R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(15
+ ,10
+ ,12
+ ,16
+ ,6
+ ,2
+ ,0
+ ,0
+ ,12
+ ,9
+ ,7
+ ,12
+ ,6
+ ,1
+ ,1
+ ,2
+ ,9
+ ,12
+ ,11
+ ,11
+ ,4
+ ,1
+ ,2
+ ,1
+ ,10
+ ,12
+ ,11
+ ,12
+ ,6
+ ,0
+ ,0
+ ,0
+ ,13
+ ,9
+ ,14
+ ,14
+ ,6
+ ,0
+ ,0
+ ,0
+ ,16
+ ,11
+ ,16
+ ,16
+ ,7
+ ,1
+ ,0
+ ,0
+ ,14
+ ,12
+ ,13
+ ,13
+ ,6
+ ,0
+ ,0
+ ,0
+ ,16
+ ,11
+ ,13
+ ,14
+ ,7
+ ,1
+ ,1
+ ,0
+ ,10
+ ,12
+ ,5
+ ,13
+ ,6
+ ,0
+ ,0
+ ,0
+ ,8
+ ,12
+ ,8
+ ,13
+ ,4
+ ,2
+ ,0
+ ,1
+ ,12
+ ,11
+ ,14
+ ,13
+ ,5
+ ,1
+ ,0
+ ,0
+ ,15
+ ,11
+ ,15
+ ,15
+ ,8
+ ,0
+ ,0
+ ,0
+ ,14
+ ,12
+ ,8
+ ,14
+ ,4
+ ,0
+ ,1
+ ,0
+ ,14
+ ,6
+ ,13
+ ,12
+ ,6
+ ,1
+ ,1
+ ,2
+ ,12
+ ,13
+ ,12
+ ,12
+ ,6
+ ,1
+ ,2
+ ,1
+ ,12
+ ,11
+ ,11
+ ,12
+ ,5
+ ,0
+ ,0
+ ,0
+ ,10
+ ,12
+ ,8
+ ,11
+ ,4
+ ,0
+ ,0
+ ,0
+ ,4
+ ,10
+ ,4
+ ,10
+ ,2
+ ,0
+ ,0
+ ,0
+ ,14
+ ,11
+ ,15
+ ,15
+ ,8
+ ,0
+ ,1
+ ,0
+ ,15
+ ,12
+ ,12
+ ,16
+ ,7
+ ,0
+ ,0
+ ,0
+ ,16
+ ,12
+ ,14
+ ,14
+ ,6
+ ,0
+ ,0
+ ,0
+ ,12
+ ,12
+ ,9
+ ,13
+ ,4
+ ,0
+ ,1
+ ,0
+ ,12
+ ,11
+ ,16
+ ,13
+ ,4
+ ,0
+ ,0
+ ,0
+ ,12
+ ,12
+ ,10
+ ,13
+ ,4
+ ,0
+ ,0
+ ,1
+ ,12
+ ,12
+ ,8
+ ,13
+ ,5
+ ,1
+ ,0
+ ,1
+ ,12
+ ,12
+ ,14
+ ,14
+ ,4
+ ,0
+ ,0
+ ,0
+ ,11
+ ,6
+ ,6
+ ,9
+ ,4
+ ,3
+ ,2
+ ,1
+ ,11
+ ,5
+ ,16
+ ,14
+ ,6
+ ,1
+ ,0
+ ,0
+ ,11
+ ,12
+ ,11
+ ,12
+ ,6
+ ,1
+ ,1
+ ,0
+ ,11
+ ,14
+ ,7
+ ,13
+ ,6
+ ,1
+ ,1
+ ,0
+ ,11
+ ,12
+ ,13
+ ,11
+ ,4
+ ,3
+ ,1
+ ,1
+ ,11
+ ,9
+ ,7
+ ,13
+ ,2
+ ,0
+ ,0
+ ,0
+ ,15
+ ,11
+ ,14
+ ,15
+ ,7
+ ,0
+ ,0
+ ,0
+ ,15
+ ,11
+ ,17
+ ,16
+ ,6
+ ,0
+ ,0
+ ,0
+ ,9
+ ,11
+ ,15
+ ,15
+ ,7
+ ,0
+ ,0
+ ,0
+ ,16
+ ,12
+ ,8
+ ,14
+ ,4
+ ,0
+ ,0
+ ,0
+ ,13
+ ,10
+ ,8
+ ,8
+ ,4
+ ,0
+ ,2
+ ,1
+ ,9
+ ,12
+ ,11
+ ,11
+ ,4
+ ,1
+ ,0
+ ,0
+ ,16
+ ,11
+ ,16
+ ,15
+ ,6
+ ,0
+ ,0
+ ,0
+ ,12
+ ,12
+ ,10
+ ,15
+ ,6
+ ,0
+ ,0
+ ,0
+ ,15
+ ,9
+ ,5
+ ,11
+ ,3
+ ,0
+ ,0
+ ,2
+ ,5
+ ,15
+ ,8
+ ,12
+ ,3
+ ,0
+ ,0
+ ,0
+ ,11
+ ,11
+ ,8
+ ,12
+ ,6
+ ,2
+ ,2
+ ,0
+ ,17
+ ,11
+ ,15
+ ,14
+ ,5
+ ,2
+ ,2
+ ,0
+ ,9
+ ,15
+ ,6
+ ,8
+ ,4
+ ,0
+ ,1
+ ,1
+ ,13
+ ,12
+ ,16
+ ,16
+ ,6
+ ,0
+ ,0
+ ,0
+ ,16
+ ,9
+ ,16
+ ,16
+ ,6
+ ,0
+ ,0
+ ,0
+ ,16
+ ,12
+ ,16
+ ,14
+ ,6
+ ,0
+ ,0
+ ,0
+ ,14
+ ,9
+ ,19
+ ,12
+ ,6
+ ,2
+ ,0
+ ,2
+ ,16
+ ,11
+ ,14
+ ,15
+ ,6
+ ,1
+ ,0
+ ,0
+ ,11
+ ,12
+ ,15
+ ,12
+ ,6
+ ,0
+ ,0
+ ,0
+ ,11
+ ,11
+ ,11
+ ,14
+ ,5
+ ,0
+ ,0
+ ,0
+ ,11
+ ,6
+ ,14
+ ,17
+ ,6
+ ,0
+ ,0
+ ,0
+ ,12
+ ,10
+ ,12
+ ,13
+ ,6
+ ,0
+ ,0
+ ,0
+ ,12
+ ,12
+ ,15
+ ,13
+ ,6
+ ,1
+ ,1
+ ,1
+ ,12
+ ,13
+ ,14
+ ,12
+ ,5
+ ,0
+ ,0
+ ,0
+ ,14
+ ,11
+ ,13
+ ,16
+ ,6
+ ,0
+ ,0
+ ,0
+ ,10
+ ,10
+ ,11
+ ,12
+ ,5
+ ,2
+ ,0
+ ,0
+ ,9
+ ,11
+ ,8
+ ,10
+ ,4
+ ,0
+ ,2
+ ,0
+ ,12
+ ,7
+ ,11
+ ,15
+ ,5
+ ,0
+ ,0
+ ,1
+ ,10
+ ,11
+ ,9
+ ,12
+ ,4
+ ,0
+ ,0
+ ,0
+ ,14
+ ,11
+ ,10
+ ,16
+ ,6
+ ,0
+ ,0
+ ,0
+ ,8
+ ,7
+ ,4
+ ,13
+ ,6
+ ,0
+ ,0
+ ,0
+ ,16
+ ,12
+ ,15
+ ,15
+ ,7
+ ,1
+ ,0
+ ,0
+ ,14
+ ,14
+ ,17
+ ,18
+ ,6
+ ,1
+ ,0
+ ,0
+ ,14
+ ,11
+ ,12
+ ,12
+ ,4
+ ,0
+ ,0
+ ,0
+ ,12
+ ,12
+ ,12
+ ,13
+ ,4
+ ,0
+ ,0
+ ,0
+ ,14
+ ,11
+ ,15
+ ,14
+ ,6
+ ,1
+ ,0
+ ,0
+ ,7
+ ,12
+ ,13
+ ,12
+ ,3
+ ,1
+ ,1
+ ,1
+ ,19
+ ,12
+ ,15
+ ,15
+ ,6
+ ,0
+ ,0
+ ,0
+ ,15
+ ,12
+ ,14
+ ,16
+ ,4
+ ,0
+ ,0
+ ,0
+ ,8
+ ,12
+ ,8
+ ,14
+ ,5
+ ,0
+ ,0
+ ,0
+ ,10
+ ,15
+ ,15
+ ,15
+ ,6
+ ,0
+ ,0
+ ,0
+ ,13
+ ,11
+ ,12
+ ,13
+ ,7
+ ,0
+ ,0
+ ,0
+ ,13
+ ,13
+ ,14
+ ,13
+ ,3
+ ,0
+ ,0
+ ,0
+ ,10
+ ,10
+ ,10
+ ,11
+ ,5
+ ,0
+ ,0
+ ,0
+ ,12
+ ,12
+ ,7
+ ,12
+ ,3
+ ,0
+ ,0
+ ,0
+ ,15
+ ,13
+ ,16
+ ,18
+ ,8
+ ,0
+ ,1
+ ,1
+ ,7
+ ,14
+ ,12
+ ,12
+ ,4
+ ,1
+ ,0
+ ,0
+ ,14
+ ,11
+ ,15
+ ,16
+ ,6
+ ,0
+ ,0
+ ,0
+ ,10
+ ,11
+ ,7
+ ,9
+ ,4
+ ,0
+ ,0
+ ,0
+ ,6
+ ,7
+ ,9
+ ,11
+ ,4
+ ,0
+ ,3
+ ,0
+ ,11
+ ,11
+ ,15
+ ,10
+ ,5
+ ,2
+ ,0
+ ,0
+ ,12
+ ,12
+ ,7
+ ,11
+ ,4
+ ,0
+ ,0
+ ,0
+ ,14
+ ,12
+ ,15
+ ,13
+ ,6
+ ,0
+ ,0
+ ,2
+ ,12
+ ,10
+ ,14
+ ,13
+ ,7
+ ,0
+ ,0
+ ,0
+ ,14
+ ,12
+ ,14
+ ,15
+ ,7
+ ,0
+ ,0
+ ,0
+ ,11
+ ,8
+ ,8
+ ,13
+ ,4
+ ,2
+ ,2
+ ,0
+ ,10
+ ,7
+ ,8
+ ,9
+ ,5
+ ,1
+ ,0
+ ,1
+ ,13
+ ,11
+ ,14
+ ,13
+ ,6
+ ,0
+ ,0
+ ,1
+ ,8
+ ,11
+ ,10
+ ,12
+ ,4
+ ,0
+ ,0
+ ,0
+ ,9
+ ,11
+ ,12
+ ,13
+ ,5
+ ,0
+ ,0
+ ,0
+ ,6
+ ,9
+ ,15
+ ,11
+ ,6
+ ,0
+ ,0
+ ,0
+ ,12
+ ,12
+ ,12
+ ,14
+ ,5
+ ,1
+ ,0
+ ,2
+ ,14
+ ,13
+ ,13
+ ,13
+ ,5
+ ,0
+ ,0
+ ,0
+ ,11
+ ,9
+ ,12
+ ,12
+ ,4
+ ,0
+ ,0
+ ,0
+ ,8
+ ,11
+ ,10
+ ,15
+ ,2
+ ,1
+ ,0
+ ,1
+ ,7
+ ,12
+ ,8
+ ,12
+ ,3
+ ,0
+ ,0
+ ,0
+ ,9
+ ,9
+ ,6
+ ,12
+ ,5
+ ,0
+ ,2
+ ,1
+ ,14
+ ,12
+ ,13
+ ,13
