R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(18.2
+ ,2687
+ ,1870
+ ,1890
+ ,145.7
+ ,352.2
+ ,0
+ ,0
+ ,143.8
+ ,13271
+ ,9115
+ ,8190
+ ,-279.0
+ ,83.0
+ ,0
+ ,0
+ ,23.4
+ ,13621
+ ,4848
+ ,4572
+ ,485.0
+ ,898.9
+ ,0
+ ,0
+ ,1.1
+ ,3614
+ ,367
+ ,90
+ ,14.1
+ ,24.6
+ ,1
+ ,0
+ ,49.5
+ ,6425
+ ,6131
+ ,2448
+ ,345.8
+ ,682.5
+ ,1
+ ,0
+ ,4.8
+ ,1022
+ ,1754
+ ,1370
+ ,72.0
+ ,119.5
+ ,0
+ ,1
+ ,20.8
+ ,1093
+ ,1679
+ ,1070
+ ,100.9
+ ,164.5
+ ,0
+ ,1
+ ,19.4
+ ,1529
+ ,1295
+ ,444
+ ,25.6
+ ,137.0
+ ,0
+ ,0
+ ,2.1
+ ,2788
+ ,271
+ ,304
+ ,23.5
+ ,28.9
+ ,1
+ ,0
+ ,79.4
+ ,19788
+ ,9084
+ ,10636
+ ,1092.9
+ ,2576.8
+ ,1
+ ,0
+ ,2.8
+ ,327
+ ,542
+ ,959
+ ,54.1
+ ,72.5
+ ,1
+ ,0
+ ,3.8
+ ,1117
+ ,1038
+ ,478
+ ,59.7
+ ,91.7
+ ,0
+ ,0
+ ,4.1
+ ,5401
+ ,550
+ ,376
+ ,25.6
+ ,37.5
+ ,1
+ ,0
+ ,13.2
+ ,1128
+ ,1516
+ ,430
+ ,-47.0
+ ,26.7
+ ,0
+ ,1
+ ,2.8
+ ,1633
+ ,701
+ ,679
+ ,74.3
+ ,135.9
+ ,0
+ ,0
+ ,48.5
+ ,44736
+ ,16197
+ ,4653
+ ,-732.5
+ ,-651.9
+ ,1
+ ,0
+ ,6.2
+ ,5651
+ ,1254
+ ,2002
+ ,310.7
+ ,407.9
+ ,0
+ ,0
+ ,10.8
+ ,5835
+ ,4053
+ ,1601
+ ,-93.8
+ ,173.8
+ ,0
+ ,0
+ ,3.8
+ ,278
+ ,205
+ ,853
+ ,44.8
+ ,50.5
+ ,1
+ ,0
+ ,21.9
+ ,5074
+ ,2557
+ ,1892
+ ,239.9
+ ,578.3
+ ,1
+ ,0
+ ,12.6
+ ,866
+ ,1487
+ ,944
+ ,71.7
+ ,115.4
+ ,0
+ ,0
+ ,128.0
+ ,4418
+ ,8793
+ ,4459
+ ,283.6
+ ,456.5
+ ,1
+ ,0
+ ,87.3
+ ,6914
+ ,7029
+ ,7957
+ ,400.6
+ ,754.7
+ ,0
+ ,1
+ ,16.0
+ ,862
+ ,1601
+ ,1093
+ ,66.9
+ ,106.8
+ ,1
+ ,0
+ ,0.7
+ ,401
+ ,176
+ ,1084
+ ,55.6
+ ,57.0
+ ,1
+ ,0
+ ,22.5
+ ,430
+ ,1155
+ ,1045
+ ,55.7
+ ,70.8
+ ,0
+ ,1
+ ,15.4
+ ,799
+ ,1140
+ ,683
+ ,57.6
+ ,89.2
+ ,0
+ ,0
+ ,3.0
+ ,4789
+ ,453
+ ,367
+ ,40.2
+ ,51.4
+ ,1
+ ,0
+ ,2.1
+ ,2548
+ ,264
+ ,181
+ ,22.2
+ ,26.2
+ ,1
+ ,0
+ ,4.1
+ ,5249
+ ,527
+ ,346
+ ,37.8
+ ,56.2
+ ,1
+ ,0
+ ,6.4
+ ,3494
+ ,1653
+ ,1442
+ ,160.9
+ ,320.3
+ ,0
+ ,0
+ ,26.6
+ ,1804
+ ,2564
+ ,483
+ ,70.5
+ ,164.9
+ ,0
+ ,1
+ ,304.0
+ ,26432
+ ,28285
+ ,33172
+ ,2336.0
+ ,3562.0
+ ,0
+ ,1
+ ,18.6
+ ,623
+ ,2247
+ ,797
+ ,57.0
+ ,93.8
+ ,1
+ ,0
+ ,65.0
+ ,1608
+ ,6615
+ ,829
+ ,56.1
+ ,134.0
+ ,1
+ ,0
+ ,66.2
+ ,4662
+ ,4781
+ ,2988
+ ,28.7
+ ,371.5
+ ,0
+ ,1
+ ,83.0
+ ,5769
+ ,6571
+ ,9462
+ ,482.0
+ ,792.0
