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(13
+ ,26
+ ,9
+ ,15
+ ,6
+ ,25
+ ,25
+ ,16
+ ,20
+ ,9
+ ,15
+ ,6
+ ,25
+ ,24
+ ,19
+ ,21
+ ,9
+ ,14
+ ,13
+ ,19
+ ,21
+ ,15
+ ,31
+ ,14
+ ,10
+ ,8
+ ,18
+ ,23
+ ,14
+ ,21
+ ,8
+ ,10
+ ,7
+ ,18
+ ,17
+ ,13
+ ,18
+ ,8
+ ,12
+ ,9
+ ,22
+ ,19
+ ,19
+ ,26
+ ,11
+ ,18
+ ,5
+ ,29
+ ,18
+ ,15
+ ,22
+ ,10
+ ,12
+ ,8
+ ,26
+ ,27
+ ,14
+ ,22
+ ,9
+ ,14
+ ,9
+ ,25
+ ,23
+ ,15
+ ,29
+ ,15
+ ,18
+ ,11
+ ,23
+ ,23
+ ,16
+ ,15
+ ,14
+ ,9
+ ,8
+ ,23
+ ,29
+ ,16
+ ,16
+ ,11
+ ,11
+ ,11
+ ,23
+ ,21
+ ,16
+ ,24
+ ,14
+ ,11
+ ,12
+ ,24
+ ,26
+ ,17
+ ,17
+ ,6
+ ,17
+ ,8
+ ,30
+ ,25
+ ,15
+ ,19
+ ,20
+ ,8
+ ,7
+ ,19
+ ,25
+ ,15
+ ,22
+ ,9
+ ,16
+ ,9
+ ,24
+ ,23
+ ,20
+ ,31
+ ,10
+ ,21
+ ,12
+ ,32
+ ,26
+ ,18
+ ,28
+ ,8
+ ,24
+ ,20
+ ,30
+ ,20
+ ,16
+ ,38
+ ,11
+ ,21
+ ,7
+ ,29
+ ,29
+ ,16
+ ,26
+ ,14
+ ,14
+ ,8
+ ,17
+ ,24
+ ,19
+ ,25
+ ,11
+ ,7
+ ,8
+ ,25
+ ,23
+ ,16
+ ,25
+ ,16
+ ,18
+ ,16
+ ,26
+ ,24
+ ,17
+ ,29
+ ,14
+ ,18
+ ,10
+ ,26
+ ,30
+ ,17
+ ,28
+ ,11
+ ,13
+ ,6
+ ,25
+ ,22
+ ,16
+ ,15
+ ,11
+ ,11
+ ,8
+ ,23
+ ,22
+ ,15
+ ,18
+ ,12
+ ,13
+ ,9
+ ,21
+ ,13
+ ,14
+ ,21
+ ,9
+ ,13
+ ,9
+ ,19
+ ,24
+ ,15
+ ,25
+ ,7
+ ,18
+ ,11
+ ,35
+ ,17
+ ,12
+ ,23
+ ,13
+ ,14
+ ,12
+ ,19
+ ,24
+ ,14
+ ,23
+ ,10
+ ,12
+ ,8
+ ,20
+ ,21
+ ,16
+ ,19
+ ,9
+ ,9
+ ,7
+ ,21
+ ,23
+ ,14
+ ,18
+ ,9
+ ,12
+ ,8
+ ,21
+ ,24
+ ,7
+ ,18
+ ,13
+ ,8
+ ,9
+ ,24
+ ,24
+ ,10
+ ,26
+ ,16
+ ,5
+ ,4
+ ,23
+ ,24
+ ,14
+ ,18
+ ,12
+ ,10
+ ,8
+ ,19
+ ,23
+ ,16
+ ,18
+ ,6
+ ,11
+ ,8
+ ,17
+ ,26
+ ,16
+ ,28
+ ,14
+ ,11
+ ,8
+ ,24
+ ,24
+ ,16
+ ,17
+ ,14
+ ,12
+ ,6
+ ,15
+ ,21
+ ,14
+ ,29
+ ,10
+ ,12
+ ,8
+ ,25
+ ,23
+ ,20
+ ,12
+ ,4
+ ,15
+ ,4
+ ,27
+ ,28
+ ,14
+ ,25
+ ,12
+ ,12
+ ,7
+ ,29
+ ,23
+ ,14
+ ,28
+ ,12
+ ,16
+ ,14
+ ,27
+ ,22
+ ,11
+ ,20
+ ,14
+ ,14
+ ,10
+ ,18
+ ,24
+ ,15
+ ,17
+ ,9
+ ,17
+ ,9
+ ,25
+ ,21
+ ,16
+ ,17
+ ,9
+ ,13
+ ,6
+ ,22
+ ,23
+ ,14
+ ,20
+ ,10
+ ,10
+ ,8
+ ,26
+ ,23
+ ,16
+ ,31
+ ,14
+ ,17
+ ,11
+ ,23
+ ,20
+ ,14
+ ,21
+ ,10
+ ,12
+ ,8
+ ,16
+ ,23
+ ,12
+ ,19
+ ,9
+ ,13
+ ,8
+ ,27
+ ,21
+ ,16
+ ,23
+ ,14
+ ,13
+ ,10
+ ,25
+ ,27
+ ,9
+ ,15
+ ,8
+ ,11
+ ,8
+ ,14
+ ,12
+ ,14
+ ,24
+ ,9
+ ,13
+ ,10
+ ,19
+ ,15
+ ,16
+ ,28
+ ,8
+ ,12
+ ,7
+ ,20
+ ,22
+ ,16
+ ,16
+ ,9
+ ,12
+ ,8
+ ,16
+ ,21
+ ,15
+ ,19
+ ,9
+ ,12
+ ,7
+ ,18
+ ,21
+ ,16
+ ,21
+ ,9
+ ,9
+ ,9
+ ,22
+ ,20
+ ,12
+ ,21
+ ,15
+ ,7
+ ,5
+ ,21
+ ,24
+ ,16
+ ,20
+ ,8
+ ,17
+ ,7
+ ,22
+ ,24
+ ,16
+ ,16
+ ,10
+ ,12
+ ,7
+ ,22
+ ,29
+ ,14
+ ,25
+ ,8
+ ,12
+ ,7
+ ,32
+ ,25
+ ,16
+ ,30
+ ,14
+ ,9
+ ,9
+ ,23
+ ,14
+ ,17
+ ,29
+ ,11
+ ,9
+ ,5
+ ,31
+ ,30
+ ,18
+ ,22
+ ,10
+ ,13
+ ,8
+ ,18
+ ,19
+ ,18
+ ,19
+ ,12
+ ,10
+ ,8
+ ,23
+ ,29
+ ,12
+ ,33
+ ,14
+ ,11
+ ,8
+ ,26
+ ,25
+ ,16
+ ,17
+ ,9
+ ,12
+ ,9
+ ,24
+ ,25
+ ,10
+ ,9
+ ,13
+ ,10
+ ,6
+ ,19
+ ,25
+ ,14
+ ,14
+ ,15
+ ,13
+ ,8
+ ,14
+ ,16
+ ,18
+ ,15
+ ,8
+ ,6
+ ,6
+ ,20
+ ,25
+ ,18
+ ,12
+ ,7
+ ,7
+ ,4
+ ,22
+ ,28
+ ,16
+ ,21
+ ,10
+ ,13
+ ,6
+ ,24
+ ,24
+ ,16
+ ,20
+ ,10
+ ,11
+ ,4
+ ,25
+ ,25
+ ,16
+ ,29
+ ,13
+ ,18
+ ,12
+ ,21
+ ,21
+ ,13
+ ,33
+ ,11
+ ,9
+ ,6
+ ,28
+ ,22
+ ,16
+ ,21
+ ,8
+ ,9
+ ,11
+ ,24
+ ,20
+ ,16
+ ,15
+ ,12
+ ,11
+ ,8
+ ,20
+ ,25
+ ,20
+ ,19
+ ,9
+ ,11
+ ,10
+ ,21
+ ,27
+ ,16
+ ,23
+ ,10
+ ,15
+ ,10
+ ,23
+ ,21
+ ,15
+ ,20
+ ,11
+ ,8
+ ,4
+ ,13
+ ,13
+ ,15
+ ,20
+ ,11
+ ,11
+ ,8
+ ,24
+ ,26
+ ,16
+ ,18
+ ,10
+ ,14
+ ,9
+ ,21
+ ,26
+ ,14
+ ,31
+ ,16
+ ,14
+ ,9
+ ,21
+ ,25
+ ,15
+ ,18
+ ,16
+ ,12
+ ,7
+ ,17
+ ,22
+ ,12
+ ,13
+ ,8
+ ,12
+ ,7
+ ,14
+ ,19
+ ,17
+ ,9
+ ,6
+ ,8
+ ,11
+ ,29
+ ,23
+ ,16
+ ,20
+ ,11
+ ,11
+ ,8
+ ,25
+ ,25
+ ,15
+ ,18
+ ,12
+ ,10
+ ,8
+ ,16
+ ,15
+ ,13
+ ,23
+ ,14
+ ,17
+ ,7
+ ,25
+ ,21
+ ,16
+ ,17
+ ,9
+ ,16
+ ,5
+ ,25
+ ,23
+ ,16
+ ,17
+ ,11
+ ,13
+ ,7
+ ,21
+ ,25
+ ,16
+ ,16
+ ,8
+ ,15
+ ,9
+ ,23
+ ,24
+ ,16
+ ,31
+ ,8
+ ,11
+ ,8
+ ,22
+ ,24
+ ,14
+ ,15
+ ,7
+ ,12
+ ,6
+ ,19
+ ,21
+ ,16
+ ,28
+ ,16
+ ,16
+ ,8
+ ,24
+ ,24
+ ,16
+ ,26
+ ,13
+ ,20
+ ,10
+ ,26
+ ,22
+ ,20
+ ,20
+ ,8
+ ,16
+ ,10
+ ,25
+ ,24
+ ,15
+ ,19
+ ,11
+ ,11
+ ,8
+ ,20
+ ,28
+ ,16
+ ,25
+ ,14
+ ,15
+ ,11
+ ,22
+ ,21
+ ,13
+ ,18
+ ,10
+ ,15
+ ,8
+ ,14
+ ,17
+ ,17
+ ,20
+ ,10
+ ,12
+ ,8
+ ,20
+ ,28
+ ,16
+ ,33
+ ,14
+ ,9
+ ,6
+ ,32
+ ,24
+ ,12
+ ,24
+ ,14