+ ,5
+ ,2
+ ,1
+ ,0
+ ,13
+ ,12
+ ,7
+ ,12
+ ,5
+ ,0
+ ,0
+ ,0
+ ,15
+ ,12
+ ,13
+ ,13
+ ,6
+ ,0
+ ,0
+ ,0
+ ,5
+ ,14
+ ,4
+ ,5
+ ,2
+ ,0
+ ,0
+ ,0
+ ,15
+ ,11
+ ,14
+ ,13
+ ,5
+ ,3
+ ,1
+ ,0
+ ,13
+ ,12
+ ,13
+ ,13
+ ,5
+ ,0
+ ,1
+ ,0
+ ,12
+ ,8
+ ,13
+ ,13
+ ,5
+ ,0
+ ,0
+ ,0
+ ,6
+ ,12
+ ,6
+ ,11
+ ,2
+ ,1
+ ,0
+ ,0
+ ,7
+ ,12
+ ,7
+ ,12
+ ,4
+ ,0
+ ,0
+ ,0
+ ,13
+ ,12
+ ,5
+ ,12
+ ,3
+ ,0
+ ,0
+ ,0
+ ,16
+ ,11
+ ,14
+ ,15
+ ,8
+ ,1
+ ,1
+ ,0
+ ,10
+ ,11
+ ,13
+ ,15
+ ,6
+ ,0
+ ,0
+ ,0
+ ,16
+ ,12
+ ,16
+ ,16
+ ,7
+ ,0
+ ,0
+ ,0
+ ,15
+ ,10
+ ,16
+ ,13
+ ,6
+ ,0
+ ,0
+ ,0
+ ,8
+ ,13
+ ,7
+ ,10
+ ,3
+ ,0
+ ,0
+ ,0
+ ,11
+ ,8
+ ,14
+ ,15
+ ,5
+ ,0
+ ,0
+ ,0
+ ,13
+ ,12
+ ,11
+ ,13
+ ,6
+ ,0
+ ,3
+ ,1
+ ,16
+ ,11
+ ,17
+ ,16
+ ,7
+ ,1
+ ,0
+ ,0
+ ,11
+ ,10
+ ,5
+ ,13
+ ,3
+ ,0
+ ,0
+ ,0
+ ,14
+ ,13
+ ,10
+ ,16
+ ,8
+ ,0
+ ,0
+ ,0
+ ,9
+ ,10
+ ,11
+ ,13
+ ,3
+ ,2
+ ,1
+ ,0
+ ,8
+ ,10
+ ,10
+ ,14
+ ,3
+ ,0
+ ,0
+ ,0
+ ,8
+ ,7
+ ,9
+ ,15
+ ,4
+ ,1
+ ,0
+ ,1
+ ,11
+ ,10
+ ,12
+ ,14
+ ,5
+ ,2
+ ,0
+ ,0
+ ,12
+ ,8
+ ,15
+ ,13
+ ,7
+ ,0
+ ,0
+ ,0
+ ,11
+ ,12
+ ,7
+ ,13
+ ,6
+ ,4
+ ,0
+ ,0
+ ,14
+ ,12
+ ,13
+ ,15
+ ,6
+ ,0
+ ,1
+ ,2
+ ,11
+ ,12
+ ,8
+ ,16
+ ,6
+ ,2
+ ,1
+ ,0
+ ,14
+ ,11
+ ,16
+ ,12
+ ,5
+ ,0
+ ,0
+ ,0
+ ,13
+ ,13
+ ,15
+ ,14
+ ,6
+ ,2
+ ,1
+ ,2
+ ,12
+ ,12
+ ,6
+ ,14
+ ,5
+ ,0
+ ,0
+ ,0
+ ,4
+ ,8
+ ,6
+ ,4
+ ,4
+ ,0
+ ,0
+ ,0
+ ,15
+ ,11
+ ,12
+ ,13
+ ,6
+ ,2
+ ,1
+ ,1
+ ,10
+ ,12
+ ,8
+ ,16
+ ,4
+ ,0
+ ,0
+ ,0
+ ,13
+ ,13
+ ,11
+ ,15
+ ,6
+ ,1
+ ,2
+ ,1
+ ,15
+ ,12
+ ,13
+ ,14
+ ,6
+ ,1
+ ,1
+ ,2
+ ,12
+ ,10
+ ,14
+ ,14
+ ,5
+ ,1
+ ,2
+ ,1
+ ,13
+ ,12
+ ,14
+ ,14
+ ,6
+ ,0
+ ,0
+ ,0
+ ,8
+ ,10
+ ,10
+ ,6
+ ,4
+ ,0
+ ,0
+ ,0
+ ,10
+ ,13
+ ,4
+ ,13
+ ,6
+ ,2
+ ,0
+ ,0
+ ,15
+ ,11
+ ,16
+ ,14
+ ,6
+ ,0
+ ,0
+ ,0
+ ,16
+ ,12
+ ,12
+ ,15
+ ,8
+ ,0
+ ,0
+ ,0
+ ,16
+ ,12
+ ,15
+ ,16
+ ,7
+ ,0
+ ,0
+ ,0
+ ,14
+ ,10
+ ,12
+ ,15
+ ,6
+ ,0
+ ,0
+ ,0
+ ,14
+ ,11
+ ,14
+ ,12
+ ,6
+ ,1
+ ,1
+ ,1
+ ,12
+ ,11
+ ,11
+ ,14
+ ,2
+ ,1
+ ,1
+ ,1
+ ,15
+ ,11
+ ,16
+ ,11
+ ,5
+ ,0
+ ,1
+ ,2
+ ,13
+ ,8
+ ,14
+ ,14
+ ,5
+ ,1
+ ,1
+ ,1
+ ,16
+ ,11
+ ,14
+ ,14
+ ,6
+ ,0
+ ,0
+ ,0
+ ,14
+ ,12
+ ,15
+ ,14
+ ,6
+ ,0
+ ,0
+ ,0
+ ,8
+ ,11
+ ,9
+ ,12
+ ,4
+ ,0
+ ,0
+ ,0
+ ,16
+ ,12
+ ,15
+ ,14
+ ,6
+ ,0
+ ,1
+ ,0
+ ,16
+ ,12
+ ,14
+ ,16
+ ,8
+ ,1
+ ,1
+ ,1
+ ,12
+ ,12
+ ,15
+ ,13
+ ,6
+ ,0
+ ,0
+ ,0
+ ,11
+ ,8
+ ,10
+ ,14
+ ,5
+ ,0
+ ,3
+ ,1
+ ,16
+ ,12
+ ,14
+ ,16
+ ,8
+ ,1
+ ,1
+ ,1
+ ,9
+ ,11
+ ,9
+ ,12
+ ,4
+ ,0
+ ,0
+ ,0)
+ ,dim=c(8
+ ,156)
+ ,dimnames=list(c('Popularity'
+ ,'FindingFriends'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity'
+ ,'B'
+ ,'2B'
+ ,'3B')
+ ,1:156))
> y <- array(NA,dim=c(8,156),dimnames=list(c('Popularity','FindingFriends','KnowingPeople','Liked','Celebrity','B','2B','3B'),1:156))
> 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'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Popularity FindingFriends KnowingPeople Liked Celebrity B 2B 3B
1 15 10 12 16 6 2 0 0
2 12 9 7 12 6 1 1 2
3 9 12 11 11 4 1 2 1
4 10 12 11 12 6 0 0 0
5 13 9 14 14 6 0 0 0
6 16 11 16 16 7 1 0 0
7 14 12 13 13 6 0 0 0
8 16 11 13 14 7 1 1 0
9 10 12 5 13 6 0 0 0
10 8 12 8 13 4 2 0 1
11 12 11 14 13 5 1 0 0
12 15 11 15 15 8 0 0 0
13 14 12 8 14 4 0 1 0
14 14 6 13 12 6 1 1 2
15 12 13 12 12 6 1 2 1
16 12 11 11 12 5 0 0 0
17 10 12 8 11 4 0 0 0
18 4 10 4 10 2 0 0 0
19 14 11 15 15 8 0 1 0
20 15 12 12 16 7 0 0 0
21 16 12 14 14 6 0 0 0
22 12 12 9 13 4 0 1 0
23 12 11 16 13 4 0 0 0
24 12 12 10 13 4 0 0 1
25 12 12 8 13 5 1 0 1
26 12 12 14 14 4 0 0 0
27 11 6 6 9 4 3 2 1
28 11 5 16 14 6 1 0 0
29 11 12 11 12 6 1 1 0
30 11 14 7 13 6 1 1 0
31 11 12 13 11 4 3 1 1
32 11 9 7 13 2 0 0 0
33 15 11 14 15 7 0 0 0
34 15 11 17 16 6 0 0 0
35 9 11 15 15 7 0 0 0
36 16 12 8 14 4 0 0 0
37 13 10 8 8 4 0 2 1
38 9 12 11 11 4 1 0 0
39 16 11 16 15 6 0 0 0
40 12 12 10 15 6 0 0 0
41 15 9 5 11 3 0 0 2
42 5 15 8 12 3 0 0 0
43 11 11 8 12 6 2 2 0
44 17 11 15 14 5 2 2 0
45 9 15 6 8 4 0 1 1
46 13 12 16 16 6 0 0 0
47 16 9 16 16 6 0 0 0
48 16 12 16 14 6 0 0 0
49 14 9 19 12 6 2 0 2
50 16 11 14 15 6 1 0 0
51 11 12 15 12 6 0 0 0
52 11 11 11 14 5 0 0 0
53 11 6 14 17 6 0 0 0
54 12 10 12 13 6 0 0 0
55 12 12 15 13 6 1 1 1
56 12 13 14 12 5 0 0 0
57 14 11 13 16 6 0 0 0
58 10 10 11 12 5 2 0 0
59 9 11 8 10 4 0 2 0
60 12 7 11 15 5 0 0 1
61 10 11 9 12 4 0 0 0
62 14 11 10 16 6 0 0 0
63 8 7 4 13 6 0 0 0
64 16 12 15 15 7 1 0 0
65 14 14 17 18 6 1 0 0
