R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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.
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(384
+ ,257.9
+ ,367.6
+ ,275.8
+ ,457.1
+ ,319.4
+ ,429.4
+ ,299.8
+ ,442.2
+ ,331.1
+ ,507.5
+ ,339.3
+ ,348.5
+ ,209.6
+ ,393.2
+ ,280.9
+ ,426.8
+ ,285.5
+ ,403
+ ,247.6
+ ,454.8
+ ,275.1
+ ,413
+ ,262.3
+ ,388.9
+ ,267.8
+ ,406.5
+ ,448.2
+ ,447.4
+ ,563.4
+ ,474.4
+ ,346.6
+ ,428.5
+ ,455.1
+ ,472.8
+ ,424.4
+ ,411
+ ,381.2
+ ,463.9
+ ,382.9
+ ,497.3
+ ,466.6
+ ,474
+ ,400.2
+ ,518.1
+ ,493.6
+ ,566
+ ,367.5
+ ,509.4
+ ,307.1
+ ,445.1
+ ,316.7
+ ,466.6
+ ,314.2
+ ,600.5
+ ,403.7
+ ,538.7
+ ,370.6
+ ,548
+ ,343.7
+ ,591.9
+ ,383
+ ,547.3
+ ,365.4
+ ,610.2
+ ,417.2
+ ,621.6
+ ,411
+ ,582.4
+ ,420.8
+ ,635.8
+ ,493
+ ,663.9
+ ,471.8
+ ,624.2
+ ,452.4
+ ,654.1
+ ,464.8
+ ,723.5
+ ,541.5
+ ,641.2
+ ,484
+ ,565.5
+ ,449.4
+ ,698.6
+ ,436.8
+ ,651
+ ,490
+ ,721.6
+ ,475.4
+ ,643.5
+ ,393.6
+ ,604
+ ,486.8
+ ,618.2
+ ,536.7
+ ,783.5
+ ,467
+ ,672.9
+ ,475.5
+ ,726.7
+ ,532.8
+ ,738.6
+ ,554.1
+ ,692.2
+ ,507.3
+ ,669.5
+ ,455.2
+ ,546.2
+ ,465.3
+ ,715
+ ,563.2
+ ,789.8
+ ,680.1
+ ,684
+ ,518.2
+ ,639
+ ,426.6
+ ,768.5
+ ,612.4
+ ,643.8
+ ,518.1
+ ,623
+ ,540
+ ,692.8
+ ,541.7
+ ,936.5
+ ,627.6
+ ,795.9
+ ,637
+ ,695.7
+ ,564.2
+ ,648.3
+ ,665
+ ,675.2
+ ,703.2
+ ,826.5
+ ,824.4
+ ,742.4
+ ,700.3
+ ,793.9
+ ,1219.6
+ ,685.3
+ ,764.7
+ ,756.1
+ ,479.9
+ ,704
+ ,543.4
+ ,860.6
+ ,593.3
+ ,795.9
+ ,584.3
+ ,816.7
+ ,645.9
+ ,777.9
+ ,548.9
+ ,746.4
+ ,421.8
+ ,694.7
+ ,460.3
+ ,909.2
+ ,553.4
+ ,783.6
+ ,424.4
+ ,730.4
+ ,470.2
+ ,847.7
+ ,547.2
+ ,758.7
+ ,444.8
+ ,839.2
+ ,526.7
+ ,784.8
+ ,598.3
+ ,906.1
+ ,543.5
+ ,838.2
+ ,641.2
+ ,729
+ ,525
+ ,768.1
+ ,521.5
+ ,710.5
+ ,551.8
+ ,863
+ ,543.7
+ ,778.3
+ ,472.1
+ ,827.7
+ ,488
+ ,853.1
+ ,642.8
+ ,859.3
+ ,601.7
+ ,779.2
+ ,553.9
+ ,724.6
+ ,522.5
+ ,829.2
+ ,568.4
+ ,862.9
+ ,675.4
+ ,601.6
+ ,499.1
+ ,964.9
+ ,549.4
+ ,766.3
+ ,531.2
+ ,847.8
+ ,583.3
+ ,992.7
+ ,526.5
+ ,865.3
+ ,513.2
+ ,1054.1
+ ,729.1
+ ,972.5
+ ,753.7
+ ,857.4
+ ,571.7
+ ,1043.3
+ ,680.9
+ ,1061
+ ,757.6
+ ,989.4
+ ,805.4
+ ,963.2
+ ,687.7
+ ,1181.9
+ ,950.8
+ ,1256.4
+ ,1062
+ ,1492.7
+ ,1110.6
+ ,1360.8
+ ,1098.9
+ ,1342.8
+ ,1067
+ ,1464
+ ,1360.1)
+ ,dim=c(2
+ ,120)
+ ,dimnames=list(c('yt'
+ ,'xt')
+ ,1:120))
> y <- array(NA,dim=c(2,120),dimnames=list(c('yt','xt'),1:120))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
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
yt xt t
1 384.0 257.9 1
2 367.6 275.8 2
3 457.1 319.4 3
4 429.4 299.8 4
5 442.2 331.1 5
6 507.5 339.3 6
7 348.5 209.6 7
8 393.2 280.9 8
9 426.8 285.5 9
10 403.0 247.6 10
11 454.8 275.1 11
12 413.0 262.3 12
13 388.9 267.8 13
14 406.5 448.2 14
15 447.4 563.4 15
16 474.4 346.6 16
17 428.5 455.1 17
18 472.8 424.4 18
19 411.0 381.2 19
20 463.9 382.9 20
21 497.3 466.6 21
22 474.0 400.2 22
23 518.1 493.6 23
24 566.0 367.5 24
