R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(4
+ ,1
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,4
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,1
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,4
+ ,1
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,1
+ ,1
+ ,4
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,1
+ ,1
+ ,4
+ ,1
+ ,1
+ ,0
+ ,1
+ ,1
+ ,1
+ ,0
+ ,4
+ ,1
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,1
+ ,0
+ ,1
+ ,1
+ ,1
+ ,1
+ ,4
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,4
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,1
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,1
+ ,4
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,1
+ ,4
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,4
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,1
+ ,1
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,1
+ ,4
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,1
+ ,1
+ ,1
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,4
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,1
+ ,4
+ ,1
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,4
+ ,1
+ ,1
+ ,0
+ ,1
+ ,1
+ ,1
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,1
+ ,1
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,1
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,4
+ ,1
+ ,1
+ ,0
+ ,1
+ ,1
+ ,1
+ ,1
+ ,4
+ ,1
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,1
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,1
+ ,0
+ ,1
+ ,1
+ ,1
+ ,0
+ ,4
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,4
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,1
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,1
+ ,1
+ ,4
+ ,0
+ ,1
+ ,0
+ ,1
+ ,1
+ ,0
+ ,1
+ ,4
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,1
+ ,1
+ ,0
+ ,0
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,1
+ ,4
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,1
+ ,2
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,1
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,2
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,1
+ ,2
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,1
+ ,2
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,1
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,1
+ ,0
+ ,2
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,1
+ ,0
+ ,0
+ ,0
+ ,2
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,2
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,1
+ ,2
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,1
+ ,2
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,1
+ ,0
+ ,1
+ ,1
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,1
+ ,2
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,1
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,1
+ ,2
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,1
+ ,2
+ ,1
+ ,0
+ ,1
+ ,1
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,1
+ ,0
+ ,1
+ ,1
+ ,0
+ ,1
+ ,1
+ ,2
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,1
+ ,1
+ ,2
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,1
+ ,1
+ ,0
+ ,1
+ ,2
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,2
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,1
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,0
+ ,2
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,2
+ ,0
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,1
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,1
+ ,1
+ ,2
+ ,0
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,1
+ ,2
+ ,1
+ ,0
+ ,1
+ ,1
+ ,1
+ ,0
+ ,0
+ ,2
+ ,1
+ ,0
+ ,1
+ ,1
+ ,1
+ ,1
+ ,0
+ ,2
+ ,1
+ ,0
+ ,1
+ ,1
+ ,0
+ ,0
+ ,0)
+ ,dim=c(8
+ ,154)
+ ,dimnames=list(c('weeks'
