R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'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(4.031636
+ ,0.5215052
+ ,9.166456
+ ,1.303763
+ ,3.702076
+ ,0.4248284
+ ,7.970589
+ ,1.416094
+ ,3.056176
+ ,0.4250311
+ ,7.104091
+ ,1.052458
+ ,3.280707
+ ,0.4771938
+ ,6.621064
+ ,1.312283
+ ,2.984728
+ ,0.8280212
+ ,7.529215
+ ,1.309429
+ ,3.693712
+ ,0.6156186
+ ,8.170938
+ ,1.492409
+ ,3.226317
+ ,0.366627
+ ,8.15745
+ ,1.026556
+ ,2.190349
+ ,0.4308883
+ ,7.378962
+ ,1.005406
+ ,2.599515
+ ,0.2810287
+ ,7.921496
+ ,1.334886
+ ,3.080288
+ ,0.4646245
+ ,8.15674
+ ,1.393873
+ ,2.929672
+ ,0.2693951
+ ,8.856365
+ ,1.128092
+ ,2.922548
+ ,0.5779049
+ ,8.817177
+ ,1.122787
+ ,3.234943
+ ,0.5661151
+ ,8.734347
+ ,1.213104
+ ,2.983081
+ ,0.5077584
+ ,9.345927
+ ,1.253528
+ ,3.284389
+ ,0.7507175
+ ,8.99297
+ ,1.094796
+ ,3.806511
+ ,0.6808395
+ ,10.78512
+ ,0.9129438
+ ,3.784579
+ ,0.7661091
+ ,8.886867
+ ,1.19513
+ ,2.645654
+ ,0.4561473
+ ,8.818847
+ ,0.9274994
+ ,3.092081
+ ,0.4977496
+ ,8.823744
+ ,0.9653326
+ ,3.204859
+ ,0.4193273
+ ,9.165298
+ ,1.198078
+ ,3.107225
+ ,0.6095514
+ ,8.652657
+ ,0.966362
+ ,3.466909
+ ,0.457337
+ ,8.173054
+ ,0.9736851
+ ,2.984404
+ ,0.5705478
+ ,7.563416
+ ,0.9948013
+ ,3.218072
+ ,0.3478996
+ ,7.595809
+ ,0.8262616
+ ,2.82731
+ ,0.3874993
+ ,8.381467
+ ,0.6888877
+ ,3.182049
+ ,0.5824285
+ ,7.216432
+ ,0.7813066
+ ,2.236319
+ ,0.2391033
+ ,6.540178
+ ,0.6047907
+ ,2.033218
+ ,0.2367445
+ ,6.238914
+ ,1.08624
+ ,1.644804
+ ,0.2626158
+ ,5.487288
+ ,0.7740255
+ ,1.627971
+ ,0.4240934
+ ,5.759462
+ ,1.026032
+ ,1.677559
+ ,0.365275
+ ,5.993215
+ ,0.6764351
+ ,2.330828
+ ,0.3750758
+ ,7.474726
+ ,0.830525
+ ,2.493615
+ ,0.4090056
+ ,7.348907
+ ,0.7916238
+ ,2.257172
+ ,0.3891676
+ ,7.303379
+ ,0.7523907
+ ,2.655517
+ ,0.240261
+ ,7.119314
+ ,0.6702018
+ ,2.298655
+ ,0.1589496
+ ,6.99378
+ ,0.8803359
+ ,2.600402
+ ,0.4393373
+ ,6.958153
+ ,0.9142966
+ ,3.04523
+ ,0.5094681
+ ,7.595706
+ ,0.9610421
+ ,2.790583
+ ,0.3743465
+ ,8.088153
+ ,0.9301944
+ ,3.227052
+ ,0.4339828
+ ,7.555753
+ ,0.8679657
+ ,2.967479
+ ,0.4130557
+ ,7.315433
+ ,0.9891596
+ ,2.938817
+ ,0.3288928
+ ,7.893427
+ ,0.9972879
+ ,3.277961
+ ,0.5186648
+ ,8.858794
+ ,0.7987437
+ ,3.423985
+ ,0.5486504
+ ,8.839367
+ ,0.9753785
+ ,3.072646
+ ,0.5469111
+ ,8.014733
+ ,0.9347208
+ ,2.754253
+ ,0.4963494
+ ,7.873465
+ ,0.9732341
+ ,2.910431
+ ,0.5308929
+ ,8.930377
+ ,0.8152998
+ ,3.174369
+ ,0.5957761
+ ,10.50055
+ ,0.9402092
+ ,3.068387
+ ,0.5570584
+ ,12.61144
+ ,0.794493
+ ,3.089543
+ ,0.5731325
+ ,11.41787
+ ,0.9313403
+ ,2.906654
+ ,0.5005416
+ ,11.87249
+ ,0.9220503
+ ,2.931161
+ ,0.5431269
+ ,11.06082
+ ,0.7845167
+ ,3.02566
+ ,0.5593657
+ ,12.04331
+ ,0.8220981
+ ,2.939551
+ ,0.6911693
+ ,9.776299
+ ,0.8910255
+ ,2.691019
+ ,0.4403485
+ ,9.557194
+ ,0.8073056
+ ,3.19812
+ ,0.5676662
+ ,9.20259
+ ,0.9514406
+ ,3.07639
+ ,0.5969114
+ ,10.22402
+ ,1.147907
+ ,2.863873
+ ,0.4735537
+ ,9.350807
+ ,1.172609
+ ,3.013802
+ ,0.5923935
+ ,8.300913
+ ,1.281051
+ ,3.053364
+ ,0.5975556
