R version 2.12.1 (2010-12-16)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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(2.04
+ ,2.01
+ ,1070
+ ,5
+ ,2.56
+ ,3.40
+ ,1254
+ ,6
+ ,3.75
+ ,3.68
+ ,1466
+ ,6
+ ,1.10
+ ,1.54
+ ,706
+ ,4
+ ,3.00
+ ,3.32
+ ,1160
+ ,5
+ ,0.05
+ ,0.33
+ ,756
+ ,3
+ ,1.38
+ ,0.36
+ ,1058
+ ,2
+ ,1.50
+ ,1.97
+ ,1008
+ ,7
+ ,1.38
+ ,2.03
+ ,1104
+ ,4
+ ,4.01
+ ,2.05
+ ,1200
+ ,7
+ ,1.50
+ ,2.13
+ ,896
+ ,7
+ ,1.29
+ ,1.34
+ ,848
+ ,3
+ ,1.90
+ ,1.51
+ ,958
+ ,5
+ ,3.11
+ ,3.12
+ ,1246
+ ,6
+ ,1.92
+ ,2.14
+ ,1106
+ ,4
+ ,0.81
+ ,2.60
+ ,790
+ ,5
+ ,1.01
+ ,1.90
+ ,954
+ ,4
+ ,3.66
+ ,3.06
+ ,1500
+ ,6
+ ,2.00
+ ,1.60
+ ,1046
+ ,5
+ ,2.05
+ ,1.96
+ ,1054
+ ,4
+ ,2.60
+ ,1.96
+ ,1198
+ ,6
+ ,2.55
+ ,1.56
+ ,940
+ ,3
+ ,0.38
+ ,1.60
+ ,456
+ ,6
+ ,2.48
+ ,1.92
+ ,1150
+ ,7
+ ,2.74
+ ,3.09
+ ,636
+ ,6
+ ,1.77
+ ,0.78
+ ,744
+ ,5
+ ,1.61
+ ,2.12
+ ,644
+ ,5
+ ,0.99
+ ,1.85
+ ,842
+ ,3
+ ,1.62
+ ,1.78
+ ,852
+ ,5
+ ,2.03
+ ,1.03
+ ,1170
+ ,3
+ ,3.50
+ ,3.44
+ ,1034
+ ,10
+ ,3.18
+ ,2.42
+ ,1202
+ ,5
+ ,2.39
+ ,1.74
+ ,1018
+ ,5
+ ,1.48
+ ,1.89
+ ,1180
+ ,5
+ ,1.54
+ ,1.43
+ ,952
+ ,3
+ ,1.57
+ ,1.64
+ ,1038
+ ,4
+ ,2.46
+ ,2.69
+ ,1090
+ ,6
+ ,2.42
+ ,1.79
+ ,694
+ ,5
+ ,2.11
+ ,2.72
+ ,1096
+ ,6
+ ,2.04
+ ,2.15
+ ,1114
+ ,5
+ ,1.68
+ ,2.22
+ ,1256
+ ,6
+ ,1.64
+ ,1.55
+ ,1208
+ ,5
+ ,2.41
+ ,2.34
+ ,820
+ ,6
+ ,2.10
+ ,2.92
+ ,1222
+ ,4
+ ,1.40
+ ,2.10
+ ,1120
+ ,5
+ ,2.03
+ ,1.64
+ ,886
+ ,4
+ ,1.99
+ ,2.83
+ ,1126
+ ,7
+ ,2.24
+ ,1.76
+ ,1158
+ ,4
+ ,0.45
+ ,1.81
+ ,676
+ ,6
+ ,2.31
+ ,2.68
+ ,1214
+ ,7
+ ,2.41
+ ,2.55
+ ,1136
+ ,6
+ ,2.56
+ ,2.70
+ ,1264
+ ,6
+ ,2.50
+ ,1.66
+ ,1116
+ ,3
+ ,2.92
+ ,2.23
+ ,1292
+ ,4
+ ,2.35
+ ,2.01
+ ,604
+ ,5
+ ,2.82
+ ,1.24
+ ,854
+ ,6
+ ,1.80
+ ,1.95
+ ,814
+ ,6
+ ,1.29
+ ,1.73
+ ,778
+ ,3
+ ,1.68
+ ,1.08
+ ,800
+ ,2
+ ,3.44
+ ,3.46
+ ,1424
+ ,7
+ ,1.90
+ ,3.01
+ ,950
+ ,6
+ ,2.06
+ ,0.54
+ ,1056
+ ,3
+ ,3.30
+ ,3.20
+ ,956
+ ,8
+ ,1.80
+ ,1.50
+ ,1352
+ ,5
+ ,2.00
+ ,1.71
+ ,852
+ ,5
+ ,1.68
+ ,1.99
+ ,1168
+ ,5
+ ,1.94
+ ,2.76
+ ,970
+ ,6
+ ,0.97
+ ,1.56
+ ,776
+ ,4
+ ,1.12
+ ,1.78
+ ,854
+ ,6
+ ,1.31
+ ,1.32
+ ,1232
+ ,5
+ ,1.68
+ ,0.87
+ ,1140
+ ,6
+ ,3.09
+ ,1.75
+ ,1084
+ ,4
+ ,1.87
+ ,1.41
+ ,954
+ ,2
+ ,2.00
+ ,2.77
+ ,1000
+ ,4
+ ,2.39
+ ,1.78
+ ,1084
+ ,4
+ ,1.50
+ ,1.34
+ ,1058
+ ,4
+ ,1.82
+ ,1.52
+ ,816
+ ,5
+ ,1.80
+ ,2.97
+ ,1146
+ ,7
+ ,2.01
+ ,1.75
+ ,1000
+ ,6
+ ,1.88
+ ,1.64
+ ,856
+ ,4
+ ,1.64
+ ,1.80
+ ,798
+ ,4
+ ,2.42
+ ,3.37
+ ,1324
+ ,6
+ ,0.22
+ ,1.15
+ ,704
+ ,6
+ ,2.31
+ ,1.72
+ ,1222
+ ,5
+ ,0.95
+ ,2.27
+ ,948
+ ,6
+ ,1.99
+ ,2.85
+ ,1182
+ ,8
+ ,1.86
+ ,2.21
+ ,1000
+ ,6
+ ,1.79
+ ,1.94
+ ,910
+ ,6
+ ,3.02
+ ,4.25
+ ,1374
+ ,9
+ ,1.85
+ ,1.83
+ ,1014
+ ,6
+ ,1.98
+ ,2.75
+ ,1420
+ ,7
+ ,2.15
+ ,1.71
+ ,400
+ ,6
+ ,1.46
+ ,2.20
+ ,998
+ ,7
+ ,2.29
+ ,2.13
+ ,776
+ ,6
+ ,2.39