+ ,'uselimit'
+ ,'T40'
+ ,'T20'
+ ,'Used'
+ ,'CorrectAnalysis'
+ ,'useful'
+ ,'outcome')
+ ,1:154))
> y <- array(NA,dim=c(8,154),dimnames=list(c('weeks','uselimit','T40','T20','Used','CorrectAnalysis','useful','outcome'),1:154))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
weeks uselimit T40 T20 Used CorrectAnalysis useful outcome
1 4 1 1 0 0 0 0 1
2 4 0 0 0 0 0 0 0
3 4 0 0 0 0 0 0 0
4 4 0 0 0 0 0 0 0
5 4 0 0 0 0 0 0 0
6 4 1 0 0 0 0 1 1
7 4 0 0 0 0 0 0 0
8 4 0 1 0 0 0 0 0
9 4 0 0 0 0 0 0 1
10 4 1 0 0 0 0 0 0
11 4 1 1 0 0 0 0 0
12 4 0 0 0 0 0 0 0
13 4 0 0 0 1 0 1 0
14 4 1 1 0 0 0 0 0
15 4 0 0 0 1 0 1 1
16 4 0 1 0 1 0 1 1
17 4 1 1 0 1 1 1 0
18 4 1 1 0 0 0 0 0
19 4 0 0 0 0 0 0 1
20 4 0 1 0 1 1 1 1
21 4 1 0 0 0 0 1 0
22 4 1 0 0 1 0 1 1
23 4 0 0 0 0 0 1 1
24 4 1 0 0 0 0 1 1
25 4 0 1 0 1 0 0 1
26 4 0 0 0 1 0 1 0
27 4 1 0 0 0 0 0 1
28 4 0 0 0 1 0 0 0
29 4 0 0 0 0 0 0 1
30 4 0 0 0 0 0 1 0
31 4 0 0 0 0 0 0 0
32 4 1 0 0 0 0 0 0
33 4 1 0 0 0 0 1 0
34 4 0 1 0 0 0 0 1
35 4 0 0 0 0 0 0 0
36 4 0 0 0 0 0 0 0
37 4 1 1 0 1 0 1 0
38 4 0 0 0 1 0 0 1
39 4 0 0 0 0 0 1 1
40 4 0 1 0 0 0 1 0
41 4 0 0 0 1 1 1 1
42 4 0 0 0 1 0 0 1
43 4 1 0 0 0 0 1 1
44 4 1 1 0 0 0 0 0
45 4 0 0 0 0 0 1 0
46 4 0 0 0 0 0 1 1
47 4 0 0 0 0 0 0 0
48 4 0 0 0 0 0 0 1
49 4 0 0 0 0 0 1 1
50 4 0 0 0 0 0 0 0
51 4 0 1 0 1 0 0 0
52 4 1 1 0 1 1 1 0
53 4 0 0 0 0 0 0 1
54 4 0 0 0 1 1 0 0
55 4 0 0 0 0 0 0 0
56 4 0 1 0 1 0 0 1
57 4 0 0 0 1 0 1 1
58 4 0 0 0 0 0 0 1
59 4 0 0 0 0 0 0 1
60 4 1 1 0 1 1 1 1
61 4 1 1 0 0 0 0 1
62 4 0 0 0 1 0 1 0
63 4 0 0 0 0 0 0 0
64 4 1 1 0 0 0 0 1
65 4 0 0 0 0 0 0 0
66 4 0 0 0 0 0 0 0
67 4 0 1 0 1 1 1 0
68 4 1 0 0 0 0 0 0
69 4 0 0 0 0 0 0 1
70 4 0 0 0 1 0 0 0
71 4 0 0 0 0 0 0 0
72 4 0 0 0 0 0 0 1
73 4 0 0 0 1 0 0 1
74 4 1 0 0 1 0 0 0
75 4 0 0 0 0 0 0 1
76 4 0 1 0 0 0 1 1
77 4 0 0 0 0 0 0 1
78 4 0 0 0 1 0 1 1
79 4 0 1 0 1 1 0 1
80 4 0 1 0 0 0 1 0
81 4 0 0 0 0 0 0 0
82 4 1 0 0 1 0 0 1
83 4 0 0 0 0 0 0 0
84 4 0 0 0 1 1 0 0
85 4 0 0 0 0 0 1 1
86 4 1 0 0 0 0 0 0
87 2 1 0 1 0 0 0 1
88 2 1 0 0 1 0 0 1
89 2 0 0 1 0 0 0 0
90 2 0 0 1 0 0 0 1
91 2 0 0 1 0 0 1 0
92 2 1 0 0 0 0 0 0
93 2 1 0 1 0 0 1 0
94 2 0 0 1 0 0 0 0
95 2 0 0 0 0 0 0 0
96 2 0 0 1 0 0 0 1
97 2 1 0 0 0 0 0 0
98 2 0 0 1 0 0 0 0
99 2 1 0 1 0 0 0 0
100 2 0 0 1 0 0 0 1
101 2 1 0 1 0 0 0 1
102 2 0 0 1 0 0 0 0
103 2 0 0 1 0 0 0 0
104 2 0 0 1 0 0 0 0
105 2 0 0 0 1 0 0 0
106 2 0 0 1 0 0 0 0
107 2 0 0 1 0 0 0 0
108 2 1 0 0 1 0 0 0
109 2 0 0 1 0 0 0 0
110 2 1 0 1 0 0 0 0
111 2 1 0 0 1 0 1 0
112 2 0 0 0 0 0 0 0
113 2 0 0 1 1 0 0 0
114 2 1 0 0 1 0 0 0
115 2 1 0 1 0 0 0 0
116 2 0 0 1 0 0 0 0
117 2 1 0 1 0 0 0 1
118 2 1 0 1 0 0 0 0
119 2 0 0 1 0 0 0 0
120 2 0 0 1 0 0 0 1
121 2 1 0 1 0 0 0 0
122 2 0 0 1 0 0 0 0
123 2 1 0 0 1 0 0 0
124 2 0 0 1 1 0 1 1
125 2 0 0 1 0 0 0 1
126 2 0 0 0 0 0 0 0
127 2 0 0 1 0 0 1 0
128 2 0 0 1 0 0 0 1
129 2 0 0 1 0 0 0 0
130 2 0 0 1 0 0 0 1
131 2 1 0 1 0 0 0 0
132 2 1 0 1 0 0 0 1
133 2 1 0 1 1 0 0 0
134 2 0 0 1 0 0 0 0
135 2 0 0 1 0 0 0 0
136 2 0 0 1 0 0 0 0
137 2 1 0 1 1 0 1 1
138 2 1 0 0 1 0 1 1
139 2 0 0 0 0 0 0 0
140 2 0 0 1 0 0 0 0
141 2 0 0 1 1 1 0 1
142 2 0 0 0 1 0 0 1
143 2 1 0 1 0 0 0 0
144 2 0 0 1 0 0 1 1
145 2 0 0 1 0 0 1 0
146 2 0 0 0 0 0 0 1
147 2 0 0 0 1 0 0 0
148 2 0 0 0 0 0 0 0
149 2 1 0 1 0 0 0 0
150 2 0 0 1 0 0 1 1
151 2 0 0 1 0 0 0 1
152 2 1 0 1 1 1 0 0
153 2 1 0 1 1 1 1 0
154 2 1 0 1 1 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) uselimit T40 T20
3.6076 -0.1798 0.3992 -1.5938
Used CorrectAnalysis useful outcome
-0.3584 0.3399 0.2132 0.1448
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.7524 -0.1116 0.1660 0.3924 0.9307
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.60756 0.08689 41.517 < 2e-16 ***
uselimit -0.17979 0.10035 -1.792 0.07528 .
