R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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(41
+ ,38
+ ,13
+ ,12
+ ,14
+ ,12
+ ,53
+ ,32
+ ,39
+ ,32
+ ,16
+ ,11
+ ,18
+ ,11
+ ,86
+ ,51
+ ,30
+ ,35
+ ,19
+ ,15
+ ,11
+ ,14
+ ,66
+ ,42
+ ,31
+ ,33
+ ,15
+ ,6
+ ,12
+ ,12
+ ,67
+ ,41
+ ,34
+ ,37
+ ,14
+ ,13
+ ,16
+ ,21
+ ,76
+ ,46
+ ,35
+ ,29
+ ,13
+ ,10
+ ,18
+ ,12
+ ,78
+ ,47
+ ,39
+ ,31
+ ,19
+ ,12
+ ,14
+ ,22
+ ,53
+ ,37
+ ,34
+ ,36
+ ,15
+ ,14
+ ,14
+ ,11
+ ,80
+ ,49
+ ,36
+ ,35
+ ,14
+ ,12
+ ,15
+ ,10
+ ,74
+ ,45
+ ,37
+ ,38
+ ,15
+ ,6
+ ,15
+ ,13
+ ,76
+ ,47
+ ,38
+ ,31
+ ,16
+ ,10
+ ,17
+ ,10
+ ,79
+ ,49
+ ,36
+ ,34
+ ,16
+ ,12
+ ,19
+ ,8
+ ,54
+ ,33
+ ,38
+ ,35
+ ,16
+ ,12
+ ,10
+ ,15
+ ,67
+ ,42
+ ,39
+ ,38
+ ,16
+ ,11
+ ,16
+ ,14
+ ,54
+ ,33
+ ,33
+ ,37
+ ,17
+ ,15
+ ,18
+ ,10
+ ,87
+ ,53
+ ,32
+ ,33
+ ,15
+ ,12
+ ,14
+ ,14
+ ,58
+ ,36
+ ,36
+ ,32
+ ,15
+ ,10
+ ,14
+ ,14
+ ,75
+ ,45
+ ,38
+ ,38
+ ,20
+ ,12
+ ,17
+ ,11
+ ,88
+ ,54
+ ,39
+ ,38
+ ,18
+ ,11
+ ,14
+ ,10
+ ,64
+ ,41
+ ,32
+ ,32
+ ,16
+ ,12
+ ,16
+ ,13
+ ,57
+ ,36
+ ,32
+ ,33
+ ,16
+ ,11
+ ,18
+ ,7
+ ,66
+ ,41
+ ,31
+ ,31
+ ,16
+ ,12
+ ,11
+ ,14
+ ,68
+ ,44
+ ,39
+ ,38
+ ,19
+ ,13
+ ,14
+ ,12
+ ,54
+ ,33
+ ,37
+ ,39
+ ,16
+ ,11
+ ,12
+ ,14
+ ,56
+ ,37
+ ,39
+ ,32
+ ,17
+ ,9
+ ,17
+ ,11
+ ,86
+ ,52
+ ,41
+ ,32
+ ,17
+ ,13
+ ,9
+ ,9
+ ,80
+ ,47
+ ,36
+ ,35
+ ,16
+ ,10
+ ,16
+ ,11
+ ,76
+ ,43
+ ,33
+ ,37
+ ,15
+ ,14
+ ,14
+ ,15
+ ,69
+ ,44
+ ,33
+ ,33
+ ,16
+ ,12
+ ,15
+ ,14
+ ,78
+ ,45
+ ,34
+ ,33
+ ,14
+ ,10
+ ,11
+ ,13
+ ,67
+ ,44
+ ,31
+ ,28
+ ,15
+ ,12
+ ,16
+ ,9
+ ,80
+ ,49
+ ,27
+ ,32
+ ,12
+ ,8
+ ,13
+ ,15
+ ,54
+ ,33
+ ,37
+ ,31
+ ,14
+ ,10
+ ,17
+ ,10
+ ,71
+ ,43
+ ,34
+ ,37
+ ,16
+ ,12
+ ,15
+ ,11
+ ,84
+ ,54
+ ,34
+ ,30
+ ,14
+ ,12