+ ,24
+ ,20
+ ,21
+ ,10
+ ,16
+ ,22
+ ,10
+ ,15
+ ,6
+ ,22
+ ,20
+ ,16
+ ,32
+ ,12
+ ,18
+ ,12
+ ,28
+ ,22
+ ,17
+ ,31
+ ,9
+ ,17
+ ,9
+ ,25
+ ,19
+ ,13
+ ,13
+ ,16
+ ,12
+ ,5
+ ,17
+ ,22
+ ,12
+ ,18
+ ,8
+ ,15
+ ,10
+ ,21
+ ,22
+ ,18
+ ,17
+ ,9
+ ,11
+ ,5
+ ,23
+ ,26
+ ,14
+ ,29
+ ,16
+ ,11
+ ,6
+ ,27
+ ,24
+ ,14
+ ,22
+ ,13
+ ,15
+ ,10
+ ,22
+ ,22
+ ,13
+ ,18
+ ,13
+ ,12
+ ,6
+ ,19
+ ,20
+ ,16
+ ,22
+ ,8
+ ,14
+ ,10
+ ,20
+ ,20
+ ,13
+ ,25
+ ,14
+ ,11
+ ,5
+ ,17
+ ,15
+ ,16
+ ,20
+ ,11
+ ,20
+ ,13
+ ,24
+ ,20
+ ,13
+ ,20
+ ,9
+ ,11
+ ,7
+ ,21
+ ,20
+ ,16
+ ,17
+ ,8
+ ,12
+ ,9
+ ,21
+ ,24
+ ,15
+ ,21
+ ,13
+ ,17
+ ,11
+ ,23
+ ,22
+ ,16
+ ,26
+ ,13
+ ,12
+ ,8
+ ,24
+ ,29
+ ,15
+ ,10
+ ,10
+ ,11
+ ,5
+ ,19
+ ,23
+ ,17
+ ,15
+ ,8
+ ,10
+ ,4
+ ,22
+ ,24
+ ,15
+ ,20
+ ,7
+ ,11
+ ,9
+ ,26
+ ,22
+ ,12
+ ,14
+ ,11
+ ,12
+ ,7
+ ,17
+ ,16
+ ,16
+ ,16
+ ,11
+ ,9
+ ,5
+ ,17
+ ,23
+ ,10
+ ,23
+ ,14
+ ,8
+ ,5
+ ,19
+ ,27
+ ,16
+ ,11
+ ,6
+ ,6
+ ,4
+ ,15
+ ,16
+ ,14
+ ,19
+ ,10
+ ,12
+ ,7
+ ,17
+ ,21
+ ,15
+ ,30
+ ,9
+ ,15
+ ,9
+ ,27
+ ,26
+ ,13
+ ,21
+ ,12
+ ,13
+ ,8
+ ,19
+ ,22
+ ,15
+ ,20
+ ,11
+ ,17
+ ,8
+ ,21
+ ,23
+ ,11
+ ,22
+ ,14
+ ,14
+ ,11
+ ,25
+ ,19
+ ,12
+ ,30
+ ,12
+ ,16
+ ,10
+ ,19
+ ,18
+ ,8
+ ,25
+ ,14
+ ,15
+ ,9
+ ,22
+ ,24
+ ,16
+ ,28
+ ,8
+ ,16
+ ,12
+ ,18
+ ,24
+ ,15
+ ,23
+ ,14
+ ,11
+ ,10
+ ,20
+ ,29
+ ,17
+ ,23
+ ,8
+ ,11
+ ,10
+ ,15
+ ,22
+ ,16
+ ,21
+ ,11
+ ,16
+ ,7
+ ,20
+ ,24
+ ,10
+ ,30
+ ,12
+ ,15
+ ,10
+ ,29
+ ,22
+ ,18
+ ,22
+ ,9
+ ,14
+ ,6
+ ,19
+ ,12
+ ,13
+ ,32
+ ,16
+ ,9
+ ,6
+ ,29
+ ,26
+ ,15
+ ,22
+ ,11
+ ,13
+ ,11
+ ,24
+ ,18
+ ,16
+ ,15
+ ,11
+ ,11
+ ,8
+ ,23
+ ,22
+ ,16
+ ,21
+ ,12
+ ,14
+ ,9
+ ,22
+ ,24
+ ,14
+ ,27
+ ,15
+ ,11
+ ,9
+ ,23
+ ,21
+ ,10
+ ,22
+ ,13
+ ,12
+ ,13
+ ,22
+ ,15
+ ,17
+ ,9
+ ,6
+ ,8
+ ,11
+ ,29
+ ,23
+ ,13
+ ,29
+ ,11
+ ,7
+ ,4
+ ,26
+ ,22
+ ,15
+ ,20
+ ,7
+ ,11
+ ,9
+ ,26
+ ,22
+ ,16
+ ,16
+ ,8
+ ,13
+ ,5
+ ,21
+ ,24
+ ,12
+ ,16
+ ,8
+ ,9
+ ,4
+ ,18
+ ,23
+ ,13
+ ,16
+ ,9
+ ,12
+ ,9
+ ,10
+ ,13)
+ ,dim=c(7
+ ,150)
+ ,dimnames=list(c('Confidence'
+ ,'Concern'
+ ,'Doubts'
+ ,'ParentalExpectations'
+ ,'ParentalCriticism'
+ ,'PersonalStandards'
+ ,'Organization')
+ ,1:150))
> y <- array(NA,dim=c(7,150),dimnames=list(c('Confidence','Concern','Doubts','ParentalExpectations','ParentalCriticism','PersonalStandards','Organization'),1:150))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Confidence Concern Doubts ParentalExpectations ParentalCriticism
1 13 26 9 15 6
2 16 20 9 15 6
3 19 21 9 14 13
4 15 31 14 10 8
5 14 21 8 10 7
6 13 18 8 12 9
7 19 26 11 18 5
8 15 22 10 12 8
9 14 22 9 14 9
10 15 29 15 18 11
11 16 15 14 9 8
12 16 16 11 11 11
13 16 24 14 11 12
14 17 17 6 17 8
15 15 19 20 8 7
16 15 22 9 16 9
17 20 31 10 21 12
18 18 28 8 24 20
19 16 38 11 21 7
20 16 26 14 14 8
21 19 25 11 7 8
22 16 25 16 18 16
23 17 29 14 18 10
24 17 28 11 13 6
25 16 15 11 11 8
26 15 18 12 13 9
27 14 21 9 13 9
28 15 25 7 18 11
29 12 23 13 14 12
30 14 23 10 12 8
31 16 19 9 9 7
32 14 18 9 12 8
33 7 18 13 8 9
34 10 26 16 5 4
35 14 18 12 10 8
36 16 18 6 11 8
37 16 28 14 11 8
38 16 17 14 12 6
39 14 29 10 12 8
40 20 12 4 15 4
41 14 25 12 12 7
42 14 28 12 16 14
43 11 20 14 14 10
44 15 17 9 17 9
45 16 17 9 13 6
46 14 20 10 10 8
47 16 31 14 17 11
48 14 21 10 12 8
49 12 19 9 13 8
50 16 23 14 13 10
51 9 15 8 11 8
52 14 24 9 13 10
53 16 28 8 12 7
54 16 16 9 12 8
55 15 19 9 12 7
56 16 21 9 9 9
57 12 21 15 7 5
58 16 20 8 17 7
59 16 16 10 12 7
60 14 25 8 12 7
61 16 30 14 9 9
62 17 29 11 9 5
63 18 22 10 13 8
64 18 19 12 10 8
65 12 33 14 11 8
66 16 17 9 12 9
67 10 9 13 10 6
68 14 14 15 13 8
69 18 15 8 6 6
70 18 12 7 7 4
71 16 21 10 13 6
72 16 20 10 11 4
73 16 29 13 18 12
74 13 33 11 9 6
75 16 21 8 9 11
76 16 15 12 11 8
77 20 19 9 11 10
78 16 23 10 15 10
79 15 20 11 8 4
80 15 20 11 11 8
81 16 18 10 14 9
82 14 31 16 14 9
83 15 18 16 12 7
84 12 13 8 12 7
85 17 9 6 8 11
86 16 20 11 11 8
87 15 18 12 10 8
88 13 23 14 17 7
89 16 17 9 16 5
90 16 17 11 13 7
91 16 16 8 15 9
92 16 31 8 11 8
93 14 15 7 12 6
94 16 28 16 16 8
95 16 26 13 20 10
96 20 20 8 16 10
97 15 19 11 11 8
98 16 25 14 15 11
99 13 18 10 15 8
100 17 20 10 12 8
101 16 33 14 9 6
102 12 24 14 24 20
103 16 22 10 15 6
104 16 32 12 18 12