25 509.4 307.1 25
26 445.1 316.7 26
27 466.6 314.2 27
28 600.5 403.7 28
29 538.7 370.6 29
30 548.0 343.7 30
31 591.9 383.0 31
32 547.3 365.4 32
33 610.2 417.2 33
34 621.6 411.0 34
35 582.4 420.8 35
36 635.8 493.0 36
37 663.9 471.8 37
38 624.2 452.4 38
39 654.1 464.8 39
40 723.5 541.5 40
41 641.2 484.0 41
42 565.5 449.4 42
43 698.6 436.8 43
44 651.0 490.0 44
45 721.6 475.4 45
46 643.5 393.6 46
47 604.0 486.8 47
48 618.2 536.7 48
49 783.5 467.0 49
50 672.9 475.5 50
51 726.7 532.8 51
52 738.6 554.1 52
53 692.2 507.3 53
54 669.5 455.2 54
55 546.2 465.3 55
56 715.0 563.2 56
57 789.8 680.1 57
58 684.0 518.2 58
59 639.0 426.6 59
60 768.5 612.4 60
61 643.8 518.1 61
62 623.0 540.0 62
63 692.8 541.7 63
64 936.5 627.6 64
65 795.9 637.0 65
66 695.7 564.2 66
67 648.3 665.0 67
68 675.2 703.2 68
69 826.5 824.4 69
70 742.4 700.3 70
71 793.9 1219.6 71
72 685.3 764.7 72
73 756.1 479.9 73
74 704.0 543.4 74
75 860.6 593.3 75
76 795.9 584.3 76
77 816.7 645.9 77
78 777.9 548.9 78
79 746.4 421.8 79
80 694.7 460.3 80
81 909.2 553.4 81
82 783.6 424.4 82
83 730.4 470.2 83
84 847.7 547.2 84
85 758.7 444.8 85
86 839.2 526.7 86
87 784.8 598.3 87
88 906.1 543.5 88
89 838.2 641.2 89
90 729.0 525.0 90
91 768.1 521.5 91
92 710.5 551.8 92
93 863.0 543.7 93
94 778.3 472.1 94
95 827.7 488.0 95
96 853.1 642.8 96
97 859.3 601.7 97
98 779.2 553.9 98
99 724.6 522.5 99
100 829.2 568.4 100
101 862.9 675.4 101
102 601.6 499.1 102
103 964.9 549.4 103
104 766.3 531.2 104
105 847.8 583.3 105
106 992.7 526.5 106
107 865.3 513.2 107
108 1054.1 729.1 108
109 972.5 753.7 109
110 857.4 571.7 110
111 1043.3 680.9 111
112 1061.0 757.6 112
113 989.4 805.4 113
114 963.2 687.7 114
115 1181.9 950.8 115
116 1256.4 1062.0 116
117 1492.7 1110.6 117
118 1360.8 1098.9 118
119 1342.8 1067.0 119
120 1464.0 1360.1 120
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) xt t
219.1512 0.5321 3.4823
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-321.49 -43.41 10.84 49.25 275.12
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 219.15121 21.41977 10.231 <2e-16 ***
xt 0.53214 0.05389 9.874 <2e-16 ***
t 3.48234 0.30485 11.423 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 80.89 on 117 degrees of freedom
Multiple R-squared: 0.8719, Adjusted R-squared: 0.8697
F-statistic: 398.1 on 2 and 117 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,] 4.829694e-02 9.659388e-02 0.9517031
[2,] 1.311128e-02 2.622256e-02 0.9868887
[3,] 6.941152e-03 1.388230e-02 0.9930588
[4,] 1.847029e-03 3.694058e-03 0.9981530
[5,] 5.491589e-04 1.098318e-03 0.9994508
[6,] 2.373413e-04 4.746827e-04 0.9997627
[7,] 6.522048e-05 1.304410e-04 0.9999348
[8,] 5.996411e-05 1.199282e-04 0.9999400
[9,] 4.241140e-03 8.482280e-03 0.9957589
[10,] 2.606525e-03 5.213049e-03 0.9973935
[11,] 2.287941e-03 4.575881e-03 0.9977121
[12,] 1.194382e-03 2.388765e-03 0.9988056
[13,] 6.608085e-04 1.321617e-03 0.9993392
[14,] 3.515218e-04 7.030435e-04 0.9996485
