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
+ ,5
+ ,8
+ ,0
+ ,11
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,5
+ ,7
+ ,0
+ ,11
+ ,14
+ ,15
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,8
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,18
+ ,4
+ ,5
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,5
+ ,8
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,12
+ ,14
+ ,15
+ ,17
+ ,4
+ ,5
+ ,8
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,12
+ ,14
+ ,15
+ ,18
+ ,4
+ ,6
+ ,8
+ ,0
+ ,12
+ ,14
+ ,15
+ ,18
+ ,4
+ ,5
+ ,8
+ ,0
+ ,12
+ ,13
+ ,15
+ ,17
+ ,4
+ ,5
+ ,8
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,8
+ ,0
+ ,12
+ ,13
+ ,15
+ ,18
+ ,4
+ ,5
+ ,7
+ ,0
+ ,11
+ ,14
+ ,15
+ ,17
+ ,4
+ ,5
+ ,7
+ ,0
+ ,12
+ ,14
+ ,15
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,15
+ ,18
+ ,4
+ ,5
+ ,7
+ ,0
+ ,11
+ ,14
+ ,15
+ ,18
+ ,4
+ ,6
+ ,8
+ ,0
+ ,12
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,12
+ ,14
+ ,15
+ ,17
+ ,4
+ ,5
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,12
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,15
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,5
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,5
+ ,7
+ ,0
+ ,11
+ ,14
+ ,15
+ ,17
+ ,4
+ ,6
+ ,8
+ ,0
+ ,11
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,5
+ ,8
+ ,0
+ ,12
+ ,14
+ ,15
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,12
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,15
+ ,18
+ ,4
+ ,6
+ ,8
+ ,0
+ ,11
+ ,14
+ ,15
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,12
+ ,13
+ ,15
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,12
+ ,14
+ ,16
+ ,18
+ ,4
+ ,5
+ ,7
+ ,0
+ ,11
+ ,14
+ ,15
+ ,18
+ ,4
+ ,5
+ ,8
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,15
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,15
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,15
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,8
+ ,0
+ ,12
+ ,14
+ ,16
+ ,17
+ ,4
+ ,5
+ ,8
+ ,0
+ ,12
+ ,13
+ ,15
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,12
+ ,13
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,8
+ ,0
+ ,12
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,12
+ ,14
+ ,15
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,18
+ ,4
+ ,5
+ ,8
+ ,0
+ ,12
+ ,13
+ ,15
+ ,18
+ ,4
+ ,5
+ ,8
+ ,0
+ ,11
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,12
+ ,14
+ ,15
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,5
+ ,8
+ ,0
+ ,11
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,8
+ ,0
+ ,12
+ ,13
+ ,15
+ ,17
+ ,4
+ ,5
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,12
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,12
+ ,14
+ ,16
+ ,18
+ ,4
+ ,5
+ ,7
+ ,0
+ ,12
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,8
+ ,0
+ ,11
+ ,14
+ ,15
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,12
+ ,14
+ ,15
+ ,18
+ ,4
+ ,6
+ ,8
+ ,0
+ ,12
+ ,13
+ ,16
+ ,18
+ ,4
+ ,6
+ ,8
+ ,0
+ ,11
+ ,14
+ ,15
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,5
+ ,7
+ ,0
+ ,12
+ ,14
+ ,16
+ ,18
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,12
+ ,13
+ ,16
+ ,17
+ ,4
+ ,6
+ ,7
+ ,0
+ ,11
+ ,14
+ ,15
+ ,18
+ ,4
+ ,5
+ ,7
+ ,0
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,5
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,18
+ ,2
+ ,5
+ ,0
+ ,10
+ ,12
+ ,14
+ ,16
+ ,18
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,18
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,15
+ ,17
+ ,2
+ ,5
+ ,0
+ ,10
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,5
+ ,0
+ ,9
+ ,11
+ ,14
+ ,15
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,10
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,18
+ ,2
+ ,5
+ ,0
+ ,10
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,5
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,18
+ ,2
+ ,5
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,18
