R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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.
Natural language support but running in an English locale
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(9
+ ,24
+ ,14
+ ,11
+ ,12
+ ,24
+ ,26
+ ,9
+ ,25
+ ,11
+ ,7
+ ,8
+ ,25
+ ,23
+ ,9
+ ,17
+ ,6
+ ,17
+ ,8
+ ,30
+ ,25
+ ,9
+ ,18
+ ,12
+ ,10
+ ,8
+ ,19
+ ,23
+ ,9
+ ,18
+ ,8
+ ,12
+ ,9
+ ,22
+ ,19
+ ,9
+ ,16
+ ,10
+ ,12
+ ,7
+ ,22
+ ,29
+ ,10
+ ,20
+ ,10
+ ,11
+ ,4
+ ,25
+ ,25
+ ,10
+ ,16
+ ,11
+ ,11
+ ,11
+ ,23
+ ,21
+ ,10
+ ,18
+ ,16
+ ,12
+ ,7
+ ,17
+ ,22
+ ,10
+ ,17
+ ,11
+ ,13
+ ,7
+ ,21
+ ,25
+ ,10
+ ,23
+ ,13
+ ,14
+ ,12
+ ,19
+ ,24
+ ,10
+ ,30
+ ,12
+ ,16
+ ,10
+ ,19
+ ,18
+ ,10
+ ,23
+ ,8
+ ,11
+ ,10
+ ,15
+ ,22
+ ,10
+ ,18
+ ,12
+ ,10
+ ,8
+ ,16
+ ,15
+ ,10
+ ,15
+ ,11
+ ,11
+ ,8
+ ,23
+ ,22
+ ,10
+ ,12
+ ,4
+ ,15
+ ,4
+ ,27
+ ,28
+ ,10
+ ,21
+ ,9
+ ,9
+ ,9
+ ,22
+ ,20
+ ,10
+ ,15
+ ,8
+ ,11
+ ,8
+ ,14
+ ,12
+ ,10
+ ,20
+ ,8
+ ,17
+ ,7
+ ,22
+ ,24
+ ,10
+ ,31
+ ,14
+ ,17
+ ,11
+ ,23
+ ,20
+ ,10
+ ,27
+ ,15
+ ,11
+ ,9
+ ,23
+ ,21
+ ,10
+ ,34
+ ,16
+ ,18
+ ,11
+ ,21
+ ,20
+ ,10
+ ,21
+ ,9
+ ,14
+ ,13
+ ,19
+ ,21
+ ,10
+ ,31
+ ,14
+ ,10
+ ,8
+ ,18
+ ,23
+ ,10
+ ,19
+ ,11
+ ,11
+ ,8
+ ,20
+ ,28
+ ,10
+ ,16
+ ,8
+ ,15
+ ,9
+ ,23
+ ,24
+ ,10
+ ,20
+ ,9
+ ,15
+ ,6
+ ,25
+ ,24
+ ,10
+ ,21
+ ,9
+ ,13
+ ,9
+ ,19
+ ,24
+ ,10
+ ,22
+ ,9
+ ,16
+ ,9
+ ,24
+ ,23
+ ,10
+ ,17
+ ,9
+ ,13
+ ,6
+ ,22
+ ,23
+ ,10
+ ,24
+ ,10
+ ,9
+ ,6
+ ,25
+ ,29
+ ,10
+ ,25
+ ,16
+ ,18
+ ,16
+ ,26
+ ,24
+ ,10
+ ,26
+ ,11
+ ,18
+ ,5
+ ,29
+ ,18
+ ,10
+ ,25
+ ,8
+ ,12
+ ,7
+ ,32
+ ,25
+ ,10
+ ,17
+ ,9
+ ,17
+ ,9
+ ,25
+ ,21
+ ,10
+ ,32
+ ,16
+ ,9
+ ,6
+ ,29
+ ,26
+ ,10
+ ,33
+ ,11
+ ,9
+ ,6
+ ,28
+ ,22
+ ,10
+ ,13
+ ,16
+ ,12
+ ,5
+ ,17
+ ,22
+ ,10
+ ,32
+ ,12
+ ,18
+ ,12
+ ,28
+ ,22
+ ,10
+ ,25
+ ,12
+ ,12
+ ,7
+ ,29
+ ,23
+ ,10
+ ,29
+ ,14
+ ,18
+ ,10
+ ,26
+ ,30
+ ,10
+ ,22
+ ,9
+ ,14
+ ,9
+ ,25
+ ,23
+ ,10
+ ,18
+ ,10
+ ,15
+ ,8
+ ,14
+ ,17
+ ,10
+ ,17
+ ,9
+ ,16
+ ,5
+ ,25
+ ,23
+ ,10
+ ,20
+ ,10
+ ,10
+ ,8
+ ,26
+ ,23
+ ,10
+ ,15
+ ,12
+ ,11
+ ,8
+ ,20
+ ,25
+ ,10
+ ,20
+ ,14
+ ,14
+ ,10
+ ,18
+ ,24
+ ,10
+ ,33
+ ,14
+ ,9
+ ,6
+ ,32
+ ,24
+ ,10
+ ,29
+ ,10
+ ,12
+ ,8
+ ,25
+ ,23
+ ,10
+ ,23
+ ,14
+ ,17
+ ,7
+ ,25
+ ,21
+ ,10
+ ,26
+ ,16
+ ,5
+ ,4
+ ,23
+ ,24
+ ,10
+ ,18
+ ,9
+ ,12
+ ,8
+ ,21
+ ,24
+ ,10
+ ,20
+ ,10
+ ,12
+ ,8
+ ,20
+ ,28
+ ,10
+ ,11
+ ,6
+ ,6
+ ,4
+ ,15
+ ,16
+ ,10
+ ,28
+ ,8
+ ,24
+ ,20
+ ,30
+ ,20
+ ,10
+ ,26
+ ,13
+ ,12
+ ,8
+ ,24
+ ,29
+ ,10
+ ,22
+ ,10
+ ,12
+ ,8
+ ,26
+ ,27
+ ,10
+ ,17
+ ,8
+ ,14
+ ,6
+ ,24
+ ,22
+ ,10
+ ,12
+ ,7
+ ,7
+ ,4
+ ,22
+ ,28
+ ,10
+ ,14
+ ,15
+ ,13
+ ,8
+ ,14
+ ,16
+ ,10
+ ,17
+ ,9
+ ,12
+ ,9
+ ,24
+ ,25
+ ,10
+ ,21
+ ,10
+ ,13
+ ,6
+ ,24
+ ,24
+ ,10
+ ,19
+ ,12
+ ,14
+ ,7
+ ,24
+ ,28
+ ,10
+ ,18
+ ,13
+ ,8
+ ,9
+ ,24
+ ,24
+ ,10
+ ,10
+ ,10
+ ,11
+ ,5
+ ,19
+ ,23
+ ,10
+ ,29
+ ,11
+ ,9
+ ,5
+ ,31
+ ,30
+ ,10
+ ,31
+ ,8
+ ,11
+ ,8
+ ,22
+ ,24
+ ,10
+ ,19
+ ,9
+ ,13
+ ,8
+ ,27
+ ,21
+ ,10
+ ,9
+ ,13
+ ,10
+ ,6
+ ,19
+ ,25
+ ,10
+ ,20
+ ,11
+ ,11
+ ,8
+ ,25
+ ,25
+ ,10
+ ,28
+ ,8
+ ,12
+ ,7
+ ,20
+ ,22
+ ,10
+ ,19
+ ,9
+ ,9
+ ,7
+ ,21
+ ,23
+ ,10
+ ,30
+ ,9
+ ,15
+ ,9
+ ,27
+ ,26
+ ,10
+ ,29
+ ,15
+ ,18
+ ,11
+ ,23
+ ,23
+ ,10
+ ,26
+ ,9
+ ,15
+ ,6
+ ,25
+ ,25
+ ,10
+ ,23
+ ,10
+ ,12
+ ,8
+ ,20
+ ,21
+ ,10
+ ,13
+ ,14
+ ,13
+ ,6
+ ,21
+ ,25
+ ,10
+ ,21
+ ,12
+ ,14
+ ,9
+ ,22
+ ,24
+ ,10
+ ,19
+ ,12
+ ,10
+ ,8
+ ,23
+ ,29
+ ,10
+ ,28
+ ,11
+ ,13
+ ,6
+ ,25
+ ,22
+ ,10
+ ,23
+ ,14
+ ,13
+ ,10
+ ,25
+ ,27
+ ,10
+ ,18
+ ,6
+ ,11
+ ,8
+ ,17
+ ,26
+ ,10
+ ,21
+ ,12
+ ,13
+ ,8
+ ,19
+ ,22
+ ,10
+ ,20
+ ,8
+ ,16
+ ,10
+ ,25
+ ,24
+ ,10
+ ,23
+ ,14
+ ,8
+ ,5
+ ,19
+ ,27
+ ,10
+ ,21
+ ,11
+ ,16
+ ,7
+ ,20
+ ,24
+ ,10
+ ,21
+ ,10
+ ,11
+ ,5
+ ,26
+ ,24
+ ,10
+ ,15
+ ,14
+ ,9
+ ,8
+ ,23
+ ,29
+ ,10
+ ,28
+ ,12
+ ,16
+ ,14
+ ,27
+ ,22
+ ,10
+ ,19
+ ,10
+ ,12
+ ,7
+ ,17
+ ,21
+ ,10
+ ,26
+ ,14
+ ,14
+ ,8
+ ,17
+ ,24
+ ,10
+ ,10
+ ,5
+ ,8
+ ,6
+ ,19
+ ,24
+ ,10
+ ,16
+ ,11
+ ,9
+ ,5
+ ,17
+ ,23
+ ,10
+ ,22
+ ,10
+ ,15
+ ,6
+ ,22
+ ,20
+ ,10
+ ,19
+ ,9
+ ,11
+ ,10
+ ,21
+ ,27
+ ,10
+ ,31
+ ,10
+ ,21
+ ,12
+ ,32
+ ,26
+ ,10
+ ,31
+ ,16
+ ,14
+ ,9
+ ,21
+ ,25
+ ,10
+ ,29
+ ,13
+ ,18
+ ,12
+ ,21
+ ,21
+ ,10
+ ,19
+ ,9
+ ,12
+ ,7
+ ,18
+ ,21
+ ,10
+ ,22
+ ,10
+ ,13
+ ,8
+ ,18
+ ,19
+ ,10
+ ,23
+ ,10
+ ,15
+ ,10
+ ,23
+ ,21
+ ,10
+ ,15
+ ,7
+ ,12
+ ,6
+ ,19