+ ,0
+ ,1
+ ,62.0
+ ,6259
+ ,4152
+ ,3090
+ ,283.7
+ ,524.5
+ ,1
+ ,0
+ ,1.6
+ ,1654
+ ,451
+ ,779
+ ,84.8
+ ,130.4
+ ,0
+ ,0
+ ,400.2
+ ,52634
+ ,50056
+ ,95697
+ ,6555.0
+ ,9874.0
+ ,0
+ ,1
+ ,23.3
+ ,999
+ ,1878
+ ,393
+ ,-173.5
+ ,-108.1
+ ,1
+ ,0
+ ,4.6
+ ,1679
+ ,1354
+ ,687
+ ,93.8
+ ,154.6
+ ,0
+ ,0
+ ,164.6
+ ,4178
+ ,17124
+ ,2091
+ ,180.8
+ ,390.4
+ ,1
+ ,0
+ ,1.9
+ ,223
+ ,557
+ ,1040
+ ,60.6
+ ,63.7
+ ,0
+ ,0
+ ,57.5
+ ,6307
+ ,8199
+ ,598
+ ,-771.5
+ ,-524.3
+ ,0
+ ,1
+ ,2.4
+ ,3720
+ ,356
+ ,211
+ ,26.6
+ ,34.8
+ ,1
+ ,0
+ ,77.3
+ ,3442
+ ,5080
+ ,2673
+ ,235.4
+ ,361.5
+ ,1
+ ,0
+ ,15.8
+ ,33406
+ ,3222
+ ,1413
+ ,201.7
+ ,246.7
+ ,1
+ ,0
+ ,0.6
+ ,1257
+ ,355
+ ,181
+ ,167.5
+ ,304.0
+ ,0
+ ,0
+ ,3.5
+ ,1743
+ ,597
+ ,717
+ ,121.6
+ ,172.4
+ ,0
+ ,0
+ ,9.0
+ ,12505
+ ,1302
+ ,702
+ ,108.4
+ ,131.4
+ ,1
+ ,0
+ ,62.0
+ ,3940
+ ,4317
+ ,3940
+ ,315.2
+ ,566.3
+ ,0
+ ,1
+ ,7.4
+ ,8998
+ ,882
+ ,988
+ ,93.0
+ ,119.0
+ ,1
+ ,0
+ ,15.6
+ ,21419
+ ,2516
+ ,930
+ ,107.6
+ ,164.7
+ ,1
+ ,0
+ ,25.2
+ ,2366
+ ,3305
+ ,1117
+ ,131.2
+ ,256.5
+ ,0
+ ,1
+ ,25.4
+ ,2448
+ ,3484
+ ,1036
+ ,48.8
+ ,257.1
+ ,1
+ ,0
+ ,3.5
+ ,1440
+ ,1617
+ ,639
+ ,81.7
+ ,126.4
+ ,0
+ ,0
+ ,27.3
+ ,14045
+ ,15636
+ ,2754
+ ,418.0
+ ,1462.0
+ ,0
+ ,0
+ ,37.5
+ ,4084
+ ,4346
+ ,3023
+ ,302.7
+ ,521.7
+ ,0
+ ,1
+ ,3.4
+ ,3010
+ ,749
+ ,1120
+ ,146.3
+ ,209.2
+ ,0
+ ,0
+ ,14.3
+ ,1286
+ ,1734
+ ,361
+ ,69.2
+ ,145.7
+ ,1
+ ,0
+ ,6.1
+ ,707
+ ,706
+ ,275
+ ,61.4
+ ,77.8
+ ,1
+ ,0
+ ,4.9
+ ,3086
+ ,1739
+ ,1507
+ ,202.7
+ ,335.2
+ ,0
+ ,0
+ ,3.3
+ ,252
+ ,312
+ ,883
+ ,41.7
+ ,60.6
+ ,1
+ ,0
+ ,7.0
+ ,11052
+ ,1097
+ ,606
+ ,64.9
+ ,97.6
+ ,1
+ ,0
+ ,8.2
+ ,9672
+ ,1037
+ ,829
+ ,92.6
+ ,118.2
+ ,1
+ ,0
+ ,43.5
+ ,1112
+ ,3689
+ ,542
+ ,30.3
+ ,96.9
+ ,1
+ ,0
+ ,48.5
+ ,1104
+ ,5123
+ ,910
+ ,63.7
+ ,133.3
+ ,1
+ ,0
+ ,5.4
+ ,478
+ ,672
+ ,866
+ ,67.1
+ ,101.6
+ ,0
+ ,1
+ ,49.5
+ ,10348
+ ,5721
+ ,1915
+ ,223.6
+ ,322.5
+ ,0
+ ,1
+ ,29.1
+ ,2769
+ ,3725
+ ,663
+ ,-208.4
+ ,12.4
+ ,1
+ ,0
+ ,2.6
+ ,752
+ ,2149
+ ,101
+ ,11.1
+ ,15.2
+ ,0
+ ,1
+ ,0.8
+ ,4989
+ ,518
+ ,53
+ ,-3.1
+ ,-0.3
+ ,1
+ ,0
+ ,184.8
+ ,10528
+ ,14992
+ ,5377
+ ,312.7
+ ,710.7
+ ,0
+ ,1
+ ,2.3
+ ,1995
+ ,2662
