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(24
+ ,14
+ ,11
+ ,12
+ ,24
+ ,26
+ ,25
+ ,11
+ ,7
+ ,8
+ ,25
+ ,23
+ ,17
+ ,6
+ ,17
+ ,8
+ ,30
+ ,25
+ ,18
+ ,12
+ ,10
+ ,8
+ ,19
+ ,23
+ ,18
+ ,8
+ ,12
+ ,9
+ ,22
+ ,19
+ ,16
+ ,10
+ ,12
+ ,7
+ ,22
+ ,29
+ ,20
+ ,10
+ ,11
+ ,4
+ ,25
+ ,25
+ ,16
+ ,11
+ ,11
+ ,11
+ ,23
+ ,21
+ ,18
+ ,16
+ ,12
+ ,7
+ ,17
+ ,22
+ ,17
+ ,11
+ ,13
+ ,7
+ ,21
+ ,25
+ ,23
+ ,13
+ ,14
+ ,12
+ ,19
+ ,24
+ ,30
+ ,12
+ ,16
+ ,10
+ ,19
+ ,18
+ ,23
+ ,8
+ ,11
+ ,10
+ ,15
+ ,22
+ ,18
+ ,12
+ ,10
+ ,8
+ ,16
+ ,15
+ ,15
+ ,11
+ ,11
+ ,8
+ ,23
+ ,22
+ ,12
+ ,4
+ ,15
+ ,4
+ ,27
+ ,28
+ ,21
+ ,9
+ ,9
+ ,9
+ ,22
+ ,20
+ ,15
+ ,8
+ ,11
+ ,8
+ ,14
+ ,12
+ ,20
+ ,8
+ ,17
+ ,7
+ ,22
+ ,24
+ ,31
+ ,14
+ ,17
+ ,11
+ ,23
+ ,20
+ ,27
+ ,15
+ ,11
+ ,9
+ ,23
+ ,21
+ ,34
+ ,16
+ ,18
+ ,11
+ ,21
+ ,20
+ ,21
+ ,9
+ ,14
+ ,13
+ ,19
+ ,21
+ ,31
+ ,14
+ ,10
+ ,8
+ ,18
+ ,23
+ ,19
+ ,11
+ ,11
+ ,8
+ ,20
+ ,28
+ ,16
+ ,8
+ ,15
+ ,9
+ ,23
+ ,24
+ ,20
+ ,9
+ ,15
+ ,6
+ ,25
+ ,24
+ ,21
+ ,9
+ ,13
+ ,9
+ ,19
+ ,24
+ ,22
+ ,9
+ ,16
+ ,9
+ ,24
+ ,23
+ ,17
+ ,9
+ ,13
+ ,6
+ ,22
+ ,23
+ ,24
+ ,10
+ ,9
+ ,6
+ ,25
+ ,29
+ ,25
+ ,16
+ ,18
+ ,16
+ ,26
+ ,24
+ ,26
+ ,11
+ ,18
+ ,5
+ ,29
+ ,18
+ ,25
+ ,8
+ ,12
+ ,7
+ ,32
+ ,25
+ ,17
+ ,9
+ ,17
+ ,9
+ ,25
+ ,21
+ ,32
+ ,16
+ ,9
+ ,6
+ ,29
+ ,26
+ ,33
+ ,11
+ ,9
+ ,6
+ ,28
+ ,22
+ ,13
+ ,16
+ ,12
+ ,5
+ ,17
+ ,22
+ ,32
+ ,12
+ ,18
+ ,12
+ ,28
+ ,22
+ ,25
+ ,12
+ ,12
+ ,7
+ ,29
+ ,23
+ ,29
+ ,14
+ ,18
+ ,10
+ ,26
+ ,30
+ ,22
+ ,9
+ ,14
+ ,9
+ ,25
+ ,23
+ ,18
+ ,10
+ ,15
+ ,8
+ ,14
+ ,17
+ ,17
+ ,9
+ ,16
+ ,5
+ ,25
+ ,23
+ ,20
+ ,10
+ ,10
+ ,8
+ ,26
+ ,23
+ ,15
+ ,12
+ ,11
+ ,8
+ ,20
+ ,25
+ ,20
+ ,14
+ ,14
+ ,10
+ ,18
+ ,24
+ ,33
+ ,14
+ ,9
+ ,6
+ ,32
+ ,24
+ ,29
+ ,10
+ ,12
+ ,8
+ ,25
+ ,23
+ ,23
+ ,14
+ ,17
+ ,7
+ ,25
+ ,21
+ ,26
+ ,16
+ ,5
+ ,4
+ ,23
+ ,24
+ ,18
+ ,9
+ ,12
+ ,8
+ ,21
+ ,24
+ ,20
+ ,10
+ ,12
+ ,8
+ ,20
+ ,28
+ ,11
+ ,6
+ ,6
+ ,4
+ ,15
+ ,16
+ ,28
+ ,8
+ ,24
+ ,20
+ ,30
+ ,20
+ ,26
+ ,13
+ ,12
+ ,8
+ ,24
+ ,29
+ ,22
+ ,10
+ ,12
+ ,8
+ ,26
+ ,27
+ ,17
+ ,8
+ ,14
+ ,6
+ ,24
+ ,22
+ ,12
+ ,7
+ ,7
+ ,4
+ ,22
+ ,28
+ ,14
+ ,15
+ ,13
+ ,8
+ ,14
+ ,16
+ ,17
+ ,9
+ ,12
+ ,9
+ ,24
+ ,25
+ ,21
+ ,10
+ ,13
+ ,6
+ ,24
+ ,24
+ ,19
+ ,12
+ ,14
+ ,7
+ ,24
+ ,28
+ ,18
+ ,13
+ ,8
+ ,9
+ ,24
+ ,24
+ ,10
+ ,10
+ ,11
+ ,5
+ ,19
+ ,23
+ ,29
+ ,11
+ ,9
+ ,5
+ ,31
+ ,30
+ ,31
+ ,8
+ ,11
+ ,8
+ ,22
+ ,24
+ ,19
+ ,9
+ ,13
+ ,8
+ ,27
+ ,21
+ ,9
+ ,13
+ ,10
+ ,6
+ ,19
+ ,25
+ ,20
+ ,11
+ ,11
+ ,8
+ ,25
+ ,25
+ ,28
+ ,8
+ ,12
+ ,7
+ ,20
+ ,22
+ ,19
+ ,9
+ ,9
+ ,7
+ ,21
+ ,23
+ ,30
+ ,9
+ ,15
+ ,9
+ ,27
+ ,26
+ ,29
+ ,15
+ ,18
+ ,11
+ ,23
+ ,23
+ ,26
+ ,9
+ ,15
+ ,6
+ ,25
+ ,25
+ ,23
+ ,10
+ ,12
+ ,8
+ ,20
+ ,21
+ ,13
+ ,14
+ ,13
+ ,6
+ ,21
+ ,25
+ ,21
+ ,12
+ ,14
+ ,9
+ ,22
+ ,24
+ ,19
+ ,12
+ ,10
+ ,8
+ ,23
+ ,29
+ ,28
+ ,11
+ ,13
+ ,6
+ ,25
+ ,22
+ ,23
+ ,14
+ ,13
+ ,10
+ ,25
+ ,27
+ ,18
+ ,6
+ ,11
+ ,8
+ ,17
+ ,26
+ ,21
+ ,12
+ ,13
+ ,8
+ ,19
+ ,22
+ ,20
+ ,8
+ ,16
+ ,10
+ ,25
+ ,24
+ ,23
+ ,14
+ ,8
+ ,5
+ ,19
+ ,27
+ ,21
+ ,11
+ ,16
+ ,7
+ ,20
+ ,24
+ ,21
+ ,10
+ ,11
+ ,5
+ ,26
+ ,24
+ ,15
+ ,14
+ ,9
+ ,8
+ ,23
+ ,29
+ ,28
+ ,12
+ ,16
+ ,14
+ ,27
+ ,22
+ ,19
+ ,10
+ ,12
+ ,7
+ ,17
+ ,21
+ ,26
+ ,14
+ ,14
+ ,8
+ ,17
+ ,24
+ ,10
+ ,5
+ ,8
+ ,6
+ ,19
+ ,24
+ ,16
+ ,11
+ ,9
+ ,5
+ ,17
+ ,23
+ ,22
+ ,10
+ ,15
+ ,6
+ ,22
+ ,20
+ ,19
+ ,9
+ ,11
+ ,10
+ ,21
+ ,27
+ ,31
+ ,10
+ ,21
+ ,12
+ ,32
+ ,26
+ ,31
+ ,16
+ ,14
+ ,9
+ ,21
+ ,25
+ ,29
+ ,13
+ ,18
+ ,12
+ ,21
+ ,21
+ ,19
+ ,9
+ ,12
+ ,7
+ ,18
+ ,21
+ ,22
+ ,10
+ ,13
+ ,8
+ ,18
+ ,19
+ ,23
+ ,10
+ ,15
+ ,10
+ ,23
+ ,21
+ ,15
+ ,7
+ ,12
+ ,6
+ ,19
+ ,21
+ ,20
+ ,9
+ ,19
+ ,10
+ ,20
+ ,16
+ ,18
+ ,8
+ ,15
+ ,10
+ ,21
+ ,22
+ ,23
+ ,14
+ ,11
+ ,10
+ ,20
+ ,29
+ ,25
+ ,14
+ ,11
+ ,5
+ ,17
+ ,15
+ ,21
+ ,8
+ ,10
+ ,7
+ ,18
+ ,17
+ ,24
+ ,9
+ ,13
+ ,10
+ ,19
+ ,15
+ ,25
+ ,14
+ ,15
+ ,11
+ ,22
+ ,21
+ ,17
+ ,14
+ ,12
+ ,6
+ ,15
+ ,21
+ ,13
+ ,8
+ ,12
+ ,7
+ ,14
+ ,19
+ ,28
+ ,8
+ ,16
+ ,12
+ ,18
+ ,24
+ ,21
+ ,8
+ ,9
+ ,11
+ ,24
+ ,20
+ ,25
+ ,7
+ ,18
+ ,11
+ ,35
+ ,17
+ ,9
+ ,6
+ ,8
+ ,11
+ ,29
+ ,23
+ ,16
+ ,8
+ ,13
+ ,5
+ ,21
+ ,24
+ ,19
+ ,6
+ ,17
+ ,8
+ ,25
+ ,14
+ ,17
+ ,11
+ ,9
+ ,6
+ ,20
+ ,19
+ ,25
+ ,14
+ ,15
+ ,9
+ ,22
+ ,24
+ ,20
+ ,11
+ ,8
+ ,4
+ ,13
+ ,13
+ ,29
+ ,11
+ ,7
+ ,4
+ ,26
+ ,22
+ ,14
+ ,11
+ ,12
+ ,7
+ ,17
+ ,16
+ ,22
+ ,14
+ ,14
+ ,11
+ ,25
+ ,19
+ ,15
+ ,8
+ ,6
+ ,6
+ ,20
+ ,25
+ ,19
+ ,20
+ ,8
+ ,7
+ ,19
+ ,25
+ ,20
+ ,11
+ ,17
+ ,8
+ ,21
+ ,23
+ ,15
+ ,8
+ ,10
+ ,4
+ ,22
+ ,24
+ ,20
+ ,11
+ ,11
+ ,8
+ ,24
+ ,26
+ ,18
+ ,10
+ ,14
+ ,9
+ ,21
+ ,26
+ ,33
+ ,14
+ ,11
+ ,8
+ ,26
+ ,25
+ ,22
+ ,11
+ ,13
+ ,11
+ ,24
+ ,18
+ ,16
+ ,9
+ ,12
+ ,8
+ ,16
+ ,21
+ ,17
+ ,9
+ ,11
+ ,5
+ ,23
+ ,26
+ ,16
+ ,8
+ ,9
+ ,4
+ ,18
+ ,23
+ ,21
+ ,10
+ ,12
+ ,8
+ ,16
+ ,23
+ ,26
+ ,13
+ ,20
+ ,10
+ ,26
+ ,22
+ ,18
+ ,13
+ ,12
+ ,6
+ ,19
+ ,20
+ ,18
+ ,12
+ ,13
+ ,9
+ ,21
+ ,13
