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(22
+ ,27
+ ,5
+ ,26
+ ,49
+ ,35
+ ,23
+ ,36
+ ,4
+ ,25
+ ,45
+ ,34
+ ,27
+ ,25
+ ,4
+ ,17
+ ,54
+ ,13
+ ,19
+ ,27
+ ,3
+ ,37
+ ,36
+ ,35
+ ,15
+ ,25
+ ,3
+ ,35
+ ,36
+ ,28
+ ,29
+ ,44
+ ,3
+ ,15
+ ,53
+ ,32
+ ,25
+ ,50
+ ,4
+ ,27
+ ,46
+ ,35
+ ,25
+ ,41
+ ,4
+ ,36
+ ,42
+ ,36
+ ,21
+ ,48
+ ,5
+ ,25
+ ,41
+ ,27
+ ,22
+ ,43
+ ,4
+ ,30
+ ,45
+ ,29
+ ,22
+ ,47
+ ,2
+ ,27
+ ,47
+ ,27
+ ,24
+ ,41
+ ,3
+ ,33
+ ,42
+ ,28
+ ,22
+ ,44
+ ,2
+ ,29
+ ,45
+ ,29
+ ,23
+ ,47
+ ,5
+ ,30
+ ,40
+ ,28
+ ,19
+ ,40
+ ,3
+ ,25
+ ,45
+ ,30
+ ,19
+ ,46
+ ,3
+ ,23
+ ,40
+ ,25
+ ,21
+ ,28
+ ,3
+ ,26
+ ,42
+ ,15
+ ,20
+ ,56
+ ,3
+ ,24
+ ,45
+ ,33
+ ,23
+ ,49
+ ,4
+ ,35
+ ,47
+ ,31
+ ,11
+ ,25
+ ,4
+ ,39
+ ,31
+ ,37
+ ,21
+ ,41
+ ,4
+ ,23
+ ,46
+ ,37
+ ,19
+ ,26
+ ,3
+ ,32
+ ,34
+ ,34
+ ,21
+ ,50
+ ,5
+ ,29
+ ,43
+ ,32
+ ,23
+ ,47
+ ,4
+ ,26
+ ,45
+ ,21
+ ,19
+ ,52
+ ,2
+ ,21
+ ,42
+ ,25
+ ,22
+ ,37
+ ,5
+ ,35
+ ,51
+ ,32
+ ,19
+ ,41
+ ,3
+ ,23
+ ,44
+ ,28
+ ,23
+ ,45
+ ,4
+ ,21
+ ,47
+ ,22
+ ,29
+ ,26
+ ,4
+ ,28
+ ,47
+ ,25
+ ,27
+ ,3
+ ,30
+ ,41
+ ,26
+ ,18
+ ,52
+ ,4
+ ,21
+ ,44
+ ,34
+ ,30
+ ,46
+ ,2
+ ,29
+ ,51
+ ,34
+ ,26
+ ,58
+ ,3
+ ,28
+ ,46
+ ,36
+ ,20
+ ,54
+ ,5
+ ,19
+ ,47
+ ,36
+ ,22
+ ,29
+ ,3
+ ,26
+ ,46
+ ,26
+ ,20
+ ,50
+ ,3
+ ,33
+ ,38
+ ,26
+ ,21
+ ,43
+ ,2
+ ,34
+ ,50
+ ,34
+ ,18
+ ,30
+ ,3
+ ,33
+ ,48
+ ,33
+ ,21
+ ,47
+ ,2
+ ,40
+ ,36
+ ,31
+ ,27
+ ,45
+ ,3
+ ,24
+ ,51
+ ,33
+ ,48
+ ,1
+ ,35
+ ,35
+ ,22
+ ,18
+ ,48
+ ,3
+ ,35
+ ,49
+ ,29
+ ,24
+ ,26
+ ,4
+ ,32
+ ,38
+ ,24
+ ,24
+ ,46
+ ,5
+ ,20
+ ,47
+ ,37
+ ,17
+ ,3
+ ,35
+ ,36
+ ,32
+ ,22
+ ,50
+ ,3
+ ,35
+ ,47
+ ,23
+ ,21
+ ,25
+ ,4
+ ,21
+ ,46
+ ,29
+ ,23
+ ,47
+ ,2
+ ,33
+ ,43
+ ,35
+ ,19
+ ,47
+ ,2
+ ,40
+ ,53
+ ,20
+ ,22
+ ,41
+ ,3
+ ,22
+ ,55
+ ,28
+ ,19
+ ,45
+ ,2
+ ,35
+ ,39
+ ,26
+ ,24
+ ,41
+ ,4
+ ,20
+ ,55
+ ,36
+ ,22
+ ,45
+ ,5
+ ,28
+ ,41
+ ,26
+ ,26
+ ,40
+ ,3
+ ,46
+ ,33
+ ,33
+ ,22
+ ,29
+ ,4
+ ,18
+ ,52
+ ,25
+ ,23
+ ,34
+ ,5
+ ,22
+ ,42
+ ,29
+ ,27
+ ,45
+ ,5
+ ,20
+ ,56
+ ,32
+ ,21
+ ,52
+ ,3
+ ,25
+ ,46
+ ,35
+ ,16
+ ,41
+ ,4
+ ,31
+ ,33
+ ,24
+ ,21
+ ,48
+ ,3
+ ,21
+ ,51
+ ,31
+ ,18
+ ,45
+ ,3
+ ,23
+ ,46
+ ,29
+ ,25
+ ,54
+ ,2
+ ,26
+ ,46
+ ,27
+ ,20
+ ,25
+ ,3
+ ,34
+ ,50
+ ,29
+ ,24
+ ,26
+ ,4
+ ,31
+ ,46
+ ,29
+ ,20
+ ,28
+ ,4
+ ,23
+ ,51
+ ,27
+ ,24
+ ,50
+ ,4
+ ,31
+ ,48
+ ,34
+ ,23
+ ,48
+ ,4
+ ,26
+ ,44
+ ,32
+ ,23
+ ,51
+ ,3
+ ,36
+ ,38
+ ,31
+ ,22
+ ,53
+ ,3
+ ,28
+ ,42
+ ,31
+ ,22
+ ,37
+ ,3
+ ,34
+ ,39
+ ,31
+ ,20
+ ,56
+ ,2
+ ,25
+ ,45
+ ,16
+ ,14
+ ,43
+ ,3
+ ,33
+ ,31
+ ,25
+ ,21
+ ,34
+ ,3
+ ,46
+ ,29
+ ,27
+ ,23
+ ,42
+ ,3
+ ,24
+ ,48
+ ,32
+ ,17
+ ,32
+ ,3
+ ,32
+ ,38
+ ,28
+ ,25
+ ,31
+ ,5
+ ,33
+ ,55
+ ,25
+ ,10
+ ,46
+ ,3
+ ,42
+ ,32
+ ,25
+ ,25
+ ,30
+ ,5
+ ,17
+ ,51
+ ,36
+ ,23
+ ,47
+ ,4
+ ,36
+ ,53
+ ,36
+ ,27
+ ,33
+ ,4
+ ,40
+ ,47
+ ,36
+ ,16
+ ,25
+ ,4
+ ,30
+ ,45
+ ,27
+ ,19
+ ,25
+ ,5
+ ,19
+ ,33
+ ,29
+ ,23
+ ,21
+ ,4
+ ,33
+ ,49
+ ,32
+ ,19
+ ,36
+ ,5
+ ,35
+ ,46
+ ,29
+ ,19
+ ,50
+ ,3
+ ,23
+ ,42
+ ,31
+ ,26
+ ,48
+ ,3
+ ,15
+ ,56
+ ,34
+ ,19
+ ,48
+ ,2
+ ,38
+ ,35
+ ,27
+ ,22
+ ,25
+ ,3
+ ,37
+ ,40
+ ,28
+ ,21
+ ,48
+ ,4
+ ,23
+ ,44
+ ,32
+ ,22
+ ,49
+ ,5
+ ,41
+ ,46
+ ,33
+ ,20
+ ,27
+ ,5
+ ,34
+ ,46
+ ,29
+ ,20
+ ,28
+ ,3
+ ,38
+ ,39
+ ,32
+ ,20
+ ,43
+ ,2
+ ,45
+ ,35
+ ,35
+ ,21
+ ,48
+ ,3
+ ,27
+ ,48
+ ,33
+ ,21
+ ,48
+ ,4
+ ,46
+ ,42
+ ,27
+ ,14
+ ,25
+ ,1
+ ,26
+ ,39
+ ,16
+ ,28
+ ,49
+ ,4
+ ,44
+ ,39
+ ,32
+ ,24
+ ,26
+ ,3
+ ,36
+ ,41
+ ,26
+ ,24
+ ,51
+ ,3
+ ,20
+ ,52
+ ,32
+ ,24
+ ,25
+ ,4
+ ,44
+ ,45
+ ,38
+ ,19
+ ,29
+ ,3
+ ,27
+ ,42
+ ,24
+ ,19
+ ,29
+ ,4
+ ,27
+ ,44
+ ,26
+ ,14
+ ,43
+ ,2
+ ,41
+ ,33
+ ,19
+ ,29
+ ,46
+ ,3
+ ,30
+ ,42
+ ,37
+ ,22
+ ,44
+ ,3
+ ,33
+ ,46
+ ,25
+ ,21
+ ,25
+ ,3
+ ,37
+ ,45
+ ,24
+ ,15
+ ,51
+ ,2
+ ,30
+ ,40
+ ,23
+ ,23
+ ,42
+ ,5
+ ,20
+ ,48
+ ,28
+ ,24
+ ,53
+ ,5
+ ,44
+ ,32
+ ,38
+ ,20
+ ,25
+ ,4
+ ,20
+ ,53
+ ,28
+ ,25
+ ,49
+ ,2
+ ,33
+ ,39
+ ,28
+ ,25
+ ,51
+ ,3
+ ,31
+ ,45
+ ,26
+ ,19
+ ,20
+ ,3
+ ,23
+ ,36
+ ,21
+ ,23
+ ,44
+ ,3
+ ,33
+ ,38
+ ,35
+ ,22
+ ,38
+ ,4
+ ,33
+ ,49
+ ,31
+ ,19
+ ,46
+ ,5
+ ,32
+ ,46
+ ,34
+ ,24
+ ,42
+ ,4
+ ,25
+ ,43
+ ,30
+ ,21
+ ,29
+ ,22
+ ,37
+ ,30
+ ,19
+ ,46
+ ,4
+ ,16
+ ,48
+ ,24
+ ,21
+ ,49
+ ,2
+ ,36
+ ,45
+ ,27
+ ,18
+ ,51
+ ,3
+ ,35
+ ,32
+ ,26
+ ,24
+ ,38
+ ,3
+ ,25
+ ,46
+ ,30
+ ,7
+ ,41
+ ,1
+ ,27
+ ,20
+ ,15
+ ,24
+ ,47
+ ,3
+ ,32
+ ,42
+ ,28
+ ,24
+ ,44
+ ,3
+ ,36
+ ,45
+ ,34
+ ,23
+ ,47
+ ,3
+ ,51
+ ,29
+ ,29
+ ,24
+ ,46
+ ,3
+ ,30
+ ,51
+ ,26
+ ,27
+ ,44
+ ,4
+ ,20
+ ,55
+ ,31
+ ,20
+ ,28
+ ,3
+ ,29
+ ,50
+ ,28
+ ,20
+ ,47
+ ,4
+ ,26
+ ,44
+ ,33
+ ,22
+ ,28
+ ,4
+ ,20
+ ,41
+ ,32
+ ,19
+ ,41
+ ,5
+ ,40
+ ,40
+ ,33
+ ,18
+ ,45
+ ,4
+ ,29
+ ,47
+ ,31
+ ,14
+ ,46
+ ,4
+ ,32
+ ,42
+ ,37
+ ,24
+ ,46
+ ,4
+ ,33
+ ,40
+ ,27
+ ,29
+ ,22
+ ,3
+ ,32
+ ,51
+ ,19
+ ,25
+ ,33
+ ,3
+ ,34
+ ,43
+ ,27
+ ,24
+ ,41
+ ,4
+ ,24
+ ,45
+ ,31
+ ,20
+ ,47
+ ,5
+ ,25
+ ,41
+ ,38
+ ,18
+ ,25
+ ,3
+ ,41
+ ,41
+ ,22
+ ,25
+ ,42
+ ,3
+ ,39
+ ,37
+ ,35
+ ,21
+ ,47
+ ,3
+ ,21
+ ,46
+ ,35
+ ,21
+ ,50
+ ,3
+ ,38
+ ,38
+ ,30
+ ,21
+ ,55
+ ,5
+ ,28
+ ,39
+ ,41
+ ,23
+ ,21
+ ,3
+ ,37
+ ,45
+ ,25
+ ,18
+ ,3
+ ,26
+ ,46
+ ,28
+ ,23
+ ,52
+ ,3
+ ,30
+ ,39