+ ,341
+ ,34.7
+ ,100.7
+ ,0
+ ,0
+ ,8.0
+ ,2286
+ ,2235
+ ,2306
+ ,195.3
+ ,219.0
+ ,0
+ ,0
+ ,10.3
+ ,952
+ ,1307
+ ,309
+ ,35.4
+ ,92.8
+ ,1
+ ,0
+ ,50.0
+ ,2957
+ ,2806
+ ,457
+ ,40.6
+ ,93.5
+ ,1
+ ,0
+ ,118.1
+ ,2535
+ ,5958
+ ,1921
+ ,177.0
+ ,288.0
+ ,1
+ ,0)
+ ,dim=c(8
+ ,79)
+ ,dimnames=list(c('Aantal_Werknemers'
+ ,'Activa'
+ ,'Omzet'
+ ,'Marktwaarde'
+ ,'Winst'
+ ,'Cashflow'
+ ,'Dienst'
+ ,'Product')
+ ,1:79))
> y <- array(NA,dim=c(8,79),dimnames=list(c('Aantal_Werknemers','Activa','Omzet','Marktwaarde','Winst','Cashflow','Dienst','Product'),1:79))
> 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'
> 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
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> 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
Aantal_Werknemers Activa Omzet Marktwaarde Winst Cashflow Dienst Product
1 18.2 2687 1870 1890 145.7 352.2 0 0
2 143.8 13271 9115 8190 -279.0 83.0 0 0
3 23.4 13621 4848 4572 485.0 898.9 0 0
4 1.1 3614 367 90 14.1 24.6 1 0
5 49.5 6425 6131 2448 345.8 682.5 1 0
6 4.8 1022 1754 1370 72.0 119.5 0 1
7 20.8 1093 1679 1070 100.9 164.5 0 1
8 19.4 1529 1295 444 25.6 137.0 0 0
9 2.1 2788 271 304 23.5 28.9 1 0
10 79.4 19788 9084 10636 1092.9 2576.8 1 0
11 2.8 327 542 959 54.1 72.5 1 0
12 3.8 1117 1038 478 59.7 91.7 0 0
13 4.1 5401 550 376 25.6 37.5 1 0
14 13.2 1128 1516 430 -47.0 26.7 0 1
15 2.8 1633 701 679 74.3 135.9 0 0
16 48.5 44736 16197 4653 -732.5 -651.9 1 0
17 6.2 5651 1254 2002 310.7 407.9 0 0
18 10.8 5835 4053 1601 -93.8 173.8 0 0
19 3.8 278 205 853 44.8 50.5 1 0
20 21.9 5074 2557 1892 239.9 578.3 1 0
21 12.6 866 1487 944 71.7 115.4 0 0
22 128.0 4418 8793 4459 283.6 456.5 1 0
23 87.3 6914 7029 7957 400.6 754.7 0 1
24 16.0 862 1601 1093 66.9 106.8 1 0
25 0.7 401 176 1084 55.6 57.0 1 0
26 22.5 430 1155 1045 55.7 70.8 0 1
27 15.4 799 1140 683 57.6 89.2 0 0
28 3.0 4789 453 367 40.2 51.4 1 0
29 2.1 2548 264 181 22.2 26.2 1 0
30 4.1 5249 527 346 37.8 56.2 1 0
31 6.4 3494 1653 1442 160.9 320.3 0 0
32 26.6 1804 2564 483 70.5 164.9 0 1
33 304.0 26432 28285 33172 2336.0 3562.0 0 1
34 18.6 623 2247 797 57.0 93.8 1 0
35 65.0 1608 6615 829 56.1 134.0 1 0
36 66.2 4662 4781 2988 28.7 371.5 0 1
