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(8.8
+ ,99.4
+ ,8.9
+ ,8.6
+ ,8.4
+ ,8.4
+ ,8.4
+ ,8.3
+ ,99.8
+ ,8.8
+ ,8.9
+ ,8.6
+ ,8.4
+ ,8.4
+ ,7.5
+ ,99.9
+ ,8.3
+ ,8.8
+ ,8.9
+ ,8.6
+ ,8.4
+ ,7.2
+ ,100
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.9
+ ,8.6
+ ,7.4
+ ,100.1
+ ,7.2
+ ,7.5
+ ,8.3
+ ,8.8
+ ,8.9
+ ,8.8
+ ,100.1
+ ,7.4
+ ,7.2
+ ,7.5
+ ,8.3
+ ,8.8
+ ,9.3
+ ,100.2
+ ,8.8
+ ,7.4
+ ,7.2
+ ,7.5
+ ,8.3
+ ,9.3
+ ,100.3
+ ,9.3
+ ,8.8
+ ,7.4
+ ,7.2
+ ,7.5
+ ,8.7
+ ,100
+ ,9.3
+ ,9.3
+ ,8.8
+ ,7.4
+ ,7.2
+ ,8.2
+ ,99.9
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.8
+ ,7.4
+ ,8.3
+ ,99.4
+ ,8.2
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.8
+ ,8.5
+ ,99.8
+ ,8.3
+ ,8.2
+ ,8.7
+ ,9.3
+ ,9.3
+ ,8.6
+ ,99.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.7
+ ,9.3
+ ,8.5
+ ,100
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.7
+ ,8.2
+ ,99.9
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.3
+ ,8.2
+ ,8.1
+ ,100.3
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.3
+ ,7.9
+ ,100.6
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.5
+ ,8.6
+ ,100.7
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.6
+ ,8.7
+ ,100.8
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,8.7
+ ,100.8
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.2
+ ,8.5
+ ,100.6
+ ,8.7
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.1
+ ,8.4
+ ,101.1
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.6
+ ,7.9
+ ,8.5
+ ,101.1
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.6
+ ,8.7
+ ,100.9
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,8.7
+ ,101.1
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.6
+ ,101.2
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.5
+ ,8.5
+ ,101.4
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8.4
+ ,8.3
+ ,101.9
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.5
+ ,8
+ ,102.1
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.7
+ ,8.2
+ ,102.1
+ ,8
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8.1
+ ,103
+ ,8.2
+ ,8
+ ,8.3
+ ,8.5
+ ,8.6
+ ,8.1
+ ,103.4
+ ,8.1
+ ,8.2
+ ,8
+ ,8.3
+ ,8.5
+ ,8
+ ,103.2
+ ,8.1
+ ,8.1
+ ,8.2
+ ,8
+ ,8.3
+ ,7.9
+ ,103.1
+ ,8
+ ,8.1
+ ,8.1
+ ,8.2
+ ,8
+ ,7.9
+ ,103
+ ,7.9
+ ,8
+ ,8.1
+ ,8.1
+ ,8.2
+ ,8
+ ,103.7
+ ,7.9
+ ,7.9
+ ,8
+ ,8.1
+ ,8.1
+ ,8
+ ,103.4
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,8.1
+ ,7.9
+ ,103.5
+ ,8
+ ,8
+ ,7.9
+ ,7.9
+ ,8
+ ,8
+ ,103.8
+ ,7.9
+ ,8
+ ,8
+ ,7.9
+ ,7.9
+ ,7.7
+ ,104
+ ,8
+ ,7.9
+ ,8