+ ,21
+ ,10
+ ,20
+ ,9
+ ,19
+ ,10
+ ,20
+ ,16
+ ,10
+ ,18
+ ,8
+ ,15
+ ,10
+ ,21
+ ,22
+ ,10
+ ,23
+ ,14
+ ,11
+ ,10
+ ,20
+ ,29
+ ,10
+ ,25
+ ,14
+ ,11
+ ,5
+ ,17
+ ,15
+ ,10
+ ,21
+ ,8
+ ,10
+ ,7
+ ,18
+ ,17
+ ,10
+ ,24
+ ,9
+ ,13
+ ,10
+ ,19
+ ,15
+ ,10
+ ,25
+ ,14
+ ,15
+ ,11
+ ,22
+ ,21
+ ,10
+ ,17
+ ,14
+ ,12
+ ,6
+ ,15
+ ,21
+ ,10
+ ,13
+ ,8
+ ,12
+ ,7
+ ,14
+ ,19
+ ,10
+ ,28
+ ,8
+ ,16
+ ,12
+ ,18
+ ,24
+ ,10
+ ,21
+ ,8
+ ,9
+ ,11
+ ,24
+ ,20
+ ,10
+ ,25
+ ,7
+ ,18
+ ,11
+ ,35
+ ,17
+ ,10
+ ,9
+ ,6
+ ,8
+ ,11
+ ,29
+ ,23
+ ,10
+ ,16
+ ,8
+ ,13
+ ,5
+ ,21
+ ,24
+ ,10
+ ,19
+ ,6
+ ,17
+ ,8
+ ,25
+ ,14
+ ,10
+ ,17
+ ,11
+ ,9
+ ,6
+ ,20
+ ,19
+ ,10
+ ,25
+ ,14
+ ,15
+ ,9
+ ,22
+ ,24
+ ,10
+ ,20
+ ,11
+ ,8
+ ,4
+ ,13
+ ,13
+ ,10
+ ,29
+ ,11
+ ,7
+ ,4
+ ,26
+ ,22
+ ,10
+ ,14
+ ,11
+ ,12
+ ,7
+ ,17
+ ,16
+ ,10
+ ,22
+ ,14
+ ,14
+ ,11
+ ,25
+ ,19
+ ,10
+ ,15
+ ,8
+ ,6
+ ,6
+ ,20
+ ,25
+ ,10
+ ,19
+ ,20
+ ,8
+ ,7
+ ,19
+ ,25
+ ,10
+ ,20
+ ,11
+ ,17
+ ,8
+ ,21
+ ,23
+ ,10
+ ,15
+ ,8
+ ,10
+ ,4
+ ,22
+ ,24
+ ,10
+ ,20
+ ,11
+ ,11
+ ,8
+ ,24
+ ,26
+ ,10
+ ,18
+ ,10
+ ,14
+ ,9
+ ,21
+ ,26
+ ,10
+ ,33
+ ,14
+ ,11
+ ,8
+ ,26
+ ,25
+ ,10
+ ,22
+ ,11
+ ,13
+ ,11
+ ,24
+ ,18
+ ,10
+ ,16
+ ,9
+ ,12
+ ,8
+ ,16
+ ,21
+ ,10
+ ,17
+ ,9
+ ,11
+ ,5
+ ,23
+ ,26
+ ,10
+ ,16
+ ,8
+ ,9
+ ,4
+ ,18
+ ,23
+ ,10
+ ,21
+ ,10
+ ,12
+ ,8
+ ,16
+ ,23
+ ,10
+ ,26
+ ,13
+ ,20
+ ,10
+ ,26
+ ,22
+ ,10
+ ,18
+ ,13
+ ,12
+ ,6
+ ,19
+ ,20
+ ,10
+ ,18
+ ,12
+ ,13
+ ,9
+ ,21
+ ,13
+ ,10
+ ,17
+ ,8
+ ,12
+ ,9
+ ,21
+ ,24
+ ,10
+ ,22
+ ,13
+ ,12
+ ,13
+ ,22
+ ,15
+ ,10
+ ,30
+ ,14
+ ,9
+ ,9
+ ,23
+ ,14
+ ,10
+ ,30
+ ,12
+ ,15
+ ,10
+ ,29
+ ,22
+ ,10
+ ,24
+ ,14
+ ,24
+ ,20
+ ,21
+ ,10
+ ,10
+ ,21
+ ,15
+ ,7
+ ,5
+ ,21
+ ,24
+ ,10
+ ,21
+ ,13
+ ,17
+ ,11
+ ,23
+ ,22
+ ,10
+ ,29
+ ,16
+ ,11
+ ,6
+ ,27
+ ,24
+ ,10
+ ,31
+ ,9
+ ,17
+ ,9
+ ,25
+ ,19
+ ,10
+ ,20
+ ,9
+ ,11
+ ,7
+ ,21
+ ,20
+ ,10
+ ,16
+ ,9
+ ,12
+ ,9
+ ,10
+ ,13
+ ,10
+ ,22
+ ,8
+ ,14
+ ,10
+ ,20
+ ,20
+ ,10
+ ,20
+ ,7
+ ,11
+ ,9
+ ,26
+ ,22
+ ,10
+ ,28
+ ,16
+ ,16
+ ,8
+ ,24
+ ,24
+ ,10
+ ,38
+ ,11
+ ,21
+ ,7
+ ,29
+ ,29
+ ,10
+ ,22
+ ,9
+ ,14
+ ,6
+ ,19
+ ,12
+ ,10
+ ,20
+ ,11
+ ,20
+ ,13
+ ,24
+ ,20
+ ,10
+ ,17
+ ,9
+ ,13
+ ,6
+ ,19
+ ,21
+ ,10
+ ,28
+ ,14
+ ,11
+ ,8
+ ,24
+ ,24
+ ,10
+ ,22
+ ,13
+ ,15
+ ,10
+ ,22
+ ,22
+ ,10
+ ,31
+ ,16
+ ,19
+ ,16
+ ,17
+ ,20)
+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('Month'
+ ,'CM'
+ ,'D'
+ ,'PE'
+ ,'PC'
+ ,'PS'
+ ,'O')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('Month','CM','D','PE','PC','PS','O'),1:159))
> 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 = '7'
> #'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
O Month CM D PE PC PS
1 26 9 24 14 11 12 24
2 23 9 25 11 7 8 25
3 25 9 17 6 17 8 30
4 23 9 18 12 10 8 19
5 19 9 18 8 12 9 22
6 29 9 16 10 12 7 22
7 25 10 20 10 11 4 25
8 21 10 16 11 11 11 23
9 22 10 18 16 12 7 17
10 25 10 17 11 13 7 21
11 24 10 23 13 14 12 19
12 18 10 30 12 16 10 19
13 22 10 23 8 11 10 15
14 15 10 18 12 10 8 16
15 22 10 15 11 11 8 23
16 28 10 12 4 15 4 27
17 20 10 21 9 9 9 22
18 12 10 15 8 11 8 14
19 24 10 20 8 17 7 22
20 20 10 31 14 17 11 23
21 21 10 27 15 11 9 23
22 20 10 34 16 18 11 21
23 21 10 21 9 14 13 19
24 23 10 31 14 10 8 18
25 28 10 19 11 11 8 20
26 24 10 16 8 15 9 23
27 24 10 20 9 15 6 25
28 24 10 21 9 13 9 19
29 23 10 22 9 16 9 24
30 23 10 17 9 13 6 22
31 29 10 24 10 9 6 25
32 24 10 25 16 18 16 26
33 18 10 26 11 18 5 29
34 25 10 25 8 12 7 32
35 21 10 17 9 17 9 25
36 26 10 32 16 9 6 29
37 22 10 33 11 9 6 28
38 22 10 13 16 12 5 17
39 22 10 32 12 18 12 28
40 23 10 25 12 12 7 29
41 30 10 29 14 18 10 26
42 23 10 22 9 14 9 25
43 17 10 18 10 15 8 14
44 23 10 17 9 16 5 25
45 23 10 20 10 10 8 26
46 25 10 15 12 11 8 20
47 24 10 20 14 14 10 18
48 24 10 33 14 9 6 32
49 23 10 29 10 12 8 25
50 21 10 23 14 17 7 25
51 24 10 26 16 5 4 23
52 24 10 18 9 12 8 21
53 28 10 20 10 12 8 20
54 16 10 11 6 6 4 15
55 20 10 28 8 24 20 30
56 29 10 26 13 12 8 24
57 27 10 22 10 12 8 26
58 22 10 17 8 14 6 24
59 28 10 12 7 7 4 22
60 16 10 14 15 13 8 14
61 25 10 17 9 12 9 24
62 24 10 21 10 13 6 24
63 28 10 19 12 14 7 24
64 24 10 18 13 8 9 24
65 23 10 10 10 11 5 19
66 30 10 29 11 9 5 31
67 24 10 31 8 11 8 22
68 21 10 19 9 13 8 27
69 25 10 9 13 10 6 19
70 25 10 20 11 11 8 25
71 22 10 28 8 12 7 20
72 23 10 19 9 9 7 21
73 26 10 30 9 15 9 27
74 23 10 29 15 18 11 23
75 25 10 26 9 15 6 25