T40 0.39921 0.14264 2.799 0.00582 **
T20 -1.59378 0.10527 -15.140 < 2e-16 ***
Used -0.35844 0.11818 -3.033 0.00287 **
CorrectAnalysis 0.33991 0.20034 1.697 0.09189 .
useful 0.21321 0.11027 1.934 0.05511 .
outcome 0.14484 0.09618 1.506 0.13424
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5694 on 146 degrees of freedom
Multiple R-squared: 0.6883, Adjusted R-squared: 0.6734
F-statistic: 46.07 on 7 and 146 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,] 5.822058e-49 1.164412e-48 1.000000e+00
[2,] 8.360088e-61 1.672018e-60 1.000000e+00
[3,] 2.981255e-86 5.962511e-86 1.000000e+00
[4,] 3.534644e-90 7.069288e-90 1.000000e+00
[5,] 5.534773e-105 1.106955e-104 1.000000e+00
[6,] 0.000000e+00 0.000000e+00 1.000000e+00
[7,] 2.806482e-145 5.612964e-145 1.000000e+00
[8,] 3.914057e-151 7.828114e-151 1.000000e+00
[9,] 7.114211e-165 1.422842e-164 1.000000e+00
[10,] 1.063469e-188 2.126938e-188 1.000000e+00
[11,] 6.762851e-221 1.352570e-220 1.000000e+00
[12,] 1.579212e-212 3.158424e-212 1.000000e+00
[13,] 6.071370e-224 1.214274e-223 1.000000e+00
[14,] 7.183946e-242 1.436789e-241 1.000000e+00
[15,] 5.174273e-260 1.034855e-259 1.000000e+00
[16,] 1.203398e-300 2.406795e-300 1.000000e+00
[17,] 8.075652e-289 1.615130e-288 1.000000e+00
[18,] 5.411977e-299 1.082395e-298 1.000000e+00
[19,] 6.764747e-319 1.352949e-318 1.000000e+00
[20,] 0.000000e+00 0.000000e+00 1.000000e+00
[21,] 0.000000e+00 0.000000e+00 1.000000e+00
[22,] 0.000000e+00 0.000000e+00 1.000000e+00
[23,] 0.000000e+00 0.000000e+00 1.000000e+00
[24,] 0.000000e+00 0.000000e+00 1.000000e+00
[25,] 0.000000e+00 0.000000e+00 1.000000e+00
[26,] 0.000000e+00 0.000000e+00 1.000000e+00
[27,] 0.000000e+00 0.000000e+00 1.000000e+00
[28,] 0.000000e+00 0.000000e+00 1.000000e+00
[29,] 0.000000e+00 0.000000e+00 1.000000e+00
[30,] 0.000000e+00 0.000000e+00 1.000000e+00
[31,] 0.000000e+00 0.000000e+00 1.000000e+00
[32,] 0.000000e+00 0.000000e+00 1.000000e+00
[33,] 0.000000e+00 0.000000e+00 1.000000e+00
[34,] 0.000000e+00 0.000000e+00 1.000000e+00
[35,] 0.000000e+00 0.000000e+00 1.000000e+00
[36,] 0.000000e+00 0.000000e+00 1.000000e+00
[37,] 0.000000e+00 0.000000e+00 1.000000e+00
[38,] 0.000000e+00 0.000000e+00 1.000000e+00
[39,] 0.000000e+00 0.000000e+00 1.000000e+00
[40,] 0.000000e+00 0.000000e+00 1.000000e+00
[41,] 0.000000e+00 0.000000e+00 1.000000e+00
[42,] 0.000000e+00 0.000000e+00 1.000000e+00
[43,] 0.000000e+00 0.000000e+00 1.000000e+00
[44,] 0.000000e+00 0.000000e+00 1.000000e+00
[45,] 0.000000e+00 0.000000e+00 1.000000e+00
[46,] 0.000000e+00 0.000000e+00 1.000000e+00
[47,] 0.000000e+00 0.000000e+00 1.000000e+00
[48,] 0.000000e+00 0.000000e+00 1.000000e+00
[49,] 0.000000e+00 0.000000e+00 1.000000e+00
[50,] 0.000000e+00 0.000000e+00 1.000000e+00
[51,] 0.000000e+00 0.000000e+00 1.000000e+00
[52,] 0.000000e+00 0.000000e+00 1.000000e+00
[53,] 0.000000e+00 0.000000e+00 1.000000e+00
[54,] 0.000000e+00 0.000000e+00 1.000000e+00
[55,] 0.000000e+00 0.000000e+00 1.000000e+00
[56,] 0.000000e+00 0.000000e+00 1.000000e+00
[57,] 0.000000e+00 0.000000e+00 1.000000e+00
[58,] 0.000000e+00 0.000000e+00 1.000000e+00
[59,] 0.000000e+00 0.000000e+00 1.000000e+00
[60,] 0.000000e+00 0.000000e+00 1.000000e+00
[61,] 0.000000e+00 0.000000e+00 1.000000e+00
[62,] 0.000000e+00 0.000000e+00 1.000000e+00
[63,] 0.000000e+00 0.000000e+00 1.000000e+00
[64,] 0.000000e+00 0.000000e+00 1.000000e+00
[65,] 0.000000e+00 0.000000e+00 1.000000e+00
[66,] 0.000000e+00 0.000000e+00 1.000000e+00
[67,] 0.000000e+00 0.000000e+00 1.000000e+00
[68,] 0.000000e+00 0.000000e+00 1.000000e+00
[69,] 0.000000e+00 0.000000e+00 1.000000e+00
[70,] 0.000000e+00 0.000000e+00 1.000000e+00
[71,] 0.000000e+00 0.000000e+00 1.000000e+00
[72,] 0.000000e+00 0.000000e+00 1.000000e+00
[73,] 0.000000e+00 0.000000e+00 1.000000e+00
[74,] 0.000000e+00 0.000000e+00 1.000000e+00
[75,] 0.000000e+00 0.000000e+00 1.000000e+00
[76,] 1.000000e+00 1.725567e-18 8.627835e-19
[77,] 2.719663e-08 5.439327e-08 1.000000e+00
[78,] 1.000000e+00 0.000000e+00 0.000000e+00
[79,] 1.000000e+00 0.000000e+00 0.000000e+00
[80,] 1.000000e+00 0.000000e+00 0.000000e+00
[81,] 1.000000e+00 0.000000e+00 0.000000e+00