105 17 31 9 17 9
106 13 13 16 12 5
107 12 18 8 15 10
108 18 17 9 11 5
109 14 29 16 11 6
110 14 22 13 15 10
111 13 18 13 12 6
112 16 22 8 14 10
113 13 25 14 11 5
114 16 20 11 20 13
115 13 20 9 11 7
116 16 17 8 12 9
117 15 21 13 17 11
118 16 26 13 12 8
119 15 10 10 11 5
120 17 15 8 10 4
121 15 20 7 11 9
122 12 14 11 12 7
123 16 16 11 9 5
124 10 23 14 8 5
125 16 11 6 6 4
126 14 19 10 12 7
127 15 30 9 15 9
128 13 21 12 13 8
129 15 20 11 17 8
130 11 22 14 14 11
131 12 30 12 16 10
132 8 25 14 15 9
133 16 28 8 16 12
134 15 23 14 11 10
135 17 23 8 11 10
136 16 21 11 16 7
137 10 30 12 15 10
138 18 22 9 14 6
139 13 32 16 9 6
140 15 22 11 13 11
141 16 15 11 11 8
142 16 21 12 14 9
143 14 27 15 11 9
144 10 22 13 12 13
145 17 9 6 8 11
146 13 29 11 7 4
147 15 20 7 11 9
148 16 16 8 13 5
149 12 16 8 9 4
150 13 16 9 12 9
PersonalStandards Organization
1 25 25
2 25 24
3 19 21
4 18 23
5 18 17
6 22 19
7 29 18
8 26 27
9 25 23
10 23 23
11 23 29
12 23 21
13 24 26
14 30 25
15 19 25
16 24 23
17 32 26
18 30 20
19 29 29
20 17 24
21 25 23
22 26 24
23 26 30
24 25 22
25 23 22
26 21 13
27 19 24
28 35 17
29 19 24
30 20 21
31 21 23
32 21 24
33 24 24
34 23 24
35 19 23
36 17 26
37 24 24
38 15 21
39 25 23
40 27 28
41 29 23
42 27 22
43 18 24
44 25 21
45 22 23
46 26 23
47 23 20
48 16 23
49 27 21
50 25 27
51 14 12
52 19 15
53 20 22
54 16 21
55 18 21
56 22 20
57 21 24
58 22 24
59 22 29
60 32 25
61 23 14
62 31 30
63 18 19
64 23 29
65 26 25
66 24 25
67 19 25
68 14 16
69 20 25
70 22 28
71 24 24
72 25 25
73 21 21
74 28 22
75 24 20
76 20 25
77 21 27
78 23 21
79 13 13
80 24 26
81 21 26
82 21 25
83 17 22
84 14 19
85 29 23
86 25 25
87 16 15
88 25 21
89 25 23
90 21 25
91 23 24
92 22 24
93 19 21
94 24 24
95 26 22
96 25 24
97 20 28
98 22 21
99 14 17
100 20 28
101 32 24
102 21 10
103 22 20
104 28 22
105 25 19
106 17 22
107 21 22
108 23 26
109 27 24
110 22 22
111 19 20
112 20 20
113 17 15
114 24 20
115 21 20
116 21 24
117 23 22
118 24 29
119 19 23
120 22 24
121 26 22
122 17 16
123 17 23
124 19 27
125 15 16
126 17 21
127 27 26
128 19 22
129 21 23
130 25 19
131 19 18
132 22 24
133 18 24
134 20 29
135 15 22
136 20 24
137 29 22
138 19 12
139 29 26
140 24 18
141 23 22
142 22 24
143 23 21
144 22 15
145 29 23
146 26 22
147 26 22
148 21 24
149 18 23
150 10 13
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Concern Doubts
12.325616 0.006185 -0.278785
ParentalExpectations ParentalCriticism PersonalStandards
0.103633 0.008670 0.027180
Organization
0.157966
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.1633 -1.1053 0.1497 1.3253 4.4789
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.325616 1.462481 8.428 3.43e-14 ***
Concern 0.006185 0.037772 0.164 0.87017
Doubts -0.278785 0.069179 -4.030 9.03e-05 ***
ParentalExpectations 0.103633 0.062960 1.646 0.10196
ParentalCriticism 0.008670 0.079066 0.110 0.91283
PersonalStandards 0.027180 0.049093 0.554 0.58069
Organization 0.157966 0.049734 3.176 0.00183 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.066 on 143 degrees of freedom
Multiple R-squared: 0.2072, Adjusted R-squared: 0.174
F-statistic: 6.23 on 6 and 143 DF, p-value: 7.82e-06
> 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,] 0.92916237 0.14167525 0.07083763
[2,] 0.87587095 0.24825811 0.12412905
[3,] 0.79967379 0.40065242 0.20032621
[4,] 0.70821724 0.58356552 0.29178276
[5,] 0.62904737 0.74190525 0.37095263
[6,] 0.54886434 0.90227132 0.45113566
[7,] 0.46461161 0.92922322 0.53538839
[8,] 0.44945728 0.89891456 0.55054272
[9,] 0.45454300 0.90908600 0.54545700
[10,] 0.36987399 0.73974798 0.63012601
[11,] 0.31656004 0.63312008 0.68343996
[12,] 0.46031512 0.92063023 0.53968488
[13,] 0.44377572 0.88755144 0.55622428
[14,] 0.38509652 0.77019305 0.61490348
[15,] 0.32686104 0.65372207 0.67313896
[16,] 0.26943345 0.53886689 0.73056655
[17,] 0.22973448 0.45946896 0.77026552
[18,] 0.18408338 0.36816676 0.81591662
[19,] 0.26561983 0.53123967 0.73438017
[20,] 0.36404281 0.72808562 0.63595719
[21,] 0.31463927 0.62927854 0.68536073
[22,] 0.27260253 0.54520506 0.72739747
[23,] 0.23264867 0.46529734 0.76735133
[24,] 0.92204214 0.15591572 0.07795786
[25,] 0.95190467 0.09619066 0.04809533
[26,] 0.93564395 0.12871211 0.06435605
[27,] 0.92131793 0.15736413 0.07868207