+ ,8.365779
+ ,1.165962
+ ,2.864753
+ ,0.6334127
+ ,8.133595
+ ,0.9789106
+ ,3.057062
+ ,0.6057115
+ ,7.66047
+ ,1.410951
+ ,2.959365
+ ,0.7046107
+ ,8.074839
+ ,1.197838
+ ,3.252258
+ ,0.4805263
+ ,7.848597
+ ,1.288368
+ ,3.602988
+ ,0.702686
+ ,7.99822
+ ,1.102253
+ ,3.497704
+ ,0.7009017
+ ,7.396895
+ ,1.197657
+ ,3.296867
+ ,0.6030854
+ ,7.900419
+ ,1.299984
+ ,3.602417
+ ,0.6980919
+ ,8.1005
+ ,1.198611
+ ,3.3001
+ ,0.597656
+ ,7.899453
+ ,1.299252
+ ,3.40193
+ ,0.8023421
+ ,7.599783
+ ,1.097604
+ ,3.502591
+ ,0.6017109
+ ,8.100929
+ ,1.39977
+ ,3.402348
+ ,0.5993127
+ ,9.002175
+ ,1.398396
+ ,3.498551
+ ,0.6025625
+ ,10.2989
+ ,1.40188
+ ,3.199823
+ ,0.7016625
+ ,10.10152
+ ,1.699717
+ ,2.700064
+ ,0.4995714
+ ,10.69915
+ ,1.39761
+ ,2.801034
+ ,0.4980918
+ ,9.69814
+ ,1.500135
+ ,2.898628
+ ,0.497569
+ ,9.800951
+ ,1.400136
+ ,2.800854
+ ,0.600183
+ ,10.90047
+ ,1.400427
+ ,2.399942
+ ,0.3339542
+ ,10.69785
+ ,1.341477
+ ,2.402724
+ ,0.274437
+ ,9.297252
+ ,1.33858
+ ,2.202331
+ ,0.3209428
+ ,10.39744
+ ,1.482977
+ ,2.102594
+ ,0.5406671
+ ,10.90072
+ ,1.163253
+ ,1.798293
+ ,0.4050209
+ ,12.90127
+ ,1.328468
+ ,1.202484
+ ,0.2885961
+ ,13.09906
+ ,1.23455
+ ,1.400201
+ ,0.3275942
+ ,11.69828
+ ,1.484741
+ ,1.200832
+ ,0.3132606
+ ,11.09987
+ ,1.336579
+ ,1.298083
+ ,0.2575562
+ ,11.30157
+ ,1.339292
+ ,1.099742
+ ,0.2138386
+ ,10.70211
+ ,1.405225
+ ,1.001377
+ ,0.1861856
+ ,10.09931
+ ,1.333491
+ ,0.8361743
+ ,0.1592713
+ ,9.591119
+ ,1.14974)
+ ,dim=c(4
+ ,90)
+ ,dimnames=list(c('firearmsuicide'
+ ,'firearmhomicide'
+ ,'nonfirearmsuicide'
+ ,'nonfirearmhomicide')
+ ,1:90))
> y <- array(NA,dim=c(4,90),dimnames=list(c('firearmsuicide','firearmhomicide','nonfirearmsuicide','nonfirearmhomicide'),1:90))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = '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
firearmsuicide firearmhomicide nonfirearmsuicide nonfirearmhomicide t
1 4.0316360 0.5215052 9.166456 1.3037630 1
2 3.7020760 0.4248284 7.970589 1.4160940 2
3 3.0561760 0.4250311 7.104091 1.0524580 3
4 3.2807070 0.4771938 6.621064 1.3122830 4
5 2.9847280 0.8280212 7.529215 1.3094290 5
6 3.6937120 0.6156186 8.170938 1.4924090 6
7 3.2263170 0.3666270 8.157450 1.0265560 7
8 2.1903490 0.4308883 7.378962 1.0054060 8
9 2.5995150 0.2810287 7.921496 1.3348860 9
10 3.0802880 0.4646245 8.156740 1.3938730 10
11 2.9296720 0.2693951 8.856365 1.1280920 11
12 2.9225480 0.5779049 8.817177 1.1227870 12
13 3.2349430 0.5661151 8.734347 1.2131040 13
14 2.9830810 0.5077584 9.345927 1.2535280 14
15 3.2843890 0.7507175 8.992970 1.0947960 15
16 3.8065110 0.6808395 10.785120 0.9129438 16
17 3.7845790 0.7661091 8.886867 1.1951300 17
18 2.6456540 0.4561473 8.818847 0.9274994 18
19 3.0920810 0.4977496 8.823744 0.9653326 19
20 3.2048590 0.4193273 9.165298 1.1980780 20
21 3.1072250 0.6095514 8.652657 0.9663620 21
22 3.4669090 0.4573370 8.173054 0.9736851 22
23 2.9844040 0.5705478 7.563416 0.9948013 23
24 3.2180720 0.3478996 7.595809 0.8262616 24
25 2.8273100 0.3874993 8.381467 0.6888877 25
26 3.1820490 0.5824285 7.216432 0.7813066 26