+ ,8
+ ,7.9
+ ,7.2
+ ,104.2
+ ,7.7
+ ,8
+ ,7.9
+ ,8
+ ,8
+ ,7.5
+ ,104.4
+ ,7.2
+ ,7.7
+ ,8
+ ,7.9
+ ,8
+ ,7.3
+ ,104.4
+ ,7.5
+ ,7.2
+ ,7.7
+ ,8
+ ,7.9
+ ,7
+ ,104.9
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7.7
+ ,8
+ ,7
+ ,105.3
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7.7
+ ,7
+ ,105.2
+ ,7
+ ,7
+ ,7.3
+ ,7.5
+ ,7.2
+ ,7.2
+ ,105.4
+ ,7
+ ,7
+ ,7
+ ,7.3
+ ,7.5
+ ,7.3
+ ,105.4
+ ,7.2
+ ,7
+ ,7
+ ,7
+ ,7.3
+ ,7.1
+ ,105.5
+ ,7.3
+ ,7.2
+ ,7
+ ,7
+ ,7
+ ,6.8
+ ,105.7
+ ,7.1
+ ,7.3
+ ,7.2
+ ,7
+ ,7
+ ,6.4
+ ,105.6
+ ,6.8
+ ,7.1
+ ,7.3
+ ,7.2
+ ,7
+ ,6.1
+ ,105.8
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.3
+ ,7.2
+ ,6.5
+ ,105.4
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.3
+ ,7.7
+ ,105.5
+ ,6.5
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.1
+ ,7.9
+ ,105.8
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.4
+ ,6.8
+ ,7.5
+ ,106.1
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.4
+ ,6.9
+ ,106
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.1
+ ,6.6
+ ,105.5
+ ,6.9
+ ,7.5
+ ,7.9
+ ,7.7
+ ,6.5
+ ,6.9
+ ,105.4
+ ,6.6
+ ,6.9
+ ,7.5
+ ,7.9
+ ,7.7
+ ,7.7
+ ,106
+ ,6.9
+ ,6.6
+ ,6.9
+ ,7.5
+ ,7.9
+ ,8
+ ,106.1
+ ,7.7
+ ,6.9
+ ,6.6
+ ,6.9
+ ,7.5
+ ,8
+ ,106.4
+ ,8
+ ,7.7
+ ,6.9
+ ,6.6
+ ,6.9
+ ,7.7
+ ,106
+ ,8
+ ,8
+ ,7.7
+ ,6.9
+ ,6.6
+ ,7.3
+ ,106
+ ,7.7
+ ,8
+ ,8
+ ,7.7
+ ,6.9
+ ,7.4
+ ,106
+ ,7.3
+ ,7.7
+ ,8
+ ,8
+ ,7.7
+ ,8.1
+ ,106
+ ,7.4
+ ,7.3
+ ,7.7
+ ,8
+ ,8
+ ,8.3
+ ,106.1
+ ,8.1
+ ,7.4
+ ,7.3
+ ,7.7
+ ,8
+ ,8.2
+ ,106.1
+ ,8.3
+ ,8.1
+ ,7.4
+ ,7.3
+ ,7.7)
+ ,dim=c(7
+ ,68)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4'
+ ,'Y5')
+ ,1:68))
> y <- array(NA,dim=c(7,68),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4','Y5'),1:68))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X Y1 Y2 Y3 Y4 Y5 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.8 99.4 8.9 8.6 8.4 8.4 8.4 1 0 0 0 0 0 0 0 0 0 0 1
2 8.3 99.8 8.8 8.9 8.6 8.4 8.4 0 1 0 0 0 0 0 0 0 0 0 2
3 7.5 99.9 8.3 8.8 8.9 8.6 8.4 0 0 1 0 0 0 0 0 0 0 0 3
4 7.2 100.0 7.5 8.3 8.8 8.9 8.6 0 0 0 1 0 0 0 0 0 0 0 4
5 7.4 100.1 7.2 7.5 8.3 8.8 8.9 0 0 0 0 1 0 0 0 0 0 0 5
6 8.8 100.1 7.4 7.2 7.5 8.3 8.8 0 0 0 0 0 1 0 0 0 0 0 6
7 9.3 100.2 8.8 7.4 7.2 7.5 8.3 0 0 0 0 0 0 1 0 0 0 0 7
8 9.3 100.3 9.3 8.8 7.4 7.2 7.5 0 0 0 0 0 0 0 1 0 0 0 8
9 8.7 100.0 9.3 9.3 8.8 7.4 7.2 0 0 0 0 0 0 0 0 1 0 0 9
10 8.2 99.9 8.7 9.3 9.3 8.8 7.4 0 0 0 0 0 0 0 0 0 1 0 10
11 8.3 99.4 8.2 8.7 9.3 9.3 8.8 0 0 0 0 0 0 0 0 0 0 1 11
12 8.5 99.8 8.3 8.2 8.7 9.3 9.3 0 0 0 0 0 0 0 0 0 0 0 12
