R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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(18,15,15,0,17,3,21,2,22,3,24,12,17,3,25,0,16,12,18,15,21,0,19,10,18,20,20,20,25,2,28,3,19,16,20,4,25,2,20,4,21,0,21,0,23,15,19,9,23,1,20,15,19,5,17,4,19,15,21,4,18,12,18,2,24,4,22,2,20,4,17,8,25,30,24,6,18,6,21,7,13,4,21,17,21,5,16,0,18,3,19,4,22,15,18,0,18,8,20,10,19,4,18,0,20,6,20,11,23,10,17,0,17,0,18,0,22,0,16,0,18,0,14,0,13,7,21,4,25,12,16,6,17,12,22,10,24,9,18,0,18,16,18,2,19,0,15,0,25,1,22,10,15,14,21,12,16,12,23,12,20,5,19,0,20,4,18,3,18,0,20,3,20,0,16,12,18,12,18,15,16,0,23,8,14,6,21,14,13,5,27,10,20,16,22,4,21,0,19,8,22,12,12,6,28,4,21,20,18,0,21,13,19,0,23,0,21,0,21,0,22,10,18,6,15,16,23,6,24,0,18,4,15,9,19,17,17,12,14,3,16,8,22,3,15,0,23,10,24,3,24,0,20,8,9,0,23,4,18,13,20,12,25,16,17,20,21,20,26,14,20,12,21,15,15,9,20,4,20,8,16,0,19,13,22,0,17,21,25,0,19,1,17,16,21,12,12,2),dim=c(2,149),dimnames=list(c('Perf','Sport
'),1:149))
> y <- array(NA,dim=c(2,149),dimnames=list(c('Perf','Sport
'),1:149))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Perf Sport\r
1 18 15
2 15 0
3 17 3
4 21 2
5 22 3
6 24 12
7 17 3
8 25 0
9 16 12
10 18 15
11 21 0
12 19 10
13 18 20
14 20 20
15 25 2
16 28 3
17 19 16
18 20 4
19 25 2
20 20 4
21 21 0
22 21 0
23 23 15
24 19 9
25 23 1
26 20 15
27 19 5
28 17 4
29 19 15
30 21 4
31 18 12
32 18 2
33 24 4
34 22 2
35 20 4
36 17 8
37 25 30
38 24 6
39 18 6
40 21 7
41 13 4
42 21 17
43 21 5
44 16 0
45 18 3
46 19 4
47 22 15
48 18 0
49 18 8
50 20 10
51 19 4
52 18 0
53 20 6
54 20 11
55 23 10
56 17 0
57 17 0
58 18 0
59 22 0
60 16 0
61 18 0
62 14 0
63 13 7
64 21 4
65 25 12
66 16 6
67 17 12
68 22 10
69 24 9
70 18 0
71 18 16
72 18 2
73 19 0
74 15 0
75 25 1
76 22 10
77 15 14
78 21 12
79 16 12
80 23 12
81 20 5
82 19 0
83 20 4
84 18 3
85 18 0
86 20 3
87 20 0
88 16 12
89 18 12
90 18 15
91 16 0
92 23 8
93 14 6
94 21 14
95 13 5
96 27 10
97 20 16
98 22 4
99 21 0
100 19 8
101 22 12
102 12 6
103 28 4
104 21 20
105 18 0
106 21 13
107 19 0
108 23 0
109 21 0
110 21 0
111 22 10
112 18 6
113 15 16
114 23 6
115 24 0
116 18 4
117 15 9
118 19 17
119 17 12
120 14 3
121 16 8
122 22 3
123 15 0
124 23 10
125 24 3
126 24 0
127 20 8
128 9 0
129 23 4
130 18 13
131 20 12
132 25 16
133 17 20
134 21 20
135 26 14
136 20 12
137 21 15
138 15 9
139 20 4
140 20 8
141 16 0
142 19 13
143 22 0
144 17 21
145 25 0
146 19 1
147 17 16
148 21 12
149 12 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `Sport\r`
19.29892 0.03802
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.29892 -1.90718 0.09282 2.24488 8.58703
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.29892 0.41199 46.843 <2e-16 ***
`Sport\r` 0.03802 0.04400 0.864 0.389
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.361 on 147 degrees of freedom
Multiple R-squared: 0.005054, Adjusted R-squared: -0.001715
F-statistic: 0.7467 on 1 and 147 DF, p-value: 0.3889
> 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,] 0.590260185 0.81947963 0.4097398
[2,] 0.651848061 0.69630388 0.3481519
[3,] 0.550327600 0.89934480 0.4496724
[4,] 0.765996148 0.46800770 0.2340039
[5,] 0.764717994 0.47056401 0.2352820
[6,] 0.682091064 0.63581787 0.3179089