27 2.2363190 0.2391033 6.540178 0.6047907 27
28 2.0332180 0.2367445 6.238914 1.0862400 28
29 1.6448040 0.2626158 5.487288 0.7740255 29
30 1.6279710 0.4240934 5.759462 1.0260320 30
31 1.6775590 0.3652750 5.993215 0.6764351 31
32 2.3308280 0.3750758 7.474726 0.8305250 32
33 2.4936150 0.4090056 7.348907 0.7916238 33
34 2.2571720 0.3891676 7.303379 0.7523907 34
35 2.6555170 0.2402610 7.119314 0.6702018 35
36 2.2986550 0.1589496 6.993780 0.8803359 36
37 2.6004020 0.4393373 6.958153 0.9142966 37
38 3.0452300 0.5094681 7.595706 0.9610421 38
39 2.7905830 0.3743465 8.088153 0.9301944 39
40 3.2270520 0.4339828 7.555753 0.8679657 40
41 2.9674790 0.4130557 7.315433 0.9891596 41
42 2.9388170 0.3288928 7.893427 0.9972879 42
43 3.2779610 0.5186648 8.858794 0.7987437 43
44 3.4239850 0.5486504 8.839367 0.9753785 44
45 3.0726460 0.5469111 8.014733 0.9347208 45
46 2.7542530 0.4963494 7.873465 0.9732341 46
47 2.9104310 0.5308929 8.930377 0.8152998 47
48 3.1743690 0.5957761 10.500550 0.9402092 48
49 3.0683870 0.5570584 12.611440 0.7944930 49
50 3.0895430 0.5731325 11.417870 0.9313403 50
51 2.9066540 0.5005416 11.872490 0.9220503 51
52 2.9311610 0.5431269 11.060820 0.7845167 52
53 3.0256600 0.5593657 12.043310 0.8220981 53
54 2.9395510 0.6911693 9.776299 0.8910255 54
55 2.6910190 0.4403485 9.557194 0.8073056 55
56 3.1981200 0.5676662 9.202590 0.9514406 56
57 3.0763900 0.5969114 10.224020 1.1479070 57
58 2.8638730 0.4735537 9.350807 1.1726090 58
59 3.0138020 0.5923935 8.300913 1.2810510 59
60 3.0533640 0.5975556 8.365779 1.1659620 60
61 2.8647530 0.6334127 8.133595 0.9789106 61
62 3.0570620 0.6057115 7.660470 1.4109510 62
63 2.9593650 0.7046107 8.074839 1.1978380 63
64 3.2522580 0.4805263 7.848597 1.2883680 64
65 3.6029880 0.7026860 7.998220 1.1022530 65
66 3.4977040 0.7009017 7.396895 1.1976570 66
67 3.2968670 0.6030854 7.900419 1.2999840 67
68 3.6024170 0.6980919 8.100500 1.1986110 68
69 3.3001000 0.5976560 7.899453 1.2992520 69
70 3.4019300 0.8023421 7.599783 1.0976040 70
71 3.5025910 0.6017109 8.100929 1.3997700 71
72 3.4023480 0.5993127 9.002175 1.3983960 72
73 3.4985510 0.6025625 10.298900 1.4018800 73
74 3.1998230 0.7016625 10.101520 1.6997170 74
75 2.7000640 0.4995714 10.699150 1.3976100 75
76 2.8010340 0.4980918 9.698140 1.5001350 76
77 2.8986280 0.4975690 9.800951 1.4001360 77
78 2.8008540 0.6001830 10.900470 1.4004270 78
79 2.3999420 0.3339542 10.697850 1.3414770 79
80 2.4027240 0.2744370 9.297252 1.3385800 80
81 2.2023310 0.3209428 10.397440 1.4829770 81
82 2.1025940 0.5406671 10.900720 1.1632530 82
83 1.7982930 0.4050209 12.901270 1.3284680 83
84 1.2024840 0.2885961 13.099060 1.2345500 84
85 1.4002010 0.3275942 11.698280 1.4847410 85
86 1.2008320 0.3132606 11.099870 1.3365790 86
87 1.2980830 0.2575562 11.301570 1.3392920 87
88 1.0997420 0.2138386 10.702110 1.4052250 88
89 1.0013770 0.1861856 10.099310 1.3334910 89
90 0.8361743 0.1592713 9.591119 1.1497400 90
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) firearmhomicide nonfirearmsuicide nonfirearmhomicide
1.711996 3.036706 -0.013063 0.158490