66 14 11 12 12 4 0 0 0
67 12 12 12 13 4 0 0 0
68 14 11 15 14 6 1 0 0
69 7 12 13 12 3 1 1 1
70 19 12 15 15 6 0 0 0
71 15 12 14 16 4 0 0 0
72 8 12 8 14 5 0 0 0
73 10 15 15 15 6 0 0 0
74 13 11 12 13 7 0 0 0
75 13 13 14 13 3 0 0 0
76 10 10 10 11 5 0 0 0
77 12 12 7 12 3 0 0 0
78 15 13 16 18 8 0 1 1
79 7 14 12 12 4 1 0 0
80 14 11 15 16 6 0 0 0
81 10 11 7 9 4 0 0 0
82 6 7 9 11 4 0 3 0
83 11 11 15 10 5 2 0 0
84 12 12 7 11 4 0 0 0
85 14 12 15 13 6 0 0 2
86 12 10 14 13 7 0 0 0
87 14 12 14 15 7 0 0 0
88 11 8 8 13 4 2 2 0
89 10 7 8 9 5 1 0 1
90 13 11 14 13 6 0 0 1
91 8 11 10 12 4 0 0 0
92 9 11 12 13 5 0 0 0
93 6 9 15 11 6 0 0 0
94 12 12 12 14 5 1 0 2
95 14 13 13 13 5 0 0 0
96 11 9 12 12 4 0 0 0
97 8 11 10 15 2 1 0 1
98 7 12 8 12 3 0 0 0
99 9 9 6 12 5 0 2 1
100 14 12 13 13 5 2 1 0
101 13 12 7 12 5 0 0 0
102 15 12 13 13 6 0 0 0
103 5 14 4 5 2 0 0 0
104 15 11 14 13 5 3 1 0
105 13 12 13 13 5 0 1 0
106 12 8 13 13 5 0 0 0
107 6 12 6 11 2 1 0 0
108 7 12 7 12 4 0 0 0
109 13 12 5 12 3 0 0 0
110 16 11 14 15 8 1 1 0
111 10 11 13 15 6 0 0 0
112 16 12 16 16 7 0 0 0
113 15 10 16 13 6 0 0 0
114 8 13 7 10 3 0 0 0
115 11 8 14 15 5 0 0 0
116 13 12 11 13 6 0 3 1
117 16 11 17 16 7 1 0 0
118 11 10 5 13 3 0 0 0
119 14 13 10 16 8 0 0 0
120 9 10 11 13 3 2 1 0
121 8 10 10 14 3 0 0 0
122 8 7 9 15 4 1 0 1
123 11 10 12 14 5 2 0 0
124 12 8 15 13 7 0 0 0
125 11 12 7 13 6 4 0 0
126 14 12 13 15 6 0 1 2
127 11 12 8 16 6 2 1 0
128 14 11 16 12 5 0 0 0
129 13 13 15 14 6 2 1 2
130 12 12 6 14 5 0 0 0
131 4 8 6 4 4 0 0 0
132 15 11 12 13 6 2 1 1
133 10 12 8 16 4 0 0 0
134 13 13 11 15 6 1 2 1
135 15 12 13 14 6 1 1 2
136 12 10 14 14 5 1 2 1
137 13 12 14 14 6 0 0 0
138 8 10 10 6 4 0 0 0
139 10 13 4 13 6 2 0 0
140 15 11 16 14 6 0 0 0
141 16 12 12 15 8 0 0 0
142 16 12 15 16 7 0 0 0
143 14 10 12 15 6 0 0 0
144 14 11 14 12 6 1 1 1
145 12 11 11 14 2 1 1 1
146 15 11 16 11 5 0 1 2
147 13 8 14 14 5 1 1 1
148 16 11 14 14 6 0 0 0
149 14 12 15 14 6 0 0 0
150 8 11 9 12 4 0 0 0
151 16 12 15 14 6 0 1 0
152 16 12 14 16 8 1 1 1
153 12 12 15 13 6 0 0 0
154 11 8 10 14 5 0 3 1
155 16 12 14 16 8 1 1 1
156 9 11 9 12 4 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) FindingFriends KnowingPeople Liked Celebrity
-0.34096 0.11785 0.24088 0.37207 0.61092
B `2B` `3B`
-0.04365 0.17183 0.50219
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.09123 -1.27976 -0.08508 1.29622 6.14596
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.34096 1.46415 -0.233 0.816184
FindingFriends 0.11785 0.09637 1.223 0.223335
KnowingPeople 0.24088 0.06160 3.910 0.000140 ***
Liked 0.37207 0.09686 3.841 0.000181 ***
Celebrity 0.61092 0.15633 3.908 0.000141 ***
B -0.04365 0.22313 -0.196 0.845159
`2B` 0.17183 0.26854 0.640 0.523248
`3B` 0.50219 0.31640 1.587 0.114600
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.097 on 148 degrees of freedom
Multiple R-squared: 0.5129, Adjusted R-squared: 0.4899
F-statistic: 22.27 on 7 and 148 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.28405131 0.5681026142 0.7159486929
[2,] 0.24572743 0.4914548611 0.7542725695
[3,] 0.29353173 0.5870634609 0.7064682695
[4,] 0.20030187 0.4006037476 0.7996981262
[5,] 0.12247104 0.2449420738 0.8775289631
[6,] 0.10024194 0.2004838739 0.8997580631
[7,] 0.07879206 0.1575841230 0.9212079385
[8,] 0.12240480 0.2448096024 0.8775951988
[9,] 0.21772666 0.4354533202 0.7822733399
[10,] 0.15770574 0.3154114812 0.8422942594
[11,] 0.18072914 0.3614582878 0.8192708561
[12,] 0.13218322 0.2643664363 0.8678167819
[13,] 0.10039876 0.2007975253 0.8996012373
[14,] 0.06930459 0.1386091831 0.9306954085
[15,] 0.05245507 0.1049101496 0.9475449252
[16,] 0.03998449 0.0799689703 0.9600155149
[17,] 0.05982729 0.1196545794 0.9401727103
[18,] 0.11787353 0.2357470660 0.8821264670
[19,] 0.09246158 0.1849231561 0.9075384219
[20,] 0.07104353 0.1420870592 0.9289564704
[21,] 0.05116021 0.1023204199 0.9488397900
[22,] 0.04454485 0.0890896977 0.9554551512
[23,] 0.03142902 0.0628580412 0.9685709794
[24,] 0.02216364 0.0443272895 0.9778363552
[25,] 0.18267389 0.3653477839 0.8173261081
[26,] 0.37052036 0.7410407296 0.6294796352
[27,] 0.51737782 0.9652443513 0.4826221757
[28,] 0.46312899 0.9262579855 0.5368710073
[29,] 0.44821666 0.8964333278 0.5517833361
[30,] 0.41082175 0.8216434933 0.5891782534
[31,] 0.67543708 0.6491258478 0.3245629239
[32,] 0.83048370 0.3390326022 0.1695163011
[33,] 0.79462739 0.4107452285 0.2053726142
