R version 2.12.0 (2010-10-15)
Copyright (C) 2010 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(13
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+ ,4
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+ ,3
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+ ,0
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+ ,14
+ ,12
+ ,4
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+ ,10
+ ,2
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+ ,8
+ ,3
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+ ,5
+ ,13)
+ ,dim=c(3
+ ,156)
+ ,dimnames=list(c('IEP'
+ ,'WP'
+ ,'HS')
+ ,1:156))
> y <- array(NA,dim=c(3,156),dimnames=list(c('IEP','WP','HS'),1:156))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'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
IEP WP HS
1 13 5 14
2 12 3 18
3 15 0 11
4 12 7 12
5 10 4 16
6 12 1 18
7 15 6 14
8 9 3 14
9 12 12 15
10 11 0 15
11 11 5 17
12 11 6 19
13 15 6 10
14 7 6 16
15 11 2 18
16 11 1 14
17 10 5 14
18 14 7 17
19 10 3 14
20 6 3 16
21 11 3 18
22 15 7 11
23 11 8 14
24 12 6 12
25 14 3 17
26 15 5 9
27 9 5 16
28 13 10 14
29 13 2 15
30 16 6 11
31 13 4 16
32 12 6 13
33 14 8 17
34 11 4 15
35 9 5 14
36 16 10 16
37 12 6 9
38 10 7 15
39 13 4 17
40 16 10 13
41 14 4 15
42 15 3 16
43 5 3 16
44 8 3 12
45 11 3 12
46 16 7 11
47 17 15 15
48 9 0 15
49 9 0 17
50 13 4 13
51 10 5 16
52 6 5 14
53 12 2 11
54 8 3 12
55 14 0 12
56 12 9 15
57 11 2 16
58 16 7 15
59 8 7 12
60 15 0 12
61 7 0 8
62 16 10 13
63 14 2 11
64 16 1 14
65 9 8 15
66 14 6 10
67 11 11 11
68 13 3 12
69 15 8 15
70 5 6 15
71 15 9 14
72 13 9 16
73 11 8 15
74 11 8 15
75 12 7 13
76 12 6 12
77 12 5 17
78 12 4 13
79 14 6 15
80 6 3 13
81 7 2 15
82 14 12 16
83 14 8 15
84 10 5 16
85 13 9 15
86 12 6 14
87 9 5 15
88 12 2 14
89 16 4 13
90 10 7 7
91 14 5 17
92 10 6 13
93 16 7 15
94 15 8 14
95 12 6 13
96 10 0 16
97 8 1 12
98 8 5 14
99 11 5 17
100 13 5 15
101 16 7 17
102 16 7 12
103 14 1 16
104 11 3 11
105 4 4 15
106 14 8 9
107 9 6 16
108 14 6 15
109 8 2 10
110 8 2 10
111 11 3 15
112 12 3 11
113 11 0 13
114 14 2 14
115 15 8 18
116 16 8 16
117 16 0 14
118 11 5 14
119 14 9 14
120 14 6 14
121 12 6 12
122 14 3 14
123 8 9 15
124 13 7 15
125 16 8 15
126 12 0 13
127 16 7 17
128 12 0 17
129 11 5 19
130 4 0 15
131 16 14 13
132 15 5 9
133 10 2 15
134 13 8 15
135 15 4 15
136 12 2 16
137 14 6 11
138 7 3 14
139 19 5 11
140 12 9 15
141 12 3 13
142 13 3 15
143 15 0 16
144 8 10 14
145 12 4 15
146 10 2 16
147 8 3 16
148 10 10 11
149 15 7 12
150 16 0 9
151 13 6 16
152 16 8 13
153 9 0 16
154 14 4 12
155 14 10 9
156 12 5 13
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) WP HS
12.4179 0.2727 -0.1239
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.6495 -1.6042 0.2265 2.1420 6.5821
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.41786 1.42825 8.694 5.06e-15 ***
WP 0.27267 0.07259 3.756 0.000245 ***
HS -0.12393 0.09663 -1.283 0.201569
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.813 on 153 degrees of freedom
Multiple R-squared: 0.09425, Adjusted R-squared: 0.08241
F-statistic: 7.96 on 2 and 153 DF, p-value: 0.0005142
> 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,] 0.08560241 0.17120482 0.9143976
[2,] 0.17547323 0.35094647 0.8245268
[3,] 0.34538724 0.69077448 0.6546128
[4,] 0.23144934 0.46289868 0.7685507
[5,] 0.16129095 0.32258189 0.8387091
[6,] 0.09844364 0.19688727 0.9015564
[7,] 0.05731355 0.11462710 0.9426864