t
-0.009606
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.30287 -0.24348 0.04365 0.31893 0.65870
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.711996 0.318443 5.376 6.57e-07 ***
firearmhomicide 3.036706 0.300588 10.103 3.19e-16 ***
nonfirearmsuicide -0.013063 0.032495 -0.402 0.689
nonfirearmhomicide 0.158490 0.205871 0.770 0.444
t -0.009606 0.002101 -4.572 1.63e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4239 on 85 degrees of freedom
Multiple R-squared: 0.6297, Adjusted R-squared: 0.6123
F-statistic: 36.14 on 4 and 85 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,] 0.4008221 0.8016442181 0.5991778910
[2,] 0.4323258 0.8646516343 0.5676741829
[3,] 0.3289218 0.6578436626 0.6710781687
[4,] 0.2422887 0.4845774063 0.7577112968
[5,] 0.1868200 0.3736400212 0.8131799894
[6,] 0.2106649 0.4213298343 0.7893350829
[7,] 0.1421643 0.2843286458 0.8578356771
[8,] 0.1880119 0.3760238828 0.8119880586
[9,] 0.1626089 0.3252177997 0.8373911002
[10,] 0.3516760 0.7033519502 0.6483240249
[11,] 0.2857827 0.5715653859 0.7142173071
[12,] 0.2902302 0.5804603191 0.7097698404
[13,] 0.2532827 0.5065654139 0.7467172931
[14,] 0.2490959 0.4981918488 0.7509040756
[15,] 0.4683961 0.9367921644 0.5316039178
[16,] 0.4449566 0.8899132065 0.5550433968
[17,] 0.5511962 0.8976075370 0.4488037685
[18,] 0.4797601 0.9595201609 0.5202399195
[19,] 0.4542927 0.9085854028 0.5457072986
[20,] 0.3978003 0.7956006555 0.6021996723
[21,] 0.3902099 0.7804197764 0.6097901118
[22,] 0.4250298 0.8500596766 0.5749701617
[23,] 0.7684129 0.4631742416 0.2315871208
[24,] 0.8871562 0.2256875533 0.1128437767
[25,] 0.8905043 0.2189914628 0.1094957314
[26,] 0.8910570 0.2178860765 0.1089430382
[27,] 0.9139670 0.1720659794 0.0860329897
[28,] 0.9303549 0.1392902087 0.0696451043
[29,] 0.9114038 0.1771923626 0.0885961813
[30,] 0.9332406 0.1335187616 0.0667593808
[31,] 0.9451855 0.1096289234 0.0548144617
[32,] 0.9303745 0.1392509980 0.0696254990
[33,] 0.9463466 0.1073067232 0.0536533616
[34,] 0.9394133 0.1211734639 0.0605867319
[35,] 0.9216021 0.1567958618 0.0783979309
[36,] 0.9030655 0.1938690168 0.0969345084
[37,] 0.8776431 0.2447138079 0.1223569040
[38,] 0.8535712 0.2928575572 0.1464287786
[39,] 0.8696085 0.2607829906 0.1303914953
[40,] 0.8530119 0.2939762838 0.1469881419
[41,] 0.8532089 0.2935822571 0.1467911286
[42,] 0.8955911 0.2088178325 0.1044089163
[43,] 0.8814777 0.2370446123 0.1185223061
[44,] 0.8747472 0.2505056198 0.1252528099
[45,] 0.8498675 0.3002649778 0.1501324889
[46,] 0.8521018 0.2957964823 0.1478982411
[47,] 0.8390786 0.3218428351 0.1609214176
[48,] 0.8100122 0.3799755683 0.1899877842
[49,] 0.7965607 0.4068785445 0.2034392723
[50,] 0.7486605 0.5026790539 0.2513395269
[51,] 0.6952936 0.6094127121 0.3047063561
[52,] 0.6878360 0.6243279474 0.3121639737
[53,] 0.6494872 0.7010256180 0.3505128090
[54,] 0.6409957 0.7180086703 0.3590043351
[55,] 0.8061221 0.3877558403 0.1938779202
[56,] 0.9703327 0.0593345389 0.0296672694
[57,] 0.9788758 0.0422484960 0.0211242480
[58,] 0.9764720 0.0470559991 0.0235279996