+ ,45
+ ,13
+ ,49
+ ,4
+ ,25
+ ,21
+ ,21
+ ,23
+ ,46
+ ,4
+ ,38
+ ,31
+ ,33
+ ,17
+ ,4
+ ,31
+ ,35
+ ,25
+ ,24
+ ,45
+ ,3
+ ,31
+ ,49
+ ,29
+ ,16
+ ,52
+ ,3
+ ,27
+ ,40
+ ,31
+ ,23
+ ,3
+ ,21
+ ,45
+ ,29
+ ,20
+ ,40
+ ,4
+ ,26
+ ,46
+ ,31
+ ,24
+ ,49
+ ,4
+ ,37
+ ,45
+ ,31
+ ,15
+ ,38
+ ,5
+ ,28
+ ,34
+ ,25
+ ,20
+ ,32
+ ,5
+ ,29
+ ,41
+ ,27
+ ,27
+ ,46
+ ,4
+ ,33
+ ,43
+ ,26
+ ,27
+ ,32
+ ,3
+ ,41
+ ,45
+ ,26
+ ,19
+ ,41
+ ,3
+ ,19
+ ,48
+ ,23
+ ,22
+ ,43
+ ,3
+ ,37
+ ,43
+ ,27
+ ,16
+ ,44
+ ,4
+ ,36
+ ,45
+ ,24
+ ,21
+ ,47
+ ,5
+ ,27
+ ,45
+ ,35
+ ,18
+ ,28
+ ,3
+ ,33
+ ,34
+ ,24
+ ,22
+ ,52
+ ,1
+ ,29
+ ,40
+ ,32
+ ,18
+ ,27
+ ,2
+ ,42
+ ,40
+ ,24
+ ,24
+ ,45
+ ,5
+ ,27
+ ,55
+ ,24
+ ,24
+ ,27
+ ,4
+ ,47
+ ,44
+ ,38
+ ,19
+ ,25
+ ,4
+ ,17
+ ,44
+ ,36
+ ,26
+ ,28
+ ,4
+ ,34
+ ,48
+ ,24
+ ,28
+ ,25
+ ,3
+ ,32
+ ,51
+ ,18
+ ,23
+ ,52
+ ,4
+ ,25
+ ,49
+ ,34
+ ,22
+ ,44
+ ,3
+ ,27
+ ,33
+ ,23
+ ,20
+ ,43
+ ,3
+ ,37
+ ,43
+ ,35
+ ,20
+ ,47
+ ,4
+ ,34
+ ,44
+ ,22
+ ,27
+ ,52
+ ,4
+ ,27
+ ,44
+ ,34
+ ,19
+ ,40
+ ,2
+ ,37
+ ,41
+ ,28
+ ,23
+ ,42
+ ,3
+ ,32
+ ,45
+ ,34
+ ,19
+ ,45
+ ,5
+ ,26
+ ,44
+ ,32
+ ,21
+ ,45
+ ,2
+ ,29
+ ,44
+ ,24
+ ,13
+ ,50
+ ,5
+ ,28
+ ,40
+ ,34
+ ,18
+ ,49
+ ,3
+ ,19
+ ,48
+ ,33
+ ,19
+ ,52
+ ,2
+ ,46
+ ,49
+ ,33
+ ,23
+ ,48
+ ,3
+ ,31
+ ,46
+ ,29
+ ,30
+ ,51
+ ,3
+ ,42
+ ,49
+ ,38
+ ,22
+ ,49
+ ,4
+ ,33
+ ,55
+ ,24
+ ,23
+ ,31
+ ,4
+ ,39
+ ,51
+ ,25
+ ,22
+ ,43
+ ,3
+ ,27
+ ,46
+ ,37
+ ,22
+ ,31
+ ,3
+ ,35
+ ,37
+ ,33
+ ,23
+ ,28
+ ,4
+ ,23
+ ,43
+ ,30
+ ,27
+ ,43
+ ,4
+ ,32
+ ,41
+ ,22
+ ,23
+ ,31
+ ,3
+ ,22
+ ,45
+ ,28
+ ,18
+ ,51
+ ,3
+ ,17
+ ,39
+ ,24
+ ,24
+ ,58
+ ,4
+ ,35
+ ,38
+ ,33
+ ,19
+ ,25
+ ,5
+ ,34
+ ,41
+ ,37)
+ ,dim=c(6
+ ,195)
+ ,dimnames=list(c('Behoefte_affiliatie'
+ ,'leeftijd'
+ ,'opleiding'
+ ,'Neuroticisme'
+ ,'Extraversie'
+ ,'Openheid
')
+ ,1:195))
> y <- array(NA,dim=c(6,195),dimnames=list(c('Behoefte_affiliatie','leeftijd','opleiding','Neuroticisme','Extraversie','Openheid
'),1:195))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> ylab = ''
> xlab = ''
> main = ''
> #'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
Behoefte_affiliatie leeftijd opleiding Neuroticisme Extraversie Openheid\r
1 22 27 5 26 49 35
2 23 36 4 25 45 34
3 27 25 4 17 54 13
4 19 27 3 37 36 35
5 15 25 3 35 36 28
6 29 44 3 15 53 32
7 25 50 4 27 46 35
8 25 41 4 36 42 36
9 21 48 5 25 41 27
10 22 43 4 30 45 29
11 22 47 2 27 47 27
12 24 41 3 33 42 28
13 22 44 2 29 45 29
14 23 47 5 30 40 28
15 19 40 3 25 45 30
16 19 46 3 23 40 25
17 21 28 3 26 42 15
18 20 56 3 24 45 33
19 23 49 4 35 47 31
20 11 25 4 39 31 37
21 21 41 4 23 46 37
22 19 26 3 32 34 34
23 21 50 5 29 43 32
24 23 47 4 26 45 21
25 19 52 2 21 42 25
26 22 37 5 35 51 32
27 19 41 3 23 44 28
28 23 45 4 21 47 22
29 29 26 4 28 47 25
30 27 3 30 41 26 18
31 52 4 21 44 34 30
32 46 2 29 51 34 26
33 58 3 28 46 36 20
34 54 5 19 47 36 22
35 29 3 26 46 26 20
36 50 3 33 38 26 21
37 43 2 34 50 34 18
38 30 3 33 48 33 21
39 47 2 40 36 31 27
40 45 3 24 51 33 48
41 1 35 35 22 18 48
42 3 35 49 29 24 26
43 4 32 38 24 24 46
44 5 20 47 37 17 3
45 35 36 32 22 50 3
46 35 47 23 21 25 4
47 21 46 29 23 47 2
48 33 43 35 19 47 2
49 40 53 20 22 41 3
50 22 55 28 19 45 2
51 35 39 26 24 41 4
52 20 55 36 22 45 5
53 28 41 26 26 40 3
54 46 33 33 22 29 4
55 18 52 25 23 34 5
56 22 42 29 27 45 5
57 20 56 32 21 52 3
58 25 46 35 16 41 4
59 31 33 24 21 48 3
60 21 51 31 18 45 3
61 23 46 29 25 54 2
62 26 46 27 20 25 3
63 34 50 29 24 26 4
64 31 46 29 20 28 4
65 23 51 27 24 50 4
66 31 48 34 23 48 4
67 26 44 32 23 51 3
68 36 38 31 22 53 3
69 28 42 31 22 37 3
70 34 39 31 20 56 2
71 25 45 16 14 43 3
72 33 31 25 21 34 3
73 46 29 27 23 42 3
74 24 48 32 17 32 3
75 32 38 28 25 31 5
76 33 55 25 10 46 3
77 42 32 25 25 30 5
78 17 51 36 23 47 4
79 36 53 36 27 33 4
80 40 47 36 16 25 4
81 30 45 27 19 25 5
82 19 33 29 23 21 4
83 33 49 32 19 36 5
84 35 46 29 19 50 3
85 23 42 31 26 48 3
86 15 56 34 19 48 2
87 38 35 27 22 25 3
88 37 40 28 21 48 4
89 23 44 32 22 49 5
90 41 46 33 20 27 5
91 34 46 29 20 28 3
92 38 39 32 20 43 2
93 45 35 35 21 48 3
94 27 48 33 21 48 4
95 46 42 27 14 25 1
96 26 39 16 28 49 4
97 44 39 32 24 26 3
98 36 41 26 24 51 3
99 20 52 32 24 25 4
100 44 45 38 19 29 3
101 27 42 24 19 29 4
102 27 44 26 14 43 2
103 41 33 19 29 46 3
104 30 42 37 22 44 3
105 33 46 25 21 25 3
106 37 45 24 15 51 2
107 30 40 23 23 42 5
108 20 48 28 24 53 5
109 44 32 38 20 25 4
110 20 53 28 25 49 2
111 33 39 28 25 51 3
112 31 45 26 19 20 3
113 23 36 21 23 44 3
114 33 38 35 22 38 4
115 33 49 31 19 46 5
116 32 46 34 24 42 4
117 25 43 30 21 29 22
118 37 30 19 46 4 16
119 48 24 21 49 2 36
120 45 27 18 51 3 35
121 32 26 24 38 3 25
122 46 30 7 41 1 27
123 20 15 24 47 3 32
124 42 28 24 44 3 36
125 45 34 23 47 3 51
126 29 29 24 46 3 30
127 51 26 27 44 4 20
128 55 31 20 28 3 29
129 50 28 20 47 4 26
130 44 33 22 28 4 20
131 41 32 19 41 5 40
132 40 33 18 45 4 29
133 47 31 14 46 4 32
134 42 37 24 46 4 33
135 40 27 29 22 3 32
136 51 19 25 33 3 34
137 43 27 24 41 4 24
138 45 31 20 47 5 25
139 41 38 18 25 3 41
140 41 22 25 42 3 39
141 37 35 21 47 3 21
142 46 35 21 50 3 38
143 38 30 21 55 5 28
144 39 41 23 21 3 37
145 45 25 18 3 26 46
146 28 23 52 3 30 39
147 45 13 49 4 25 21
