R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(3,0,3.21,0,3.37,0,3.51,0,3.75,0,4.11,0,4.25,0,4.25,0,4.5,0,4.7,0,4.75,0,4.75,0,4.75,0,4.75,0,4.75,0,4.75,0,4.58,0,4.5,0,4.5,0,4.49,0,4.03,0,3.75,0,3.39,0,3.25,0,3.25,0,3.25,0,3.25,0,3.25,0,3.25,0,3.25,0,3.25,0,3.25,0,3.25,0,3.25,0,3.25,0,2.85,0,2.75,0,2.75,0,2.55,0,2.5,0,2.5,0,2.1,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2,0,2.21,0,2.25,0,2.25,0,2.45,0,2.5,0,2.5,0,2.64,0,2.75,0,2.93,0,3,0,3.17,0,3.25,0,3.39,0,3.5,0,3.5,0,3.65,0,3.75,0,3.75,0,3.9,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,4,0,4.18,0,4.25,0,4.25,0,3.97,1,3.42,1,2.75,1,2.31,1,2,1,1.66,1,1.31,1,1.09,1,1,1,1,1,1,1,1,1,1,1),dim=c(2,118),dimnames=list(c('Rente','Crisis'),1:118))
> y <- array(NA,dim=c(2,118),dimnames=list(c('Rente','Crisis'),1:118))
> 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 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Rente Crisis M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 3.00 0 1 0 0 0 0 0 0 0 0 0 0
2 3.21 0 0 1 0 0 0 0 0 0 0 0 0
3 3.37 0 0 0 1 0 0 0 0 0 0 0 0
4 3.51 0 0 0 0 1 0 0 0 0 0 0 0
5 3.75 0 0 0 0 0 1 0 0 0 0 0 0
6 4.11 0 0 0 0 0 0 1 0 0 0 0 0
7 4.25 0 0 0 0 0 0 0 1 0 0 0 0
8 4.25 0 0 0 0 0 0 0 0 1 0 0 0
9 4.50 0 0 0 0 0 0 0 0 0 1 0 0
10 4.70 0 0 0 0 0 0 0 0 0 0 1 0
11 4.75 0 0 0 0 0 0 0 0 0 0 0 1
12 4.75 0 0 0 0 0 0 0 0 0 0 0 0
13 4.75 0 1 0 0 0 0 0 0 0 0 0 0
14 4.75 0 0 1 0 0 0 0 0 0 0 0 0
15 4.75 0 0 0 1 0 0 0 0 0 0 0 0
16 4.75 0 0 0 0 1 0 0 0 0 0 0 0
17 4.58 0 0 0 0 0 1 0 0 0 0 0 0
18 4.50 0 0 0 0 0 0 1 0 0 0 0 0
19 4.50 0 0 0 0 0 0 0 1 0 0 0 0
20 4.49 0 0 0 0 0 0 0 0 1 0 0 0
21 4.03 0 0 0 0 0 0 0 0 0 1 0 0
22 3.75 0 0 0 0 0 0 0 0 0 0 1 0
23 3.39 0 0 0 0 0 0 0 0 0 0 0 1
24 3.25 0 0 0 0 0 0 0 0 0 0 0 0
25 3.25 0 1 0 0 0 0 0 0 0 0 0 0
26 3.25 0 0 1 0 0 0 0 0 0 0 0 0
27 3.25 0 0 0 1 0 0 0 0 0 0 0 0
28 3.25 0 0 0 0 1 0 0 0 0 0 0 0
29 3.25 0 0 0 0 0 1 0 0 0 0 0 0
30 3.25 0 0 0 0 0 0 1 0 0 0 0 0
31 3.25 0 0 0 0 0 0 0 1 0 0 0 0
32 3.25 0 0 0 0 0 0 0 0 1 0 0 0
33 3.25 0 0 0 0 0 0 0 0 0 1 0 0
34 3.25 0 0 0 0 0 0 0 0 0 0 1 0
35 3.25 0 0 0 0 0 0 0 0 0 0 0 1
36 2.85 0 0 0 0 0 0 0 0 0 0 0 0
37 2.75 0 1 0 0 0 0 0 0 0 0 0 0
38 2.75 0 0 1 0 0 0 0 0 0 0 0 0
39 2.55 0 0 0 1 0 0 0 0 0 0 0 0
40 2.50 0 0 0 0 1 0 0 0 0 0 0 0
41 2.50 0 0 0 0 0 1 0 0 0 0 0 0
42 2.10 0 0 0 0 0 0 1 0 0 0 0 0
43 2.00 0 0 0 0 0 0 0 1 0 0 0 0
44 2.00 0 0 0 0 0 0 0 0 1 0 0 0
45 2.00 0 0 0 0 0 0 0 0 0 1 0 0
46 2.00 0 0 0 0 0 0 0 0 0 0 1 0
47 2.00 0 0 0 0 0 0 0 0 0 0 0 1
48 2.00 0 0 0 0 0 0 0 0 0 0 0 0
49 2.00 0 1 0 0 0 0 0 0 0 0 0 0
50 2.00 0 0 1 0 0 0 0 0 0 0 0 0
51 2.00 0 0 0 1 0 0 0 0 0 0 0 0
52 2.00 0 0 0 0 1 0 0 0 0 0 0 0
53 2.00 0 0 0 0 0 1 0 0 0 0 0 0
54 2.00 0 0 0 0 0 0 1 0 0 0 0 0
55 2.00 0 0 0 0 0 0 0 1 0 0 0 0
56 2.00 0 0 0 0 0 0 0 0 1 0 0 0
57 2.00 0 0 0 0 0 0 0 0 0 1 0 0
58 2.00 0 0 0 0 0 0 0 0 0 0 1 0
59 2.00 0 0 0 0 0 0 0 0 0 0 0 1
60 2.00 0 0 0 0 0 0 0 0 0 0 0 0
61 2.00 0 1 0 0 0 0 0 0 0 0 0 0