[8,] 0.03777481 0.07554963 0.9622252
[9,] 0.13726916 0.27453832 0.8627308
[10,] 0.09205435 0.18410870 0.9079457
[11,] 0.06677952 0.13355903 0.9332205
[12,] 0.05891757 0.11783513 0.9410824
[13,] 0.07320366 0.14640732 0.9267963
[14,] 0.06292896 0.12585792 0.9370710
[15,] 0.17764427 0.35528855 0.8223557
[16,] 0.13513224 0.27026447 0.8648678
[17,] 0.11536958 0.23073916 0.8846304
[18,] 0.09090667 0.18181334 0.9090933
[19,] 0.06647362 0.13294724 0.9335264
[20,] 0.08193385 0.16386770 0.9180662
[21,] 0.06502537 0.13005075 0.9349746
[22,] 0.06121818 0.12243635 0.9387818
[23,] 0.04461017 0.08922034 0.9553898
[24,] 0.03590943 0.07181887 0.9640906
[25,] 0.03781392 0.07562783 0.9621861
[26,] 0.03112827 0.06225653 0.9688717
[27,] 0.02207400 0.04414799 0.9779260
[28,] 0.02300840 0.04601680 0.9769916
[29,] 0.01633554 0.03267108 0.9836645
[30,] 0.02037582 0.04075164 0.9796242
[31,] 0.02989874 0.05979748 0.9701013
[32,] 0.02520093 0.05040185 0.9747991
[33,] 0.02313019 0.04626037 0.9768698
[34,] 0.01955091 0.03910181 0.9804491
[35,] 0.01977183 0.03954366 0.9802282
[36,] 0.01825747 0.03651494 0.9817425
[37,] 0.02525307 0.05050614 0.9747469
[38,] 0.09398764 0.18797528 0.9060124
[39,] 0.12660122 0.25320244 0.8733988
[40,] 0.10404210 0.20808420 0.8959579
[41,] 0.10496707 0.20993414 0.8950329
[42,] 0.09701964 0.19403928 0.9029804
[43,] 0.08133589 0.16267178 0.9186641
[44,] 0.06577316 0.13154633 0.9342268
[45,] 0.05264331 0.10528661 0.9473567
[46,] 0.04482491 0.08964983 0.9551751
[47,] 0.11720613 0.23441226 0.8827939
[48,] 0.09452364 0.18904728 0.9054764
[49,] 0.11436492 0.22872984 0.8856351
[50,] 0.12260075 0.24520151 0.8773992
[51,] 0.10221282 0.20442564 0.8977872
[52,] 0.08243556 0.16487113 0.9175644
[53,] 0.09574396 0.19148792 0.9042560
[54,] 0.15043515 0.30087030 0.8495649
[55,] 0.18352871 0.36705741 0.8164713
[56,] 0.24037065 0.48074131 0.7596293
[57,] 0.23018031 0.46036062 0.7698197
[58,] 0.22103448 0.44206897 0.7789655
[59,] 0.30886032 0.61772064 0.6911397
[60,] 0.34034646 0.68069292 0.6596535
[61,] 0.30477347 0.60954694 0.6952265
[62,] 0.31213067 0.62426135 0.6878693
[63,] 0.27922411 0.55844821 0.7207759
[64,] 0.26627427 0.53254854 0.7337257
[65,] 0.50141759 0.99716483 0.4985824
[66,] 0.47588664 0.95177328 0.5241134
[67,] 0.43037849 0.86075698 0.5696215
[68,] 0.40185385 0.80370769 0.5981462
[69,] 0.37409073 0.74818147 0.6259093
[70,] 0.33362491 0.66724983 0.6663751
[71,] 0.29405020 0.58810040 0.7059498
[72,] 0.25657694 0.51315388 0.7434231
[73,] 0.22066216 0.44132433 0.7793378
[74,] 0.20102064 0.40204128 0.7989794
[75,] 0.30890545 0.61781090 0.6910946
[76,] 0.35541303 0.71082606 0.6445870
[77,] 0.31413061 0.62826123 0.6858694
[78,] 0.28166794 0.56333587 0.7183321
[79,] 0.26007729 0.52015457 0.7399227
[80,] 0.22420705 0.44841410 0.7757929
[81,] 0.19169324 0.38338647 0.8083068
[82,] 0.19549394 0.39098788 0.8045061
[83,] 0.16725650 0.33451301 0.8327435
[84,] 0.19983573 0.39967147 0.8001643
[85,] 0.21479373 0.42958747 0.7852063
[86,] 0.20206844 0.40413687 0.7979316
[87,] 0.19498536 0.38997072 0.8050146
[88,] 0.20988869 0.41977738 0.7901113
[89,] 0.19383362 0.38766725 0.8061664
[90,] 0.16411281 0.32822562 0.8358872
[91,] 0.13751313 0.27502625 0.8624869
[92,] 0.14611164 0.29222328 0.8538884
[93,] 0.17875116 0.35750232 0.8212488
[94,] 0.15183657 0.30367315 0.8481634
[95,] 0.12830789 0.25661577 0.8716921
[96,] 0.14492002 0.28984004 0.8550800
[97,] 0.14854682 0.29709363 0.8514532
