.1409 -8715.2989
5 31035.9780 20107.1409
6 5918.2266 31035.9780
7 -4817.6593 5918.2266
8 2457.7733 -4817.6593
9 66405.9012 2457.7733
10 -21082.7923 66405.9012
11 -1855.9595 -21082.7923
12 5090.3166 -1855.9595
13 -7698.0879 5090.3166
14 10682.0941 -7698.0879
15 3541.6615 10682.0941
16 -61041.2802 3541.6615
17 -5029.8573 -61041.2802
18 -35779.8877 -5029.8573
19 -36806.8531 -35779.8877
20 4614.7005 -36806.8531
21 -22420.1932 4614.7005
22 17812.5375 -22420.1932
23 -10147.6650 17812.5375
24 11391.7243 -10147.6650
25 7614.1403 11391.7243
26 4598.1601 7614.1403
27 11804.5558 4598.1601
28 68679.5141 11804.5558
29 42248.3958 68679.5141
30 -3021.2417 42248.3958
31 24681.2294 -3021.2417
32 -9028.3599 24681.2294
33 -11904.9523 -9028.3599
34 37716.5798 -11904.9523
35 -5775.5833 37716.5798
36 26390.6098 -5775.5833
37 -14988.0437 26390.6098
38 -43774.0046 -14988.0437
39 -20658.9359 -43774.0046
40 -2697.3977 -20658.9359
41 3233.8193 -2697.3977
42 -12792.3118 3233.8193
43 -7080.3703 -12792.3118
44 -25153.1812 -7080.3703
45 13490.3272 -25153.1812
46 6125.8608 13490.3272
47 21496.2204 6125.8608
48 1602.8128 21496.2204
49 -5288.5864 1602.8128
50 12174.0261 -5288.5864
51 4760.2519 12174.0261
52 47896.2730 4760.2519
53 28433.9503 47896.2730
54 4490.3784 28433.9503
55 21443.9453 4490.3784
56 11761.8909 21443.9453
57 -3820.1680 11761.8909
58 -11284.3617 -3820.1680
59 -12230.2945 -11284.3617
60 -5284.9272 -12230.2945
61 -12219.3255 -5284.9272
62 -17042.9438 -12219.3255
63 -3119.3488 -17042.9438
64 15792.3031 -3119.3488
65 -13091.7756 15792.3031
66 -17898.0554 -13091.7756
67 -16605.8481 -17898.0554
68 -15495.2989 -16605.8481
69 1100.1543 -15495.2989
70 29704.2518 1100.1543
71 -1458.4204 29704.2518
72 -30136.6669 -1458.4204
73 -15841.3197 -30136.6669
74 -29053.4977 -15841.3197
75 -4029.3158 -29053.4977
76 1134.5668 -4029.3158
77 -20968.9711 1134.5668
78 -13783.1270 -20968.9711
79 -24888.1510 -13783.1270
80 -24508.8133 -24888.1510
81 62278.0814 -24508.8133
82 9121.1991 62278.0814
83 -4483.0257 9121.1991
84 -14116.9217 -4483.0257
85 13121.6709 -14116.9217
86 19367.7121 13121.6709
87 -15254.6848 19367.7121
88 -8803.8620 -15254.6848
89 -5467.5686 -8803.8620
90 -362.8310 -5467.5686
91 -13112.6623 -362.8310
92 15666.6548 -13112.6623
93 -2062.5172 15666.6548
94 -6472.7580 -2062.5172
95 47193.5739 -6472.7580
96 -19104.7439 47193.5739
97 -4385.5438 -19104.7439
98 22360.9540 -4385.5438
99 13436.7559 22360.9540
100 5043.8222 13436.7559
101 -28248.2438 5043.8222
102 13048.1146 -28248.2438
103 1640.2118 13048.1146
104 6572.1910 1640.2118
105 18501.3590 6572.1910
106 -27556.7903 18501.3590
107 8695.6217 -27556.7903
108 -5775.5833 8695.6217
109 -12255.4992 -5775.5833
110 -24958.4419 -12255.4992
111 -2050.2980 -24958.4419
112 -27119.4451 -2050.2980
113 -8026.1036 -27119.4451
114 -2717.5833 -8026.1036
115 -5775.5833 -2717.5833
116 18340.2929 -5775.5833
117 -9806.4606 18340.2929
118 28235.2255 -9806.4606
119 -18994.0690 28235.2255
120 15847.3871 -18994.0690
121 -8758.5303 15847.3871
122 -1957.6448 -8758.5303
123 -8580.0384 -1957.6448
124 8966.1372 -8580.0384
125 -5127.8008 8966.1372
126 -12740.0406 -5127.8008
127 13799.2272 -12740.0406
128 -18324.0710 13799.2272
129 3891.8862 -18324.0710
130 634.3582 3891.8862
131 24161.2842 634.3582
132 2005.4265 24161.2842