13 8.6 99.6 8.5 8.3 8.2 8.7 9.3 1 0 0 0 0 0 0 0 0 0 0 13
14 8.5 100.0 8.6 8.5 8.3 8.2 8.7 0 1 0 0 0 0 0 0 0 0 0 14
15 8.2 99.9 8.5 8.6 8.5 8.3 8.2 0 0 1 0 0 0 0 0 0 0 0 15
16 8.1 100.3 8.2 8.5 8.6 8.5 8.3 0 0 0 1 0 0 0 0 0 0 0 16
17 7.9 100.6 8.1 8.2 8.5 8.6 8.5 0 0 0 0 1 0 0 0 0 0 0 17
18 8.6 100.7 7.9 8.1 8.2 8.5 8.6 0 0 0 0 0 1 0 0 0 0 0 18
19 8.7 100.8 8.6 7.9 8.1 8.2 8.5 0 0 0 0 0 0 1 0 0 0 0 19
20 8.7 100.8 8.7 8.6 7.9 8.1 8.2 0 0 0 0 0 0 0 1 0 0 0 20
21 8.5 100.6 8.7 8.7 8.6 7.9 8.1 0 0 0 0 0 0 0 0 1 0 0 21
22 8.4 101.1 8.5 8.7 8.7 8.6 7.9 0 0 0 0 0 0 0 0 0 1 0 22
23 8.5 101.1 8.4 8.5 8.7 8.7 8.6 0 0 0 0 0 0 0 0 0 0 1 23
24 8.7 100.9 8.5 8.4 8.5 8.7 8.7 0 0 0 0 0 0 0 0 0 0 0 24
25 8.7 101.1 8.7 8.5 8.4 8.5 8.7 1 0 0 0 0 0 0 0 0 0 0 25
26 8.6 101.2 8.7 8.7 8.5 8.4 8.5 0 1 0 0 0 0 0 0 0 0 0 26
27 8.5 101.4 8.6 8.7 8.7 8.5 8.4 0 0 1 0 0 0 0 0 0 0 0 27
28 8.3 101.9 8.5 8.6 8.7 8.7 8.5 0 0 0 1 0 0 0 0 0 0 0 28
29 8.0 102.1 8.3 8.5 8.6 8.7 8.7 0 0 0 0 1 0 0 0 0 0 0 29
30 8.2 102.1 8.0 8.3 8.5 8.6 8.7 0 0 0 0 0 1 0 0 0 0 0 30
31 8.1 103.0 8.2 8.0 8.3 8.5 8.6 0 0 0 0 0 0 1 0 0 0 0 31
32 8.1 103.4 8.1 8.2 8.0 8.3 8.5 0 0 0 0 0 0 0 1 0 0 0 32
33 8.0 103.2 8.1 8.1 8.2 8.0 8.3 0 0 0 0 0 0 0 0 1 0 0 33
34 7.9 103.1 8.0 8.1 8.1 8.2 8.0 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 103.0 7.9 8.0 8.1 8.1 8.2 0 0 0 0 0 0 0 0 0 0 1 35
36 8.0 103.7 7.9 7.9 8.0 8.1 8.1 0 0 0 0 0 0 0 0 0 0 0 36
37 8.0 103.4 8.0 7.9 7.9 8.0 8.1 1 0 0 0 0 0 0 0 0 0 0 37
38 7.9 103.5 8.0 8.0 7.9 7.9 8.0 0 1 0 0 0 0 0 0 0 0 0 38
39 8.0 103.8 7.9 8.0 8.0 7.9 7.9 0 0 1 0 0 0 0 0 0 0 0 39
40 7.7 104.0 8.0 7.9 8.0 8.0 7.9 0 0 0 1 0 0 0 0 0 0 0 40
41 7.2 104.2 7.7 8.0 7.9 8.0 8.0 0 0 0 0 1 0 0 0 0 0 0 41
42 7.5 104.4 7.2 7.7 8.0 7.9 8.0 0 0 0 0 0 1 0 0 0 0 0 42
43 7.3 104.4 7.5 7.2 7.7 8.0 7.9 0 0 0 0 0 0 1 0 0 0 0 43
44 7.0 104.9 7.3 7.5 7.2 7.7 8.0 0 0 0 0 0 0 0 1 0 0 0 44
45 7.0 105.3 7.0 7.3 7.5 7.2 7.7 0 0 0 0 0 0 0 0 1 0 0 45
46 7.0 105.2 7.0 7.0 7.3 7.5 7.2 0 0 0 0 0 0 0 0 0 1 0 46
47 7.2 105.4 7.0 7.0 7.0 7.3 7.5 0 0 0 0 0 0 0 0 0 0 1 47
48 7.3 105.4 7.2 7.0 7.0 7.0 7.3 0 0 0 0 0 0 0 0 0 0 0 48
49 7.1 105.5 7.3 7.2 7.0 7.0 7.0 1 0 0 0 0 0 0 0 0 0 0 49
50 6.8 105.7 7.1 7.3 7.2 7.0 7.0 0 1 0 0 0 0 0 0 0 0 0 50
51 6.4 105.6 6.8 7.1 7.3 7.2 7.0 0 0 1 0 0 0 0 0 0 0 0 51
52 6.1 105.8 6.4 6.8 7.1 7.3 7.2 0 0 0 1 0 0 0 0 0 0 0 52
53 6.5 105.4 6.1 6.4 6.8 7.1 7.3 0 0 0 0 1 0 0 0 0 0 0 53
54 7.7 105.5 6.5 6.1 6.4 6.8 7.1 0 0 0 0 0 1 0 0 0 0 0 54
55 7.9 105.8 7.7 6.5 6.1 6.4 6.8 0 0 0 0 0 0 1 0 0 0 0 55