[98,] 0.15447648 0.30895297 0.8455235
[99,] 0.13103638 0.26207276 0.8689636
[100,] 0.36753056 0.73506111 0.6324694
[101,] 0.32280484 0.64560968 0.6771952
[102,] 0.33742783 0.67485567 0.6625722
[103,] 0.30643540 0.61287080 0.6935646
[104,] 0.35862944 0.71725888 0.6413706
[105,] 0.43612007 0.87224013 0.5638799
[106,] 0.39119910 0.78239820 0.6088009
[107,] 0.35014204 0.70028407 0.6498580
[108,] 0.31090967 0.62181934 0.6890903
[109,] 0.29334711 0.58669422 0.7066529
[110,] 0.29962817 0.59925634 0.7003718
[111,] 0.33243495 0.66486991 0.6675650
[112,] 0.41409722 0.82819445 0.5859028
[113,] 0.37360078 0.74720156 0.6263992
[114,] 0.32813278 0.65626556 0.6718672
[115,] 0.29292201 0.58584402 0.7070780
[116,] 0.25529657 0.51059313 0.7447034
[117,] 0.23390899 0.46781799 0.7660910
[118,] 0.31984389 0.63968779 0.6801561
[119,] 0.27125325 0.54250650 0.7287468
[120,] 0.28788060 0.57576121 0.7121194
[121,] 0.24201760 0.48403519 0.7579824
[122,] 0.31744741 0.63489483 0.6825526
[123,] 0.28794007 0.57588014 0.7120599
[124,] 0.25319517 0.50639033 0.7468048
[125,] 0.57617193 0.84765613 0.4238281
[126,] 0.60309684 0.79380632 0.3969032
[127,] 0.54719282 0.90561436 0.4528072
[128,] 0.51052755 0.97894490 0.4894725
[129,] 0.46852762 0.93705525 0.5314724
[130,] 0.50507746 0.98984507 0.4949225
[131,] 0.44148246 0.88296492 0.5585175
[132,] 0.37158909 0.74317818 0.6284109
[133,] 0.57790918 0.84418163 0.4220908
[134,] 0.76364021 0.47271959 0.2363598
[135,] 0.71469969 0.57060062 0.2853003
[136,] 0.64781395 0.70437210 0.3521860
[137,] 0.58021081 0.83957838 0.4197892
[138,] 0.66812104 0.66375792 0.3318790
[139,] 0.73294231 0.53411537 0.2670577
[140,] 0.64614906 0.70770187 0.3538509
[141,] 0.54065825 0.91868351 0.4593418
[142,] 0.56680127 0.86639745 0.4331987
[143,] 0.79291326 0.41417347 0.2070867
[144,] 0.68109457 0.63781086 0.3189054
[145,] 0.75511008 0.48977984 0.2448899
> postscript(file="/var/www/rcomp/tmp/104921292938456.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/204921292938456.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/304921292938456.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4m5qq1292938456.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5m5qq1292938456.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 156
Frequency = 1
1 2 3 4 5 6
0.953857302 0.994937088 3.945413832 -0.839354340 -1.525602805 1.540280586
7 8 9 10 11 12
2.681185553 -2.500799200 -1.830910871 0.441150119 -0.674340483 -0.699144088
13 14 15 16 17 18
2.185449265 -5.070946304 0.267608837 0.044544298 -2.046142698 1.780316019
19 20 21 22 23 24
-1.500799200 -5.252931056 -0.005062912 2.036711588 -1.864157946 -0.566682591
25 26 27 28 29 30
2.871003016 2.334186942 -2.798274555 -0.409501444 1.895806621 3.309383337
31 32 33 34 35 36
1.474397195 -0.442748519 1.507644270 -0.649536877 -3.046142698 2.838366700
37 38 39 40 41 42
-0.938484807 -2.467552125 1.598331267 2.466564484 2.350463123 3.747068944
43 44 45 46 47 48
-6.252931056 -3.748667344 -0.748667344 3.036711588 2.351073882 -1.558849881
49 50 51 52 53 54
-1.310981737 1.102594979 -1.798274555 -6.046142698 0.400070334 -3.748667344
55 56 57 58 59 60
3.069347904 -1.012895623 0.019740693 3.532447875 -4.839354340 4.069347904
61 62 63 64 65 66
-4.426388384 2.466564484 2.400070334 5.044544298 -3.740223874 1.185449265
67 68 69 70 71 72
-3.053975409 1.251332656 2.259776126 -7.194880376 1.863170305 0.111038449
73 74 75 76 77 78