+ ,17
+ ,8
+ ,12
+ ,9
+ ,21
+ ,24
+ ,22
+ ,13
+ ,12
+ ,13
+ ,22
+ ,15
+ ,30
+ ,14
+ ,9
+ ,9
+ ,23
+ ,14
+ ,30
+ ,12
+ ,15
+ ,10
+ ,29
+ ,22
+ ,24
+ ,14
+ ,24
+ ,20
+ ,21
+ ,10
+ ,21
+ ,15
+ ,7
+ ,5
+ ,21
+ ,24
+ ,21
+ ,13
+ ,17
+ ,11
+ ,23
+ ,22
+ ,29
+ ,16
+ ,11
+ ,6
+ ,27
+ ,24
+ ,31
+ ,9
+ ,17
+ ,9
+ ,25
+ ,19
+ ,20
+ ,9
+ ,11
+ ,7
+ ,21
+ ,20
+ ,16
+ ,9
+ ,12
+ ,9
+ ,10
+ ,13
+ ,22
+ ,8
+ ,14
+ ,10
+ ,20
+ ,20
+ ,20
+ ,7
+ ,11
+ ,9
+ ,26
+ ,22
+ ,28
+ ,16
+ ,16
+ ,8
+ ,24
+ ,24
+ ,38
+ ,11
+ ,21
+ ,7
+ ,29
+ ,29
+ ,22
+ ,9
+ ,14
+ ,6
+ ,19
+ ,12
+ ,20
+ ,11
+ ,20
+ ,13
+ ,24
+ ,20
+ ,17
+ ,9
+ ,13
+ ,6
+ ,19
+ ,21
+ ,28
+ ,14
+ ,11
+ ,8
+ ,24
+ ,24
+ ,22
+ ,13
+ ,15
+ ,10
+ ,22
+ ,22
+ ,31
+ ,16
+ ,19
+ ,16
+ ,17
+ ,20)
+ ,dim=c(6
+ ,159)
+ ,dimnames=list(c('Concern(Mistakes)'
+ ,'Doubts(actions)'
+ ,'Parental-Expectations'
+ ,'Parental-Criticism'
+ ,'Personal-Standards'
+ ,'Organization')
+ ,1:159))
> y <- array(NA,dim=c(6,159),dimnames=list(c('Concern(Mistakes)','Doubts(actions)','Parental-Expectations','Parental-Criticism','Personal-Standards','Organization'),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 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '5'
> #'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
Personal-Standards Concern(Mistakes) Doubts(actions) Parental-Expectations
1 24 24 14 11
2 25 25 11 7
3 30 17 6 17
4 19 18 12 10
5 22 18 8 12
6 22 16 10 12
7 25 20 10 11
8 23 16 11 11
9 17 18 16 12
10 21 17 11 13
11 19 23 13 14
12 19 30 12 16
13 15 23 8 11
14 16 18 12 10
15 23 15 11 11
16 27 12 4 15
17 22 21 9 9
18 14 15 8 11
19 22 20 8 17
20 23 31 14 17
21 23 27 15 11
22 21 34 16 18
23 19 21 9 14
24 18 31 14 10
25 20 19 11 11
26 23 16 8 15
27 25 20 9 15
28 19 21 9 13
29 24 22 9 16
30 22 17 9 13
31 25 24 10 9
32 26 25 16 18
33 29 26 11 18
34 32 25 8 12
35 25 17 9 17
36 29 32 16 9
37 28 33 11 9
38 17 13 16 12
39 28 32 12 18
40 29 25 12 12
41 26 29 14 18
42 25 22 9 14
43 14 18 10 15
44 25 17 9 16
45 26 20 10 10
46 20 15 12 11
47 18 20 14 14
48 32 33 14 9
49 25 29 10 12
50 25 23 14 17
51 23 26 16 5
52 21 18 9 12
53 20 20 10 12
54 15 11 6 6
55 30 28 8 24
56 24 26 13 12
57 26 22 10 12
58 24 17 8 14
59 22 12 7 7
60 14 14 15 13
61 24 17 9 12
62 24 21 10 13
63 24 19 12 14
64 24 18 13 8
65 19 10 10 11
66 31 29 11 9
67 22 31 8 11
68 27 19 9 13
69 19 9 13 10
70 25 20 11 11
71 20 28 8 12
72 21 19 9 9
73 27 30 9 15
74 23 29 15 18
75 25 26 9 15
76 20 23 10 12
77 21 13 14 13
78 22 21 12 14
79 23 19 12 10
80 25 28 11 13
81 25 23 14 13
82 17 18 6 11
83 19 21 12 13
84 25 20 8 16
85 19 23 14 8
86 20 21 11 16
87 26 21 10 11
88 23 15 14 9
89 27 28 12 16
90 17 19 10 12
91 17 26 14 14
92 19 10 5 8
93 17 16 11 9
94 22 22 10 15
95 21 19 9 11
96 32 31 10 21
97 21 31 16 14
98 21 29 13 18
99 18 19 9 12
100 18 22 10 13
101 23 23 10 15
102 19 15 7 12
103 20 20 9 19
104 21 18 8 15
105 20 23 14 11
106 17 25 14 11
107 18 21 8 10
108 19 24 9 13
109 22 25 14 15
110 15 17 14 12
111 14 13 8 12
112 18 28 8 16
113 24 21 8 9
114 35 25 7 18
115 29 9 6 8
116 21 16 8 13
117 25 19 6 17
118 20 17 11 9
119 22 25 14 15
120 13 20 11 8
121 26 29 11 7
122 17 14 11 12
123 25 22 14 14
124 20 15 8 6
125 19 19 20 8
126 21 20 11 17
127 22 15 8 10
128 24 20 11 11
129 21 18 10 14
130 26 33 14 11
131 24 22 11 13
132 16 16 9 12
133 23 17 9 11
134 18 16 8 9
135 16 21 10 12
136 26 26 13 20
137 19 18 13 12
138 21 18 12 13
139 21 17 8 12
140 22 22 13 12
141 23 30 14 9
142 29 30 12 15
143 21 24 14 24
144 21 21 15 7
145 23 21 13 17
146 27 29 16 11
147 25 31 9 17
148 21 20 9 11
149 10 16 9 12
150 20 22 8 14
151 26 20 7 11
152 24 28 16 16
153 29 38 11 21
154 19 22 9 14
155 24 20 11 20
156 19 17 9 13
157 24 28 14 11
158 22 22 13 15
159 17 31 16 19
Parental-Criticism Organization M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 12 26 1 0 0 0 0 0 0 0 0 0 0 1
2 8 23 0 1 0 0 0 0 0 0 0 0 0 2
3 8 25 0 0 1 0 0 0 0 0 0 0 0 3
4 8 23 0 0 0 1 0 0 0 0 0 0 0 4
5 9 19 0 0 0 0 1 0 0 0 0 0 0 5
6 7 29 0 0 0 0 0 1 0 0 0 0 0 6
7 4 25 0 0 0 0 0 0 1 0 0 0 0 7
8 11 21 0 0 0 0 0 0 0 1 0 0 0 8
9 7 22 0 0 0 0 0 0 0 0 1 0 0 9
10 7 25 0 0 0 0 0 0 0 0 0 1 0 10
11 12 24 0 0 0 0 0 0 0 0 0 0 1 11
12 10 18 0 0 0 0 0 0 0 0 0 0 0 12
13 10 22 1 0 0 0 0 0 0 0 0 0 0 13
14 8 15 0 1 0 0 0 0 0 0 0 0 0 14
15 8 22 0 0 1 0 0 0 0 0 0 0 0 15
16 4 28 0 0 0 1 0 0 0 0 0 0 0 16
17 9 20 0 0 0 0 1 0 0 0 0 0 0 17
18 8 12 0 0 0 0 0 1 0 0 0 0 0 18
19 7 24 0 0 0 0 0 0 1 0 0 0 0 19
20 11 20 0 0 0 0 0 0 0 1 0 0 0 20
21 9 21 0 0 0 0 0 0 0 0 1 0 0 21
22 11 20 0 0 0 0 0 0 0 0 0 1 0 22
23 13 21 0 0 0 0 0 0 0 0 0 0 1 23
24 8 23 0 0 0 0 0 0 0 0 0 0 0 24
25 8 28 1 0 0 0 0 0 0 0 0 0 0 25
26 9 24 0 1 0 0 0 0 0 0 0 0 0 26
27 6 24 0 0 1 0 0 0 0 0 0 0 0 27
28 9 24 0 0 0 1 0 0 0 0 0 0 0 28
29 9 23 0 0 0 0 1 0 0 0 0 0 0 29
30 6 23 0 0 0 0 0 1 0 0 0 0 0 30
31 6 29 0 0 0 0 0 0 1 0 0 0 0 31
32 16 24 0 0 0 0 0 0 0 1 0 0 0 32
33 5 18 0 0 0 0 0 0 0 0 1 0 0 33