62 2.00 0 0 1 0 0 0 0 0 0 0 0 0
63 2.00 0 0 0 1 0 0 0 0 0 0 0 0
64 2.00 0 0 0 0 1 0 0 0 0 0 0 0
65 2.00 0 0 0 0 0 1 0 0 0 0 0 0
66 2.00 0 0 0 0 0 0 1 0 0 0 0 0
67 2.00 0 0 0 0 0 0 0 1 0 0 0 0
68 2.00 0 0 0 0 0 0 0 0 1 0 0 0
69 2.00 0 0 0 0 0 0 0 0 0 1 0 0
70 2.00 0 0 0 0 0 0 0 0 0 0 1 0
71 2.00 0 0 0 0 0 0 0 0 0 0 0 1
72 2.21 0 0 0 0 0 0 0 0 0 0 0 0
73 2.25 0 1 0 0 0 0 0 0 0 0 0 0
74 2.25 0 0 1 0 0 0 0 0 0 0 0 0
75 2.45 0 0 0 1 0 0 0 0 0 0 0 0
76 2.50 0 0 0 0 1 0 0 0 0 0 0 0
77 2.50 0 0 0 0 0 1 0 0 0 0 0 0
78 2.64 0 0 0 0 0 0 1 0 0 0 0 0
79 2.75 0 0 0 0 0 0 0 1 0 0 0 0
80 2.93 0 0 0 0 0 0 0 0 1 0 0 0
81 3.00 0 0 0 0 0 0 0 0 0 1 0 0
82 3.17 0 0 0 0 0 0 0 0 0 0 1 0
83 3.25 0 0 0 0 0 0 0 0 0 0 0 1
84 3.39 0 0 0 0 0 0 0 0 0 0 0 0
85 3.50 0 1 0 0 0 0 0 0 0 0 0 0
86 3.50 0 0 1 0 0 0 0 0 0 0 0 0
87 3.65 0 0 0 1 0 0 0 0 0 0 0 0
88 3.75 0 0 0 0 1 0 0 0 0 0 0 0
89 3.75 0 0 0 0 0 1 0 0 0 0 0 0
90 3.90 0 0 0 0 0 0 1 0 0 0 0 0
91 4.00 0 0 0 0 0 0 0 1 0 0 0 0
92 4.00 0 0 0 0 0 0 0 0 1 0 0 0
93 4.00 0 0 0 0 0 0 0 0 0 1 0 0
94 4.00 0 0 0 0 0 0 0 0 0 0 1 0
95 4.00 0 0 0 0 0 0 0 0 0 0 0 1
96 4.00 0 0 0 0 0 0 0 0 0 0 0 0
97 4.00 0 1 0 0 0 0 0 0 0 0 0 0
98 4.00 0 0 1 0 0 0 0 0 0 0 0 0
99 4.00 0 0 0 1 0 0 0 0 0 0 0 0
100 4.00 0 0 0 0 1 0 0 0 0 0 0 0
101 4.00 0 0 0 0 0 1 0 0 0 0 0 0
102 4.00 0 0 0 0 0 0 1 0 0 0 0 0
103 4.18 0 0 0 0 0 0 0 1 0 0 0 0
104 4.25 0 0 0 0 0 0 0 0 1 0 0 0
105 4.25 0 0 0 0 0 0 0 0 0 1 0 0
106 3.97 1 0 0 0 0 0 0 0 0 0 1 0
107 3.42 1 0 0 0 0 0 0 0 0 0 0 1
108 2.75 1 0 0 0 0 0 0 0 0 0 0 0
109 2.31 1 1 0 0 0 0 0 0 0 0 0 0
110 2.00 1 0 1 0 0 0 0 0 0 0 0 0
111 1.66 1 0 0 1 0 0 0 0 0 0 0 0
112 1.31 1 0 0 0 1 0 0 0 0 0 0 0
113 1.09 1 0 0 0 0 1 0 0 0 0 0 0
114 1.00 1 0 0 0 0 0 1 0 0 0 0 0
115 1.00 1 0 0 0 0 0 0 1 0 0 0 0
116 1.00 1 0 0 0 0 0 0 0 1 0 0 0
117 1.00 1 0 0 0 0 0 0 0 0 1 0 0
118 1.00 1 0 0 0 0 0 0 0 0 0 1 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Crisis M1 M2 M3 M4
3.17111 -1.33998 -0.05611 -0.06611 -0.06911 -0.08011
M5 M6 M7 M8 M9 M10
-0.09511 -0.08711 -0.04411 -0.02011 -0.03411 0.08089
M11
0.09556
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.26666 -0.97828 0.03845 0.85950 2.05798
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.17111 0.33295 9.524 7.17e-16 ***
Crisis -1.33998 0.29342 -4.567 1.35e-05 ***
M1 -0.05611 0.45675 -0.123 0.902
M2 -0.06611 0.45675 -0.145 0.885
M3 -0.06911 0.45675 -0.151 0.880
M4 -0.08011 0.45675 -0.175 0.861
M5 -0.09511 0.45675 -0.208 0.835
M6 -0.08711 0.45675 -0.191 0.849
M7 -0.04411 0.45675 -0.097 0.923
M8 -0.02011 0.45675 -0.044 0.965
M9 -0.03411 0.45675 -0.075 0.941
M10 0.08089 0.45748 0.177 0.860
M11 0.09556 0.46861 0.204 0.839
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9941 on 105 degrees of freedom
Multiple R-squared: 0.1672, Adjusted R-squared: 0.07204
F-statistic: 1.757 on 12 and 105 DF, p-value: 0.06514
> 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,] 0.8318570 0.33628598 0.16814299