148 21 23 46 4 38 31
149 33 17 4 31 35 25
150 24 45 3 31 49 29
151 16 52 3 27 40 31
152 23 3 21 45 29 20
153 40 4 26 46 31 24
154 49 4 37 45 31 15
155 38 5 28 34 25 20
156 32 5 29 41 27 27
157 46 4 33 43 26 27
158 32 3 41 45 26 19
159 41 3 19 48 23 22
160 43 3 37 43 27 16
161 44 4 36 45 24 21
162 47 5 27 45 35 18
163 28 3 33 34 24 22
164 52 1 29 40 32 18
165 27 2 42 40 24 24
166 45 5 27 55 24 24
167 27 4 47 44 38 19
168 25 4 17 44 36 26
169 28 4 34 48 24 28
170 25 3 32 51 18 23
171 52 4 25 49 34 22
172 44 3 27 33 23 20
173 43 3 37 43 35 20
174 47 4 34 44 22 27
175 52 4 27 44 34 19
176 40 2 37 41 28 23
177 42 3 32 45 34 19
178 45 5 26 44 32 21
179 45 2 29 44 24 13
180 50 5 28 40 34 18
181 49 3 19 48 33 19
182 52 2 46 49 33 23
183 48 3 31 46 29 30
184 51 3 42 49 38 22
185 49 4 33 55 24 23
186 31 4 39 51 25 22
187 43 3 27 46 37 22
188 31 3 35 37 33 23
189 28 4 23 43 30 27
190 43 4 32 41 22 23
191 31 3 22 45 28 18
192 51 3 17 39 24 24
193 58 4 35 38 33 19
194 25 5 34 41 37 22
195 27 5 26 49 35 23
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) leeftijd opleiding Neuroticisme Extraversie
59.83314 -0.32187 -0.08942 0.01061 -0.30954
`Openheid\r`
-0.30532
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-38.407 -4.929 0.268 7.274 18.398
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 59.83314 7.06459 8.469 6.81e-15 ***
leeftijd -0.32187 0.06258 -5.143 6.73e-07 ***
opleiding -0.08942 0.07822 -1.143 0.254
Neuroticisme 0.01061 0.09117 0.116 0.908
Extraversie -0.30954 0.05836 -5.304 3.14e-07 ***
`Openheid\r` -0.30532 0.07488 -4.078 6.70e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.324 on 189 degrees of freedom
Multiple R-squared: 0.3622, Adjusted R-squared: 0.3453
F-statistic: 21.47 on 5 and 189 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 1.145892e-02 2.291785e-02 0.9885411
[2,] 5.580255e-03 1.116051e-02 0.9944197
[3,] 3.830395e-03 7.660790e-03 0.9961696
[4,] 1.446882e-03 2.893763e-03 0.9985531
[5,] 4.068297e-04 8.136594e-04 0.9995932
[6,] 1.108453e-04 2.216906e-04 0.9998892
[7,] 7.234206e-05 1.446841e-04 0.9999277
[8,] 1.754479e-05 3.508959e-05 0.9999825
[9,] 5.376351e-06 1.075270e-05 0.9999946
[10,] 2.890507e-06 5.781015e-06 0.9999971
[11,] 1.200169e-06 2.400338e-06 0.9999988
[12,] 9.133592e-07 1.826718e-06 0.9999991
[13,] 2.612329e-07 5.224658e-07 0.9999997
[14,] 2.522526e-07 5.045052e-07 0.9999997
[15,] 7.286404e-08 1.457281e-07 0.9999999
[16,] 1.846637e-08 3.693275e-08 1.0000000
[17,] 5.445030e-09 1.089006e-08 1.0000000
[18,] 4.410303e-09 8.820606e-09 1.0000000
[19,] 1.958966e-09 3.917932e-09 1.0000000
[20,] 5.154375e-10 1.030875e-09 1.0000000
[21,] 1.688198e-09 3.376396e-09 1.0000000
[22,] 5.977288e-10 1.195458e-09 1.0000000
[23,] 1.506817e-04 3.013633e-04 0.9998493
[24,] 8.041576e-05 1.608315e-04 0.9999196
[25,] 3.659071e-04 7.318142e-04 0.9996341
[26,] 1.647330e-03 3.294659e-03 0.9983527
[27,] 2.663321e-03 5.326642e-03 0.9973367
[28,] 2.758548e-03 5.517097e-03 0.9972415
[29,] 4.570074e-03 9.140147e-03 0.9954299
[30,] 2.445120e-02 4.890241e-02 0.9755488
[31,] 1.834191e-02 3.668383e-02 0.9816581
[32,] 1.465260e-02 2.930521e-02 0.9853474
[33,] 4.461984e-02 8.923967e-02 0.9553802
[34,] 1.543527e-01 3.087053e-01 0.8456473
[35,] 1.944954e-01 3.889908e-01 0.8055046
[36,] 3.794247e-01 7.588494e-01 0.6205753
[37,] 3.365930e-01 6.731859e-01 0.6634070
[38,] 7.802384e-01 4.395232e-01 0.2197616
[39,] 7.705110e-01 4.589781e-01 0.2294890
[40,] 7.431219e-01 5.137562e-01 0.2568781
[41,] 8.426455e-01 3.147090e-01 0.1573545
[42,] 8.157055e-01 3.685890e-01 0.1842945
[43,] 7.956944e-01 4.086113e-01 0.2043056
[44,] 7.744545e-01 4.510909e-01 0.2255455
[45,] 7.386582e-01 5.226836e-01 0.2613418
[46,] 8.652086e-01 2.695828e-01 0.1347914
[47,] 8.552111e-01 2.895777e-01 0.1447889
[48,] 8.560808e-01 2.878385e-01 0.1439192
[49,] 8.443511e-01 3.112978e-01 0.1556489
[50,] 8.185754e-01 3.628491e-01 0.1814246
[51,] 7.913918e-01 4.172163e-01 0.2086082
[52,] 7.676275e-01 4.647451e-01 0.2323725
[53,] 7.637256e-01 4.725488e-01 0.2362744
[54,] 7.607276e-01 4.785447e-01 0.2392724
[55,] 7.926213e-01 4.147574e-01 0.2073787
[56,] 7.854982e-01 4.290036e-01 0.2145018
[57,] 7.588639e-01 4.822723e-01 0.2411361
[58,] 7.296985e-01 5.406030e-01 0.2703015
[59,] 6.976887e-01 6.046226e-01 0.3023113
[60,] 6.705092e-01 6.589817e-01 0.3294908
[61,] 6.340033e-01 7.319933e-01 0.3659967
[62,] 5.992892e-01 8.014215e-01 0.4007108
[63,] 5.652256e-01 8.695488e-01 0.4347744
[64,] 5.259812e-01 9.480375e-01 0.4740188
[65,] 5.571204e-01 8.857592e-01 0.4428796
[66,] 5.310714e-01 9.378571e-01 0.4689286
[67,] 4.967245e-01 9.934491e-01 0.5032755
[68,] 4.975102e-01 9.950204e-01 0.5024898
[69,] 5.026292e-01 9.947416e-01 0.4973708
[70,] 5.064258e-01 9.871484e-01 0.4935742
[71,] 5.345128e-01 9.309744e-01 0.4654872
[72,] 5.973396e-01 8.053208e-01 0.4026604
[73,] 5.679744e-01 8.640512e-01 0.4320256
[74,] 6.602305e-01 6.795390e-01 0.3397695
[75,] 6.423772e-01 7.152455e-01 0.3576228
[76,] 6.221471e-01 7.557059e-01 0.3778529
[77,] 6.119804e-01 7.760393e-01 0.3880196
[78,] 6.288448e-01 7.423104e-01 0.3711552
[79,] 6.031370e-01 7.937260e-01 0.3968630
[80,] 5.820967e-01 8.358067e-01 0.4179033