[7,] 0.597617373 0.80476525 0.4023826
[8,] 0.501305253 0.99738949 0.4986947
[9,] 0.410596896 0.82119379 0.5894031
[10,] 0.343616904 0.68723381 0.6563831
[11,] 0.452966565 0.90593313 0.5470334
[12,] 0.746054287 0.50789143 0.2539457
[13,] 0.680614272 0.63877146 0.3193857
[14,] 0.613028137 0.77394373 0.3869719
[15,] 0.644901101 0.71019780 0.3550989
[16,] 0.580933060 0.83813388 0.4190669
[17,] 0.515698660 0.96860268 0.4843013
[18,] 0.451038686 0.90207737 0.5489613
[19,] 0.462520334 0.92504067 0.5374797
[20,] 0.404891447 0.80978289 0.5951086
[21,] 0.368969930 0.73793986 0.6310301
[22,] 0.310214512 0.62042902 0.6897855
[23,] 0.270185834 0.54037167 0.7298142
[24,] 0.284973753 0.56994751 0.7150262
[25,] 0.235411344 0.47082269 0.7645887
[26,] 0.192713843 0.38542769 0.8072862
[27,] 0.164366694 0.32873339 0.8356333
[28,] 0.154349009 0.30869802 0.8456510
[29,] 0.165810882 0.33162176 0.8341891
[30,] 0.139424089 0.27884818 0.8605759
[31,] 0.111119972 0.22223994 0.8888800
[32,] 0.109665306 0.21933061 0.8903347
[33,] 0.200154282 0.40030856 0.7998457
[34,] 0.215127225 0.43025445 0.7848728
[35,] 0.195750863 0.39150173 0.8042491
[36,] 0.162665234 0.32533047 0.8373348
[37,] 0.322584274 0.64516855 0.6774157
[38,] 0.279948048 0.55989610 0.7200520
[39,] 0.241165239 0.48233048 0.7588348
[40,] 0.262770680 0.52554136 0.7372293
[41,] 0.235979202 0.47195840 0.7640208
[42,] 0.200909508 0.40181902 0.7990905
[43,] 0.178056634 0.35611327 0.8219434
[44,] 0.155996165 0.31199233 0.8440038
[45,] 0.136840266 0.27368053 0.8631597
[46,] 0.110853892 0.22170778 0.8891461
[47,] 0.090059462 0.18011892 0.9099405
[48,] 0.076067317 0.15213463 0.9239327
[49,] 0.059719321 0.11943864 0.9402807
[50,] 0.046221817 0.09244363 0.9537782
[51,] 0.045162655 0.09032531 0.9548373
[52,] 0.041324161 0.08264832 0.9586758
[53,] 0.037359090 0.07471818 0.9626409
[54,] 0.030108747 0.06021749 0.9698913
[55,] 0.026934075 0.05386815 0.9730659
[56,] 0.028136537 0.05627307 0.9718635
[57,] 0.022344579 0.04468916 0.9776554
[58,] 0.036057467 0.07211493 0.9639425
[59,] 0.077207899 0.15441580 0.9227921
[60,] 0.064270854 0.12854171 0.9357291
[61,] 0.087670223 0.17534045 0.9123298
[62,] 0.091053305 0.18210661 0.9089467
[63,] 0.086278610 0.17255722 0.9137214
[64,] 0.076411254 0.15282251 0.9235887
[65,] 0.088005172 0.17601034 0.9119948
[66,] 0.072908354 0.14581671 0.9270916
[67,] 0.063511920 0.12702384 0.9364881
[68,] 0.052144438 0.10428888 0.9478556
[69,] 0.040731770 0.08146354 0.9592682
[70,] 0.047883554 0.09576711 0.9521164
[71,] 0.074142737 0.14828547 0.9258573
[72,] 0.065527161 0.13105432 0.9344728
[73,] 0.084348734 0.16869747 0.9156513
[74,] 0.069575428 0.13915086 0.9304246
[75,] 0.073749675 0.14749935 0.9262503
[76,] 0.072319549 0.14463910 0.9276805
[77,] 0.057713109 0.11542622 0.9422869
[78,] 0.045353849 0.09070770 0.9546462
[79,] 0.035388457 0.07077691 0.9646115
[80,] 0.028410273 0.05682055 0.9715897
[81,] 0.022439636 0.04487927 0.9775604
[82,] 0.016963699 0.03392740 0.9830363
[83,] 0.012722469 0.02544494 0.9872775
[84,] 0.013653232 0.02730646 0.9863468
[85,] 0.010888619 0.02177724 0.9891114
[86,] 0.008714068 0.01742814 0.9912859
