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)
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> x <- array(list(158258
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+ ,dim=c(6
+ ,144)
+ ,dimnames=list(c('A'
+ ,'B'
+ ,'C'
+ ,'D'
+ ,'E'
+ ,'F')
+ ,1:144))
> y <- array(NA,dim=c(6,144),dimnames=list(c('A','B','C','D','E','F'),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 = '5'
> #'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
E A B C D F
1 20465 158258 48 18 63 23975
2 33629 186930 53 20 56 85634
3 1423 7215 0 0 0 1929
4 25629 128162 51 27 63 36294
5 54002 226974 76 31 116 72255
6 151036 500344 125 36 138 189748
7 33287 171007 59 23 71 61834
8 31172 179835 80 30 107 68167
9 28113 154581 55 30 50 38462
10 57803 278960 67 26 79 101219
11 49830 121844 50 24 58 43270
12 52143 183086 77 30 91 76183
13 21055 98796 44 22 41 31476
14 47007 209322 79 25 91 62157
15 28735 157125 51 18 61 46261
16 59147 154565 54 22 74 50063
17 78950 134198 75 33 131 64483
18 13497 69128 2 15 45 2341
19 46154 150680 73 34 110 48149
20 53249 27997 13 18 41 12743
21 10726 69919 19 15 37 18743
22 83700 233044 93 30 84 97057
23 40400 195820 38 25 67 17675
24 33797 127994 48 34 69 33106
25 36205 145433 50 21 58 53311
26 30165 170864 48 21 60 42754
27 58534 199655 60 25 88 59056
28 44663 188633 81 31 75 101621
29 92556 354266 60 31 98 118120
30 40078 192399 52 20 67 79572
31 34711 165753 50 28 84 42744
32 31076 173721 60 20 58 65931
33 74608 126739 53 17 35 38575
34 58092 224762 76 25 74 28795
35 42009 219428 63 24 89 94440
36 0 0 0 0 0 0
37 36022 217267 54 27 75 38229
38 23333 99706 44 14 39 31972
39 53349 136733 36 35 101 40071
40 92596 249965 83 34 135 132480
41 49598 232951 105 22 76 62797
42 44093 143755 37 34 118 40429
43 84205 95734 25 23 76 45545
44 63369 191416 63 24 65 57568
45 60132 114820 55 26 97 39019
46 37403 157625 41 22 67 53866
47 24460 81293 23 35 63 38345
48 46456 210040 63 24 96 50210
49 66616 223771 54 31 112 80947
50 41554 160344 68 26 75 43461
51 22346 48188 12 22 39 14812
52 30874 145235 84 21 63 37819
53 68701 287839 66 27 93 102738
54 35728 235223 56 30 76 54509
55 29010 195583 67 33 117 62956
56 23110 145942 40 11 30 55411
57 38844 207309 53 26 65 50611
58 27084 93764 26 26 78 26692
59 35139 151985 67 23 87 60056
60 57476 190545 36 38 85 25155
61 33277 146414 50 30 111 42840
62 31141 130794 48 19 60 39358
63 61281 124234 46 19 53 47241
64 25820 112718 53 26 67 49611
65 23284 160817 27 26 90 41833
66 35378 99070 38 33 100 48930
67 74990 178653 68 36 135 110600
68 29653 138708 93 25 71 52235
69 64622 114408 59 24 75 53986
70 4157 31970 5 21 42 4105
71 29245 224494 53 19 42 59331
72 50008 123328 36 12 8 47796
73 52338 113504 72 30 86 38302
74 13310 105932 49 21 41 14063
75 92901 162203 81 34 118 54414
76 10956 100098 27 32 91 9903
77 34241 174768 94 28 102 53987
78 75043 156752 71 28 89 88937
79 21152 77269 18 21 46 21928
80 42249 84971 34 31 60 29487
81 42005 80522 54 26 69 35334
82 41152 276525 44 29 95 57596
83 14399 62974 26 23 17 29750
84 28263 120296 44 25 61 41029
85 17215 75555 35 22 55 12416
86 48140 157988 32 26 55 51158
87 62897 223247 55 33 124 79935
88 22883 115019 58 24 73 26552
89 41622 99602 44 24 73 25807
90 40715 151804 39 21 67 50620
91 65897 146005 49 28 66 61467
92 76542 163444 72 27 75 65292