[2,] 0.7693736 0.46125275 0.23062638
[3,] 0.6808626 0.63827472 0.31913736
[4,] 0.5867413 0.82651742 0.41325871
[5,] 0.4961209 0.99224173 0.50387914
[6,] 0.4124696 0.82493915 0.58753043
[7,] 0.3752000 0.75039994 0.62480003
[8,] 0.3937279 0.78745572 0.60627214
[9,] 0.4293459 0.85869188 0.57065406
[10,] 0.3692028 0.73840556 0.63079722
[11,] 0.3240123 0.64802456 0.67598772
[12,] 0.2905863 0.58117266 0.70941367
[13,] 0.2668939 0.53378779 0.73310610
[14,] 0.2483694 0.49673886 0.75163057
[15,] 0.2446185 0.48923690 0.75538155
[16,] 0.2467335 0.49346691 0.75326655
[17,] 0.2465290 0.49305805 0.75347098
[18,] 0.2354890 0.47097795 0.76451102
[19,] 0.2199970 0.43999391 0.78000305
[20,] 0.1943154 0.38863081 0.80568460
[21,] 0.1914398 0.38287966 0.80856017
[22,] 0.1755661 0.35113211 0.82443394
[23,] 0.1646235 0.32924707 0.83537646
[24,] 0.1717915 0.34358304 0.82820848
[25,] 0.1851412 0.37028236 0.81485882
[26,] 0.1982489 0.39649781 0.80175109
[27,] 0.2648130 0.52962606 0.73518697
[28,] 0.3534169 0.70683378 0.64658311
[29,] 0.4387238 0.87744766 0.56127617
[30,] 0.5083943 0.98321149 0.49160575
[31,] 0.5730767 0.85384659 0.42692329
[32,] 0.6242272 0.75154553 0.37577277
[33,] 0.6529184 0.69416321 0.34708160
[34,] 0.6676392 0.66472151 0.33236076
[35,] 0.6823349 0.63533025 0.31766513
[36,] 0.6949362 0.61012756 0.30506378
[37,] 0.7061756 0.58764875 0.29382438
[38,] 0.7155965 0.56880701 0.28440350
[39,] 0.7233645 0.55327095 0.27663548
[40,] 0.7334178 0.53316444 0.26658222
[41,] 0.7442806 0.51143877 0.25571938
[42,] 0.7523983 0.49520333 0.24760166
[43,] 0.7694497 0.46110058 0.23055029
[44,] 0.7894383 0.42112344 0.21056172
[45,] 0.8010741 0.39785184 0.19892592
[46,] 0.8087268 0.38254636 0.19127318
[47,] 0.8146744 0.37065117 0.18532559
[48,] 0.8210006 0.35799873 0.17899937
[49,] 0.8260603 0.34787938 0.17393969
[50,] 0.8294834 0.34103314 0.17051657
[51,] 0.8348928 0.33021440 0.16510720
[52,] 0.8456051 0.30878977 0.15439489
[53,] 0.8598313 0.28033733 0.14016867
[54,] 0.8744298 0.25114049 0.12557025
[55,] 0.9087089 0.18258211 0.09129106
[56,] 0.9466176 0.10676473 0.05338236
[57,] 0.9616107 0.07677858 0.03838929
[58,] 0.9711587 0.05768270 0.02884135
[59,] 0.9780288 0.04394238 0.02197119
[60,] 0.9799470 0.04010607 0.02005303
[61,] 0.9802877 0.03942466 0.01971233
[62,] 0.9799997 0.04000066 0.02000033
[63,] 0.9781016 0.04379689 0.02189845
[64,] 0.9759516 0.04809683 0.02404841
[65,] 0.9711862 0.05762751 0.02881376
[66,] 0.9645213 0.07095743 0.03547871
[67,] 0.9680126 0.06397473 0.03198737
[68,] 0.9783861 0.04322789 0.02161394
[69,] 0.9789089 0.04218212 0.02109106
[70,] 0.9745401 0.05091982 0.02545991
[71,] 0.9668094 0.06638120 0.03319060
[72,] 0.9524189 0.09516211 0.04758106
[73,] 0.9308581 0.13828378 0.06914189
[74,] 0.9015507 0.19689869 0.09844935
[75,] 0.8664200 0.26715993 0.13357996
[76,] 0.8234179 0.35316429 0.17658214
[77,] 0.7696603 0.46067939 0.23033970
[78,] 0.7059854 0.58802923 0.29401461
