R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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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(1.579
+ ,9.769
+ ,2.146
+ ,9.321
+ ,2.462
+ ,9.939
+ ,3.695
+ ,9.336
+ ,4.831
+ ,10.195
+ ,5.134
+ ,9.464
+ ,6.250
+ ,10.010
+ ,5.760
+ ,10.213
+ ,6.249
+ ,9.563
+ ,2.917
+ ,9.890
+ ,1.741
+ ,9.305
+ ,2.359
+ ,9.391
+ ,1.511
+ ,9.928
+ ,2.059
+ ,8.686
+ ,2.635
+ ,9.843
+ ,2.867
+ ,9.627
+ ,4.403
+ ,10.074
+ ,5.720
+ ,9.503
+ ,4.502
+ ,10.119
+ ,5.749
+ ,10.000
+ ,5.627
+ ,9.313
+ ,2.846
+ ,9.866
+ ,1.762
+ ,9.172
+ ,2.429
+ ,9.241
+ ,1.169
+ ,9.659
+ ,2.154
+ ,8.904
+ ,2.249
+ ,9.755
+ ,2.687
+ ,9.080
+ ,4.359
+ ,9.435
+ ,5.382
+ ,8.971
+ ,4.459
+ ,10.063
+ ,6.398
+ ,9.793
+ ,4.596
+ ,9.454
+ ,3.024
+ ,9.759
+ ,1.887
+ ,8.820
+ ,2.070
+ ,9.403
+ ,1.351
+ ,9.676
+ ,2.218
+ ,8.642
+ ,2.461
+ ,9.402
+ ,3.028
+ ,9.610
+ ,4.784
+ ,9.294
+ ,4.975
+ ,9.448
+ ,4.607
+ ,10.319
+ ,6.249
+ ,9.548
+ ,4.809
+ ,9.801
+ ,3.157
+ ,9.596
+ ,1.910
+ ,8.923
+ ,2.228
+ ,9.746
+ ,1.594
+ ,9.829
+ ,2.467
+ ,9.125
+ ,2.222
+ ,9.782
+ ,3.607
+ ,9.441
+ ,4.685
+ ,9.162
+ ,4.962
+ ,9.915
+ ,5.770
+ ,10.444
+ ,5.480
+ ,10.209
+ ,5.000
+ ,9.985
+ ,3.228
+ ,9.842
+ ,1.993
+ ,9.429
+ ,2.288
+ ,10.132
+ ,1.580
+ ,9.849
+ ,2.111
+ ,9.172
+ ,2.192
+ ,10.313
+ ,3.601
+ ,9.819
+ ,4.665
+ ,9.955
+ ,4.876
+ ,10.048
+ ,5.813
+ ,10.082
+ ,5.589
+ ,10.541
+ ,5.331
+ ,10.208
+ ,3.075
+ ,10.233
+ ,2.002
+ ,9.439
+ ,2.306
+ ,9.963
+ ,1.507
+ ,10.158
+ ,1.992
+ ,9.225
+ ,2.487
+ ,10.474
+ ,3.490
+ ,9.757
+ ,4.647
+ ,10.490
+ ,5.594
+ ,10.281
+ ,5.611
+ ,10.444
+ ,5.788
+ ,10.640
+ ,6.204
+ ,10.695
+ ,3.013
+ ,10.786
+ ,1.931
+ ,9.832
+ ,2.549
+ ,9.747
+ ,1.504
+ ,10.411
+ ,2.090
+ ,9.511
+ ,2.702
+ ,10.402
+ ,2.939
+ ,9.701
+ ,4.500
+ ,10.540
+ ,6.208
+ ,10.112
+ ,6.415
+ ,10.915
+ ,5.657
+ ,11.183
+ ,5.964
+ ,10.384
+ ,3.163
+ ,10.834
+ ,1.997
+ ,9.886
+ ,2.422
+ ,10.216)
+ ,dim=c(2
+ ,96)
+ ,dimnames=list(c('huwelijken'
+ ,'geboortes')
+ ,1:96))
> y <- array(NA,dim=c(2,96),dimnames=list(c('huwelijken','geboortes'),1:96))
> 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 = 'Include Monthly 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.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
huwelijken geboortes M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 1.579 9.769 1 0 0 0 0 0 0 0 0 0 0 1
2 2.146 9.321 0 1 0 0 0 0 0 0 0 0 0 2
3 2.462 9.939 0 0 1 0 0 0 0 0 0 0 0 3
4 3.695 9.336 0 0 0 1 0 0 0 0 0 0 0 4
5 4.831 10.195 0 0 0 0 1 0 0 0 0 0 0 5
6 5.134 9.464 0 0 0 0 0 1 0 0 0 0 0 6
7 6.250 10.010 0 0 0 0 0 0 1 0 0 0 0 7
8 5.760 10.213 0 0 0 0 0 0 0 1 0 0 0 8
9 6.249 9.563 0 0 0 0 0 0 0 0 1 0 0 9
10 2.917 9.890 0 0 0 0 0 0 0 0 0 1 0 10
11 1.741 9.305 0 0 0 0 0 0 0 0 0 0 1 11
12 2.359 9.391 0 0 0 0 0 0 0 0 0 0 0 12
13 1.511 9.928 1 0 0 0 0 0 0 0 0 0 0 13