37 83.0 5769 6571 9462 482.0 792.0 0 1
38 62.0 6259 4152 3090 283.7 524.5 1 0
39 1.6 1654 451 779 84.8 130.4 0 0
40 400.2 52634 50056 95697 6555.0 9874.0 0 1
41 23.3 999 1878 393 -173.5 -108.1 1 0
42 4.6 1679 1354 687 93.8 154.6 0 0
43 164.6 4178 17124 2091 180.8 390.4 1 0
44 1.9 223 557 1040 60.6 63.7 0 0
45 57.5 6307 8199 598 -771.5 -524.3 0 1
46 2.4 3720 356 211 26.6 34.8 1 0
47 77.3 3442 5080 2673 235.4 361.5 1 0
48 15.8 33406 3222 1413 201.7 246.7 1 0
49 0.6 1257 355 181 167.5 304.0 0 0
50 3.5 1743 597 717 121.6 172.4 0 0
51 9.0 12505 1302 702 108.4 131.4 1 0
52 62.0 3940 4317 3940 315.2 566.3 0 1
53 7.4 8998 882 988 93.0 119.0 1 0
54 15.6 21419 2516 930 107.6 164.7 1 0
55 25.2 2366 3305 1117 131.2 256.5 0 1
56 25.4 2448 3484 1036 48.8 257.1 1 0
57 3.5 1440 1617 639 81.7 126.4 0 0
58 27.3 14045 15636 2754 418.0 1462.0 0 0
59 37.5 4084 4346 3023 302.7 521.7 0 1
60 3.4 3010 749 1120 146.3 209.2 0 0
61 14.3 1286 1734 361 69.2 145.7 1 0
62 6.1 707 706 275 61.4 77.8 1 0
63 4.9 3086 1739 1507 202.7 335.2 0 0
64 3.3 252 312 883 41.7 60.6 1 0
65 7.0 11052 1097 606 64.9 97.6 1 0
66 8.2 9672 1037 829 92.6 118.2 1 0
67 43.5 1112 3689 542 30.3 96.9 1 0
68 48.5 1104 5123 910 63.7 133.3 1 0
69 5.4 478 672 866 67.1 101.6 0 1
70 49.5 10348 5721 1915 223.6 322.5 0 1
71 29.1 2769 3725 663 -208.4 12.4 1 0
72 2.6 752 2149 101 11.1 15.2 0 1
73 0.8 4989 518 53 -3.1 -0.3 1 0
74 184.8 10528 14992 5377 312.7 710.7 0 1
75 2.3 1995 2662 341 34.7 100.7 0 0
76 8.0 2286 2235 2306 195.3 219.0 0 0
77 10.3 952 1307 309 35.4 92.8 1 0
78 50.0 2957 2806 457 40.6 93.5 1 0
79 118.1 2535 5958 1921 177.0 288.0 1 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Activa Omzet Marktwaarde Winst Cashflow
-2.1200016 -0.0015291 0.0100733 0.0006661 0.0374020 -0.0315420
Dienst Product
12.3288015 14.2438423
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-77.964 -9.773 -3.441 4.345 81.992
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.1200016 5.1128573 -0.415 0.67965
Activa -0.0015291 0.0004476 -3.416 0.00105 **
Omzet 0.0100733 0.0009712 10.373 7.24e-16 ***
Marktwaarde 0.0006661 0.0011999 0.555 0.58057
Winst 0.0374020 0.0274722 1.361 0.17768
Cashflow -0.0315420 0.0175943 -1.793 0.07727 .