[28,] 0.90958768 0.18082464 0.09041232
[29,] 0.90369399 0.19261201 0.09630601
[30,] 0.88555045 0.22889909 0.11444955
[31,] 0.90363979 0.19272042 0.09636021
[32,] 0.88226655 0.23546689 0.11773345
[33,] 0.86398357 0.27203286 0.13601643
[34,] 0.92026208 0.15947584 0.07973792
[35,] 0.90533201 0.18933597 0.09466799
[36,] 0.88183947 0.23632105 0.11816053
[37,] 0.85832113 0.28335773 0.14167887
[38,] 0.83995631 0.32008738 0.16004369
[39,] 0.81360073 0.37279854 0.18639927
[40,] 0.85969521 0.28060959 0.14030479
[41,] 0.83703636 0.32592729 0.16296364
[42,] 0.92551654 0.14896692 0.07448346
[43,] 0.90706086 0.18587828 0.09293914
[44,] 0.88873847 0.22252306 0.11126153
[45,] 0.87334287 0.25331426 0.12665713
[46,] 0.84604971 0.30790058 0.15395029
[47,] 0.83617751 0.32764498 0.16382249
[48,] 0.81815811 0.36368378 0.18184189
[49,] 0.78457545 0.43084909 0.21542455
[50,] 0.74650479 0.50699043 0.25349521
[51,] 0.74768973 0.50462054 0.25231027
[52,] 0.80804694 0.38390613 0.19195306
[53,] 0.78501609 0.42996781 0.21498391
[54,] 0.83809998 0.32380005 0.16190002
[55,] 0.86127623 0.27744754 0.13872377
[56,] 0.87424047 0.25151906 0.12575953
[57,] 0.84826786 0.30346428 0.15173214
[58,] 0.92192779 0.15614442 0.07807221
[59,] 0.91060686 0.17878629 0.08939314
[60,] 0.92904363 0.14191275 0.07095637
[61,] 0.92758719 0.14482562 0.07241281
[62,] 0.91014053 0.17971895 0.08985947
[63,] 0.89005339 0.21989322 0.10994661
[64,] 0.87719330 0.24561339 0.12280670
[65,] 0.86805420 0.26389161 0.13194580
[66,] 0.84731564 0.30536873 0.15268436
[67,] 0.82822318 0.34355363 0.17177682
[68,] 0.90519289 0.18961423 0.09480711
[69,] 0.88635764 0.22728472 0.11364236
[70,] 0.89170253 0.21659494 0.10829747
[71,] 0.86758542 0.26482917 0.13241458
[72,] 0.84000274 0.31999453 0.15999726
[73,] 0.81302999 0.37394001 0.18697001
[74,] 0.81425290 0.37149420 0.18574710
[75,] 0.84318411 0.31363179 0.15681589
[76,] 0.81854951 0.36290099 0.18145049
[77,] 0.79090305 0.41819390 0.20909695
[78,] 0.79894194 0.40211612 0.20105806
[79,] 0.78252310 0.43495379 0.21747690
[80,] 0.74989209 0.50021581 0.25010791
[81,] 0.71376096 0.57247808 0.28623904
[82,] 0.67172291 0.65655417 0.32827709
[83,] 0.62671510 0.74656979 0.37328490
[84,] 0.61845754 0.76308492 0.38154246
[85,] 0.62100672 0.75798655 0.37899328
[86,] 0.57802797 0.84394405 0.42197203
[87,] 0.66516200 0.66967601 0.33483800
[88,] 0.61902895 0.76194210 0.38097105
[89,] 0.64826816 0.70346368 0.35173184
[90,] 0.61868524 0.76262951 0.38131476
[91,] 0.59820774 0.80358452 0.40179226
[92,] 0.61111675 0.77776649 0.38888325
[93,] 0.57777196 0.84445608 0.42222804
[94,] 0.53239737 0.93520525 0.46760263
[95,] 0.51112139 0.97775721 0.48887861
[96,] 0.50397991 0.99204019 0.49602009
[97,] 0.45017114 0.90034227 0.54982886
[98,] 0.58660369 0.82679262 0.41339631
[99,] 0.59000615 0.81998771 0.40999385
[100,] 0.58644988 0.82710023 0.41355012
[101,] 0.53551944 0.92896112 0.46448056
[102,] 0.48143838 0.96287676 0.51856162
[103,] 0.42951657 0.85903314 0.57048343
[104,] 0.39781760 0.79563519 0.60218240
[105,] 0.36025719 0.72051437 0.63974281
[106,] 0.34379479 0.68758958 0.65620521
[107,] 0.29025869 0.58051738 0.70974131
[108,] 0.26478682 0.52957363 0.73521318
[109,] 0.26751933 0.53503866 0.73248067
[110,] 0.21900938 0.43801877 0.78099062
[111,] 0.18792434 0.37584869 0.81207566
[112,] 0.15440735 0.30881470 0.84559265
[113,] 0.14053714 0.28107428 0.85946286
[114,] 0.13110620 0.26221239 0.86889380
[115,] 0.17743381 0.35486762 0.82256619
[116,] 0.14286271 0.28572543 0.85713729
[117,] 0.11046370 0.22092740 0.88953630
[118,] 0.08326754 0.16653507 0.91673246
[119,] 0.06299011 0.12598022 0.93700989
[120,] 0.04431208 0.08862416 0.95568792
[121,] 0.03631640 0.07263279 0.96368360
[122,] 0.02690943 0.05381887 0.97309057
[123,] 0.19617354 0.39234708 0.80382646
[124,] 0.14283935 0.28567871 0.85716065
[125,] 0.10327202 0.20654403 0.89672798
[126,] 0.31528380 0.63056759 0.68471620
[127,] 0.24258855 0.48517710 0.75741145
[128,] 0.58448193 0.83103615 0.41551807
[129,] 0.57540536 0.84918928 0.42459464
[130,] 0.43855980 0.87711961 0.56144020
[131,] 0.31320890 0.62641779 0.68679110
