R version 2.12.1 (2010-12-16)
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
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(63031
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+ ,42419
+ ,228
+ ,12
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+ ,6585)
+ ,dim=c(5
+ ,144)
+ ,dimnames=list(c('Yt'
+ ,'X_1t'
+ ,'X_2t'
+ ,'X_3t'
+ ,'X_4t')
+ ,1:144))
> y <- array(NA,dim=c(5,144),dimnames=list(c('Yt','X_1t','X_2t','X_3t','X_4t'),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
Yt X_1t X_2t X_3t X_4t
1 63031 256 13 5 10345
2 66751 160 26 7 17607
3 7176 70 0 0 1423
4 78306 360 37 12 20050
5 137944 721 47 15 21212
6 261308 938 80 16 93979
7 69266 287 21 12 15524
8 80226 149 36 13 16182
9 73226 311 35 15 19238
10 178519 617 40 13 28909
11 66476 262 35 6 22357
12 98606 385 46 16 25560
13 50001 369 20 7 9954
14 91093 558 24 12 18490
15 73884 220 19 9 17777
16 72377 313 15 10 25268
17 69388 229 48 16 37525
18 15629 88 0 5 6023
19 71693 494 38 20 25042
20 19920 155 12 7 35713
21 39403 234 10 13 7039
22 99933 361 51 13 40841
23 56088 280 4 11 9214
24 62006 331 24 9 17446
25 81665 378 39 10 10295
26 65223 227 19 7 13206
27 88794 396 23 13 26093
28 90642 179 39 15 20744
29 203699 509 37 13 68013
30 99340 504 20 7 12840
31 56695 225 20 14 12672
32 108143 366 41 11 10872
33 58313 341 26 3 21325
34 29101 171 0 8 24542
35 113060 437 31 12 16401
36 0 0 0 0 0
37 65773 313 8 12 12821
38 67047 366 35 8 14662
39 41953 232 3 20 22190
40 109835 389 47 18 37929
41 82577 340 42 9 18009
42 59588 316 11 14 11076
43 40064 140 10 7 24981
44 70227 419 26 13 30691
45 60437 226 27 11 29164
46 47000 161 0 11 13985
47 40295 103 15 14 7588
48 103397 356 32 9 20023
49 78982 293 13 12 25524
50 60206 414 24 11 14717
51 39887 156 10 17 6832
52 49791 189 14 10 9624
53 129283 442 24 11 24300
54 104816 321 29 12 21790
55 101395 367 40 17 16493
56 72824 309 22 6 9269
57 76018 235 27 8 20105
58 33891 137 8 12 11216
59 62164 194 27 13 15569
60 28266 220 0 14 21799
61 35093 149 0 17 3772
62 35252 306 17 8 6057
63 36977 178 7 9 20828
64 42406 145 18 9 9976
65 56353 144 7 9 14055
66 58817 270 24 15 17455
67 76053 301 18 16 39553
68 70872 501 39 13 14818
69 42372 153 17 12 17065
70 19144 40 0 10 1536
71 114177 500 39 9 11938
72 53544 199 20 3 24589
73 51379 242 29 12 21332
74 40756 265 27 8 13229
75 46357 293 23 17 11331
76 17799 141 0 9 853
77 71154 234 31 8 19821
78 58305 336 19 9 34666
79 27454 124 12 12 15051
80 34323 241 23 5 27969
81 44761 127 33 14 17897
82 113862 327 21 14 6031
83 35027 175 17 10 7153
84 62396 331 27 12 13365
85 29613 176 14 10 11197
86 65559 281 12 12 25291
87 109788 291 21 17 28994
88 27883 137 14 11 10461
89 40181 155 14 10 16415
90 53398 194 22 11 8495
91 56435 300 25 7 18318
92 77283 370 36 10 25143
93 71738 187 10 11 20471
94 48096 210 16 5 14561
95 25214 185 12 6 16902
96 119332 445 20 14 12994
97 79201 234 38 13 29697
98 19349 67 13 1 3895
99 78760 316 12 13 9807
100 54133 336 11 9 10711
101 21623 116 8 1 2325
102 25497 141 22 6 19000
103 69535 236 14 12 22418
104 30709 98 7 9 7872
105 37043 97 14 9 5650
106 24716 152 2 12 3979
107 54865 132 35 10 14956
108 27246 97 5 2 3738
109 0 0 0 0 0
110 38814 165 34 8 10586
111 27646 153 12 7 18122