[34,] 0.83868794 0.3226241123 0.1613120562
[35,] 0.81475541 0.3704891751 0.1852445875
[36,] 0.80776641 0.3844671833 0.1922335916
[37,] 0.78254813 0.4349037490 0.2174518745
[38,] 0.80099088 0.3980182382 0.1990091191
[39,] 0.76309585 0.4738082995 0.2369041497
[40,] 0.78097321 0.4380535891 0.2190267945
[41,] 0.75708285 0.4858342973 0.2429171487
[42,] 0.73212818 0.5357436389 0.2678718194
[43,] 0.84031241 0.3193751883 0.1596875941
[44,] 0.80763907 0.3847218600 0.1923609300
[45,] 0.80437985 0.3912403002 0.1956201501
[46,] 0.77127525 0.4574494918 0.2287247459
[47,] 0.73226895 0.5354620982 0.2677310491
[48,] 0.69219270 0.6156145979 0.3078072989
[49,] 0.66119496 0.6776100716 0.3388050358
[50,] 0.64331134 0.7133773142 0.3566886571
[51,] 0.59641816 0.8071636857 0.4035818429
[52,] 0.55601333 0.8879733337 0.4439866669
[53,] 0.54296789 0.9140642219 0.4570321110
[54,] 0.52928514 0.9414297247 0.4707148623
[55,] 0.53201740 0.9359652064 0.4679826032
[56,] 0.59681332 0.8063733699 0.4031866850
[57,] 0.55328945 0.8934210974 0.4467105487
[58,] 0.51164577 0.9767084515 0.4883542257
[59,] 0.68391140 0.6321771998 0.3160885999
[60,] 0.84808273 0.3038345373 0.1519172686
[61,] 0.84459873 0.3108025478 0.1554012739
[62,] 0.88082926 0.2383414781 0.1191707391
[63,] 0.94545968 0.1090806351 0.0545403176
[64,] 0.93120991 0.1375801792 0.0687900896
[65,] 0.92354053 0.1529189346 0.0764594673
[66,] 0.90501195 0.1899760974 0.0949880487
[67,] 0.92413494 0.1517301230 0.0758650615
[68,] 0.93176781 0.1364643750 0.0682321875
[69,] 0.97124566 0.0575086830 0.0287543415
[70,] 0.96252747 0.0749450657 0.0374725329
[71,] 0.95939744 0.0812051222 0.0406025611
[72,] 0.97970514 0.0405897108 0.0202948554
[73,] 0.97366612 0.0526677540 0.0263338770
[74,] 0.97911814 0.0417637209 0.0208818604
[75,] 0.97228802 0.0554239552 0.0277119776
[76,] 0.96707085 0.0658582925 0.0329291462
[77,] 0.95781481 0.0843703793 0.0421851896
[78,] 0.95110407 0.0977918678 0.0488959339
[79,] 0.95168556 0.0966288809 0.0483144404
[80,] 0.93812715 0.1237456921 0.0618728460
[81,] 0.94100263 0.1179947354 0.0589973677
[82,] 0.95273532 0.0945293634 0.0472646817
[83,] 0.99689501 0.0062099873 0.0031049937
[84,] 0.99588101 0.0082379855 0.0041189927
[85,] 0.99494139 0.0101172190 0.0050586095
[86,] 0.99307954 0.0138409163 0.0069204581
[87,] 0.99398754 0.0120249293 0.0060124647
[88,] 0.99484427 0.0103114648 0.0051557324
[89,] 0.99312713 0.0137457347 0.0068728674
[90,] 0.99169441 0.0166111702 0.0083055851
[91,] 0.99447398 0.0110520370 0.0055260185
[92,] 0.99440656 0.0111868832 0.0055934416
[93,] 0.99221801 0.0155639709 0.0077819855
[94,] 0.99401935 0.0119613018 0.0059806509
[95,] 0.99144142 0.0171171697 0.0085585848
[96,] 0.98849767 0.0230046560 0.0115023280
[97,] 0.98740006 0.0251998760 0.0125999380
[98,] 0.99163489 0.0167302128 0.0083651064
[99,] 0.99907421 0.0018515749 0.0009257874
[100,] 0.99867367 0.0026526682 0.0013263341
[101,] 0.99967487 0.0006502628 0.0003251314
[102,] 0.99945649 0.0010870271 0.0005435136
[103,] 0.99934363 0.0013127402 0.0006563701
[104,] 0.99897546 0.0020490769 0.0010245384
[105,] 0.99862705 0.0027459021 0.0013729510
[106,] 0.99775706 0.0044858777 0.0022429389
[107,] 0.99642791 0.0071441723 0.0035720861
[108,] 0.99923340 0.0015332018 0.0007666009
[109,] 0.99878932 0.0024213662 0.0012106831
[110,] 0.99809216 0.0038156831 0.0019078415
[111,] 0.99799260 0.0040148082 0.0020074041
[112,] 0.99797816 0.0040436780 0.0020218390
[113,] 0.99708216 0.0058356757 0.0029178379
[114,] 0.99771141 0.0045771877 0.0022885938
[115,] 0.99613409 0.0077318161 0.0038659081
[116,] 0.99442637 0.0111472548 0.0055736274
[117,] 0.99219413 0.0156117443 0.0078058722
[118,] 0.98849186 0.0230162805 0.0115081403
[119,] 0.99468801 0.0106239834 0.0053119917
[120,] 0.99655263 0.0068947329 0.0034473665
[121,] 0.99461720 0.0107655965 0.0053827982
[122,] 0.99395792 0.0120841540 0.0060420770
[123,] 0.99080626 0.0183874713 0.0091937357
[124,] 0.98402299 0.0319540203 0.0159770101
[125,] 0.97216222 0.0556755564 0.0278377782
[126,] 0.96854283 0.0629143393 0.0314571697
[127,] 0.95735279 0.0852944248 0.0426472124
[128,] 0.93483513 0.1303297375 0.0651648688
[129,] 0.92147888 0.1570422453 0.0785211226
[130,] 0.87192986 0.2561402754 0.1280701377
[131,] 0.87711486 0.2457702900 0.1228851450
[132,] 0.80285413 0.3942917459 0.1971458730
[133,] 0.73551679 0.5289664196 0.2644832098
[134,] 0.71361285 0.5727743027 0.2863871514