34 7 25 0 0 0 0 0 0 0 0 0 1 0 34
35 9 21 0 0 0 0 0 0 0 0 0 0 1 35
36 6 26 0 0 0 0 0 0 0 0 0 0 0 36
37 6 22 1 0 0 0 0 0 0 0 0 0 0 37
38 5 22 0 1 0 0 0 0 0 0 0 0 0 38
39 12 22 0 0 1 0 0 0 0 0 0 0 0 39
40 7 23 0 0 0 1 0 0 0 0 0 0 0 40
41 10 30 0 0 0 0 1 0 0 0 0 0 0 41
42 9 23 0 0 0 0 0 1 0 0 0 0 0 42
43 8 17 0 0 0 0 0 0 1 0 0 0 0 43
44 5 23 0 0 0 0 0 0 0 1 0 0 0 44
45 8 23 0 0 0 0 0 0 0 0 1 0 0 45
46 8 25 0 0 0 0 0 0 0 0 0 1 0 46
47 10 24 0 0 0 0 0 0 0 0 0 0 1 47
48 6 24 0 0 0 0 0 0 0 0 0 0 0 48
49 8 23 1 0 0 0 0 0 0 0 0 0 0 49
50 7 21 0 1 0 0 0 0 0 0 0 0 0 50
51 4 24 0 0 1 0 0 0 0 0 0 0 0 51
52 8 24 0 0 0 1 0 0 0 0 0 0 0 52
53 8 28 0 0 0 0 1 0 0 0 0 0 0 53
54 4 16 0 0 0 0 0 1 0 0 0 0 0 54
55 20 20 0 0 0 0 0 0 1 0 0 0 0 55
56 8 29 0 0 0 0 0 0 0 1 0 0 0 56
57 8 27 0 0 0 0 0 0 0 0 1 0 0 57
58 6 22 0 0 0 0 0 0 0 0 0 1 0 58
59 4 28 0 0 0 0 0 0 0 0 0 0 1 59
60 8 16 0 0 0 0 0 0 0 0 0 0 0 60
61 9 25 1 0 0 0 0 0 0 0 0 0 0 61
62 6 24 0 1 0 0 0 0 0 0 0 0 0 62
63 7 28 0 0 1 0 0 0 0 0 0 0 0 63
64 9 24 0 0 0 1 0 0 0 0 0 0 0 64
65 5 23 0 0 0 0 1 0 0 0 0 0 0 65
66 5 30 0 0 0 0 0 1 0 0 0 0 0 66
67 8 24 0 0 0 0 0 0 1 0 0 0 0 67
68 8 21 0 0 0 0 0 0 0 1 0 0 0 68
69 6 25 0 0 0 0 0 0 0 0 1 0 0 69
70 8 25 0 0 0 0 0 0 0 0 0 1 0 70
71 7 22 0 0 0 0 0 0 0 0 0 0 1 71
72 7 23 0 0 0 0 0 0 0 0 0 0 0 72
73 9 26 1 0 0 0 0 0 0 0 0 0 0 73
74 11 23 0 1 0 0 0 0 0 0 0 0 0 74
75 6 25 0 0 1 0 0 0 0 0 0 0 0 75
76 8 21 0 0 0 1 0 0 0 0 0 0 0 76
77 6 25 0 0 0 0 1 0 0 0 0 0 0 77
78 9 24 0 0 0 0 0 1 0 0 0 0 0 78
79 8 29 0 0 0 0 0 0 1 0 0 0 0 79
80 6 22 0 0 0 0 0 0 0 1 0 0 0 80
81 10 27 0 0 0 0 0 0 0 0 1 0 0 81
82 8 26 0 0 0 0 0 0 0 0 0 1 0 82
83 8 22 0 0 0 0 0 0 0 0 0 0 1 83
84 10 24 0 0 0 0 0 0 0 0 0 0 0 84
85 5 27 1 0 0 0 0 0 0 0 0 0 0 85
86 7 24 0 1 0 0 0 0 0 0 0 0 0 86
87 5 24 0 0 1 0 0 0 0 0 0 0 0 87
88 8 29 0 0 0 1 0 0 0 0 0 0 0 88
89 14 22 0 0 0 0 1 0 0 0 0 0 0 89
90 7 21 0 0 0 0 0 1 0 0 0 0 0 90
91 8 24 0 0 0 0 0 0 1 0 0 0 0 91
92 6 24 0 0 0 0 0 0 0 1 0 0 0 92
93 5 23 0 0 0 0 0 0 0 0 1 0 0 93
94 6 20 0 0 0 0 0 0 0 0 0 1 0 94
95 10 27 0 0 0 0 0 0 0 0 0 0 1 95
96 12 26 0 0 0 0 0 0 0 0 0 0 0 96
97 9 25 1 0 0 0 0 0 0 0 0 0 0 97
98 12 21 0 1 0 0 0 0 0 0 0 0 0 98
99 7 21 0 0 1 0 0 0 0 0 0 0 0 99
100 8 19 0 0 0 1 0 0 0 0 0 0 0 100
101 10 21 0 0 0 0 1 0 0 0 0 0 0 101
102 6 21 0 0 0 0 0 1 0 0 0 0 0 102
103 10 16 0 0 0 0 0 0 1 0 0 0 0 103
104 10 22 0 0 0 0 0 0 0 1 0 0 0 104
105 10 29 0 0 0 0 0 0 0 0 1 0 0 105
106 5 15 0 0 0 0 0 0 0 0 0 1 0 106
107 7 17 0 0 0 0 0 0 0 0 0 0 1 107
108 10 15 0 0 0 0 0 0 0 0 0 0 0 108
109 11 21 1 0 0 0 0 0 0 0 0 0 0 109
110 6 21 0 1 0 0 0 0 0 0 0 0 0 110
111 7 19 0 0 1 0 0 0 0 0 0 0 0 111
112 12 24 0 0 0 1 0 0 0 0 0 0 0 112
113 11 20 0 0 0 0 1 0 0 0 0 0 0 113
114 11 17 0 0 0 0 0 1 0 0 0 0 0 114
115 11 23 0 0 0 0 0 0 1 0 0 0 0 115
116 5 24 0 0 0 0 0 0 0 1 0 0 0 116
117 8 14 0 0 0 0 0 0 0 0 1 0 0 117
118 6 19 0 0 0 0 0 0 0 0 0 1 0 118
119 9 24 0 0 0 0 0 0 0 0 0 0 1 119
120 4 13 0 0 0 0 0 0 0 0 0 0 0 120
121 4 22 1 0 0 0 0 0 0 0 0 0 0 121
122 7 16 0 1 0 0 0 0 0 0 0 0 0 122
123 11 19 0 0 1 0 0 0 0 0 0 0 0 123
124 6 25 0 0 0 1 0 0 0 0 0 0 0 124
125 7 25 0 0 0 0 1 0 0 0 0 0 0 125
126 8 23 0 0 0 0 0 1 0 0 0 0 0 126
127 4 24 0 0 0 0 0 0 1 0 0 0 0 127
128 8 26 0 0 0 0 0 0 0 1 0 0 0 128
129 9 26 0 0 0 0 0 0 0 0 1 0 0 129
130 8 25 0 0 0 0 0 0 0 0 0 1 0 130
131 11 18 0 0 0 0 0 0 0 0 0 0 1 131
132 8 21 0 0 0 0 0 0 0 0 0 0 0 132
133 5 26 1 0 0 0 0 0 0 0 0 0 0 133
134 4 23 0 1 0 0 0 0 0 0 0 0 0 134
135 8 23 0 0 1 0 0 0 0 0 0 0 0 135
136 10 22 0 0 0 1 0 0 0 0 0 0 0 136
137 6 20 0 0 0 0 1 0 0 0 0 0 0 137
138 9 13 0 0 0 0 0 1 0 0 0 0 0 138
139 9 24 0 0 0 0 0 0 1 0 0 0 0 139
140 13 15 0 0 0 0 0 0 0 1 0 0 0 140
141 9 14 0 0 0 0 0 0 0 0 1 0 0 141
142 10 22 0 0 0 0 0 0 0 0 0 1 0 142
143 20 10 0 0 0 0 0 0 0 0 0 0 1 143
144 5 24 0 0 0 0 0 0 0 0 0 0 0 144
145 11 22 1 0 0 0 0 0 0 0 0 0 0 145
146 6 24 0 1 0 0 0 0 0 0 0 0 0 146
147 9 19 0 0 1 0 0 0 0 0 0 0 0 147
148 7 20 0 0 0 1 0 0 0 0 0 0 0 148
149 9 13 0 0 0 0 1 0 0 0 0 0 0 149
150 10 20 0 0 0 0 0 1 0 0 0 0 0 150
151 9 22 0 0 0 0 0 0 1 0 0 0 0 151
152 8 24 0 0 0 0 0 0 0 1 0 0 0 152
153 7 29 0 0 0 0 0 0 0 0 1 0 0 153
154 6 12 0 0 0 0 0 0 0 0 0 1 0 154
155 13 20 0 0 0 0 0 0 0 0 0 0 1 155
156 6 21 0 0 0 0 0 0 0 0 0 0 0 156
157 8 24 1 0 0 0 0 0 0 0 0 0 0 157
158 10 22 0 1 0 0 0 0 0 0 0 0 0 158
159 16 20 0 0 1 0 0 0 0 0 0 0 0 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `Concern(Mistakes)` `Doubts(actions)`
7.358069 0.335230 -0.366768
`Parental-Expectations` `Parental-Criticism` Organization
0.161871 0.062092 0.391606
M1 M2 M3
-0.094792 0.289928 0.669864
M4 M5 M6
0.159287 0.365862 1.011994
M7 M8 M9
0.433807 1.671176 1.418630
M10 M11 t
0.923488 -0.528232 -0.003924
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.2638 -2.3247 0.1432 2.1717 11.0099
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.358069 2.610736 2.818 0.00552 **