[15,] 1.868137e-04 3.736275e-04 0.9998132
[16,] 1.201731e-04 2.403462e-04 0.9998798
[17,] 5.805791e-05 1.161158e-04 0.9999419
[18,] 3.916427e-05 7.832853e-05 0.9999608
[19,] 2.016207e-04 4.032415e-04 0.9997984
[20,] 1.153650e-04 2.307301e-04 0.9998846
[21,] 7.398343e-05 1.479669e-04 0.9999260
[22,] 3.477164e-05 6.954329e-05 0.9999652
[23,] 1.274425e-04 2.548850e-04 0.9998726
[24,] 7.136137e-05 1.427227e-04 0.9999286
[25,] 4.223189e-05 8.446377e-05 0.9999578
[26,] 4.227610e-05 8.455221e-05 0.9999577
[27,] 2.042522e-05 4.085044e-05 0.9999796
[28,] 1.869625e-05 3.739250e-05 0.9999813
[29,] 1.750357e-05 3.500715e-05 0.9999825
[30,] 8.421586e-06 1.684317e-05 0.9999916
[31,] 5.788307e-06 1.157661e-05 0.9999942
[32,] 6.183196e-06 1.236639e-05 0.9999938
[33,] 3.131576e-06 6.263152e-06 0.9999969
[34,] 1.984953e-06 3.969906e-06 0.9999980
[35,] 3.509405e-06 7.018811e-06 0.9999965
[36,] 1.721727e-06 3.443455e-06 0.9999983
[37,] 2.205388e-06 4.410775e-06 0.9999978
[38,] 2.433124e-06 4.866248e-06 0.9999976
[39,] 1.238546e-06 2.477092e-06 0.9999988
[40,] 1.410005e-06 2.820010e-06 0.9999986
[41,] 8.747587e-07 1.749517e-06 0.9999991
[42,] 1.059657e-06 2.119315e-06 0.9999989
[43,] 1.096617e-06 2.193234e-06 0.9999989
[44,] 5.836721e-06 1.167344e-05 0.9999942
[45,] 3.688210e-06 7.376421e-06 0.9999963
[46,] 2.547211e-06 5.094422e-06 0.9999975
[47,] 1.787935e-06 3.575870e-06 0.9999982
[48,] 1.187496e-06 2.374993e-06 0.9999988
[49,] 9.710361e-07 1.942072e-06 0.9999990
[50,] 1.585303e-05 3.170607e-05 0.9999841
[51,] 9.876524e-06 1.975305e-05 0.9999901
[52,] 6.723001e-06 1.344600e-05 0.9999933
[53,] 5.007671e-06 1.001534e-05 0.9999950
[54,] 5.001402e-06 1.000280e-05 0.9999950
[55,] 3.390877e-06 6.781755e-06 0.9999966
[56,] 4.488774e-06 8.977548e-06 0.9999955
[57,] 9.567439e-06 1.913488e-05 0.9999904
[58,] 6.654107e-06 1.330821e-05 0.9999933
[59,] 1.984178e-04 3.968357e-04 0.9998016
[60,] 1.643848e-04 3.287696e-04 0.9998356
[61,] 1.468537e-04 2.937075e-04 0.9998531
[62,] 4.387205e-04 8.774410e-04 0.9995613
[63,] 7.906956e-04 1.581391e-03 0.9992093
[64,] 4.949315e-04 9.898630e-04 0.9995051
[65,] 3.742130e-04 7.484261e-04 0.9996258
[66,] 1.447161e-02 2.894323e-02 0.9855284
[67,] 9.220241e-02 1.844048e-01 0.9077976
[68,] 7.467113e-02 1.493423e-01 0.9253289
[69,] 7.842647e-02 1.568529e-01 0.9215735
[70,] 7.244111e-02 1.448822e-01 0.9275589
[71,] 5.612337e-02 1.122467e-01 0.9438766
[72,] 4.737909e-02 9.475819e-02 0.9526209
[73,] 3.654648e-02 7.309297e-02 0.9634535
[74,] 3.090621e-02 6.181243e-02 0.9690938
[75,] 3.071261e-02 6.142523e-02 0.9692874
[76,] 4.525492e-02 9.050984e-02 0.9547451
[77,] 4.716109e-02 9.432217e-02 0.9528389
[78,] 4.016314e-02 8.032629e-02 0.9598369
[79,] 3.565664e-02 7.131329e-02 0.9643434
[80,] 3.228899e-02 6.457798e-02 0.9677110
[81,] 3.179685e-02 6.359370e-02 0.9682031
[82,] 2.515880e-02 5.031759e-02 0.9748412
[83,] 5.063098e-02 1.012620e-01 0.9493690
[84,] 3.737387e-02 7.474774e-02 0.9626261