[59,] 0.9805042 0.0389916652 0.0194958326
[60,] 0.9928050 0.0143899916 0.0071949958
[61,] 0.9900497 0.0199005638 0.0099502819
[62,] 0.9960890 0.0078220156 0.0039110078
[63,] 0.9987292 0.0025415242 0.0012707621
[64,] 0.9988286 0.0023427614 0.0011713807
[65,] 0.9985836 0.0028327629 0.0014163814
[66,] 0.9978590 0.0042820096 0.0021410048
[67,] 0.9970335 0.0059329484 0.0029664742
[68,] 0.9987500 0.0025000685 0.0012500343
[69,] 0.9995927 0.0008145401 0.0004072701
[70,] 0.9988426 0.0023148097 0.0011574049
[71,] 0.9966602 0.0066796717 0.0033398358
[72,] 0.9903042 0.0193915898 0.0096957949
[73,] 0.9730961 0.0538077794 0.0269038897
[74,] 0.9833081 0.0333837666 0.0166918833
[75,] 0.9854140 0.0291720134 0.0145860067
> postscript(file="/var/www/rcomp/tmp/1g9wb1292944675.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2g9wb1292944675.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39jvw1292944675.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/49jvw1292944675.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/59jvw1292944675.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 = 90
Frequency = 1
1 2 3 4 5 6
0.658699413 0.598899093 0.008302983 0.036547672 -1.302868907 0.040107972
7 8 9 10 11 12
0.412090210 -0.816231953 0.012487693 -0.060935368 0.442172134 -0.491870353
13 14 15 16 17 18
-0.149463466 -0.212924707 -0.619259234 0.176901157 -0.163884754 -0.310412624
19 20 21 22 23 24
0.013354343 0.341457927 -0.294196697 0.529897885 -0.298099525 0.648426703
25 26 27 28 29 30
0.179053961 -0.078410326 0.047185250 -0.219387128 -0.637094403 -1.171066250
31 32 33 34 35 36
-0.874796692 -0.246751870 -0.172871730 -0.333843127 0.536915137 0.401633979
37 38 39 40 41 42
-0.144315672 0.098071761 0.274677509 0.542562423 0.333797563 0.576581959
43 44 45 46 47 48
0.393128633 0.429452664 0.088672855 -0.074522433 0.025201136 0.102428965
49 50 51 52 53 54
0.174297324 0.118966116 0.173531698 0.089520349 0.151191395 -0.366099401
55 56 57 58 59 60
0.167050205 0.269654638 0.050927061 0.207295070 -0.024953471 0.027626715
61 62 63 64 65 66
-0.233652933 -0.022272166 -0.371501436 0.594172471 0.311326937 0.198091520
67 68 69 70 71 72
0.294259880 0.339589580 0.333296018 -0.148794800 0.529386584 0.458023438
73 74 75 76 77 78
0.570351332 -0.069490752 0.109735624 0.195479071 0.321458701 -0.064000362
79 80 81 82 83 84
0.359848372 0.535135298 0.194610691 -0.505510582 -0.388338830 -0.603524963
85 86 87 88 89 90
-0.572579336 -0.703150339 -0.424930439 -0.499188540 -0.500478876 -0.551860624
> postscript(file="/var/www/rcomp/tmp/61acz1292944675.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 = 90
Frequency = 1
lag(myerror, k = 1) myerror
0 0.658699413 NA
1 0.598899093 0.658699413
2 0.008302983 0.598899093
3 0.036547672 0.008302983
4 -1.302868907 0.036547672
5 0.040107972 -1.302868907
6 0.412090210 0.040107972
7 -0.816231953 0.412090210
8 0.012487693 -0.816231953
9 -0.060935368 0.012487693
10 0.442172134 -0.060935368
11 -0.491870353 0.442172134
12 -0.149463466 -0.491870353
13 -0.212924707 -0.149463466
14 -0.619259234 -0.212924707