93 37477 151517 39 25 83 55516
94 53216 133686 28 15 55 42006
95 40911 58128 24 13 27 26273
96 57021 234325 49 36 115 90248
97 73116 195576 96 24 76 61476
98 3895 19349 13 1 0 9604
99 46609 213189 32 24 83 45108
100 29351 151672 41 31 90 47232
101 2325 59117 24 4 4 3439
102 31747 71931 52 20 56 30553
103 32665 126653 57 23 63 24751
104 19249 113552 28 23 52 34458
105 15292 85338 36 12 24 24649
106 5842 27676 2 16 17 2342
107 33994 138522 80 29 105 52739
108 13018 122417 29 26 20 6245
109 0 0 0 0 0 0
110 98177 87592 46 25 51 35381
111 37941 107205 25 21 76 19595
112 31032 144664 51 23 59 50848
113 32683 136540 59 21 70 39443
114 34545 71894 36 21 38 27023
115 0 3616 0 0 0 0
116 0 0 0 0 0 0
117 27525 175055 38 23 81 61022
118 66856 144618 68 33 78 63528
119 28549 152826 28 28 67 34835
120 38610 113245 36 23 89 37172
121 2781 43410 7 1 3 13
122 41211 175762 70 29 87 62548
123 22698 93634 30 17 48 31334
124 41194 117426 59 31 66 20839
125 32689 60493 3 12 32 5084
126 5752 19764 10 2 4 9927
127 26757 164062 46 21 70 53229
128 22527 128144 34 26 94 29877
129 44810 154959 54 29 91 37310
130 0 11796 1 2 1 0
131 0 10674 0 0 0 0
132 100674 138547 35 18 39 50067
133 0 6836 0 1 0 0
134 57786 154135 48 21 45 47708
135 0 5118 5 0 0 0
136 5444 40248 8 4 7 6012
137 0 0 0 0 0 0
138 28470 120460 36 25 75 27749
139 61849 88837 21 26 52 47555
140 0 7131 0 0 0 0
141 2179 9056 0 4 1 1336
142 8019 68916 15 17 49 11017
143 39644 132697 50 21 69 55184
144 23494 100681 17 22 56 43485
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) A B C D F
1.297e+03 -4.211e-03 1.040e+02 6.155e+02 -2.188e+01 4.755e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-30028 -10108 -3256 7150 62286
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.297e+03 3.499e+03 0.371 0.7114
A -4.211e-03 3.911e-02 -0.108 0.9144
B 1.040e+02 9.250e+01 1.124 0.2628
C 6.155e+02 3.032e+02 2.030 0.0442 *
D -2.188e+01 9.576e+01 -0.228 0.8196
F 4.755e-01 9.465e-02 5.024 1.54e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 15930 on 138 degrees of freedom
Multiple R-squared: 0.6116, Adjusted R-squared: 0.5975
F-statistic: 43.45 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,] 0.3684959 0.736991828 0.631504086
[2,] 0.3863283 0.772656594 0.613671703
[3,] 0.6751477 0.649704579 0.324852289
[4,] 0.5654291 0.869141709 0.434570855
[5,] 0.4497625 0.899525015 0.550237493
[6,] 0.3496432 0.699286496 0.650356752
[7,] 0.2639789 0.527957898 0.736021051
[8,] 0.4323602 0.864720356 0.567639822
[9,] 0.5706503 0.858699432 0.429349716
[10,] 0.4847776 0.969555163 0.515222418
[11,] 0.3996973 0.799394536 0.600302732
[12,] 0.6569542 0.686091522 0.343045761
[13,] 0.6132514 0.773497223 0.386748612
[14,] 0.6611306 0.677738847 0.338869423
[15,] 0.6502617 0.699476691 0.349738346
[16,] 0.5903658 0.819268363 0.409634181
[17,] 0.5233546 0.953290746 0.476645373
[18,] 0.4591466 0.918293275 0.540853362
[19,] 0.4065924 0.813184737 0.593407632
[20,] 0.4644046 0.928809180 0.535595410
[21,] 0.4153191 0.830638270 0.584680865
[22,] 0.4083222 0.816644381 0.591677809
[23,] 0.3730009 0.746001823 0.626999089
[24,] 0.3452600 0.690520072 0.654739964
[25,] 0.8222797 0.355440676 0.177720338
[26,] 0.8302731 0.339453803 0.169726902
[27,] 0.8527852 0.294429647 0.147214824
[28,] 0.8173165 0.365367055 0.182683527