+ ,14
+ ,13
+ ,74
+ ,42
+ ,32
+ ,33
+ ,7
+ ,7
+ ,16
+ ,8
+ ,71
+ ,44
+ ,29
+ ,31
+ ,10
+ ,6
+ ,9
+ ,20
+ ,63
+ ,37
+ ,36
+ ,33
+ ,14
+ ,12
+ ,15
+ ,12
+ ,71
+ ,43
+ ,29
+ ,31
+ ,16
+ ,10
+ ,17
+ ,10
+ ,76
+ ,46
+ ,35
+ ,33
+ ,16
+ ,10
+ ,13
+ ,10
+ ,69
+ ,42
+ ,37
+ ,32
+ ,16
+ ,10
+ ,15
+ ,9
+ ,74
+ ,45
+ ,34
+ ,33
+ ,14
+ ,12
+ ,16
+ ,14
+ ,75
+ ,44
+ ,38
+ ,32
+ ,20
+ ,15
+ ,16
+ ,8
+ ,54
+ ,33
+ ,35
+ ,33
+ ,14
+ ,10
+ ,12
+ ,14
+ ,52
+ ,31
+ ,38
+ ,28
+ ,14
+ ,10
+ ,12
+ ,11
+ ,69
+ ,42
+ ,37
+ ,35
+ ,11
+ ,12
+ ,11
+ ,13
+ ,68
+ ,40
+ ,38
+ ,39
+ ,14
+ ,13
+ ,15
+ ,9
+ ,65
+ ,43
+ ,33
+ ,34
+ ,15
+ ,11
+ ,15
+ ,11
+ ,75
+ ,46
+ ,36
+ ,38
+ ,16
+ ,11
+ ,17
+ ,15
+ ,74
+ ,42
+ ,38
+ ,32
+ ,14
+ ,12
+ ,13
+ ,11
+ ,75
+ ,45
+ ,32
+ ,38
+ ,16
+ ,14
+ ,16
+ ,10
+ ,72
+ ,44
+ ,32
+ ,30
+ ,14
+ ,10
+ ,14
+ ,14
+ ,67
+ ,40
+ ,32
+ ,33
+ ,12
+ ,12
+ ,11
+ ,18
+ ,63
+ ,37
+ ,34
+ ,38
+ ,16
+ ,13
+ ,12
+ ,14
+ ,62
+ ,46
+ ,32
+ ,32
+ ,9
+ ,5
+ ,12
+ ,11
+ ,63
+ ,36
+ ,37
+ ,32
+ ,14
+ ,6
+ ,15
+ ,12
+ ,76
+ ,47
+ ,39
+ ,34
+ ,16
+ ,12
+ ,16
+ ,13
+ ,74
+ ,45
+ ,29
+ ,34
+ ,16
+ ,12
+ ,15
+ ,9
+ ,67
+ ,42
+ ,37
+ ,36
+ ,15
+ ,11
+ ,12
+ ,10
+ ,73
+ ,43
+ ,35
+ ,34
+ ,16
+ ,10
+ ,12
+ ,15
+ ,70
+ ,43
+ ,30
+ ,28
+ ,12
+ ,7
+ ,8
+ ,20
+ ,53
+ ,32
+ ,38
+ ,34
+ ,16
+ ,12
+ ,13
+ ,12
+ ,77
+ ,45
+ ,34
+ ,35
+ ,16
+ ,14
+ ,11
+ ,12
+ ,77
+ ,45
+ ,31
+ ,35
+ ,14
+ ,11
+ ,14
+ ,14
+ ,52
+ ,31
+ ,34
+ ,31
+ ,16
+ ,12
+ ,15
+ ,13
+ ,54
+ ,33
+ ,35
+ ,37
+ ,17
+ ,13
+ ,10
+ ,11
+ ,80
+ ,49
+ ,36
+ ,35
+ ,18
+ ,14
+ ,11
+ ,17
+ ,66
+ ,42
+ ,30
+ ,27
+ ,18
+ ,11
+ ,12
+ ,12
+ ,73
+ ,41
+ ,39
+ ,40
+ ,12
+ ,12
+ ,15
+ ,13
+ ,63
+ ,38
+ ,35
+ ,37
+ ,16
+ ,12
+ ,15
+ ,14
+ ,69
+ ,42
+ ,38
+ ,36
+ ,10
+ ,8
+ ,14
+ ,13
+ ,67
+ ,44
+ ,31
+ ,38