-1.740223874 -1.740223874 -0.715420268 -0.566682591 0.325659517 0.102594979
79 80 81 82 83 84
1.805119624 -5.624733272 -4.104193379 0.293023201 1.259776126 -1.798274555
85 86 87 88 89 90
-0.012895623 -0.318814447 -2.922208626 0.771872549 4.102594979 -3.459024700
91 92 93 94 95 96
2.325659517 -2.442748519 3.532447875 2.135842054 -0.442748519 -0.434915809
97 98 99 100 101 102
-3.203323845 -4.046142698 -0.674340483 1.077791374 3.780316019 3.160645660
103 104 105 106 107 108
3.292412442 -0.872601416 -7.649536877 0.516171695 -3.070946304 1.805119624
109 110 111 112 113 114
-3.723863738 -3.723863738 -0.376865128 0.127398584 0.193281976 2.771872549
115 116 117 118 119 120
2.631578342 3.383710198 5.317216048 -1.046142698 0.863170305 1.681185553
121 122 123 124 125 126
-0.566682591 2.499200800 -5.012895623 0.532447875 3.259776126 1.193281976
127 128 129 130 131 132
3.780316019 1.689018263 -0.426472339 -6.558849881 1.375877487 2.334186942
133 134 135 136 137 138
-1.104193379 0.259776126 3.350463123 1.019740693 1.309383337 -4.500799200
139 140 141 142 143 144
6.582055086 -1.012895623 0.375266728 1.623134872 4.565084191 -5.409501444
145 146 147 148 149 150
0.350463123 -0.980259307 -3.252931056 -3.781303660 2.160645660 4.697545688
151 152 153 154 155 156
0.929053696 3.011907982 -1.434915809 1.978660907 -0.029171803 -0.170076770
> postscript(file="/var/www/rcomp/tmp/6m5qq1292938456.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 0.953857302 NA
1 0.994937088 0.953857302
2 3.945413832 0.994937088
3 -0.839354340 3.945413832
4 -1.525602805 -0.839354340
5 1.540280586 -1.525602805
6 2.681185553 1.540280586
7 -2.500799200 2.681185553
8 -1.830910871 -2.500799200
9 0.441150119 -1.830910871
10 -0.674340483 0.441150119
11 -0.699144088 -0.674340483
12 2.185449265 -0.699144088
13 -5.070946304 2.185449265
14 0.267608837 -5.070946304
15 0.044544298 0.267608837
16 -2.046142698 0.044544298
17 1.780316019 -2.046142698
18 -1.500799200 1.780316019
19 -5.252931056 -1.500799200
20 -0.005062912 -5.252931056
21 2.036711588 -0.005062912
22 -1.864157946 2.036711588
23 -0.566682591 -1.864157946
24 2.871003016 -0.566682591
25 2.334186942 2.871003016
26 -2.798274555 2.334186942
27 -0.409501444 -2.798274555
28 1.895806621 -0.409501444
29 3.309383337 1.895806621
30 1.474397195 3.309383337
31 -0.442748519 1.474397195
32 1.507644270 -0.442748519
33 -0.649536877 1.507644270
34 -3.046142698 -0.649536877
35 2.838366700 -3.046142698
36 -0.938484807 2.838366700
37 -2.467552125 -0.938484807
38 1.598331267 -2.467552125
39 2.466564484 1.598331267
40 2.350463123 2.466564484
41 3.747068944 2.350463123
42 -6.252931056 3.747068944
43 -3.748667344 -6.252931056
44 -0.748667344 -3.748667344
45 3.036711588 -0.748667344
46 2.351073882 3.036711588
47 -1.558849881 2.351073882
48 -1.310981737 -1.558849881
49 1.102594979 -1.310981737
50 -1.798274555 1.102594979
51 -6.046142698 -1.798274555
52 0.400070334 -6.046142698
53 -3.748667344 0.400070334
54 3.069347904 -3.748667344
55 -1.012895623 3.069347904
56 0.019740693 -1.012895623
57 3.532447875 0.019740693
58 -4.839354340 3.532447875
59 4.069347904 -4.839354340
60 -4.426388384 4.069347904
61 2.466564484 -4.426388384
62 2.400070334 2.466564484
63 5.044544298 2.400070334
64 -3.740223874 5.044544298
65 1.185449265 -3.740223874
66 -3.053975409 1.185449265
67 1.251332656 -3.053975409
68 2.259776126 1.251332656
69 -7.194880376 2.259776126