+ ,2.38
+ ,1134
+ ,7
+ ,1.80
+ ,1.64
+ ,772
+ ,4
+ ,2.64
+ ,1.87
+ ,1304
+ ,6
+ ,2.08
+ ,2.53
+ ,1212
+ ,4
+ ,0.70
+ ,1.78
+ ,818
+ ,6
+ ,0.89
+ ,1.20
+ ,864
+ ,2)
+ ,dim=c(4
+ ,100)
+ ,dimnames=list(c('CollegeGPA'
+ ,'HighSchoolGPA'
+ ,'SATtotal'
+ ,'Qualityoflettersofrecommendation')
+ ,1:100))
> y <- array(NA,dim=c(4,100),dimnames=list(c('CollegeGPA','HighSchoolGPA','SATtotal','Qualityoflettersofrecommendation'),1:100))
> 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 = '4'
> #'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
Qualityoflettersofrecommendation CollegeGPA HighSchoolGPA SATtotal
1 5 2.04 2.01 1070
2 6 2.56 3.40 1254
3 6 3.75 3.68 1466
4 4 1.10 1.54 706
5 5 3.00 3.32 1160
6 3 0.05 0.33 756
7 2 1.38 0.36 1058
8 7 1.50 1.97 1008
9 4 1.38 2.03 1104
10 7 4.01 2.05 1200
11 7 1.50 2.13 896
12 3 1.29 1.34 848
13 5 1.90 1.51 958
14 6 3.11 3.12 1246
15 4 1.92 2.14 1106
16 5 0.81 2.60 790
17 4 1.01 1.90 954
18 6 3.66 3.06 1500
19 5 2.00 1.60 1046
20 4 2.05 1.96 1054
21 6 2.60 1.96 1198
22 3 2.55 1.56 940
23 6 0.38 1.60 456
24 7 2.48 1.92 1150
25 6 2.74 3.09 636
26 5 1.77 0.78 744
27 5 1.61 2.12 644
28 3 0.99 1.85 842
29 5 1.62 1.78 852
30 3 2.03 1.03 1170
31 10 3.50 3.44 1034
32 5 3.18 2.42 1202
33 5 2.39 1.74 1018
34 5 1.48 1.89 1180
35 3 1.54 1.43 952
36 4 1.57 1.64 1038
37 6 2.46 2.69 1090
38 5 2.42 1.79 694
39 6 2.11 2.72 1096
40 5 2.04 2.15 1114
41 6 1.68 2.22 1256
42 5 1.64 1.55 1208
43 6 2.41 2.34 820
44 4 2.10 2.92 1222
45 5 1.40 2.10 1120
46 4 2.03 1.64 886
47 7 1.99 2.83 1126
48 4 2.24 1.76 1158
49 6 0.45 1.81 676
50 7 2.31 2.68 1214
51 6 2.41 2.55 1136
52 6 2.56 2.70 1264
53 3 2.50 1.66 1116
54 4 2.92 2.23 1292
55 5 2.35 2.01 604
56 6 2.82 1.24 854
57 6 1.80 1.95 814
58 3 1.29 1.73 778
59 2 1.68 1.08 800
60 7 3.44 3.46 1424
61 6 1.90 3.01 950
62 3 2.06 0.54 1056
63 8 3.30 3.20 956
64 5 1.80 1.50 1352
65 5 2.00 1.71 852
66 5 1.68 1.99 1168
67 6 1.94 2.76 970
68 4 0.97 1.56 776
69 6 1.12 1.78 854
70 5 1.31 1.32 1232
71 6 1.68 0.87 1140
72 4 3.09 1.75 1084
73 2 1.87 1.41 954
74 4 2.00 2.77 1000
75 4 2.39 1.78 1084
76 4 1.50 1.34 1058
77 5 1.82 1.52 816
78 7 1.80 2.97 1146
79 6 2.01 1.75 1000
80 4 1.88 1.64 856
81 4 1.64 1.80 798
82 6 2.42 3.37 1324
83 6 0.22 1.15 704
84 5 2.31 1.72 1222
85 6 0.95 2.27 948
86 8 1.99 2.85 1182
87 6 1.86 2.21 1000
88 6 1.79 1.94 910
89 9 3.02 4.25 1374
90 6 1.85 1.83 1014
91 7 1.98 2.75 1420
92 6 2.15 1.71 400
93 7 1.46 2.20 998
94 6 2.29 2.13 776
95 7 2.39 2.38 1134
96 4 1.80 1.64 772
97 6 2.64 1.87 1304
98 4 2.08 2.53 1212
99 6 0.70 1.78 818
100 2 0.89 1.20 864
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) CollegeGPA HighSchoolGPA SATtotal
2.8792692 0.0907256 1.3191699 -0.0005631
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.3718 -0.8369 -0.0182 0.9222 2.8475