[29,] 0.7900000 0.420000081 0.210000040
[30,] 0.7487953 0.502409410 0.251204705
[31,] 0.7233850 0.553230042 0.276615021
[32,] 0.6832728 0.633454332 0.316727166
[33,] 0.6334688 0.733062435 0.366531217
[34,] 0.5852585 0.829483035 0.414741517
[35,] 0.8770326 0.245934716 0.122967358
[36,] 0.8778747 0.244250662 0.122125331
[37,] 0.8883630 0.223274062 0.111637031
[38,] 0.8657720 0.268455965 0.134227982
[39,] 0.8623415 0.275316908 0.137658454
[40,] 0.8398205 0.320359091 0.160179546
[41,] 0.8098092 0.380381566 0.190190783
[42,] 0.7723872 0.455225617 0.227612809
[43,] 0.7330158 0.533968441 0.266984221
[44,] 0.6947401 0.610519786 0.305259893
[45,] 0.6507080 0.698583996 0.349291998
[46,] 0.6371254 0.725749198 0.362874599
[47,] 0.7329857 0.534028632 0.267014316
[48,] 0.7248888 0.550222406 0.275111203
[49,] 0.6874359 0.625128219 0.312564109
[50,] 0.6462359 0.707528163 0.353764081
[51,] 0.6331073 0.733785390 0.366892695
[52,] 0.6599630 0.680074076 0.340037038
[53,] 0.6379518 0.724096493 0.362048246
[54,] 0.5924907 0.815018622 0.407509311
[55,] 0.6483089 0.703382196 0.351691098
[56,] 0.6618533 0.676293419 0.338146710
[57,] 0.6649841 0.670031845 0.335015922
[58,] 0.6376278 0.724744413 0.362372207
[59,] 0.6121109 0.775778108 0.387889054
[60,] 0.6448921 0.710215782 0.355107891
[61,] 0.6661297 0.667740621 0.333870310
[62,] 0.6376287 0.724742622 0.362371311
[63,] 0.6447424 0.710515288 0.355257644
[64,] 0.6460751 0.707849799 0.353924899
[65,] 0.6202189 0.759562269 0.379781135
[66,] 0.5943193 0.811361356 0.405680678
[67,] 0.8188871 0.362225787 0.181112893
[68,] 0.8059129 0.388174288 0.194087144
[69,] 0.8138434 0.372313240 0.186156620
[70,] 0.7905518 0.418896432 0.209448216
[71,] 0.7541042 0.491791635 0.245895817
[72,] 0.7230694 0.553861192 0.276930596
[73,] 0.6838106 0.632378815 0.316189408
[74,] 0.6483848 0.703230489 0.351615244
[75,] 0.6991590 0.601681914 0.300840957
[76,] 0.6862917 0.627416548 0.313708274
[77,] 0.6435778 0.712844337 0.356422168
[78,] 0.6019872 0.796025565 0.398012782
[79,] 0.5556299 0.888740124 0.444370062
[80,] 0.5214335 0.957133027 0.478566514
[81,] 0.5016729 0.996654232 0.498327116
[82,] 0.4507448 0.901489661 0.549255170
[83,] 0.4296404 0.859280803 0.570359599
[84,] 0.4480982 0.896196432 0.551901784
[85,] 0.4066671 0.813334128 0.593332936
[86,] 0.4544293 0.908858551 0.545570725
[87,] 0.4481620 0.896323988 0.551838006
[88,] 0.4240683 0.848136587 0.575931707
[89,] 0.4432362 0.886472478 0.556763761
[90,] 0.3959996 0.791999283 0.604000358
[91,] 0.3932439 0.786487748 0.606756126
[92,] 0.3847713 0.769542624 0.615228688
[93,] 0.3364378 0.672875564 0.663562218
[94,] 0.2909058 0.581811600 0.709094200
[95,] 0.2478741 0.495748138 0.752125931
[96,] 0.2532886 0.506577117 0.746711441
[97,] 0.2260574 0.452114837 0.773942581
[98,] 0.2194172 0.438834399 0.780582800
[99,] 0.2135689 0.427137702 0.786431149
[100,] 0.2806402 0.561280474 0.719359763
[101,] 0.2335845 0.467168998 0.766415501
[102,] 0.7906002 0.418799601 0.209399800
[103,] 0.8306703 0.338659486 0.169329743
[104,] 0.8452634 0.309473234 0.154736617
[105,] 0.8041718 0.391656440 0.195828220
[106,] 0.7642384 0.471523175 0.235761588
[107,] 0.7099842 0.580031576 0.290015788
[108,] 0.6497762 0.700447620 0.350223810
[109,] 0.6708740 0.658252060 0.329126030
[110,] 0.6115649 0.776870288 0.388435144