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,10
+ ,12
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,5
+ ,0
+ ,10
+ ,12
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,5
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,5
+ ,0
+ ,10
+ ,12
+ ,14
+ ,15
+ ,17
+ ,2
+ ,6
+ ,0
+ ,10
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,12
+ ,14
+ ,16
+ ,17
+ ,2
+ ,5
+ ,0
+ ,10
+ ,12
+ ,14
+ ,16
+ ,17
+ ,2
+ ,5
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,5
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,18
+ ,2
+ ,5
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,18
+ ,2
+ ,5
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,5
+ ,0
+ ,10
+ ,12
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,12
+ ,14
+ ,15
+ ,18
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,18
+ ,2
+ ,6
+ ,0
+ ,10
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,15
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,18
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,18
+ ,2
+ ,5
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,5
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,18
+ ,2
+ ,5
+ ,0
+ ,9
+ ,12
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,5
+ ,0
+ ,9
+ ,12
+ ,14
+ ,15
+ ,18
+ ,2
+ ,5
+ ,0
+ ,10
+ ,12
+ ,14
+ ,15
+ ,18
+ ,2
+ ,6
+ ,0
+ ,10
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,12
+ ,13
+ ,16
+ ,18
+ ,2
+ ,6
+ ,0
+ ,10
+ ,12
+ ,14
+ ,16
+ ,18
+ ,2
+ ,5
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,15
+ ,18
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,15
+ ,17
+ ,2
+ ,6
+ ,0
+ ,10
+ ,11
+ ,14
+ ,16
+ ,18
+ ,2
+ ,6
+ ,0
+ ,10
+ ,12
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,10
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,5
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,17
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,15
+ ,18
+ ,2
+ ,6
+ ,0
+ ,9
+ ,11
+ ,14
+ ,16
+ ,18
+ ,2
+ ,5
+ ,0
+ ,9
+ ,12
+ ,13
+ ,16
+ ,17
+ ,2
+ ,5
+ ,0
+ ,9
+ ,12
+ ,13
+ ,15
+ ,17
+ ,2
+ ,5
+ ,0
+ ,9
+ ,12
+ ,14
+ ,16
+ ,17)
+ ,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\r\r
1 4 5 8 0 11 14 16 18
2 4 6 7 0 11 14 16 17
3 4 6 7 0 11 14 16 17
4 4 6 7 0 11 14 16 17
5 4 6 7 0 11 14 16 17
6 4 5 7 0 11 14 15 18
7 4 6 7 0 11 14 16 17
8 4 6 8 0 11 14 16 17
9 4 6 7 0 11 14 16 18
10 4 5 7 0 11 14 16 17
11 4 5 8 0 11 14 16 17
12 4 6 7 0 11 14 16 17
13 4 6 7 0 12 14 15 17
14 4 5 8 0 11 14 16 17
15 4 6 7 0 12 14 15 18
16 4 6 8 0 12 14 15 18
17 4 5 8 0 12 13 15 17
18 4 5 8 0 11 14 16 17
19 4 6 7 0 11 14 16 18
20 4 6 8 0 12 13 15 18
21 4 5 7 0 11 14 15 17
22 4 5 7 0 12 14 15 18
23 4 6 7 0 11 14 15 18
24 4 5 7 0 11 14 15 18
25 4 6 8 0 12 14 16 18
26 4 6 7 0 12 14 15 17
27 4 5 7 0 11 14 16 18
28 4 6 7 0 12 14 16 17
29 4 6 7 0 11 14 16 18
30 4 6 7 0 11 14 15 17
31 4 6 7 0 11 14 16 17
32 4 5 7 0 11 14 16 17
33 4 5 7 0 11 14 15 17
34 4 6 8 0 11 14 16 18
35 4 6 7 0 11 14 16 17
36 4 6 7 0 11 14 16 17
37 4 5 8 0 12 14 15 17
38 4 6 7 0 12 14 16 18
39 4 6 7 0 11 14 15 18
40 4 6 8 0 11 14 15 17
41 4 6 7 0 12 13 15 18
42 4 6 7 0 12 14 16 18
43 4 5 7 0 11 14 15 18
44 4 5 8 0 11 14 16 17
45 4 6 7 0 11 14 15 17
46 4 6 7 0 11 14 15 18
47 4 6 7 0 11 14 16 17
48 4 6 7 0 11 14 16 18
49 4 6 7 0 11 14 15 18
50 4 6 7 0 11 14 16 17
51 4 6 8 0 12 14 16 17
52 4 5 8 0 12 13 15 17
53 4 6 7 0 11 14 16 18
54 4 6 7 0 12 13 16 17
55 4 6 7 0 11 14 16 17
56 4 6 8 0 12 14 16 18
57 4 6 7 0 12 14 15 18
58 4 6 7 0 11 14 16 18
59 4 6 7 0 11 14 16 18
60 4 5 8 0 12 13 15 18
61 4 5 8 0 11 14 16 18
62 4 6 7 0 12 14 15 17
63 4 6 7 0 11 14 16 17
64 4 5 8 0 11 14 16 18
65 4 6 7 0 11 14 16 17
66 4 6 7 0 11 14 16 17
67 4 6 8 0 12 13 15 17
68 4 5 7 0 11 14 16 17
69 4 6 7 0 11 14 16 18
70 4 6 7 0 12 14 16 17
71 4 6 7 0 11 14 16 17
72 4 6 7 0 11 14 16 18
73 4 6 7 0 12 14 16 18
74 4 5 7 0 12 14 16 17
75 4 6 7 0 11 14 16 18
76 4 6 8 0 11 14 15 18
77 4 6 7 0 11 14 16 18
78 4 6 7 0 12 14 15 18