70 1.863170305 -7.194880376
71 0.111038449 1.863170305
72 -1.740223874 0.111038449
73 -1.740223874 -1.740223874
74 -0.715420268 -1.740223874
75 -0.566682591 -0.715420268
76 0.325659517 -0.566682591
77 0.102594979 0.325659517
78 1.805119624 0.102594979
79 -5.624733272 1.805119624
80 -4.104193379 -5.624733272
81 0.293023201 -4.104193379
82 1.259776126 0.293023201
83 -1.798274555 1.259776126
84 -0.012895623 -1.798274555
85 -0.318814447 -0.012895623
86 -2.922208626 -0.318814447
87 0.771872549 -2.922208626
88 4.102594979 0.771872549
89 -3.459024700 4.102594979
90 2.325659517 -3.459024700
91 -2.442748519 2.325659517
92 3.532447875 -2.442748519
93 2.135842054 3.532447875
94 -0.442748519 2.135842054
95 -0.434915809 -0.442748519
96 -3.203323845 -0.434915809
97 -4.046142698 -3.203323845
98 -0.674340483 -4.046142698
99 1.077791374 -0.674340483
100 3.780316019 1.077791374
101 3.160645660 3.780316019
102 3.292412442 3.160645660
103 -0.872601416 3.292412442
104 -7.649536877 -0.872601416
105 0.516171695 -7.649536877
106 -3.070946304 0.516171695
107 1.805119624 -3.070946304
108 -3.723863738 1.805119624
109 -3.723863738 -3.723863738
110 -0.376865128 -3.723863738
111 0.127398584 -0.376865128
112 0.193281976 0.127398584
113 2.771872549 0.193281976
114 2.631578342 2.771872549
115 3.383710198 2.631578342
116 5.317216048 3.383710198
117 -1.046142698 5.317216048
118 0.863170305 -1.046142698
119 1.681185553 0.863170305
120 -0.566682591 1.681185553
121 2.499200800 -0.566682591
122 -5.012895623 2.499200800
123 0.532447875 -5.012895623
124 3.259776126 0.532447875
125 1.193281976 3.259776126
126 3.780316019 1.193281976
127 1.689018263 3.780316019
128 -0.426472339 1.689018263
129 -6.558849881 -0.426472339
130 1.375877487 -6.558849881
131 2.334186942 1.375877487
132 -1.104193379 2.334186942
133 0.259776126 -1.104193379
134 3.350463123 0.259776126
135 1.019740693 3.350463123
136 1.309383337 1.019740693
137 -4.500799200 1.309383337
138 6.582055086 -4.500799200
139 -1.012895623 6.582055086
140 0.375266728 -1.012895623
141 1.623134872 0.375266728
142 4.565084191 1.623134872
143 -5.409501444 4.565084191
144 0.350463123 -5.409501444
145 -0.980259307 0.350463123
146 -3.252931056 -0.980259307
147 -3.781303660 -3.252931056
148 2.160645660 -3.781303660
149 4.697545688 2.160645660
150 0.929053696 4.697545688
151 3.011907982 0.929053696
152 -1.434915809 3.011907982
153 1.978660907 -1.434915809
154 -0.029171803 1.978660907
155 -0.170076770 -0.029171803
156 NA -0.170076770
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.994937088 0.953857302
[2,] 3.945413832 0.994937088
[3,] -0.839354340 3.945413832
[4,] -1.525602805 -0.839354340
[5,] 1.540280586 -1.525602805
[6,] 2.681185553 1.540280586
[7,] -2.500799200 2.681185553
[8,] -1.830910871 -2.500799200
[9,] 0.441150119 -1.830910871
[10,] -0.674340483 0.441150119
[11,] -0.699144088 -0.674340483
[12,] 2.185449265 -0.699144088
[13,] -5.070946304 2.185449265
[14,] 0.267608837 -5.070946304
[15,] 0.044544298 0.267608837
[16,] -2.046142698 0.044544298
[17,] 1.780316019 -2.046142698
[18,] -1.500799200 1.780316019
[19,] -5.252931056 -1.500799200
[20,] -0.005062912 -5.252931056
[21,] 2.036711588 -0.005062912
[22,] -1.864157946 2.036711588
[23,] -0.566682591 -1.864157946
[24,] 2.871003016 -0.566682591
[25,] 2.334186942 2.871003016
[26,] -2.798274555 2.334186942
[27,] -0.409501444 -2.798274555
[28,] 1.895806621 -0.409501444
[29,] 3.309383337 1.895806621