[85,] 3.617994e-02 7.235989e-02 0.9638201
[86,] 2.920154e-02 5.840308e-02 0.9707985
[87,] 3.870858e-02 7.741715e-02 0.9612914
[88,] 3.768864e-02 7.537728e-02 0.9623114
[89,] 3.280467e-02 6.560935e-02 0.9671953
[90,] 3.801873e-02 7.603746e-02 0.9619813
[91,] 2.717949e-02 5.435898e-02 0.9728205
[92,] 2.219247e-02 4.438494e-02 0.9778075
[93,] 1.707263e-02 3.414526e-02 0.9829274
[94,] 1.712454e-02 3.424908e-02 0.9828755
[95,] 1.169268e-02 2.338535e-02 0.9883073
[96,] 7.664378e-03 1.532876e-02 0.9923356
[97,] 1.394443e-01 2.788885e-01 0.8605557
[98,] 1.857074e-01 3.714147e-01 0.8142926
[99,] 2.051171e-01 4.102342e-01 0.7948829
[100,] 1.934906e-01 3.869812e-01 0.8065094
[101,] 3.508547e-01 7.017094e-01 0.6491453
[102,] 2.850609e-01 5.701217e-01 0.7149391
[103,] 2.979832e-01 5.959664e-01 0.7020168
[104,] 2.283286e-01 4.566572e-01 0.7716714
[105,] 1.679736e-01 3.359473e-01 0.8320264
[106,] 1.730689e-01 3.461378e-01 0.8269311
[107,] 1.456988e-01 2.913976e-01 0.8543012
[108,] 1.199607e-01 2.399213e-01 0.8800393
[109,] 9.899360e-02 1.979872e-01 0.9010064
> postscript(file="/var/www/html/rcomp/tmp/1azqg1259329055.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2s2in1259329055.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3tffq1259329055.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4kl181259329055.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5bde41259329055.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 = 120
Frequency = 1
1 2 3 4 5 6
24.1279933 -5.2796245 57.5368045 36.7843713 29.4461008 86.9002241
7 8 9 10 11 12
-6.5637871 -3.2875878 24.3822332 17.2679301 50.9517850 12.4808117
13 14 15 16 17 18
-18.0282918 -99.9083762 -123.7930464 15.0921846 -92.0271593 -34.8728578
19 20 21 22 23 24
-77.1668282 -28.6538062 -43.2761213 -34.7244841 -43.8085403 67.7117508
25 26 27 28 29 30
39.7705585 -33.1203118 -13.7723092 69.0189738 21.3504071 41.4825832
31 32 33 34 35 36
60.9872067 22.2704969 54.1233922 65.3403064 17.4430085 28.9402833
37 38 39 40 41 42
64.8392714 31.9804105 51.7995531 76.9022058 21.7178125 -39.0525468
43 44 45 46 47 48
97.2700522 17.8779539 92.7648294 54.7113958 -37.8662327 -53.7022748
49 50 51 52 53 54
145.2054187 26.5999005 46.4260354 43.5091475 18.5308748 20.0729350
55 56 57 58 59 60
-112.0840044 1.1373172 10.2480121 -12.8811473 -12.6196260 14.5267429
61 62 63 64 65 66
-63.4749625 -99.4111334 -33.9981114 160.5088693 11.4244267 -53.5182513
67 68 69 70 71 72
-158.0401305 -154.9501549 -71.6276546 -93.1716401 -321.4933796 -191.5060298
73 74 75 76 77 78
27.3646026 -62.0085197 64.5554382 1.1623395 -14.2997201 -4.9646522
79 80 81 82 83 84
27.6877771 -47.9818888 113.4936965 53.0571886 -27.9970866 44.8459246
85 86 87 88 89 90
6.8545391 40.2900728 -55.6933693 91.2854641 -32.0867866 -82.9346642
91 92 93 94 95 96
-45.4545234 -122.6606555 30.6673213 -19.4139226 18.0427361 -42.4146090
97 98 99 100 101 102
-17.8260697 -75.9722040 -117.3454058 -40.6528948 -67.3740313 -238.3403997