[81,] 5.586799e-01 8.826402e-01 0.4413201
[82,] 5.981571e-01 8.036858e-01 0.4018429
[83,] 5.703242e-01 8.593516e-01 0.4296758
[84,] 5.482743e-01 9.034515e-01 0.4517257
[85,] 5.937089e-01 8.125823e-01 0.4062911
[86,] 5.526819e-01 8.946363e-01 0.4473181
[87,] 5.989642e-01 8.020715e-01 0.4010358
[88,] 5.788046e-01 8.423907e-01 0.4211954
[89,] 5.971377e-01 8.057247e-01 0.4028623
[90,] 5.757269e-01 8.485461e-01 0.4242731
[91,] 5.851420e-01 8.297160e-01 0.4148580
[92,] 6.366055e-01 7.267889e-01 0.3633945
[93,] 6.150445e-01 7.699110e-01 0.3849555
[94,] 5.818111e-01 8.363779e-01 0.4181889
[95,] 5.682678e-01 8.634643e-01 0.4317322
[96,] 5.267289e-01 9.465421e-01 0.4732711
[97,] 4.890102e-01 9.780203e-01 0.5109898
[98,] 4.840851e-01 9.681702e-01 0.5159149
[99,] 4.432512e-01 8.865024e-01 0.5567488
[100,] 4.231131e-01 8.462261e-01 0.5768869
[101,] 4.207057e-01 8.414113e-01 0.5792943
[102,] 3.978120e-01 7.956241e-01 0.6021880
[103,] 3.644818e-01 7.289635e-01 0.6355182
[104,] 3.313349e-01 6.626699e-01 0.6686651
[105,] 3.452325e-01 6.904650e-01 0.6547675
[106,] 3.072225e-01 6.144451e-01 0.6927775
[107,] 2.850532e-01 5.701065e-01 0.7149468
[108,] 2.567797e-01 5.135593e-01 0.7432203
[109,] 2.335238e-01 4.670475e-01 0.7664762
[110,] 2.177410e-01 4.354819e-01 0.7822590
[111,] 2.691910e-01 5.383821e-01 0.7308090
[112,] 2.775299e-01 5.550599e-01 0.7224701
[113,] 2.773209e-01 5.546418e-01 0.7226791
[114,] 2.639759e-01 5.279518e-01 0.7360241
[115,] 4.644936e-01 9.289872e-01 0.5355064
[116,] 4.556163e-01 9.112325e-01 0.5443837
[117,] 5.266866e-01 9.466268e-01 0.4733134
[118,] 5.555564e-01 8.888873e-01 0.4444436
[119,] 5.483863e-01 9.032275e-01 0.4516137
[120,] 6.390286e-01 7.219428e-01 0.3609714
[121,] 6.331501e-01 7.336997e-01 0.3668499
[122,] 5.993304e-01 8.013392e-01 0.4006696
[123,] 5.715285e-01 8.569429e-01 0.4284715
[124,] 5.288282e-01 9.423436e-01 0.4711718
[125,] 5.100021e-01 9.799959e-01 0.4899979
[126,] 4.781714e-01 9.563427e-01 0.5218286
[127,] 4.409046e-01 8.818092e-01 0.5590954
[128,] 4.433798e-01 8.867595e-01 0.5566202
[129,] 3.990747e-01 7.981493e-01 0.6009253
[130,] 3.654780e-01 7.309559e-01 0.6345220
[131,] 3.474508e-01 6.949016e-01 0.6525492
[132,] 3.073774e-01 6.147549e-01 0.6926226
[133,] 2.716633e-01 5.433266e-01 0.7283367
[134,] 2.852265e-01 5.704530e-01 0.7147735
[135,] 2.497083e-01 4.994166e-01 0.7502917
[136,] 2.652288e-01 5.304576e-01 0.7347712
[137,] 4.118952e-01 8.237904e-01 0.5881048
[138,] 3.811166e-01 7.622333e-01 0.6188834
[139,] 3.997749e-01 7.995497e-01 0.6002251
[140,] 3.590553e-01 7.181107e-01 0.6409447
[141,] 3.150505e-01 6.301010e-01 0.6849495
[142,] 2.734385e-01 5.468769e-01 0.7265615
[143,] 2.372029e-01 4.744057e-01 0.7627971
[144,] 3.962798e-01 7.925596e-01 0.6037202
[145,] 3.468595e-01 6.937190e-01 0.6531405
[146,] 3.160261e-01 6.320521e-01 0.6839739
[147,] 2.726924e-01 5.453848e-01 0.7273076
[148,] 2.379212e-01 4.758424e-01 0.7620788
[149,] 2.408580e-01 4.817160e-01 0.7591420
[150,] 2.372864e-01 4.745728e-01 0.7627136
[151,] 1.988109e-01 3.976218e-01 0.8011891
[152,] 1.627582e-01 3.255164e-01 0.8372418
[153,] 1.370372e-01 2.740744e-01 0.8629628
[154,] 1.154269e-01 2.308539e-01 0.8845731
[155,] 1.217476e-01 2.434952e-01 0.8782524
[156,] 1.047491e-01 2.094982e-01 0.8952509
[157,] 1.257840e-01 2.515679e-01 0.8742160
[158,] 1.140611e-01 2.281223e-01 0.8859389
[159,] 1.649482e-01 3.298964e-01 0.8350518
[160,] 2.030216e-01 4.060431e-01 0.7969784
[161,] 1.955943e-01 3.911886e-01 0.8044057
[162,] 3.019372e-01 6.038744e-01 0.6980628
[163,] 3.189872e-01 6.379745e-01 0.6810128
[164,] 2.590628e-01 5.181255e-01 0.7409372
[165,] 2.072319e-01 4.144637e-01 0.7927681
[166,] 1.814384e-01 3.628768e-01 0.8185616
[167,] 1.857027e-01 3.714054e-01 0.8142973
[168,] 1.563471e-01 3.126942e-01 0.8436529
[169,] 1.160174e-01 2.320348e-01 0.8839826
[170,] 1.020606e-01 2.041212e-01 0.8979394
[171,] 9.017417e-02 1.803483e-01 0.9098258
[172,] 1.351662e-01 2.703324e-01 0.8648338
[173,] 1.115345e-01 2.230690e-01 0.8884655
[174,] 7.726311e-02 1.545262e-01 0.9227369
[175,] 5.402914e-02 1.080583e-01 0.9459709
[176,] 5.125555e-02 1.025111e-01 0.9487445
[177,] 8.654840e-02 1.730968e-01 0.9134516
[178,] 4.484174e-02 8.968347e-02 0.9551583
> postscript(file="/var/www/html/rcomp/tmp/1qvl91293537510.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qvl91293537510.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3qvl91293537510.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4j43c1293537510.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5j43c1293537510.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 = 195
Frequency = 1
1 2 3 4 5 6
-3.11750372 -0.84294153 -3.92456096 -10.43706280 -17.19684023 9.61438130
7 8 9 10 11 12
6.25693535 2.33176400 -2.26644182 -1.16946725 -0.02055920 -1.16840379
13 14 15 16 17 18
-1.01583475 -0.64557260 -4.86615434 -5.98800480 -12.24767794 2.21039289
19 20 21 22 23 24
2.93845988 -19.94966246 0.01314423 -11.63030120 0.48056055 -1.28210891
25 26 27 28 29 30
-3.50589127 -0.29111566 -5.44324796 -0.94841680 -0.22230514 -16.07586251
31 32 33 34 35 36
14.54954430 7.32565625 18.39829974 14.83726245 -13.87595541 8.14019488
37 38 39 40 41 42