76 21 10 23 10 12 8 20
77 25 10 13 14 13 6 21
78 24 10 21 12 14 9 22
79 29 10 19 12 10 8 23
80 22 10 28 11 13 6 25
81 27 10 23 14 13 10 25
82 26 10 18 6 11 8 17
83 22 10 21 12 13 8 19
84 24 10 20 8 16 10 25
85 27 10 23 14 8 5 19
86 24 10 21 11 16 7 20
87 24 10 21 10 11 5 26
88 29 10 15 14 9 8 23
89 22 10 28 12 16 14 27
90 21 10 19 10 12 7 17
91 24 10 26 14 14 8 17
92 24 10 10 5 8 6 19
93 23 10 16 11 9 5 17
94 20 10 22 10 15 6 22
95 27 10 19 9 11 10 21
96 26 10 31 10 21 12 32
97 25 10 31 16 14 9 21
98 21 10 29 13 18 12 21
99 21 10 19 9 12 7 18
100 19 10 22 10 13 8 18
101 21 10 23 10 15 10 23
102 21 10 15 7 12 6 19
103 16 10 20 9 19 10 20
104 22 10 18 8 15 10 21
105 29 10 23 14 11 10 20
106 15 10 25 14 11 5 17
107 17 10 21 8 10 7 18
108 15 10 24 9 13 10 19
109 21 10 25 14 15 11 22
110 21 10 17 14 12 6 15
111 19 10 13 8 12 7 14
112 24 10 28 8 16 12 18
113 20 10 21 8 9 11 24
114 17 10 25 7 18 11 35
115 23 10 9 6 8 11 29
116 24 10 16 8 13 5 21
117 14 10 19 6 17 8 25
118 19 10 17 11 9 6 20
119 24 10 25 14 15 9 22
120 13 10 20 11 8 4 13
121 22 10 29 11 7 4 26
122 16 10 14 11 12 7 17
123 19 10 22 14 14 11 25
124 25 10 15 8 6 6 20
125 25 10 19 20 8 7 19
126 23 10 20 11 17 8 21
127 24 10 15 8 10 4 22
128 26 10 20 11 11 8 24
129 26 10 18 10 14 9 21
130 25 10 33 14 11 8 26
131 18 10 22 11 13 11 24
132 21 10 16 9 12 8 16
133 26 10 17 9 11 5 23
134 23 10 16 8 9 4 18
135 23 10 21 10 12 8 16
136 22 10 26 13 20 10 26
137 20 10 18 13 12 6 19
138 13 10 18 12 13 9 21
139 24 10 17 8 12 9 21
140 15 10 22 13 12 13 22
141 14 10 30 14 9 9 23
142 22 10 30 12 15 10 29
143 10 10 24 14 24 20 21
144 24 10 21 15 7 5 21
145 22 10 21 13 17 11 23
146 24 10 29 16 11 6 27
147 19 10 31 9 17 9 25
148 20 10 20 9 11 7 21
149 13 10 16 9 12 9 10
150 20 10 22 8 14 10 20
151 22 10 20 7 11 9 26
152 24 10 28 16 16 8 24
153 29 10 38 11 21 7 29
154 12 10 22 9 14 6 19
155 20 10 20 11 20 13 24
156 21 10 17 9 13 6 19
157 24 10 28 14 11 8 24
158 22 10 22 13 15 10 22
159 20 10 31 16 19 16 17
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Month CM D PE PC
28.21032 -1.21064 -0.06629 0.22005 -0.13798 -0.26785
PS
0.41522
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.0934 -1.7735 0.2302 2.2698 7.2320
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 28.21032 14.94520 1.888 0.0610 .
Month -1.21064 1.48477 -0.815 0.4161
CM -0.06629 0.06322 -1.049 0.2960
D 0.22005 0.11277 1.951 0.0529 .
PE -0.13798 0.10525 -1.311 0.1918
PC -0.26785 0.13148 -2.037 0.0434 *
PS 0.41522 0.07626 5.445 2.04e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.503 on 152 degrees of freedom
Multiple R-squared: 0.2258, Adjusted R-squared: 0.1952
F-statistic: 7.387 on 6 and 152 DF, p-value: 5.999e-07
> 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,] 0.527028364 0.945943271 0.4729716
[2,] 0.562072177 0.875855646 0.4379278
[3,] 0.488820122 0.977640244 0.5111799
[4,] 0.540340997 0.919318006 0.4596590
[5,] 0.741634142 0.516731717 0.2583659
[6,] 0.654767780 0.690464440 0.3452322
[7,] 0.623840204 0.752319593 0.3761598
[8,] 0.538160412 0.923679176 0.4618396
[9,] 0.676102823 0.647794355 0.3238972
[10,] 0.601320656 0.797358687 0.3986793
[11,] 0.588507053 0.822985893 0.4114929
[12,] 0.524600812 0.950798376 0.4753992
[13,] 0.455193001 0.910386002 0.5448070
[14,] 0.404513233 0.809026466 0.5954868
[15,] 0.408928633 0.817857265 0.5910714
[16,] 0.571312478 0.857375044 0.4286875
[17,] 0.510036638 0.979926725 0.4899634
[18,] 0.443017031 0.886034061 0.5569830
[19,] 0.437463609 0.874927218 0.5625364
[20,] 0.373317526 0.746635051 0.6266825
[21,] 0.312925469 0.625850939 0.6870745
[22,] 0.346705303 0.693410605 0.6532947
[23,] 0.295684869 0.591369738 0.7043151
[24,] 0.521856237 0.956287527 0.4781438
[25,] 0.470720695 0.941441391 0.5292793
[26,] 0.432758548 0.865517095 0.5672415
[27,] 0.375438919 0.750877838 0.6245611
[28,] 0.348812634 0.697625268 0.6511874
[29,] 0.297200140 0.594400279 0.7027999
[30,] 0.249829277 0.499658554 0.7501707
[31,] 0.222496242 0.444992483 0.7775038
[32,] 0.375109750 0.750219500 0.6248902
[33,] 0.323329351 0.646658703 0.6766706
[34,] 0.291309744 0.582619489 0.7086903
[35,] 0.247741143 0.495482286 0.7522589
[36,] 0.211344651 0.422689302 0.7886553
[37,] 0.191943416 0.383886833 0.8080566
[38,] 0.179511293 0.359022586 0.8204887
[39,] 0.164434999 0.328869997 0.8355650
[40,] 0.134347120 0.268694240 0.8656529
[41,] 0.124730084 0.249460168 0.8752699