79 4 6 8 0 12 13 16 18
80 4 6 8 0 11 14 15 17
81 4 6 7 0 11 14 16 17
82 4 5 7 0 12 14 16 18
83 4 6 7 0 11 14 16 17
84 4 6 7 0 12 13 16 17
85 4 6 7 0 11 14 15 18
86 4 5 7 0 11 14 16 17
87 2 5 0 9 11 14 16 18
88 2 5 0 10 12 14 16 18
89 2 6 0 9 11 14 16 17
90 2 6 0 9 11 14 16 18
91 2 6 0 9 11 14 15 17
92 2 5 0 10 11 14 16 17
93 2 5 0 9 11 14 15 17
94 2 6 0 9 11 14 16 17
95 2 6 0 10 11 14 16 17
96 2 6 0 9 11 14 16 18
97 2 5 0 10 11 14 16 17
98 2 6 0 9 11 14 16 17
99 2 5 0 9 11 14 16 17
100 2 6 0 9 11 14 16 18
101 2 5 0 9 11 14 16 18
102 2 6 0 9 11 14 16 17
103 2 6 0 9 11 14 16 17
104 2 6 0 9 11 14 16 17
105 2 6 0 10 12 14 16 17
106 2 6 0 9 11 14 16 17
107 2 6 0 9 11 14 16 17
108 2 5 0 10 12 14 16 17
109 2 6 0 9 11 14 16 17
110 2 5 0 9 11 14 16 17
111 2 5 0 10 12 14 15 17
112 2 6 0 10 11 14 16 17
113 2 6 0 9 12 14 16 17
114 2 5 0 10 12 14 16 17
115 2 5 0 9 11 14 16 17
116 2 6 0 9 11 14 16 17
117 2 5 0 9 11 14 16 18
118 2 5 0 9 11 14 16 17
119 2 6 0 9 11 14 16 17
120 2 6 0 9 11 14 16 18
121 2 5 0 9 11 14 16 17
122 2 6 0 9 11 14 16 17
123 2 5 0 10 12 14 16 17
124 2 6 0 9 12 14 15 18
125 2 6 0 9 11 14 16 18
126 2 6 0 10 11 14 16 17
127 2 6 0 9 11 14 15 17
128 2 6 0 9 11 14 16 18
129 2 6 0 9 11 14 16 17
130 2 6 0 9 11 14 16 18
131 2 5 0 9 11 14 16 17
132 2 5 0 9 11 14 16 18
133 2 5 0 9 12 14 16 17
134 2 6 0 9 11 14 16 17
135 2 6 0 9 11 14 16 17
136 2 6 0 9 11 14 16 17
137 2 5 0 9 12 14 15 18
138 2 5 0 10 12 14 15 18
139 2 6 0 10 11 14 16 17
140 2 6 0 9 11 14 16 17
141 2 6 0 9 12 13 16 18
142 2 6 0 10 12 14 16 18
143 2 5 0 9 11 14 16 17
144 2 6 0 9 11 14 15 18
145 2 6 0 9 11 14 15 17
146 2 6 0 10 11 14 16 18
147 2 6 0 10 12 14 16 17
148 2 6 0 10 11 14 16 17
149 2 5 0 9 11 14 16 17
150 2 6 0 9 11 14 15 18
151 2 6 0 9 11 14 16 18
152 2 5 0 9 12 13 16 17
153 2 5 0 9 12 13 15 17
154 2 5 0 9 12 14 16 17
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Uselimit T40 T20
2.143564 0.006918 0.087269 -0.147468
Used CorrectAnalysis Useful `Outcome\\r\\r`
0.031676 0.058591 0.008312 -0.007012
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.097403 -0.038631 -0.003121 0.028555 0.114119
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.143564 0.378771 5.659 7.79e-08 ***
Uselimit 0.006918 0.008871 0.780 0.436761
T40 0.087269 0.010604 8.230 9.53e-14 ***
T20 -0.147468 0.008351 -17.659 < 2e-16 ***
Used 0.031676 0.010350 3.060 0.002631 **
CorrectAnalysis 0.058591 0.017052 3.436 0.000769 ***
Useful 0.008312 0.009762 0.852 0.395882
`Outcome\\r\\r` -0.007012 0.008473 -0.828 0.409275
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0499 on 146 degrees of freedom
Multiple R-squared: 0.9976, Adjusted R-squared: 0.9975
F-statistic: 8695 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,] 8.914512e-46 1.782902e-45 1.000000e+00
[2,] 1.466101e-56 2.932203e-56 1.000000e+00
[3,] 5.910518e-81 1.182104e-80 1.000000e+00
[4,] 8.344502e-84 1.668900e-83 1.000000e+00
[5,] 1.490286e-97 2.980571e-97 1.000000e+00
[6,] 0.000000e+00 0.000000e+00 1.000000e+00
[7,] 1.136661e-135 2.273322e-135 1.000000e+00
[8,] 2.015727e-140 4.031454e-140 1.000000e+00
[9,] 4.334469e-153 8.668939e-153 1.000000e+00
[10,] 7.684970e-176 1.536994e-175 1.000000e+00
[11,] 5.737216e-207 1.147443e-206 1.000000e+00
[12,] 1.490664e-197 2.981328e-197 1.000000e+00
[13,] 6.984912e-208 1.396982e-207 1.000000e+00
[14,] 1.012127e-224 2.024254e-224 1.000000e+00
[15,] 1.207731e-241 2.415462e-241 1.000000e+00
[16,] 3.090529e-281 6.181058e-281 1.000000e+00
[17,] 2.450534e-268 4.901069e-268 1.000000e+00
[18,] 1.656940e-277 3.313880e-277 1.000000e+00
[19,] 2.474432e-296 4.948865e-296 1.000000e+00
[20,] 3.140693e-311 6.281386e-311 1.000000e+00
[21,] 3.235023e-317 6.470046e-317 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,] 1.000000e+00 9.847788e-81 4.923894e-81
[71,] 9.999866e-01 2.689682e-05 1.344841e-05
[72,] 9.999941e-01 1.177842e-05 5.889209e-06
[73,] 1.000000e+00 1.768602e-44 8.843011e-45