+ ,14
+ ,11
+ ,16
+ ,15
+ ,54
+ ,33
+ ,34
+ ,39
+ ,18
+ ,14
+ ,15
+ ,13
+ ,81
+ ,48
+ ,38
+ ,41
+ ,18
+ ,14
+ ,15
+ ,10
+ ,69
+ ,40
+ ,34
+ ,27
+ ,16
+ ,12
+ ,13
+ ,11
+ ,84
+ ,50
+ ,39
+ ,30
+ ,17
+ ,9
+ ,12
+ ,19
+ ,80
+ ,49
+ ,37
+ ,37
+ ,16
+ ,13
+ ,17
+ ,13
+ ,70
+ ,43
+ ,34
+ ,31
+ ,16
+ ,11
+ ,13
+ ,17
+ ,69
+ ,44
+ ,28
+ ,31
+ ,13
+ ,12
+ ,15
+ ,13
+ ,77
+ ,47
+ ,37
+ ,27
+ ,16
+ ,12
+ ,13
+ ,9
+ ,54
+ ,33
+ ,33
+ ,36
+ ,16
+ ,12
+ ,15
+ ,11
+ ,79
+ ,46
+ ,37
+ ,38
+ ,20
+ ,12
+ ,16
+ ,10
+ ,30
+ ,0
+ ,35
+ ,37
+ ,16
+ ,12
+ ,15
+ ,9
+ ,71
+ ,45
+ ,37
+ ,33
+ ,15
+ ,12
+ ,16
+ ,12
+ ,73
+ ,43
+ ,32
+ ,34
+ ,15
+ ,11
+ ,15
+ ,12
+ ,72
+ ,44
+ ,33
+ ,31
+ ,16
+ ,10
+ ,14
+ ,13
+ ,77
+ ,47
+ ,38
+ ,39
+ ,14
+ ,9
+ ,15
+ ,13
+ ,75
+ ,45
+ ,33
+ ,34
+ ,16
+ ,12
+ ,14
+ ,12
+ ,69
+ ,42
+ ,29
+ ,32
+ ,16
+ ,12
+ ,13
+ ,15
+ ,54
+ ,33
+ ,33
+ ,33
+ ,15
+ ,12
+ ,7
+ ,22
+ ,70
+ ,43
+ ,31
+ ,36
+ ,12
+ ,9
+ ,17
+ ,13
+ ,73
+ ,46
+ ,36
+ ,32
+ ,17
+ ,15
+ ,13
+ ,15
+ ,54
+ ,33
+ ,35
+ ,41
+ ,16
+ ,12
+ ,15
+ ,13
+ ,77
+ ,46
+ ,32
+ ,28
+ ,15
+ ,12
+ ,14
+ ,15
+ ,82
+ ,48
+ ,29
+ ,30
+ ,13
+ ,12
+ ,13
+ ,10
+ ,80
+ ,47
+ ,39
+ ,36
+ ,16
+ ,10
+ ,16
+ ,11
+ ,80
+ ,47
+ ,37
+ ,35
+ ,16
+ ,13
+ ,12
+ ,16
+ ,69
+ ,43
+ ,35
+ ,31
+ ,16
+ ,9
+ ,14
+ ,11
+ ,78
+ ,46
+ ,37
+ ,34
+ ,16
+ ,12
+ ,17
+ ,11
+ ,81
+ ,48
+ ,32
+ ,36
+ ,14
+ ,10
+ ,15
+ ,10
+ ,76
+ ,46
+ ,38
+ ,36
+ ,16
+ ,14
+ ,17
+ ,10
+ ,76
+ ,45
+ ,37
+ ,35
+ ,16
+ ,11
+ ,12
+ ,16
+ ,73
+ ,45
+ ,36
+ ,37
+ ,20
+ ,15
+ ,16
+ ,12
+ ,85
+ ,52
+ ,32
+ ,28
+ ,15
+ ,11
+ ,11
+ ,11
+ ,66
+ ,42
+ ,33
+ ,39
+ ,16
+ ,11
+ ,15
+ ,16
+ ,79
+ ,47
+ ,40
+ ,32
+ ,13
+ ,12
+ ,9
+ ,19
+ ,68
+ ,41
+ ,38
+ ,35
+ ,17
+ ,12
+ ,16
+ ,11
+ ,76
+ ,47
+ ,41
+ ,39
+ ,16
+ ,12
+ ,15
+ ,16
+ ,71
+ ,43