103 104 105 106 107 108
94.7107030 -97.6867238 -47.3934699 124.2496400 0.4447358 70.8737432
109 110 111 112 113 114
-27.2992009 -49.0323813 75.2757781 48.6784308 -51.8401209 -18.8897911
115 116 117 118 119 120
56.3222906 68.1661735 275.1219112 145.9655858 141.4584531 103.2063872
> postscript(file="/var/www/html/rcomp/tmp/6mmqy1259329055.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 = 120
Frequency = 1
lag(myerror, k = 1) myerror
0 24.1279933 NA
1 -5.2796245 24.1279933
2 57.5368045 -5.2796245
3 36.7843713 57.5368045
4 29.4461008 36.7843713
5 86.9002241 29.4461008
6 -6.5637871 86.9002241
7 -3.2875878 -6.5637871
8 24.3822332 -3.2875878
9 17.2679301 24.3822332
10 50.9517850 17.2679301
11 12.4808117 50.9517850
12 -18.0282918 12.4808117
13 -99.9083762 -18.0282918
14 -123.7930464 -99.9083762
15 15.0921846 -123.7930464
16 -92.0271593 15.0921846
17 -34.8728578 -92.0271593
18 -77.1668282 -34.8728578
19 -28.6538062 -77.1668282
20 -43.2761213 -28.6538062
21 -34.7244841 -43.2761213
22 -43.8085403 -34.7244841
23 67.7117508 -43.8085403
24 39.7705585 67.7117508
25 -33.1203118 39.7705585
26 -13.7723092 -33.1203118
27 69.0189738 -13.7723092
28 21.3504071 69.0189738
29 41.4825832 21.3504071
30 60.9872067 41.4825832
31 22.2704969 60.9872067
32 54.1233922 22.2704969
33 65.3403064 54.1233922
34 17.4430085 65.3403064
35 28.9402833 17.4430085
36 64.8392714 28.9402833
37 31.9804105 64.8392714
38 51.7995531 31.9804105
39 76.9022058 51.7995531
40 21.7178125 76.9022058
41 -39.0525468 21.7178125
42 97.2700522 -39.0525468
43 17.8779539 97.2700522
44 92.7648294 17.8779539
45 54.7113958 92.7648294
46 -37.8662327 54.7113958
47 -53.7022748 -37.8662327
48 145.2054187 -53.7022748
49 26.5999005 145.2054187
50 46.4260354 26.5999005
51 43.5091475 46.4260354
52 18.5308748 43.5091475
53 20.0729350 18.5308748
54 -112.0840044 20.0729350
55 1.1373172 -112.0840044
56 10.2480121 1.1373172
57 -12.8811473 10.2480121
58 -12.6196260 -12.8811473
59 14.5267429 -12.6196260
60 -63.4749625 14.5267429
61 -99.4111334 -63.4749625
62 -33.9981114 -99.4111334
63 160.5088693 -33.9981114
64 11.4244267 160.5088693
65 -53.5182513 11.4244267
66 -158.0401305 -53.5182513
67 -154.9501549 -158.0401305
68 -71.6276546 -154.9501549
69 -93.1716401 -71.6276546
70 -321.4933796 -93.1716401
71 -191.5060298 -321.4933796
72 27.3646026 -191.5060298
73 -62.0085197 27.3646026
74 64.5554382 -62.0085197
75 1.1623395 64.5554382
76 -14.2997201 1.1623395
77 -4.9646522 -14.2997201
78 27.6877771 -4.9646522
79 -47.9818888 27.6877771
80 113.4936965 -47.9818888
81 53.0571886 113.4936965
82 -27.9970866 53.0571886
83 44.8459246 -27.9970866
84 6.8545391 44.8459246
85 40.2900728 6.8545391
86 -55.6933693 40.2900728
87 91.2854641 -55.6933693
88 -32.0867866 91.2854641
89 -82.9346642 -32.0867866
90 -45.4545234 -82.9346642
91 -122.6606555 -45.4545234
92 30.6673213 -122.6606555
93 -19.4139226 30.6673213
94 18.0427361 -19.4139226
95 -42.4146090 18.0427361