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.8792692 0.5759568 4.999 2.59e-06 ***
CollegeGPA 0.0907256 0.2039012 0.445 0.657
HighSchoolGPA 1.3191699 0.1999748 6.597 2.29e-09 ***
SATtotal -0.0005631 0.0006536 -0.862 0.391
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.179 on 96 degrees of freedom
Multiple R-squared: 0.3974, Adjusted R-squared: 0.3785
F-statistic: 21.1 on 3 and 96 DF, p-value: 1.397e-10
> 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,] 0.12819931 0.25639863 0.8718007
[2,] 0.57840388 0.84319224 0.4215961
[3,] 0.60271151 0.79457698 0.3972885
[4,] 0.73428121 0.53143758 0.2657188
[5,] 0.80727677 0.38544647 0.1927232
[6,] 0.83357667 0.33284665 0.1664233
[7,] 0.76851519 0.46296961 0.2314848
[8,] 0.69292667 0.61414665 0.3070733
[9,] 0.64794593 0.70410814 0.3520541
[10,] 0.57695737 0.84608526 0.4230426
[11,] 0.50231808 0.99536385 0.4976819
[12,] 0.42632750 0.85265500 0.5736725
[13,] 0.35697944 0.71395889 0.6430206
[14,] 0.32932183 0.65864365 0.6706782
[15,] 0.32069270 0.64138541 0.6793073
[16,] 0.46945628 0.93891256 0.5305437
[17,] 0.47367377 0.94734754 0.5263262
[18,] 0.62665907 0.74668185 0.3733409
[19,] 0.60264422 0.79471156 0.3973558
[20,] 0.58013865 0.83972270 0.4198613
[21,] 0.52127363 0.95745274 0.4787264
[22,] 0.59197349 0.81605302 0.4080265
[23,] 0.52714571 0.94570858 0.4728543
[24,] 0.49944130 0.99888260 0.5005587
[25,] 0.77813252 0.44373497 0.2218675
[26,] 0.74684076 0.50631849 0.2531592
[27,] 0.69430727 0.61138547 0.3056927
[28,] 0.65843492 0.68313015 0.3415651
[29,] 0.67121188 0.65757623 0.3287881
[30,] 0.62333856 0.75332288 0.3766614
[31,] 0.56488121 0.87023759 0.4351188
[32,] 0.51703834 0.96592332 0.4829617
[33,] 0.46176026 0.92352052 0.5382397
[34,] 0.40432466 0.80864931 0.5956753
[35,] 0.40189408 0.80378816 0.5981059
[36,] 0.36921389 0.73842778 0.6307861
[37,] 0.31611425 0.63222849 0.6838858
[38,] 0.43166310 0.86332619 0.5683369
[39,] 0.38333969 0.76667938 0.6166603
[40,] 0.35224614 0.70449228 0.6477539
[41,] 0.34358317 0.68716635 0.6564168
[42,] 0.31084937 0.62169874 0.6891506
[43,] 0.30849059 0.61698119 0.6915094
[44,] 0.30698475 0.61396950 0.6930152
[45,] 0.25915082 0.51830163 0.7408492
[46,] 0.21520516 0.43041033 0.7847948
[47,] 0.26152542 0.52305083 0.7384746
[48,] 0.28114723 0.56229446 0.7188528
[49,] 0.24146110 0.48292220 0.7585389
[50,] 0.29148124 0.58296249 0.7085188
[51,] 0.26699661 0.53399321 0.7330034
[52,] 0.34396926 0.68793852 0.6560307
[53,] 0.45695769 0.91391537 0.5430423
[54,] 0.39969420 0.79938841 0.6003058
[55,] 0.35866959 0.71733918 0.6413304
[56,] 0.30504032 0.61008064 0.6949597
[57,] 0.30756116 0.61512232 0.6924388
[58,] 0.27587318 0.55174635 0.7241268
[59,] 0.22733372 0.45466744 0.7726663
[60,] 0.18856196 0.37712392 0.8114380
[61,] 0.15121504 0.30243009 0.8487850
[62,] 0.13291258 0.26582515 0.8670874
[63,] 0.12292898 0.24585797 0.8770710