14 2.059 8.686 0 1 0 0 0 0 0 0 0 0 0 14
15 2.635 9.843 0 0 1 0 0 0 0 0 0 0 0 15
16 2.867 9.627 0 0 0 1 0 0 0 0 0 0 0 16
17 4.403 10.074 0 0 0 0 1 0 0 0 0 0 0 17
18 5.720 9.503 0 0 0 0 0 1 0 0 0 0 0 18
19 4.502 10.119 0 0 0 0 0 0 1 0 0 0 0 19
20 5.749 10.000 0 0 0 0 0 0 0 1 0 0 0 20
21 5.627 9.313 0 0 0 0 0 0 0 0 1 0 0 21
22 2.846 9.866 0 0 0 0 0 0 0 0 0 1 0 22
23 1.762 9.172 0 0 0 0 0 0 0 0 0 0 1 23
24 2.429 9.241 0 0 0 0 0 0 0 0 0 0 0 24
25 1.169 9.659 1 0 0 0 0 0 0 0 0 0 0 25
26 2.154 8.904 0 1 0 0 0 0 0 0 0 0 0 26
27 2.249 9.755 0 0 1 0 0 0 0 0 0 0 0 27
28 2.687 9.080 0 0 0 1 0 0 0 0 0 0 0 28
29 4.359 9.435 0 0 0 0 1 0 0 0 0 0 0 29
30 5.382 8.971 0 0 0 0 0 1 0 0 0 0 0 30
31 4.459 10.063 0 0 0 0 0 0 1 0 0 0 0 31
32 6.398 9.793 0 0 0 0 0 0 0 1 0 0 0 32
33 4.596 9.454 0 0 0 0 0 0 0 0 1 0 0 33
34 3.024 9.759 0 0 0 0 0 0 0 0 0 1 0 34
35 1.887 8.820 0 0 0 0 0 0 0 0 0 0 1 35
36 2.070 9.403 0 0 0 0 0 0 0 0 0 0 0 36
37 1.351 9.676 1 0 0 0 0 0 0 0 0 0 0 37
38 2.218 8.642 0 1 0 0 0 0 0 0 0 0 0 38
39 2.461 9.402 0 0 1 0 0 0 0 0 0 0 0 39
40 3.028 9.610 0 0 0 1 0 0 0 0 0 0 0 40
41 4.784 9.294 0 0 0 0 1 0 0 0 0 0 0 41
42 4.975 9.448 0 0 0 0 0 1 0 0 0 0 0 42
43 4.607 10.319 0 0 0 0 0 0 1 0 0 0 0 43
44 6.249 9.548 0 0 0 0 0 0 0 1 0 0 0 44
45 4.809 9.801 0 0 0 0 0 0 0 0 1 0 0 45
46 3.157 9.596 0 0 0 0 0 0 0 0 0 1 0 46
47 1.910 8.923 0 0 0 0 0 0 0 0 0 0 1 47
48 2.228 9.746 0 0 0 0 0 0 0 0 0 0 0 48
49 1.594 9.829 1 0 0 0 0 0 0 0 0 0 0 49
50 2.467 9.125 0 1 0 0 0 0 0 0 0 0 0 50
51 2.222 9.782 0 0 1 0 0 0 0 0 0 0 0 51
52 3.607 9.441 0 0 0 1 0 0 0 0 0 0 0 52
53 4.685 9.162 0 0 0 0 1 0 0 0 0 0 0 53
54 4.962 9.915 0 0 0 0 0 1 0 0 0 0 0 54
55 5.770 10.444 0 0 0 0 0 0 1 0 0 0 0 55
56 5.480 10.209 0 0 0 0 0 0 0 1 0 0 0 56
57 5.000 9.985 0 0 0 0 0 0 0 0 1 0 0 57
58 3.228 9.842 0 0 0 0 0 0 0 0 0 1 0 58
59 1.993 9.429 0 0 0 0 0 0 0 0 0 0 1 59
60 2.288 10.132 0 0 0 0 0 0 0 0 0 0 0 60
61 1.580 9.849 1 0 0 0 0 0 0 0 0 0 0 61
62 2.111 9.172 0 1 0 0 0 0 0 0 0 0 0 62
63 2.192 10.313 0 0 1 0 0 0 0 0 0 0 0 63
64 3.601 9.819 0 0 0 1 0 0 0 0 0 0 0 64
65 4.665 9.955 0 0 0 0 1 0 0 0 0 0 0 65
66 4.876 10.048 0 0 0 0 0 1 0 0 0 0 0 66
67 5.813 10.082 0 0 0 0 0 0 1 0 0 0 0 67
68 5.589 10.541 0 0 0 0 0 0 0 1 0 0 0 68
69 5.331 10.208 0 0 0 0 0 0 0 0 1 0 0 69
70 3.075 10.233 0 0 0 0 0 0 0 0 0 1 0 70
71 2.002 9.439 0 0 0 0 0 0 0 0 0 0 1 71
72 2.306 9.963 0 0 0 0 0 0 0 0 0 0 0 72
73 1.507 10.158 1 0 0 0 0 0 0 0 0 0 0 73
74 1.992 9.225 0 1 0 0 0 0 0 0 0 0 0 74
75 2.487 10.474 0 0 1 0 0 0 0 0 0 0 0 75
76 3.490 9.757 0 0 0 1 0 0 0 0 0 0 0 76
77 4.647 10.490 0 0 0 0 1 0 0 0 0 0 0 77
78 5.594 10.281 0 0 0 0 0 1 0 0 0 0 0 78
79 5.611 10.444 0 0 0 0 0 0 1 0 0 0 0 79
80 5.788 10.640 0 0 0 0 0 0 0 1 0 0 0 80
81 6.204 10.695 0 0 0 0 0 0 0 0 1 0 0 81
82 3.013 10.786 0 0 0 0 0 0 0 0 0 1 0 82
83 1.931 9.832 0 0 0 0 0 0 0 0 0 0 1 83