Dienst 12.3288015 6.1397495 2.008 0.04845 *
Product 14.2438423 7.5683765 1.882 0.06393 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 22.38 on 71 degrees of freedom
Multiple R-squared: 0.8904, Adjusted R-squared: 0.8796
F-statistic: 82.44 on 7 and 71 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,] 0.090716671 0.18143334 0.909283329
[2,] 0.032703851 0.06540770 0.967296149
[3,] 0.010133986 0.02026797 0.989866014
[4,] 0.008420946 0.01684189 0.991579054
[5,] 0.002695350 0.00539070 0.997304650
[6,] 0.073864272 0.14772854 0.926135728
[7,] 0.046245751 0.09249150 0.953754249
[8,] 0.076965758 0.15393152 0.923034242
[9,] 0.067036528 0.13407306 0.932963472
[10,] 0.050803828 0.10160766 0.949196172
[11,] 0.030861969 0.06172394 0.969138031
[12,] 0.030107754 0.06021551 0.969892246
[13,] 0.067751490 0.13550298 0.932248510
[14,] 0.056382092 0.11276418 0.943617908
[15,] 0.050696179 0.10139236 0.949303821
[16,] 0.045925872 0.09185174 0.954074128
[17,] 0.030391265 0.06078253 0.969608735
[18,] 0.024860026 0.04972005 0.975139974
[19,] 0.015890116 0.03178023 0.984109884
[20,] 0.013013816 0.02602763 0.986986184
[21,] 0.008457892 0.01691578 0.991542108
[22,] 0.007392791 0.01478558 0.992607209
[23,] 0.052283801 0.10456760 0.947716199
[24,] 0.048530640 0.09706128 0.951469360
[25,] 0.036601047 0.07320209 0.963398953
[26,] 0.054228386 0.10845677 0.945771614
[27,] 0.044442218 0.08888444 0.955557782
[28,] 0.095610475 0.19122095 0.904389525
[29,] 0.070682772 0.14136554 0.929317228
[30,] 0.938712647 0.12257471 0.061287353
[31,] 0.919962172 0.16007566 0.080037828
[32,] 0.896488016 0.20702397 0.103511984
[33,] 0.968225552 0.06354890 0.031774448
[34,] 0.953894344 0.09221131 0.046105656
[35,] 0.975204913 0.04959017 0.024795087
[36,] 0.962438438 0.07512312 0.037561562
[37,] 0.954796886 0.09040623 0.045203114
[38,] 0.966916254 0.06616749 0.033083746
[39,] 0.990383995 0.01923201 0.009616005
[40,] 0.990798413 0.01840317 0.009201587
[41,] 0.984784878 0.03043024 0.015215122
[42,] 0.978952111 0.04209578 0.021047889
[43,] 0.967398202 0.06520360 0.032601798
[44,] 0.951869046 0.09626191 0.048130954
[45,] 0.936500124 0.12699975 0.063499876
[46,] 0.907264291 0.18547142 0.092735709
[47,] 0.873671909 0.25265618 0.126328091
[48,] 0.978694523 0.04261095 0.021305477
[49,] 0.975785584 0.04842883 0.024214416
[50,] 0.969920914 0.06015817 0.030079086
[51,] 0.959310111 0.08137978 0.040689889
[52,] 0.938807875 0.12238425 0.061192125
[53,] 0.906424103 0.18715179 0.093575897
[54,] 0.867650888 0.26469822 0.132349112
[55,] 0.784922898 0.43015420 0.215077102
[56,] 0.667470035 0.66505993 0.332529965
[57,] 0.529411050 0.94117790 0.470588950
[58,] 0.680139696 0.63972061 0.319860304
> postscript(file="/var/fisher/rcomp/tmp/1aame1351700586.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2huis1351700586.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3jxqe1351700586.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4fdw31351700586.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/54vu11351700586.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 = 79
Frequency = 1
1 2 3 4 5 6