[30,] 1.474397195 3.309383337
[31,] -0.442748519 1.474397195
[32,] 1.507644270 -0.442748519
[33,] -0.649536877 1.507644270
[34,] -3.046142698 -0.649536877
[35,] 2.838366700 -3.046142698
[36,] -0.938484807 2.838366700
[37,] -2.467552125 -0.938484807
[38,] 1.598331267 -2.467552125
[39,] 2.466564484 1.598331267
[40,] 2.350463123 2.466564484
[41,] 3.747068944 2.350463123
[42,] -6.252931056 3.747068944
[43,] -3.748667344 -6.252931056
[44,] -0.748667344 -3.748667344
[45,] 3.036711588 -0.748667344
[46,] 2.351073882 3.036711588
[47,] -1.558849881 2.351073882
[48,] -1.310981737 -1.558849881
[49,] 1.102594979 -1.310981737
[50,] -1.798274555 1.102594979
[51,] -6.046142698 -1.798274555
[52,] 0.400070334 -6.046142698
[53,] -3.748667344 0.400070334
[54,] 3.069347904 -3.748667344
[55,] -1.012895623 3.069347904
[56,] 0.019740693 -1.012895623
[57,] 3.532447875 0.019740693
[58,] -4.839354340 3.532447875
[59,] 4.069347904 -4.839354340
[60,] -4.426388384 4.069347904
[61,] 2.466564484 -4.426388384
[62,] 2.400070334 2.466564484
[63,] 5.044544298 2.400070334
[64,] -3.740223874 5.044544298
[65,] 1.185449265 -3.740223874
[66,] -3.053975409 1.185449265
[67,] 1.251332656 -3.053975409
[68,] 2.259776126 1.251332656
[69,] -7.194880376 2.259776126
[70,] 1.863170305 -7.194880376
[71,] 0.111038449 1.863170305
[72,] -1.740223874 0.111038449
[73,] -1.740223874 -1.740223874
[74,] -0.715420268 -1.740223874
[75,] -0.566682591 -0.715420268
[76,] 0.325659517 -0.566682591
[77,] 0.102594979 0.325659517
[78,] 1.805119624 0.102594979
[79,] -5.624733272 1.805119624
[80,] -4.104193379 -5.624733272
[81,] 0.293023201 -4.104193379
[82,] 1.259776126 0.293023201
[83,] -1.798274555 1.259776126
[84,] -0.012895623 -1.798274555
[85,] -0.318814447 -0.012895623
[86,] -2.922208626 -0.318814447
[87,] 0.771872549 -2.922208626
[88,] 4.102594979 0.771872549
[89,] -3.459024700 4.102594979
[90,] 2.325659517 -3.459024700
[91,] -2.442748519 2.325659517
[92,] 3.532447875 -2.442748519
[93,] 2.135842054 3.532447875
[94,] -0.442748519 2.135842054
[95,] -0.434915809 -0.442748519
[96,] -3.203323845 -0.434915809
[97,] -4.046142698 -3.203323845
[98,] -0.674340483 -4.046142698
[99,] 1.077791374 -0.674340483
[100,] 3.780316019 1.077791374
[101,] 3.160645660 3.780316019
[102,] 3.292412442 3.160645660
[103,] -0.872601416 3.292412442
[104,] -7.649536877 -0.872601416
[105,] 0.516171695 -7.649536877
[106,] -3.070946304 0.516171695
[107,] 1.805119624 -3.070946304
[108,] -3.723863738 1.805119624
[109,] -3.723863738 -3.723863738
[110,] -0.376865128 -3.723863738
[111,] 0.127398584 -0.376865128
[112,] 0.193281976 0.127398584
[113,] 2.771872549 0.193281976
[114,] 2.631578342 2.771872549
[115,] 3.383710198 2.631578342
[116,] 5.317216048 3.383710198
[117,] -1.046142698 5.317216048
[118,] 0.863170305 -1.046142698
[119,] 1.681185553 0.863170305
[120,] -0.566682591 1.681185553
[121,] 2.499200800 -0.566682591
[122,] -5.012895623 2.499200800
[123,] 0.532447875 -5.012895623
[124,] 3.259776126 0.532447875
[125,] 1.193281976 3.259776126
[126,] 3.780316019 1.193281976
[127,] 1.689018263 3.780316019
[128,] -0.426472339 1.689018263
[129,] -6.558849881 -0.426472339
[130,] 1.375877487 -6.558849881
[131,] 2.334186942 1.375877487
[132,] -1.104193379 2.334186942
[133,] 0.259776126 -1.104193379
[134,] 3.350463123 0.259776126
[135,] 1.019740693 3.350463123
[136,] 1.309383337 1.019740693
[137,] -4.500799200 1.309383337