[87,] 0.008559083 0.01711817 0.9914409
[88,] 0.008503713 0.01700743 0.9914963
[89,] 0.014160371 0.02832074 0.9858396
[90,] 0.010757141 0.02151428 0.9892429
[91,] 0.023679888 0.04735978 0.9763201
[92,] 0.059390178 0.11878036 0.9406098
[93,] 0.046414404 0.09282881 0.9535856
[94,] 0.040968188 0.08193638 0.9590318
[95,] 0.033220979 0.06644196 0.9667790
[96,] 0.025295763 0.05059153 0.9747042
[97,] 0.021496934 0.04299387 0.9785031
[98,] 0.059170344 0.11834069 0.9408297
[99,] 0.174887811 0.34977562 0.8251122
[100,] 0.148096895 0.29619379 0.8519031
[101,] 0.124833676 0.24966735 0.8751663
[102,] 0.104195921 0.20839184 0.8958041
[103,] 0.083096831 0.16619366 0.9169032
[104,] 0.084348345 0.16869669 0.9156517
[105,] 0.069881090 0.13976218 0.9301189
[106,] 0.057577841 0.11515568 0.9424222
[107,] 0.050625172 0.10125034 0.9493748
[108,] 0.039884824 0.07976965 0.9601152
[109,] 0.049202794 0.09840559 0.9507972
[110,] 0.049931598 0.09986320 0.9500684
[111,] 0.066122297 0.13224459 0.9338777
[112,] 0.051532508 0.10306502 0.9484675
[113,] 0.059559823 0.11911965 0.9404402
[114,] 0.045433134 0.09086627 0.9545669
[115,] 0.039550935 0.07910187 0.9604491
[116,] 0.054747196 0.10949439 0.9452528
[117,] 0.054540444 0.10908089 0.9454596
[118,] 0.047434511 0.09486902 0.9525655
[119,] 0.052979498 0.10595900 0.9470205
[120,] 0.049843459 0.09968692 0.9501565
[121,] 0.061231651 0.12246330 0.9387683
[122,] 0.084818966 0.16963793 0.9151810
[123,] 0.063375933 0.12675187 0.9366241
[124,] 0.347806092 0.69561218 0.6521939
[125,] 0.349004917 0.69800983 0.6509951
[126,] 0.299640786 0.59928157 0.7003592
[127,] 0.239561011 0.47912202 0.7604390
[128,] 0.321957119 0.64391424 0.6780429
[129,] 0.291709032 0.58341806 0.7082910
[130,] 0.234876579 0.46975316 0.7651234
[131,] 0.452017948 0.90403590 0.5479821
[132,] 0.378765302 0.75753060 0.6212347
[133,] 0.347066035 0.69413207 0.6529340
[134,] 0.355353924 0.71070785 0.6446461
[135,] 0.271678987 0.54335797 0.7283210
[136,] 0.200656193 0.40131239 0.7993438
[137,] 0.194859750 0.38971950 0.8051403
[138,] 0.125055279 0.25011056 0.8749447
[139,] 0.085848304 0.17169661 0.9141517
[140,] 0.043642249 0.08728450 0.9563578
> postscript(file="/var/www/rcomp/tmp/13e921289896937.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2dn851289896937.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3dn851289896937.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4ow7p1289896937.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5ow7p1289896937.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 = 149
Frequency = 1
1 2 3 4 5 6
-1.86916459 -4.29891993 -2.41296886 1.62504745 2.58703114 4.24488434
7 8 9 10 11 12
-2.41296886 5.70108007 -3.75511566 -1.86916459 1.70108007 -0.67908304
13 14 15 16 17 18
-2.05924614 -0.05924614 5.62504745 8.58703114 -0.90718090 0.54901483
19 20 21 22 23 24
5.62504745 0.54901483 1.70108007 1.70108007 3.13083541 -0.64106672
25 26 27 28 29 30
3.66306376 0.13083541 -0.48900148 -2.45098517 -0.86916459 1.54901483
31 32 33 34 35 36
-1.75511566 -1.37495255 4.54901483 2.62504745 0.54901483 -2.60305041
37 38 39 40 41 42
4.56059076 4.47298221 -1.52701779 1.43496590 -6.45098517 1.05480279