112 65373 226 34 11 17899
113 43021 182 30 14 10913
114 43116 172 21 4 18060
115 3058 1 0 0 0
116 0 0 0 0 0
117 96347 196 28 13 15452
118 48626 263 16 17 33996
119 73073 304 12 13 8877
120 45266 183 14 12 18708
121 43410 292 7 1 2781
122 83842 257 41 12 20854
123 39296 141 21 6 8179
124 35223 189 28 11 7139
125 39841 129 1 8 13798
126 19764 75 10 2 5619
127 59975 301 31 12 13050
128 64589 204 7 12 11297
129 63339 257 26 14 16170
130 11796 79 1 2 0
131 7627 25 0 0 0
132 68998 217 12 9 20539
133 6836 11 0 1 0
134 28834 209 17 3 10056
135 5118 6 5 0 0
136 20898 115 4 2 2418
137 0 0 0 0 0
138 42690 167 6 12 11806
139 14507 75 0 14 15924
140 7131 27 0 0 0
141 4194 14 0 0 0
142 21416 96 15 4 7084
143 30591 95 0 7 14831
144 42419 228 12 10 6585
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X_1t X_2t X_3t X_4t
-2688.8440 147.0815 513.0889 469.1092 0.6747
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-44051 -10145 -444 8109 60554
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2688.8440 3246.1996 -0.828 0.408918
X_1t 147.0815 14.6636 10.030 < 2e-16 ***
X_2t 513.0889 145.2960 3.531 0.000561 ***
X_3t 469.1092 347.6423 1.349 0.179402
X_4t 0.6747 0.1560 4.325 2.90e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 16470 on 139 degrees of freedom
Multiple R-squared: 0.8163, Adjusted R-squared: 0.8111
F-statistic: 154.5 on 4 and 139 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,] 0.3882694 0.7765387001 0.6117306500
[2,] 0.3507499 0.7014997058 0.6492501471
[3,] 0.9199377 0.1601246970 0.0800623485
[4,] 0.8853125 0.2293750808 0.1146875404
[5,] 0.8373584 0.3252831971 0.1626415985
[6,] 0.8731008 0.2537983313 0.1268991657
[7,] 0.8700683 0.2598634235 0.1299317118
[8,] 0.8408107 0.3183785082 0.1591892541
[9,] 0.7946566 0.4106867314 0.2053433657
[10,] 0.8670662 0.2658676831 0.1329338415
[11,] 0.8198198 0.3603603083 0.1801801541
[12,] 0.9224385 0.1551229593 0.0775614797
[13,] 0.9614924 0.0770151281 0.0385075640
[14,] 0.9501665 0.0996670331 0.0498335165
[15,] 0.9406665 0.1186669457 0.0593334728
[16,] 0.9362148 0.1275703295 0.0637851648
[17,] 0.9240516 0.1518967469 0.0759483735
[18,] 0.9017529 0.1964941663 0.0982470832
[19,] 0.8855840 0.2288319158 0.1144159579
[20,] 0.8547248 0.2905503206 0.1452751603
[21,] 0.9085082 0.1829836884 0.0914918442
[22,] 0.9981983 0.0036034385 0.0018017192
[23,] 0.9973091 0.0053817110 0.0026908555
[24,] 0.9960099 0.0079801260 0.0039900630
[25,] 0.9972925 0.0054149602 0.0027074801
[26,] 0.9978922 0.0042155597 0.0021077799
[27,] 0.9973801 0.0052397775 0.0026198887
[28,] 0.9977231 0.0045537950 0.0022768975
[29,] 0.9965387 0.0069226982 0.0034613491
[30,] 0.9951620 0.0096760215 0.0048380108
[31,] 0.9948543 0.0102914937 0.0051457469
[32,] 0.9938334 0.0123331752 0.0061665876
[33,] 0.9913257 0.0173486064 0.0086743032
[34,] 0.9878960 0.0242080246 0.0121040123
[35,] 0.9835494 0.0329012971 0.0164506485
[36,] 0.9777058 0.0445884148 0.0222942074
[37,] 0.9862423 0.0275154393 0.0137577197
[38,] 0.9821872 0.0356255064 0.0178127532
[39,] 0.9803500 0.0392999806 0.0196499903
[40,] 0.9762872 0.0474256422 0.0237128211
[41,] 0.9794306 0.0411388431 0.0205694216
[42,] 0.9754864 0.0490271443 0.0245135721
[43,] 0.9825552 0.0348895410 0.0174447705