[64,] 0.10576317 0.21152633 0.8942368
[65,] 0.22308310 0.44616619 0.7769169
[66,] 0.18983972 0.37967944 0.8101603
[67,] 0.34446804 0.68893608 0.6555320
[68,] 0.58024397 0.83951207 0.4197560
[69,] 0.55554546 0.88890909 0.4444545
[70,] 0.49238621 0.98477241 0.5076138
[71,] 0.42284445 0.84568891 0.5771555
[72,] 0.36304328 0.72608656 0.6369567
[73,] 0.34134605 0.68269209 0.6586540
[74,] 0.31575359 0.63150718 0.6842464
[75,] 0.33639935 0.67279869 0.6636007
[76,] 0.38555180 0.77110359 0.6144482
[77,] 0.49971286 0.99942573 0.5002871
[78,] 0.41399913 0.82799826 0.5860009
[79,] 0.33407272 0.66814544 0.6659273
[80,] 0.33428234 0.66856469 0.6657177
[81,] 0.25450031 0.50900061 0.7454997
[82,] 0.19604042 0.39208084 0.8039596
[83,] 0.16508624 0.33017247 0.8349138
[84,] 0.13729305 0.27458610 0.8627069
[85,] 0.08737291 0.17474583 0.9126271
[86,] 0.05012344 0.10024688 0.9498766
[87,] 0.04888222 0.09776443 0.9511178
> postscript(file="/var/www/rcomp/tmp/1kutb1322142836.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2qvq41322142836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3wbo41322142836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4g51q1322142836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/533uh1322142836.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 = 100
Frequency = 1
1 2 3 4 5
-0.1133730469 -0.8905876530 -1.2485433340 -0.6130464040 -1.8779039422
6 7 8 9 10
0.1065656170 -0.8836209579 1.9534739219 -1.0607324131 1.7283325404
11 12 13 14 15
1.6793405015 -1.2864913169 0.4958472281 -0.5756239256 -1.2537067593
16 17 18 19 20
-0.9377563355 -0.9401355639 -0.4033476053 0.4176014253 -1.0573312737
21 22 23 24 25
0.9738547949 -1.6392185760 1.2323530000 2.0104802759 -0.8459661324
26 27 28 29 30
1.3501339854 -0.4593466674 -1.9354288015 0.1053868509 -0.7633701843
31 32 33 34 35
2.8474833320 -0.6833318441 0.1817680854 0.1576737538 -1.3693365021
36 37 38 39 40
-0.6006580761 -0.0372514891 -0.0693538087 -0.0416940626 -0.2732808029
41 42 43 44 45
0.7469975239 0.6074419641 0.2769595496 -2.2336712542 -0.1458793526
46 47 48 49 50
-0.7279817713 0.8409770734 -0.7521736620 1.0728566558 1.0593723959
51 52 53 54 55
0.1778707799 0.0384621620 -1.6674951834 -1.3584226740 -0.4038986138
56 57 58 59 60
1.7099939907 0.8433998848 -1.8403839633 -2.0059185428 0.0461491414
61 62 63 64 65
-0.4874122900 -0.1838911493 1.1383080919 0.7399695246 0.1632529983
66 67 68 69 70
0.0008545429 -0.1499870220 -0.5882190661 1.1518758531 0.9543046870
71 72 73 74 75
2.4625582227 -0.8577675239 -2.3717663828 -2.1517295152 -0.8338346727
76 77 78 79 80
-0.1872944950 0.4099545937 0.6847929937 1.1929164833 -0.7312656692
81 82 83 84 85
-0.9532179977 -0.7988945658 1.9801422195 0.3302801909 0.5738365783
86 87 88 89 90
1.8461267978 0.5997071945 0.9115556196 1.0139551492 1.1097822772
91 92 93 94 95