[135,] 0.58090993 0.8381801453 0.4190900727
> postscript(file="/var/www/html/rcomp/tmp/1uabe1293203553.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2njsz1293203553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3njsz1293203553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4njsz1293203553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5gar21293203553.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 = 156
Frequency = 1
1 2 3 4 5 6
1.74051170 0.33119764 -2.06161883 -1.85332033 0.03343624 1.00456323
7 8 9 10 11 12
1.29284170 2.29952450 -0.78009940 -2.69580001 -0.17562164 -0.03705014
13 14 15 16 17 18
3.17517867 1.23945361 -1.01425517 0.87544506 0.46323068 -2.74363585
19 20 21 22 23 24
-1.20888244 0.80658900 2.67988610 1.30636955 -0.09012465 0.73512801
25 26 27 28 29 30
0.64963102 -0.09828321 2.68134699 -1.93327468 -0.98149900 -0.62574288
31 32 33 34 35 36
-0.28424400 2.53534739 0.81474756 0.33094258 -5.42613480 5.34701097
37 38 39 40 41 42
3.96929443 -1.21576277 1.94389818 -0.72865769 6.14596031 -4.65147735
43 44 45 46 47 48
-0.26918053 3.91141180 0.03364123 -1.54602510 1.80752503 2.19812138
49 50 51 52 53 54
-0.34390478 2.46931654 -1.81684978 -0.86870142 -2.72923334 -0.23057584
55 56 57 58 59 60
-1.81929316 -0.08290212 0.29447203 -0.91939764 -0.39051062 -0.27156595
61 62 63 64 65 66
-0.03187487 1.01711912 -1.94996681 1.49966879 -1.72310040 3.24547804
67 68 69 70 71 72
0.75555476 0.60050742 -4.13270916 5.06693050 2.15757031 -3.26390438
73 74 75 76 77 78
-4.28661963 0.04065877 1.76685533 -0.39374929 2.94295515 -2.30387608
79 80 81 82 83 84
-4.06441846 -0.18729269 1.56610957 -3.70389833 -0.25663065 2.70411305
85 86 87 88 89 90
-0.19330596 -1.32325591 -0.30310248 0.93412706 0.72717420 -0.33238208
91 92 93 94 95 96
-2.27275723 -2.73751054 -6.09122640 -1.18816314 1.78590700 0.48117813
97 98 99 100 101 102
-2.62568411 -2.29792721 -1.53029910 1.81923201 2.72112446 2.29284170
103 104 105 106 107 108
-0.35466983 2.73985333 0.73192476 0.37515723 -1.78952027 -2.66796019
109 110 111 112 113 114
4.42471988 1.07565355 -3.33345473 0.84305955 1.80589471 -0.43074841
115 116 117 118 119 120
-1.60987161 -0.24308192 0.76368087 2.28834673 -0.44041166 -1.24147248
121 122 123 124 125 126
-2.28813832 -3.13523225 -0.90442648 -1.32843818 -0.08724961 -0.62752001
127 128 129 130 131 132
-1.70349124 1.67103325 -1.76775428 1.21786034 -1.97909173 2.06485761
133 134 135 136 137 138
-1.39713551 -0.88959253 0.78820686 -1.27570089 -0.32011390 0.07753225
139 140 141 142 143 144
-0.56975982 1.31597142 1.56774690 1.08394192 1.02527768 0.91151249
145 146 147 148 149 150
1.33367448 1.86689125 0.13183149 2.79773615 0.43900374 -2.03187487
151 152 153 154 155 156
2.26717145 0.08353880 -1.18892302 -1.29195728 0.08353880 -1.03187487
> postscript(file="/var/www/html/rcomp/tmp/6gar21293203553.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 1.74051170 NA
1 0.33119764 1.74051170
2 -2.06161883 0.33119764
3 -1.85332033 -2.06161883
4 0.03343624 -1.85332033
5 1.00456323 0.03343624
6 1.29284170 1.00456323
7 2.29952450 1.29284170
8 -0.78009940 2.29952450
9 -2.69580001 -0.78009940
10 -0.17562164 -2.69580001
11 -0.03705014 -0.17562164
12 3.17517867 -0.03705014
13 1.23945361 3.17517867
14 -1.01425517 1.23945361
15 0.87544506 -1.01425517
16 0.46323068 0.87544506
17 -2.74363585 0.46323068
18 -1.20888244 -2.74363585
19 0.80658900 -1.20888244
20 2.67988610 0.80658900
21 1.30636955 2.67988610
22 -0.09012465 1.30636955
23 0.73512801 -0.09012465
24 0.64963102 0.73512801
25 -0.09828321 0.64963102
26 2.68134699 -0.09828321
27 -1.93327468 2.68134699
28 -0.98149900 -1.93327468
29 -0.62574288 -0.98149900
30 -0.28424400 -0.62574288
31 2.53534739 -0.28424400
32 0.81474756 2.53534739
33 0.33094258 0.81474756
34 -5.42613480 0.33094258
35 5.34701097 -5.42613480
36 3.96929443 5.34701097
37 -1.21576277 3.96929443
38 1.94389818 -1.21576277
39 -0.72865769 1.94389818
40 6.14596031 -0.72865769
41 -4.65147735 6.14596031
42 -0.26918053 -4.65147735
43 3.91141180 -0.26918053
44 0.03364123 3.91141180
45 -1.54602510 0.03364123
46 1.80752503 -1.54602510
47 2.19812138 1.80752503
48 -0.34390478 2.19812138
49 2.46931654 -0.34390478
50 -1.81684978 2.46931654
51 -0.86870142 -1.81684978
52 -2.72923334 -0.86870142
53 -0.23057584 -2.72923334
54 -1.81929316 -0.23057584
55 -0.08290212 -1.81929316
56 0.29447203 -0.08290212