43 44 45 46 47 48
1.51099852 -3.29891993 -1.41296886 -0.45098517 2.13083541 -1.29891993
49 50 51 52 53 54
-1.60305041 0.32091696 -0.45098517 -1.29891993 0.47298221 0.28290065
55 56 57 58 59 60
3.32091696 -2.29891993 -2.29891993 -1.29891993 2.70108007 -3.29891993
61 62 63 64 65 66
-1.29891993 -5.29891993 -6.56503410 1.54901483 5.24488434 -3.52701779
67 68 69 70 71 72
-2.75511566 2.32091696 4.35893328 -1.29891993 -1.90718090 -1.37495255
73 74 75 76 77 78
-0.29891993 -4.29891993 5.66306376 2.32091696 -4.83114828 1.24488434
79 80 81 82 83 84
-3.75511566 3.24488434 0.51099852 -0.29891993 0.54901483 -1.41296886
85 86 87 88 89 90
-1.29891993 0.58703114 0.70108007 -3.75511566 -1.75511566 -1.86916459
91 92 93 94 95 96
-3.29891993 3.39694959 -5.52701779 1.16885172 -6.48900148 7.32091696
97 98 99 100 101 102
0.09281910 2.54901483 1.70108007 -0.60305041 2.24488434 -7.52701779
103 104 105 106 107 108
8.54901483 0.94075386 -1.29891993 1.20686803 -0.29891993 3.70108007
109 110 111 112 113 114
1.70108007 1.70108007 2.32091696 -1.52701779 -4.90718090 3.47298221
115 116 117 118 119 120
4.70108007 -1.45098517 -4.64106672 -0.94519721 -2.75511566 -5.41296886
121 122 123 124 125 126
-3.60305041 2.58703114 -4.29891993 3.32091696 4.58703114 4.70108007
127 128 129 130 131 132
0.39694959 -10.29891993 3.54901483 -1.79313197 0.24488434 5.09281910
133 134 135 136 137 138
-3.05924614 0.94075386 6.16885172 0.24488434 1.13083541 -4.64106672
139 140 141 142 143 144
0.54901483 0.39694959 -3.29891993 -0.79313197 2.70108007 -3.09726245
145 146 147 148 149
5.70108007 -0.33693624 -2.90718090 1.24488434 -7.37495255
> postscript(file="/var/www/rcomp/tmp/6ow7p1289896937.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 = 149
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.86916459 NA
1 -4.29891993 -1.86916459
2 -2.41296886 -4.29891993
3 1.62504745 -2.41296886
4 2.58703114 1.62504745
5 4.24488434 2.58703114
6 -2.41296886 4.24488434
7 5.70108007 -2.41296886
8 -3.75511566 5.70108007
9 -1.86916459 -3.75511566
10 1.70108007 -1.86916459
11 -0.67908304 1.70108007
12 -2.05924614 -0.67908304
13 -0.05924614 -2.05924614
14 5.62504745 -0.05924614
15 8.58703114 5.62504745
16 -0.90718090 8.58703114
17 0.54901483 -0.90718090
18 5.62504745 0.54901483
19 0.54901483 5.62504745
20 1.70108007 0.54901483
21 1.70108007 1.70108007
22 3.13083541 1.70108007
23 -0.64106672 3.13083541
24 3.66306376 -0.64106672
25 0.13083541 3.66306376
26 -0.48900148 0.13083541
27 -2.45098517 -0.48900148
28 -0.86916459 -2.45098517
29 1.54901483 -0.86916459
30 -1.75511566 1.54901483
31 -1.37495255 -1.75511566
32 4.54901483 -1.37495255
33 2.62504745 4.54901483
34 0.54901483 2.62504745
35 -2.60305041 0.54901483
36 4.56059076 -2.60305041
37 4.47298221 4.56059076
38 -1.52701779 4.47298221
39 1.43496590 -1.52701779
40 -6.45098517 1.43496590
41 1.05480279 -6.45098517
42 1.51099852 1.05480279
43 -3.29891993 1.51099852
44 -1.41296886 -3.29891993
45 -0.45098517 -1.41296886
46 2.13083541 -0.45098517
47 -1.29891993 2.13083541
48 -1.60305041 -1.29891993
49 0.32091696 -1.60305041
50 -0.45098517 0.32091696
51 -1.29891993 -0.45098517
52 0.47298221 -1.29891993
53 0.28290065 0.47298221