[74,] 2.090071e-16 4.180143e-16 1.000000e+00
[75,] 1.000000e+00 4.809443e-78 2.404721e-78
[76,] 1.000000e+00 1.725567e-18 8.627835e-19
[77,] 7.396545e-20 1.479309e-19 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 3.952525e-323 1.976263e-323
[114,] 1.000000e+00 6.527753e-316 3.263877e-316
[115,] 1.000000e+00 3.907568e-301 1.953784e-301
[116,] 1.000000e+00 1.505264e-282 7.526319e-283
[117,] 1.000000e+00 1.227816e-272 6.139080e-273
[118,] 1.000000e+00 5.740197e-285 2.870098e-285
[119,] 1.000000e+00 2.503507e-245 1.251753e-245
[120,] 1.000000e+00 8.135137e-228 4.067568e-228
[121,] 1.000000e+00 1.056210e-211 5.281050e-212
[122,] 1.000000e+00 1.348760e-200 6.743800e-201
[123,] 1.000000e+00 3.545410e-209 1.772705e-209
[124,] 1.000000e+00 8.094817e-178 4.047408e-178
[125,] 1.000000e+00 5.713641e-155 2.856820e-155
[126,] 1.000000e+00 1.819529e-142 9.097646e-143
[127,] 1.000000e+00 1.329989e-138 6.649946e-139
[128,] 1.000000e+00 0.000000e+00 0.000000e+00
[129,] 1.000000e+00 5.507852e-99 2.753926e-99
[130,] 1.000000e+00 1.598957e-85 7.994787e-86
[131,] 1.000000e+00 9.996366e-82 4.998183e-82
[132,] 1.000000e+00 6.219205e-57 3.109603e-57
[133,] 1.000000e+00 6.471392e-46 3.235696e-46
> postscript(file="/var/fisher/rcomp/tmp/1pp6n1356045832.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/24unh1356045832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/36ra31356045832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4ikzt1356045832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/59zrj1356045832.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
-0.051796575 0.021542868 0.021542868 0.021542868 0.021542868 0.043784838
7 8 9 10 11 12
0.021542868 -0.065726542 0.028554740 0.028460963 -0.058808447 0.021542868
13 14 15 16 17 18
-0.001821116 -0.058808447 0.005190756 -0.082078654 -0.023581505 -0.058808447
19 20 21 22 23 24
0.028554740 -0.023487728 0.036772966 0.012108851 0.036866743 0.043784838
25 26 27 28 29 30
-0.090390657 -0.001821116 0.035472835 -0.010133119 0.028554740 0.029854871
31 32 33 34 35 36
0.021542868 0.028460963 0.036772966 -0.058714670 0.021542868 0.021542868
37 38 39 40 41 42
-0.082172431 -0.003121247 0.036866743 -0.057414539 0.063781682 -0.003121247
43 44 45 46 47 48
0.043784838 -0.058808447 0.029854871 0.036866743 0.021542868 0.028554740
49 50 51 52 53 54
0.036866743 0.021542868 -0.097402529 -0.023581505 0.028554740 0.048457806
55 56 57 58 59 60
0.021542868 -0.090390657 0.005190756 0.028554740 0.028554740 -0.016569633
61 62 63 64 65 66
-0.051796575 -0.001821116 0.021542868 -0.051796575 0.021542868 0.021542868
67 68 69 70 71 72
-0.030499601 0.028460963 0.028554740 -0.010133119 0.021542868 0.028554740
73 74 75 76 77 78
-0.003121247 -0.003215024 0.028554740 -0.050402667 0.028554740 0.005190756
79 80 81 82 83 84
-0.031799731 -0.057414539 0.021542868 0.003796848 0.021542868 0.048457806
85 86 87 88 89 90
0.036866743 0.028460963 -0.026430856 0.089360983 -0.040360824 -0.033348952
91 92 93 94 95 96
-0.032048821 0.114025098 -0.025130726 -0.040360824 0.107107003 -0.033348952
97 98 99 100 101 102
0.114025098 -0.040360824 -0.033442729 -0.033348952 -0.026430856 -0.040360824
103 104 105 106 107 108
-0.040360824 -0.040360824 0.075431015 -0.040360824 -0.040360824 0.082349111
109 110 111 112 113 114
-0.040360824 -0.033442729 0.090661114 0.107107003 -0.072036811 0.082349111
115 116 117 118 119 120
-0.033442729 -0.040360824 -0.026430856 -0.033442729 -0.040360824 -0.033348952
121 122 123 124 125 126
-0.033442729 -0.040360824 0.082349111 -0.056712936 -0.033348952 0.107107003
127 128 129 130 131 132
-0.032048821 -0.033348952 -0.040360824 -0.033348952 -0.033442729 -0.026430856
133 134 135 136 137 138
-0.065118716 -0.040360824 -0.040360824 -0.040360824 -0.049794841 0.097672986
139 140 141 142 143 144
0.107107003 -0.040360824 -0.006434013 0.082442888 -0.033442729 -0.025036949
145 146 147 148 149 150
-0.032048821 0.114118875 0.075431015 0.107107003 -0.033442729 -0.025036949