57 -0.91939764 0.29447203
58 -0.39051062 -0.91939764
59 -0.27156595 -0.39051062
60 -0.03187487 -0.27156595
61 1.01711912 -0.03187487
62 -1.94996681 1.01711912
63 1.49966879 -1.94996681
64 -1.72310040 1.49966879
65 3.24547804 -1.72310040
66 0.75555476 3.24547804
67 0.60050742 0.75555476
68 -4.13270916 0.60050742
69 5.06693050 -4.13270916
70 2.15757031 5.06693050
71 -3.26390438 2.15757031
72 -4.28661963 -3.26390438
73 0.04065877 -4.28661963
74 1.76685533 0.04065877
75 -0.39374929 1.76685533
76 2.94295515 -0.39374929
77 -2.30387608 2.94295515
78 -4.06441846 -2.30387608
79 -0.18729269 -4.06441846
80 1.56610957 -0.18729269
81 -3.70389833 1.56610957
82 -0.25663065 -3.70389833
83 2.70411305 -0.25663065
84 -0.19330596 2.70411305
85 -1.32325591 -0.19330596
86 -0.30310248 -1.32325591
87 0.93412706 -0.30310248
88 0.72717420 0.93412706
89 -0.33238208 0.72717420
90 -2.27275723 -0.33238208
91 -2.73751054 -2.27275723
92 -6.09122640 -2.73751054
93 -1.18816314 -6.09122640
94 1.78590700 -1.18816314
95 0.48117813 1.78590700
96 -2.62568411 0.48117813
97 -2.29792721 -2.62568411
98 -1.53029910 -2.29792721
99 1.81923201 -1.53029910
100 2.72112446 1.81923201
101 2.29284170 2.72112446
102 -0.35466983 2.29284170
103 2.73985333 -0.35466983
104 0.73192476 2.73985333
105 0.37515723 0.73192476
106 -1.78952027 0.37515723
107 -2.66796019 -1.78952027
108 4.42471988 -2.66796019
109 1.07565355 4.42471988
110 -3.33345473 1.07565355
111 0.84305955 -3.33345473
112 1.80589471 0.84305955
113 -0.43074841 1.80589471
114 -1.60987161 -0.43074841
115 -0.24308192 -1.60987161
116 0.76368087 -0.24308192
117 2.28834673 0.76368087
118 -0.44041166 2.28834673
119 -1.24147248 -0.44041166
120 -2.28813832 -1.24147248
121 -3.13523225 -2.28813832
122 -0.90442648 -3.13523225
123 -1.32843818 -0.90442648
124 -0.08724961 -1.32843818
125 -0.62752001 -0.08724961
126 -1.70349124 -0.62752001
127 1.67103325 -1.70349124
128 -1.76775428 1.67103325
129 1.21786034 -1.76775428
130 -1.97909173 1.21786034
131 2.06485761 -1.97909173
132 -1.39713551 2.06485761
133 -0.88959253 -1.39713551
134 0.78820686 -0.88959253
135 -1.27570089 0.78820686
136 -0.32011390 -1.27570089
137 0.07753225 -0.32011390
138 -0.56975982 0.07753225
139 1.31597142 -0.56975982
140 1.56774690 1.31597142
141 1.08394192 1.56774690
142 1.02527768 1.08394192
143 0.91151249 1.02527768
144 1.33367448 0.91151249
145 1.86689125 1.33367448
146 0.13183149 1.86689125
147 2.79773615 0.13183149
148 0.43900374 2.79773615
149 -2.03187487 0.43900374
150 2.26717145 -2.03187487
151 0.08353880 2.26717145
152 -1.18892302 0.08353880
153 -1.29195728 -1.18892302
154 0.08353880 -1.29195728
155 -1.03187487 0.08353880
156 NA -1.03187487
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.33119764 1.74051170
[2,] -2.06161883 0.33119764
[3,] -1.85332033 -2.06161883
[4,] 0.03343624 -1.85332033
[5,] 1.00456323 0.03343624
[6,] 1.29284170 1.00456323
[7,] 2.29952450 1.29284170
[8,] -0.78009940 2.29952450
[9,] -2.69580001 -0.78009940
[10,] -0.17562164 -2.69580001
[11,] -0.03705014 -0.17562164
[12,] 3.17517867 -0.03705014
[13,] 1.23945361 3.17517867
[14,] -1.01425517 1.23945361
[15,] 0.87544506 -1.01425517
[16,] 0.46323068 0.87544506
[17,] -2.74363585 0.46323068
[18,] -1.20888244 -2.74363585
[19,] 0.80658900 -1.20888244
[20,] 2.67988610 0.80658900
[21,] 1.30636955 2.67988610
[22,] -0.09012465 1.30636955
[23,] 0.73512801 -0.09012465
[24,] 0.64963102 0.73512801
[25,] -0.09828321 0.64963102
[26,] 2.68134699 -0.09828321
[27,] -1.93327468 2.68134699
[28,] -0.98149900 -1.93327468
[29,] -0.62574288 -0.98149900
[30,] -0.28424400 -0.62574288
[31,] 2.53534739 -0.28424400
[32,] 0.81474756 2.53534739
[33,] 0.33094258 0.81474756
[34,] -5.42613480 0.33094258
[35,] 5.34701097 -5.42613480
[36,] 3.96929443 5.34701097
[37,] -1.21576277 3.96929443
[38,] 1.94389818 -1.21576277
[39,] -0.72865769 1.94389818
[40,] 6.14596031 -0.72865769
[41,] -4.65147735 6.14596031
[42,] -0.26918053 -4.65147735
[43,] 3.91141180 -0.26918053
[44,] 0.03364123 3.91141180
[45,] -1.54602510 0.03364123
[46,] 1.80752503 -1.54602510
[47,] 2.19812138 1.80752503
[48,] -0.34390478 2.19812138
[49,] 2.46931654 -0.34390478
[50,] -1.81684978 2.46931654
[51,] -0.86870142 -1.81684978
[52,] -2.72923334 -0.86870142
[53,] -0.23057584 -2.72923334
[54,] -1.81929316 -0.23057584
[55,] -0.08290212 -1.81929316
[56,] 0.29447203 -0.08290212
[57,] -0.91939764 0.29447203
[58,] -0.39051062 -0.91939764