[44,] 0.9775701 0.0448597706 0.0224298853
[45,] 0.9712469 0.0575062092 0.0287531046
[46,] 0.9902117 0.0195766311 0.0097883156
[47,] 0.9945004 0.0109992679 0.0054996339
[48,] 0.9933659 0.0132681808 0.0066340904
[49,] 0.9919127 0.0161745000 0.0080872500
[50,] 0.9916889 0.0166221744 0.0083110872
[51,] 0.9885617 0.0228765965 0.0114382983
[52,] 0.9849357 0.0301286891 0.0150643446
[53,] 0.9884172 0.0231656759 0.0115828379
[54,] 0.9860336 0.0279328005 0.0139664002
[55,] 0.9905094 0.0189812721 0.0094906361
[56,] 0.9877100 0.0245799520 0.0122899760
[57,] 0.9833679 0.0332642302 0.0166321151
[58,] 0.9860349 0.0279301140 0.0139650570
[59,] 0.9828172 0.0343655420 0.0171827710
[60,] 0.9778468 0.0443064692 0.0221532346
[61,] 0.9937077 0.0125846944 0.0062923472
[62,] 0.9912597 0.0174805436 0.0087402718
[63,] 0.9887777 0.0224445451 0.0112222726
[64,] 0.9868437 0.0263125951 0.0131562976
[65,] 0.9842928 0.0314143626 0.0157071813
[66,] 0.9834489 0.0331021633 0.0165510816
[67,] 0.9870047 0.0259905273 0.0129952636
[68,] 0.9933103 0.0133793403 0.0066896702
[69,] 0.9929188 0.0141624116 0.0070812058
[70,] 0.9919242 0.0161515006 0.0080757503
[71,] 0.9930242 0.0139516429 0.0069758214
[72,] 0.9919779 0.0160442190 0.0080221095
[73,] 0.9951161 0.0097677403 0.0048838701
[74,] 0.9933807 0.0132385872 0.0066192936
[75,] 0.9994772 0.0010456364 0.0005228182
[76,] 0.9992953 0.0014094840 0.0007047420
[77,] 0.9992169 0.0015662434 0.0007831217
[78,] 0.9992327 0.0015346548 0.0007673274
[79,] 0.9988216 0.0023567997 0.0011783998
[80,] 0.9997600 0.0004800197 0.0002400098
[81,] 0.9997203 0.0005594788 0.0002797394
[82,] 0.9995538 0.0008923707 0.0004461854
[83,] 0.9992980 0.0014040878 0.0007020439
[84,] 0.9990989 0.0018022584 0.0009011292
[85,] 0.9988662 0.0022675872 0.0011337936
[86,] 0.9994577 0.0010846736 0.0005423368
[87,] 0.9991314 0.0017371506 0.0008685753
[88,] 0.9992842 0.0014316514 0.0007158257
[89,] 0.9998162 0.0003676526 0.0001838263
[90,] 0.9997402 0.0005196924 0.0002598462
[91,] 0.9995691 0.0008618364 0.0004309182
[92,] 0.9995656 0.0008687736 0.0004343868
[93,] 0.9994055 0.0011890933 0.0005945466
[94,] 0.9990222 0.0019555271 0.0009777635
[95,] 0.9991588 0.0016824843 0.0008412421
[96,] 0.9990099 0.0019802425 0.0009901213
[97,] 0.9984190 0.0031619461 0.0015809730
[98,] 0.9977548 0.0044903282 0.0022451641
[99,] 0.9970719 0.0058562843 0.0029281421
[100,] 0.9958804 0.0082391911 0.0041195955
[101,] 0.9943647 0.0112706169 0.0056353085
[102,] 0.9913223 0.0173554844 0.0086777422
[103,] 0.9891123 0.0217754440 0.0108877220
[104,] 0.9873175 0.0253649410 0.0126824705
[105,] 0.9810169 0.0379662876 0.0189831438
[106,] 0.9800755 0.0398489953 0.0199244976
[107,] 0.9703970 0.0592059335 0.0296029667
[108,] 0.9574777 0.0850446859 0.0425223429
[109,] 0.9398414 0.1203172561 0.0601586280
[110,] 0.9976029 0.0047941705 0.0023970853
[111,] 0.9996821 0.0006358056 0.0003179028
[112,] 0.9997423 0.0005154882 0.0002577441
[113,] 0.9996122 0.0007756909 0.0003878454
[114,] 0.9992440 0.0015119306 0.0007559653
[115,] 0.9996220 0.0007559321 0.0003779661
[116,] 0.9994930 0.0010139201 0.0005069601
[117,] 0.9989042 0.0021915437 0.0010957718
[118,] 0.9977006 0.0045988135 0.0022994068
[119,] 0.9953475 0.0093049894 0.0046524947