54 3.32091696 0.28290065
55 -2.29891993 3.32091696
56 -2.29891993 -2.29891993
57 -1.29891993 -2.29891993
58 2.70108007 -1.29891993
59 -3.29891993 2.70108007
60 -1.29891993 -3.29891993
61 -5.29891993 -1.29891993
62 -6.56503410 -5.29891993
63 1.54901483 -6.56503410
64 5.24488434 1.54901483
65 -3.52701779 5.24488434
66 -2.75511566 -3.52701779
67 2.32091696 -2.75511566
68 4.35893328 2.32091696
69 -1.29891993 4.35893328
70 -1.90718090 -1.29891993
71 -1.37495255 -1.90718090
72 -0.29891993 -1.37495255
73 -4.29891993 -0.29891993
74 5.66306376 -4.29891993
75 2.32091696 5.66306376
76 -4.83114828 2.32091696
77 1.24488434 -4.83114828
78 -3.75511566 1.24488434
79 3.24488434 -3.75511566
80 0.51099852 3.24488434
81 -0.29891993 0.51099852
82 0.54901483 -0.29891993
83 -1.41296886 0.54901483
84 -1.29891993 -1.41296886
85 0.58703114 -1.29891993
86 0.70108007 0.58703114
87 -3.75511566 0.70108007
88 -1.75511566 -3.75511566
89 -1.86916459 -1.75511566
90 -3.29891993 -1.86916459
91 3.39694959 -3.29891993
92 -5.52701779 3.39694959
93 1.16885172 -5.52701779
94 -6.48900148 1.16885172
95 7.32091696 -6.48900148
96 0.09281910 7.32091696
97 2.54901483 0.09281910
98 1.70108007 2.54901483
99 -0.60305041 1.70108007
100 2.24488434 -0.60305041
101 -7.52701779 2.24488434
102 8.54901483 -7.52701779
103 0.94075386 8.54901483
104 -1.29891993 0.94075386
105 1.20686803 -1.29891993
106 -0.29891993 1.20686803
107 3.70108007 -0.29891993
108 1.70108007 3.70108007
109 1.70108007 1.70108007
110 2.32091696 1.70108007
111 -1.52701779 2.32091696
112 -4.90718090 -1.52701779
113 3.47298221 -4.90718090
114 4.70108007 3.47298221
115 -1.45098517 4.70108007
116 -4.64106672 -1.45098517
117 -0.94519721 -4.64106672
118 -2.75511566 -0.94519721
119 -5.41296886 -2.75511566
120 -3.60305041 -5.41296886
121 2.58703114 -3.60305041
122 -4.29891993 2.58703114
123 3.32091696 -4.29891993
124 4.58703114 3.32091696
125 4.70108007 4.58703114
126 0.39694959 4.70108007
127 -10.29891993 0.39694959
128 3.54901483 -10.29891993
129 -1.79313197 3.54901483
130 0.24488434 -1.79313197
131 5.09281910 0.24488434
132 -3.05924614 5.09281910
133 0.94075386 -3.05924614
134 6.16885172 0.94075386
135 0.24488434 6.16885172
136 1.13083541 0.24488434
137 -4.64106672 1.13083541
138 0.54901483 -4.64106672
139 0.39694959 0.54901483
140 -3.29891993 0.39694959
141 -0.79313197 -3.29891993
142 2.70108007 -0.79313197
143 -3.09726245 2.70108007
144 5.70108007 -3.09726245
145 -0.33693624 5.70108007
146 -2.90718090 -0.33693624
147 1.24488434 -2.90718090
148 -7.37495255 1.24488434
149 NA -7.37495255
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -4.29891993 -1.86916459
[2,] -2.41296886 -4.29891993
[3,] 1.62504745 -2.41296886
[4,] 2.58703114 1.62504745
[5,] 4.24488434 2.58703114
[6,] -2.41296886 4.24488434
[7,] 5.70108007 -2.41296886
[8,] -3.75511566 5.70108007
[9,] -1.86916459 -3.75511566
[10,] 1.70108007 -1.86916459
[11,] -0.67908304 1.70108007
[12,] -2.05924614 -0.67908304
[13,] -0.05924614 -2.05924614
[14,] 5.62504745 -0.05924614
[15,] 8.58703114 5.62504745
[16,] -0.90718090 8.58703114
[17,] 0.54901483 -0.90718090
[18,] 5.62504745 0.54901483
[19,] 0.54901483 5.62504745