1.1129668067 0.8951268140 1.6480629656 0.5400962722 1.4028179855
96 97 98 99 100
-0.7713073005 1.1486387510 -1.7230114115 1.1697093288 -2.0565078181
> postscript(file="/var/www/rcomp/tmp/6hv5r1322142836.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 = 100
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.1133730469 NA
1 -0.8905876530 -0.1133730469
2 -1.2485433340 -0.8905876530
3 -0.6130464040 -1.2485433340
4 -1.8779039422 -0.6130464040
5 0.1065656170 -1.8779039422
6 -0.8836209579 0.1065656170
7 1.9534739219 -0.8836209579
8 -1.0607324131 1.9534739219
9 1.7283325404 -1.0607324131
10 1.6793405015 1.7283325404
11 -1.2864913169 1.6793405015
12 0.4958472281 -1.2864913169
13 -0.5756239256 0.4958472281
14 -1.2537067593 -0.5756239256
15 -0.9377563355 -1.2537067593
16 -0.9401355639 -0.9377563355
17 -0.4033476053 -0.9401355639
18 0.4176014253 -0.4033476053
19 -1.0573312737 0.4176014253
20 0.9738547949 -1.0573312737
21 -1.6392185760 0.9738547949
22 1.2323530000 -1.6392185760
23 2.0104802759 1.2323530000
24 -0.8459661324 2.0104802759
25 1.3501339854 -0.8459661324
26 -0.4593466674 1.3501339854
27 -1.9354288015 -0.4593466674
28 0.1053868509 -1.9354288015
29 -0.7633701843 0.1053868509
30 2.8474833320 -0.7633701843
31 -0.6833318441 2.8474833320
32 0.1817680854 -0.6833318441
33 0.1576737538 0.1817680854
34 -1.3693365021 0.1576737538
35 -0.6006580761 -1.3693365021
36 -0.0372514891 -0.6006580761
37 -0.0693538087 -0.0372514891
38 -0.0416940626 -0.0693538087
39 -0.2732808029 -0.0416940626
40 0.7469975239 -0.2732808029
41 0.6074419641 0.7469975239
42 0.2769595496 0.6074419641
43 -2.2336712542 0.2769595496
44 -0.1458793526 -2.2336712542
45 -0.7279817713 -0.1458793526
46 0.8409770734 -0.7279817713
47 -0.7521736620 0.8409770734
48 1.0728566558 -0.7521736620
49 1.0593723959 1.0728566558
50 0.1778707799 1.0593723959
51 0.0384621620 0.1778707799
52 -1.6674951834 0.0384621620
53 -1.3584226740 -1.6674951834
54 -0.4038986138 -1.3584226740
55 1.7099939907 -0.4038986138
56 0.8433998848 1.7099939907
57 -1.8403839633 0.8433998848
58 -2.0059185428 -1.8403839633
59 0.0461491414 -2.0059185428
60 -0.4874122900 0.0461491414
61 -0.1838911493 -0.4874122900
62 1.1383080919 -0.1838911493
63 0.7399695246 1.1383080919
64 0.1632529983 0.7399695246
65 0.0008545429 0.1632529983
66 -0.1499870220 0.0008545429
67 -0.5882190661 -0.1499870220
68 1.1518758531 -0.5882190661
69 0.9543046870 1.1518758531
70 2.4625582227 0.9543046870
71 -0.8577675239 2.4625582227
72 -2.3717663828 -0.8577675239
73 -2.1517295152 -2.3717663828
74 -0.8338346727 -2.1517295152
75 -0.1872944950 -0.8338346727
76 0.4099545937 -0.1872944950
77 0.6847929937 0.4099545937
78 1.1929164833 0.6847929937
79 -0.7312656692 1.1929164833
80 -0.9532179977 -0.7312656692
81 -0.7988945658 -0.9532179977
82 1.9801422195 -0.7988945658
83 0.3302801909 1.9801422195
84 0.5738365783 0.3302801909