2.34081923 -9.79909501 8.84513382 12.60792391 -24.44394769 -26.12607210
43 44 45 46 47 48
-20.91590806 -38.40704571 5.77552122 1.08872399 -6.51856579 5.09478888
49 50 51 52 53 54
12.38841512 -3.28777920 3.70283624 -3.68824512 -3.28949286 9.70429158
55 56 57 58 59 60
-11.05308769 -6.55160911 -2.15731803 -3.15437046 -0.51396768 -4.99107257
61 62 63 64 65 66
-2.37299607 -8.17016577 1.86860877 -1.75737450 -1.55939036 5.49248492
67 68 69 70 71 72
-0.35055678 8.25846611 -3.40669004 7.22485586 -4.84032986 -3.40185983
73 74 75 76 77 78
11.58835190 -6.88069021 -2.24088212 8.15427641 5.25006279 -7.67258638
79 80 81 82 83 84
7.59516178 7.30427687 -3.87079046 -20.14033826 4.26877259 8.75780672
85 86 87 88 89 90
-5.04417259 -8.50073795 0.26800974 8.40216536 -3.34838919 8.59610268
91 92 93 94 95 96
0.93730476 7.29025156 15.11344658 1.42427555 9.99534307 -3.75751107
97 98 99 100 101 102
8.29095128 8.13666930 -11.52891186 11.74039778 -7.17184218 -2.57328141
103 104 105 106 107 108
8.33497001 1.29664025 -1.35962178 10.03546146 -0.61809416 -4.20164417
109 110 111 112 113 114
6.61259164 -4.75700846 4.66116321 -5.11855950 -9.07599725 1.27837407
115 116 117 118 119 120
7.27475490 3.98088632 -2.83886463 -5.84251562 8.86060232 6.54095531
121 122 123 124 125 126
-9.15968309 4.56733636 -22.65851461 4.77894746 14.16875341 -9.75231824
127 128 129 130 131 132
8.82788204 16.41934522 9.64576133 3.80359505 6.49150706 2.01345787
133 134 135 136 137 138
8.91736830 7.04817341 1.91627623 10.47754984 2.13458808 5.61560206
139 140 141 142 143 144
8.18928300 2.87430724 -2.84784352 11.31078658 -0.78574467 6.42317206
145 146 147 148 149 150
16.88432895 1.38192473 7.84083197 -6.13146897 -2.86547780 2.61241086
151 152 153 154 155 156
-5.26726862 -19.38384737 0.21490442 7.46129234 -4.23561091 -7.46411234
157 158 159 160 161 162
6.24095562 -9.82930451 -2.84112077 0.22779173 2.03700784 7.04304799
163 164 165 166 167 168
-14.13113591 10.05881742 -14.10119424 3.36395062 -10.24578988 -13.41035443
169 170 171 172 173 174
-12.03641794 -18.95281007 12.41163830 0.42274375 3.92539994 6.08160981
175 176 177 178 179 180
11.72756191 -0.62608118 1.84220226 4.95157138 -0.66466772 9.87596886
181 182 183 184 185 186
7.33832362 13.64158102 9.55299529 13.84814007 7.27330196 -10.14350264
187 188 189 190 191 192
4.22905759 -7.89292304 -11.41512453 0.71330067 -12.21460522 7.99567985
193 194 195
18.19706018 -12.44818801 -11.56220243
> postscript(file="/var/www/html/rcomp/tmp/6j43c1293537510.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 = 195
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.11750372 NA
1 -0.84294153 -3.11750372
2 -3.92456096 -0.84294153
3 -10.43706280 -3.92456096
4 -17.19684023 -10.43706280
5 9.61438130 -17.19684023
6 6.25693535 9.61438130
7 2.33176400 6.25693535
8 -2.26644182 2.33176400
9 -1.16946725 -2.26644182
10 -0.02055920 -1.16946725
11 -1.16840379 -0.02055920
12 -1.01583475 -1.16840379
13 -0.64557260 -1.01583475
14 -4.86615434 -0.64557260
15 -5.98800480 -4.86615434
16 -12.24767794 -5.98800480
17 2.21039289 -12.24767794
18 2.93845988 2.21039289
19 -19.94966246 2.93845988
20 0.01314423 -19.94966246
21 -11.63030120 0.01314423
22 0.48056055 -11.63030120
23 -1.28210891 0.48056055
24 -3.50589127 -1.28210891
25 -0.29111566 -3.50589127
26 -5.44324796 -0.29111566
27 -0.94841680 -5.44324796
28 -0.22230514 -0.94841680
29 -16.07586251 -0.22230514
30 14.54954430 -16.07586251
31 7.32565625 14.54954430
32 18.39829974 7.32565625
33 14.83726245 18.39829974
34 -13.87595541 14.83726245
35 8.14019488 -13.87595541
36 2.34081923 8.14019488
37 -9.79909501 2.34081923
38 8.84513382 -9.79909501
39 12.60792391 8.84513382
40 -24.44394769 12.60792391
41 -26.12607210 -24.44394769
42 -20.91590806 -26.12607210
43 -38.40704571 -20.91590806
44 5.77552122 -38.40704571
45 1.08872399 5.77552122
46 -6.51856579 1.08872399
47 5.09478888 -6.51856579
48 12.38841512 5.09478888
49 -3.28777920 12.38841512
50 3.70283624 -3.28777920
51 -3.68824512 3.70283624
52 -3.28949286 -3.68824512
53 9.70429158 -3.28949286
54 -11.05308769 9.70429158
55 -6.55160911 -11.05308769
56 -2.15731803 -6.55160911
57 -3.15437046 -2.15731803
58 -0.51396768 -3.15437046
59 -4.99107257 -0.51396768
60 -2.37299607 -4.99107257
61 -8.17016577 -2.37299607
62 1.86860877 -8.17016577
63 -1.75737450 1.86860877
64 -1.55939036 -1.75737450
65 5.49248492 -1.55939036
66 -0.35055678 5.49248492
67 8.25846611 -0.35055678
68 -3.40669004 8.25846611
69 7.22485586 -3.40669004
70 -4.84032986 7.22485586
71 -3.40185983 -4.84032986
72 11.58835190 -3.40185983
73 -6.88069021 11.58835190
74 -2.24088212 -6.88069021
75 8.15427641 -2.24088212
76 5.25006279 8.15427641
77 -7.67258638 5.25006279
78 7.59516178 -7.67258638
79 7.30427687 7.59516178
80 -3.87079046 7.30427687
81 -20.14033826 -3.87079046
82 4.26877259 -20.14033826
83 8.75780672 4.26877259
84 -5.04417259 8.75780672
85 -8.50073795 -5.04417259
86 0.26800974 -8.50073795
87 8.40216536 0.26800974
88 -3.34838919 8.40216536
89 8.59610268 -3.34838919
90 0.93730476 8.59610268
91 7.29025156 0.93730476
92 15.11344658 7.29025156
93 1.42427555 15.11344658
94 9.99534307 1.42427555
95 -3.75751107 9.99534307
96 8.29095128 -3.75751107
97 8.13666930 8.29095128
98 -11.52891186 8.13666930