`Concern(Mistakes)` 0.335230 0.059844 5.602 1.08e-07 ***
`Doubts(actions)` -0.366768 0.116654 -3.144 0.00203 **
`Parental-Expectations` 0.161871 0.106932 1.514 0.13232
`Parental-Criticism` 0.062092 0.139806 0.444 0.65763
Organization 0.391606 0.078570 4.984 1.80e-06 ***
M1 -0.094792 1.371116 -0.069 0.94498
M2 0.289928 1.372927 0.211 0.83306
M3 0.669864 1.365334 0.491 0.62446
M4 0.159287 1.407608 0.113 0.91006
M5 0.365862 1.402591 0.261 0.79459
M6 1.011994 1.405064 0.720 0.47256
M7 0.433807 1.426762 0.304 0.76154
M8 1.671176 1.400483 1.193 0.23476
M9 1.418630 1.385153 1.024 0.30751
M10 0.923488 1.374666 0.672 0.50282
M11 -0.528232 1.425316 -0.371 0.71149
t -0.003924 0.006235 -0.629 0.53015
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.488 on 141 degrees of freedom
Multiple R-squared: 0.3894, Adjusted R-squared: 0.3158
F-statistic: 5.29 on 17 and 141 DF, p-value: 6.342e-09
> 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,] 0.79485807 0.4102839 0.20514193
[2,] 0.67901911 0.6419618 0.32098089
[3,] 0.55409826 0.8918035 0.44590174
[4,] 0.45651878 0.9130376 0.54348122
[5,] 0.37105488 0.7421098 0.62894512
[6,] 0.26787508 0.5357502 0.73212492
[7,] 0.18820793 0.3764159 0.81179207
[8,] 0.17108478 0.3421696 0.82891522
[9,] 0.11665805 0.2333161 0.88334195
[10,] 0.11474560 0.2294912 0.88525440
[11,] 0.07548555 0.1509711 0.92451445
[12,] 0.05686782 0.1137356 0.94313218
[13,] 0.18723453 0.3744691 0.81276547
[14,] 0.31974363 0.6394873 0.68025637
[15,] 0.40323856 0.8064771 0.59676144
[16,] 0.52514425 0.9497115 0.47485575
[17,] 0.49177070 0.9835414 0.50822930
[18,] 0.44013752 0.8802750 0.55986248
[19,] 0.37793066 0.7558613 0.62206934
[20,] 0.48340406 0.9668081 0.51659594
[21,] 0.44927983 0.8985597 0.55072017
[22,] 0.39999589 0.7999918 0.60000411
[23,] 0.39513030 0.7902606 0.60486970
[24,] 0.40894179 0.8178836 0.59105821
[25,] 0.35950630 0.7190126 0.64049370
[26,] 0.30957524 0.6191505 0.69042476
[27,] 0.29015150 0.5803030 0.70984850
[28,] 0.40547584 0.8109517 0.59452416
[29,] 0.34854812 0.6970962 0.65145188
[30,] 0.33670966 0.6734193 0.66329034
[31,] 0.37480207 0.7496041 0.62519793
[32,] 0.33403505 0.6680701 0.66596495
[33,] 0.39770504 0.7954101 0.60229496
[34,] 0.36817220 0.7363444 0.63182780
[35,] 0.47664761 0.9532952 0.52335239
[36,] 0.52500191 0.9499962 0.47499809
[37,] 0.49388164 0.9877633 0.50611836
[38,] 0.44846347 0.8969269 0.55153653
[39,] 0.40127642 0.8025528 0.59872358
[40,] 0.35798183 0.7159637 0.64201817
[41,] 0.33349653 0.6669931 0.66650347
[42,] 0.30757116 0.6151423 0.69242884
[43,] 0.28553857 0.5710771 0.71446143
[44,] 0.30954469 0.6190894 0.69045531
[45,] 0.26967857 0.5393571 0.73032143
[46,] 0.26539868 0.5307974 0.73460132
[47,] 0.33211622 0.6642324 0.66788378
[48,] 0.34049331 0.6809866 0.65950669
[49,] 0.30053559 0.6010712 0.69946441
[50,] 0.27436292 0.5487258 0.72563708
[51,] 0.30905289 0.6181058 0.69094711
[52,] 0.27185067 0.5437013 0.72814933
[53,] 0.23265981 0.4653196 0.76734019
[54,] 0.20511875 0.4102375 0.79488125
[55,] 0.21049952 0.4209990 0.78950048
[56,] 0.18953912 0.3790782 0.81046088
[57,] 0.18278147 0.3655629 0.81721853
[58,] 0.15099850 0.3019970 0.84900150
[59,] 0.12347320 0.2469464 0.87652680
[60,] 0.10503528 0.2100706 0.89496472
[61,] 0.08860353 0.1772071 0.91139647
[62,] 0.18551062 0.3710212 0.81448938
[63,] 0.15393519 0.3078704 0.84606481
[64,] 0.13368774 0.2673755 0.86631226
[65,] 0.11902560 0.2380512 0.88097440
[66,] 0.10995307 0.2199061 0.89004693
[67,] 0.14991520 0.2998304 0.85008480
[68,] 0.16081520 0.3216304 0.83918480
[69,] 0.15658273 0.3131655 0.84341727
[70,] 0.16020691 0.3204138 0.83979309
[71,] 0.22250211 0.4450042 0.77749789
[72,] 0.19852216 0.3970443 0.80147784
[73,] 0.18742275 0.3748455 0.81257725
[74,] 0.15560827 0.3112165 0.84439173
[75,] 0.13137896 0.2627579 0.86862104
[76,] 0.17762060 0.3552412 0.82237940
[77,] 0.16740658 0.3348132 0.83259342
[78,] 0.15192951 0.3038590 0.84807049
[79,] 0.13836108 0.2767222 0.86163892
[80,] 0.11737430 0.2347486 0.88262570
[81,] 0.09953958 0.1990792 0.90046042
[82,] 0.08641271 0.1728254 0.91358729
[83,] 0.08283893 0.1656779 0.91716107
[84,] 0.06792131 0.1358426 0.93207869
[85,] 0.08203834 0.1640767 0.91796166
[86,] 0.07842286 0.1568457 0.92157714
[87,] 0.07478805 0.1495761 0.92521195
[88,] 0.05833687 0.1166737 0.94166313
[89,] 0.05517558 0.1103512 0.94482442
[90,] 0.06575833 0.1315167 0.93424167
[91,] 0.06417704 0.1283541 0.93582296
[92,] 0.31590324 0.6318065 0.68409676
[93,] 0.29106787 0.5821357 0.70893213
[94,] 0.69530915 0.6093817 0.30469085
[95,] 0.90381761 0.1923648 0.09618239
[96,] 0.87599170 0.2480166 0.12400830
[97,] 0.91007998 0.1798400 0.08992002
[98,] 0.88278443 0.2344311 0.11721557
[99,] 0.90480240 0.1903952 0.09519760
[100,] 0.93726038 0.1254792 0.06273962
[101,] 0.92210787 0.1557843 0.07789213
[102,] 0.90465072 0.1906986 0.09534928
[103,] 0.96694365 0.0661127 0.03305635