96 -17.8260697 -42.4146090
97 -75.9722040 -17.8260697
98 -117.3454058 -75.9722040
99 -40.6528948 -117.3454058
100 -67.3740313 -40.6528948
101 -238.3403997 -67.3740313
102 94.7107030 -238.3403997
103 -97.6867238 94.7107030
104 -47.3934699 -97.6867238
105 124.2496400 -47.3934699
106 0.4447358 124.2496400
107 70.8737432 0.4447358
108 -27.2992009 70.8737432
109 -49.0323813 -27.2992009
110 75.2757781 -49.0323813
111 48.6784308 75.2757781
112 -51.8401209 48.6784308
113 -18.8897911 -51.8401209
114 56.3222906 -18.8897911
115 68.1661735 56.3222906
116 275.1219112 68.1661735
117 145.9655858 275.1219112
118 141.4584531 145.9655858
119 103.2063872 141.4584531
120 NA 103.2063872
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.2796245 24.1279933
[2,] 57.5368045 -5.2796245
[3,] 36.7843713 57.5368045
[4,] 29.4461008 36.7843713
[5,] 86.9002241 29.4461008
[6,] -6.5637871 86.9002241
[7,] -3.2875878 -6.5637871
[8,] 24.3822332 -3.2875878
[9,] 17.2679301 24.3822332
[10,] 50.9517850 17.2679301
[11,] 12.4808117 50.9517850
[12,] -18.0282918 12.4808117
[13,] -99.9083762 -18.0282918
[14,] -123.7930464 -99.9083762
[15,] 15.0921846 -123.7930464
[16,] -92.0271593 15.0921846
[17,] -34.8728578 -92.0271593
[18,] -77.1668282 -34.8728578
[19,] -28.6538062 -77.1668282
[20,] -43.2761213 -28.6538062
[21,] -34.7244841 -43.2761213
[22,] -43.8085403 -34.7244841
[23,] 67.7117508 -43.8085403
[24,] 39.7705585 67.7117508
[25,] -33.1203118 39.7705585
[26,] -13.7723092 -33.1203118
[27,] 69.0189738 -13.7723092
[28,] 21.3504071 69.0189738
[29,] 41.4825832 21.3504071
[30,] 60.9872067 41.4825832
[31,] 22.2704969 60.9872067
[32,] 54.1233922 22.2704969
[33,] 65.3403064 54.1233922
[34,] 17.4430085 65.3403064
[35,] 28.9402833 17.4430085
[36,] 64.8392714 28.9402833
[37,] 31.9804105 64.8392714
[38,] 51.7995531 31.9804105
[39,] 76.9022058 51.7995531
[40,] 21.7178125 76.9022058
[41,] -39.0525468 21.7178125
[42,] 97.2700522 -39.0525468
[43,] 17.8779539 97.2700522
[44,] 92.7648294 17.8779539
[45,] 54.7113958 92.7648294
[46,] -37.8662327 54.7113958
[47,] -53.7022748 -37.8662327
[48,] 145.2054187 -53.7022748
[49,] 26.5999005 145.2054187
[50,] 46.4260354 26.5999005
[51,] 43.5091475 46.4260354
[52,] 18.5308748 43.5091475
[53,] 20.0729350 18.5308748
[54,] -112.0840044 20.0729350
[55,] 1.1373172 -112.0840044
[56,] 10.2480121 1.1373172
[57,] -12.8811473 10.2480121
[58,] -12.6196260 -12.8811473
[59,] 14.5267429 -12.6196260
[60,] -63.4749625 14.5267429
[61,] -99.4111334 -63.4749625
[62,] -33.9981114 -99.4111334
[63,] 160.5088693 -33.9981114
[64,] 11.4244267 160.5088693
[65,] -53.5182513 11.4244267
[66,] -158.0401305 -53.5182513
[67,] -154.9501549 -158.0401305
[68,] -71.6276546 -154.9501549
[69,] -93.1716401 -71.6276546
[70,] -321.4933796 -93.1716401
[71,] -191.5060298 -321.4933796
[72,] 27.3646026 -191.5060298
[73,] -62.0085197 27.3646026
[74,] 64.5554382 -62.0085197
[75,] 1.1623395 64.5554382
[76,] -14.2997201 1.1623395
[77,] -4.9646522 -14.2997201