99 11.74039778 -11.52891186
100 -7.17184218 11.74039778
101 -2.57328141 -7.17184218
102 8.33497001 -2.57328141
103 1.29664025 8.33497001
104 -1.35962178 1.29664025
105 10.03546146 -1.35962178
106 -0.61809416 10.03546146
107 -4.20164417 -0.61809416
108 6.61259164 -4.20164417
109 -4.75700846 6.61259164
110 4.66116321 -4.75700846
111 -5.11855950 4.66116321
112 -9.07599725 -5.11855950
113 1.27837407 -9.07599725
114 7.27475490 1.27837407
115 3.98088632 7.27475490
116 -2.83886463 3.98088632
117 -5.84251562 -2.83886463
118 8.86060232 -5.84251562
119 6.54095531 8.86060232
120 -9.15968309 6.54095531
121 4.56733636 -9.15968309
122 -22.65851461 4.56733636
123 4.77894746 -22.65851461
124 14.16875341 4.77894746
125 -9.75231824 14.16875341
126 8.82788204 -9.75231824
127 16.41934522 8.82788204
128 9.64576133 16.41934522
129 3.80359505 9.64576133
130 6.49150706 3.80359505
131 2.01345787 6.49150706
132 8.91736830 2.01345787
133 7.04817341 8.91736830
134 1.91627623 7.04817341
135 10.47754984 1.91627623
136 2.13458808 10.47754984
137 5.61560206 2.13458808
138 8.18928300 5.61560206
139 2.87430724 8.18928300
140 -2.84784352 2.87430724
141 11.31078658 -2.84784352
142 -0.78574467 11.31078658
143 6.42317206 -0.78574467
144 16.88432895 6.42317206
145 1.38192473 16.88432895
146 7.84083197 1.38192473
147 -6.13146897 7.84083197
148 -2.86547780 -6.13146897
149 2.61241086 -2.86547780
150 -5.26726862 2.61241086
151 -19.38384737 -5.26726862
152 0.21490442 -19.38384737
153 7.46129234 0.21490442
154 -4.23561091 7.46129234
155 -7.46411234 -4.23561091
156 6.24095562 -7.46411234
157 -9.82930451 6.24095562
158 -2.84112077 -9.82930451
159 0.22779173 -2.84112077
160 2.03700784 0.22779173
161 7.04304799 2.03700784
162 -14.13113591 7.04304799
163 10.05881742 -14.13113591
164 -14.10119424 10.05881742
165 3.36395062 -14.10119424
166 -10.24578988 3.36395062
167 -13.41035443 -10.24578988
168 -12.03641794 -13.41035443
169 -18.95281007 -12.03641794
170 12.41163830 -18.95281007
171 0.42274375 12.41163830
172 3.92539994 0.42274375
173 6.08160981 3.92539994
174 11.72756191 6.08160981
175 -0.62608118 11.72756191
176 1.84220226 -0.62608118
177 4.95157138 1.84220226
178 -0.66466772 4.95157138
179 9.87596886 -0.66466772
180 7.33832362 9.87596886
181 13.64158102 7.33832362
182 9.55299529 13.64158102
183 13.84814007 9.55299529
184 7.27330196 13.84814007
185 -10.14350264 7.27330196
186 4.22905759 -10.14350264
187 -7.89292304 4.22905759
188 -11.41512453 -7.89292304
189 0.71330067 -11.41512453
190 -12.21460522 0.71330067
191 7.99567985 -12.21460522
192 18.19706018 7.99567985
193 -12.44818801 18.19706018
194 -11.56220243 -12.44818801
195 NA -11.56220243
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.84294153 -3.11750372
[2,] -3.92456096 -0.84294153
[3,] -10.43706280 -3.92456096
[4,] -17.19684023 -10.43706280
[5,] 9.61438130 -17.19684023
[6,] 6.25693535 9.61438130
[7,] 2.33176400 6.25693535
[8,] -2.26644182 2.33176400
[9,] -1.16946725 -2.26644182
[10,] -0.02055920 -1.16946725
[11,] -1.16840379 -0.02055920
[12,] -1.01583475 -1.16840379
[13,] -0.64557260 -1.01583475
[14,] -4.86615434 -0.64557260
[15,] -5.98800480 -4.86615434
[16,] -12.24767794 -5.98800480
[17,] 2.21039289 -12.24767794
[18,] 2.93845988 2.21039289
[19,] -19.94966246 2.93845988
[20,] 0.01314423 -19.94966246
[21,] -11.63030120 0.01314423
[22,] 0.48056055 -11.63030120
[23,] -1.28210891 0.48056055
[24,] -3.50589127 -1.28210891
[25,] -0.29111566 -3.50589127
[26,] -5.44324796 -0.29111566
[27,] -0.94841680 -5.44324796
[28,] -0.22230514 -0.94841680
[29,] -16.07586251 -0.22230514
[30,] 14.54954430 -16.07586251
[31,] 7.32565625 14.54954430
[32,] 18.39829974 7.32565625
[33,] 14.83726245 18.39829974
[34,] -13.87595541 14.83726245
[35,] 8.14019488 -13.87595541
[36,] 2.34081923 8.14019488
[37,] -9.79909501 2.34081923
[38,] 8.84513382 -9.79909501
[39,] 12.60792391 8.84513382
[40,] -24.44394769 12.60792391
[41,] -26.12607210 -24.44394769
[42,] -20.91590806 -26.12607210
[43,] -38.40704571 -20.91590806
[44,] 5.77552122 -38.40704571
[45,] 1.08872399 5.77552122
[46,] -6.51856579 1.08872399
[47,] 5.09478888 -6.51856579
[48,] 12.38841512 5.09478888
[49,] -3.28777920 12.38841512
[50,] 3.70283624 -3.28777920
[51,] -3.68824512 3.70283624
[52,] -3.28949286 -3.68824512
[53,] 9.70429158 -3.28949286
[54,] -11.05308769 9.70429158
[55,] -6.55160911 -11.05308769
[56,] -2.15731803 -6.55160911
[57,] -3.15437046 -2.15731803
[58,] -0.51396768 -3.15437046
[59,] -4.99107257 -0.51396768
[60,] -2.37299607 -4.99107257
[61,] -8.17016577 -2.37299607
[62,] 1.86860877 -8.17016577
[63,] -1.75737450 1.86860877
[64,] -1.55939036 -1.75737450
[65,] 5.49248492 -1.55939036
[66,] -0.35055678 5.49248492
[67,] 8.25846611 -0.35055678
[68,] -3.40669004 8.25846611
[69,] 7.22485586 -3.40669004
[70,] -4.84032986 7.22485586
[71,] -3.40185983 -4.84032986
[72,] 11.58835190 -3.40185983
[73,] -6.88069021 11.58835190
[74,] -2.24088212 -6.88069021
[75,] 8.15427641 -2.24088212
[76,] 5.25006279 8.15427641
[77,] -7.67258638 5.25006279
[78,] 7.59516178 -7.67258638
[79,] 7.30427687 7.59516178
[80,] -3.87079046 7.30427687
[81,] -20.14033826 -3.87079046
[82,] 4.26877259 -20.14033826
[83,] 8.75780672 4.26877259