[104,] 0.94962586 0.1007483 0.05037414
[105,] 0.92925433 0.1414913 0.07074567
[106,] 0.90756196 0.1848761 0.09243804
[107,] 0.88554761 0.2289048 0.11445239
[108,] 0.84324046 0.3135191 0.15675954
[109,] 0.81656058 0.3668788 0.18343942
[110,] 0.80513792 0.3897242 0.19486208
[111,] 0.75670580 0.4865884 0.24329420
[112,] 0.72996540 0.5400692 0.27003460
[113,] 0.63856443 0.7228711 0.36143557
[114,] 0.64974185 0.7005163 0.35025815
[115,] 0.73794757 0.5241049 0.26205243
[116,] 0.62603673 0.7479265 0.37396327
[117,] 0.59539457 0.8092109 0.40460543
[118,] 0.69254411 0.6149118 0.30745589
> postscript(file="/var/www/html/rcomp/tmp/1c7cm1291054671.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/25zup1291054671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35zup1291054671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45zup1291054671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55zup1291054671.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 = 159
Frequency = 1
1 2 3 4 5 6
1.122449042 2.376786160 5.446849934 -1.256959668 1.253906133 -1.776183883
7 8 9 10 11 12
2.379578033 1.985603776 -2.899654573 -1.235890043 -3.138811389 -4.226418603
13 14 15 16 17 18
-8.005238677 -1.215514318 3.144281018 3.348338142 0.756076751 -3.370321330
19 20 21 22 23 24
-2.072767589 -2.475072261 0.192887394 -4.153559748 -2.775613406 -5.643648689
25 26 27 28 29 30
-2.742382469 0.639055087 1.475167523 -4.208094533 0.160017517 -0.134153806
31 32 33 34 35 36
-0.234037104 1.278175229 5.398217613 6.238007165 4.375145159 4.912979248
37 38 39 40 41 42
3.409047078 0.143169173 1.524850014 6.276037083 -1.432740455 0.888636454
43 44 45 46 47 48
-5.571707033 1.838075842 3.240574597 -0.795756002 -2.500914341 7.674510088
49 50 51 52 53 54
0.428881181 3.562487604 1.868230238 -0.884271979 -3.957039621 -2.130386979
55 56 57 58 59 60
3.012786836 -2.553262156 0.727033681 1.927184892 1.599851522 -1.181086386
61 62 63 64 65 66
2.286652103 1.347715219 0.585313050 4.215279012 0.748520788 3.686212582
67 68 69 70 71 72
-4.662821270 4.344336746 0.463549721 2.255496815 -3.995964406 -0.042429374
73 74 75 76 77 78
0.098530578 -1.181402257 -0.739476703 -1.924663660 2.087944653 -0.926178698
79 80 81 82 83 84
0.077935958 -0.159532577 0.666994102 -7.252406783 -1.359158848 1.591680390
85 86 87 88 89 90
-2.684076449 -2.739050037 3.451707551 2.624097369 2.565518166 -4.319427129
91 92 93 94 95 96
-6.177503307 -2.252778962 -3.515250749 -0.252830115 -1.500391774 3.968024307
97 98 99 100 101 102
-3.021672392 -3.099647949 -3.308751554 -2.873921799 0.357059296 -1.969636274
103 104 105 106 107 108
-0.753569469 -2.385477302 -4.698306015 -2.076754462 -1.226327049 -1.278231722
109 110 111 112 113 114
0.583625598 -3.319261055 -4.833843326 -8.263762630 2.641807071 11.009891887
115 116 117 118 119 120
9.857983302 -1.816944816 3.782601682 1.247089503 0.005668742 -3.191573615
121 122 123 124 125 126
3.527488269 0.529147617 4.824675882 -0.259037070 1.212780160 -1.801291085
127 128 129 130 131 132
1.346519945 0.343780943 -2.643762581 0.233240532 4.507333706 -3.565805423
133 134 135 136 137 138
1.587794868 -2.263888021 -6.316491005 2.594622096 0.400355042 3.784476038
139 140 141 142 143 144
-0.911054397 2.289282458 2.356267002 3.955630191 2.777709902 1.826552135
145 146 147 148 149 150
1.983684353 4.519825832 -0.293493911 0.612337637 -6.794205500 -2.941637777
151 152 153 154 155 156
3.708656094 -0.436186920 -3.071151875 -0.089451943 3.231472183 -0.844552355
157 158 159
1.425216916 0.700576946 -6.829018712
> postscript(file="/var/www/html/rcomp/tmp/6y8ts1291054671.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 1.122449042 NA
1 2.376786160 1.122449042
2 5.446849934 2.376786160
3 -1.256959668 5.446849934
4 1.253906133 -1.256959668
5 -1.776183883 1.253906133
6 2.379578033 -1.776183883
7 1.985603776 2.379578033
8 -2.899654573 1.985603776
9 -1.235890043 -2.899654573
10 -3.138811389 -1.235890043
11 -4.226418603 -3.138811389
12 -8.005238677 -4.226418603
13 -1.215514318 -8.005238677
14 3.144281018 -1.215514318
15 3.348338142 3.144281018
16 0.756076751 3.348338142
17 -3.370321330 0.756076751
18 -2.072767589 -3.370321330
19 -2.475072261 -2.072767589
20 0.192887394 -2.475072261
21 -4.153559748 0.192887394
22 -2.775613406 -4.153559748
23 -5.643648689 -2.775613406
24 -2.742382469 -5.643648689
25 0.639055087 -2.742382469
26 1.475167523 0.639055087
27 -4.208094533 1.475167523
28 0.160017517 -4.208094533
29 -0.134153806 0.160017517
30 -0.234037104 -0.134153806
31 1.278175229 -0.234037104
32 5.398217613 1.278175229
33 6.238007165 5.398217613
34 4.375145159 6.238007165
35 4.912979248 4.375145159
36 3.409047078 4.912979248
37 0.143169173 3.409047078
38 1.524850014 0.143169173
39 6.276037083 1.524850014
40 -1.432740455 6.276037083
41 0.888636454 -1.432740455
42 -5.571707033 0.888636454
43 1.838075842 -5.571707033