> postscript(file="/var/www/html/rcomp/tmp/1nlwr1290453222.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2nlwr1290453222.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3gcdc1290453222.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4gcdc1290453222.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5gcdc1290453222.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 = 150
Frequency = 1
1 2 3 4 5 6
-3.21251175 -0.01743734 3.65629533 1.15750535 -0.49689394 -2.12759783
7 8 9 10 11 12
4.03986969 -0.95854183 -1.79422013 0.45768669 1.27640002 1.46430772
13 14 15 16 17 18
1.42550629 -0.35371155 2.77725863 -0.97430515 3.01330377 1.09618016
19 20 21 22 23 24
-1.10020840 1.64311348 4.47889476 1.47835250 1.00027201 2.01385118
25 26 27 28 29 30
1.33853832 1.85888436 -1.67928698 -1.12622500 -2.70615968 -0.85385166
31 32 33 34 35 36
0.86855921 -1.60278994 -7.16332965 -2.99502191 -0.34684294 -0.54272334
37 38 39 40 41 42
1.75138019 2.45164013 -1.34279278 1.96923295 -0.86053353 -1.14198566
43 44 45 46 47 48
-3.36429888 -0.75826278 0.44788857 -1.10704456 1.74406130 -1.04869238
49 50 51 52 53 54
-3.40179187 1.05662096 -4.67353931 -0.28482139 0.40835874 1.01937760
55 56 57 58 59 60
-0.04486678 1.28556527 -1.40445841 -0.43061763 -0.11997289 -2.37314635
61 62 63 64 65 66
3.54444166 1.00406115 3.41899218 2.59045773 -2.49186986 0.15521812
67 68 69 70 71 72
-4.31098478 1.44501446 2.64533051 1.77055073 0.48960855 0.53525371
73 74 75 76 77 78
1.26173741 -1.68408300 0.93507865 1.22496740 4.00342012 0.73636887
79 80 81 82 83 84
2.34668676 -0.35142833 0.14412837 -0.10559702 1.78202928 -2.86189111
85 86 87 88 89 90
0.94556046 0.77935706 1.99842258 -1.38410446 0.06412051 0.70803757
91 92 93 94 95 96
-0.24313435 0.11447483 -1.59620780 1.79078755 0.79650080 3.56546292
97 98 99 100 101 102
-0.55245385 1.85764996 -1.33888176 1.05894349 1.72762060 -1.38209117
103 104 105 106 107 108
0.96238138 0.61617062 1.47108124 -0.16970578 -3.89388276 2.16274724
109 110 111 112 113 114
0.23856614 -0.55187601 -0.78408518 0.52812210 0.40789802 0.62031937
115 116 117 118 119 120
-1.87099401 0.11593916 0.21119285 0.59150390 0.06744378 1.35174613
121 122 123 124 125 126
-0.89773775 -1.63936405 1.57074590 -4.21878224 1.68743176 -0.73890132
127 128 129 130 131 132
-1.47558841 -1.51832960 -0.41778657 -2.78577247 -2.27036917 -6.59890592
133 134 135 136 137 138
-0.31109487 0.08385610 1.65280506 0.55754634 -5.07040120 4.13249430
139 140 141 142 143 144
-0.94301468 0.66665028 1.33853832 0.97189543 0.52875680 -3.16123031
145 146 147 148 149 150
0.94556046 -1.38037717 -0.89773775 0.05317322 -3.28411955 -0.56248669
> postscript(file="/var/www/html/rcomp/tmp/69luf1290453222.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 = 150
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.21251175 NA
1 -0.01743734 -3.21251175
2 3.65629533 -0.01743734
3 1.15750535 3.65629533
4 -0.49689394 1.15750535
5 -2.12759783 -0.49689394
6 4.03986969 -2.12759783
7 -0.95854183 4.03986969
8 -1.79422013 -0.95854183
9 0.45768669 -1.79422013
10 1.27640002 0.45768669
11 1.46430772 1.27640002
12 1.42550629 1.46430772
13 -0.35371155 1.42550629
14 2.77725863 -0.35371155
15 -0.97430515 2.77725863
16 3.01330377 -0.97430515
17 1.09618016 3.01330377
18 -1.10020840 1.09618016
19 1.64311348 -1.10020840
20 4.47889476 1.64311348
21 1.47835250 4.47889476
22 1.00027201 1.47835250
23 2.01385118 1.00027201
24 1.33853832 2.01385118
25 1.85888436 1.33853832
26 -1.67928698 1.85888436
27 -1.12622500 -1.67928698
28 -2.70615968 -1.12622500
29 -0.85385166 -2.70615968
30 0.86855921 -0.85385166
31 -1.60278994 0.86855921
32 -7.16332965 -1.60278994
33 -2.99502191 -7.16332965
34 -0.34684294 -2.99502191
35 -0.54272334 -0.34684294
36 1.75138019 -0.54272334
37 2.45164013 1.75138019
38 -1.34279278 2.45164013
39 1.96923295 -1.34279278
40 -0.86053353 1.96923295
41 -1.14198566 -0.86053353
42 -3.36429888 -1.14198566
43 -0.75826278 -3.36429888
44 0.44788857 -0.75826278
45 -1.10704456 0.44788857
46 1.74406130 -1.10704456
47 -1.04869238 1.74406130
48 -3.40179187 -1.04869238
49 1.05662096 -3.40179187
50 -4.67353931 1.05662096
51 -0.28482139 -4.67353931
52 0.40835874 -0.28482139
53 1.01937760 0.40835874
54 -0.04486678 1.01937760
55 1.28556527 -0.04486678
56 -1.40445841 1.28556527