84 2.549 9.747 0 0 0 0 0 0 0 0 0 0 0 84
85 1.504 10.411 1 0 0 0 0 0 0 0 0 0 0 85
86 2.090 9.511 0 1 0 0 0 0 0 0 0 0 0 86
87 2.702 10.402 0 0 1 0 0 0 0 0 0 0 0 87
88 2.939 9.701 0 0 0 1 0 0 0 0 0 0 0 88
89 4.500 10.540 0 0 0 0 1 0 0 0 0 0 0 89
90 6.208 10.112 0 0 0 0 0 1 0 0 0 0 0 90
91 6.415 10.915 0 0 0 0 0 0 1 0 0 0 0 91
92 5.657 11.183 0 0 0 0 0 0 0 1 0 0 0 92
93 5.964 10.384 0 0 0 0 0 0 0 0 1 0 0 93
94 3.163 10.834 0 0 0 0 0 0 0 0 0 1 0 94
95 1.997 9.886 0 0 0 0 0 0 0 0 0 0 1 95
96 2.422 10.216 0 0 0 0 0 0 0 0 0 0 0 96
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) geboortes M1 M2 M3 M4
2.023395 0.020691 -0.838997 -0.143412 0.107296 0.927474
M5 M6 M7 M8 M9 M10
2.288324 3.037102 3.095090 3.499186 3.143006 0.717777
M11 t
-0.418680 0.001975
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.928928 -0.163207 -0.001151 0.166785 0.910574
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.023395 1.284063 1.576 0.118928
geboortes 0.020691 0.138189 0.150 0.881347
M1 -0.838997 0.190709 -4.399 3.24e-05 ***
M2 -0.143412 0.202170 -0.709 0.480113
M3 0.107296 0.192387 0.558 0.578564
M4 0.927474 0.186992 4.960 3.75e-06 ***
M5 2.288324 0.188961 12.110 < 2e-16 ***
M6 3.037102 0.186338 16.299 < 2e-16 ***
M7 3.095090 0.204729 15.118 < 2e-16 ***
M8 3.499186 0.202291 17.298 < 2e-16 ***
M9 3.143006 0.188645 16.661 < 2e-16 ***
M10 0.717777 0.193704 3.706 0.000382 ***
M11 -0.418680 0.192952 -2.170 0.032910 *
t 0.001975 0.001887 1.047 0.298308
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3721 on 82 degrees of freedom
Multiple R-squared: 0.9532, Adjusted R-squared: 0.9457
F-statistic: 128.4 on 13 and 82 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,] 0.4099811 0.81996217 0.59001891
[2,] 0.6842690 0.63146200 0.31573100
[3,] 0.9755853 0.04882945 0.02441472
[4,] 0.9567090 0.08658202 0.04329101
[5,] 0.9486381 0.10272387 0.05136193
[6,] 0.9232996 0.15340079 0.07670040
[7,] 0.8928486 0.21430287 0.10715144
[8,] 0.8650598 0.26988048 0.13494024
[9,] 0.8082823 0.38343531 0.19171765
[10,] 0.7965522 0.40689556 0.20344778
[11,] 0.7334368 0.53312638 0.26656319
[12,] 0.7228673 0.55426535 0.27713267
[13,] 0.6534589 0.69308215 0.34654108
[14,] 0.5859186 0.82816277 0.41408138
[15,] 0.6716588 0.65668247 0.32834123
[16,] 0.8740972 0.25180570 0.12590285
[17,] 0.9455207 0.10895861 0.05447930
[18,] 0.9456900 0.10862004 0.05431002
[19,] 0.9292300 0.14154002 0.07077001
[20,] 0.9053940 0.18921207 0.09460603
[21,] 0.8920526 0.21589476 0.10794738
[22,] 0.8776679 0.24466417 0.12233208
[23,] 0.8446873 0.31062534 0.15531267
[24,] 0.8421724 0.31565524 0.15782762
[25,] 0.8204679 0.35906411 0.17953206
[26,] 0.7808999 0.43820020 0.21910010
[27,] 0.8809371 0.23812582 0.11906291
[28,] 0.9215934 0.15681318 0.07840659
[29,] 0.9394643 0.12107146 0.06053573
[30,] 0.9322842 0.13543170 0.06771585