44 3.240574597 1.838075842
45 -0.795756002 3.240574597
46 -2.500914341 -0.795756002
47 7.674510088 -2.500914341
48 0.428881181 7.674510088
49 3.562487604 0.428881181
50 1.868230238 3.562487604
51 -0.884271979 1.868230238
52 -3.957039621 -0.884271979
53 -2.130386979 -3.957039621
54 3.012786836 -2.130386979
55 -2.553262156 3.012786836
56 0.727033681 -2.553262156
57 1.927184892 0.727033681
58 1.599851522 1.927184892
59 -1.181086386 1.599851522
60 2.286652103 -1.181086386
61 1.347715219 2.286652103
62 0.585313050 1.347715219
63 4.215279012 0.585313050
64 0.748520788 4.215279012
65 3.686212582 0.748520788
66 -4.662821270 3.686212582
67 4.344336746 -4.662821270
68 0.463549721 4.344336746
69 2.255496815 0.463549721
70 -3.995964406 2.255496815
71 -0.042429374 -3.995964406
72 0.098530578 -0.042429374
73 -1.181402257 0.098530578
74 -0.739476703 -1.181402257
75 -1.924663660 -0.739476703
76 2.087944653 -1.924663660
77 -0.926178698 2.087944653
78 0.077935958 -0.926178698
79 -0.159532577 0.077935958
80 0.666994102 -0.159532577
81 -7.252406783 0.666994102
82 -1.359158848 -7.252406783
83 1.591680390 -1.359158848
84 -2.684076449 1.591680390
85 -2.739050037 -2.684076449
86 3.451707551 -2.739050037
87 2.624097369 3.451707551
88 2.565518166 2.624097369
89 -4.319427129 2.565518166
90 -6.177503307 -4.319427129
91 -2.252778962 -6.177503307
92 -3.515250749 -2.252778962
93 -0.252830115 -3.515250749
94 -1.500391774 -0.252830115
95 3.968024307 -1.500391774
96 -3.021672392 3.968024307
97 -3.099647949 -3.021672392
98 -3.308751554 -3.099647949
99 -2.873921799 -3.308751554
100 0.357059296 -2.873921799
101 -1.969636274 0.357059296
102 -0.753569469 -1.969636274
103 -2.385477302 -0.753569469
104 -4.698306015 -2.385477302
105 -2.076754462 -4.698306015
106 -1.226327049 -2.076754462
107 -1.278231722 -1.226327049
108 0.583625598 -1.278231722
109 -3.319261055 0.583625598
110 -4.833843326 -3.319261055
111 -8.263762630 -4.833843326
112 2.641807071 -8.263762630
113 11.009891887 2.641807071
114 9.857983302 11.009891887
115 -1.816944816 9.857983302
116 3.782601682 -1.816944816
117 1.247089503 3.782601682
118 0.005668742 1.247089503
119 -3.191573615 0.005668742
120 3.527488269 -3.191573615
121 0.529147617 3.527488269
122 4.824675882 0.529147617
123 -0.259037070 4.824675882
124 1.212780160 -0.259037070
125 -1.801291085 1.212780160
126 1.346519945 -1.801291085
127 0.343780943 1.346519945
128 -2.643762581 0.343780943
129 0.233240532 -2.643762581
130 4.507333706 0.233240532
131 -3.565805423 4.507333706
132 1.587794868 -3.565805423
133 -2.263888021 1.587794868
134 -6.316491005 -2.263888021
135 2.594622096 -6.316491005
136 0.400355042 2.594622096
137 3.784476038 0.400355042
138 -0.911054397 3.784476038
139 2.289282458 -0.911054397
140 2.356267002 2.289282458
141 3.955630191 2.356267002
142 2.777709902 3.955630191
143 1.826552135 2.777709902
144 1.983684353 1.826552135
145 4.519825832 1.983684353
146 -0.293493911 4.519825832
147 0.612337637 -0.293493911
148 -6.794205500 0.612337637
149 -2.941637777 -6.794205500
150 3.708656094 -2.941637777
151 -0.436186920 3.708656094
152 -3.071151875 -0.436186920
153 -0.089451943 -3.071151875
154 3.231472183 -0.089451943
155 -0.844552355 3.231472183
156 1.425216916 -0.844552355
157 0.700576946 1.425216916
158 -6.829018712 0.700576946
159 NA -6.829018712
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.376786160 1.122449042
[2,] 5.446849934 2.376786160
[3,] -1.256959668 5.446849934
[4,] 1.253906133 -1.256959668
[5,] -1.776183883 1.253906133
[6,] 2.379578033 -1.776183883
[7,] 1.985603776 2.379578033
[8,] -2.899654573 1.985603776
[9,] -1.235890043 -2.899654573
[10,] -3.138811389 -1.235890043
[11,] -4.226418603 -3.138811389
[12,] -8.005238677 -4.226418603
[13,] -1.215514318 -8.005238677
[14,] 3.144281018 -1.215514318
[15,] 3.348338142 3.144281018
[16,] 0.756076751 3.348338142
[17,] -3.370321330 0.756076751
[18,] -2.072767589 -3.370321330
[19,] -2.475072261 -2.072767589
[20,] 0.192887394 -2.475072261
[21,] -4.153559748 0.192887394
[22,] -2.775613406 -4.153559748
[23,] -5.643648689 -2.775613406
[24,] -2.742382469 -5.643648689
[25,] 0.639055087 -2.742382469
[26,] 1.475167523 0.639055087
[27,] -4.208094533 1.475167523
[28,] 0.160017517 -4.208094533
[29,] -0.134153806 0.160017517
[30,] -0.234037104 -0.134153806
[31,] 1.278175229 -0.234037104
[32,] 5.398217613 1.278175229
[33,] 6.238007165 5.398217613
[34,] 4.375145159 6.238007165
[35,] 4.912979248 4.375145159
[36,] 3.409047078 4.912979248
[37,] 0.143169173 3.409047078
[38,] 1.524850014 0.143169173
[39,] 6.276037083 1.524850014
[40,] -1.432740455 6.276037083
[41,] 0.888636454 -1.432740455
[42,] -5.571707033 0.888636454
[43,] 1.838075842 -5.571707033
[44,] 3.240574597 1.838075842
[45,] -0.795756002 3.240574597
[46,] -2.500914341 -0.795756002
[47,] 7.674510088 -2.500914341
[48,] 0.428881181 7.674510088
[49,] 3.562487604 0.428881181