57 -0.43061763 -1.40445841
58 -0.11997289 -0.43061763
59 -2.37314635 -0.11997289
60 3.54444166 -2.37314635
61 1.00406115 3.54444166
62 3.41899218 1.00406115
63 2.59045773 3.41899218
64 -2.49186986 2.59045773
65 0.15521812 -2.49186986
66 -4.31098478 0.15521812
67 1.44501446 -4.31098478
68 2.64533051 1.44501446
69 1.77055073 2.64533051
70 0.48960855 1.77055073
71 0.53525371 0.48960855
72 1.26173741 0.53525371
73 -1.68408300 1.26173741
74 0.93507865 -1.68408300
75 1.22496740 0.93507865
76 4.00342012 1.22496740
77 0.73636887 4.00342012
78 2.34668676 0.73636887
79 -0.35142833 2.34668676
80 0.14412837 -0.35142833
81 -0.10559702 0.14412837
82 1.78202928 -0.10559702
83 -2.86189111 1.78202928
84 0.94556046 -2.86189111
85 0.77935706 0.94556046
86 1.99842258 0.77935706
87 -1.38410446 1.99842258
88 0.06412051 -1.38410446
89 0.70803757 0.06412051
90 -0.24313435 0.70803757
91 0.11447483 -0.24313435
92 -1.59620780 0.11447483
93 1.79078755 -1.59620780
94 0.79650080 1.79078755
95 3.56546292 0.79650080
96 -0.55245385 3.56546292
97 1.85764996 -0.55245385
98 -1.33888176 1.85764996
99 1.05894349 -1.33888176
100 1.72762060 1.05894349
101 -1.38209117 1.72762060
102 0.96238138 -1.38209117
103 0.61617062 0.96238138
104 1.47108124 0.61617062
105 -0.16970578 1.47108124
106 -3.89388276 -0.16970578
107 2.16274724 -3.89388276
108 0.23856614 2.16274724
109 -0.55187601 0.23856614
110 -0.78408518 -0.55187601
111 0.52812210 -0.78408518
112 0.40789802 0.52812210
113 0.62031937 0.40789802
114 -1.87099401 0.62031937
115 0.11593916 -1.87099401
116 0.21119285 0.11593916
117 0.59150390 0.21119285
118 0.06744378 0.59150390
119 1.35174613 0.06744378
120 -0.89773775 1.35174613
121 -1.63936405 -0.89773775
122 1.57074590 -1.63936405
123 -4.21878224 1.57074590
124 1.68743176 -4.21878224
125 -0.73890132 1.68743176
126 -1.47558841 -0.73890132
127 -1.51832960 -1.47558841
128 -0.41778657 -1.51832960
129 -2.78577247 -0.41778657
130 -2.27036917 -2.78577247
131 -6.59890592 -2.27036917
132 -0.31109487 -6.59890592
133 0.08385610 -0.31109487
134 1.65280506 0.08385610
135 0.55754634 1.65280506
136 -5.07040120 0.55754634
137 4.13249430 -5.07040120
138 -0.94301468 4.13249430
139 0.66665028 -0.94301468
140 1.33853832 0.66665028
141 0.97189543 1.33853832
142 0.52875680 0.97189543
143 -3.16123031 0.52875680
144 0.94556046 -3.16123031
145 -1.38037717 0.94556046
146 -0.89773775 -1.38037717
147 0.05317322 -0.89773775
148 -3.28411955 0.05317322
149 -0.56248669 -3.28411955
150 NA -0.56248669
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.01743734 -3.21251175
[2,] 3.65629533 -0.01743734
[3,] 1.15750535 3.65629533
[4,] -0.49689394 1.15750535
[5,] -2.12759783 -0.49689394
[6,] 4.03986969 -2.12759783
[7,] -0.95854183 4.03986969
[8,] -1.79422013 -0.95854183
[9,] 0.45768669 -1.79422013
[10,] 1.27640002 0.45768669
[11,] 1.46430772 1.27640002
[12,] 1.42550629 1.46430772
[13,] -0.35371155 1.42550629
[14,] 2.77725863 -0.35371155
[15,] -0.97430515 2.77725863
[16,] 3.01330377 -0.97430515
[17,] 1.09618016 3.01330377
[18,] -1.10020840 1.09618016
[19,] 1.64311348 -1.10020840
[20,] 4.47889476 1.64311348
[21,] 1.47835250 4.47889476
[22,] 1.00027201 1.47835250
[23,] 2.01385118 1.00027201
[24,] 1.33853832 2.01385118
[25,] 1.85888436 1.33853832
[26,] -1.67928698 1.85888436
[27,] -1.12622500 -1.67928698
[28,] -2.70615968 -1.12622500
[29,] -0.85385166 -2.70615968
[30,] 0.86855921 -0.85385166
[31,] -1.60278994 0.86855921
[32,] -7.16332965 -1.60278994
[33,] -2.99502191 -7.16332965
[34,] -0.34684294 -2.99502191
[35,] -0.54272334 -0.34684294
[36,] 1.75138019 -0.54272334
[37,] 2.45164013 1.75138019
[38,] -1.34279278 2.45164013
[39,] 1.96923295 -1.34279278
[40,] -0.86053353 1.96923295
[41,] -1.14198566 -0.86053353
[42,] -3.36429888 -1.14198566
[43,] -0.75826278 -3.36429888
[44,] 0.44788857 -0.75826278
[45,] -1.10704456 0.44788857
[46,] 1.74406130 -1.10704456
[47,] -1.04869238 1.74406130
[48,] -3.40179187 -1.04869238
[49,] 1.05662096 -3.40179187
[50,] -4.67353931 1.05662096
[51,] -0.28482139 -4.67353931
[52,] 0.40835874 -0.28482139
[53,] 1.01937760 0.40835874
[54,] -0.04486678 1.01937760
[55,] 1.28556527 -0.04486678
[56,] -1.40445841 1.28556527
[57,] -0.43061763 -1.40445841