9.9923146 81.9922962 4.6802413 -7.0909216 -5.6806767 -23.2659182
7 8 9 10 11 12
-5.8635591 13.8810559 -6.7454056 41.2593831 -12.7439428 -2.4869933
13 14 15 16 17 18
-3.4155635 -10.1565374 1.4109463 -52.7252336 4.2407098 -11.0609925
19 20 21 22 23 24
-8.6996365 1.7001434 1.3946081 36.7936419 18.4644997 -8.8796458
25 26 27 28 29 30
-11.6722100 -1.1471886 7.4624174 -4.5759007 -6.9964913 -3.2627871
31 32 33 34 35 36
0.3359115 -6.3506533 50.2558274 -12.9950881 -7.8089264 21.6984149
37 38 39 40 41 42
14.1568314 23.4120405 2.1285833 -33.1369621 -1.4811646 -3.4414366
43 44 45 46 47 48
-7.5570767 -2.1999020 -15.6512355 -5.7444206 21.9994223 23.5123598
49 50 51 52 53 54
4.2694252 2.6836330 4.4197973 15.8630483 1.6824140 13.3495860
55 56 57 58 59 60
-14.1589809 -10.5668803 -7.9611290 -77.9644433 -9.0373534 2.9583494
61 62 63 64 65 66
-9.6425406 -10.1651841 -3.7909908 -9.9027019 2.8878559 2.0472930
67 68 69 70 71 72
-0.6068501 -10.4104595 -12.6440327 -3.8965673 -6.6538062 -30.0245556
73 74 75 76 77 78
-6.9269178 44.8949225 -17.6933636 -10.8312411 -10.2216946 17.1732324
79
52.9349572
> postscript(file="/var/fisher/rcomp/tmp/6rqr21351700586.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 = 79
Frequency = 1
lag(myerror, k = 1) myerror
0 9.9923146 NA
1 81.9922962 9.9923146
2 4.6802413 81.9922962
3 -7.0909216 4.6802413
4 -5.6806767 -7.0909216
5 -23.2659182 -5.6806767
6 -5.8635591 -23.2659182
7 13.8810559 -5.8635591
8 -6.7454056 13.8810559
9 41.2593831 -6.7454056
10 -12.7439428 41.2593831
11 -2.4869933 -12.7439428
12 -3.4155635 -2.4869933
13 -10.1565374 -3.4155635
14 1.4109463 -10.1565374
15 -52.7252336 1.4109463
16 4.2407098 -52.7252336
17 -11.0609925 4.2407098
18 -8.6996365 -11.0609925
19 1.7001434 -8.6996365
20 1.3946081 1.7001434
21 36.7936419 1.3946081
22 18.4644997 36.7936419
23 -8.8796458 18.4644997
24 -11.6722100 -8.8796458
25 -1.1471886 -11.6722100
26 7.4624174 -1.1471886
27 -4.5759007 7.4624174
28 -6.9964913 -4.5759007
29 -3.2627871 -6.9964913
30 0.3359115 -3.2627871
31 -6.3506533 0.3359115
32 50.2558274 -6.3506533
33 -12.9950881 50.2558274
34 -7.8089264 -12.9950881
35 21.6984149 -7.8089264
36 14.1568314 21.6984149
37 23.4120405 14.1568314
38 2.1285833 23.4120405
39 -33.1369621 2.1285833
40 -1.4811646 -33.1369621
41 -3.4414366 -1.4811646
42 -7.5570767 -3.4414366
43 -2.1999020 -7.5570767
44 -15.6512355 -2.1999020
45 -5.7444206 -15.6512355
46 21.9994223 -5.7444206
47 23.5123598 21.9994223
48 4.2694252 23.5123598
49 2.6836330 4.2694252
50 4.4197973 2.6836330
51 15.8630483 4.4197973
52 1.6824140 15.8630483
53 13.3495860 1.6824140
54 -14.1589809 13.3495860
55 -10.5668803 -14.1589809
56 -7.9611290 -10.5668803
57 -77.9644433 -7.9611290
58 -9.0373534 -77.9644433
59 2.9583494 -9.0373534
60 -9.6425406 2.9583494
61 -10.1651841 -9.6425406
62 -3.7909908 -10.1651841
63 -9.9027019 -3.7909908
64 2.8878559 -9.9027019
65 2.0472930 2.8878559
66 -0.6068501 2.0472930
67 -10.4104595 -0.6068501
68 -12.6440327 -10.4104595
69 -3.8965673 -12.6440327
70 -6.6538062 -3.8965673
71 -30.0245556 -6.6538062
72 -6.9269178 -30.0245556
73 44.8949225 -6.9269178