[50,] 1.868230238 3.562487604
[51,] -0.884271979 1.868230238
[52,] -3.957039621 -0.884271979
[53,] -2.130386979 -3.957039621
[54,] 3.012786836 -2.130386979
[55,] -2.553262156 3.012786836
[56,] 0.727033681 -2.553262156
[57,] 1.927184892 0.727033681
[58,] 1.599851522 1.927184892
[59,] -1.181086386 1.599851522
[60,] 2.286652103 -1.181086386
[61,] 1.347715219 2.286652103
[62,] 0.585313050 1.347715219
[63,] 4.215279012 0.585313050
[64,] 0.748520788 4.215279012
[65,] 3.686212582 0.748520788
[66,] -4.662821270 3.686212582
[67,] 4.344336746 -4.662821270
[68,] 0.463549721 4.344336746
[69,] 2.255496815 0.463549721
[70,] -3.995964406 2.255496815
[71,] -0.042429374 -3.995964406
[72,] 0.098530578 -0.042429374
[73,] -1.181402257 0.098530578
[74,] -0.739476703 -1.181402257
[75,] -1.924663660 -0.739476703
[76,] 2.087944653 -1.924663660
[77,] -0.926178698 2.087944653
[78,] 0.077935958 -0.926178698
[79,] -0.159532577 0.077935958
[80,] 0.666994102 -0.159532577
[81,] -7.252406783 0.666994102
[82,] -1.359158848 -7.252406783
[83,] 1.591680390 -1.359158848
[84,] -2.684076449 1.591680390
[85,] -2.739050037 -2.684076449
[86,] 3.451707551 -2.739050037
[87,] 2.624097369 3.451707551
[88,] 2.565518166 2.624097369
[89,] -4.319427129 2.565518166
[90,] -6.177503307 -4.319427129
[91,] -2.252778962 -6.177503307
[92,] -3.515250749 -2.252778962
[93,] -0.252830115 -3.515250749
[94,] -1.500391774 -0.252830115
[95,] 3.968024307 -1.500391774
[96,] -3.021672392 3.968024307
[97,] -3.099647949 -3.021672392
[98,] -3.308751554 -3.099647949
[99,] -2.873921799 -3.308751554
[100,] 0.357059296 -2.873921799
[101,] -1.969636274 0.357059296
[102,] -0.753569469 -1.969636274
[103,] -2.385477302 -0.753569469
[104,] -4.698306015 -2.385477302
[105,] -2.076754462 -4.698306015
[106,] -1.226327049 -2.076754462
[107,] -1.278231722 -1.226327049
[108,] 0.583625598 -1.278231722
[109,] -3.319261055 0.583625598
[110,] -4.833843326 -3.319261055
[111,] -8.263762630 -4.833843326
[112,] 2.641807071 -8.263762630
[113,] 11.009891887 2.641807071
[114,] 9.857983302 11.009891887
[115,] -1.816944816 9.857983302
[116,] 3.782601682 -1.816944816
[117,] 1.247089503 3.782601682
[118,] 0.005668742 1.247089503
[119,] -3.191573615 0.005668742
[120,] 3.527488269 -3.191573615
[121,] 0.529147617 3.527488269
[122,] 4.824675882 0.529147617
[123,] -0.259037070 4.824675882
[124,] 1.212780160 -0.259037070
[125,] -1.801291085 1.212780160
[126,] 1.346519945 -1.801291085
[127,] 0.343780943 1.346519945
[128,] -2.643762581 0.343780943
[129,] 0.233240532 -2.643762581
[130,] 4.507333706 0.233240532
[131,] -3.565805423 4.507333706
[132,] 1.587794868 -3.565805423
[133,] -2.263888021 1.587794868
[134,] -6.316491005 -2.263888021
[135,] 2.594622096 -6.316491005
[136,] 0.400355042 2.594622096
[137,] 3.784476038 0.400355042
[138,] -0.911054397 3.784476038
[139,] 2.289282458 -0.911054397
[140,] 2.356267002 2.289282458
[141,] 3.955630191 2.356267002
[142,] 2.777709902 3.955630191
[143,] 1.826552135 2.777709902
[144,] 1.983684353 1.826552135
[145,] 4.519825832 1.983684353
[146,] -0.293493911 4.519825832
[147,] 0.612337637 -0.293493911
[148,] -6.794205500 0.612337637
[149,] -2.941637777 -6.794205500
[150,] 3.708656094 -2.941637777
[151,] -0.436186920 3.708656094
[152,] -3.071151875 -0.436186920
[153,] -0.089451943 -3.071151875
[154,] 3.231472183 -0.089451943
[155,] -0.844552355 3.231472183
[156,] 1.425216916 -0.844552355
[157,] 0.700576946 1.425216916
[158,] -6.829018712 0.700576946
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.376786160 1.122449042
2 5.446849934 2.376786160
3 -1.256959668 5.446849934
4 1.253906133 -1.256959668
5 -1.776183883 1.253906133
6 2.379578033 -1.776183883
7 1.985603776 2.379578033
8 -2.899654573 1.985603776
9 -1.235890043 -2.899654573
10 -3.138811389 -1.235890043
11 -4.226418603 -3.138811389
12 -8.005238677 -4.226418603
13 -1.215514318 -8.005238677
14 3.144281018 -1.215514318
15 3.348338142 3.144281018
16 0.756076751 3.348338142
17 -3.370321330 0.756076751
18 -2.072767589 -3.370321330
19 -2.475072261 -2.072767589
20 0.192887394 -2.475072261
21 -4.153559748 0.192887394
22 -2.775613406 -4.153559748
23 -5.643648689 -2.775613406
24 -2.742382469 -5.643648689
25 0.639055087 -2.742382469
26 1.475167523 0.639055087
27 -4.208094533 1.475167523
28 0.160017517 -4.208094533
29 -0.134153806 0.160017517
30 -0.234037104 -0.134153806
31 1.278175229 -0.234037104
32 5.398217613 1.278175229
33 6.238007165 5.398217613
34 4.375145159 6.238007165
35 4.912979248 4.375145159
36 3.409047078 4.912979248
37 0.143169173 3.409047078
38 1.524850014 0.143169173
39 6.276037083 1.524850014
40 -1.432740455 6.276037083
41 0.888636454 -1.432740455
42 -5.571707033 0.888636454
43 1.838075842 -5.571707033
44 3.240574597 1.838075842
45 -0.795756002 3.240574597
46 -2.500914341 -0.795756002
47 7.674510088 -2.500914341
48 0.428881181 7.674510088
49 3.562487604 0.428881181