15 0.176901157 -0.619259234
16 -0.163884754 0.176901157
17 -0.310412624 -0.163884754
18 0.013354343 -0.310412624
19 0.341457927 0.013354343
20 -0.294196697 0.341457927
21 0.529897885 -0.294196697
22 -0.298099525 0.529897885
23 0.648426703 -0.298099525
24 0.179053961 0.648426703
25 -0.078410326 0.179053961
26 0.047185250 -0.078410326
27 -0.219387128 0.047185250
28 -0.637094403 -0.219387128
29 -1.171066250 -0.637094403
30 -0.874796692 -1.171066250
31 -0.246751870 -0.874796692
32 -0.172871730 -0.246751870
33 -0.333843127 -0.172871730
34 0.536915137 -0.333843127
35 0.401633979 0.536915137
36 -0.144315672 0.401633979
37 0.098071761 -0.144315672
38 0.274677509 0.098071761
39 0.542562423 0.274677509
40 0.333797563 0.542562423
41 0.576581959 0.333797563
42 0.393128633 0.576581959
43 0.429452664 0.393128633
44 0.088672855 0.429452664
45 -0.074522433 0.088672855
46 0.025201136 -0.074522433
47 0.102428965 0.025201136
48 0.174297324 0.102428965
49 0.118966116 0.174297324
50 0.173531698 0.118966116
51 0.089520349 0.173531698
52 0.151191395 0.089520349
53 -0.366099401 0.151191395
54 0.167050205 -0.366099401
55 0.269654638 0.167050205
56 0.050927061 0.269654638
57 0.207295070 0.050927061
58 -0.024953471 0.207295070
59 0.027626715 -0.024953471
60 -0.233652933 0.027626715
61 -0.022272166 -0.233652933
62 -0.371501436 -0.022272166
63 0.594172471 -0.371501436
64 0.311326937 0.594172471
65 0.198091520 0.311326937
66 0.294259880 0.198091520
67 0.339589580 0.294259880
68 0.333296018 0.339589580
69 -0.148794800 0.333296018
70 0.529386584 -0.148794800
71 0.458023438 0.529386584
72 0.570351332 0.458023438
73 -0.069490752 0.570351332
74 0.109735624 -0.069490752
75 0.195479071 0.109735624
76 0.321458701 0.195479071
77 -0.064000362 0.321458701
78 0.359848372 -0.064000362
79 0.535135298 0.359848372
80 0.194610691 0.535135298
81 -0.505510582 0.194610691
82 -0.388338830 -0.505510582
83 -0.603524963 -0.388338830
84 -0.572579336 -0.603524963
85 -0.703150339 -0.572579336
86 -0.424930439 -0.703150339
87 -0.499188540 -0.424930439
88 -0.500478876 -0.499188540
89 -0.551860624 -0.500478876
90 NA -0.551860624
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.598899093 0.658699413
[2,] 0.008302983 0.598899093
[3,] 0.036547672 0.008302983
[4,] -1.302868907 0.036547672
[5,] 0.040107972 -1.302868907
[6,] 0.412090210 0.040107972
[7,] -0.816231953 0.412090210
[8,] 0.012487693 -0.816231953
[9,] -0.060935368 0.012487693
[10,] 0.442172134 -0.060935368
[11,] -0.491870353 0.442172134
[12,] -0.149463466 -0.491870353
[13,] -0.212924707 -0.149463466
[14,] -0.619259234 -0.212924707
[15,] 0.176901157 -0.619259234
[16,] -0.163884754 0.176901157
[17,] -0.310412624 -0.163884754
[18,] 0.013354343 -0.310412624
[19,] 0.341457927 0.013354343
[20,] -0.294196697 0.341457927
[21,] 0.529897885 -0.294196697
[22,] -0.298099525 0.529897885
[23,] 0.648426703 -0.298099525
[24,] 0.179053961 0.648426703
[25,] -0.078410326 0.179053961
[26,] 0.047185250 -0.078410326
[27,] -0.219387128 0.047185250
[28,] -0.637094403 -0.219387128
[29,] -1.171066250 -0.637094403