133 -714.8442 2005.4265
134 -5947.5575 -714.8442
135 5707.9329 -5947.5575
136 -5775.5833 5707.9329
137 -4546.4825 -5775.5833
138 -3279.3480 -4546.4825
139 1355.4167 -3279.3480
140 -2277.0453 1355.4167
141 -13861.7670 -2277.0453
142 -4051.7417 -13861.7670
143 6606.4319 -4051.7417
144 NA 6606.4319
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 7564.2045 12762.3594
[2,] -184.9160 7564.2045
[3,] -8715.2989 -184.9160
[4,] 20107.1409 -8715.2989
[5,] 31035.9780 20107.1409
[6,] 5918.2266 31035.9780
[7,] -4817.6593 5918.2266
[8,] 2457.7733 -4817.6593
[9,] 66405.9012 2457.7733
[10,] -21082.7923 66405.9012
[11,] -1855.9595 -21082.7923
[12,] 5090.3166 -1855.9595
[13,] -7698.0879 5090.3166
[14,] 10682.0941 -7698.0879
[15,] 3541.6615 10682.0941
[16,] -61041.2802 3541.6615
[17,] -5029.8573 -61041.2802
[18,] -35779.8877 -5029.8573
[19,] -36806.8531 -35779.8877
[20,] 4614.7005 -36806.8531
[21,] -22420.1932 4614.7005
[22,] 17812.5375 -22420.1932
[23,] -10147.6650 17812.5375
[24,] 11391.7243 -10147.6650
[25,] 7614.1403 11391.7243
[26,] 4598.1601 7614.1403
[27,] 11804.5558 4598.1601
[28,] 68679.5141 11804.5558
[29,] 42248.3958 68679.5141
[30,] -3021.2417 42248.3958
[31,] 24681.2294 -3021.2417
[32,] -9028.3599 24681.2294
[33,] -11904.9523 -9028.3599
[34,] 37716.5798 -11904.9523
[35,] -5775.5833 37716.5798
[36,] 26390.6098 -5775.5833
[37,] -14988.0437 26390.6098
[38,] -43774.0046 -14988.0437
[39,] -20658.9359 -43774.0046
[40,] -2697.3977 -20658.9359
[41,] 3233.8193 -2697.3977
[42,] -12792.3118 3233.8193
[43,] -7080.3703 -12792.3118
[44,] -25153.1812 -7080.3703
[45,] 13490.3272 -25153.1812
[46,] 6125.8608 13490.3272
[47,] 21496.2204 6125.8608
[48,] 1602.8128 21496.2204
[49,] -5288.5864 1602.8128
[50,] 12174.0261 -5288.5864
[51,] 4760.2519 12174.0261
[52,] 47896.2730 4760.2519
[53,] 28433.9503 47896.2730
[54,] 4490.3784 28433.9503
[55,] 21443.9453 4490.3784
[56,] 11761.8909 21443.9453
[57,] -3820.1680 11761.8909
[58,] -11284.3617 -3820.1680
[59,] -12230.2945 -11284.3617
[60,] -5284.9272 -12230.2945
[61,] -12219.3255 -5284.9272
[62,] -17042.9438 -12219.3255
[63,] -3119.3488 -17042.9438
[64,] 15792.3031 -3119.3488
[65,] -13091.7756 15792.3031
[66,] -17898.0554 -13091.7756
[67,] -16605.8481 -17898.0554
[68,] -15495.2989 -16605.8481
[69,] 1100.1543 -15495.2989
[70,] 29704.2518 1100.1543
[71,] -1458.4204 29704.2518
[72,] -30136.6669 -1458.4204
[73,] -15841.3197 -30136.6669
[74,] -29053.4977 -15841.3197
[75,] -4029.3158 -29053.4977
[76,] 1134.5668 -4029.3158
[77,] -20968.9711 1134.5668
[78,] -13783.1270 -20968.9711
[79,] -24888.1510 -13783.1270
[80,] -24508.8133 -24888.1510
[81,] 62278.0814 -24508.8133
[82,] 9121.1991 62278.0814
[83,] -4483.0257 9121.1991
[84,] -14116.9217 -4483.0257
[85,] 13121.6709 -14116.9217
[86,] 19367.7121 13121.6709
[87,] -15254.6848 19367.7121
[88,] -8803.8620 -15254.6848
[89,] -5467.5686 -8803.8620
[90,] -362.8310 -5467.5686
[91,] -13112.6623 -362.8310
[92,] 15666.6548 -13112.6623
[93,] -2062.5172 15666.6548
[94,] -6472.7580 -2062.5172
[95,] 47193.5739 -6472.7580
[96,] -19104.7439 47193.5739
[97,] -4385.5438 -19104.7439
[98,] 22360.9540 -4385.5438
[99,] 13436.7559 22360.9540
[100,] 5043.8222 13436.7559
[101,] -28248.2438 5043.8222
[102,] 13048.1146 -28248.2438
[103,] 1640.2118 13048.1146
[104,] 6572.1910 1640.2118