56 7.5 106.1 7.9 7.7 6.5 6.1 6.4 0 0 0 0 0 0 0 1 0 0 0 56
57 6.9 106.0 7.5 7.9 7.7 6.5 6.1 0 0 0 0 0 0 0 0 1 0 0 57
58 6.6 105.5 6.9 7.5 7.9 7.7 6.5 0 0 0 0 0 0 0 0 0 1 0 58
59 6.9 105.4 6.6 6.9 7.5 7.9 7.7 0 0 0 0 0 0 0 0 0 0 1 59
60 7.7 106.0 6.9 6.6 6.9 7.5 7.9 0 0 0 0 0 0 0 0 0 0 0 60
61 8.0 106.1 7.7 6.9 6.6 6.9 7.5 1 0 0 0 0 0 0 0 0 0 0 61
62 8.0 106.4 8.0 7.7 6.9 6.6 6.9 0 1 0 0 0 0 0 0 0 0 0 62
63 7.7 106.0 8.0 8.0 7.7 6.9 6.6 0 0 1 0 0 0 0 0 0 0 0 63
64 7.3 106.0 7.7 8.0 8.0 7.7 6.9 0 0 0 1 0 0 0 0 0 0 0 64
65 7.4 106.0 7.3 7.7 8.0 8.0 7.7 0 0 0 0 1 0 0 0 0 0 0 65
66 8.1 106.0 7.4 7.3 7.7 8.0 8.0 0 0 0 0 0 1 0 0 0 0 0 66
67 8.3 106.1 8.1 7.4 7.3 7.7 8.0 0 0 0 0 0 0 1 0 0 0 0 67
68 8.2 106.1 8.3 8.1 7.4 7.3 7.7 0 0 0 0 0 0 0 1 0 0 0 68
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
19.42508 -0.17458 1.38018 -0.69401 -0.20040 0.21750
Y5 M1 M2 M3 M4 M5
0.05085 -0.22626 -0.12951 -0.14849 -0.16973 -0.11923
M6 M7 M8 M9 M10 M11
0.48905 -0.41556 -0.10456 0.01589 -0.03509 0.05062
t
0.01621
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.266644 -0.105373 -0.002965 0.105067 0.344305
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.425085 6.034377 3.219 0.002284 **
X -0.174580 0.055399 -3.151 0.002771 **
Y1 1.380180 0.146104 9.447 1.29e-12 ***
Y2 -0.694005 0.247281 -2.807 0.007166 **
Y3 -0.200396 0.270402 -0.741 0.462167
Y4 0.217500 0.248385 0.876 0.385489
Y5 0.050848 0.134054 0.379 0.706096
M1 -0.226258 0.102885 -2.199 0.032619 *
M2 -0.129506 0.116627 -1.110 0.272234
M3 -0.148491 0.113998 -1.303 0.198810
M4 -0.169728 0.108223 -1.568 0.123242
M5 -0.119228 0.106469 -1.120 0.268240
M6 0.489047 0.103489 4.726 1.97e-05 ***
M7 -0.415558 0.118310 -3.512 0.000965 ***
M8 -0.104561 0.152152 -0.687 0.495186
M9 0.015894 0.162364 0.098 0.922420
M10 -0.035093 0.131428 -0.267 0.790577
M11 0.050620 0.107891 0.469 0.641027
t 0.016213 0.005522 2.936 0.005048 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1614 on 49 degrees of freedom
Multiple R-squared: 0.9608, Adjusted R-squared: 0.9465
F-statistic: 66.81 on 18 and 49 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.071080176 0.14216035 0.9289198
[2,] 0.033191866 0.06638373 0.9668081
[3,] 0.016336398 0.03267280 0.9836636
[4,] 0.006053379 0.01210676 0.9939466
[5,] 0.005140485 0.01028097 0.9948595
[6,] 0.139111180 0.27822236 0.8608888
[7,] 0.091482107 0.18296421 0.9085179
[8,] 0.087107570 0.17421514 0.9128924
[9,] 0.158212515 0.31642503 0.8417875
[10,] 0.122116624 0.24423325 0.8778834