[58,] -0.11997289 -0.43061763
[59,] -2.37314635 -0.11997289
[60,] 3.54444166 -2.37314635
[61,] 1.00406115 3.54444166
[62,] 3.41899218 1.00406115
[63,] 2.59045773 3.41899218
[64,] -2.49186986 2.59045773
[65,] 0.15521812 -2.49186986
[66,] -4.31098478 0.15521812
[67,] 1.44501446 -4.31098478
[68,] 2.64533051 1.44501446
[69,] 1.77055073 2.64533051
[70,] 0.48960855 1.77055073
[71,] 0.53525371 0.48960855
[72,] 1.26173741 0.53525371
[73,] -1.68408300 1.26173741
[74,] 0.93507865 -1.68408300
[75,] 1.22496740 0.93507865
[76,] 4.00342012 1.22496740
[77,] 0.73636887 4.00342012
[78,] 2.34668676 0.73636887
[79,] -0.35142833 2.34668676
[80,] 0.14412837 -0.35142833
[81,] -0.10559702 0.14412837
[82,] 1.78202928 -0.10559702
[83,] -2.86189111 1.78202928
[84,] 0.94556046 -2.86189111
[85,] 0.77935706 0.94556046
[86,] 1.99842258 0.77935706
[87,] -1.38410446 1.99842258
[88,] 0.06412051 -1.38410446
[89,] 0.70803757 0.06412051
[90,] -0.24313435 0.70803757
[91,] 0.11447483 -0.24313435
[92,] -1.59620780 0.11447483
[93,] 1.79078755 -1.59620780
[94,] 0.79650080 1.79078755
[95,] 3.56546292 0.79650080
[96,] -0.55245385 3.56546292
[97,] 1.85764996 -0.55245385
[98,] -1.33888176 1.85764996
[99,] 1.05894349 -1.33888176
[100,] 1.72762060 1.05894349
[101,] -1.38209117 1.72762060
[102,] 0.96238138 -1.38209117
[103,] 0.61617062 0.96238138
[104,] 1.47108124 0.61617062
[105,] -0.16970578 1.47108124
[106,] -3.89388276 -0.16970578
[107,] 2.16274724 -3.89388276
[108,] 0.23856614 2.16274724
[109,] -0.55187601 0.23856614
[110,] -0.78408518 -0.55187601
[111,] 0.52812210 -0.78408518
[112,] 0.40789802 0.52812210
[113,] 0.62031937 0.40789802
[114,] -1.87099401 0.62031937
[115,] 0.11593916 -1.87099401
[116,] 0.21119285 0.11593916
[117,] 0.59150390 0.21119285
[118,] 0.06744378 0.59150390
[119,] 1.35174613 0.06744378
[120,] -0.89773775 1.35174613
[121,] -1.63936405 -0.89773775
[122,] 1.57074590 -1.63936405
[123,] -4.21878224 1.57074590
[124,] 1.68743176 -4.21878224
[125,] -0.73890132 1.68743176
[126,] -1.47558841 -0.73890132
[127,] -1.51832960 -1.47558841
[128,] -0.41778657 -1.51832960
[129,] -2.78577247 -0.41778657
[130,] -2.27036917 -2.78577247
[131,] -6.59890592 -2.27036917
[132,] -0.31109487 -6.59890592
[133,] 0.08385610 -0.31109487
[134,] 1.65280506 0.08385610
[135,] 0.55754634 1.65280506
[136,] -5.07040120 0.55754634
[137,] 4.13249430 -5.07040120
[138,] -0.94301468 4.13249430
[139,] 0.66665028 -0.94301468
[140,] 1.33853832 0.66665028
[141,] 0.97189543 1.33853832
[142,] 0.52875680 0.97189543
[143,] -3.16123031 0.52875680
[144,] 0.94556046 -3.16123031
[145,] -1.38037717 0.94556046
[146,] -0.89773775 -1.38037717
[147,] 0.05317322 -0.89773775
[148,] -3.28411955 0.05317322
[149,] -0.56248669 -3.28411955
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.01743734 -3.21251175
2 3.65629533 -0.01743734
3 1.15750535 3.65629533
4 -0.49689394 1.15750535
5 -2.12759783 -0.49689394
6 4.03986969 -2.12759783
7 -0.95854183 4.03986969
8 -1.79422013 -0.95854183
9 0.45768669 -1.79422013
10 1.27640002 0.45768669
11 1.46430772 1.27640002
12 1.42550629 1.46430772
13 -0.35371155 1.42550629
14 2.77725863 -0.35371155
15 -0.97430515 2.77725863
16 3.01330377 -0.97430515
17 1.09618016 3.01330377
18 -1.10020840 1.09618016
19 1.64311348 -1.10020840
20 4.47889476 1.64311348
21 1.47835250 4.47889476
22 1.00027201 1.47835250
23 2.01385118 1.00027201
24 1.33853832 2.01385118
25 1.85888436 1.33853832
26 -1.67928698 1.85888436
27 -1.12622500 -1.67928698
28 -2.70615968 -1.12622500
29 -0.85385166 -2.70615968
30 0.86855921 -0.85385166
31 -1.60278994 0.86855921
32 -7.16332965 -1.60278994
33 -2.99502191 -7.16332965
34 -0.34684294 -2.99502191
35 -0.54272334 -0.34684294
36 1.75138019 -0.54272334
37 2.45164013 1.75138019
38 -1.34279278 2.45164013
39 1.96923295 -1.34279278
40 -0.86053353 1.96923295
41 -1.14198566 -0.86053353
42 -3.36429888 -1.14198566
43 -0.75826278 -3.36429888
44 0.44788857 -0.75826278
45 -1.10704456 0.44788857
46 1.74406130 -1.10704456
47 -1.04869238 1.74406130
48 -3.40179187 -1.04869238
49 1.05662096 -3.40179187
50 -4.67353931 1.05662096
51 -0.28482139 -4.67353931
52 0.40835874 -0.28482139
53 1.01937760 0.40835874
54 -0.04486678 1.01937760
55 1.28556527 -0.04486678