[82,] 1.000000e+00 0.000000e+00 0.000000e+00
[83,] 1.000000e+00 0.000000e+00 0.000000e+00
[84,] 1.000000e+00 0.000000e+00 0.000000e+00
[85,] 1.000000e+00 0.000000e+00 0.000000e+00
[86,] 1.000000e+00 0.000000e+00 0.000000e+00
[87,] 1.000000e+00 0.000000e+00 0.000000e+00
[88,] 1.000000e+00 0.000000e+00 0.000000e+00
[89,] 1.000000e+00 0.000000e+00 0.000000e+00
[90,] 1.000000e+00 0.000000e+00 0.000000e+00
[91,] 1.000000e+00 0.000000e+00 0.000000e+00
[92,] 1.000000e+00 0.000000e+00 0.000000e+00
[93,] 1.000000e+00 0.000000e+00 0.000000e+00
[94,] 1.000000e+00 0.000000e+00 0.000000e+00
[95,] 1.000000e+00 0.000000e+00 0.000000e+00
[96,] 1.000000e+00 0.000000e+00 0.000000e+00
[97,] 1.000000e+00 0.000000e+00 0.000000e+00
[98,] 1.000000e+00 0.000000e+00 0.000000e+00
[99,] 1.000000e+00 0.000000e+00 0.000000e+00
[100,] 1.000000e+00 0.000000e+00 0.000000e+00
[101,] 1.000000e+00 0.000000e+00 0.000000e+00
[102,] 1.000000e+00 0.000000e+00 0.000000e+00
[103,] 1.000000e+00 0.000000e+00 0.000000e+00
[104,] 1.000000e+00 0.000000e+00 0.000000e+00
[105,] 1.000000e+00 0.000000e+00 0.000000e+00
[106,] 1.000000e+00 0.000000e+00 0.000000e+00
[107,] 1.000000e+00 0.000000e+00 0.000000e+00
[108,] 1.000000e+00 0.000000e+00 0.000000e+00
[109,] 1.000000e+00 0.000000e+00 0.000000e+00
[110,] 1.000000e+00 0.000000e+00 0.000000e+00
[111,] 1.000000e+00 0.000000e+00 0.000000e+00
[112,] 1.000000e+00 0.000000e+00 0.000000e+00
[113,] 1.000000e+00 0.000000e+00 0.000000e+00
[114,] 1.000000e+00 0.000000e+00 0.000000e+00
[115,] 1.000000e+00 8.893182e-323 4.446591e-323
[116,] 1.000000e+00 2.609495e-303 1.304747e-303
[117,] 1.000000e+00 2.406144e-292 1.203072e-292
[118,] 1.000000e+00 1.251262e-303 6.256309e-304
[119,] 1.000000e+00 6.293505e-263 3.146752e-263
[120,] 1.000000e+00 2.251000e-244 1.125500e-244
[121,] 1.000000e+00 3.316047e-227 1.658023e-227
[122,] 1.000000e+00 4.705588e-215 2.352794e-215
[123,] 1.000000e+00 1.427753e-222 7.138764e-223
[124,] 1.000000e+00 3.610384e-190 1.805192e-190
[125,] 1.000000e+00 2.802131e-166 1.401065e-166
[126,] 1.000000e+00 9.748056e-153 4.874028e-153
[127,] 1.000000e+00 7.891768e-148 3.945884e-148
[128,] 1.000000e+00 0.000000e+00 0.000000e+00
[129,] 1.000000e+00 3.005973e-106 1.502986e-106
[130,] 1.000000e+00 9.650293e-92 4.825147e-92
[131,] 1.000000e+00 6.551151e-87 3.275576e-87
[132,] 1.000000e+00 4.179806e-61 2.089903e-61
[133,] 1.000000e+00 4.828536e-49 2.414268e-49
> postscript(file="/var/wessaorg/rcomp/tmp/1ktpx1355926481.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2493f1355926481.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3hcd11355926481.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4hsmp1355926481.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5hglc1355926481.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 154
Frequency = 1
1 2 3 4 5 6
0.028178157 0.392438107 0.392438107 0.392438107 0.392438107 0.214172426
7 8 9 10 11 12
0.392438107 -0.006770111 0.247599422 0.572225059 0.173016842 0.392438107
13 14 15 16 17 18
0.537667981 0.173016842 0.392829297 -0.006378921 -0.021663445 0.173016842
19 20 21 22 23 24
0.247599422 -0.346289082 0.359011110 0.572616249 0.034385473 0.214172426
25 26 27 28 29 30
0.206835028 0.537667981 0.427386375 0.750881930 0.247599422 0.179224158
31 32 33 34 35 36
0.392438107 0.572225059 0.359011110 -0.151608795 0.392438107 0.392438107
37 38 39 40 41 42
0.318246716 0.606043246 0.034385473 -0.219984060 0.052919135 0.606043246
43 44 45 46 47 48
0.214172426 0.173016842 0.179224158 0.034385473 0.392438107 0.247599422
49 50 51 52 53 54
0.034385473 0.392438107 0.351673712 -0.021663445 0.247599422 0.410971769
55 56 57 58 59 60
0.392438107 0.206835028 0.392829297 0.247599422 0.247599422 -0.166502130
61 62 63 64 65 66
0.028178157 0.537667981 0.392438107 0.028178157 0.392438107 0.392438107
67 68 69 70 71 72
-0.201450398 0.572225059 0.247599422 0.750881930 0.392438107 0.247599422
73 74 75 76 77 78
0.606043246 0.930668882 0.247599422 -0.364822744 0.247599422 0.392829297
79 80 81 82 83 84
-0.133075133 -0.219984060 0.392438107 0.785830198 0.392438107 0.410971769
85 86 87 88 89 90
0.034385473 0.572225059 0.021167835 -1.214169802 -0.013780433 -0.158619117