50 1.868230238 3.562487604
51 -0.884271979 1.868230238
52 -3.957039621 -0.884271979
53 -2.130386979 -3.957039621
54 3.012786836 -2.130386979
55 -2.553262156 3.012786836
56 0.727033681 -2.553262156
57 1.927184892 0.727033681
58 1.599851522 1.927184892
59 -1.181086386 1.599851522
60 2.286652103 -1.181086386
61 1.347715219 2.286652103
62 0.585313050 1.347715219
63 4.215279012 0.585313050
64 0.748520788 4.215279012
65 3.686212582 0.748520788
66 -4.662821270 3.686212582
67 4.344336746 -4.662821270
68 0.463549721 4.344336746
69 2.255496815 0.463549721
70 -3.995964406 2.255496815
71 -0.042429374 -3.995964406
72 0.098530578 -0.042429374
73 -1.181402257 0.098530578
74 -0.739476703 -1.181402257
75 -1.924663660 -0.739476703
76 2.087944653 -1.924663660
77 -0.926178698 2.087944653
78 0.077935958 -0.926178698
79 -0.159532577 0.077935958
80 0.666994102 -0.159532577
81 -7.252406783 0.666994102
82 -1.359158848 -7.252406783
83 1.591680390 -1.359158848
84 -2.684076449 1.591680390
85 -2.739050037 -2.684076449
86 3.451707551 -2.739050037
87 2.624097369 3.451707551
88 2.565518166 2.624097369
89 -4.319427129 2.565518166
90 -6.177503307 -4.319427129
91 -2.252778962 -6.177503307
92 -3.515250749 -2.252778962
93 -0.252830115 -3.515250749
94 -1.500391774 -0.252830115
95 3.968024307 -1.500391774
96 -3.021672392 3.968024307
97 -3.099647949 -3.021672392
98 -3.308751554 -3.099647949
99 -2.873921799 -3.308751554
100 0.357059296 -2.873921799
101 -1.969636274 0.357059296
102 -0.753569469 -1.969636274
103 -2.385477302 -0.753569469
104 -4.698306015 -2.385477302
105 -2.076754462 -4.698306015
106 -1.226327049 -2.076754462
107 -1.278231722 -1.226327049
108 0.583625598 -1.278231722
109 -3.319261055 0.583625598
110 -4.833843326 -3.319261055
111 -8.263762630 -4.833843326
112 2.641807071 -8.263762630
113 11.009891887 2.641807071
114 9.857983302 11.009891887
115 -1.816944816 9.857983302
116 3.782601682 -1.816944816
117 1.247089503 3.782601682
118 0.005668742 1.247089503
119 -3.191573615 0.005668742
120 3.527488269 -3.191573615
121 0.529147617 3.527488269
122 4.824675882 0.529147617
123 -0.259037070 4.824675882
124 1.212780160 -0.259037070
125 -1.801291085 1.212780160
126 1.346519945 -1.801291085
127 0.343780943 1.346519945
128 -2.643762581 0.343780943
129 0.233240532 -2.643762581
130 4.507333706 0.233240532
131 -3.565805423 4.507333706
132 1.587794868 -3.565805423
133 -2.263888021 1.587794868
134 -6.316491005 -2.263888021
135 2.594622096 -6.316491005
136 0.400355042 2.594622096
137 3.784476038 0.400355042
138 -0.911054397 3.784476038
139 2.289282458 -0.911054397
140 2.356267002 2.289282458
141 3.955630191 2.356267002
142 2.777709902 3.955630191
143 1.826552135 2.777709902
144 1.983684353 1.826552135
145 4.519825832 1.983684353
146 -0.293493911 4.519825832
147 0.612337637 -0.293493911
148 -6.794205500 0.612337637
149 -2.941637777 -6.794205500
150 3.708656094 -2.941637777
151 -0.436186920 3.708656094
152 -3.071151875 -0.436186920
153 -0.089451943 -3.071151875
154 3.231472183 -0.089451943
155 -0.844552355 3.231472183
156 1.425216916 -0.844552355
157 0.700576946 1.425216916
158 -6.829018712 0.700576946
> 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/7qzsc1291054671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8qzsc1291054671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9qzsc1291054671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1018ax1291054671.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11n98l1291054671.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/12q9691291054671.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/13m1m01291054671.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/1472lo1291054671.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/15tk1u1291054671.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/16pch21291054671.tab")
+ }
>
> try(system("convert tmp/1c7cm1291054671.ps tmp/1c7cm1291054671.png",intern=TRUE))
character(0)
> try(system("convert tmp/25zup1291054671.ps tmp/25zup1291054671.png",intern=TRUE))
character(0)
> try(system("convert tmp/35zup1291054671.ps tmp/35zup1291054671.png",intern=TRUE))
character(0)
> try(system("convert tmp/45zup1291054671.ps tmp/45zup1291054671.png",intern=TRUE))
character(0)
> try(system("convert tmp/55zup1291054671.ps tmp/55zup1291054671.png",intern=TRUE))
character(0)
> try(system("convert tmp/6y8ts1291054671.ps tmp/6y8ts1291054671.png",intern=TRUE))
character(0)
> try(system("convert tmp/7qzsc1291054671.ps tmp/7qzsc1291054671.png",intern=TRUE))
character(0)
> try(system("convert tmp/8qzsc1291054671.ps tmp/8qzsc1291054671.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qzsc1291054671.ps tmp/9qzsc1291054671.png",intern=TRUE))
character(0)
> try(system("convert tmp/1018ax1291054671.ps tmp/1018ax1291054671.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.269 1.748 9.419