85 1.8461267978 0.5738365783
86 0.5997071945 1.8461267978
87 0.9115556196 0.5997071945
88 1.0139551492 0.9115556196
89 1.1097822772 1.0139551492
90 1.1129668067 1.1097822772
91 0.8951268140 1.1129668067
92 1.6480629656 0.8951268140
93 0.5400962722 1.6480629656
94 1.4028179855 0.5400962722
95 -0.7713073005 1.4028179855
96 1.1486387510 -0.7713073005
97 -1.7230114115 1.1486387510
98 1.1697093288 -1.7230114115
99 -2.0565078181 1.1697093288
100 NA -2.0565078181
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.8905876530 -0.1133730469
[2,] -1.2485433340 -0.8905876530
[3,] -0.6130464040 -1.2485433340
[4,] -1.8779039422 -0.6130464040
[5,] 0.1065656170 -1.8779039422
[6,] -0.8836209579 0.1065656170
[7,] 1.9534739219 -0.8836209579
[8,] -1.0607324131 1.9534739219
[9,] 1.7283325404 -1.0607324131
[10,] 1.6793405015 1.7283325404
[11,] -1.2864913169 1.6793405015
[12,] 0.4958472281 -1.2864913169
[13,] -0.5756239256 0.4958472281
[14,] -1.2537067593 -0.5756239256
[15,] -0.9377563355 -1.2537067593
[16,] -0.9401355639 -0.9377563355
[17,] -0.4033476053 -0.9401355639
[18,] 0.4176014253 -0.4033476053
[19,] -1.0573312737 0.4176014253
[20,] 0.9738547949 -1.0573312737
[21,] -1.6392185760 0.9738547949
[22,] 1.2323530000 -1.6392185760
[23,] 2.0104802759 1.2323530000
[24,] -0.8459661324 2.0104802759
[25,] 1.3501339854 -0.8459661324
[26,] -0.4593466674 1.3501339854
[27,] -1.9354288015 -0.4593466674
[28,] 0.1053868509 -1.9354288015
[29,] -0.7633701843 0.1053868509
[30,] 2.8474833320 -0.7633701843
[31,] -0.6833318441 2.8474833320
[32,] 0.1817680854 -0.6833318441
[33,] 0.1576737538 0.1817680854
[34,] -1.3693365021 0.1576737538
[35,] -0.6006580761 -1.3693365021
[36,] -0.0372514891 -0.6006580761
[37,] -0.0693538087 -0.0372514891
[38,] -0.0416940626 -0.0693538087
[39,] -0.2732808029 -0.0416940626
[40,] 0.7469975239 -0.2732808029
[41,] 0.6074419641 0.7469975239
[42,] 0.2769595496 0.6074419641
[43,] -2.2336712542 0.2769595496
[44,] -0.1458793526 -2.2336712542
[45,] -0.7279817713 -0.1458793526
[46,] 0.8409770734 -0.7279817713
[47,] -0.7521736620 0.8409770734
[48,] 1.0728566558 -0.7521736620
[49,] 1.0593723959 1.0728566558
[50,] 0.1778707799 1.0593723959
[51,] 0.0384621620 0.1778707799
[52,] -1.6674951834 0.0384621620
[53,] -1.3584226740 -1.6674951834
[54,] -0.4038986138 -1.3584226740
[55,] 1.7099939907 -0.4038986138
[56,] 0.8433998848 1.7099939907
[57,] -1.8403839633 0.8433998848
[58,] -2.0059185428 -1.8403839633
[59,] 0.0461491414 -2.0059185428
[60,] -0.4874122900 0.0461491414
[61,] -0.1838911493 -0.4874122900
[62,] 1.1383080919 -0.1838911493
[63,] 0.7399695246 1.1383080919
[64,] 0.1632529983 0.7399695246
[65,] 0.0008545429 0.1632529983
[66,] -0.1499870220 0.0008545429
[67,] -0.5882190661 -0.1499870220
[68,] 1.1518758531 -0.5882190661
[69,] 0.9543046870 1.1518758531
[70,] 2.4625582227 0.9543046870
[71,] -0.8577675239 2.4625582227