[105,] 18501.3590 6572.1910
[106,] -27556.7903 18501.3590
[107,] 8695.6217 -27556.7903
[108,] -5775.5833 8695.6217
[109,] -12255.4992 -5775.5833
[110,] -24958.4419 -12255.4992
[111,] -2050.2980 -24958.4419
[112,] -27119.4451 -2050.2980
[113,] -8026.1036 -27119.4451
[114,] -2717.5833 -8026.1036
[115,] -5775.5833 -2717.5833
[116,] 18340.2929 -5775.5833
[117,] -9806.4606 18340.2929
[118,] 28235.2255 -9806.4606
[119,] -18994.0690 28235.2255
[120,] 15847.3871 -18994.0690
[121,] -8758.5303 15847.3871
[122,] -1957.6448 -8758.5303
[123,] -8580.0384 -1957.6448
[124,] 8966.1372 -8580.0384
[125,] -5127.8008 8966.1372
[126,] -12740.0406 -5127.8008
[127,] 13799.2272 -12740.0406
[128,] -18324.0710 13799.2272
[129,] 3891.8862 -18324.0710
[130,] 634.3582 3891.8862
[131,] 24161.2842 634.3582
[132,] 2005.4265 24161.2842
[133,] -714.8442 2005.4265
[134,] -5947.5575 -714.8442
[135,] 5707.9329 -5947.5575
[136,] -5775.5833 5707.9329
[137,] -4546.4825 -5775.5833
[138,] -3279.3480 -4546.4825
[139,] 1355.4167 -3279.3480
[140,] -2277.0453 1355.4167
[141,] -13861.7670 -2277.0453
[142,] -4051.7417 -13861.7670
[143,] 6606.4319 -4051.7417
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 7564.2045 12762.3594
2 -184.9160 7564.2045
3 -8715.2989 -184.9160
4 20107.1409 -8715.2989
5 31035.9780 20107.1409
6 5918.2266 31035.9780
7 -4817.6593 5918.2266
8 2457.7733 -4817.6593
9 66405.9012 2457.7733
10 -21082.7923 66405.9012
11 -1855.9595 -21082.7923
12 5090.3166 -1855.9595
13 -7698.0879 5090.3166
14 10682.0941 -7698.0879
15 3541.6615 10682.0941
16 -61041.2802 3541.6615
17 -5029.8573 -61041.2802
18 -35779.8877 -5029.8573
19 -36806.8531 -35779.8877
20 4614.7005 -36806.8531
21 -22420.1932 4614.7005
22 17812.5375 -22420.1932
23 -10147.6650 17812.5375
24 11391.7243 -10147.6650
25 7614.1403 11391.7243
26 4598.1601 7614.1403
27 11804.5558 4598.1601
28 68679.5141 11804.5558
29 42248.3958 68679.5141
30 -3021.2417 42248.3958
31 24681.2294 -3021.2417
32 -9028.3599 24681.2294
33 -11904.9523 -9028.3599
34 37716.5798 -11904.9523
35 -5775.5833 37716.5798
36 26390.6098 -5775.5833
37 -14988.0437 26390.6098
38 -43774.0046 -14988.0437
39 -20658.9359 -43774.0046
40 -2697.3977 -20658.9359
41 3233.8193 -2697.3977
42 -12792.3118 3233.8193
43 -7080.3703 -12792.3118
44 -25153.1812 -7080.3703
45 13490.3272 -25153.1812
46 6125.8608 13490.3272
47 21496.2204 6125.8608
48 1602.8128 21496.2204
49 -5288.5864 1602.8128
50 12174.0261 -5288.5864
51 4760.2519 12174.0261
52 47896.2730 4760.2519
53 28433.9503 47896.2730
54 4490.3784 28433.9503
55 21443.9453 4490.3784
56 11761.8909 21443.9453
57 -3820.1680 11761.8909
58 -11284.3617 -3820.1680
59 -12230.2945 -11284.3617
60 -5284.9272 -12230.2945
61 -12219.3255 -5284.9272
62 -17042.9438 -12219.3255
63 -3119.3488 -17042.9438
64 15792.3031 -3119.3488
65 -13091.7756 15792.3031
66 -17898.0554 -13091.7756
67 -16605.8481 -17898.0554
68 -15495.2989 -16605.8481
69 1100.1543 -15495.2989
70 29704.2518 1100.1543
71 -1458.4204 29704.2518
72 -30136.6669 -1458.4204
73 -15841.3197 -30136.6669
74 -29053.4977 -15841.3197
75 -4029.3158 -29053.4977
76 1134.5668 -4029.3158
77 -20968.9711 1134.5668
78 -13783.1270 -20968.9711
79 -24888.1510 -13783.1270
80 -24508.8133 -24888.1510
81 62278.0814 -24508.8133
82 9121.1991 62278.0814
83 -4483.0257 9121.1991