[111,] 0.7163382 0.567323547 0.283661774
[112,] 0.8228885 0.354223048 0.177111524
[113,] 0.7867433 0.426513342 0.213256671
[114,] 0.7445567 0.510886571 0.255443286
[115,] 0.6877655 0.624468995 0.312234497
[116,] 0.6347111 0.730577749 0.365288874
[117,] 0.6770923 0.645815461 0.322907731
[118,] 0.5955363 0.808927389 0.404463694
[119,] 0.7192250 0.561549913 0.280774956
[120,] 0.6656517 0.668696630 0.334348315
[121,] 0.6243328 0.751334435 0.375667218
[122,] 0.5238461 0.952307749 0.476153874
[123,] 0.4138393 0.827678502 0.586160749
[124,] 0.9979107 0.004178627 0.002089314
[125,] 0.9923967 0.015206533 0.007603267
[126,] 0.9749226 0.050154702 0.025077351
[127,] 0.9256704 0.148659191 0.074329595
> postscript(file="/var/www/rcomp/tmp/100sy1324507011.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2cbwi1324507011.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/354vo1324507011.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/47r9f1324507011.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5x6d11324507011.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
-6259.5972 -24199.3942 -760.8478 -12932.1249 -5146.0417 29476.0383
7 8 9 10 11 12
-15433.4412 -26227.9705 -13914.7876 -11694.9268 9766.2315 -9093.0928
13 14 15 16 17 18
-12014.3198 -4579.2360 -8947.6540 17155.8736 22308.0884 2921.3899
19 20 21 22 23 24
-3518.2115 34475.6608 -9588.8417 10931.0689 13648.1699 -7114.3366
25 26 27 28 29 30
-6687.5654 -7348.6041 10292.0350 -30027.6649 13404.3598 -14500.1000
31 32 33 34 35 36
-6811.0863 -18123.0209 40291.0187 22375.3386 -22650.2341 -1296.9468
37 38 39 40 41 42
-3133.5005 -5087.8344 10494.9396 2747.3752 -3378.6115 1981.6957
43 44 45 46 47 48
46558.7648 15600.3053 21162.1922 -5184.7824 -17286.0593 2942.7954
49 50 51 52 53 54
5521.7205 -1169.6416 271.9498 -8079.2161 -1687.1263 -13126.1392
55 56 57 58 59 60
-26121.5277 -14196.2095 -5740.6026 -3512.3221 -13298.0590 19744.8693
61 62 63 64 65 66
-9012.5998 -3695.4079 22723.2785 -18643.6859 -14071.3387 -10846.1533
67 68 69 70 71 72
-4425.5365 -19405.9909 18867.0827 -11484.7489 -15608.2130 15546.8720
73 74 75 76 77 78
9232.8261 -11353.4339 39641.1597 -15142.8849 -16771.6963 9442.4865
79 80 81 82 83 84
-4038.7483 5983.1230 4134.5345 -6716.9643 -17268.9691 -10667.5407
85 86 87 88 89 90
-5646.4396 5052.8282 1209.1873 -9763.5644 10720.7811 469.7888
91 92 93 94 95 96
15098.7436 22418.7099 -7209.7057 21565.3465 17458.1103 -10943.7187
97 98 99 100 101 102
20315.0937 -3854.9346 8474.8112 -15143.8794 -5228.9510 -269.3504
103 104 105 106 107 108
1424.6253 -13887.0810 -7972.1469 -6136.7118 -15671.7867 -9315.4088
109 110 111 112 113 114
-1296.9468 61367.7317 13913.9927 -12005.7787 -4325.9423 4861.8043
115 116 117 118 119 120
-1281.7184 -1296.9468 -18389.4993 10280.6819 -7350.4840 4159.4546
121 122 123 124 125 126
382.7785 -12316.1313 -5638.7315 6708.3793 22230.8749 -2365.8096
127 128 129 130 131 132
-15339.6067 -9921.0702 4948.0501 -2560.4602 -1251.9945 62286.0018
133 134 135 136 137 138
-1883.6909 17518.0596 -1795.3954 -1683.3041 -1296.9468 -3006.6234
139 140 141 142 143 144
21262.2984 -1266.9154 -2155.3654 -9178.7202 -3952.2244 -12141.8009
> postscript(file="/var/www/rcomp/tmp/6ugpa1324507011.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 -6259.5972 NA
1 -24199.3942 -6259.5972