[42,] 0.102953974 0.205907948 0.8970460
[43,] 0.087835501 0.175671002 0.9121645
[44,] 0.147397588 0.294795177 0.8526024
[45,] 0.176789106 0.353578211 0.8232109
[46,] 0.165267187 0.330534373 0.8347328
[47,] 0.222686975 0.445373951 0.7773130
[48,] 0.215131909 0.430263818 0.7848681
[49,] 0.184153643 0.368307286 0.8158464
[50,] 0.197351138 0.394702276 0.8026489
[51,] 0.225091999 0.450183998 0.7749080
[52,] 0.198880237 0.397760474 0.8011198
[53,] 0.167118258 0.334236517 0.8328817
[54,] 0.182344871 0.364689742 0.8176551
[55,] 0.153254813 0.306509625 0.8467452
[56,] 0.126438969 0.252877937 0.8735610
[57,] 0.122883295 0.245766591 0.8771167
[58,] 0.112248217 0.224496435 0.8877518
[59,] 0.111344923 0.222689847 0.8886551
[60,] 0.094756585 0.189513169 0.9052434
[61,] 0.077360514 0.154721029 0.9226395
[62,] 0.062868134 0.125736267 0.9371319
[63,] 0.049656216 0.099312432 0.9503438
[64,] 0.046615423 0.093230846 0.9533846
[65,] 0.037341364 0.074682728 0.9626586
[66,] 0.031031437 0.062062875 0.9689686
[67,] 0.023800976 0.047601951 0.9761990
[68,] 0.018685901 0.037371803 0.9813141
[69,] 0.014957141 0.029914281 0.9850429
[70,] 0.022478108 0.044956216 0.9775219
[71,] 0.017921206 0.035842412 0.9820788
[72,] 0.017525364 0.035050728 0.9824746
[73,] 0.031350586 0.062701172 0.9686494
[74,] 0.024177388 0.048354776 0.9758226
[75,] 0.020172652 0.040345304 0.9798273
[76,] 0.022091304 0.044182607 0.9779087
[77,] 0.019577907 0.039155814 0.9804221
[78,] 0.014883396 0.029766791 0.9851166
[79,] 0.019434845 0.038869690 0.9805652
[80,] 0.015281934 0.030563868 0.9847181
[81,] 0.011435040 0.022870080 0.9885650
[82,] 0.011493602 0.022987203 0.9885064
[83,] 0.010048797 0.020097593 0.9899512
[84,] 0.007706349 0.015412698 0.9922937
[85,] 0.006370954 0.012741909 0.9936290
[86,] 0.011676430 0.023352861 0.9883236
[87,] 0.010575073 0.021150146 0.9894249
[88,] 0.010057932 0.020115863 0.9899421
[89,] 0.007696459 0.015392918 0.9923035
[90,] 0.005678852 0.011357704 0.9943211
[91,] 0.004347520 0.008695041 0.9956525
[92,] 0.003212266 0.006424531 0.9967877
[93,] 0.002285633 0.004571267 0.9977144
[94,] 0.002443965 0.004887929 0.9975560
[95,] 0.001914652 0.003829303 0.9980853
[96,] 0.008114130 0.016228260 0.9918859
[97,] 0.018167185 0.036334369 0.9818328
[98,] 0.018029551 0.036059103 0.9819704
[99,] 0.022721154 0.045442307 0.9772788
[100,] 0.017529650 0.035059300 0.9824703
[101,] 0.012803546 0.025607091 0.9871965
[102,] 0.009269762 0.018539524 0.9907302
[103,] 0.022733095 0.045466189 0.9772669
[104,] 0.020596448 0.041192896 0.9794036
[105,] 0.057193263 0.114386525 0.9428067
[106,] 0.048450184 0.096900367 0.9515498
[107,] 0.039264729 0.078529458 0.9607353
[108,] 0.115712576 0.231425152 0.8842874
[109,] 0.110870624 0.221741247 0.8891294
[110,] 0.098297554 0.196595109 0.9017024
[111,] 0.171823905 0.343647809 0.8281761
[112,] 0.163706792 0.327413583 0.8362932
[113,] 0.195062024 0.390124048 0.8049380
[114,] 0.188042981 0.376085961 0.8119570
[115,] 0.187225642 0.374451284 0.8127744
[116,] 0.169993307 0.339986613 0.8300067
[117,] 0.138332383 0.276664766 0.8616676
[118,] 0.108170210 0.216340421 0.8918298
[119,] 0.111622507 0.223245015 0.8883775
[120,] 0.160124831 0.320249662 0.8398752
[121,] 0.137944003 0.275888006 0.8620560
[122,] 0.120411351 0.240822702 0.8795886
[123,] 0.104288623 0.208577246 0.8957114
[124,] 0.095663044 0.191326087 0.9043370
[125,] 0.078090526 0.156181053 0.9219095
[126,] 0.106568502 0.213137003 0.8934315
[127,] 0.079019524 0.158039049 0.9209805
[128,] 0.057629542 0.115259084 0.9423705
[129,] 0.146858067 0.293716133 0.8531419
[130,] 0.208120045 0.416240089 0.7918800
[131,] 0.188513100 0.377026200 0.8114869
[132,] 0.406025650 0.812051300 0.5939744
[133,] 0.356376041 0.712752081 0.6436240
[134,] 0.666004373 0.667991253 0.3339956
[135,] 0.604797947 0.790404107 0.3952021
[136,] 0.494173129 0.988346259 0.5058269
[137,] 0.423286504 0.846573007 0.5767135
[138,] 0.433583275 0.867166550 0.5664167
[139,] 0.303283812 0.606567624 0.6967162
[140,] 0.211402600 0.422805200 0.7885974
> postscript(file="/var/www/html/freestat/rcomp/tmp/1cl0u1290532142.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/25vhf1290532142.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/35vhf1290532142.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4x4y01290532142.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5x4y01290532142.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 159