[120,] 0.9910407 0.0179186399 0.0089593199
[121,] 0.9966702 0.0066596518 0.0033298259
[122,] 0.9953204 0.0093591862 0.0046795931
[123,] 0.9897504 0.0204991886 0.0102495943
[124,] 0.9777435 0.0445129230 0.0222564615
[125,] 0.9962533 0.0074933890 0.0037466945
[126,] 0.9892687 0.0214626181 0.0107313090
[127,] 0.9956726 0.0086547104 0.0043273552
[128,] 0.9880981 0.0238038809 0.0119019405
[129,] 0.9737495 0.0525010805 0.0262505402
> postscript(file="/var/www/rcomp/tmp/1frn01322168361.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2cm781322168361.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3afbf1322168361.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/46k291322168361.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5e2fn1322168361.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
12071.7489 17403.6979 -1390.9258 -10095.3612 -10875.9741 14076.1093
7 8 9 10 11 12
2864.5967 25512.4641 -7801.6802 44332.3575 -5227.0196 -3684.1036
13 14 15 16 17 18
-21844.5030 -18707.8475 18250.5166 -405.8187 -19056.0719 -1034.4511
19 20 21 22 23 24
-44051.2306 -33724.3435 -8303.5846 -10294.9965 4164.9967 -12295.6566
25 26 27 28 29 30
-2890.3179 12582.1110 -2265.2303 25960.6595 60553.8748 5691.3778
31 32 33 34 35 36
911.6977 23468.0847 -18288.0643 -13671.8784 18873.7912 2688.8440
37 38 39 40 41 42
4041.2840 -15699.0783 -15373.5883 -2839.8111 -2663.8332 -3885.1351
43 44 45 46 47 48
-3107.3145 -28856.5343 -8804.4438 11413.1692 8451.1410 19574.9532
49 50 51 52 53 54
9056.0535 -25400.4474 1915.9936 6314.0162 33092.8452 25081.5913
55 56 57 58 59 60
10479.0798 9708.4720 12972.0452 -871.5158 5863.1733 -22677.8896
61 62 63 64 65 66
5346.9633 -23627.9964 -8380.4317 3579.8698 20565.9236 -9333.4125
67 68 69 70 71 72
-8956.5172 -36233.2323 -3307.8030 10222.1884 11038.3546 -1295.1019
73 74 75 76 77 78
-16426.9682 -22063.3240 -21469.6975 -5048.1299 6394.3792 -25784.5497
79 80 81 82 83 84
-10036.1964 -31451.4169 -6803.6557 47043.8226 -6262.9837 -12098.8961
85 86 87 88 89 90
-13013.1904 -1931.6800 31364.8315 -8979.5592 -2876.9411 5373.4981
91 92 93 94 95 96
-13470.3184 -14573.9965 22820.2091 -481.2050 -19682.3389 30973.5216
97 98 99 100 101 102
1841.1052 2416.2534 16099.0498 -9689.9620 1107.9465 -19474.1132
103 104 105 106 107 108
9575.1555 5859.1977 10247.7882 -4291.5708 5399.4197 9642.3345
109 110 111 112 113 114
2688.8440 -11105.6260 -13835.9454 140.1651 -10381.9306 -4329.1364
115 116 117 118 119 120
5599.7625 2688.8440 39317.8586 -26488.1740 12804.4770 -4395.4749
121 122 123 124 125 126
-2785.9614 7995.2379 2138.6506 -14229.7663 9981.1806 1561.6165
127 128 129 130 131 132
-11947.2835 20430.4637 -2589.4622 1414.0980 6638.8065 15533.9268
133 134 135 136 137 138
7437.8383 -16131.5761 4358.9105 2050.5294 2688.8440 4143.1577
139 140 141 142 143 144
-11146.3472 5848.6435 4823.7030 -4367.1592 6017.2089 -3717.6425
> postscript(file="/var/www/rcomp/tmp/6vsv61322168361.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 12071.7489 NA
1 17403.6979 12071.7489
2 -1390.9258 17403.6979
3 -10095.3612 -1390.9258
4 -10875.9741 -10095.3612
5 14076.1093 -10875.9741
6 2864.5967 14076.1093
7 25512.4641 2864.5967
8 -7801.6802 25512.4641
9 44332.3575 -7801.6802