[20,] 1.70108007 0.54901483
[21,] 1.70108007 1.70108007
[22,] 3.13083541 1.70108007
[23,] -0.64106672 3.13083541
[24,] 3.66306376 -0.64106672
[25,] 0.13083541 3.66306376
[26,] -0.48900148 0.13083541
[27,] -2.45098517 -0.48900148
[28,] -0.86916459 -2.45098517
[29,] 1.54901483 -0.86916459
[30,] -1.75511566 1.54901483
[31,] -1.37495255 -1.75511566
[32,] 4.54901483 -1.37495255
[33,] 2.62504745 4.54901483
[34,] 0.54901483 2.62504745
[35,] -2.60305041 0.54901483
[36,] 4.56059076 -2.60305041
[37,] 4.47298221 4.56059076
[38,] -1.52701779 4.47298221
[39,] 1.43496590 -1.52701779
[40,] -6.45098517 1.43496590
[41,] 1.05480279 -6.45098517
[42,] 1.51099852 1.05480279
[43,] -3.29891993 1.51099852
[44,] -1.41296886 -3.29891993
[45,] -0.45098517 -1.41296886
[46,] 2.13083541 -0.45098517
[47,] -1.29891993 2.13083541
[48,] -1.60305041 -1.29891993
[49,] 0.32091696 -1.60305041
[50,] -0.45098517 0.32091696
[51,] -1.29891993 -0.45098517
[52,] 0.47298221 -1.29891993
[53,] 0.28290065 0.47298221
[54,] 3.32091696 0.28290065
[55,] -2.29891993 3.32091696
[56,] -2.29891993 -2.29891993
[57,] -1.29891993 -2.29891993
[58,] 2.70108007 -1.29891993
[59,] -3.29891993 2.70108007
[60,] -1.29891993 -3.29891993
[61,] -5.29891993 -1.29891993
[62,] -6.56503410 -5.29891993
[63,] 1.54901483 -6.56503410
[64,] 5.24488434 1.54901483
[65,] -3.52701779 5.24488434
[66,] -2.75511566 -3.52701779
[67,] 2.32091696 -2.75511566
[68,] 4.35893328 2.32091696
[69,] -1.29891993 4.35893328
[70,] -1.90718090 -1.29891993
[71,] -1.37495255 -1.90718090
[72,] -0.29891993 -1.37495255
[73,] -4.29891993 -0.29891993
[74,] 5.66306376 -4.29891993
[75,] 2.32091696 5.66306376
[76,] -4.83114828 2.32091696
[77,] 1.24488434 -4.83114828
[78,] -3.75511566 1.24488434
[79,] 3.24488434 -3.75511566
[80,] 0.51099852 3.24488434
[81,] -0.29891993 0.51099852
[82,] 0.54901483 -0.29891993
[83,] -1.41296886 0.54901483
[84,] -1.29891993 -1.41296886
[85,] 0.58703114 -1.29891993
[86,] 0.70108007 0.58703114
[87,] -3.75511566 0.70108007
[88,] -1.75511566 -3.75511566
[89,] -1.86916459 -1.75511566
[90,] -3.29891993 -1.86916459
[91,] 3.39694959 -3.29891993
[92,] -5.52701779 3.39694959
[93,] 1.16885172 -5.52701779
[94,] -6.48900148 1.16885172
[95,] 7.32091696 -6.48900148
[96,] 0.09281910 7.32091696
[97,] 2.54901483 0.09281910
[98,] 1.70108007 2.54901483
[99,] -0.60305041 1.70108007
[100,] 2.24488434 -0.60305041
[101,] -7.52701779 2.24488434
[102,] 8.54901483 -7.52701779
[103,] 0.94075386 8.54901483
[104,] -1.29891993 0.94075386
[105,] 1.20686803 -1.29891993
[106,] -0.29891993 1.20686803
[107,] 3.70108007 -0.29891993
[108,] 1.70108007 3.70108007
[109,] 1.70108007 1.70108007
[110,] 2.32091696 1.70108007
[111,] -1.52701779 2.32091696
[112,] -4.90718090 -1.52701779
[113,] 3.47298221 -4.90718090
[114,] 4.70108007 3.47298221
[115,] -1.45098517 4.70108007
[116,] -4.64106672 -1.45098517
[117,] -0.94519721 -4.64106672
[118,] -2.75511566 -0.94519721
[119,] -5.41296886 -2.75511566
[120,] -3.60305041 -5.41296886
[121,] 2.58703114 -3.60305041
[122,] -4.29891993 2.58703114
[123,] 3.32091696 -4.29891993
[124,] 4.58703114 3.32091696
[125,] 4.70108007 4.58703114
[126,] 0.39694959 4.70108007