[31,] 0.9161953 0.16760940 0.08380470
[32,] 0.9063711 0.18725779 0.09362890
[33,] 0.9072712 0.18545750 0.09272875
[34,] 0.9474819 0.10503617 0.05251809
[35,] 0.9263841 0.14723174 0.07361587
[36,] 0.9531112 0.09377756 0.04688878
[37,] 0.9395473 0.12090550 0.06045275
[38,] 0.9331531 0.13369370 0.06684685
[39,] 0.9551107 0.08977852 0.04488926
[40,] 0.9403664 0.11926718 0.05963359
[41,] 0.9618104 0.07637930 0.03818965
[42,] 0.9577176 0.08456480 0.04228240
[43,] 0.9464867 0.10702667 0.05351334
[44,] 0.9246233 0.15075338 0.07537669
[45,] 0.9055785 0.18884292 0.09442146
[46,] 0.8786842 0.24263158 0.12131579
[47,] 0.8467962 0.30640761 0.15320380
[48,] 0.8885565 0.22288705 0.11144353
[49,] 0.8665330 0.26693410 0.13346705
[50,] 0.9578053 0.08438949 0.04219475
[51,] 0.9473672 0.10526554 0.05263277
[52,] 0.9193131 0.16137376 0.08068688
[53,] 0.9600460 0.07990803 0.03995402
[54,] 0.9361044 0.12779125 0.06389562
[55,] 0.9071277 0.18574460 0.09287230
[56,] 0.8599191 0.28016174 0.14008087
[57,] 0.7939077 0.41218451 0.20609226
[58,] 0.7056003 0.58879940 0.29439970
[59,] 0.6183980 0.76320407 0.38160204
[60,] 0.6763720 0.64725602 0.32362801
[61,] 0.5889379 0.82212416 0.41106208
[62,] 0.6529760 0.69404796 0.34702398
[63,] 0.9404178 0.11916439 0.05958219
> postscript(file="/var/www/html/rcomp/tmp/1jhmx1290938866.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2jhmx1290938866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3jhmx1290938866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4brmi1290938866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5brmi1290938866.ps",horizontal=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 = 96
Frequency = 1
1 2 3 4 5
0.1904993418 0.0692084342 0.1197389055 0.5430623427 0.2984635522
6 7 8 9 10
-0.1341651999 0.9105744912 0.0103035638 0.8669573230 -0.0485548983
11 12 13 14 15
-0.0779688572 0.1175960925 0.0955067997 -0.0283557240 0.2710224816
16 17 18 19 20
-0.3146613665 -0.1507356051 0.4273251372 -0.8633835179 -0.0199920529
21 22 23 24 25
0.2264272608 -0.1427610497 -0.0779197266 0.1669969643 -0.2646301399
26 27 28 29 30
0.0384309849 -0.1368594675 -0.5070463028 -0.2052170007 0.0766298410
31 32 33 34 35
-0.9289275682 0.6095881865 -0.8311928495 0.0137501238 0.0306606585
36 37 38 39 40
-0.2190576498 -0.1066846083 0.0841492106 0.0587416082 -0.2007150796
41 42 43 44 45
0.1989976552 -0.3639423309 -0.8099271043 0.4419546710 -0.6490752356
46 47 48 49 50
0.1264199741 0.0278267933 -0.0918572733 0.1094469935 0.2994528948
51 52 53 54 55
-0.2118235697 0.3580789147 0.0790260951 -0.4103075962 0.3267838361
56 57 58 59 60
-0.3644245823 -0.4855850442 0.1686273446 0.0766545923 -0.0635465952
61 62 63 64 65
0.0713304531 -0.0812222934 -0.2765130372 0.3205551181 0.0189156747
66 67 68 69 70
-0.5227621812 0.3535711277 -0.2859966086 -0.1829017885 -0.0161654305
71 72 73 74 75