10 -5227.0196 44332.3575
11 -3684.1036 -5227.0196
12 -21844.5030 -3684.1036
13 -18707.8475 -21844.5030
14 18250.5166 -18707.8475
15 -405.8187 18250.5166
16 -19056.0719 -405.8187
17 -1034.4511 -19056.0719
18 -44051.2306 -1034.4511
19 -33724.3435 -44051.2306
20 -8303.5846 -33724.3435
21 -10294.9965 -8303.5846
22 4164.9967 -10294.9965
23 -12295.6566 4164.9967
24 -2890.3179 -12295.6566
25 12582.1110 -2890.3179
26 -2265.2303 12582.1110
27 25960.6595 -2265.2303
28 60553.8748 25960.6595
29 5691.3778 60553.8748
30 911.6977 5691.3778
31 23468.0847 911.6977
32 -18288.0643 23468.0847
33 -13671.8784 -18288.0643
34 18873.7912 -13671.8784
35 2688.8440 18873.7912
36 4041.2840 2688.8440
37 -15699.0783 4041.2840
38 -15373.5883 -15699.0783
39 -2839.8111 -15373.5883
40 -2663.8332 -2839.8111
41 -3885.1351 -2663.8332
42 -3107.3145 -3885.1351
43 -28856.5343 -3107.3145
44 -8804.4438 -28856.5343
45 11413.1692 -8804.4438
46 8451.1410 11413.1692
47 19574.9532 8451.1410
48 9056.0535 19574.9532
49 -25400.4474 9056.0535
50 1915.9936 -25400.4474
51 6314.0162 1915.9936
52 33092.8452 6314.0162
53 25081.5913 33092.8452
54 10479.0798 25081.5913
55 9708.4720 10479.0798
56 12972.0452 9708.4720
57 -871.5158 12972.0452
58 5863.1733 -871.5158
59 -22677.8896 5863.1733
60 5346.9633 -22677.8896
61 -23627.9964 5346.9633
62 -8380.4317 -23627.9964
63 3579.8698 -8380.4317
64 20565.9236 3579.8698
65 -9333.4125 20565.9236
66 -8956.5172 -9333.4125
67 -36233.2323 -8956.5172
68 -3307.8030 -36233.2323
69 10222.1884 -3307.8030
70 11038.3546 10222.1884
71 -1295.1019 11038.3546
72 -16426.9682 -1295.1019
73 -22063.3240 -16426.9682
74 -21469.6975 -22063.3240
75 -5048.1299 -21469.6975
76 6394.3792 -5048.1299
77 -25784.5497 6394.3792
78 -10036.1964 -25784.5497
79 -31451.4169 -10036.1964
80 -6803.6557 -31451.4169
81 47043.8226 -6803.6557
82 -6262.9837 47043.8226
83 -12098.8961 -6262.9837
84 -13013.1904 -12098.8961
85 -1931.6800 -13013.1904
86 31364.8315 -1931.6800
87 -8979.5592 31364.8315
88 -2876.9411 -8979.5592
89 5373.4981 -2876.9411
90 -13470.3184 5373.4981
91 -14573.9965 -13470.3184
92 22820.2091 -14573.9965
93 -481.2050 22820.2091
94 -19682.3389 -481.2050
95 30973.5216 -19682.3389
96 1841.1052 30973.5216
97 2416.2534 1841.1052
98 16099.0498 2416.2534
99 -9689.9620 16099.0498
100 1107.9465 -9689.9620
101 -19474.1132 1107.9465
102 9575.1555 -19474.1132
103 5859.1977 9575.1555
104 10247.7882 5859.1977
105 -4291.5708 10247.7882
106 5399.4197 -4291.5708
107 9642.3345 5399.4197
108 2688.8440 9642.3345
109 -11105.6260 2688.8440
110 -13835.9454 -11105.6260
111 140.1651 -13835.9454
112 -10381.9306 140.1651
113 -4329.1364 -10381.9306
114 5599.7625 -4329.1364
115 2688.8440 5599.7625
116 39317.8586 2688.8440
117 -26488.1740 39317.8586
118 12804.4770 -26488.1740
119 -4395.4749 12804.4770
120 -2785.9614 -4395.4749
121 7995.2379 -2785.9614
122 2138.6506 7995.2379
123 -14229.7663 2138.6506
124 9981.1806 -14229.7663
125 1561.6165 9981.1806
126 -11947.2835 1561.6165
127 20430.4637 -11947.2835
128 -2589.4622 20430.4637
129 1414.0980 -2589.4622
130 6638.8065 1414.0980
131 15533.9268 6638.8065
132 7437.8383 15533.9268
133 -16131.5761 7437.8383
134 4358.9105 -16131.5761
135 2050.5294 4358.9105
136 2688.8440 2050.5294