[127,] -10.29891993 0.39694959
[128,] 3.54901483 -10.29891993
[129,] -1.79313197 3.54901483
[130,] 0.24488434 -1.79313197
[131,] 5.09281910 0.24488434
[132,] -3.05924614 5.09281910
[133,] 0.94075386 -3.05924614
[134,] 6.16885172 0.94075386
[135,] 0.24488434 6.16885172
[136,] 1.13083541 0.24488434
[137,] -4.64106672 1.13083541
[138,] 0.54901483 -4.64106672
[139,] 0.39694959 0.54901483
[140,] -3.29891993 0.39694959
[141,] -0.79313197 -3.29891993
[142,] 2.70108007 -0.79313197
[143,] -3.09726245 2.70108007
[144,] 5.70108007 -3.09726245
[145,] -0.33693624 5.70108007
[146,] -2.90718090 -0.33693624
[147,] 1.24488434 -2.90718090
[148,] -7.37495255 1.24488434
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -4.29891993 -1.86916459
2 -2.41296886 -4.29891993
3 1.62504745 -2.41296886
4 2.58703114 1.62504745
5 4.24488434 2.58703114
6 -2.41296886 4.24488434
7 5.70108007 -2.41296886
8 -3.75511566 5.70108007
9 -1.86916459 -3.75511566
10 1.70108007 -1.86916459
11 -0.67908304 1.70108007
12 -2.05924614 -0.67908304
13 -0.05924614 -2.05924614
14 5.62504745 -0.05924614
15 8.58703114 5.62504745
16 -0.90718090 8.58703114
17 0.54901483 -0.90718090
18 5.62504745 0.54901483
19 0.54901483 5.62504745
20 1.70108007 0.54901483
21 1.70108007 1.70108007
22 3.13083541 1.70108007
23 -0.64106672 3.13083541
24 3.66306376 -0.64106672
25 0.13083541 3.66306376
26 -0.48900148 0.13083541
27 -2.45098517 -0.48900148
28 -0.86916459 -2.45098517
29 1.54901483 -0.86916459
30 -1.75511566 1.54901483
31 -1.37495255 -1.75511566
32 4.54901483 -1.37495255
33 2.62504745 4.54901483
34 0.54901483 2.62504745
35 -2.60305041 0.54901483
36 4.56059076 -2.60305041
37 4.47298221 4.56059076
38 -1.52701779 4.47298221
39 1.43496590 -1.52701779
40 -6.45098517 1.43496590
41 1.05480279 -6.45098517
42 1.51099852 1.05480279
43 -3.29891993 1.51099852
44 -1.41296886 -3.29891993
45 -0.45098517 -1.41296886
46 2.13083541 -0.45098517
47 -1.29891993 2.13083541
48 -1.60305041 -1.29891993
49 0.32091696 -1.60305041
50 -0.45098517 0.32091696
51 -1.29891993 -0.45098517
52 0.47298221 -1.29891993
53 0.28290065 0.47298221
54 3.32091696 0.28290065
55 -2.29891993 3.32091696
56 -2.29891993 -2.29891993
57 -1.29891993 -2.29891993
58 2.70108007 -1.29891993
59 -3.29891993 2.70108007
60 -1.29891993 -3.29891993
61 -5.29891993 -1.29891993
62 -6.56503410 -5.29891993
63 1.54901483 -6.56503410
64 5.24488434 1.54901483
65 -3.52701779 5.24488434
66 -2.75511566 -3.52701779
67 2.32091696 -2.75511566
68 4.35893328 2.32091696
69 -1.29891993 4.35893328
70 -1.90718090 -1.29891993
71 -1.37495255 -1.90718090
72 -0.29891993 -1.37495255
73 -4.29891993 -0.29891993
74 5.66306376 -4.29891993
75 2.32091696 5.66306376
76 -4.83114828 2.32091696
77 1.24488434 -4.83114828
78 -3.75511566 1.24488434
79 3.24488434 -3.75511566
80 0.51099852 3.24488434
81 -0.29891993 0.51099852
82 0.54901483 -0.29891993
83 -1.41296886 0.54901483
84 -1.29891993 -1.41296886
85 0.58703114 -1.29891993
86 0.70108007 0.58703114
87 -3.75511566 0.70108007
88 -1.75511566 -3.75511566
89 -1.86916459 -1.75511566
90 -3.29891993 -1.86916459
91 3.39694959 -3.29891993
92 -5.52701779 3.39694959
93 1.16885172 -5.52701779
94 -6.48900148 1.16885172