74 -17.6933636 44.8949225
75 -10.8312411 -17.6933636
76 -10.2216946 -10.8312411
77 17.1732324 -10.2216946
78 52.9349572 17.1732324
79 NA 52.9349572
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 81.9922962 9.9923146
[2,] 4.6802413 81.9922962
[3,] -7.0909216 4.6802413
[4,] -5.6806767 -7.0909216
[5,] -23.2659182 -5.6806767
[6,] -5.8635591 -23.2659182
[7,] 13.8810559 -5.8635591
[8,] -6.7454056 13.8810559
[9,] 41.2593831 -6.7454056
[10,] -12.7439428 41.2593831
[11,] -2.4869933 -12.7439428
[12,] -3.4155635 -2.4869933
[13,] -10.1565374 -3.4155635
[14,] 1.4109463 -10.1565374
[15,] -52.7252336 1.4109463
[16,] 4.2407098 -52.7252336
[17,] -11.0609925 4.2407098
[18,] -8.6996365 -11.0609925
[19,] 1.7001434 -8.6996365
[20,] 1.3946081 1.7001434
[21,] 36.7936419 1.3946081
[22,] 18.4644997 36.7936419
[23,] -8.8796458 18.4644997
[24,] -11.6722100 -8.8796458
[25,] -1.1471886 -11.6722100
[26,] 7.4624174 -1.1471886
[27,] -4.5759007 7.4624174
[28,] -6.9964913 -4.5759007
[29,] -3.2627871 -6.9964913
[30,] 0.3359115 -3.2627871
[31,] -6.3506533 0.3359115
[32,] 50.2558274 -6.3506533
[33,] -12.9950881 50.2558274
[34,] -7.8089264 -12.9950881
[35,] 21.6984149 -7.8089264
[36,] 14.1568314 21.6984149
[37,] 23.4120405 14.1568314
[38,] 2.1285833 23.4120405
[39,] -33.1369621 2.1285833
[40,] -1.4811646 -33.1369621
[41,] -3.4414366 -1.4811646
[42,] -7.5570767 -3.4414366
[43,] -2.1999020 -7.5570767
[44,] -15.6512355 -2.1999020
[45,] -5.7444206 -15.6512355
[46,] 21.9994223 -5.7444206
[47,] 23.5123598 21.9994223
[48,] 4.2694252 23.5123598
[49,] 2.6836330 4.2694252
[50,] 4.4197973 2.6836330
[51,] 15.8630483 4.4197973
[52,] 1.6824140 15.8630483
[53,] 13.3495860 1.6824140
[54,] -14.1589809 13.3495860
[55,] -10.5668803 -14.1589809
[56,] -7.9611290 -10.5668803
[57,] -77.9644433 -7.9611290
[58,] -9.0373534 -77.9644433
[59,] 2.9583494 -9.0373534
[60,] -9.6425406 2.9583494
[61,] -10.1651841 -9.6425406
[62,] -3.7909908 -10.1651841
[63,] -9.9027019 -3.7909908
[64,] 2.8878559 -9.9027019
[65,] 2.0472930 2.8878559
[66,] -0.6068501 2.0472930
[67,] -10.4104595 -0.6068501
[68,] -12.6440327 -10.4104595
[69,] -3.8965673 -12.6440327
[70,] -6.6538062 -3.8965673
[71,] -30.0245556 -6.6538062
[72,] -6.9269178 -30.0245556
[73,] 44.8949225 -6.9269178
[74,] -17.6933636 44.8949225
[75,] -10.8312411 -17.6933636
[76,] -10.2216946 -10.8312411
[77,] 17.1732324 -10.2216946
[78,] 52.9349572 17.1732324
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 81.9922962 9.9923146
2 4.6802413 81.9922962
3 -7.0909216 4.6802413
4 -5.6806767 -7.0909216
5 -23.2659182 -5.6806767
6 -5.8635591 -23.2659182
7 13.8810559 -5.8635591
8 -6.7454056 13.8810559
9 41.2593831 -6.7454056
10 -12.7439428 41.2593831
11 -2.4869933 -12.7439428
12 -3.4155635 -2.4869933
13 -10.1565374 -3.4155635
14 1.4109463 -10.1565374
15 -52.7252336 1.4109463
16 4.2407098 -52.7252336
17 -11.0609925 4.2407098
18 -8.6996365 -11.0609925
19 1.7001434 -8.6996365
20 1.3946081 1.7001434
21 36.7936419 1.3946081
22 18.4644997 36.7936419
23 -8.8796458 18.4644997
24 -11.6722100 -8.8796458
25 -1.1471886 -11.6722100
26 7.4624174 -1.1471886
27 -4.5759007 7.4624174