0.0617449585 -0.0657526008 -0.0317656879 -0.2250216256 -0.0085469606
76 77 78 79 80
0.1871352118 -0.0338565555 0.1667141679 0.1203783817 -0.1127477111
81 82 83 84 85
0.6563191330 -0.1133100926 -0.0410891980 0.1580138545 -0.0637031520
86 87 88 89 90
-0.1566418815 0.1842400397 -0.3864088384 -0.2055938157 0.7605081622
91 92 93 94 95
0.8909303537 -0.2786854665 0.3990512010 0.0119940285 0.0000907792
96
-0.0023927921
> postscript(file="/var/www/html/rcomp/tmp/6brmi1290938866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 96
Frequency = 1
lag(myerror, k = 1) myerror
0 0.1904993418 NA
1 0.0692084342 0.1904993418
2 0.1197389055 0.0692084342
3 0.5430623427 0.1197389055
4 0.2984635522 0.5430623427
5 -0.1341651999 0.2984635522
6 0.9105744912 -0.1341651999
7 0.0103035638 0.9105744912
8 0.8669573230 0.0103035638
9 -0.0485548983 0.8669573230
10 -0.0779688572 -0.0485548983
11 0.1175960925 -0.0779688572
12 0.0955067997 0.1175960925
13 -0.0283557240 0.0955067997
14 0.2710224816 -0.0283557240
15 -0.3146613665 0.2710224816
16 -0.1507356051 -0.3146613665
17 0.4273251372 -0.1507356051
18 -0.8633835179 0.4273251372
19 -0.0199920529 -0.8633835179
20 0.2264272608 -0.0199920529
21 -0.1427610497 0.2264272608
22 -0.0779197266 -0.1427610497
23 0.1669969643 -0.0779197266
24 -0.2646301399 0.1669969643
25 0.0384309849 -0.2646301399
26 -0.1368594675 0.0384309849
27 -0.5070463028 -0.1368594675
28 -0.2052170007 -0.5070463028
29 0.0766298410 -0.2052170007
30 -0.9289275682 0.0766298410
31 0.6095881865 -0.9289275682
32 -0.8311928495 0.6095881865
33 0.0137501238 -0.8311928495
34 0.0306606585 0.0137501238
35 -0.2190576498 0.0306606585
36 -0.1066846083 -0.2190576498
37 0.0841492106 -0.1066846083
38 0.0587416082 0.0841492106
39 -0.2007150796 0.0587416082
40 0.1989976552 -0.2007150796
41 -0.3639423309 0.1989976552
42 -0.8099271043 -0.3639423309
43 0.4419546710 -0.8099271043
44 -0.6490752356 0.4419546710
45 0.1264199741 -0.6490752356
46 0.0278267933 0.1264199741
47 -0.0918572733 0.0278267933
48 0.1094469935 -0.0918572733
49 0.2994528948 0.1094469935
50 -0.2118235697 0.2994528948
51 0.3580789147 -0.2118235697
52 0.0790260951 0.3580789147
53 -0.4103075962 0.0790260951
54 0.3267838361 -0.4103075962
55 -0.3644245823 0.3267838361
56 -0.4855850442 -0.3644245823
57 0.1686273446 -0.4855850442
58 0.0766545923 0.1686273446
59 -0.0635465952 0.0766545923
60 0.0713304531 -0.0635465952
61 -0.0812222934 0.0713304531
62 -0.2765130372 -0.0812222934
63 0.3205551181 -0.2765130372
64 0.0189156747 0.3205551181
65 -0.5227621812 0.0189156747
66 0.3535711277 -0.5227621812
67 -0.2859966086 0.3535711277
68 -0.1829017885 -0.2859966086
69 -0.0161654305 -0.1829017885
70 0.0617449585 -0.0161654305
71 -0.0657526008 0.0617449585
72 -0.0317656879 -0.0657526008
73 -0.2250216256 -0.0317656879
74 -0.0085469606 -0.2250216256
75 0.1871352118 -0.0085469606