95 7.32091696 -6.48900148
96 0.09281910 7.32091696
97 2.54901483 0.09281910
98 1.70108007 2.54901483
99 -0.60305041 1.70108007
100 2.24488434 -0.60305041
101 -7.52701779 2.24488434
102 8.54901483 -7.52701779
103 0.94075386 8.54901483
104 -1.29891993 0.94075386
105 1.20686803 -1.29891993
106 -0.29891993 1.20686803
107 3.70108007 -0.29891993
108 1.70108007 3.70108007
109 1.70108007 1.70108007
110 2.32091696 1.70108007
111 -1.52701779 2.32091696
112 -4.90718090 -1.52701779
113 3.47298221 -4.90718090
114 4.70108007 3.47298221
115 -1.45098517 4.70108007
116 -4.64106672 -1.45098517
117 -0.94519721 -4.64106672
118 -2.75511566 -0.94519721
119 -5.41296886 -2.75511566
120 -3.60305041 -5.41296886
121 2.58703114 -3.60305041
122 -4.29891993 2.58703114
123 3.32091696 -4.29891993
124 4.58703114 3.32091696
125 4.70108007 4.58703114
126 0.39694959 4.70108007
127 -10.29891993 0.39694959
128 3.54901483 -10.29891993
129 -1.79313197 3.54901483
130 0.24488434 -1.79313197
131 5.09281910 0.24488434
132 -3.05924614 5.09281910
133 0.94075386 -3.05924614
134 6.16885172 0.94075386
135 0.24488434 6.16885172
136 1.13083541 0.24488434
137 -4.64106672 1.13083541
138 0.54901483 -4.64106672
139 0.39694959 0.54901483
140 -3.29891993 0.39694959
141 -0.79313197 -3.29891993
142 2.70108007 -0.79313197
143 -3.09726245 2.70108007
144 5.70108007 -3.09726245
145 -0.33693624 5.70108007
146 -2.90718090 -0.33693624
147 1.24488434 -2.90718090
148 -7.37495255 1.24488434
> 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/7ho7a1289896937.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8ho7a1289896937.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9ho7a1289896937.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/109x6v1289896937.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11dx4j1289896937.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/12gglp1289896937.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/13cqjg1289896937.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/14g8z41289896937.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/15j9fs1289896937.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/16f0vi1289896937.tab")
+ }
>
> try(system("convert tmp/13e921289896937.ps tmp/13e921289896937.png",intern=TRUE))
character(0)
> try(system("convert tmp/2dn851289896937.ps tmp/2dn851289896937.png",intern=TRUE))
character(0)
> try(system("convert tmp/3dn851289896937.ps tmp/3dn851289896937.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ow7p1289896937.ps tmp/4ow7p1289896937.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ow7p1289896937.ps tmp/5ow7p1289896937.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ow7p1289896937.ps tmp/6ow7p1289896937.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ho7a1289896937.ps tmp/7ho7a1289896937.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ho7a1289896937.ps tmp/8ho7a1289896937.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ho7a1289896937.ps tmp/9ho7a1289896937.png",intern=TRUE))
character(0)
> try(system("convert tmp/109x6v1289896937.ps tmp/109x6v1289896937.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.96 2.09 7.07