Frequency = 1
1 2 3 4 5 6
1.96251135 -2.34958653 -0.47597203 -0.12842019 -3.95008146 4.94154886
7 8 9 10 11 12
0.23017892 -1.54966450 0.04058636 2.55163464 3.81694154 -1.75870827
13 14 15 16 17 18
3.62841177 -5.67212272 -1.41949741 3.74161327 -2.17455278 -7.02239333
19 20 21 22 23 24
2.54735007 -1.38755577 -2.23634272 -0.66035853 1.83239245 1.91913215
25 26 27 28 29 30
6.09132191 2.12670137 0.53784093 3.62302313 1.02716350 0.30866200
31 32 33 34 35 36
4.75507738 2.00616635 -7.01928506 -0.96327865 -1.58153008 -0.69574575
37 38 39 40 41 42
-3.11400402 -0.82656371 -0.55143571 -2.59780961 7.10434010 0.33598470
43 44 45 46 47 48
-1.71169192 -0.79089940 -1.25163056 2.60611102 3.27754552 -3.43501661
49 50 51 52 53 54
0.03616821 -2.81971030 -1.68979814 2.18788569 6.51563974 -5.02397346
55 56 57 58 59 60
0.20380878 5.59237379 3.15691216 -1.16374788 4.05372334 -4.35304918
61 62 63 64 65 66
2.14378695 0.52334337 4.35649574 -0.22202881 0.32642491 3.10732939
67 68 69 70 71 72
2.71651800 -3.09915274 1.72986142 1.08152153 1.21821426 0.57238877
73 74 75 76 77 78
3.17385721 1.39779580 1.93558758 -0.28548694 1.35848355 1.85520909
79 80 81 82 83 84
5.48764030 -1.64788364 3.43191049 6.37091803 0.69503647 1.96725672
85 86 87 88 89 90
3.89408269 2.64595813 -0.85090100 4.64440268 -0.14164781 0.42715626
91 92 93 94 95 96
3.55481593 2.28056374 1.05860118 -2.30396842 5.65189108 2.57543368
97 98 99 100 101 102
3.05315260 0.93617260 0.23198443 -1.38336120 -0.58150654 -0.27615264
103 104 105 106 107 108
-3.76275749 1.35756745 7.23204148 -6.72895774 -3.69134740 -4.91025628
109 110 111 112 113 114
-0.64604462 -0.02302248 -0.28484256 5.93980821 -2.24924838 -8.08961650
115 116 117 118 119 120
-1.81872048 1.60978839 -8.05665574 -3.85291534 1.81826084 -7.42118871
121 122 123 124 125 126
-3.36038689 -5.12434574 -4.22855309 2.26070108 0.84433400 1.57027624
127 128 129 130 131 132
0.44649096 2.49673982 4.51164700 0.86794823 -4.29117565 1.13139497
133 134 135 136 137 138
2.34963593 1.03567501 3.24280404 -0.59852625 -2.39755811 -9.06642619
139 140 141 142 143 144
2.60948831 -6.50311770 -9.09338344 -2.04887152 -7.64466975 -0.42696285
145 146 147 148 149 150
0.16957962 -1.78822197 -2.65345458 -2.08535962 -4.10944797 -0.10003008
151 152 153 154 155 156
-1.18566365 0.61673762 5.72584353 -8.97624731 -0.92220156 -0.44568310
157 158 159
0.36692929 0.10728125 2.27885794
> postscript(file="/var/www/html/freestat/rcomp/tmp/6qvg31290532142.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 1.96251135 NA
1 -2.34958653 1.96251135
2 -0.47597203 -2.34958653
3 -0.12842019 -0.47597203
4 -3.95008146 -0.12842019
5 4.94154886 -3.95008146
6 0.23017892 4.94154886
7 -1.54966450 0.23017892
8 0.04058636 -1.54966450
9 2.55163464 0.04058636
10 3.81694154 2.55163464
11 -1.75870827 3.81694154
12 3.62841177 -1.75870827
13 -5.67212272 3.62841177
14 -1.41949741 -5.67212272
15 3.74161327 -1.41949741
16 -2.17455278 3.74161327
17 -7.02239333 -2.17455278
18 2.54735007 -7.02239333
19 -1.38755577 2.54735007
20 -2.23634272 -1.38755577
21 -0.66035853 -2.23634272
22 1.83239245 -0.66035853
23 1.91913215 1.83239245
24 6.09132191 1.91913215
25 2.12670137 6.09132191
26 0.53784093 2.12670137
27 3.62302313 0.53784093
28 1.02716350 3.62302313
29 0.30866200 1.02716350
30 4.75507738 0.30866200
31 2.00616635 4.75507738
32 -7.01928506 2.00616635
33 -0.96327865 -7.01928506
34 -1.58153008 -0.96327865
35 -0.69574575 -1.58153008
36 -3.11400402 -0.69574575
37 -0.82656371 -3.11400402
38 -0.55143571 -0.82656371
39 -2.59780961 -0.55143571
40 7.10434010 -2.59780961
41 0.33598470 7.10434010
42 -1.71169192 0.33598470
43 -0.79089940 -1.71169192
44 -1.25163056 -0.79089940
45 2.60611102 -1.25163056
46 3.27754552 2.60611102
47 -3.43501661 3.27754552
48 0.03616821 -3.43501661
49 -2.81971030 0.03616821
50 -1.68979814 -2.81971030
51 2.18788569 -1.68979814
52 6.51563974 2.18788569
53 -5.02397346 6.51563974
54 0.20380878 -5.02397346
55 5.59237379 0.20380878
56 3.15691216 5.59237379
57 -1.16374788 3.15691216
58 4.05372334 -1.16374788
59 -4.35304918 4.05372334
60 2.14378695 -4.35304918
61 0.52334337 2.14378695
62 4.35649574 0.52334337
63 -0.22202881 4.35649574
64 0.32642491 -0.22202881
65 3.10732939 0.32642491
66 2.71651800 3.10732939
67 -3.09915274 2.71651800
68 1.72986142 -3.09915274
69 1.08152153 1.72986142
70 1.21821426 1.08152153