[78,] 27.6877771 -4.9646522
[79,] -47.9818888 27.6877771
[80,] 113.4936965 -47.9818888
[81,] 53.0571886 113.4936965
[82,] -27.9970866 53.0571886
[83,] 44.8459246 -27.9970866
[84,] 6.8545391 44.8459246
[85,] 40.2900728 6.8545391
[86,] -55.6933693 40.2900728
[87,] 91.2854641 -55.6933693
[88,] -32.0867866 91.2854641
[89,] -82.9346642 -32.0867866
[90,] -45.4545234 -82.9346642
[91,] -122.6606555 -45.4545234
[92,] 30.6673213 -122.6606555
[93,] -19.4139226 30.6673213
[94,] 18.0427361 -19.4139226
[95,] -42.4146090 18.0427361
[96,] -17.8260697 -42.4146090
[97,] -75.9722040 -17.8260697
[98,] -117.3454058 -75.9722040
[99,] -40.6528948 -117.3454058
[100,] -67.3740313 -40.6528948
[101,] -238.3403997 -67.3740313
[102,] 94.7107030 -238.3403997
[103,] -97.6867238 94.7107030
[104,] -47.3934699 -97.6867238
[105,] 124.2496400 -47.3934699
[106,] 0.4447358 124.2496400
[107,] 70.8737432 0.4447358
[108,] -27.2992009 70.8737432
[109,] -49.0323813 -27.2992009
[110,] 75.2757781 -49.0323813
[111,] 48.6784308 75.2757781
[112,] -51.8401209 48.6784308
[113,] -18.8897911 -51.8401209
[114,] 56.3222906 -18.8897911
[115,] 68.1661735 56.3222906
[116,] 275.1219112 68.1661735
[117,] 145.9655858 275.1219112
[118,] 141.4584531 145.9655858
[119,] 103.2063872 141.4584531
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.2796245 24.1279933
2 57.5368045 -5.2796245
3 36.7843713 57.5368045
4 29.4461008 36.7843713
5 86.9002241 29.4461008
6 -6.5637871 86.9002241
7 -3.2875878 -6.5637871
8 24.3822332 -3.2875878
9 17.2679301 24.3822332
10 50.9517850 17.2679301
11 12.4808117 50.9517850
12 -18.0282918 12.4808117
13 -99.9083762 -18.0282918
14 -123.7930464 -99.9083762
15 15.0921846 -123.7930464
16 -92.0271593 15.0921846
17 -34.8728578 -92.0271593
18 -77.1668282 -34.8728578
19 -28.6538062 -77.1668282
20 -43.2761213 -28.6538062
21 -34.7244841 -43.2761213
22 -43.8085403 -34.7244841
23 67.7117508 -43.8085403
24 39.7705585 67.7117508
25 -33.1203118 39.7705585
26 -13.7723092 -33.1203118
27 69.0189738 -13.7723092
28 21.3504071 69.0189738
29 41.4825832 21.3504071
30 60.9872067 41.4825832
31 22.2704969 60.9872067
32 54.1233922 22.2704969
33 65.3403064 54.1233922
34 17.4430085 65.3403064
35 28.9402833 17.4430085
36 64.8392714 28.9402833
37 31.9804105 64.8392714
38 51.7995531 31.9804105
39 76.9022058 51.7995531
40 21.7178125 76.9022058
41 -39.0525468 21.7178125
42 97.2700522 -39.0525468
43 17.8779539 97.2700522
44 92.7648294 17.8779539
45 54.7113958 92.7648294
46 -37.8662327 54.7113958
47 -53.7022748 -37.8662327
48 145.2054187 -53.7022748
49 26.5999005 145.2054187
50 46.4260354 26.5999005
51 43.5091475 46.4260354
52 18.5308748 43.5091475
53 20.0729350 18.5308748
54 -112.0840044 20.0729350
55 1.1373172 -112.0840044
56 10.2480121 1.1373172
57 -12.8811473 10.2480121
58 -12.6196260 -12.8811473
59 14.5267429 -12.6196260
60 -63.4749625 14.5267429
61 -99.4111334 -63.4749625
62 -33.9981114 -99.4111334
63 160.5088693 -33.9981114