91 92 93 94 95 96
-0.226994382 -1.427774941 -0.047207429 -0.013780433 -1.607561893 -0.158619117
97 98 99 100 101 102
-1.427774941 -0.013780433 0.166006520 -0.158619117 0.021167835 -0.013780433
103 104 105 106 107 108
-0.013780433 -0.013780433 -1.249118070 -0.013780433 -0.013780433 -1.069331118
109 110 111 112 113 114
-0.013780433 0.166006520 -1.282545067 -1.607561893 0.344663390 -1.069331118
115 116 117 118 119 120
0.166006520 -0.013780433 0.021167835 0.166006520 -0.013780433 -0.158619117
121 122 123 124 125 126
0.166006520 -0.013780433 -1.069331118 -0.013389243 -0.158619117 -1.607561893
127 128 129 130 131 132
-0.226994382 -0.158619117 -0.013780433 -0.158619117 0.166006520 0.021167835
133 134 135 136 137 138
0.524450343 -0.013780433 -0.013780433 -0.013780433 0.166397710 -1.427383751
139 140 141 142 143 144
-1.607561893 -0.013780433 -0.140085455 -1.393956754 0.166006520 -0.371833066
145 146 147 148 149 150
-0.226994382 -1.752400578 -1.249118070 -1.607561893 0.166006520 -0.371833066
151 152 153 154
-0.158619117 0.184540182 -0.028673767 0.524450343
> postscript(file="/var/wessaorg/rcomp/tmp/6xv981355926481.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 154
Frequency = 1
lag(myerror, k = 1) myerror
0 0.028178157 NA
1 0.392438107 0.028178157
2 0.392438107 0.392438107
3 0.392438107 0.392438107
4 0.392438107 0.392438107
5 0.214172426 0.392438107
6 0.392438107 0.214172426
7 -0.006770111 0.392438107
8 0.247599422 -0.006770111
9 0.572225059 0.247599422
10 0.173016842 0.572225059
11 0.392438107 0.173016842
12 0.537667981 0.392438107
13 0.173016842 0.537667981
14 0.392829297 0.173016842
15 -0.006378921 0.392829297
16 -0.021663445 -0.006378921
17 0.173016842 -0.021663445
18 0.247599422 0.173016842
19 -0.346289082 0.247599422
20 0.359011110 -0.346289082
21 0.572616249 0.359011110
22 0.034385473 0.572616249
23 0.214172426 0.034385473
24 0.206835028 0.214172426
25 0.537667981 0.206835028
26 0.427386375 0.537667981
27 0.750881930 0.427386375
28 0.247599422 0.750881930
29 0.179224158 0.247599422
30 0.392438107 0.179224158
31 0.572225059 0.392438107
32 0.359011110 0.572225059
33 -0.151608795 0.359011110
34 0.392438107 -0.151608795
35 0.392438107 0.392438107
36 0.318246716 0.392438107
37 0.606043246 0.318246716
38 0.034385473 0.606043246
39 -0.219984060 0.034385473
40 0.052919135 -0.219984060
41 0.606043246 0.052919135
42 0.214172426 0.606043246
43 0.173016842 0.214172426
44 0.179224158 0.173016842
45 0.034385473 0.179224158
46 0.392438107 0.034385473
47 0.247599422 0.392438107
48 0.034385473 0.247599422
49 0.392438107 0.034385473
50 0.351673712 0.392438107
51 -0.021663445 0.351673712
52 0.247599422 -0.021663445
53 0.410971769 0.247599422
54 0.392438107 0.410971769
55 0.206835028 0.392438107
56 0.392829297 0.206835028
57 0.247599422 0.392829297
58 0.247599422 0.247599422
59 -0.166502130 0.247599422
60 0.028178157 -0.166502130
61 0.537667981 0.028178157
62 0.392438107 0.537667981
63 0.028178157 0.392438107
64 0.392438107 0.028178157
65 0.392438107 0.392438107
66 -0.201450398 0.392438107
67 0.572225059 -0.201450398
68 0.247599422 0.572225059
69 0.750881930 0.247599422
70 0.392438107 0.750881930
71 0.247599422 0.392438107
72 0.606043246 0.247599422
73 0.930668882 0.606043246
74 0.247599422 0.930668882
75 -0.364822744 0.247599422
76 0.247599422 -0.364822744
77 0.392829297 0.247599422
78 -0.133075133 0.392829297
79 -0.219984060 -0.133075133
80 0.392438107 -0.219984060
81 0.785830198 0.392438107
82 0.392438107 0.785830198
83 0.410971769 0.392438107
84 0.034385473 0.410971769
85 0.572225059 0.034385473
86 0.021167835 0.572225059
87 -1.214169802 0.021167835
88 -0.013780433 -1.214169802
89 -0.158619117 -0.013780433
90 -0.226994382 -0.158619117
91 -1.427774941 -0.226994382
92 -0.047207429 -1.427774941
93 -0.013780433 -0.047207429
94 -1.607561893 -0.013780433
95 -0.158619117 -1.607561893
96 -1.427774941 -0.158619117
97 -0.013780433 -1.427774941
98 0.166006520 -0.013780433
99 -0.158619117 0.166006520
100 0.021167835 -0.158619117
101 -0.013780433 0.021167835
102 -0.013780433 -0.013780433
103 -0.013780433 -0.013780433
104 -1.249118070 -0.013780433
105 -0.013780433 -1.249118070