71 0.57238877 1.21821426
72 3.17385721 0.57238877
73 1.39779580 3.17385721
74 1.93558758 1.39779580
75 -0.28548694 1.93558758
76 1.35848355 -0.28548694
77 1.85520909 1.35848355
78 5.48764030 1.85520909
79 -1.64788364 5.48764030
80 3.43191049 -1.64788364
81 6.37091803 3.43191049
82 0.69503647 6.37091803
83 1.96725672 0.69503647
84 3.89408269 1.96725672
85 2.64595813 3.89408269
86 -0.85090100 2.64595813
87 4.64440268 -0.85090100
88 -0.14164781 4.64440268
89 0.42715626 -0.14164781
90 3.55481593 0.42715626
91 2.28056374 3.55481593
92 1.05860118 2.28056374
93 -2.30396842 1.05860118
94 5.65189108 -2.30396842
95 2.57543368 5.65189108
96 3.05315260 2.57543368
97 0.93617260 3.05315260
98 0.23198443 0.93617260
99 -1.38336120 0.23198443
100 -0.58150654 -1.38336120
101 -0.27615264 -0.58150654
102 -3.76275749 -0.27615264
103 1.35756745 -3.76275749
104 7.23204148 1.35756745
105 -6.72895774 7.23204148
106 -3.69134740 -6.72895774
107 -4.91025628 -3.69134740
108 -0.64604462 -4.91025628
109 -0.02302248 -0.64604462
110 -0.28484256 -0.02302248
111 5.93980821 -0.28484256
112 -2.24924838 5.93980821
113 -8.08961650 -2.24924838
114 -1.81872048 -8.08961650
115 1.60978839 -1.81872048
116 -8.05665574 1.60978839
117 -3.85291534 -8.05665574
118 1.81826084 -3.85291534
119 -7.42118871 1.81826084
120 -3.36038689 -7.42118871
121 -5.12434574 -3.36038689
122 -4.22855309 -5.12434574
123 2.26070108 -4.22855309
124 0.84433400 2.26070108
125 1.57027624 0.84433400
126 0.44649096 1.57027624
127 2.49673982 0.44649096
128 4.51164700 2.49673982
129 0.86794823 4.51164700
130 -4.29117565 0.86794823
131 1.13139497 -4.29117565
132 2.34963593 1.13139497
133 1.03567501 2.34963593
134 3.24280404 1.03567501
135 -0.59852625 3.24280404
136 -2.39755811 -0.59852625
137 -9.06642619 -2.39755811
138 2.60948831 -9.06642619
139 -6.50311770 2.60948831
140 -9.09338344 -6.50311770
141 -2.04887152 -9.09338344
142 -7.64466975 -2.04887152
143 -0.42696285 -7.64466975
144 0.16957962 -0.42696285
145 -1.78822197 0.16957962
146 -2.65345458 -1.78822197
147 -2.08535962 -2.65345458
148 -4.10944797 -2.08535962
149 -0.10003008 -4.10944797
150 -1.18566365 -0.10003008
151 0.61673762 -1.18566365
152 5.72584353 0.61673762
153 -8.97624731 5.72584353
154 -0.92220156 -8.97624731
155 -0.44568310 -0.92220156
156 0.36692929 -0.44568310
157 0.10728125 0.36692929
158 2.27885794 0.10728125
159 NA 2.27885794
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.34958653 1.96251135
[2,] -0.47597203 -2.34958653
[3,] -0.12842019 -0.47597203
[4,] -3.95008146 -0.12842019
[5,] 4.94154886 -3.95008146
[6,] 0.23017892 4.94154886
[7,] -1.54966450 0.23017892
[8,] 0.04058636 -1.54966450
[9,] 2.55163464 0.04058636
[10,] 3.81694154 2.55163464
[11,] -1.75870827 3.81694154
[12,] 3.62841177 -1.75870827
[13,] -5.67212272 3.62841177
[14,] -1.41949741 -5.67212272
[15,] 3.74161327 -1.41949741
[16,] -2.17455278 3.74161327
[17,] -7.02239333 -2.17455278
[18,] 2.54735007 -7.02239333
[19,] -1.38755577 2.54735007
[20,] -2.23634272 -1.38755577
[21,] -0.66035853 -2.23634272
[22,] 1.83239245 -0.66035853
[23,] 1.91913215 1.83239245
[24,] 6.09132191 1.91913215
[25,] 2.12670137 6.09132191
[26,] 0.53784093 2.12670137
[27,] 3.62302313 0.53784093
[28,] 1.02716350 3.62302313
[29,] 0.30866200 1.02716350
[30,] 4.75507738 0.30866200
[31,] 2.00616635 4.75507738
[32,] -7.01928506 2.00616635
[33,] -0.96327865 -7.01928506
[34,] -1.58153008 -0.96327865
[35,] -0.69574575 -1.58153008
[36,] -3.11400402 -0.69574575
[37,] -0.82656371 -3.11400402
[38,] -0.55143571 -0.82656371
[39,] -2.59780961 -0.55143571
[40,] 7.10434010 -2.59780961
[41,] 0.33598470 7.10434010
[42,] -1.71169192 0.33598470
[43,] -0.79089940 -1.71169192
[44,] -1.25163056 -0.79089940
[45,] 2.60611102 -1.25163056
[46,] 3.27754552 2.60611102
[47,] -3.43501661 3.27754552
[48,] 0.03616821 -3.43501661
[49,] -2.81971030 0.03616821
[50,] -1.68979814 -2.81971030
[51,] 2.18788569 -1.68979814
[52,] 6.51563974 2.18788569
[53,] -5.02397346 6.51563974
[54,] 0.20380878 -5.02397346
[55,] 5.59237379 0.20380878
[56,] 3.15691216 5.59237379
[57,] -1.16374788 3.15691216
[58,] 4.05372334 -1.16374788
[59,] -4.35304918 4.05372334
[60,] 2.14378695 -4.35304918
[61,] 0.52334337 2.14378695
[62,] 4.35649574 0.52334337
[63,] -0.22202881 4.35649574
[64,] 0.32642491 -0.22202881
[65,] 3.10732939 0.32642491
[66,] 2.71651800 3.10732939
[67,] -3.09915274 2.71651800
[68,] 1.72986142 -3.09915274