56 -1.40445841 1.28556527
57 -0.43061763 -1.40445841
58 -0.11997289 -0.43061763
59 -2.37314635 -0.11997289
60 3.54444166 -2.37314635
61 1.00406115 3.54444166
62 3.41899218 1.00406115
63 2.59045773 3.41899218
64 -2.49186986 2.59045773
65 0.15521812 -2.49186986
66 -4.31098478 0.15521812
67 1.44501446 -4.31098478
68 2.64533051 1.44501446
69 1.77055073 2.64533051
70 0.48960855 1.77055073
71 0.53525371 0.48960855
72 1.26173741 0.53525371
73 -1.68408300 1.26173741
74 0.93507865 -1.68408300
75 1.22496740 0.93507865
76 4.00342012 1.22496740
77 0.73636887 4.00342012
78 2.34668676 0.73636887
79 -0.35142833 2.34668676
80 0.14412837 -0.35142833
81 -0.10559702 0.14412837
82 1.78202928 -0.10559702
83 -2.86189111 1.78202928
84 0.94556046 -2.86189111
85 0.77935706 0.94556046
86 1.99842258 0.77935706
87 -1.38410446 1.99842258
88 0.06412051 -1.38410446
89 0.70803757 0.06412051
90 -0.24313435 0.70803757
91 0.11447483 -0.24313435
92 -1.59620780 0.11447483
93 1.79078755 -1.59620780
94 0.79650080 1.79078755
95 3.56546292 0.79650080
96 -0.55245385 3.56546292
97 1.85764996 -0.55245385
98 -1.33888176 1.85764996
99 1.05894349 -1.33888176
100 1.72762060 1.05894349
101 -1.38209117 1.72762060
102 0.96238138 -1.38209117
103 0.61617062 0.96238138
104 1.47108124 0.61617062
105 -0.16970578 1.47108124
106 -3.89388276 -0.16970578
107 2.16274724 -3.89388276
108 0.23856614 2.16274724
109 -0.55187601 0.23856614
110 -0.78408518 -0.55187601
111 0.52812210 -0.78408518
112 0.40789802 0.52812210
113 0.62031937 0.40789802
114 -1.87099401 0.62031937
115 0.11593916 -1.87099401
116 0.21119285 0.11593916
117 0.59150390 0.21119285
118 0.06744378 0.59150390
119 1.35174613 0.06744378
120 -0.89773775 1.35174613
121 -1.63936405 -0.89773775
122 1.57074590 -1.63936405
123 -4.21878224 1.57074590
124 1.68743176 -4.21878224
125 -0.73890132 1.68743176
126 -1.47558841 -0.73890132
127 -1.51832960 -1.47558841
128 -0.41778657 -1.51832960
129 -2.78577247 -0.41778657
130 -2.27036917 -2.78577247
131 -6.59890592 -2.27036917
132 -0.31109487 -6.59890592
133 0.08385610 -0.31109487
134 1.65280506 0.08385610
135 0.55754634 1.65280506
136 -5.07040120 0.55754634
137 4.13249430 -5.07040120
138 -0.94301468 4.13249430
139 0.66665028 -0.94301468
140 1.33853832 0.66665028
141 0.97189543 1.33853832
142 0.52875680 0.97189543
143 -3.16123031 0.52875680
144 0.94556046 -3.16123031
145 -1.38037717 0.94556046
146 -0.89773775 -1.38037717
147 0.05317322 -0.89773775
148 -3.28411955 0.05317322
149 -0.56248669 -3.28411955
> 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/79luf1290453222.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8jdbi1290453222.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jdbi1290453222.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10c4t31290453222.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ynr91290453222.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/12158x1290453222.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/13qo581290453222.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/141x4t1290453222.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/15fp2k1290453222.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/16tz0t1290453222.tab")
+ }
>
> try(system("convert tmp/1nlwr1290453222.ps tmp/1nlwr1290453222.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nlwr1290453222.ps tmp/2nlwr1290453222.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gcdc1290453222.ps tmp/3gcdc1290453222.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gcdc1290453222.ps tmp/4gcdc1290453222.png",intern=TRUE))
character(0)
> try(system("convert tmp/5gcdc1290453222.ps tmp/5gcdc1290453222.png",intern=TRUE))
character(0)
> try(system("convert tmp/69luf1290453222.ps tmp/69luf1290453222.png",intern=TRUE))
character(0)
> try(system("convert tmp/79luf1290453222.ps tmp/79luf1290453222.png",intern=TRUE))
character(0)
> try(system("convert tmp/8jdbi1290453222.ps tmp/8jdbi1290453222.png",intern=TRUE))
character(0)
> try(system("convert tmp/9jdbi1290453222.ps tmp/9jdbi1290453222.png",intern=TRUE))
character(0)
> try(system("convert tmp/10c4t31290453222.ps tmp/10c4t31290453222.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.965 1.732 8.580