106 -0.013780433 -0.013780433
107 -1.069331118 -0.013780433
108 -0.013780433 -1.069331118
109 0.166006520 -0.013780433
110 -1.282545067 0.166006520
111 -1.607561893 -1.282545067
112 0.344663390 -1.607561893
113 -1.069331118 0.344663390
114 0.166006520 -1.069331118
115 -0.013780433 0.166006520
116 0.021167835 -0.013780433
117 0.166006520 0.021167835
118 -0.013780433 0.166006520
119 -0.158619117 -0.013780433
120 0.166006520 -0.158619117
121 -0.013780433 0.166006520
122 -1.069331118 -0.013780433
123 -0.013389243 -1.069331118
124 -0.158619117 -0.013389243
125 -1.607561893 -0.158619117
126 -0.226994382 -1.607561893
127 -0.158619117 -0.226994382
128 -0.013780433 -0.158619117
129 -0.158619117 -0.013780433
130 0.166006520 -0.158619117
131 0.021167835 0.166006520
132 0.524450343 0.021167835
133 -0.013780433 0.524450343
134 -0.013780433 -0.013780433
135 -0.013780433 -0.013780433
136 0.166397710 -0.013780433
137 -1.427383751 0.166397710
138 -1.607561893 -1.427383751
139 -0.013780433 -1.607561893
140 -0.140085455 -0.013780433
141 -1.393956754 -0.140085455
142 0.166006520 -1.393956754
143 -0.371833066 0.166006520
144 -0.226994382 -0.371833066
145 -1.752400578 -0.226994382
146 -1.249118070 -1.752400578
147 -1.607561893 -1.249118070
148 0.166006520 -1.607561893
149 -0.371833066 0.166006520
150 -0.158619117 -0.371833066
151 0.184540182 -0.158619117
152 -0.028673767 0.184540182
153 0.524450343 -0.028673767
154 NA 0.524450343
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.392438107 0.028178157
[2,] 0.392438107 0.392438107
[3,] 0.392438107 0.392438107
[4,] 0.392438107 0.392438107
[5,] 0.214172426 0.392438107
[6,] 0.392438107 0.214172426
[7,] -0.006770111 0.392438107
[8,] 0.247599422 -0.006770111
[9,] 0.572225059 0.247599422
[10,] 0.173016842 0.572225059
[11,] 0.392438107 0.173016842
[12,] 0.537667981 0.392438107
[13,] 0.173016842 0.537667981
[14,] 0.392829297 0.173016842
[15,] -0.006378921 0.392829297
[16,] -0.021663445 -0.006378921
[17,] 0.173016842 -0.021663445
[18,] 0.247599422 0.173016842
[19,] -0.346289082 0.247599422
[20,] 0.359011110 -0.346289082
[21,] 0.572616249 0.359011110
[22,] 0.034385473 0.572616249
[23,] 0.214172426 0.034385473
[24,] 0.206835028 0.214172426
[25,] 0.537667981 0.206835028
[26,] 0.427386375 0.537667981
[27,] 0.750881930 0.427386375
[28,] 0.247599422 0.750881930
[29,] 0.179224158 0.247599422
[30,] 0.392438107 0.179224158
[31,] 0.572225059 0.392438107
[32,] 0.359011110 0.572225059
[33,] -0.151608795 0.359011110
[34,] 0.392438107 -0.151608795
[35,] 0.392438107 0.392438107
[36,] 0.318246716 0.392438107
[37,] 0.606043246 0.318246716
[38,] 0.034385473 0.606043246
[39,] -0.219984060 0.034385473
[40,] 0.052919135 -0.219984060
[41,] 0.606043246 0.052919135
[42,] 0.214172426 0.606043246
[43,] 0.173016842 0.214172426
[44,] 0.179224158 0.173016842
[45,] 0.034385473 0.179224158
[46,] 0.392438107 0.034385473
[47,] 0.247599422 0.392438107
[48,] 0.034385473 0.247599422
[49,] 0.392438107 0.034385473
[50,] 0.351673712 0.392438107
[51,] -0.021663445 0.351673712
[52,] 0.247599422 -0.021663445
[53,] 0.410971769 0.247599422
[54,] 0.392438107 0.410971769
[55,] 0.206835028 0.392438107
[56,] 0.392829297 0.206835028
[57,] 0.247599422 0.392829297
[58,] 0.247599422 0.247599422
[59,] -0.166502130 0.247599422
[60,] 0.028178157 -0.166502130
[61,] 0.537667981 0.028178157
[62,] 0.392438107 0.537667981
[63,] 0.028178157 0.392438107
[64,] 0.392438107 0.028178157
[65,] 0.392438107 0.392438107
[66,] -0.201450398 0.392438107
[67,] 0.572225059 -0.201450398
[68,] 0.247599422 0.572225059
[69,] 0.750881930 0.247599422
[70,] 0.392438107 0.750881930
[71,] 0.247599422 0.392438107
[72,] 0.606043246 0.247599422
[73,] 0.930668882 0.606043246
[74,] 0.247599422 0.930668882
[75,] -0.364822744 0.247599422
[76,] 0.247599422 -0.364822744
[77,] 0.392829297 0.247599422
[78,] -0.133075133 0.392829297
[79,] -0.219984060 -0.133075133
[80,] 0.392438107 -0.219984060
[81,] 0.785830198 0.392438107
[82,] 0.392438107 0.785830198
[83,] 0.410971769 0.392438107
[84,] 0.034385473 0.410971769
[85,] 0.572225059 0.034385473
[86,] 0.021167835 0.572225059
[87,] -1.214169802 0.021167835
[88,] -0.013780433 -1.214169802