84 -14116.9217 -4483.0257
85 13121.6709 -14116.9217
86 19367.7121 13121.6709
87 -15254.6848 19367.7121
88 -8803.8620 -15254.6848
89 -5467.5686 -8803.8620
90 -362.8310 -5467.5686
91 -13112.6623 -362.8310
92 15666.6548 -13112.6623
93 -2062.5172 15666.6548
94 -6472.7580 -2062.5172
95 47193.5739 -6472.7580
96 -19104.7439 47193.5739
97 -4385.5438 -19104.7439
98 22360.9540 -4385.5438
99 13436.7559 22360.9540
100 5043.8222 13436.7559
101 -28248.2438 5043.8222
102 13048.1146 -28248.2438
103 1640.2118 13048.1146
104 6572.1910 1640.2118
105 18501.3590 6572.1910
106 -27556.7903 18501.3590
107 8695.6217 -27556.7903
108 -5775.5833 8695.6217
109 -12255.4992 -5775.5833
110 -24958.4419 -12255.4992
111 -2050.2980 -24958.4419
112 -27119.4451 -2050.2980
113 -8026.1036 -27119.4451
114 -2717.5833 -8026.1036
115 -5775.5833 -2717.5833
116 18340.2929 -5775.5833
117 -9806.4606 18340.2929
118 28235.2255 -9806.4606
119 -18994.0690 28235.2255
120 15847.3871 -18994.0690
121 -8758.5303 15847.3871
122 -1957.6448 -8758.5303
123 -8580.0384 -1957.6448
124 8966.1372 -8580.0384
125 -5127.8008 8966.1372
126 -12740.0406 -5127.8008
127 13799.2272 -12740.0406
128 -18324.0710 13799.2272
129 3891.8862 -18324.0710
130 634.3582 3891.8862
131 24161.2842 634.3582
132 2005.4265 24161.2842
133 -714.8442 2005.4265
134 -5947.5575 -714.8442
135 5707.9329 -5947.5575
136 -5775.5833 5707.9329
137 -4546.4825 -5775.5833
138 -3279.3480 -4546.4825
139 1355.4167 -3279.3480
140 -2277.0453 1355.4167
141 -13861.7670 -2277.0453
142 -4051.7417 -13861.7670
143 6606.4319 -4051.7417
> 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/7y1a21322147198.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8n88g1322147198.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/91drz1322147198.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10cpa31322147198.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11y5891322147198.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/12d9eb1322147198.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/13j0wf1322147198.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/14up3s1322147198.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/15rdve1322147198.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/16m3un1322147198.tab")
+ }
>
> try(system("convert tmp/1st4b1322147198.ps tmp/1st4b1322147198.png",intern=TRUE))
character(0)
> try(system("convert tmp/2yzxa1322147198.ps tmp/2yzxa1322147198.png",intern=TRUE))
character(0)
> try(system("convert tmp/3q9u51322147198.ps tmp/3q9u51322147198.png",intern=TRUE))
character(0)
> try(system("convert tmp/4b7041322147198.ps tmp/4b7041322147198.png",intern=TRUE))
character(0)
> try(system("convert tmp/5y9lk1322147198.ps tmp/5y9lk1322147198.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wutn1322147198.ps tmp/6wutn1322147198.png",intern=TRUE))
character(0)
> try(system("convert tmp/7y1a21322147198.ps tmp/7y1a21322147198.png",intern=TRUE))
character(0)
> try(system("convert tmp/8n88g1322147198.ps tmp/8n88g1322147198.png",intern=TRUE))
character(0)
> try(system("convert tmp/91drz1322147198.ps tmp/91drz1322147198.png",intern=TRUE))
character(0)
> try(system("convert tmp/10cpa31322147198.ps tmp/10cpa31322147198.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.574 0.550 5.207