[79,] 0.6678916 0.66421688 0.33210844
[80,] 0.7492813 0.50143740 0.25071870
[81,] 0.7614662 0.47706760 0.23853380
[82,] 0.7373365 0.52532696 0.26266348
[83,] 0.6869544 0.62609126 0.31304563
[84,] 0.6028061 0.79438788 0.39719394
[85,] 0.4855380 0.97107596 0.51446202
[86,] 0.3526134 0.70522688 0.64738656
[87,] 0.2228771 0.44575421 0.77712290
> postscript(file="/var/www/html/rcomp/tmp/109ha1258737211.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2exc81258737211.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ifva1258737211.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4whuo1258737211.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ll851258737211.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 = 118
Frequency = 1
1 2 3 4 5 6
-0.114998064 0.105001936 0.268001936 0.419001936 0.674001936 1.026001936
7 8 9 10 11 12
1.123001936 1.099001936 1.363001936 1.448003872 1.483335485 1.578891040
13 14 15 16 17 18
1.635001936 1.645001936 1.648001936 1.659001936 1.504001936 1.416001936
19 20 21 22 23 24
1.373001936 1.339001936 0.893001936 0.498003872 0.123335485 0.078891040
25 26 27 28 29 30
0.135001936 0.145001936 0.148001936 0.159001936 0.174001936 0.166001936
31 32 33 34 35 36
0.123001936 0.099001936 0.113001936 -0.001996128 -0.016664515 -0.321108960
37 38 39 40 41 42
-0.364998064 -0.354998064 -0.551998064 -0.590998064 -0.575998064 -0.983998064
43 44 45 46 47 48
-1.126998064 -1.150998064 -1.136998064 -1.251996128 -1.266664515 -1.171108960
49 50 51 52 53 54
-1.114998064 -1.104998064 -1.101998064 -1.090998064 -1.075998064 -1.083998064
55 56 57 58 59 60
-1.126998064 -1.150998064 -1.136998064 -1.251996128 -1.266664515 -1.171108960
61 62 63 64 65 66
-1.114998064 -1.104998064 -1.101998064 -1.090998064 -1.075998064 -1.083998064
67 68 69 70 71 72
-1.126998064 -1.150998064 -1.136998064 -1.251996128 -1.266664515 -0.961108960
73 74 75 76 77 78
-0.864998064 -0.854998064 -0.651998064 -0.590998064 -0.575998064 -0.443998064
79 80 81 82 83 84
-0.376998064 -0.220998064 -0.136998064 -0.081996128 -0.016664515 0.218891040
85 86 87 88 89 90
0.385001936 0.395001936 0.548001936 0.659001936 0.674001936 0.816001936
91 92 93 94 95 96
0.873001936 0.849001936 0.863001936 0.748003872 0.733335485 0.828891040
97 98 99 100 101 102
0.885001936 0.895001936 0.898001936 0.909001936 0.924001936 0.916001936
103 104 105 106 107 108
1.053001936 1.099001936 1.113001936 2.057984511 1.493316123 0.918871679
109 110 111 112 113 114
0.534982575 0.234982575 -0.102017425 -0.441017425 -0.646017425 -0.744017425
115 116 117 118
-0.787017425 -0.811017425 -0.797017425 -0.912015489
> postscript(file="/var/www/html/rcomp/tmp/6ajsb1258737211.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 = 118
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.114998064 NA
1 0.105001936 -0.114998064
2 0.268001936 0.105001936
3 0.419001936 0.268001936
4 0.674001936 0.419001936
5 1.026001936 0.674001936