151 152 153 154
-0.033348952 -0.006527790 0.001784213 -0.065118716
> postscript(file="/var/fisher/rcomp/tmp/6q5xj1356045832.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 -0.051796575 NA
1 0.021542868 -0.051796575
2 0.021542868 0.021542868
3 0.021542868 0.021542868
4 0.021542868 0.021542868
5 0.043784838 0.021542868
6 0.021542868 0.043784838
7 -0.065726542 0.021542868
8 0.028554740 -0.065726542
9 0.028460963 0.028554740
10 -0.058808447 0.028460963
11 0.021542868 -0.058808447
12 -0.001821116 0.021542868
13 -0.058808447 -0.001821116
14 0.005190756 -0.058808447
15 -0.082078654 0.005190756
16 -0.023581505 -0.082078654
17 -0.058808447 -0.023581505
18 0.028554740 -0.058808447
19 -0.023487728 0.028554740
20 0.036772966 -0.023487728
21 0.012108851 0.036772966
22 0.036866743 0.012108851
23 0.043784838 0.036866743
24 -0.090390657 0.043784838
25 -0.001821116 -0.090390657
26 0.035472835 -0.001821116
27 -0.010133119 0.035472835
28 0.028554740 -0.010133119
29 0.029854871 0.028554740
30 0.021542868 0.029854871
31 0.028460963 0.021542868
32 0.036772966 0.028460963
33 -0.058714670 0.036772966
34 0.021542868 -0.058714670
35 0.021542868 0.021542868
36 -0.082172431 0.021542868
37 -0.003121247 -0.082172431
38 0.036866743 -0.003121247
39 -0.057414539 0.036866743
40 0.063781682 -0.057414539
41 -0.003121247 0.063781682
42 0.043784838 -0.003121247
43 -0.058808447 0.043784838
44 0.029854871 -0.058808447
45 0.036866743 0.029854871
46 0.021542868 0.036866743
47 0.028554740 0.021542868
48 0.036866743 0.028554740
49 0.021542868 0.036866743
50 -0.097402529 0.021542868
51 -0.023581505 -0.097402529
52 0.028554740 -0.023581505
53 0.048457806 0.028554740
54 0.021542868 0.048457806
55 -0.090390657 0.021542868
56 0.005190756 -0.090390657
57 0.028554740 0.005190756
58 0.028554740 0.028554740
59 -0.016569633 0.028554740
60 -0.051796575 -0.016569633
61 -0.001821116 -0.051796575
62 0.021542868 -0.001821116
63 -0.051796575 0.021542868
64 0.021542868 -0.051796575
65 0.021542868 0.021542868
66 -0.030499601 0.021542868
67 0.028460963 -0.030499601
68 0.028554740 0.028460963
69 -0.010133119 0.028554740
70 0.021542868 -0.010133119
71 0.028554740 0.021542868
72 -0.003121247 0.028554740
73 -0.003215024 -0.003121247
74 0.028554740 -0.003215024
75 -0.050402667 0.028554740
76 0.028554740 -0.050402667
77 0.005190756 0.028554740
78 -0.031799731 0.005190756
79 -0.057414539 -0.031799731
80 0.021542868 -0.057414539
81 0.003796848 0.021542868
82 0.021542868 0.003796848
83 0.048457806 0.021542868
84 0.036866743 0.048457806
85 0.028460963 0.036866743
86 -0.026430856 0.028460963
87 0.089360983 -0.026430856
88 -0.040360824 0.089360983
89 -0.033348952 -0.040360824
90 -0.032048821 -0.033348952
91 0.114025098 -0.032048821
92 -0.025130726 0.114025098
93 -0.040360824 -0.025130726
94 0.107107003 -0.040360824
95 -0.033348952 0.107107003
96 0.114025098 -0.033348952
97 -0.040360824 0.114025098
98 -0.033442729 -0.040360824
99 -0.033348952 -0.033442729
100 -0.026430856 -0.033348952
101 -0.040360824 -0.026430856
102 -0.040360824 -0.040360824
103 -0.040360824 -0.040360824
104 0.075431015 -0.040360824
105 -0.040360824 0.075431015
106 -0.040360824 -0.040360824
107 0.082349111 -0.040360824
108 -0.040360824 0.082349111
109 -0.033442729 -0.040360824
110 0.090661114 -0.033442729
111 0.107107003 0.090661114
112 -0.072036811 0.107107003
113 0.082349111 -0.072036811
114 -0.033442729 0.082349111
115 -0.040360824 -0.033442729
116 -0.026430856 -0.040360824
117 -0.033442729 -0.026430856
118 -0.040360824 -0.033442729
119 -0.033348952 -0.040360824
120 -0.033442729 -0.033348952
121 -0.040360824 -0.033442729
122 0.082349111 -0.040360824
123 -0.056712936 0.082349111
124 -0.033348952 -0.056712936
125 0.107107003 -0.033348952
126 -0.032048821 0.107107003
127 -0.033348952 -0.032048821
128 -0.040360824 -0.033348952
129 -0.033348952 -0.040360824
130 -0.033442729 -0.033348952
131 -0.026430856 -0.033442729