[72,] -2.3717663828 -0.8577675239
[73,] -2.1517295152 -2.3717663828
[74,] -0.8338346727 -2.1517295152
[75,] -0.1872944950 -0.8338346727
[76,] 0.4099545937 -0.1872944950
[77,] 0.6847929937 0.4099545937
[78,] 1.1929164833 0.6847929937
[79,] -0.7312656692 1.1929164833
[80,] -0.9532179977 -0.7312656692
[81,] -0.7988945658 -0.9532179977
[82,] 1.9801422195 -0.7988945658
[83,] 0.3302801909 1.9801422195
[84,] 0.5738365783 0.3302801909
[85,] 1.8461267978 0.5738365783
[86,] 0.5997071945 1.8461267978
[87,] 0.9115556196 0.5997071945
[88,] 1.0139551492 0.9115556196
[89,] 1.1097822772 1.0139551492
[90,] 1.1129668067 1.1097822772
[91,] 0.8951268140 1.1129668067
[92,] 1.6480629656 0.8951268140
[93,] 0.5400962722 1.6480629656
[94,] 1.4028179855 0.5400962722
[95,] -0.7713073005 1.4028179855
[96,] 1.1486387510 -0.7713073005
[97,] -1.7230114115 1.1486387510
[98,] 1.1697093288 -1.7230114115
[99,] -2.0565078181 1.1697093288
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.8905876530 -0.1133730469
2 -1.2485433340 -0.8905876530
3 -0.6130464040 -1.2485433340
4 -1.8779039422 -0.6130464040
5 0.1065656170 -1.8779039422
6 -0.8836209579 0.1065656170
7 1.9534739219 -0.8836209579
8 -1.0607324131 1.9534739219
9 1.7283325404 -1.0607324131
10 1.6793405015 1.7283325404
11 -1.2864913169 1.6793405015
12 0.4958472281 -1.2864913169
13 -0.5756239256 0.4958472281
14 -1.2537067593 -0.5756239256
15 -0.9377563355 -1.2537067593
16 -0.9401355639 -0.9377563355
17 -0.4033476053 -0.9401355639
18 0.4176014253 -0.4033476053
19 -1.0573312737 0.4176014253
20 0.9738547949 -1.0573312737
21 -1.6392185760 0.9738547949
22 1.2323530000 -1.6392185760
23 2.0104802759 1.2323530000
24 -0.8459661324 2.0104802759
25 1.3501339854 -0.8459661324
26 -0.4593466674 1.3501339854
27 -1.9354288015 -0.4593466674
28 0.1053868509 -1.9354288015
29 -0.7633701843 0.1053868509
30 2.8474833320 -0.7633701843
31 -0.6833318441 2.8474833320
32 0.1817680854 -0.6833318441
33 0.1576737538 0.1817680854
34 -1.3693365021 0.1576737538
35 -0.6006580761 -1.3693365021
36 -0.0372514891 -0.6006580761
37 -0.0693538087 -0.0372514891
38 -0.0416940626 -0.0693538087
39 -0.2732808029 -0.0416940626
40 0.7469975239 -0.2732808029
41 0.6074419641 0.7469975239
42 0.2769595496 0.6074419641
43 -2.2336712542 0.2769595496
44 -0.1458793526 -2.2336712542
45 -0.7279817713 -0.1458793526
46 0.8409770734 -0.7279817713
47 -0.7521736620 0.8409770734
48 1.0728566558 -0.7521736620
49 1.0593723959 1.0728566558
50 0.1778707799 1.0593723959
51 0.0384621620 0.1778707799
52 -1.6674951834 0.0384621620
53 -1.3584226740 -1.6674951834
54 -0.4038986138 -1.3584226740
55 1.7099939907 -0.4038986138
56 0.8433998848 1.7099939907
57 -1.8403839633 0.8433998848
58 -2.0059185428 -1.8403839633
59 0.0461491414 -2.0059185428
60 -0.4874122900 0.0461491414
61 -0.1838911493 -0.4874122900