+ ,36
+ ,35
+ ,16
+ ,11
+ ,10
+ ,15
+ ,54
+ ,33
+ ,43
+ ,42
+ ,12
+ ,7
+ ,10
+ ,24
+ ,46
+ ,30
+ ,30
+ ,34
+ ,16
+ ,12
+ ,15
+ ,14
+ ,82
+ ,49
+ ,31
+ ,33
+ ,16
+ ,14
+ ,11
+ ,15
+ ,74
+ ,44
+ ,32
+ ,41
+ ,17
+ ,11
+ ,13
+ ,11
+ ,88
+ ,55
+ ,32
+ ,33
+ ,13
+ ,11
+ ,14
+ ,15
+ ,38
+ ,11
+ ,37
+ ,34
+ ,12
+ ,10
+ ,18
+ ,12
+ ,76
+ ,47
+ ,37
+ ,32
+ ,18
+ ,13
+ ,16
+ ,10
+ ,86
+ ,53
+ ,33
+ ,40
+ ,14
+ ,13
+ ,14
+ ,14
+ ,54
+ ,33
+ ,34
+ ,40
+ ,14
+ ,8
+ ,14
+ ,13
+ ,70
+ ,44
+ ,33
+ ,35
+ ,13
+ ,11
+ ,14
+ ,9
+ ,69
+ ,42
+ ,38
+ ,36
+ ,16
+ ,12
+ ,14
+ ,15
+ ,90
+ ,55
+ ,33
+ ,37
+ ,13
+ ,11
+ ,12
+ ,15
+ ,54
+ ,33
+ ,31
+ ,27
+ ,16
+ ,13
+ ,14
+ ,14
+ ,76
+ ,46
+ ,38
+ ,39
+ ,13
+ ,12
+ ,15
+ ,11
+ ,89
+ ,54
+ ,37
+ ,38
+ ,16
+ ,14
+ ,15
+ ,8
+ ,76
+ ,47
+ ,33
+ ,31
+ ,15
+ ,13
+ ,15
+ ,11
+ ,73
+ ,45
+ ,31
+ ,33
+ ,16
+ ,15
+ ,13
+ ,11
+ ,79
+ ,47
+ ,39
+ ,32
+ ,15
+ ,10
+ ,17
+ ,8
+ ,90
+ ,55
+ ,44
+ ,39
+ ,17
+ ,11
+ ,17
+ ,10
+ ,74
+ ,44
+ ,33
+ ,36
+ ,15
+ ,9
+ ,19
+ ,11
+ ,81
+ ,53
+ ,35
+ ,33
+ ,12
+ ,11
+ ,15
+ ,13
+ ,72
+ ,44
+ ,32
+ ,33
+ ,16
+ ,10
+ ,13
+ ,11
+ ,71
+ ,42
+ ,28
+ ,32
+ ,10
+ ,11
+ ,9
+ ,20
+ ,66
+ ,40
+ ,40
+ ,37
+ ,16
+ ,8
+ ,15
+ ,10
+ ,77
+ ,46
+ ,27
+ ,30
+ ,12
+ ,11
+ ,15
+ ,15
+ ,65
+ ,40
+ ,37
+ ,38
+ ,14
+ ,12
+ ,15
+ ,12
+ ,74
+ ,46
+ ,32
+ ,29
+ ,15
+ ,12
+ ,16
+ ,14
+ ,82
+ ,53
+ ,28
+ ,22
+ ,13
+ ,9
+ ,11
+ ,23
+ ,54
+ ,33
+ ,34
+ ,35
+ ,15
+ ,11
+ ,14
+ ,14
+ ,63
+ ,42
+ ,30
+ ,35
+ ,11
+ ,10
+ ,11
+ ,16
+ ,54
+ ,35
+ ,35
+ ,34
+ ,12
+ ,8
+ ,15
+ ,11
+ ,64
+ ,40
+ ,31
+ ,35
+ ,8
+ ,9
+ ,13
+ ,12
+ ,69
+ ,41
+ ,32
+ ,34
+ ,16
+ ,8
+ ,15
+ ,10
+ ,54
+ ,33
+ ,30
+ ,34
+ ,15
+ ,9
+ ,16
+ ,14
+ ,84
+ ,51
+ ,30
+ ,35
+ ,17
+ ,15
+ ,14
+ ,12
+ ,86
+ ,53
+ ,31
+ ,23
+ ,16
+ ,11
+ ,15
+ ,12