6 1.123001936 1.026001936
7 1.099001936 1.123001936
8 1.363001936 1.099001936
9 1.448003872 1.363001936
10 1.483335485 1.448003872
11 1.578891040 1.483335485
12 1.635001936 1.578891040
13 1.645001936 1.635001936
14 1.648001936 1.645001936
15 1.659001936 1.648001936
16 1.504001936 1.659001936
17 1.416001936 1.504001936
18 1.373001936 1.416001936
19 1.339001936 1.373001936
20 0.893001936 1.339001936
21 0.498003872 0.893001936
22 0.123335485 0.498003872
23 0.078891040 0.123335485
24 0.135001936 0.078891040
25 0.145001936 0.135001936
26 0.148001936 0.145001936
27 0.159001936 0.148001936
28 0.174001936 0.159001936
29 0.166001936 0.174001936
30 0.123001936 0.166001936
31 0.099001936 0.123001936
32 0.113001936 0.099001936
33 -0.001996128 0.113001936
34 -0.016664515 -0.001996128
35 -0.321108960 -0.016664515
36 -0.364998064 -0.321108960
37 -0.354998064 -0.364998064
38 -0.551998064 -0.354998064
39 -0.590998064 -0.551998064
40 -0.575998064 -0.590998064
41 -0.983998064 -0.575998064
42 -1.126998064 -0.983998064
43 -1.150998064 -1.126998064
44 -1.136998064 -1.150998064
45 -1.251996128 -1.136998064
46 -1.266664515 -1.251996128
47 -1.171108960 -1.266664515
48 -1.114998064 -1.171108960
49 -1.104998064 -1.114998064
50 -1.101998064 -1.104998064
51 -1.090998064 -1.101998064
52 -1.075998064 -1.090998064
53 -1.083998064 -1.075998064
54 -1.126998064 -1.083998064
55 -1.150998064 -1.126998064
56 -1.136998064 -1.150998064
57 -1.251996128 -1.136998064
58 -1.266664515 -1.251996128
59 -1.171108960 -1.266664515
60 -1.114998064 -1.171108960
61 -1.104998064 -1.114998064
62 -1.101998064 -1.104998064
63 -1.090998064 -1.101998064
64 -1.075998064 -1.090998064
65 -1.083998064 -1.075998064
66 -1.126998064 -1.083998064
67 -1.150998064 -1.126998064
68 -1.136998064 -1.150998064
69 -1.251996128 -1.136998064
70 -1.266664515 -1.251996128
71 -0.961108960 -1.266664515
72 -0.864998064 -0.961108960
73 -0.854998064 -0.864998064
74 -0.651998064 -0.854998064
75 -0.590998064 -0.651998064
76 -0.575998064 -0.590998064
77 -0.443998064 -0.575998064
78 -0.376998064 -0.443998064
79 -0.220998064 -0.376998064
80 -0.136998064 -0.220998064
81 -0.081996128 -0.136998064
82 -0.016664515 -0.081996128
83 0.218891040 -0.016664515
84 0.385001936 0.218891040
85 0.395001936 0.385001936
86 0.548001936 0.395001936
87 0.659001936 0.548001936
88 0.674001936 0.659001936
89 0.816001936 0.674001936
90 0.873001936 0.816001936
91 0.849001936 0.873001936
92 0.863001936 0.849001936
93 0.748003872 0.863001936
94 0.733335485 0.748003872
95 0.828891040 0.733335485
96 0.885001936 0.828891040
97 0.895001936 0.885001936
98 0.898001936 0.895001936
99 0.909001936 0.898001936
100 0.924001936 0.909001936
101 0.916001936 0.924001936
102 1.053001936 0.916001936
103 1.099001936 1.053001936
104 1.113001936 1.099001936
105 2.057984511 1.113001936
106 1.493316123 2.057984511
107 0.918871679 1.493316123
108 0.534982575 0.918871679