76 -0.0338565555 0.1871352118
77 0.1667141679 -0.0338565555
78 0.1203783817 0.1667141679
79 -0.1127477111 0.1203783817
80 0.6563191330 -0.1127477111
81 -0.1133100926 0.6563191330
82 -0.0410891980 -0.1133100926
83 0.1580138545 -0.0410891980
84 -0.0637031520 0.1580138545
85 -0.1566418815 -0.0637031520
86 0.1842400397 -0.1566418815
87 -0.3864088384 0.1842400397
88 -0.2055938157 -0.3864088384
89 0.7605081622 -0.2055938157
90 0.8909303537 0.7605081622
91 -0.2786854665 0.8909303537
92 0.3990512010 -0.2786854665
93 0.0119940285 0.3990512010
94 0.0000907792 0.0119940285
95 -0.0023927921 0.0000907792
96 NA -0.0023927921
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0692084342 0.1904993418
[2,] 0.1197389055 0.0692084342
[3,] 0.5430623427 0.1197389055
[4,] 0.2984635522 0.5430623427
[5,] -0.1341651999 0.2984635522
[6,] 0.9105744912 -0.1341651999
[7,] 0.0103035638 0.9105744912
[8,] 0.8669573230 0.0103035638
[9,] -0.0485548983 0.8669573230
[10,] -0.0779688572 -0.0485548983
[11,] 0.1175960925 -0.0779688572
[12,] 0.0955067997 0.1175960925
[13,] -0.0283557240 0.0955067997
[14,] 0.2710224816 -0.0283557240
[15,] -0.3146613665 0.2710224816
[16,] -0.1507356051 -0.3146613665
[17,] 0.4273251372 -0.1507356051
[18,] -0.8633835179 0.4273251372
[19,] -0.0199920529 -0.8633835179
[20,] 0.2264272608 -0.0199920529
[21,] -0.1427610497 0.2264272608
[22,] -0.0779197266 -0.1427610497
[23,] 0.1669969643 -0.0779197266
[24,] -0.2646301399 0.1669969643
[25,] 0.0384309849 -0.2646301399
[26,] -0.1368594675 0.0384309849
[27,] -0.5070463028 -0.1368594675
[28,] -0.2052170007 -0.5070463028
[29,] 0.0766298410 -0.2052170007
[30,] -0.9289275682 0.0766298410
[31,] 0.6095881865 -0.9289275682
[32,] -0.8311928495 0.6095881865
[33,] 0.0137501238 -0.8311928495
[34,] 0.0306606585 0.0137501238
[35,] -0.2190576498 0.0306606585
[36,] -0.1066846083 -0.2190576498
[37,] 0.0841492106 -0.1066846083
[38,] 0.0587416082 0.0841492106
[39,] -0.2007150796 0.0587416082
[40,] 0.1989976552 -0.2007150796
[41,] -0.3639423309 0.1989976552
[42,] -0.8099271043 -0.3639423309
[43,] 0.4419546710 -0.8099271043
[44,] -0.6490752356 0.4419546710
[45,] 0.1264199741 -0.6490752356
[46,] 0.0278267933 0.1264199741
[47,] -0.0918572733 0.0278267933
[48,] 0.1094469935 -0.0918572733
[49,] 0.2994528948 0.1094469935
[50,] -0.2118235697 0.2994528948
[51,] 0.3580789147 -0.2118235697
[52,] 0.0790260951 0.3580789147
[53,] -0.4103075962 0.0790260951
[54,] 0.3267838361 -0.4103075962
[55,] -0.3644245823 0.3267838361
[56,] -0.4855850442 -0.3644245823
[57,] 0.1686273446 -0.4855850442
[58,] 0.0766545923 0.1686273446
[59,] -0.0635465952 0.0766545923
[60,] 0.0713304531 -0.0635465952
[61,] -0.0812222934 0.0713304531
[62,] -0.2765130372 -0.0812222934
[63,] 0.3205551181 -0.2765130372
[64,] 0.0189156747 0.3205551181
[65,] -0.5227621812 0.0189156747
[66,] 0.3535711277 -0.5227621812