[11,] 0.169047755 0.33809551 0.8309522
[12,] 0.235731316 0.47146263 0.7642687
[13,] 0.208334747 0.41666949 0.7916653
[14,] 0.256393033 0.51278607 0.7436070
[15,] 0.191962711 0.38392542 0.8080373
[16,] 0.150430187 0.30086037 0.8495698
[17,] 0.125460807 0.25092161 0.8745392
[18,] 0.382323428 0.76464686 0.6176766
[19,] 0.468394710 0.93678942 0.5316053
[20,] 0.764225821 0.47154836 0.2357742
[21,] 0.665277536 0.66944493 0.3347225
[22,] 0.674366110 0.65126778 0.3256339
[23,] 0.664693882 0.67061224 0.3353061
[24,] 0.560692153 0.87861569 0.4393078
[25,] 0.436604016 0.87320803 0.5633960
> postscript(file="/var/www/html/rcomp/tmp/1o46p1258479363.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2npuh1258479363.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3kb721258479363.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lucb1258479363.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/559301258479363.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 = 68
Frequency = 1
1 2 3 4 5
0.0522378625 -0.1045961994 -0.2470581652 0.1371059755 0.0529972242
6 7 8 9 10
0.2977889487 0.0494913261 0.1672645932 -0.0224652676 0.1084877199
11 12 13 14 15
0.1130211242 -0.2134234332 -0.1146289330 -0.0976805505 -0.1611951055
16 17 18 19 20
0.1297690451 -0.2067136480 0.0494379774 0.0006546036 0.0181556639
21 22 23 24 25
-0.0951656544 0.0808934905 0.0208403641 -0.0322520634 0.0295330555
26 27 28 29 30
0.0247864454 0.1239060206 0.0362524515 -0.1191191445 -0.2666439161
31 32 33 34 35
0.1813865470 0.1892935462 -0.0361922819 -0.0291436804 -0.0683302527
36 37 38 39 40
0.1039263908 0.1252891587 0.0260175532 0.3443054242 -0.1449233521
41 42 43 44 45
-0.2183910228 0.0157149486 -0.1337350377 -0.2294504394 0.1630894981
46 47 48 49 50
-0.1077015282 -0.0065854324 -0.0727957944 -0.0292559218 -0.0217889910
51 52 53 54 55
-0.1846827841 -0.1728714131 0.2053309948 0.0332880912 -0.1624259093
56 57 58 59 60
-0.1147436672 -0.0092662942 -0.0525360018 -0.0589458032 0.2145449003
61 62 63 64 65
-0.0631752219 0.1732617424 0.1247246101 0.0146672930 0.2858955962
66 67 68
-0.1295860499 0.0646284702 -0.0305196967
> postscript(file="/var/www/html/rcomp/tmp/67z151258479363.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 = 68
Frequency = 1
lag(myerror, k = 1) myerror
0 0.0522378625 NA
1 -0.1045961994 0.0522378625
2 -0.2470581652 -0.1045961994
3 0.1371059755 -0.2470581652
4 0.0529972242 0.1371059755
5 0.2977889487 0.0529972242
6 0.0494913261 0.2977889487
7 0.1672645932 0.0494913261
8 -0.0224652676 0.1672645932