2 -760.8478 -24199.3942
3 -12932.1249 -760.8478
4 -5146.0417 -12932.1249
5 29476.0383 -5146.0417
6 -15433.4412 29476.0383
7 -26227.9705 -15433.4412
8 -13914.7876 -26227.9705
9 -11694.9268 -13914.7876
10 9766.2315 -11694.9268
11 -9093.0928 9766.2315
12 -12014.3198 -9093.0928
13 -4579.2360 -12014.3198
14 -8947.6540 -4579.2360
15 17155.8736 -8947.6540
16 22308.0884 17155.8736
17 2921.3899 22308.0884
18 -3518.2115 2921.3899
19 34475.6608 -3518.2115
20 -9588.8417 34475.6608
21 10931.0689 -9588.8417
22 13648.1699 10931.0689
23 -7114.3366 13648.1699
24 -6687.5654 -7114.3366
25 -7348.6041 -6687.5654
26 10292.0350 -7348.6041
27 -30027.6649 10292.0350
28 13404.3598 -30027.6649
29 -14500.1000 13404.3598
30 -6811.0863 -14500.1000
31 -18123.0209 -6811.0863
32 40291.0187 -18123.0209
33 22375.3386 40291.0187
34 -22650.2341 22375.3386
35 -1296.9468 -22650.2341
36 -3133.5005 -1296.9468
37 -5087.8344 -3133.5005
38 10494.9396 -5087.8344
39 2747.3752 10494.9396
40 -3378.6115 2747.3752
41 1981.6957 -3378.6115
42 46558.7648 1981.6957
43 15600.3053 46558.7648
44 21162.1922 15600.3053
45 -5184.7824 21162.1922
46 -17286.0593 -5184.7824
47 2942.7954 -17286.0593
48 5521.7205 2942.7954
49 -1169.6416 5521.7205
50 271.9498 -1169.6416
51 -8079.2161 271.9498
52 -1687.1263 -8079.2161
53 -13126.1392 -1687.1263
54 -26121.5277 -13126.1392
55 -14196.2095 -26121.5277
56 -5740.6026 -14196.2095
57 -3512.3221 -5740.6026
58 -13298.0590 -3512.3221
59 19744.8693 -13298.0590
60 -9012.5998 19744.8693
61 -3695.4079 -9012.5998
62 22723.2785 -3695.4079
63 -18643.6859 22723.2785
64 -14071.3387 -18643.6859
65 -10846.1533 -14071.3387
66 -4425.5365 -10846.1533
67 -19405.9909 -4425.5365
68 18867.0827 -19405.9909
69 -11484.7489 18867.0827
70 -15608.2130 -11484.7489
71 15546.8720 -15608.2130
72 9232.8261 15546.8720
73 -11353.4339 9232.8261
74 39641.1597 -11353.4339
75 -15142.8849 39641.1597
76 -16771.6963 -15142.8849
77 9442.4865 -16771.6963
78 -4038.7483 9442.4865
79 5983.1230 -4038.7483
80 4134.5345 5983.1230
81 -6716.9643 4134.5345
82 -17268.9691 -6716.9643
83 -10667.5407 -17268.9691
84 -5646.4396 -10667.5407
85 5052.8282 -5646.4396
86 1209.1873 5052.8282
87 -9763.5644 1209.1873
88 10720.7811 -9763.5644
89 469.7888 10720.7811
90 15098.7436 469.7888
91 22418.7099 15098.7436
92 -7209.7057 22418.7099
93 21565.3465 -7209.7057
94 17458.1103 21565.3465
95 -10943.7187 17458.1103
96 20315.0937 -10943.7187
97 -3854.9346 20315.0937
98 8474.8112 -3854.9346
99 -15143.8794 8474.8112
100 -5228.9510 -15143.8794
101 -269.3504 -5228.9510
102 1424.6253 -269.3504
103 -13887.0810 1424.6253
104 -7972.1469 -13887.0810
105 -6136.7118 -7972.1469
106 -15671.7867 -6136.7118
107 -9315.4088 -15671.7867
108 -1296.9468 -9315.4088
109 61367.7317 -1296.9468
110 13913.9927 61367.7317
111 -12005.7787 13913.9927
112 -4325.9423 -12005.7787
113 4861.8043 -4325.9423
114 -1281.7184 4861.8043
115 -1296.9468 -1281.7184
116 -18389.4993 -1296.9468
117 10280.6819 -18389.4993
118 -7350.4840 10280.6819
119 4159.4546 -7350.4840
120 382.7785 4159.4546
121 -12316.1313 382.7785
122 -5638.7315 -12316.1313
123 6708.3793 -5638.7315
124 22230.8749 6708.3793
125 -2365.8096 22230.8749
126 -15339.6067 -2365.8096
127 -9921.0702 -15339.6067
128 4948.0501 -9921.0702
129 -2560.4602 4948.0501
130 -1251.9945 -2560.4602