137 4143.1577 2688.8440
138 -11146.3472 4143.1577
139 5848.6435 -11146.3472
140 4823.7030 5848.6435
141 -4367.1592 4823.7030
142 6017.2089 -4367.1592
143 -3717.6425 6017.2089
144 NA -3717.6425
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 17403.6979 12071.7489
[2,] -1390.9258 17403.6979
[3,] -10095.3612 -1390.9258
[4,] -10875.9741 -10095.3612
[5,] 14076.1093 -10875.9741
[6,] 2864.5967 14076.1093
[7,] 25512.4641 2864.5967
[8,] -7801.6802 25512.4641
[9,] 44332.3575 -7801.6802
[10,] -5227.0196 44332.3575
[11,] -3684.1036 -5227.0196
[12,] -21844.5030 -3684.1036
[13,] -18707.8475 -21844.5030
[14,] 18250.5166 -18707.8475
[15,] -405.8187 18250.5166
[16,] -19056.0719 -405.8187
[17,] -1034.4511 -19056.0719
[18,] -44051.2306 -1034.4511
[19,] -33724.3435 -44051.2306
[20,] -8303.5846 -33724.3435
[21,] -10294.9965 -8303.5846
[22,] 4164.9967 -10294.9965
[23,] -12295.6566 4164.9967
[24,] -2890.3179 -12295.6566
[25,] 12582.1110 -2890.3179
[26,] -2265.2303 12582.1110
[27,] 25960.6595 -2265.2303
[28,] 60553.8748 25960.6595
[29,] 5691.3778 60553.8748
[30,] 911.6977 5691.3778
[31,] 23468.0847 911.6977
[32,] -18288.0643 23468.0847
[33,] -13671.8784 -18288.0643
[34,] 18873.7912 -13671.8784
[35,] 2688.8440 18873.7912
[36,] 4041.2840 2688.8440
[37,] -15699.0783 4041.2840
[38,] -15373.5883 -15699.0783
[39,] -2839.8111 -15373.5883
[40,] -2663.8332 -2839.8111
[41,] -3885.1351 -2663.8332
[42,] -3107.3145 -3885.1351
[43,] -28856.5343 -3107.3145
[44,] -8804.4438 -28856.5343
[45,] 11413.1692 -8804.4438
[46,] 8451.1410 11413.1692
[47,] 19574.9532 8451.1410
[48,] 9056.0535 19574.9532
[49,] -25400.4474 9056.0535
[50,] 1915.9936 -25400.4474
[51,] 6314.0162 1915.9936
[52,] 33092.8452 6314.0162
[53,] 25081.5913 33092.8452
[54,] 10479.0798 25081.5913
[55,] 9708.4720 10479.0798
[56,] 12972.0452 9708.4720
[57,] -871.5158 12972.0452
[58,] 5863.1733 -871.5158
[59,] -22677.8896 5863.1733
[60,] 5346.9633 -22677.8896
[61,] -23627.9964 5346.9633
[62,] -8380.4317 -23627.9964
[63,] 3579.8698 -8380.4317
[64,] 20565.9236 3579.8698
[65,] -9333.4125 20565.9236
[66,] -8956.5172 -9333.4125
[67,] -36233.2323 -8956.5172
[68,] -3307.8030 -36233.2323
[69,] 10222.1884 -3307.8030
[70,] 11038.3546 10222.1884
[71,] -1295.1019 11038.3546
[72,] -16426.9682 -1295.1019
[73,] -22063.3240 -16426.9682
[74,] -21469.6975 -22063.3240
[75,] -5048.1299 -21469.6975
[76,] 6394.3792 -5048.1299
[77,] -25784.5497 6394.3792
[78,] -10036.1964 -25784.5497
[79,] -31451.4169 -10036.1964
[80,] -6803.6557 -31451.4169
[81,] 47043.8226 -6803.6557
[82,] -6262.9837 47043.8226
[83,] -12098.8961 -6262.9837
[84,] -13013.1904 -12098.8961
[85,] -1931.6800 -13013.1904
[86,] 31364.8315 -1931.6800
[87,] -8979.5592 31364.8315
[88,] -2876.9411 -8979.5592
[89,] 5373.4981 -2876.9411
[90,] -13470.3184 5373.4981
[91,] -14573.9965 -13470.3184
[92,] 22820.2091 -14573.9965
[93,] -481.2050 22820.2091
[94,] -19682.3389 -481.2050
[95,] 30973.5216 -19682.3389
[96,] 1841.1052 30973.5216
[97,] 2416.2534 1841.1052
[98,] 16099.0498 2416.2534
[99,] -9689.9620 16099.0498
[100,] 1107.9465 -9689.9620
[101,] -19474.1132 1107.9465
[102,] 9575.1555 -19474.1132
[103,] 5859.1977 9575.1555
[104,] 10247.7882 5859.1977
[105,] -4291.5708 10247.7882
[106,] 5399.4197 -4291.5708