132 -0.065118716 -0.026430856
133 -0.040360824 -0.065118716
134 -0.040360824 -0.040360824
135 -0.040360824 -0.040360824
136 -0.049794841 -0.040360824
137 0.097672986 -0.049794841
138 0.107107003 0.097672986
139 -0.040360824 0.107107003
140 -0.006434013 -0.040360824
141 0.082442888 -0.006434013
142 -0.033442729 0.082442888
143 -0.025036949 -0.033442729
144 -0.032048821 -0.025036949
145 0.114118875 -0.032048821
146 0.075431015 0.114118875
147 0.107107003 0.075431015
148 -0.033442729 0.107107003
149 -0.025036949 -0.033442729
150 -0.033348952 -0.025036949
151 -0.006527790 -0.033348952
152 0.001784213 -0.006527790
153 -0.065118716 0.001784213
154 NA -0.065118716
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.021542868 -0.051796575
[2,] 0.021542868 0.021542868
[3,] 0.021542868 0.021542868
[4,] 0.021542868 0.021542868
[5,] 0.043784838 0.021542868
[6,] 0.021542868 0.043784838
[7,] -0.065726542 0.021542868
[8,] 0.028554740 -0.065726542
[9,] 0.028460963 0.028554740
[10,] -0.058808447 0.028460963
[11,] 0.021542868 -0.058808447
[12,] -0.001821116 0.021542868
[13,] -0.058808447 -0.001821116
[14,] 0.005190756 -0.058808447
[15,] -0.082078654 0.005190756
[16,] -0.023581505 -0.082078654
[17,] -0.058808447 -0.023581505
[18,] 0.028554740 -0.058808447
[19,] -0.023487728 0.028554740
[20,] 0.036772966 -0.023487728
[21,] 0.012108851 0.036772966
[22,] 0.036866743 0.012108851
[23,] 0.043784838 0.036866743
[24,] -0.090390657 0.043784838
[25,] -0.001821116 -0.090390657
[26,] 0.035472835 -0.001821116
[27,] -0.010133119 0.035472835
[28,] 0.028554740 -0.010133119
[29,] 0.029854871 0.028554740
[30,] 0.021542868 0.029854871
[31,] 0.028460963 0.021542868
[32,] 0.036772966 0.028460963
[33,] -0.058714670 0.036772966
[34,] 0.021542868 -0.058714670
[35,] 0.021542868 0.021542868
[36,] -0.082172431 0.021542868
[37,] -0.003121247 -0.082172431
[38,] 0.036866743 -0.003121247
[39,] -0.057414539 0.036866743
[40,] 0.063781682 -0.057414539
[41,] -0.003121247 0.063781682
[42,] 0.043784838 -0.003121247
[43,] -0.058808447 0.043784838
[44,] 0.029854871 -0.058808447
[45,] 0.036866743 0.029854871
[46,] 0.021542868 0.036866743
[47,] 0.028554740 0.021542868
[48,] 0.036866743 0.028554740
[49,] 0.021542868 0.036866743
[50,] -0.097402529 0.021542868
[51,] -0.023581505 -0.097402529
[52,] 0.028554740 -0.023581505
[53,] 0.048457806 0.028554740
[54,] 0.021542868 0.048457806
[55,] -0.090390657 0.021542868
[56,] 0.005190756 -0.090390657
[57,] 0.028554740 0.005190756
[58,] 0.028554740 0.028554740
[59,] -0.016569633 0.028554740
[60,] -0.051796575 -0.016569633
[61,] -0.001821116 -0.051796575
[62,] 0.021542868 -0.001821116
[63,] -0.051796575 0.021542868
[64,] 0.021542868 -0.051796575
[65,] 0.021542868 0.021542868
[66,] -0.030499601 0.021542868
[67,] 0.028460963 -0.030499601
[68,] 0.028554740 0.028460963
[69,] -0.010133119 0.028554740
[70,] 0.021542868 -0.010133119
[71,] 0.028554740 0.021542868
[72,] -0.003121247 0.028554740
[73,] -0.003215024 -0.003121247
[74,] 0.028554740 -0.003215024
[75,] -0.050402667 0.028554740
[76,] 0.028554740 -0.050402667
[77,] 0.005190756 0.028554740
[78,] -0.031799731 0.005190756
[79,] -0.057414539 -0.031799731
[80,] 0.021542868 -0.057414539
[81,] 0.003796848 0.021542868
[82,] 0.021542868 0.003796848
[83,] 0.048457806 0.021542868
[84,] 0.036866743 0.048457806
[85,] 0.028460963 0.036866743
[86,] -0.026430856 0.028460963
[87,] 0.089360983 -0.026430856
[88,] -0.040360824 0.089360983
[89,] -0.033348952 -0.040360824
[90,] -0.032048821 -0.033348952
[91,] 0.114025098 -0.032048821
[92,] -0.025130726 0.114025098
[93,] -0.040360824 -0.025130726
[94,] 0.107107003 -0.040360824
[95,] -0.033348952 0.107107003
[96,] 0.114025098 -0.033348952
[97,] -0.040360824 0.114025098
[98,] -0.033442729 -0.040360824
[99,] -0.033348952 -0.033442729
[100,] -0.026430856 -0.033348952
[101,] -0.040360824 -0.026430856
[102,] -0.040360824 -0.040360824
[103,] -0.040360824 -0.040360824