131 62286.0018 -1251.9945
132 -1883.6909 62286.0018
133 17518.0596 -1883.6909
134 -1795.3954 17518.0596
135 -1683.3041 -1795.3954
136 -1296.9468 -1683.3041
137 -3006.6234 -1296.9468
138 21262.2984 -3006.6234
139 -1266.9154 21262.2984
140 -2155.3654 -1266.9154
141 -9178.7202 -2155.3654
142 -3952.2244 -9178.7202
143 -12141.8009 -3952.2244
144 NA -12141.8009
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -24199.3942 -6259.5972
[2,] -760.8478 -24199.3942
[3,] -12932.1249 -760.8478
[4,] -5146.0417 -12932.1249
[5,] 29476.0383 -5146.0417
[6,] -15433.4412 29476.0383
[7,] -26227.9705 -15433.4412
[8,] -13914.7876 -26227.9705
[9,] -11694.9268 -13914.7876
[10,] 9766.2315 -11694.9268
[11,] -9093.0928 9766.2315
[12,] -12014.3198 -9093.0928
[13,] -4579.2360 -12014.3198
[14,] -8947.6540 -4579.2360
[15,] 17155.8736 -8947.6540
[16,] 22308.0884 17155.8736
[17,] 2921.3899 22308.0884
[18,] -3518.2115 2921.3899
[19,] 34475.6608 -3518.2115
[20,] -9588.8417 34475.6608
[21,] 10931.0689 -9588.8417
[22,] 13648.1699 10931.0689
[23,] -7114.3366 13648.1699
[24,] -6687.5654 -7114.3366
[25,] -7348.6041 -6687.5654
[26,] 10292.0350 -7348.6041
[27,] -30027.6649 10292.0350
[28,] 13404.3598 -30027.6649
[29,] -14500.1000 13404.3598
[30,] -6811.0863 -14500.1000
[31,] -18123.0209 -6811.0863
[32,] 40291.0187 -18123.0209
[33,] 22375.3386 40291.0187
[34,] -22650.2341 22375.3386
[35,] -1296.9468 -22650.2341
[36,] -3133.5005 -1296.9468
[37,] -5087.8344 -3133.5005
[38,] 10494.9396 -5087.8344
[39,] 2747.3752 10494.9396
[40,] -3378.6115 2747.3752
[41,] 1981.6957 -3378.6115
[42,] 46558.7648 1981.6957
[43,] 15600.3053 46558.7648
[44,] 21162.1922 15600.3053
[45,] -5184.7824 21162.1922
[46,] -17286.0593 -5184.7824
[47,] 2942.7954 -17286.0593
[48,] 5521.7205 2942.7954
[49,] -1169.6416 5521.7205
[50,] 271.9498 -1169.6416
[51,] -8079.2161 271.9498
[52,] -1687.1263 -8079.2161
[53,] -13126.1392 -1687.1263
[54,] -26121.5277 -13126.1392
[55,] -14196.2095 -26121.5277
[56,] -5740.6026 -14196.2095
[57,] -3512.3221 -5740.6026
[58,] -13298.0590 -3512.3221
[59,] 19744.8693 -13298.0590
[60,] -9012.5998 19744.8693
[61,] -3695.4079 -9012.5998
[62,] 22723.2785 -3695.4079
[63,] -18643.6859 22723.2785
[64,] -14071.3387 -18643.6859
[65,] -10846.1533 -14071.3387
[66,] -4425.5365 -10846.1533
[67,] -19405.9909 -4425.5365
[68,] 18867.0827 -19405.9909
[69,] -11484.7489 18867.0827
[70,] -15608.2130 -11484.7489
[71,] 15546.8720 -15608.2130
[72,] 9232.8261 15546.8720
[73,] -11353.4339 9232.8261
[74,] 39641.1597 -11353.4339
[75,] -15142.8849 39641.1597
[76,] -16771.6963 -15142.8849
[77,] 9442.4865 -16771.6963
[78,] -4038.7483 9442.4865
[79,] 5983.1230 -4038.7483
[80,] 4134.5345 5983.1230
[81,] -6716.9643 4134.5345
[82,] -17268.9691 -6716.9643
[83,] -10667.5407 -17268.9691
[84,] -5646.4396 -10667.5407
[85,] 5052.8282 -5646.4396
[86,] 1209.1873 5052.8282
[87,] -9763.5644 1209.1873
[88,] 10720.7811 -9763.5644
[89,] 469.7888 10720.7811
[90,] 15098.7436 469.7888
[91,] 22418.7099 15098.7436
[92,] -7209.7057 22418.7099
[93,] 21565.3465 -7209.7057
[94,] 17458.1103 21565.3465
[95,] -10943.7187 17458.1103
[96,] 20315.0937 -10943.7187
[97,] -3854.9346 20315.0937
[98,] 8474.8112 -3854.9346
[99,] -15143.8794 8474.8112
[100,] -5228.9510 -15143.8794
[101,] -269.3504 -5228.9510
[102,] 1424.6253 -269.3504
[103,] -13887.0810 1424.6253