9 0.1084877199 -0.0224652676
10 0.1130211242 0.1084877199
11 -0.2134234332 0.1130211242
12 -0.1146289330 -0.2134234332
13 -0.0976805505 -0.1146289330
14 -0.1611951055 -0.0976805505
15 0.1297690451 -0.1611951055
16 -0.2067136480 0.1297690451
17 0.0494379774 -0.2067136480
18 0.0006546036 0.0494379774
19 0.0181556639 0.0006546036
20 -0.0951656544 0.0181556639
21 0.0808934905 -0.0951656544
22 0.0208403641 0.0808934905
23 -0.0322520634 0.0208403641
24 0.0295330555 -0.0322520634
25 0.0247864454 0.0295330555
26 0.1239060206 0.0247864454
27 0.0362524515 0.1239060206
28 -0.1191191445 0.0362524515
29 -0.2666439161 -0.1191191445
30 0.1813865470 -0.2666439161
31 0.1892935462 0.1813865470
32 -0.0361922819 0.1892935462
33 -0.0291436804 -0.0361922819
34 -0.0683302527 -0.0291436804
35 0.1039263908 -0.0683302527
36 0.1252891587 0.1039263908
37 0.0260175532 0.1252891587
38 0.3443054242 0.0260175532
39 -0.1449233521 0.3443054242
40 -0.2183910228 -0.1449233521
41 0.0157149486 -0.2183910228
42 -0.1337350377 0.0157149486
43 -0.2294504394 -0.1337350377
44 0.1630894981 -0.2294504394
45 -0.1077015282 0.1630894981
46 -0.0065854324 -0.1077015282
47 -0.0727957944 -0.0065854324
48 -0.0292559218 -0.0727957944
49 -0.0217889910 -0.0292559218
50 -0.1846827841 -0.0217889910
51 -0.1728714131 -0.1846827841
52 0.2053309948 -0.1728714131
53 0.0332880912 0.2053309948
54 -0.1624259093 0.0332880912
55 -0.1147436672 -0.1624259093
56 -0.0092662942 -0.1147436672
57 -0.0525360018 -0.0092662942
58 -0.0589458032 -0.0525360018
59 0.2145449003 -0.0589458032
60 -0.0631752219 0.2145449003
61 0.1732617424 -0.0631752219
62 0.1247246101 0.1732617424
63 0.0146672930 0.1247246101
64 0.2858955962 0.0146672930
65 -0.1295860499 0.2858955962
66 0.0646284702 -0.1295860499
67 -0.0305196967 0.0646284702
68 NA -0.0305196967
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.1045961994 0.0522378625
[2,] -0.2470581652 -0.1045961994
[3,] 0.1371059755 -0.2470581652
[4,] 0.0529972242 0.1371059755
[5,] 0.2977889487 0.0529972242
[6,] 0.0494913261 0.2977889487
[7,] 0.1672645932 0.0494913261
[8,] -0.0224652676 0.1672645932
[9,] 0.1084877199 -0.0224652676
[10,] 0.1130211242 0.1084877199
[11,] -0.2134234332 0.1130211242
[12,] -0.1146289330 -0.2134234332
[13,] -0.0976805505 -0.1146289330
[14,] -0.1611951055 -0.0976805505
[15,] 0.1297690451 -0.1611951055
[16,] -0.2067136480 0.1297690451
[17,] 0.0494379774 -0.2067136480
[18,] 0.0006546036 0.0494379774
[19,] 0.0181556639 0.0006546036
[20,] -0.0951656544 0.0181556639
[21,] 0.0808934905 -0.0951656544