[89,] -0.158619117 -0.013780433
[90,] -0.226994382 -0.158619117
[91,] -1.427774941 -0.226994382
[92,] -0.047207429 -1.427774941
[93,] -0.013780433 -0.047207429
[94,] -1.607561893 -0.013780433
[95,] -0.158619117 -1.607561893
[96,] -1.427774941 -0.158619117
[97,] -0.013780433 -1.427774941
[98,] 0.166006520 -0.013780433
[99,] -0.158619117 0.166006520
[100,] 0.021167835 -0.158619117
[101,] -0.013780433 0.021167835
[102,] -0.013780433 -0.013780433
[103,] -0.013780433 -0.013780433
[104,] -1.249118070 -0.013780433
[105,] -0.013780433 -1.249118070
[106,] -0.013780433 -0.013780433
[107,] -1.069331118 -0.013780433
[108,] -0.013780433 -1.069331118
[109,] 0.166006520 -0.013780433
[110,] -1.282545067 0.166006520
[111,] -1.607561893 -1.282545067
[112,] 0.344663390 -1.607561893
[113,] -1.069331118 0.344663390
[114,] 0.166006520 -1.069331118
[115,] -0.013780433 0.166006520
[116,] 0.021167835 -0.013780433
[117,] 0.166006520 0.021167835
[118,] -0.013780433 0.166006520
[119,] -0.158619117 -0.013780433
[120,] 0.166006520 -0.158619117
[121,] -0.013780433 0.166006520
[122,] -1.069331118 -0.013780433
[123,] -0.013389243 -1.069331118
[124,] -0.158619117 -0.013389243
[125,] -1.607561893 -0.158619117
[126,] -0.226994382 -1.607561893
[127,] -0.158619117 -0.226994382
[128,] -0.013780433 -0.158619117
[129,] -0.158619117 -0.013780433
[130,] 0.166006520 -0.158619117
[131,] 0.021167835 0.166006520
[132,] 0.524450343 0.021167835
[133,] -0.013780433 0.524450343
[134,] -0.013780433 -0.013780433
[135,] -0.013780433 -0.013780433
[136,] 0.166397710 -0.013780433
[137,] -1.427383751 0.166397710
[138,] -1.607561893 -1.427383751
[139,] -0.013780433 -1.607561893
[140,] -0.140085455 -0.013780433
[141,] -1.393956754 -0.140085455
[142,] 0.166006520 -1.393956754
[143,] -0.371833066 0.166006520
[144,] -0.226994382 -0.371833066
[145,] -1.752400578 -0.226994382
[146,] -1.249118070 -1.752400578
[147,] -1.607561893 -1.249118070
[148,] 0.166006520 -1.607561893
[149,] -0.371833066 0.166006520
[150,] -0.158619117 -0.371833066
[151,] 0.184540182 -0.158619117
[152,] -0.028673767 0.184540182
[153,] 0.524450343 -0.028673767
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.392438107 0.028178157
2 0.392438107 0.392438107
3 0.392438107 0.392438107
4 0.392438107 0.392438107
5 0.214172426 0.392438107
6 0.392438107 0.214172426
7 -0.006770111 0.392438107
8 0.247599422 -0.006770111
9 0.572225059 0.247599422
10 0.173016842 0.572225059
11 0.392438107 0.173016842
12 0.537667981 0.392438107
13 0.173016842 0.537667981
14 0.392829297 0.173016842
15 -0.006378921 0.392829297
16 -0.021663445 -0.006378921
17 0.173016842 -0.021663445
18 0.247599422 0.173016842
19 -0.346289082 0.247599422
20 0.359011110 -0.346289082
21 0.572616249 0.359011110
22 0.034385473 0.572616249
23 0.214172426 0.034385473
24 0.206835028 0.214172426
25 0.537667981 0.206835028
26 0.427386375 0.537667981
27 0.750881930 0.427386375
28 0.247599422 0.750881930
29 0.179224158 0.247599422
30 0.392438107 0.179224158
31 0.572225059 0.392438107
32 0.359011110 0.572225059
33 -0.151608795 0.359011110
34 0.392438107 -0.151608795
35 0.392438107 0.392438107
36 0.318246716 0.392438107
37 0.606043246 0.318246716
38 0.034385473 0.606043246
39 -0.219984060 0.034385473
40 0.052919135 -0.219984060
41 0.606043246 0.052919135
42 0.214172426 0.606043246
43 0.173016842 0.214172426
44 0.179224158 0.173016842
45 0.034385473 0.179224158
46 0.392438107 0.034385473
47 0.247599422 0.392438107
48 0.034385473 0.247599422
49 0.392438107 0.034385473
50 0.351673712 0.392438107
51 -0.021663445 0.351673712
52 0.247599422 -0.021663445
53 0.410971769 0.247599422
54 0.392438107 0.410971769
55 0.206835028 0.392438107
56 0.392829297 0.206835028
57 0.247599422 0.392829297
58 0.247599422 0.247599422
59 -0.166502130 0.247599422
60 0.028178157 -0.166502130
61 0.537667981 0.028178157
62 0.392438107 0.537667981
63 0.028178157 0.392438107
64 0.392438107 0.028178157
65 0.392438107 0.392438107
66 -0.201450398 0.392438107
67 0.572225059 -0.201450398
68 0.247599422 0.572225059
69 0.750881930 0.247599422
70 0.392438107 0.750881930
71 0.247599422 0.392438107
72 0.606043246 0.247599422
73 0.930668882 0.606043246
74 0.247599422 0.930668882