[84,] -5.04417259 8.75780672
[85,] -8.50073795 -5.04417259
[86,] 0.26800974 -8.50073795
[87,] 8.40216536 0.26800974
[88,] -3.34838919 8.40216536
[89,] 8.59610268 -3.34838919
[90,] 0.93730476 8.59610268
[91,] 7.29025156 0.93730476
[92,] 15.11344658 7.29025156
[93,] 1.42427555 15.11344658
[94,] 9.99534307 1.42427555
[95,] -3.75751107 9.99534307
[96,] 8.29095128 -3.75751107
[97,] 8.13666930 8.29095128
[98,] -11.52891186 8.13666930
[99,] 11.74039778 -11.52891186
[100,] -7.17184218 11.74039778
[101,] -2.57328141 -7.17184218
[102,] 8.33497001 -2.57328141
[103,] 1.29664025 8.33497001
[104,] -1.35962178 1.29664025
[105,] 10.03546146 -1.35962178
[106,] -0.61809416 10.03546146
[107,] -4.20164417 -0.61809416
[108,] 6.61259164 -4.20164417
[109,] -4.75700846 6.61259164
[110,] 4.66116321 -4.75700846
[111,] -5.11855950 4.66116321
[112,] -9.07599725 -5.11855950
[113,] 1.27837407 -9.07599725
[114,] 7.27475490 1.27837407
[115,] 3.98088632 7.27475490
[116,] -2.83886463 3.98088632
[117,] -5.84251562 -2.83886463
[118,] 8.86060232 -5.84251562
[119,] 6.54095531 8.86060232
[120,] -9.15968309 6.54095531
[121,] 4.56733636 -9.15968309
[122,] -22.65851461 4.56733636
[123,] 4.77894746 -22.65851461
[124,] 14.16875341 4.77894746
[125,] -9.75231824 14.16875341
[126,] 8.82788204 -9.75231824
[127,] 16.41934522 8.82788204
[128,] 9.64576133 16.41934522
[129,] 3.80359505 9.64576133
[130,] 6.49150706 3.80359505
[131,] 2.01345787 6.49150706
[132,] 8.91736830 2.01345787
[133,] 7.04817341 8.91736830
[134,] 1.91627623 7.04817341
[135,] 10.47754984 1.91627623
[136,] 2.13458808 10.47754984
[137,] 5.61560206 2.13458808
[138,] 8.18928300 5.61560206
[139,] 2.87430724 8.18928300
[140,] -2.84784352 2.87430724
[141,] 11.31078658 -2.84784352
[142,] -0.78574467 11.31078658
[143,] 6.42317206 -0.78574467
[144,] 16.88432895 6.42317206
[145,] 1.38192473 16.88432895
[146,] 7.84083197 1.38192473
[147,] -6.13146897 7.84083197
[148,] -2.86547780 -6.13146897
[149,] 2.61241086 -2.86547780
[150,] -5.26726862 2.61241086
[151,] -19.38384737 -5.26726862
[152,] 0.21490442 -19.38384737
[153,] 7.46129234 0.21490442
[154,] -4.23561091 7.46129234
[155,] -7.46411234 -4.23561091
[156,] 6.24095562 -7.46411234
[157,] -9.82930451 6.24095562
[158,] -2.84112077 -9.82930451
[159,] 0.22779173 -2.84112077
[160,] 2.03700784 0.22779173
[161,] 7.04304799 2.03700784
[162,] -14.13113591 7.04304799
[163,] 10.05881742 -14.13113591
[164,] -14.10119424 10.05881742
[165,] 3.36395062 -14.10119424
[166,] -10.24578988 3.36395062
[167,] -13.41035443 -10.24578988
[168,] -12.03641794 -13.41035443
[169,] -18.95281007 -12.03641794
[170,] 12.41163830 -18.95281007
[171,] 0.42274375 12.41163830
[172,] 3.92539994 0.42274375
[173,] 6.08160981 3.92539994
[174,] 11.72756191 6.08160981
[175,] -0.62608118 11.72756191
[176,] 1.84220226 -0.62608118
[177,] 4.95157138 1.84220226
[178,] -0.66466772 4.95157138
[179,] 9.87596886 -0.66466772
[180,] 7.33832362 9.87596886
[181,] 13.64158102 7.33832362
[182,] 9.55299529 13.64158102
[183,] 13.84814007 9.55299529
[184,] 7.27330196 13.84814007
[185,] -10.14350264 7.27330196
[186,] 4.22905759 -10.14350264
[187,] -7.89292304 4.22905759
[188,] -11.41512453 -7.89292304
[189,] 0.71330067 -11.41512453
[190,] -12.21460522 0.71330067
[191,] 7.99567985 -12.21460522
[192,] 18.19706018 7.99567985
[193,] -12.44818801 18.19706018
[194,] -11.56220243 -12.44818801
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.84294153 -3.11750372
2 -3.92456096 -0.84294153
3 -10.43706280 -3.92456096
4 -17.19684023 -10.43706280
5 9.61438130 -17.19684023
6 6.25693535 9.61438130
7 2.33176400 6.25693535
8 -2.26644182 2.33176400
9 -1.16946725 -2.26644182
10 -0.02055920 -1.16946725
11 -1.16840379 -0.02055920
12 -1.01583475 -1.16840379
13 -0.64557260 -1.01583475
14 -4.86615434 -0.64557260
15 -5.98800480 -4.86615434
16 -12.24767794 -5.98800480
17 2.21039289 -12.24767794
18 2.93845988 2.21039289
19 -19.94966246 2.93845988
20 0.01314423 -19.94966246
21 -11.63030120 0.01314423
22 0.48056055 -11.63030120
23 -1.28210891 0.48056055
24 -3.50589127 -1.28210891
25 -0.29111566 -3.50589127
26 -5.44324796 -0.29111566
27 -0.94841680 -5.44324796
28 -0.22230514 -0.94841680
29 -16.07586251 -0.22230514
30 14.54954430 -16.07586251
31 7.32565625 14.54954430
32 18.39829974 7.32565625
33 14.83726245 18.39829974
34 -13.87595541 14.83726245
35 8.14019488 -13.87595541
36 2.34081923 8.14019488
37 -9.79909501 2.34081923
38 8.84513382 -9.79909501
39 12.60792391 8.84513382
40 -24.44394769 12.60792391
41 -26.12607210 -24.44394769
42 -20.91590806 -26.12607210
43 -38.40704571 -20.91590806
44 5.77552122 -38.40704571
45 1.08872399 5.77552122
46 -6.51856579 1.08872399
47 5.09478888 -6.51856579
48 12.38841512 5.09478888
49 -3.28777920 12.38841512
50 3.70283624 -3.28777920
51 -3.68824512 3.70283624
52 -3.28949286 -3.68824512
53 9.70429158 -3.28949286
54 -11.05308769 9.70429158
55 -6.55160911 -11.05308769
56 -2.15731803 -6.55160911
57 -3.15437046 -2.15731803
58 -0.51396768 -3.15437046
59 -4.99107257 -0.51396768
60 -2.37299607 -4.99107257
61 -8.17016577 -2.37299607
62 1.86860877 -8.17016577
63 -1.75737450 1.86860877
64 -1.55939036 -1.75737450
65 5.49248492 -1.55939036
66 -0.35055678 5.49248492
67 8.25846611 -0.35055678