109 0.234982575 0.534982575
110 -0.102017425 0.234982575
111 -0.441017425 -0.102017425
112 -0.646017425 -0.441017425
113 -0.744017425 -0.646017425
114 -0.787017425 -0.744017425
115 -0.811017425 -0.787017425
116 -0.797017425 -0.811017425
117 -0.912015489 -0.797017425
118 NA -0.912015489
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.105001936 -0.114998064
[2,] 0.268001936 0.105001936
[3,] 0.419001936 0.268001936
[4,] 0.674001936 0.419001936
[5,] 1.026001936 0.674001936
[6,] 1.123001936 1.026001936
[7,] 1.099001936 1.123001936
[8,] 1.363001936 1.099001936
[9,] 1.448003872 1.363001936
[10,] 1.483335485 1.448003872
[11,] 1.578891040 1.483335485
[12,] 1.635001936 1.578891040
[13,] 1.645001936 1.635001936
[14,] 1.648001936 1.645001936
[15,] 1.659001936 1.648001936
[16,] 1.504001936 1.659001936
[17,] 1.416001936 1.504001936
[18,] 1.373001936 1.416001936
[19,] 1.339001936 1.373001936
[20,] 0.893001936 1.339001936
[21,] 0.498003872 0.893001936
[22,] 0.123335485 0.498003872
[23,] 0.078891040 0.123335485
[24,] 0.135001936 0.078891040
[25,] 0.145001936 0.135001936
[26,] 0.148001936 0.145001936
[27,] 0.159001936 0.148001936
[28,] 0.174001936 0.159001936
[29,] 0.166001936 0.174001936
[30,] 0.123001936 0.166001936
[31,] 0.099001936 0.123001936
[32,] 0.113001936 0.099001936
[33,] -0.001996128 0.113001936
[34,] -0.016664515 -0.001996128
[35,] -0.321108960 -0.016664515
[36,] -0.364998064 -0.321108960
[37,] -0.354998064 -0.364998064
[38,] -0.551998064 -0.354998064
[39,] -0.590998064 -0.551998064
[40,] -0.575998064 -0.590998064
[41,] -0.983998064 -0.575998064
[42,] -1.126998064 -0.983998064
[43,] -1.150998064 -1.126998064
[44,] -1.136998064 -1.150998064
[45,] -1.251996128 -1.136998064
[46,] -1.266664515 -1.251996128
[47,] -1.171108960 -1.266664515
[48,] -1.114998064 -1.171108960
[49,] -1.104998064 -1.114998064
[50,] -1.101998064 -1.104998064
[51,] -1.090998064 -1.101998064
[52,] -1.075998064 -1.090998064
[53,] -1.083998064 -1.075998064
[54,] -1.126998064 -1.083998064
[55,] -1.150998064 -1.126998064
[56,] -1.136998064 -1.150998064
[57,] -1.251996128 -1.136998064
[58,] -1.266664515 -1.251996128
[59,] -1.171108960 -1.266664515
[60,] -1.114998064 -1.171108960
[61,] -1.104998064 -1.114998064
[62,] -1.101998064 -1.104998064
[63,] -1.090998064 -1.101998064
[64,] -1.075998064 -1.090998064
[65,] -1.083998064 -1.075998064
[66,] -1.126998064 -1.083998064
[67,] -1.150998064 -1.126998064
[68,] -1.136998064 -1.150998064
[69,] -1.251996128 -1.136998064
[70,] -1.266664515 -1.251996128
[71,] -0.961108960 -1.266664515
[72,] -0.864998064 -0.961108960
[73,] -0.854998064 -0.864998064
[74,] -0.651998064 -0.854998064
[75,] -0.590998064 -0.651998064
[76,] -0.575998064 -0.590998064
[77,] -0.443998064 -0.575998064
[78,] -0.376998064 -0.443998064
[79,] -0.220998064 -0.376998064
[80,] -0.136998064 -0.220998064
[81,] -0.081996128 -0.136998064
[82,] -0.016664515 -0.081996128
[83,] 0.218891040 -0.016664515