75 -0.364822744 0.247599422
76 0.247599422 -0.364822744
77 0.392829297 0.247599422
78 -0.133075133 0.392829297
79 -0.219984060 -0.133075133
80 0.392438107 -0.219984060
81 0.785830198 0.392438107
82 0.392438107 0.785830198
83 0.410971769 0.392438107
84 0.034385473 0.410971769
85 0.572225059 0.034385473
86 0.021167835 0.572225059
87 -1.214169802 0.021167835
88 -0.013780433 -1.214169802
89 -0.158619117 -0.013780433
90 -0.226994382 -0.158619117
91 -1.427774941 -0.226994382
92 -0.047207429 -1.427774941
93 -0.013780433 -0.047207429
94 -1.607561893 -0.013780433
95 -0.158619117 -1.607561893
96 -1.427774941 -0.158619117
97 -0.013780433 -1.427774941
98 0.166006520 -0.013780433
99 -0.158619117 0.166006520
100 0.021167835 -0.158619117
101 -0.013780433 0.021167835
102 -0.013780433 -0.013780433
103 -0.013780433 -0.013780433
104 -1.249118070 -0.013780433
105 -0.013780433 -1.249118070
106 -0.013780433 -0.013780433
107 -1.069331118 -0.013780433
108 -0.013780433 -1.069331118
109 0.166006520 -0.013780433
110 -1.282545067 0.166006520
111 -1.607561893 -1.282545067
112 0.344663390 -1.607561893
113 -1.069331118 0.344663390
114 0.166006520 -1.069331118
115 -0.013780433 0.166006520
116 0.021167835 -0.013780433
117 0.166006520 0.021167835
118 -0.013780433 0.166006520
119 -0.158619117 -0.013780433
120 0.166006520 -0.158619117
121 -0.013780433 0.166006520
122 -1.069331118 -0.013780433
123 -0.013389243 -1.069331118
124 -0.158619117 -0.013389243
125 -1.607561893 -0.158619117
126 -0.226994382 -1.607561893
127 -0.158619117 -0.226994382
128 -0.013780433 -0.158619117
129 -0.158619117 -0.013780433
130 0.166006520 -0.158619117
131 0.021167835 0.166006520
132 0.524450343 0.021167835
133 -0.013780433 0.524450343
134 -0.013780433 -0.013780433
135 -0.013780433 -0.013780433
136 0.166397710 -0.013780433
137 -1.427383751 0.166397710
138 -1.607561893 -1.427383751
139 -0.013780433 -1.607561893
140 -0.140085455 -0.013780433
141 -1.393956754 -0.140085455
142 0.166006520 -1.393956754
143 -0.371833066 0.166006520
144 -0.226994382 -0.371833066
145 -1.752400578 -0.226994382
146 -1.249118070 -1.752400578
147 -1.607561893 -1.249118070
148 0.166006520 -1.607561893
149 -0.371833066 0.166006520
150 -0.158619117 -0.371833066
151 0.184540182 -0.158619117
152 -0.028673767 0.184540182
153 0.524450343 -0.028673767
> 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/wessaorg/rcomp/tmp/7dmhm1355926481.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8q9v81355926481.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9lnze1355926481.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10kz9i1355926481.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/119t5a1355926481.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/wessaorg/rcomp/tmp/1222nq1355926481.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/wessaorg/rcomp/tmp/13f77e1355926481.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/wessaorg/rcomp/tmp/14g8d21355926481.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/wessaorg/rcomp/tmp/15x6p21355926481.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/wessaorg/rcomp/tmp/16ftpn1355926481.tab")
+ }
>
> try(system("convert tmp/1ktpx1355926481.ps tmp/1ktpx1355926481.png",intern=TRUE))
character(0)
> try(system("convert tmp/2493f1355926481.ps tmp/2493f1355926481.png",intern=TRUE))
character(0)
> try(system("convert tmp/3hcd11355926481.ps tmp/3hcd11355926481.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hsmp1355926481.ps tmp/4hsmp1355926481.png",intern=TRUE))
character(0)
> try(system("convert tmp/5hglc1355926481.ps tmp/5hglc1355926481.png",intern=TRUE))
character(0)
> try(system("convert tmp/6xv981355926481.ps tmp/6xv981355926481.png",intern=TRUE))
character(0)
> try(system("convert tmp/7dmhm1355926481.ps tmp/7dmhm1355926481.png",intern=TRUE))
character(0)
> try(system("convert tmp/8q9v81355926481.ps tmp/8q9v81355926481.png",intern=TRUE))
character(0)
> try(system("convert tmp/9lnze1355926481.ps tmp/9lnze1355926481.png",intern=TRUE))
character(0)
> try(system("convert tmp/10kz9i1355926481.ps tmp/10kz9i1355926481.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.641 1.121 10.829