[107,] 9642.3345 5399.4197
[108,] 2688.8440 9642.3345
[109,] -11105.6260 2688.8440
[110,] -13835.9454 -11105.6260
[111,] 140.1651 -13835.9454
[112,] -10381.9306 140.1651
[113,] -4329.1364 -10381.9306
[114,] 5599.7625 -4329.1364
[115,] 2688.8440 5599.7625
[116,] 39317.8586 2688.8440
[117,] -26488.1740 39317.8586
[118,] 12804.4770 -26488.1740
[119,] -4395.4749 12804.4770
[120,] -2785.9614 -4395.4749
[121,] 7995.2379 -2785.9614
[122,] 2138.6506 7995.2379
[123,] -14229.7663 2138.6506
[124,] 9981.1806 -14229.7663
[125,] 1561.6165 9981.1806
[126,] -11947.2835 1561.6165
[127,] 20430.4637 -11947.2835
[128,] -2589.4622 20430.4637
[129,] 1414.0980 -2589.4622
[130,] 6638.8065 1414.0980
[131,] 15533.9268 6638.8065
[132,] 7437.8383 15533.9268
[133,] -16131.5761 7437.8383
[134,] 4358.9105 -16131.5761
[135,] 2050.5294 4358.9105
[136,] 2688.8440 2050.5294
[137,] 4143.1577 2688.8440
[138,] -11146.3472 4143.1577
[139,] 5848.6435 -11146.3472
[140,] 4823.7030 5848.6435
[141,] -4367.1592 4823.7030
[142,] 6017.2089 -4367.1592
[143,] -3717.6425 6017.2089
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 17403.6979 12071.7489
2 -1390.9258 17403.6979
3 -10095.3612 -1390.9258
4 -10875.9741 -10095.3612
5 14076.1093 -10875.9741
6 2864.5967 14076.1093
7 25512.4641 2864.5967
8 -7801.6802 25512.4641
9 44332.3575 -7801.6802
10 -5227.0196 44332.3575
11 -3684.1036 -5227.0196
12 -21844.5030 -3684.1036
13 -18707.8475 -21844.5030
14 18250.5166 -18707.8475
15 -405.8187 18250.5166
16 -19056.0719 -405.8187
17 -1034.4511 -19056.0719
18 -44051.2306 -1034.4511
19 -33724.3435 -44051.2306
20 -8303.5846 -33724.3435
21 -10294.9965 -8303.5846
22 4164.9967 -10294.9965
23 -12295.6566 4164.9967
24 -2890.3179 -12295.6566
25 12582.1110 -2890.3179
26 -2265.2303 12582.1110
27 25960.6595 -2265.2303
28 60553.8748 25960.6595
29 5691.3778 60553.8748
30 911.6977 5691.3778
31 23468.0847 911.6977
32 -18288.0643 23468.0847
33 -13671.8784 -18288.0643
34 18873.7912 -13671.8784
35 2688.8440 18873.7912
36 4041.2840 2688.8440
37 -15699.0783 4041.2840
38 -15373.5883 -15699.0783
39 -2839.8111 -15373.5883
40 -2663.8332 -2839.8111
41 -3885.1351 -2663.8332
42 -3107.3145 -3885.1351
43 -28856.5343 -3107.3145
44 -8804.4438 -28856.5343
45 11413.1692 -8804.4438
46 8451.1410 11413.1692
47 19574.9532 8451.1410
48 9056.0535 19574.9532
49 -25400.4474 9056.0535
50 1915.9936 -25400.4474
51 6314.0162 1915.9936
52 33092.8452 6314.0162
53 25081.5913 33092.8452
54 10479.0798 25081.5913
55 9708.4720 10479.0798
56 12972.0452 9708.4720
57 -871.5158 12972.0452
58 5863.1733 -871.5158
59 -22677.8896 5863.1733
60 5346.9633 -22677.8896
61 -23627.9964 5346.9633
62 -8380.4317 -23627.9964
63 3579.8698 -8380.4317
64 20565.9236 3579.8698
65 -9333.4125 20565.9236
66 -8956.5172 -9333.4125
67 -36233.2323 -8956.5172
68 -3307.8030 -36233.2323
69 10222.1884 -3307.8030
70 11038.3546 10222.1884
71 -1295.1019 11038.3546
72 -16426.9682 -1295.1019
73 -22063.3240 -16426.9682
74 -21469.6975 -22063.3240
75 -5048.1299 -21469.6975
76 6394.3792 -5048.1299
77 -25784.5497 6394.3792
78 -10036.1964 -25784.5497
79 -31451.4169 -10036.1964
80 -6803.6557 -31451.4169
81 47043.8226 -6803.6557
82 -6262.9837 47043.8226
83 -12098.8961 -6262.9837
84 -13013.1904 -12098.8961