[104,] -7972.1469 -13887.0810
[105,] -6136.7118 -7972.1469
[106,] -15671.7867 -6136.7118
[107,] -9315.4088 -15671.7867
[108,] -1296.9468 -9315.4088
[109,] 61367.7317 -1296.9468
[110,] 13913.9927 61367.7317
[111,] -12005.7787 13913.9927
[112,] -4325.9423 -12005.7787
[113,] 4861.8043 -4325.9423
[114,] -1281.7184 4861.8043
[115,] -1296.9468 -1281.7184
[116,] -18389.4993 -1296.9468
[117,] 10280.6819 -18389.4993
[118,] -7350.4840 10280.6819
[119,] 4159.4546 -7350.4840
[120,] 382.7785 4159.4546
[121,] -12316.1313 382.7785
[122,] -5638.7315 -12316.1313
[123,] 6708.3793 -5638.7315
[124,] 22230.8749 6708.3793
[125,] -2365.8096 22230.8749
[126,] -15339.6067 -2365.8096
[127,] -9921.0702 -15339.6067
[128,] 4948.0501 -9921.0702
[129,] -2560.4602 4948.0501
[130,] -1251.9945 -2560.4602
[131,] 62286.0018 -1251.9945
[132,] -1883.6909 62286.0018
[133,] 17518.0596 -1883.6909
[134,] -1795.3954 17518.0596
[135,] -1683.3041 -1795.3954
[136,] -1296.9468 -1683.3041
[137,] -3006.6234 -1296.9468
[138,] 21262.2984 -3006.6234
[139,] -1266.9154 21262.2984
[140,] -2155.3654 -1266.9154
[141,] -9178.7202 -2155.3654
[142,] -3952.2244 -9178.7202
[143,] -12141.8009 -3952.2244
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -24199.3942 -6259.5972
2 -760.8478 -24199.3942
3 -12932.1249 -760.8478
4 -5146.0417 -12932.1249
5 29476.0383 -5146.0417
6 -15433.4412 29476.0383
7 -26227.9705 -15433.4412
8 -13914.7876 -26227.9705
9 -11694.9268 -13914.7876
10 9766.2315 -11694.9268
11 -9093.0928 9766.2315
12 -12014.3198 -9093.0928
13 -4579.2360 -12014.3198
14 -8947.6540 -4579.2360
15 17155.8736 -8947.6540
16 22308.0884 17155.8736
17 2921.3899 22308.0884
18 -3518.2115 2921.3899
19 34475.6608 -3518.2115
20 -9588.8417 34475.6608
21 10931.0689 -9588.8417
22 13648.1699 10931.0689
23 -7114.3366 13648.1699
24 -6687.5654 -7114.3366
25 -7348.6041 -6687.5654
26 10292.0350 -7348.6041
27 -30027.6649 10292.0350
28 13404.3598 -30027.6649
29 -14500.1000 13404.3598
30 -6811.0863 -14500.1000
31 -18123.0209 -6811.0863
32 40291.0187 -18123.0209
33 22375.3386 40291.0187
34 -22650.2341 22375.3386
35 -1296.9468 -22650.2341
36 -3133.5005 -1296.9468
37 -5087.8344 -3133.5005
38 10494.9396 -5087.8344
39 2747.3752 10494.9396
40 -3378.6115 2747.3752
41 1981.6957 -3378.6115
42 46558.7648 1981.6957
43 15600.3053 46558.7648
44 21162.1922 15600.3053
45 -5184.7824 21162.1922
46 -17286.0593 -5184.7824
47 2942.7954 -17286.0593
48 5521.7205 2942.7954
49 -1169.6416 5521.7205
50 271.9498 -1169.6416
51 -8079.2161 271.9498
52 -1687.1263 -8079.2161
53 -13126.1392 -1687.1263
54 -26121.5277 -13126.1392
55 -14196.2095 -26121.5277
56 -5740.6026 -14196.2095
57 -3512.3221 -5740.6026
58 -13298.0590 -3512.3221
59 19744.8693 -13298.0590
60 -9012.5998 19744.8693
61 -3695.4079 -9012.5998
62 22723.2785 -3695.4079
63 -18643.6859 22723.2785
64 -14071.3387 -18643.6859
65 -10846.1533 -14071.3387
66 -4425.5365 -10846.1533
67 -19405.9909 -4425.5365
68 18867.0827 -19405.9909
69 -11484.7489 18867.0827
70 -15608.2130 -11484.7489
71 15546.8720 -15608.2130
72 9232.8261 15546.8720
73 -11353.4339 9232.8261
74 39641.1597 -11353.4339
75 -15142.8849 39641.1597
76 -16771.6963 -15142.8849
77 9442.4865 -16771.6963
78 -4038.7483 9442.4865
79 5983.1230 -4038.7483
80 4134.5345 5983.1230
81 -6716.9643 4134.5345
82 -17268.9691 -6716.9643