[59,] -0.27156595 -0.39051062
[60,] -0.03187487 -0.27156595
[61,] 1.01711912 -0.03187487
[62,] -1.94996681 1.01711912
[63,] 1.49966879 -1.94996681
[64,] -1.72310040 1.49966879
[65,] 3.24547804 -1.72310040
[66,] 0.75555476 3.24547804
[67,] 0.60050742 0.75555476
[68,] -4.13270916 0.60050742
[69,] 5.06693050 -4.13270916
[70,] 2.15757031 5.06693050
[71,] -3.26390438 2.15757031
[72,] -4.28661963 -3.26390438
[73,] 0.04065877 -4.28661963
[74,] 1.76685533 0.04065877
[75,] -0.39374929 1.76685533
[76,] 2.94295515 -0.39374929
[77,] -2.30387608 2.94295515
[78,] -4.06441846 -2.30387608
[79,] -0.18729269 -4.06441846
[80,] 1.56610957 -0.18729269
[81,] -3.70389833 1.56610957
[82,] -0.25663065 -3.70389833
[83,] 2.70411305 -0.25663065
[84,] -0.19330596 2.70411305
[85,] -1.32325591 -0.19330596
[86,] -0.30310248 -1.32325591
[87,] 0.93412706 -0.30310248
[88,] 0.72717420 0.93412706
[89,] -0.33238208 0.72717420
[90,] -2.27275723 -0.33238208
[91,] -2.73751054 -2.27275723
[92,] -6.09122640 -2.73751054
[93,] -1.18816314 -6.09122640
[94,] 1.78590700 -1.18816314
[95,] 0.48117813 1.78590700
[96,] -2.62568411 0.48117813
[97,] -2.29792721 -2.62568411
[98,] -1.53029910 -2.29792721
[99,] 1.81923201 -1.53029910
[100,] 2.72112446 1.81923201
[101,] 2.29284170 2.72112446
[102,] -0.35466983 2.29284170
[103,] 2.73985333 -0.35466983
[104,] 0.73192476 2.73985333
[105,] 0.37515723 0.73192476
[106,] -1.78952027 0.37515723
[107,] -2.66796019 -1.78952027
[108,] 4.42471988 -2.66796019
[109,] 1.07565355 4.42471988
[110,] -3.33345473 1.07565355
[111,] 0.84305955 -3.33345473
[112,] 1.80589471 0.84305955
[113,] -0.43074841 1.80589471
[114,] -1.60987161 -0.43074841
[115,] -0.24308192 -1.60987161
[116,] 0.76368087 -0.24308192
[117,] 2.28834673 0.76368087
[118,] -0.44041166 2.28834673
[119,] -1.24147248 -0.44041166
[120,] -2.28813832 -1.24147248
[121,] -3.13523225 -2.28813832
[122,] -0.90442648 -3.13523225
[123,] -1.32843818 -0.90442648
[124,] -0.08724961 -1.32843818
[125,] -0.62752001 -0.08724961
[126,] -1.70349124 -0.62752001
[127,] 1.67103325 -1.70349124
[128,] -1.76775428 1.67103325
[129,] 1.21786034 -1.76775428
[130,] -1.97909173 1.21786034
[131,] 2.06485761 -1.97909173
[132,] -1.39713551 2.06485761
[133,] -0.88959253 -1.39713551
[134,] 0.78820686 -0.88959253
[135,] -1.27570089 0.78820686
[136,] -0.32011390 -1.27570089
[137,] 0.07753225 -0.32011390
[138,] -0.56975982 0.07753225
[139,] 1.31597142 -0.56975982
[140,] 1.56774690 1.31597142
[141,] 1.08394192 1.56774690
[142,] 1.02527768 1.08394192
[143,] 0.91151249 1.02527768
[144,] 1.33367448 0.91151249
[145,] 1.86689125 1.33367448
[146,] 0.13183149 1.86689125
[147,] 2.79773615 0.13183149
[148,] 0.43900374 2.79773615
[149,] -2.03187487 0.43900374
[150,] 2.26717145 -2.03187487
[151,] 0.08353880 2.26717145
[152,] -1.18892302 0.08353880
[153,] -1.29195728 -1.18892302
[154,] 0.08353880 -1.29195728
[155,] -1.03187487 0.08353880
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.33119764 1.74051170
2 -2.06161883 0.33119764
3 -1.85332033 -2.06161883
4 0.03343624 -1.85332033
5 1.00456323 0.03343624
6 1.29284170 1.00456323
7 2.29952450 1.29284170
8 -0.78009940 2.29952450
9 -2.69580001 -0.78009940
10 -0.17562164 -2.69580001
11 -0.03705014 -0.17562164
12 3.17517867 -0.03705014
13 1.23945361 3.17517867
14 -1.01425517 1.23945361
15 0.87544506 -1.01425517
16 0.46323068 0.87544506
17 -2.74363585 0.46323068
18 -1.20888244 -2.74363585
19 0.80658900 -1.20888244
20 2.67988610 0.80658900
21 1.30636955 2.67988610
22 -0.09012465 1.30636955
23 0.73512801 -0.09012465
24 0.64963102 0.73512801
25 -0.09828321 0.64963102
26 2.68134699 -0.09828321
27 -1.93327468 2.68134699
28 -0.98149900 -1.93327468
29 -0.62574288 -0.98149900
30 -0.28424400 -0.62574288
31 2.53534739 -0.28424400
32 0.81474756 2.53534739
33 0.33094258 0.81474756
34 -5.42613480 0.33094258
35 5.34701097 -5.42613480
36 3.96929443 5.34701097
37 -1.21576277 3.96929443
38 1.94389818 -1.21576277
39 -0.72865769 1.94389818
40 6.14596031 -0.72865769
41 -4.65147735 6.14596031
42 -0.26918053 -4.65147735
43 3.91141180 -0.26918053
44 0.03364123 3.91141180
45 -1.54602510 0.03364123
46 1.80752503 -1.54602510
47 2.19812138 1.80752503
48 -0.34390478 2.19812138
49 2.46931654 -0.34390478
50 -1.81684978 2.46931654
51 -0.86870142 -1.81684978
52 -2.72923334 -0.86870142
53 -0.23057584 -2.72923334
54 -1.81929316 -0.23057584
55 -0.08290212 -1.81929316