[104,] 0.075431015 -0.040360824
[105,] -0.040360824 0.075431015
[106,] -0.040360824 -0.040360824
[107,] 0.082349111 -0.040360824
[108,] -0.040360824 0.082349111
[109,] -0.033442729 -0.040360824
[110,] 0.090661114 -0.033442729
[111,] 0.107107003 0.090661114
[112,] -0.072036811 0.107107003
[113,] 0.082349111 -0.072036811
[114,] -0.033442729 0.082349111
[115,] -0.040360824 -0.033442729
[116,] -0.026430856 -0.040360824
[117,] -0.033442729 -0.026430856
[118,] -0.040360824 -0.033442729
[119,] -0.033348952 -0.040360824
[120,] -0.033442729 -0.033348952
[121,] -0.040360824 -0.033442729
[122,] 0.082349111 -0.040360824
[123,] -0.056712936 0.082349111
[124,] -0.033348952 -0.056712936
[125,] 0.107107003 -0.033348952
[126,] -0.032048821 0.107107003
[127,] -0.033348952 -0.032048821
[128,] -0.040360824 -0.033348952
[129,] -0.033348952 -0.040360824
[130,] -0.033442729 -0.033348952
[131,] -0.026430856 -0.033442729
[132,] -0.065118716 -0.026430856
[133,] -0.040360824 -0.065118716
[134,] -0.040360824 -0.040360824
[135,] -0.040360824 -0.040360824
[136,] -0.049794841 -0.040360824
[137,] 0.097672986 -0.049794841
[138,] 0.107107003 0.097672986
[139,] -0.040360824 0.107107003
[140,] -0.006434013 -0.040360824
[141,] 0.082442888 -0.006434013
[142,] -0.033442729 0.082442888
[143,] -0.025036949 -0.033442729
[144,] -0.032048821 -0.025036949
[145,] 0.114118875 -0.032048821
[146,] 0.075431015 0.114118875
[147,] 0.107107003 0.075431015
[148,] -0.033442729 0.107107003
[149,] -0.025036949 -0.033442729
[150,] -0.033348952 -0.025036949
[151,] -0.006527790 -0.033348952
[152,] 0.001784213 -0.006527790
[153,] -0.065118716 0.001784213
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.021542868 -0.051796575
2 0.021542868 0.021542868
3 0.021542868 0.021542868
4 0.021542868 0.021542868
5 0.043784838 0.021542868
6 0.021542868 0.043784838
7 -0.065726542 0.021542868
8 0.028554740 -0.065726542
9 0.028460963 0.028554740
10 -0.058808447 0.028460963
11 0.021542868 -0.058808447
12 -0.001821116 0.021542868
13 -0.058808447 -0.001821116
14 0.005190756 -0.058808447
15 -0.082078654 0.005190756
16 -0.023581505 -0.082078654
17 -0.058808447 -0.023581505
18 0.028554740 -0.058808447
19 -0.023487728 0.028554740
20 0.036772966 -0.023487728
21 0.012108851 0.036772966
22 0.036866743 0.012108851
23 0.043784838 0.036866743
24 -0.090390657 0.043784838
25 -0.001821116 -0.090390657
26 0.035472835 -0.001821116
27 -0.010133119 0.035472835
28 0.028554740 -0.010133119
29 0.029854871 0.028554740
30 0.021542868 0.029854871
31 0.028460963 0.021542868
32 0.036772966 0.028460963
33 -0.058714670 0.036772966
34 0.021542868 -0.058714670
35 0.021542868 0.021542868
36 -0.082172431 0.021542868
37 -0.003121247 -0.082172431
38 0.036866743 -0.003121247
39 -0.057414539 0.036866743
40 0.063781682 -0.057414539
41 -0.003121247 0.063781682
42 0.043784838 -0.003121247
43 -0.058808447 0.043784838
44 0.029854871 -0.058808447
45 0.036866743 0.029854871
46 0.021542868 0.036866743
47 0.028554740 0.021542868
48 0.036866743 0.028554740
49 0.021542868 0.036866743
50 -0.097402529 0.021542868
51 -0.023581505 -0.097402529
52 0.028554740 -0.023581505
53 0.048457806 0.028554740
54 0.021542868 0.048457806
55 -0.090390657 0.021542868
56 0.005190756 -0.090390657
57 0.028554740 0.005190756
58 0.028554740 0.028554740
59 -0.016569633 0.028554740
60 -0.051796575 -0.016569633
61 -0.001821116 -0.051796575
62 0.021542868 -0.001821116
63 -0.051796575 0.021542868
64 0.021542868 -0.051796575
65 0.021542868 0.021542868
66 -0.030499601 0.021542868
67 0.028460963 -0.030499601
68 0.028554740 0.028460963
69 -0.010133119 0.028554740
70 0.021542868 -0.010133119
71 0.028554740 0.021542868
72 -0.003121247 0.028554740
73 -0.003215024 -0.003121247
74 0.028554740 -0.003215024
75 -0.050402667 0.028554740
76 0.028554740 -0.050402667
77 0.005190756 0.028554740
78 -0.031799731 0.005190756
79 -0.057414539 -0.031799731
80 0.021542868 -0.057414539
81 0.003796848 0.021542868