83 -10667.5407 -17268.9691
84 -5646.4396 -10667.5407
85 5052.8282 -5646.4396
86 1209.1873 5052.8282
87 -9763.5644 1209.1873
88 10720.7811 -9763.5644
89 469.7888 10720.7811
90 15098.7436 469.7888
91 22418.7099 15098.7436
92 -7209.7057 22418.7099
93 21565.3465 -7209.7057
94 17458.1103 21565.3465
95 -10943.7187 17458.1103
96 20315.0937 -10943.7187
97 -3854.9346 20315.0937
98 8474.8112 -3854.9346
99 -15143.8794 8474.8112
100 -5228.9510 -15143.8794
101 -269.3504 -5228.9510
102 1424.6253 -269.3504
103 -13887.0810 1424.6253
104 -7972.1469 -13887.0810
105 -6136.7118 -7972.1469
106 -15671.7867 -6136.7118
107 -9315.4088 -15671.7867
108 -1296.9468 -9315.4088
109 61367.7317 -1296.9468
110 13913.9927 61367.7317
111 -12005.7787 13913.9927
112 -4325.9423 -12005.7787
113 4861.8043 -4325.9423
114 -1281.7184 4861.8043
115 -1296.9468 -1281.7184
116 -18389.4993 -1296.9468
117 10280.6819 -18389.4993
118 -7350.4840 10280.6819
119 4159.4546 -7350.4840
120 382.7785 4159.4546
121 -12316.1313 382.7785
122 -5638.7315 -12316.1313
123 6708.3793 -5638.7315
124 22230.8749 6708.3793
125 -2365.8096 22230.8749
126 -15339.6067 -2365.8096
127 -9921.0702 -15339.6067
128 4948.0501 -9921.0702
129 -2560.4602 4948.0501
130 -1251.9945 -2560.4602
131 62286.0018 -1251.9945
132 -1883.6909 62286.0018
133 17518.0596 -1883.6909
134 -1795.3954 17518.0596
135 -1683.3041 -1795.3954
136 -1296.9468 -1683.3041
137 -3006.6234 -1296.9468
138 21262.2984 -3006.6234
139 -1266.9154 21262.2984
140 -2155.3654 -1266.9154
141 -9178.7202 -2155.3654
142 -3952.2244 -9178.7202
143 -12141.8009 -3952.2244
> 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/7weqy1324507011.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8qvir1324507011.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9vaq81324507011.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10mnrs1324507011.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11rmgz1324507011.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/12dpeb1324507011.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/13wrcq1324507011.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/1481041324507011.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/15fr361324507011.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/16mlyh1324507011.tab")
+ }
>
> try(system("convert tmp/100sy1324507011.ps tmp/100sy1324507011.png",intern=TRUE))
character(0)
> try(system("convert tmp/2cbwi1324507011.ps tmp/2cbwi1324507011.png",intern=TRUE))
character(0)
> try(system("convert tmp/354vo1324507011.ps tmp/354vo1324507011.png",intern=TRUE))
character(0)
> try(system("convert tmp/47r9f1324507011.ps tmp/47r9f1324507011.png",intern=TRUE))
character(0)
> try(system("convert tmp/5x6d11324507011.ps tmp/5x6d11324507011.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ugpa1324507011.ps tmp/6ugpa1324507011.png",intern=TRUE))
character(0)
> try(system("convert tmp/7weqy1324507011.ps tmp/7weqy1324507011.png",intern=TRUE))
character(0)
> try(system("convert tmp/8qvir1324507011.ps tmp/8qvir1324507011.png",intern=TRUE))
character(0)
> try(system("convert tmp/9vaq81324507011.ps tmp/9vaq81324507011.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mnrs1324507011.ps tmp/10mnrs1324507011.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.980 0.230 5.181