[138,] 6.582055086 -4.500799200
[139,] -1.012895623 6.582055086
[140,] 0.375266728 -1.012895623
[141,] 1.623134872 0.375266728
[142,] 4.565084191 1.623134872
[143,] -5.409501444 4.565084191
[144,] 0.350463123 -5.409501444
[145,] -0.980259307 0.350463123
[146,] -3.252931056 -0.980259307
[147,] -3.781303660 -3.252931056
[148,] 2.160645660 -3.781303660
[149,] 4.697545688 2.160645660
[150,] 0.929053696 4.697545688
[151,] 3.011907982 0.929053696
[152,] -1.434915809 3.011907982
[153,] 1.978660907 -1.434915809
[154,] -0.029171803 1.978660907
[155,] -0.170076770 -0.029171803
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.994937088 0.953857302
2 3.945413832 0.994937088
3 -0.839354340 3.945413832
4 -1.525602805 -0.839354340
5 1.540280586 -1.525602805
6 2.681185553 1.540280586
7 -2.500799200 2.681185553
8 -1.830910871 -2.500799200
9 0.441150119 -1.830910871
10 -0.674340483 0.441150119
11 -0.699144088 -0.674340483
12 2.185449265 -0.699144088
13 -5.070946304 2.185449265
14 0.267608837 -5.070946304
15 0.044544298 0.267608837
16 -2.046142698 0.044544298
17 1.780316019 -2.046142698
18 -1.500799200 1.780316019
19 -5.252931056 -1.500799200
20 -0.005062912 -5.252931056
21 2.036711588 -0.005062912
22 -1.864157946 2.036711588
23 -0.566682591 -1.864157946
24 2.871003016 -0.566682591
25 2.334186942 2.871003016
26 -2.798274555 2.334186942
27 -0.409501444 -2.798274555
28 1.895806621 -0.409501444
29 3.309383337 1.895806621
30 1.474397195 3.309383337
31 -0.442748519 1.474397195
32 1.507644270 -0.442748519
33 -0.649536877 1.507644270
34 -3.046142698 -0.649536877
35 2.838366700 -3.046142698
36 -0.938484807 2.838366700
37 -2.467552125 -0.938484807
38 1.598331267 -2.467552125
39 2.466564484 1.598331267
40 2.350463123 2.466564484
41 3.747068944 2.350463123
42 -6.252931056 3.747068944
43 -3.748667344 -6.252931056
44 -0.748667344 -3.748667344
45 3.036711588 -0.748667344
46 2.351073882 3.036711588
47 -1.558849881 2.351073882
48 -1.310981737 -1.558849881
49 1.102594979 -1.310981737
50 -1.798274555 1.102594979
51 -6.046142698 -1.798274555
52 0.400070334 -6.046142698
53 -3.748667344 0.400070334
54 3.069347904 -3.748667344
55 -1.012895623 3.069347904
56 0.019740693 -1.012895623
57 3.532447875 0.019740693
58 -4.839354340 3.532447875
59 4.069347904 -4.839354340
60 -4.426388384 4.069347904
61 2.466564484 -4.426388384
62 2.400070334 2.466564484
63 5.044544298 2.400070334
64 -3.740223874 5.044544298
65 1.185449265 -3.740223874
66 -3.053975409 1.185449265
67 1.251332656 -3.053975409
68 2.259776126 1.251332656
69 -7.194880376 2.259776126
70 1.863170305 -7.194880376
71 0.111038449 1.863170305
72 -1.740223874 0.111038449
73 -1.740223874 -1.740223874
74 -0.715420268 -1.740223874
75 -0.566682591 -0.715420268
76 0.325659517 -0.566682591
77 0.102594979 0.325659517
78 1.805119624 0.102594979
79 -5.624733272 1.805119624
80 -4.104193379 -5.624733272
81 0.293023201 -4.104193379
82 1.259776126 0.293023201
83 -1.798274555 1.259776126
84 -0.012895623 -1.798274555
85 -0.318814447 -0.012895623
86 -2.922208626 -0.318814447
87 0.771872549 -2.922208626
88 4.102594979 0.771872549
89 -3.459024700 4.102594979
90 2.325659517 -3.459024700
91 -2.442748519 2.325659517
92 3.532447875 -2.442748519
93 2.135842054 3.532447875
94 -0.442748519 2.135842054
95 -0.434915809 -0.442748519
96 -3.203323845 -0.434915809
97 -4.046142698 -3.203323845
98 -0.674340483 -4.046142698
99 1.077791374 -0.674340483
100 3.780316019 1.077791374