82 0.021542868 0.003796848
83 0.048457806 0.021542868
84 0.036866743 0.048457806
85 0.028460963 0.036866743
86 -0.026430856 0.028460963
87 0.089360983 -0.026430856
88 -0.040360824 0.089360983
89 -0.033348952 -0.040360824
90 -0.032048821 -0.033348952
91 0.114025098 -0.032048821
92 -0.025130726 0.114025098
93 -0.040360824 -0.025130726
94 0.107107003 -0.040360824
95 -0.033348952 0.107107003
96 0.114025098 -0.033348952
97 -0.040360824 0.114025098
98 -0.033442729 -0.040360824
99 -0.033348952 -0.033442729
100 -0.026430856 -0.033348952
101 -0.040360824 -0.026430856
102 -0.040360824 -0.040360824
103 -0.040360824 -0.040360824
104 0.075431015 -0.040360824
105 -0.040360824 0.075431015
106 -0.040360824 -0.040360824
107 0.082349111 -0.040360824
108 -0.040360824 0.082349111
109 -0.033442729 -0.040360824
110 0.090661114 -0.033442729
111 0.107107003 0.090661114
112 -0.072036811 0.107107003
113 0.082349111 -0.072036811
114 -0.033442729 0.082349111
115 -0.040360824 -0.033442729
116 -0.026430856 -0.040360824
117 -0.033442729 -0.026430856
118 -0.040360824 -0.033442729
119 -0.033348952 -0.040360824
120 -0.033442729 -0.033348952
121 -0.040360824 -0.033442729
122 0.082349111 -0.040360824
123 -0.056712936 0.082349111
124 -0.033348952 -0.056712936
125 0.107107003 -0.033348952
126 -0.032048821 0.107107003
127 -0.033348952 -0.032048821
128 -0.040360824 -0.033348952
129 -0.033348952 -0.040360824
130 -0.033442729 -0.033348952
131 -0.026430856 -0.033442729
132 -0.065118716 -0.026430856
133 -0.040360824 -0.065118716
134 -0.040360824 -0.040360824
135 -0.040360824 -0.040360824
136 -0.049794841 -0.040360824
137 0.097672986 -0.049794841
138 0.107107003 0.097672986
139 -0.040360824 0.107107003
140 -0.006434013 -0.040360824
141 0.082442888 -0.006434013
142 -0.033442729 0.082442888
143 -0.025036949 -0.033442729
144 -0.032048821 -0.025036949
145 0.114118875 -0.032048821
146 0.075431015 0.114118875
147 0.107107003 0.075431015
148 -0.033442729 0.107107003
149 -0.025036949 -0.033442729
150 -0.033348952 -0.025036949
151 -0.006527790 -0.033348952
152 0.001784213 -0.006527790
153 -0.065118716 0.001784213
> 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/fisher/rcomp/tmp/7hvbj1356045832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8owt11356045832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9kes71356045832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10t0zq1356045832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/110jb41356045832.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/fisher/rcomp/tmp/12bvj81356045832.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/fisher/rcomp/tmp/13mytu1356045833.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/fisher/rcomp/tmp/14ejtx1356045833.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/fisher/rcomp/tmp/15rclk1356045833.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/fisher/rcomp/tmp/1673ds1356045833.tab")
+ }
>
> try(system("convert tmp/1pp6n1356045832.ps tmp/1pp6n1356045832.png",intern=TRUE))
character(0)
> try(system("convert tmp/24unh1356045832.ps tmp/24unh1356045832.png",intern=TRUE))
character(0)
> try(system("convert tmp/36ra31356045832.ps tmp/36ra31356045832.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ikzt1356045832.ps tmp/4ikzt1356045832.png",intern=TRUE))
character(0)
> try(system("convert tmp/59zrj1356045832.ps tmp/59zrj1356045832.png",intern=TRUE))
character(0)
> try(system("convert tmp/6q5xj1356045832.ps tmp/6q5xj1356045832.png",intern=TRUE))
character(0)
> try(system("convert tmp/7hvbj1356045832.ps tmp/7hvbj1356045832.png",intern=TRUE))
character(0)
> try(system("convert tmp/8owt11356045832.ps tmp/8owt11356045832.png",intern=TRUE))
character(0)
> try(system("convert tmp/9kes71356045832.ps tmp/9kes71356045832.png",intern=TRUE))
character(0)
> try(system("convert tmp/10t0zq1356045832.ps tmp/10t0zq1356045832.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.853 1.762 9.684