[67,] -0.2859966086 0.3535711277
[68,] -0.1829017885 -0.2859966086
[69,] -0.0161654305 -0.1829017885
[70,] 0.0617449585 -0.0161654305
[71,] -0.0657526008 0.0617449585
[72,] -0.0317656879 -0.0657526008
[73,] -0.2250216256 -0.0317656879
[74,] -0.0085469606 -0.2250216256
[75,] 0.1871352118 -0.0085469606
[76,] -0.0338565555 0.1871352118
[77,] 0.1667141679 -0.0338565555
[78,] 0.1203783817 0.1667141679
[79,] -0.1127477111 0.1203783817
[80,] 0.6563191330 -0.1127477111
[81,] -0.1133100926 0.6563191330
[82,] -0.0410891980 -0.1133100926
[83,] 0.1580138545 -0.0410891980
[84,] -0.0637031520 0.1580138545
[85,] -0.1566418815 -0.0637031520
[86,] 0.1842400397 -0.1566418815
[87,] -0.3864088384 0.1842400397
[88,] -0.2055938157 -0.3864088384
[89,] 0.7605081622 -0.2055938157
[90,] 0.8909303537 0.7605081622
[91,] -0.2786854665 0.8909303537
[92,] 0.3990512010 -0.2786854665
[93,] 0.0119940285 0.3990512010
[94,] 0.0000907792 0.0119940285
[95,] -0.0023927921 0.0000907792
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0692084342 0.1904993418
2 0.1197389055 0.0692084342
3 0.5430623427 0.1197389055
4 0.2984635522 0.5430623427
5 -0.1341651999 0.2984635522
6 0.9105744912 -0.1341651999
7 0.0103035638 0.9105744912
8 0.8669573230 0.0103035638
9 -0.0485548983 0.8669573230
10 -0.0779688572 -0.0485548983
11 0.1175960925 -0.0779688572
12 0.0955067997 0.1175960925
13 -0.0283557240 0.0955067997
14 0.2710224816 -0.0283557240
15 -0.3146613665 0.2710224816
16 -0.1507356051 -0.3146613665
17 0.4273251372 -0.1507356051
18 -0.8633835179 0.4273251372
19 -0.0199920529 -0.8633835179
20 0.2264272608 -0.0199920529
21 -0.1427610497 0.2264272608
22 -0.0779197266 -0.1427610497
23 0.1669969643 -0.0779197266
24 -0.2646301399 0.1669969643
25 0.0384309849 -0.2646301399
26 -0.1368594675 0.0384309849
27 -0.5070463028 -0.1368594675
28 -0.2052170007 -0.5070463028
29 0.0766298410 -0.2052170007
30 -0.9289275682 0.0766298410
31 0.6095881865 -0.9289275682
32 -0.8311928495 0.6095881865
33 0.0137501238 -0.8311928495
34 0.0306606585 0.0137501238
35 -0.2190576498 0.0306606585
36 -0.1066846083 -0.2190576498
37 0.0841492106 -0.1066846083
38 0.0587416082 0.0841492106
39 -0.2007150796 0.0587416082
40 0.1989976552 -0.2007150796
41 -0.3639423309 0.1989976552
42 -0.8099271043 -0.3639423309
43 0.4419546710 -0.8099271043
44 -0.6490752356 0.4419546710
45 0.1264199741 -0.6490752356
46 0.0278267933 0.1264199741
47 -0.0918572733 0.0278267933
48 0.1094469935 -0.0918572733
49 0.2994528948 0.1094469935
50 -0.2118235697 0.2994528948
51 0.3580789147 -0.2118235697
52 0.0790260951 0.3580789147
53 -0.4103075962 0.0790260951
54 0.3267838361 -0.4103075962
55 -0.3644245823 0.3267838361
56 -0.4855850442 -0.3644245823
57 0.1686273446 -0.4855850442
58 0.0766545923 0.1686273446
59 -0.0635465952 0.0766545923
60 0.0713304531 -0.0635465952
61 -0.0812222934 0.0713304531