28 -6.9964913 -4.5759007
29 -3.2627871 -6.9964913
30 0.3359115 -3.2627871
31 -6.3506533 0.3359115
32 50.2558274 -6.3506533
33 -12.9950881 50.2558274
34 -7.8089264 -12.9950881
35 21.6984149 -7.8089264
36 14.1568314 21.6984149
37 23.4120405 14.1568314
38 2.1285833 23.4120405
39 -33.1369621 2.1285833
40 -1.4811646 -33.1369621
41 -3.4414366 -1.4811646
42 -7.5570767 -3.4414366
43 -2.1999020 -7.5570767
44 -15.6512355 -2.1999020
45 -5.7444206 -15.6512355
46 21.9994223 -5.7444206
47 23.5123598 21.9994223
48 4.2694252 23.5123598
49 2.6836330 4.2694252
50 4.4197973 2.6836330
51 15.8630483 4.4197973
52 1.6824140 15.8630483
53 13.3495860 1.6824140
54 -14.1589809 13.3495860
55 -10.5668803 -14.1589809
56 -7.9611290 -10.5668803
57 -77.9644433 -7.9611290
58 -9.0373534 -77.9644433
59 2.9583494 -9.0373534
60 -9.6425406 2.9583494
61 -10.1651841 -9.6425406
62 -3.7909908 -10.1651841
63 -9.9027019 -3.7909908
64 2.8878559 -9.9027019
65 2.0472930 2.8878559
66 -0.6068501 2.0472930
67 -10.4104595 -0.6068501
68 -12.6440327 -10.4104595
69 -3.8965673 -12.6440327
70 -6.6538062 -3.8965673
71 -30.0245556 -6.6538062
72 -6.9269178 -30.0245556
73 44.8949225 -6.9269178
74 -17.6933636 44.8949225
75 -10.8312411 -17.6933636
76 -10.2216946 -10.8312411
77 17.1732324 -10.2216946
78 52.9349572 17.1732324
> 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/fisher/rcomp/tmp/76gi71351700586.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8ky7q1351700586.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9iqcp1351700586.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/103o8x1351700586.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/11mzyi1351700586.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/fisher/rcomp/tmp/12la071351700586.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/fisher/rcomp/tmp/13ccbl1351700586.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/fisher/rcomp/tmp/14wcm01351700586.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/fisher/rcomp/tmp/15naps1351700586.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/fisher/rcomp/tmp/16yaep1351700586.tab")
+ }
>
> try(system("convert tmp/1aame1351700586.ps tmp/1aame1351700586.png",intern=TRUE))
character(0)
> try(system("convert tmp/2huis1351700586.ps tmp/2huis1351700586.png",intern=TRUE))
character(0)
> try(system("convert tmp/3jxqe1351700586.ps tmp/3jxqe1351700586.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fdw31351700586.ps tmp/4fdw31351700586.png",intern=TRUE))
character(0)
> try(system("convert tmp/54vu11351700586.ps tmp/54vu11351700586.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rqr21351700586.ps tmp/6rqr21351700586.png",intern=TRUE))
character(0)
> try(system("convert tmp/76gi71351700586.ps tmp/76gi71351700586.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ky7q1351700586.ps tmp/8ky7q1351700586.png",intern=TRUE))
character(0)
> try(system("convert tmp/9iqcp1351700586.ps tmp/9iqcp1351700586.png",intern=TRUE))
character(0)
> try(system("convert tmp/103o8x1351700586.ps tmp/103o8x1351700586.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.419 1.067 7.488