[30,] -0.874796692 -1.171066250
[31,] -0.246751870 -0.874796692
[32,] -0.172871730 -0.246751870
[33,] -0.333843127 -0.172871730
[34,] 0.536915137 -0.333843127
[35,] 0.401633979 0.536915137
[36,] -0.144315672 0.401633979
[37,] 0.098071761 -0.144315672
[38,] 0.274677509 0.098071761
[39,] 0.542562423 0.274677509
[40,] 0.333797563 0.542562423
[41,] 0.576581959 0.333797563
[42,] 0.393128633 0.576581959
[43,] 0.429452664 0.393128633
[44,] 0.088672855 0.429452664
[45,] -0.074522433 0.088672855
[46,] 0.025201136 -0.074522433
[47,] 0.102428965 0.025201136
[48,] 0.174297324 0.102428965
[49,] 0.118966116 0.174297324
[50,] 0.173531698 0.118966116
[51,] 0.089520349 0.173531698
[52,] 0.151191395 0.089520349
[53,] -0.366099401 0.151191395
[54,] 0.167050205 -0.366099401
[55,] 0.269654638 0.167050205
[56,] 0.050927061 0.269654638
[57,] 0.207295070 0.050927061
[58,] -0.024953471 0.207295070
[59,] 0.027626715 -0.024953471
[60,] -0.233652933 0.027626715
[61,] -0.022272166 -0.233652933
[62,] -0.371501436 -0.022272166
[63,] 0.594172471 -0.371501436
[64,] 0.311326937 0.594172471
[65,] 0.198091520 0.311326937
[66,] 0.294259880 0.198091520
[67,] 0.339589580 0.294259880
[68,] 0.333296018 0.339589580
[69,] -0.148794800 0.333296018
[70,] 0.529386584 -0.148794800
[71,] 0.458023438 0.529386584
[72,] 0.570351332 0.458023438
[73,] -0.069490752 0.570351332
[74,] 0.109735624 -0.069490752
[75,] 0.195479071 0.109735624
[76,] 0.321458701 0.195479071
[77,] -0.064000362 0.321458701
[78,] 0.359848372 -0.064000362
[79,] 0.535135298 0.359848372
[80,] 0.194610691 0.535135298
[81,] -0.505510582 0.194610691
[82,] -0.388338830 -0.505510582
[83,] -0.603524963 -0.388338830
[84,] -0.572579336 -0.603524963
[85,] -0.703150339 -0.572579336
[86,] -0.424930439 -0.703150339
[87,] -0.499188540 -0.424930439
[88,] -0.500478876 -0.499188540
[89,] -0.551860624 -0.500478876
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.598899093 0.658699413
2 0.008302983 0.598899093
3 0.036547672 0.008302983
4 -1.302868907 0.036547672
5 0.040107972 -1.302868907
6 0.412090210 0.040107972
7 -0.816231953 0.412090210
8 0.012487693 -0.816231953
9 -0.060935368 0.012487693
10 0.442172134 -0.060935368
11 -0.491870353 0.442172134
12 -0.149463466 -0.491870353
13 -0.212924707 -0.149463466
14 -0.619259234 -0.212924707
15 0.176901157 -0.619259234
16 -0.163884754 0.176901157
17 -0.310412624 -0.163884754
18 0.013354343 -0.310412624
19 0.341457927 0.013354343
20 -0.294196697 0.341457927
21 0.529897885 -0.294196697
22 -0.298099525 0.529897885
23 0.648426703 -0.298099525
24 0.179053961 0.648426703
25 -0.078410326 0.179053961
26 0.047185250 -0.078410326
27 -0.219387128 0.047185250
28 -0.637094403 -0.219387128
29 -1.171066250 -0.637094403
30 -0.874796692 -1.171066250
31 -0.246751870 -0.874796692
32 -0.172871730 -0.246751870
33 -0.333843127 -0.172871730
34 0.536915137 -0.333843127
35 0.401633979 0.536915137
36 -0.144315672 0.401633979
37 0.098071761 -0.144315672
38 0.274677509 0.098071761
39 0.542562423 0.274677509
40 0.333797563 0.542562423
41 0.576581959 0.333797563