+ ,77
+ ,46
+ ,40
+ ,31
+ ,10
+ ,8
+ ,16
+ ,11
+ ,89
+ ,55
+ ,32
+ ,27
+ ,18
+ ,13
+ ,16
+ ,12
+ ,76
+ ,47
+ ,36
+ ,36
+ ,13
+ ,12
+ ,11
+ ,13
+ ,60
+ ,38
+ ,32
+ ,31
+ ,16
+ ,12
+ ,12
+ ,11
+ ,75
+ ,46
+ ,35
+ ,32
+ ,13
+ ,9
+ ,9
+ ,19
+ ,73
+ ,46
+ ,38
+ ,39
+ ,10
+ ,7
+ ,16
+ ,12
+ ,85
+ ,53
+ ,42
+ ,37
+ ,15
+ ,13
+ ,13
+ ,17
+ ,79
+ ,47
+ ,34
+ ,38
+ ,16
+ ,9
+ ,16
+ ,9
+ ,71
+ ,41
+ ,35
+ ,39
+ ,16
+ ,6
+ ,12
+ ,12
+ ,72
+ ,44
+ ,35
+ ,34
+ ,14
+ ,8
+ ,9
+ ,19
+ ,69
+ ,43
+ ,33
+ ,31
+ ,10
+ ,8
+ ,13
+ ,18
+ ,78
+ ,51
+ ,36
+ ,32
+ ,17
+ ,15
+ ,13
+ ,15
+ ,54
+ ,33
+ ,32
+ ,37
+ ,13
+ ,6
+ ,14
+ ,14
+ ,69
+ ,43
+ ,33
+ ,36
+ ,15
+ ,9
+ ,19
+ ,11
+ ,81
+ ,53
+ ,34
+ ,32
+ ,16
+ ,11
+ ,13
+ ,9
+ ,84
+ ,51
+ ,32
+ ,35
+ ,12
+ ,8
+ ,12
+ ,18
+ ,84
+ ,50)
+ ,dim=c(8
+ ,161)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:161))
> y <- array(NA,dim=c(8,161),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:161))
> 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 = '3'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '3'
> #'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, 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
Learning Connected Separate Software Happiness Depression Belonging
1 13 41 38 12 14 12 53
2 16 39 32 11 18 11 86
3 19 30 35 15 11 14 66
4 15 31 33 6 12 12 67
5 14 34 37 13 16 21 76
6 13 35 29 10 18 12 78
7 19 39 31 12 14 22 53
8 15 34 36 14 14 11 80
9 14 36 35 12 15 10 74
10 15 37 38 6 15 13 76
11 16 38 31 10 17 10 79
12 16 36 34 12 19 8 54
13 16 38 35 12 10 15 67
14 16 39 38 11 16 14 54
15 17 33 37 15 18 10 87
16 15 32 33 12 14 14 58
17 15 36 32 10 14 14 75
18 20 38 38 12 17 11 88
19 18 39 38 11 14 10 64
20 16 32 32 12 16 13 57
21 16 32 33 11 18 7 66
22 16 31 31 12 11 14 68