[84,] 0.385001936 0.218891040
[85,] 0.395001936 0.385001936
[86,] 0.548001936 0.395001936
[87,] 0.659001936 0.548001936
[88,] 0.674001936 0.659001936
[89,] 0.816001936 0.674001936
[90,] 0.873001936 0.816001936
[91,] 0.849001936 0.873001936
[92,] 0.863001936 0.849001936
[93,] 0.748003872 0.863001936
[94,] 0.733335485 0.748003872
[95,] 0.828891040 0.733335485
[96,] 0.885001936 0.828891040
[97,] 0.895001936 0.885001936
[98,] 0.898001936 0.895001936
[99,] 0.909001936 0.898001936
[100,] 0.924001936 0.909001936
[101,] 0.916001936 0.924001936
[102,] 1.053001936 0.916001936
[103,] 1.099001936 1.053001936
[104,] 1.113001936 1.099001936
[105,] 2.057984511 1.113001936
[106,] 1.493316123 2.057984511
[107,] 0.918871679 1.493316123
[108,] 0.534982575 0.918871679
[109,] 0.234982575 0.534982575
[110,] -0.102017425 0.234982575
[111,] -0.441017425 -0.102017425
[112,] -0.646017425 -0.441017425
[113,] -0.744017425 -0.646017425
[114,] -0.787017425 -0.744017425
[115,] -0.811017425 -0.787017425
[116,] -0.797017425 -0.811017425
[117,] -0.912015489 -0.797017425
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.105001936 -0.114998064
2 0.268001936 0.105001936
3 0.419001936 0.268001936
4 0.674001936 0.419001936
5 1.026001936 0.674001936
6 1.123001936 1.026001936
7 1.099001936 1.123001936
8 1.363001936 1.099001936
9 1.448003872 1.363001936
10 1.483335485 1.448003872
11 1.578891040 1.483335485
12 1.635001936 1.578891040
13 1.645001936 1.635001936
14 1.648001936 1.645001936
15 1.659001936 1.648001936
16 1.504001936 1.659001936
17 1.416001936 1.504001936
18 1.373001936 1.416001936
19 1.339001936 1.373001936
20 0.893001936 1.339001936
21 0.498003872 0.893001936
22 0.123335485 0.498003872
23 0.078891040 0.123335485
24 0.135001936 0.078891040
25 0.145001936 0.135001936
26 0.148001936 0.145001936
27 0.159001936 0.148001936
28 0.174001936 0.159001936
29 0.166001936 0.174001936
30 0.123001936 0.166001936
31 0.099001936 0.123001936
32 0.113001936 0.099001936
33 -0.001996128 0.113001936
34 -0.016664515 -0.001996128
35 -0.321108960 -0.016664515
36 -0.364998064 -0.321108960
37 -0.354998064 -0.364998064
38 -0.551998064 -0.354998064
39 -0.590998064 -0.551998064
40 -0.575998064 -0.590998064
41 -0.983998064 -0.575998064
42 -1.126998064 -0.983998064
43 -1.150998064 -1.126998064
44 -1.136998064 -1.150998064
45 -1.251996128 -1.136998064
46 -1.266664515 -1.251996128
47 -1.171108960 -1.266664515
48 -1.114998064 -1.171108960
49 -1.104998064 -1.114998064
50 -1.101998064 -1.104998064
51 -1.090998064 -1.101998064
52 -1.075998064 -1.090998064
53 -1.083998064 -1.075998064
54 -1.126998064 -1.083998064
55 -1.150998064 -1.126998064
56 -1.136998064 -1.150998064
57 -1.251996128 -1.136998064
58 -1.266664515 -1.251996128
59 -1.171108960 -1.266664515
60 -1.114998064 -1.171108960
61 -1.104998064 -1.114998064
62 -1.101998064 -1.104998064
63 -1.090998064 -1.101998064
64 -1.075998064 -1.090998064
65 -1.083998064 -1.075998064