62 -0.2765130372 -0.0812222934
63 0.3205551181 -0.2765130372
64 0.0189156747 0.3205551181
65 -0.5227621812 0.0189156747
66 0.3535711277 -0.5227621812
67 -0.2859966086 0.3535711277
68 -0.1829017885 -0.2859966086
69 -0.0161654305 -0.1829017885
70 0.0617449585 -0.0161654305
71 -0.0657526008 0.0617449585
72 -0.0317656879 -0.0657526008
73 -0.2250216256 -0.0317656879
74 -0.0085469606 -0.2250216256
75 0.1871352118 -0.0085469606
76 -0.0338565555 0.1871352118
77 0.1667141679 -0.0338565555
78 0.1203783817 0.1667141679
79 -0.1127477111 0.1203783817
80 0.6563191330 -0.1127477111
81 -0.1133100926 0.6563191330
82 -0.0410891980 -0.1133100926
83 0.1580138545 -0.0410891980
84 -0.0637031520 0.1580138545
85 -0.1566418815 -0.0637031520
86 0.1842400397 -0.1566418815
87 -0.3864088384 0.1842400397
88 -0.2055938157 -0.3864088384
89 0.7605081622 -0.2055938157
90 0.8909303537 0.7605081622
91 -0.2786854665 0.8909303537
92 0.3990512010 -0.2786854665
93 0.0119940285 0.3990512010
94 0.0000907792 0.0119940285
95 -0.0023927921 0.0000907792
> 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/html/rcomp/tmp/740ll1290938866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8x9k61290938866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9x9k61290938866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10x9k61290938866.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/rcomp/tmp/11t1ix1290938866.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/html/rcomp/tmp/123az01290938866.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/html/rcomp/tmp/13atwc1290938866.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/html/rcomp/tmp/1432dw1290938866.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/html/rcomp/tmp/1573uk1290938866.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/html/rcomp/tmp/163dst1290938866.tab")
+ }
>
> try(system("convert tmp/1jhmx1290938866.ps tmp/1jhmx1290938866.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jhmx1290938866.ps tmp/2jhmx1290938866.png",intern=TRUE))
character(0)
> try(system("convert tmp/3jhmx1290938866.ps tmp/3jhmx1290938866.png",intern=TRUE))
character(0)
> try(system("convert tmp/4brmi1290938866.ps tmp/4brmi1290938866.png",intern=TRUE))
character(0)
> try(system("convert tmp/5brmi1290938866.ps tmp/5brmi1290938866.png",intern=TRUE))
character(0)
> try(system("convert tmp/6brmi1290938866.ps tmp/6brmi1290938866.png",intern=TRUE))
character(0)
> try(system("convert tmp/740ll1290938866.ps tmp/740ll1290938866.png",intern=TRUE))
character(0)
> try(system("convert tmp/8x9k61290938866.ps tmp/8x9k61290938866.png",intern=TRUE))
character(0)
> try(system("convert tmp/9x9k61290938866.ps tmp/9x9k61290938866.png",intern=TRUE))
character(0)
> try(system("convert tmp/10x9k61290938866.ps tmp/10x9k61290938866.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.948 1.631 6.555