42 0.393128633 0.576581959
43 0.429452664 0.393128633
44 0.088672855 0.429452664
45 -0.074522433 0.088672855
46 0.025201136 -0.074522433
47 0.102428965 0.025201136
48 0.174297324 0.102428965
49 0.118966116 0.174297324
50 0.173531698 0.118966116
51 0.089520349 0.173531698
52 0.151191395 0.089520349
53 -0.366099401 0.151191395
54 0.167050205 -0.366099401
55 0.269654638 0.167050205
56 0.050927061 0.269654638
57 0.207295070 0.050927061
58 -0.024953471 0.207295070
59 0.027626715 -0.024953471
60 -0.233652933 0.027626715
61 -0.022272166 -0.233652933
62 -0.371501436 -0.022272166
63 0.594172471 -0.371501436
64 0.311326937 0.594172471
65 0.198091520 0.311326937
66 0.294259880 0.198091520
67 0.339589580 0.294259880
68 0.333296018 0.339589580
69 -0.148794800 0.333296018
70 0.529386584 -0.148794800
71 0.458023438 0.529386584
72 0.570351332 0.458023438
73 -0.069490752 0.570351332
74 0.109735624 -0.069490752
75 0.195479071 0.109735624
76 0.321458701 0.195479071
77 -0.064000362 0.321458701
78 0.359848372 -0.064000362
79 0.535135298 0.359848372
80 0.194610691 0.535135298
81 -0.505510582 0.194610691
82 -0.388338830 -0.505510582
83 -0.603524963 -0.388338830
84 -0.572579336 -0.603524963
85 -0.703150339 -0.572579336
86 -0.424930439 -0.703150339
87 -0.499188540 -0.424930439
88 -0.500478876 -0.499188540
89 -0.551860624 -0.500478876
> 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/71acz1292944675.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8u1uk1292944675.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9u1uk1292944675.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10nab51292944675.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qbra1292944675.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/12bbqy1292944675.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/1383671292944675.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/14b4mv1292944675.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/15eml11292944675.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/16injp1292944675.tab")
+ }
>
> try(system("convert tmp/1g9wb1292944675.ps tmp/1g9wb1292944675.png",intern=TRUE))
character(0)
> try(system("convert tmp/2g9wb1292944675.ps tmp/2g9wb1292944675.png",intern=TRUE))
character(0)
> try(system("convert tmp/39jvw1292944675.ps tmp/39jvw1292944675.png",intern=TRUE))
character(0)
> try(system("convert tmp/49jvw1292944675.ps tmp/49jvw1292944675.png",intern=TRUE))
character(0)
> try(system("convert tmp/59jvw1292944675.ps tmp/59jvw1292944675.png",intern=TRUE))
character(0)
> try(system("convert tmp/61acz1292944675.ps tmp/61acz1292944675.png",intern=TRUE))
character(0)
> try(system("convert tmp/71acz1292944675.ps tmp/71acz1292944675.png",intern=TRUE))
character(0)
> try(system("convert tmp/8u1uk1292944675.ps tmp/8u1uk1292944675.png",intern=TRUE))
character(0)
> try(system("convert tmp/9u1uk1292944675.ps tmp/9u1uk1292944675.png",intern=TRUE))
character(0)
> try(system("convert tmp/10nab51292944675.ps tmp/10nab51292944675.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.490 0.720 4.201