62 1.1383080919 -0.1838911493
63 0.7399695246 1.1383080919
64 0.1632529983 0.7399695246
65 0.0008545429 0.1632529983
66 -0.1499870220 0.0008545429
67 -0.5882190661 -0.1499870220
68 1.1518758531 -0.5882190661
69 0.9543046870 1.1518758531
70 2.4625582227 0.9543046870
71 -0.8577675239 2.4625582227
72 -2.3717663828 -0.8577675239
73 -2.1517295152 -2.3717663828
74 -0.8338346727 -2.1517295152
75 -0.1872944950 -0.8338346727
76 0.4099545937 -0.1872944950
77 0.6847929937 0.4099545937
78 1.1929164833 0.6847929937
79 -0.7312656692 1.1929164833
80 -0.9532179977 -0.7312656692
81 -0.7988945658 -0.9532179977
82 1.9801422195 -0.7988945658
83 0.3302801909 1.9801422195
84 0.5738365783 0.3302801909
85 1.8461267978 0.5738365783
86 0.5997071945 1.8461267978
87 0.9115556196 0.5997071945
88 1.0139551492 0.9115556196
89 1.1097822772 1.0139551492
90 1.1129668067 1.1097822772
91 0.8951268140 1.1129668067
92 1.6480629656 0.8951268140
93 0.5400962722 1.6480629656
94 1.4028179855 0.5400962722
95 -0.7713073005 1.4028179855
96 1.1486387510 -0.7713073005
97 -1.7230114115 1.1486387510
98 1.1697093288 -1.7230114115
99 -2.0565078181 1.1697093288
> 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/7incz1322142836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8iee31322142836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/988fz1322142836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/1035301322142836.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11gr4w1322142836.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/12w5n01322142836.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/138hip1322142836.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/14dnhv1322142836.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/15buvt1322142836.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/1689xh1322142836.tab")
+ }
>
> try(system("convert tmp/1kutb1322142836.ps tmp/1kutb1322142836.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qvq41322142836.ps tmp/2qvq41322142836.png",intern=TRUE))
character(0)
> try(system("convert tmp/3wbo41322142836.ps tmp/3wbo41322142836.png",intern=TRUE))
character(0)
> try(system("convert tmp/4g51q1322142836.ps tmp/4g51q1322142836.png",intern=TRUE))
character(0)
> try(system("convert tmp/533uh1322142836.ps tmp/533uh1322142836.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hv5r1322142836.ps tmp/6hv5r1322142836.png",intern=TRUE))
character(0)
> try(system("convert tmp/7incz1322142836.ps tmp/7incz1322142836.png",intern=TRUE))
character(0)
> try(system("convert tmp/8iee31322142836.ps tmp/8iee31322142836.png",intern=TRUE))
character(0)
> try(system("convert tmp/988fz1322142836.ps tmp/988fz1322142836.png",intern=TRUE))
character(0)
> try(system("convert tmp/1035301322142836.ps tmp/1035301322142836.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.060 0.732 7.507