68 -3.40669004 8.25846611
69 7.22485586 -3.40669004
70 -4.84032986 7.22485586
71 -3.40185983 -4.84032986
72 11.58835190 -3.40185983
73 -6.88069021 11.58835190
74 -2.24088212 -6.88069021
75 8.15427641 -2.24088212
76 5.25006279 8.15427641
77 -7.67258638 5.25006279
78 7.59516178 -7.67258638
79 7.30427687 7.59516178
80 -3.87079046 7.30427687
81 -20.14033826 -3.87079046
82 4.26877259 -20.14033826
83 8.75780672 4.26877259
84 -5.04417259 8.75780672
85 -8.50073795 -5.04417259
86 0.26800974 -8.50073795
87 8.40216536 0.26800974
88 -3.34838919 8.40216536
89 8.59610268 -3.34838919
90 0.93730476 8.59610268
91 7.29025156 0.93730476
92 15.11344658 7.29025156
93 1.42427555 15.11344658
94 9.99534307 1.42427555
95 -3.75751107 9.99534307
96 8.29095128 -3.75751107
97 8.13666930 8.29095128
98 -11.52891186 8.13666930
99 11.74039778 -11.52891186
100 -7.17184218 11.74039778
101 -2.57328141 -7.17184218
102 8.33497001 -2.57328141
103 1.29664025 8.33497001
104 -1.35962178 1.29664025
105 10.03546146 -1.35962178
106 -0.61809416 10.03546146
107 -4.20164417 -0.61809416
108 6.61259164 -4.20164417
109 -4.75700846 6.61259164
110 4.66116321 -4.75700846
111 -5.11855950 4.66116321
112 -9.07599725 -5.11855950
113 1.27837407 -9.07599725
114 7.27475490 1.27837407
115 3.98088632 7.27475490
116 -2.83886463 3.98088632
117 -5.84251562 -2.83886463
118 8.86060232 -5.84251562
119 6.54095531 8.86060232
120 -9.15968309 6.54095531
121 4.56733636 -9.15968309
122 -22.65851461 4.56733636
123 4.77894746 -22.65851461
124 14.16875341 4.77894746
125 -9.75231824 14.16875341
126 8.82788204 -9.75231824
127 16.41934522 8.82788204
128 9.64576133 16.41934522
129 3.80359505 9.64576133
130 6.49150706 3.80359505
131 2.01345787 6.49150706
132 8.91736830 2.01345787
133 7.04817341 8.91736830
134 1.91627623 7.04817341
135 10.47754984 1.91627623
136 2.13458808 10.47754984
137 5.61560206 2.13458808
138 8.18928300 5.61560206
139 2.87430724 8.18928300
140 -2.84784352 2.87430724
141 11.31078658 -2.84784352
142 -0.78574467 11.31078658
143 6.42317206 -0.78574467
144 16.88432895 6.42317206
145 1.38192473 16.88432895
146 7.84083197 1.38192473
147 -6.13146897 7.84083197
148 -2.86547780 -6.13146897
149 2.61241086 -2.86547780
150 -5.26726862 2.61241086
151 -19.38384737 -5.26726862
152 0.21490442 -19.38384737
153 7.46129234 0.21490442
154 -4.23561091 7.46129234
155 -7.46411234 -4.23561091
156 6.24095562 -7.46411234
157 -9.82930451 6.24095562
158 -2.84112077 -9.82930451
159 0.22779173 -2.84112077
160 2.03700784 0.22779173
161 7.04304799 2.03700784
162 -14.13113591 7.04304799
163 10.05881742 -14.13113591
164 -14.10119424 10.05881742
165 3.36395062 -14.10119424
166 -10.24578988 3.36395062
167 -13.41035443 -10.24578988
168 -12.03641794 -13.41035443
169 -18.95281007 -12.03641794
170 12.41163830 -18.95281007
171 0.42274375 12.41163830
172 3.92539994 0.42274375
173 6.08160981 3.92539994
174 11.72756191 6.08160981
175 -0.62608118 11.72756191
176 1.84220226 -0.62608118
177 4.95157138 1.84220226
178 -0.66466772 4.95157138
179 9.87596886 -0.66466772
180 7.33832362 9.87596886
181 13.64158102 7.33832362
182 9.55299529 13.64158102
183 13.84814007 9.55299529
184 7.27330196 13.84814007
185 -10.14350264 7.27330196
186 4.22905759 -10.14350264
187 -7.89292304 4.22905759
188 -11.41512453 -7.89292304
189 0.71330067 -11.41512453
190 -12.21460522 0.71330067
191 7.99567985 -12.21460522
192 18.19706018 7.99567985
193 -12.44818801 18.19706018
194 -11.56220243 -12.44818801
> 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/7twkw1293537510.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8m5jz1293537510.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9m5jz1293537510.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10m5jz1293537510.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11p5hn1293537510.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/12b6gt1293537510.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/130pd51293537510.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/14bgu81293537510.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/15wzbe1293537510.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/16s8r41293537510.tab")
+ }
>
> try(system("convert tmp/1qvl91293537510.ps tmp/1qvl91293537510.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qvl91293537510.ps tmp/2qvl91293537510.png",intern=TRUE))
character(0)
> try(system("convert tmp/3qvl91293537510.ps tmp/3qvl91293537510.png",intern=TRUE))
character(0)
> try(system("convert tmp/4j43c1293537510.ps tmp/4j43c1293537510.png",intern=TRUE))
character(0)
> try(system("convert tmp/5j43c1293537510.ps tmp/5j43c1293537510.png",intern=TRUE))
character(0)
> try(system("convert tmp/6j43c1293537510.ps tmp/6j43c1293537510.png",intern=TRUE))
character(0)
> try(system("convert tmp/7twkw1293537510.ps tmp/7twkw1293537510.png",intern=TRUE))
character(0)
> try(system("convert tmp/8m5jz1293537510.ps tmp/8m5jz1293537510.png",intern=TRUE))
character(0)
> try(system("convert tmp/9m5jz1293537510.ps tmp/9m5jz1293537510.png",intern=TRUE))
character(0)
> try(system("convert tmp/10m5jz1293537510.ps tmp/10m5jz1293537510.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.802 1.856 11.743