56 0.29447203 -0.08290212
57 -0.91939764 0.29447203
58 -0.39051062 -0.91939764
59 -0.27156595 -0.39051062
60 -0.03187487 -0.27156595
61 1.01711912 -0.03187487
62 -1.94996681 1.01711912
63 1.49966879 -1.94996681
64 -1.72310040 1.49966879
65 3.24547804 -1.72310040
66 0.75555476 3.24547804
67 0.60050742 0.75555476
68 -4.13270916 0.60050742
69 5.06693050 -4.13270916
70 2.15757031 5.06693050
71 -3.26390438 2.15757031
72 -4.28661963 -3.26390438
73 0.04065877 -4.28661963
74 1.76685533 0.04065877
75 -0.39374929 1.76685533
76 2.94295515 -0.39374929
77 -2.30387608 2.94295515
78 -4.06441846 -2.30387608
79 -0.18729269 -4.06441846
80 1.56610957 -0.18729269
81 -3.70389833 1.56610957
82 -0.25663065 -3.70389833
83 2.70411305 -0.25663065
84 -0.19330596 2.70411305
85 -1.32325591 -0.19330596
86 -0.30310248 -1.32325591
87 0.93412706 -0.30310248
88 0.72717420 0.93412706
89 -0.33238208 0.72717420
90 -2.27275723 -0.33238208
91 -2.73751054 -2.27275723
92 -6.09122640 -2.73751054
93 -1.18816314 -6.09122640
94 1.78590700 -1.18816314
95 0.48117813 1.78590700
96 -2.62568411 0.48117813
97 -2.29792721 -2.62568411
98 -1.53029910 -2.29792721
99 1.81923201 -1.53029910
100 2.72112446 1.81923201
101 2.29284170 2.72112446
102 -0.35466983 2.29284170
103 2.73985333 -0.35466983
104 0.73192476 2.73985333
105 0.37515723 0.73192476
106 -1.78952027 0.37515723
107 -2.66796019 -1.78952027
108 4.42471988 -2.66796019
109 1.07565355 4.42471988
110 -3.33345473 1.07565355
111 0.84305955 -3.33345473
112 1.80589471 0.84305955
113 -0.43074841 1.80589471
114 -1.60987161 -0.43074841
115 -0.24308192 -1.60987161
116 0.76368087 -0.24308192
117 2.28834673 0.76368087
118 -0.44041166 2.28834673
119 -1.24147248 -0.44041166
120 -2.28813832 -1.24147248
121 -3.13523225 -2.28813832
122 -0.90442648 -3.13523225
123 -1.32843818 -0.90442648
124 -0.08724961 -1.32843818
125 -0.62752001 -0.08724961
126 -1.70349124 -0.62752001
127 1.67103325 -1.70349124
128 -1.76775428 1.67103325
129 1.21786034 -1.76775428
130 -1.97909173 1.21786034
131 2.06485761 -1.97909173
132 -1.39713551 2.06485761
133 -0.88959253 -1.39713551
134 0.78820686 -0.88959253
135 -1.27570089 0.78820686
136 -0.32011390 -1.27570089
137 0.07753225 -0.32011390
138 -0.56975982 0.07753225
139 1.31597142 -0.56975982
140 1.56774690 1.31597142
141 1.08394192 1.56774690
142 1.02527768 1.08394192
143 0.91151249 1.02527768
144 1.33367448 0.91151249
145 1.86689125 1.33367448
146 0.13183149 1.86689125
147 2.79773615 0.13183149
148 0.43900374 2.79773615
149 -2.03187487 0.43900374
150 2.26717145 -2.03187487
151 0.08353880 2.26717145
152 -1.18892302 0.08353880
153 -1.29195728 -1.18892302
154 0.08353880 -1.29195728
155 -1.03187487 0.08353880
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7qj8m1293203553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8qj8m1293203553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jb8p1293203553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10jb8p1293203553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11mt6d1293203553.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12qu511293203553.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/134m2a1293203553.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14pm1y1293203553.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15tmz41293203553.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16w5g91293203553.tab")
+ }
>
> try(system("convert tmp/1uabe1293203553.ps tmp/1uabe1293203553.png",intern=TRUE))
character(0)
> try(system("convert tmp/2njsz1293203553.ps tmp/2njsz1293203553.png",intern=TRUE))
character(0)
> try(system("convert tmp/3njsz1293203553.ps tmp/3njsz1293203553.png",intern=TRUE))
character(0)
> try(system("convert tmp/4njsz1293203553.ps tmp/4njsz1293203553.png",intern=TRUE))
character(0)
> try(system("convert tmp/5gar21293203553.ps tmp/5gar21293203553.png",intern=TRUE))
character(0)
> try(system("convert tmp/6gar21293203553.ps tmp/6gar21293203553.png",intern=TRUE))
character(0)
> try(system("convert tmp/7qj8m1293203553.ps tmp/7qj8m1293203553.png",intern=TRUE))
character(0)
> try(system("convert tmp/8qj8m1293203553.ps tmp/8qj8m1293203553.png",intern=TRUE))
character(0)
> try(system("convert tmp/9jb8p1293203553.ps tmp/9jb8p1293203553.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jb8p1293203553.ps tmp/10jb8p1293203553.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.156 1.849 11.281