101 3.160645660 3.780316019
102 3.292412442 3.160645660
103 -0.872601416 3.292412442
104 -7.649536877 -0.872601416
105 0.516171695 -7.649536877
106 -3.070946304 0.516171695
107 1.805119624 -3.070946304
108 -3.723863738 1.805119624
109 -3.723863738 -3.723863738
110 -0.376865128 -3.723863738
111 0.127398584 -0.376865128
112 0.193281976 0.127398584
113 2.771872549 0.193281976
114 2.631578342 2.771872549
115 3.383710198 2.631578342
116 5.317216048 3.383710198
117 -1.046142698 5.317216048
118 0.863170305 -1.046142698
119 1.681185553 0.863170305
120 -0.566682591 1.681185553
121 2.499200800 -0.566682591
122 -5.012895623 2.499200800
123 0.532447875 -5.012895623
124 3.259776126 0.532447875
125 1.193281976 3.259776126
126 3.780316019 1.193281976
127 1.689018263 3.780316019
128 -0.426472339 1.689018263
129 -6.558849881 -0.426472339
130 1.375877487 -6.558849881
131 2.334186942 1.375877487
132 -1.104193379 2.334186942
133 0.259776126 -1.104193379
134 3.350463123 0.259776126
135 1.019740693 3.350463123
136 1.309383337 1.019740693
137 -4.500799200 1.309383337
138 6.582055086 -4.500799200
139 -1.012895623 6.582055086
140 0.375266728 -1.012895623
141 1.623134872 0.375266728
142 4.565084191 1.623134872
143 -5.409501444 4.565084191
144 0.350463123 -5.409501444
145 -0.980259307 0.350463123
146 -3.252931056 -0.980259307
147 -3.781303660 -3.252931056
148 2.160645660 -3.781303660
149 4.697545688 2.160645660
150 0.929053696 4.697545688
151 3.011907982 0.929053696
152 -1.434915809 3.011907982
153 1.978660907 -1.434915809
154 -0.029171803 1.978660907
155 -0.170076770 -0.029171803
> 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/www/rcomp/tmp/7ew7b1292938456.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8p5ow1292938456.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9p5ow1292938456.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10ixoh1292938456.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/rcomp/tmp/113fmm1292938456.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/www/rcomp/tmp/12w73p1292938456.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/www/rcomp/tmp/1338011292938456.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/www/rcomp/tmp/14dz041292938456.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/www/rcomp/tmp/15zzys1292938456.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/www/rcomp/tmp/16v9ej1292938456.tab")
+ }
>
> try(system("convert tmp/104921292938456.ps tmp/104921292938456.png",intern=TRUE))
character(0)
> try(system("convert tmp/204921292938456.ps tmp/204921292938456.png",intern=TRUE))
character(0)
> try(system("convert tmp/304921292938456.ps tmp/304921292938456.png",intern=TRUE))
character(0)
> try(system("convert tmp/4m5qq1292938456.ps tmp/4m5qq1292938456.png",intern=TRUE))
character(0)
> try(system("convert tmp/5m5qq1292938456.ps tmp/5m5qq1292938456.png",intern=TRUE))
character(0)
> try(system("convert tmp/6m5qq1292938456.ps tmp/6m5qq1292938456.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ew7b1292938456.ps tmp/7ew7b1292938456.png",intern=TRUE))
character(0)
> try(system("convert tmp/8p5ow1292938456.ps tmp/8p5ow1292938456.png",intern=TRUE))
character(0)
> try(system("convert tmp/9p5ow1292938456.ps tmp/9p5ow1292938456.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ixoh1292938456.ps tmp/10ixoh1292938456.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.320 1.450 5.765