[22,] 0.0208403641 0.0808934905
[23,] -0.0322520634 0.0208403641
[24,] 0.0295330555 -0.0322520634
[25,] 0.0247864454 0.0295330555
[26,] 0.1239060206 0.0247864454
[27,] 0.0362524515 0.1239060206
[28,] -0.1191191445 0.0362524515
[29,] -0.2666439161 -0.1191191445
[30,] 0.1813865470 -0.2666439161
[31,] 0.1892935462 0.1813865470
[32,] -0.0361922819 0.1892935462
[33,] -0.0291436804 -0.0361922819
[34,] -0.0683302527 -0.0291436804
[35,] 0.1039263908 -0.0683302527
[36,] 0.1252891587 0.1039263908
[37,] 0.0260175532 0.1252891587
[38,] 0.3443054242 0.0260175532
[39,] -0.1449233521 0.3443054242
[40,] -0.2183910228 -0.1449233521
[41,] 0.0157149486 -0.2183910228
[42,] -0.1337350377 0.0157149486
[43,] -0.2294504394 -0.1337350377
[44,] 0.1630894981 -0.2294504394
[45,] -0.1077015282 0.1630894981
[46,] -0.0065854324 -0.1077015282
[47,] -0.0727957944 -0.0065854324
[48,] -0.0292559218 -0.0727957944
[49,] -0.0217889910 -0.0292559218
[50,] -0.1846827841 -0.0217889910
[51,] -0.1728714131 -0.1846827841
[52,] 0.2053309948 -0.1728714131
[53,] 0.0332880912 0.2053309948
[54,] -0.1624259093 0.0332880912
[55,] -0.1147436672 -0.1624259093
[56,] -0.0092662942 -0.1147436672
[57,] -0.0525360018 -0.0092662942
[58,] -0.0589458032 -0.0525360018
[59,] 0.2145449003 -0.0589458032
[60,] -0.0631752219 0.2145449003
[61,] 0.1732617424 -0.0631752219
[62,] 0.1247246101 0.1732617424
[63,] 0.0146672930 0.1247246101
[64,] 0.2858955962 0.0146672930
[65,] -0.1295860499 0.2858955962
[66,] 0.0646284702 -0.1295860499
[67,] -0.0305196967 0.0646284702
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.1045961994 0.0522378625
2 -0.2470581652 -0.1045961994
3 0.1371059755 -0.2470581652
4 0.0529972242 0.1371059755
5 0.2977889487 0.0529972242
6 0.0494913261 0.2977889487
7 0.1672645932 0.0494913261
8 -0.0224652676 0.1672645932
9 0.1084877199 -0.0224652676
10 0.1130211242 0.1084877199
11 -0.2134234332 0.1130211242
12 -0.1146289330 -0.2134234332
13 -0.0976805505 -0.1146289330
14 -0.1611951055 -0.0976805505
15 0.1297690451 -0.1611951055
16 -0.2067136480 0.1297690451
17 0.0494379774 -0.2067136480
18 0.0006546036 0.0494379774
19 0.0181556639 0.0006546036
20 -0.0951656544 0.0181556639
21 0.0808934905 -0.0951656544
22 0.0208403641 0.0808934905
23 -0.0322520634 0.0208403641
24 0.0295330555 -0.0322520634
25 0.0247864454 0.0295330555
26 0.1239060206 0.0247864454
27 0.0362524515 0.1239060206
28 -0.1191191445 0.0362524515
29 -0.2666439161 -0.1191191445
30 0.1813865470 -0.2666439161
31 0.1892935462 0.1813865470
32 -0.0361922819 0.1892935462