64 11.4244267 160.5088693
65 -53.5182513 11.4244267
66 -158.0401305 -53.5182513
67 -154.9501549 -158.0401305
68 -71.6276546 -154.9501549
69 -93.1716401 -71.6276546
70 -321.4933796 -93.1716401
71 -191.5060298 -321.4933796
72 27.3646026 -191.5060298
73 -62.0085197 27.3646026
74 64.5554382 -62.0085197
75 1.1623395 64.5554382
76 -14.2997201 1.1623395
77 -4.9646522 -14.2997201
78 27.6877771 -4.9646522
79 -47.9818888 27.6877771
80 113.4936965 -47.9818888
81 53.0571886 113.4936965
82 -27.9970866 53.0571886
83 44.8459246 -27.9970866
84 6.8545391 44.8459246
85 40.2900728 6.8545391
86 -55.6933693 40.2900728
87 91.2854641 -55.6933693
88 -32.0867866 91.2854641
89 -82.9346642 -32.0867866
90 -45.4545234 -82.9346642
91 -122.6606555 -45.4545234
92 30.6673213 -122.6606555
93 -19.4139226 30.6673213
94 18.0427361 -19.4139226
95 -42.4146090 18.0427361
96 -17.8260697 -42.4146090
97 -75.9722040 -17.8260697
98 -117.3454058 -75.9722040
99 -40.6528948 -117.3454058
100 -67.3740313 -40.6528948
101 -238.3403997 -67.3740313
102 94.7107030 -238.3403997
103 -97.6867238 94.7107030
104 -47.3934699 -97.6867238
105 124.2496400 -47.3934699
106 0.4447358 124.2496400
107 70.8737432 0.4447358
108 -27.2992009 70.8737432
109 -49.0323813 -27.2992009
110 75.2757781 -49.0323813
111 48.6784308 75.2757781
112 -51.8401209 48.6784308
113 -18.8897911 -51.8401209
114 56.3222906 -18.8897911
115 68.1661735 56.3222906
116 275.1219112 68.1661735
117 145.9655858 275.1219112
118 141.4584531 145.9655858
119 103.2063872 141.4584531
> 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/77wxl1259329055.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8m8ny1259329055.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9hfho1259329055.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10xal11259329055.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11spu71259329055.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/12zs2i1259329055.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/139q451259329055.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/14ikc11259329055.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/15cc461259329055.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/16vu701259329055.tab")
+ }
> system("convert tmp/1azqg1259329055.ps tmp/1azqg1259329055.png")
> system("convert tmp/2s2in1259329055.ps tmp/2s2in1259329055.png")
> system("convert tmp/3tffq1259329055.ps tmp/3tffq1259329055.png")
> system("convert tmp/4kl181259329055.ps tmp/4kl181259329055.png")
> system("convert tmp/5bde41259329055.ps tmp/5bde41259329055.png")
> system("convert tmp/6mmqy1259329055.ps tmp/6mmqy1259329055.png")
> system("convert tmp/77wxl1259329055.ps tmp/77wxl1259329055.png")
> system("convert tmp/8m8ny1259329055.ps tmp/8m8ny1259329055.png")
> system("convert tmp/9hfho1259329055.ps tmp/9hfho1259329055.png")
> system("convert tmp/10xal11259329055.ps tmp/10xal11259329055.png")
>
>
> proc.time()
user system elapsed
3.298 1.657 3.739