[69,] 1.08152153 1.72986142
[70,] 1.21821426 1.08152153
[71,] 0.57238877 1.21821426
[72,] 3.17385721 0.57238877
[73,] 1.39779580 3.17385721
[74,] 1.93558758 1.39779580
[75,] -0.28548694 1.93558758
[76,] 1.35848355 -0.28548694
[77,] 1.85520909 1.35848355
[78,] 5.48764030 1.85520909
[79,] -1.64788364 5.48764030
[80,] 3.43191049 -1.64788364
[81,] 6.37091803 3.43191049
[82,] 0.69503647 6.37091803
[83,] 1.96725672 0.69503647
[84,] 3.89408269 1.96725672
[85,] 2.64595813 3.89408269
[86,] -0.85090100 2.64595813
[87,] 4.64440268 -0.85090100
[88,] -0.14164781 4.64440268
[89,] 0.42715626 -0.14164781
[90,] 3.55481593 0.42715626
[91,] 2.28056374 3.55481593
[92,] 1.05860118 2.28056374
[93,] -2.30396842 1.05860118
[94,] 5.65189108 -2.30396842
[95,] 2.57543368 5.65189108
[96,] 3.05315260 2.57543368
[97,] 0.93617260 3.05315260
[98,] 0.23198443 0.93617260
[99,] -1.38336120 0.23198443
[100,] -0.58150654 -1.38336120
[101,] -0.27615264 -0.58150654
[102,] -3.76275749 -0.27615264
[103,] 1.35756745 -3.76275749
[104,] 7.23204148 1.35756745
[105,] -6.72895774 7.23204148
[106,] -3.69134740 -6.72895774
[107,] -4.91025628 -3.69134740
[108,] -0.64604462 -4.91025628
[109,] -0.02302248 -0.64604462
[110,] -0.28484256 -0.02302248
[111,] 5.93980821 -0.28484256
[112,] -2.24924838 5.93980821
[113,] -8.08961650 -2.24924838
[114,] -1.81872048 -8.08961650
[115,] 1.60978839 -1.81872048
[116,] -8.05665574 1.60978839
[117,] -3.85291534 -8.05665574
[118,] 1.81826084 -3.85291534
[119,] -7.42118871 1.81826084
[120,] -3.36038689 -7.42118871
[121,] -5.12434574 -3.36038689
[122,] -4.22855309 -5.12434574
[123,] 2.26070108 -4.22855309
[124,] 0.84433400 2.26070108
[125,] 1.57027624 0.84433400
[126,] 0.44649096 1.57027624
[127,] 2.49673982 0.44649096
[128,] 4.51164700 2.49673982
[129,] 0.86794823 4.51164700
[130,] -4.29117565 0.86794823
[131,] 1.13139497 -4.29117565
[132,] 2.34963593 1.13139497
[133,] 1.03567501 2.34963593
[134,] 3.24280404 1.03567501
[135,] -0.59852625 3.24280404
[136,] -2.39755811 -0.59852625
[137,] -9.06642619 -2.39755811
[138,] 2.60948831 -9.06642619
[139,] -6.50311770 2.60948831
[140,] -9.09338344 -6.50311770
[141,] -2.04887152 -9.09338344
[142,] -7.64466975 -2.04887152
[143,] -0.42696285 -7.64466975
[144,] 0.16957962 -0.42696285
[145,] -1.78822197 0.16957962
[146,] -2.65345458 -1.78822197
[147,] -2.08535962 -2.65345458
[148,] -4.10944797 -2.08535962
[149,] -0.10003008 -4.10944797
[150,] -1.18566365 -0.10003008
[151,] 0.61673762 -1.18566365
[152,] 5.72584353 0.61673762
[153,] -8.97624731 5.72584353
[154,] -0.92220156 -8.97624731
[155,] -0.44568310 -0.92220156
[156,] 0.36692929 -0.44568310
[157,] 0.10728125 0.36692929
[158,] 2.27885794 0.10728125
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.34958653 1.96251135
2 -0.47597203 -2.34958653
3 -0.12842019 -0.47597203
4 -3.95008146 -0.12842019
5 4.94154886 -3.95008146
6 0.23017892 4.94154886
7 -1.54966450 0.23017892
8 0.04058636 -1.54966450
9 2.55163464 0.04058636
10 3.81694154 2.55163464
11 -1.75870827 3.81694154
12 3.62841177 -1.75870827
13 -5.67212272 3.62841177
14 -1.41949741 -5.67212272
15 3.74161327 -1.41949741
16 -2.17455278 3.74161327
17 -7.02239333 -2.17455278
18 2.54735007 -7.02239333
19 -1.38755577 2.54735007
20 -2.23634272 -1.38755577
21 -0.66035853 -2.23634272
22 1.83239245 -0.66035853
23 1.91913215 1.83239245
24 6.09132191 1.91913215
25 2.12670137 6.09132191
26 0.53784093 2.12670137
27 3.62302313 0.53784093
28 1.02716350 3.62302313
29 0.30866200 1.02716350
30 4.75507738 0.30866200
31 2.00616635 4.75507738
32 -7.01928506 2.00616635
33 -0.96327865 -7.01928506
34 -1.58153008 -0.96327865
35 -0.69574575 -1.58153008
36 -3.11400402 -0.69574575
37 -0.82656371 -3.11400402
38 -0.55143571 -0.82656371
39 -2.59780961 -0.55143571
40 7.10434010 -2.59780961
41 0.33598470 7.10434010
42 -1.71169192 0.33598470
43 -0.79089940 -1.71169192
44 -1.25163056 -0.79089940
45 2.60611102 -1.25163056
46 3.27754552 2.60611102
47 -3.43501661 3.27754552
48 0.03616821 -3.43501661
49 -2.81971030 0.03616821
50 -1.68979814 -2.81971030
51 2.18788569 -1.68979814
52 6.51563974 2.18788569
53 -5.02397346 6.51563974
54 0.20380878 -5.02397346
55 5.59237379 0.20380878
56 3.15691216 5.59237379
57 -1.16374788 3.15691216
58 4.05372334 -1.16374788
59 -4.35304918 4.05372334
60 2.14378695 -4.35304918
61 0.52334337 2.14378695
62 4.35649574 0.52334337
63 -0.22202881 4.35649574