85 -1931.6800 -13013.1904
86 31364.8315 -1931.6800
87 -8979.5592 31364.8315
88 -2876.9411 -8979.5592
89 5373.4981 -2876.9411
90 -13470.3184 5373.4981
91 -14573.9965 -13470.3184
92 22820.2091 -14573.9965
93 -481.2050 22820.2091
94 -19682.3389 -481.2050
95 30973.5216 -19682.3389
96 1841.1052 30973.5216
97 2416.2534 1841.1052
98 16099.0498 2416.2534
99 -9689.9620 16099.0498
100 1107.9465 -9689.9620
101 -19474.1132 1107.9465
102 9575.1555 -19474.1132
103 5859.1977 9575.1555
104 10247.7882 5859.1977
105 -4291.5708 10247.7882
106 5399.4197 -4291.5708
107 9642.3345 5399.4197
108 2688.8440 9642.3345
109 -11105.6260 2688.8440
110 -13835.9454 -11105.6260
111 140.1651 -13835.9454
112 -10381.9306 140.1651
113 -4329.1364 -10381.9306
114 5599.7625 -4329.1364
115 2688.8440 5599.7625
116 39317.8586 2688.8440
117 -26488.1740 39317.8586
118 12804.4770 -26488.1740
119 -4395.4749 12804.4770
120 -2785.9614 -4395.4749
121 7995.2379 -2785.9614
122 2138.6506 7995.2379
123 -14229.7663 2138.6506
124 9981.1806 -14229.7663
125 1561.6165 9981.1806
126 -11947.2835 1561.6165
127 20430.4637 -11947.2835
128 -2589.4622 20430.4637
129 1414.0980 -2589.4622
130 6638.8065 1414.0980
131 15533.9268 6638.8065
132 7437.8383 15533.9268
133 -16131.5761 7437.8383
134 4358.9105 -16131.5761
135 2050.5294 4358.9105
136 2688.8440 2050.5294
137 4143.1577 2688.8440
138 -11146.3472 4143.1577
139 5848.6435 -11146.3472
140 4823.7030 5848.6435
141 -4367.1592 4823.7030
142 6017.2089 -4367.1592
143 -3717.6425 6017.2089
> 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/7rqdv1322168361.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8112g1322168361.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9tph51322168361.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10coen1322168361.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113p2a1322168361.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/12a5jo1322168361.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/137zb31322168361.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/14r7aj1322168361.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/15xnfy1322168361.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/16phfi1322168361.tab")
+ }
>
> try(system("convert tmp/1frn01322168361.ps tmp/1frn01322168361.png",intern=TRUE))
character(0)
> try(system("convert tmp/2cm781322168361.ps tmp/2cm781322168361.png",intern=TRUE))
character(0)
> try(system("convert tmp/3afbf1322168361.ps tmp/3afbf1322168361.png",intern=TRUE))
character(0)
> try(system("convert tmp/46k291322168361.ps tmp/46k291322168361.png",intern=TRUE))
character(0)
> try(system("convert tmp/5e2fn1322168361.ps tmp/5e2fn1322168361.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vsv61322168361.ps tmp/6vsv61322168361.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rqdv1322168361.ps tmp/7rqdv1322168361.png",intern=TRUE))
character(0)
> try(system("convert tmp/8112g1322168361.ps tmp/8112g1322168361.png",intern=TRUE))
character(0)
> try(system("convert tmp/9tph51322168361.ps tmp/9tph51322168361.png",intern=TRUE))
character(0)
> try(system("convert tmp/10coen1322168361.ps tmp/10coen1322168361.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.864 0.668 6.700