33 -0.0291436804 -0.0361922819
34 -0.0683302527 -0.0291436804
35 0.1039263908 -0.0683302527
36 0.1252891587 0.1039263908
37 0.0260175532 0.1252891587
38 0.3443054242 0.0260175532
39 -0.1449233521 0.3443054242
40 -0.2183910228 -0.1449233521
41 0.0157149486 -0.2183910228
42 -0.1337350377 0.0157149486
43 -0.2294504394 -0.1337350377
44 0.1630894981 -0.2294504394
45 -0.1077015282 0.1630894981
46 -0.0065854324 -0.1077015282
47 -0.0727957944 -0.0065854324
48 -0.0292559218 -0.0727957944
49 -0.0217889910 -0.0292559218
50 -0.1846827841 -0.0217889910
51 -0.1728714131 -0.1846827841
52 0.2053309948 -0.1728714131
53 0.0332880912 0.2053309948
54 -0.1624259093 0.0332880912
55 -0.1147436672 -0.1624259093
56 -0.0092662942 -0.1147436672
57 -0.0525360018 -0.0092662942
58 -0.0589458032 -0.0525360018
59 0.2145449003 -0.0589458032
60 -0.0631752219 0.2145449003
61 0.1732617424 -0.0631752219
62 0.1247246101 0.1732617424
63 0.0146672930 0.1247246101
64 0.2858955962 0.0146672930
65 -0.1295860499 0.2858955962
66 0.0646284702 -0.1295860499
67 -0.0305196967 0.0646284702
> 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/77c211258479363.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8smiz1258479363.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/98ucl1258479363.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10lxzn1258479363.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wyk91258479363.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/12rr3z1258479363.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/13bbc21258479363.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/14z5r61258479363.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/15y16r1258479363.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/16tvha1258479363.tab")
+ }
>
> system("convert tmp/1o46p1258479363.ps tmp/1o46p1258479363.png")
> system("convert tmp/2npuh1258479363.ps tmp/2npuh1258479363.png")
> system("convert tmp/3kb721258479363.ps tmp/3kb721258479363.png")
> system("convert tmp/4lucb1258479363.ps tmp/4lucb1258479363.png")
> system("convert tmp/559301258479363.ps tmp/559301258479363.png")
> system("convert tmp/67z151258479363.ps tmp/67z151258479363.png")
> system("convert tmp/77c211258479363.ps tmp/77c211258479363.png")
> system("convert tmp/8smiz1258479363.ps tmp/8smiz1258479363.png")
> system("convert tmp/98ucl1258479363.ps tmp/98ucl1258479363.png")
> system("convert tmp/10lxzn1258479363.ps tmp/10lxzn1258479363.png")
>
>
> proc.time()
user system elapsed
2.526 1.566 3.966