64 0.32642491 -0.22202881
65 3.10732939 0.32642491
66 2.71651800 3.10732939
67 -3.09915274 2.71651800
68 1.72986142 -3.09915274
69 1.08152153 1.72986142
70 1.21821426 1.08152153
71 0.57238877 1.21821426
72 3.17385721 0.57238877
73 1.39779580 3.17385721
74 1.93558758 1.39779580
75 -0.28548694 1.93558758
76 1.35848355 -0.28548694
77 1.85520909 1.35848355
78 5.48764030 1.85520909
79 -1.64788364 5.48764030
80 3.43191049 -1.64788364
81 6.37091803 3.43191049
82 0.69503647 6.37091803
83 1.96725672 0.69503647
84 3.89408269 1.96725672
85 2.64595813 3.89408269
86 -0.85090100 2.64595813
87 4.64440268 -0.85090100
88 -0.14164781 4.64440268
89 0.42715626 -0.14164781
90 3.55481593 0.42715626
91 2.28056374 3.55481593
92 1.05860118 2.28056374
93 -2.30396842 1.05860118
94 5.65189108 -2.30396842
95 2.57543368 5.65189108
96 3.05315260 2.57543368
97 0.93617260 3.05315260
98 0.23198443 0.93617260
99 -1.38336120 0.23198443
100 -0.58150654 -1.38336120
101 -0.27615264 -0.58150654
102 -3.76275749 -0.27615264
103 1.35756745 -3.76275749
104 7.23204148 1.35756745
105 -6.72895774 7.23204148
106 -3.69134740 -6.72895774
107 -4.91025628 -3.69134740
108 -0.64604462 -4.91025628
109 -0.02302248 -0.64604462
110 -0.28484256 -0.02302248
111 5.93980821 -0.28484256
112 -2.24924838 5.93980821
113 -8.08961650 -2.24924838
114 -1.81872048 -8.08961650
115 1.60978839 -1.81872048
116 -8.05665574 1.60978839
117 -3.85291534 -8.05665574
118 1.81826084 -3.85291534
119 -7.42118871 1.81826084
120 -3.36038689 -7.42118871
121 -5.12434574 -3.36038689
122 -4.22855309 -5.12434574
123 2.26070108 -4.22855309
124 0.84433400 2.26070108
125 1.57027624 0.84433400
126 0.44649096 1.57027624
127 2.49673982 0.44649096
128 4.51164700 2.49673982
129 0.86794823 4.51164700
130 -4.29117565 0.86794823
131 1.13139497 -4.29117565
132 2.34963593 1.13139497
133 1.03567501 2.34963593
134 3.24280404 1.03567501
135 -0.59852625 3.24280404
136 -2.39755811 -0.59852625
137 -9.06642619 -2.39755811
138 2.60948831 -9.06642619
139 -6.50311770 2.60948831
140 -9.09338344 -6.50311770
141 -2.04887152 -9.09338344
142 -7.64466975 -2.04887152
143 -0.42696285 -7.64466975
144 0.16957962 -0.42696285
145 -1.78822197 0.16957962
146 -2.65345458 -1.78822197
147 -2.08535962 -2.65345458
148 -4.10944797 -2.08535962
149 -0.10003008 -4.10944797
150 -1.18566365 -0.10003008
151 0.61673762 -1.18566365
152 5.72584353 0.61673762
153 -8.97624731 5.72584353
154 -0.92220156 -8.97624731
155 -0.44568310 -0.92220156
156 0.36692929 -0.44568310
157 0.10728125 0.36692929
158 2.27885794 0.10728125
> 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/freestat/rcomp/tmp/71nfo1290532142.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/81nfo1290532142.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/91nfo1290532142.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10cwe81290532142.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11fwde1290532142.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/freestat/rcomp/tmp/12ifbk1290532142.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/freestat/rcomp/tmp/137g8e1290532142.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/freestat/rcomp/tmp/14bgpk1290532142.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/freestat/rcomp/tmp/15ez581290532142.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/freestat/rcomp/tmp/16zhme1290532142.tab")
+ }
>
> try(system("convert tmp/1cl0u1290532142.ps tmp/1cl0u1290532142.png",intern=TRUE))
character(0)
> try(system("convert tmp/25vhf1290532142.ps tmp/25vhf1290532142.png",intern=TRUE))
character(0)
> try(system("convert tmp/35vhf1290532142.ps tmp/35vhf1290532142.png",intern=TRUE))
character(0)
> try(system("convert tmp/4x4y01290532142.ps tmp/4x4y01290532142.png",intern=TRUE))
character(0)
> try(system("convert tmp/5x4y01290532142.ps tmp/5x4y01290532142.png",intern=TRUE))
character(0)
> try(system("convert tmp/6qvg31290532142.ps tmp/6qvg31290532142.png",intern=TRUE))
character(0)
> try(system("convert tmp/71nfo1290532142.ps tmp/71nfo1290532142.png",intern=TRUE))
character(0)
> try(system("convert tmp/81nfo1290532142.ps tmp/81nfo1290532142.png",intern=TRUE))
character(0)
> try(system("convert tmp/91nfo1290532142.ps tmp/91nfo1290532142.png",intern=TRUE))
character(0)
> try(system("convert tmp/10cwe81290532142.ps tmp/10cwe81290532142.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.024 2.681 23.692