66 -1.126998064 -1.083998064
67 -1.150998064 -1.126998064
68 -1.136998064 -1.150998064
69 -1.251996128 -1.136998064
70 -1.266664515 -1.251996128
71 -0.961108960 -1.266664515
72 -0.864998064 -0.961108960
73 -0.854998064 -0.864998064
74 -0.651998064 -0.854998064
75 -0.590998064 -0.651998064
76 -0.575998064 -0.590998064
77 -0.443998064 -0.575998064
78 -0.376998064 -0.443998064
79 -0.220998064 -0.376998064
80 -0.136998064 -0.220998064
81 -0.081996128 -0.136998064
82 -0.016664515 -0.081996128
83 0.218891040 -0.016664515
84 0.385001936 0.218891040
85 0.395001936 0.385001936
86 0.548001936 0.395001936
87 0.659001936 0.548001936
88 0.674001936 0.659001936
89 0.816001936 0.674001936
90 0.873001936 0.816001936
91 0.849001936 0.873001936
92 0.863001936 0.849001936
93 0.748003872 0.863001936
94 0.733335485 0.748003872
95 0.828891040 0.733335485
96 0.885001936 0.828891040
97 0.895001936 0.885001936
98 0.898001936 0.895001936
99 0.909001936 0.898001936
100 0.924001936 0.909001936
101 0.916001936 0.924001936
102 1.053001936 0.916001936
103 1.099001936 1.053001936
104 1.113001936 1.099001936
105 2.057984511 1.113001936
106 1.493316123 2.057984511
107 0.918871679 1.493316123
108 0.534982575 0.918871679
109 0.234982575 0.534982575
110 -0.102017425 0.234982575
111 -0.441017425 -0.102017425
112 -0.646017425 -0.441017425
113 -0.744017425 -0.646017425
114 -0.787017425 -0.744017425
115 -0.811017425 -0.787017425
116 -0.797017425 -0.811017425
117 -0.912015489 -0.797017425
> 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/7olw41258737211.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8dgwp1258737211.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9epv91258737211.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10lkm01258737211.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1122av1258737211.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/123h8c1258737211.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/13mllb1258737211.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/14upph1258737211.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/1593jh1258737211.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/16thw61258737211.tab")
+ }
>
> system("convert tmp/109ha1258737211.ps tmp/109ha1258737211.png")
> system("convert tmp/2exc81258737211.ps tmp/2exc81258737211.png")
> system("convert tmp/3ifva1258737211.ps tmp/3ifva1258737211.png")
> system("convert tmp/4whuo1258737211.ps tmp/4whuo1258737211.png")
> system("convert tmp/5ll851258737211.ps tmp/5ll851258737211.png")
> system("convert tmp/6ajsb1258737211.ps tmp/6ajsb1258737211.png")
> system("convert tmp/7olw41258737211.ps tmp/7olw41258737211.png")
> system("convert tmp/8dgwp1258737211.ps tmp/8dgwp1258737211.png")
> system("convert tmp/9epv91258737211.ps tmp/9epv91258737211.png")
> system("convert tmp/10lkm01258737211.ps tmp/10lkm01258737211.png")
>
>
> proc.time()
user system elapsed
3.236 1.619 3.752