23 19 39 38 13 14 12 54
24 16 37 39 11 12 14 56
25 17 39 32 9 17 11 86
26 17 41 32 13 9 9 80
27 16 36 35 10 16 11 76
28 15 33 37 14 14 15 69
29 16 33 33 12 15 14 78
30 14 34 33 10 11 13 67
31 15 31 28 12 16 9 80
32 12 27 32 8 13 15 54
33 14 37 31 10 17 10 71
34 16 34 37 12 15 11 84
35 14 34 30 12 14 13 74
36 7 32 33 7 16 8 71
37 10 29 31 6 9 20 63
38 14 36 33 12 15 12 71
39 16 29 31 10 17 10 76
40 16 35 33 10 13 10 69
41 16 37 32 10 15 9 74
42 14 34 33 12 16 14 75
43 20 38 32 15 16 8 54
44 14 35 33 10 12 14 52
45 14 38 28 10 12 11 69
46 11 37 35 12 11 13 68
47 14 38 39 13 15 9 65
48 15 33 34 11 15 11 75
49 16 36 38 11 17 15 74
50 14 38 32 12 13 11 75
51 16 32 38 14 16 10 72
52 14 32 30 10 14 14 67
53 12 32 33 12 11 18 63
54 16 34 38 13 12 14 62
55 9 32 32 5 12 11 63
56 14 37 32 6 15 12 76
57 16 39 34 12 16 13 74
58 16 29 34 12 15 9 67
59 15 37 36 11 12 10 73
60 16 35 34 10 12 15 70
61 12 30 28 7 8 20 53
62 16 38 34 12 13 12 77
63 16 34 35 14 11 12 77
64 14 31 35 11 14 14 52
65 16 34 31 12 15 13 54
66 17 35 37 13 10 11 80
67 18 36 35 14 11 17 66
68 18 30 27 11 12 12 73
69 12 39 40 12 15 13 63
70 16 35 37 12 15 14 69
71 10 38 36 8 14 13 67
72 14 31 38 11 16 15 54
73 18 34 39 14 15 13 81
74 18 38 41 14 15 10 69
75 16 34 27 12 13 11 84
76 17 39 30 9 12 19 80
77 16 37 37 13 17 13 70
78 16 34 31 11 13 17 69
79 13 28 31 12 15 13 77
80 16 37 27 12 13 9 54
81 16 33 36 12 15 11 79
82 20 37 38 12 16 10 30
83 16 35 37 12 15 9 71
84 15 37 33 12 16 12 73
85 15 32 34 11 15 12 72
86 16 33 31 10 14 13 77
87 14 38 39 9 15 13 75
88 16 33 34 12 14 12 69
89 16 29 32 12 13 15 54
90 15 33 33 12 7 22 70
91 12 31 36 9 17 13 73
92 17 36 32 15 13 15 54
93 16 35 41 12 15 13 77
94 15 32 28 12 14 15 82
95 13 29 30 12 13 10 80
96 16 39 36 10 16 11 80
97 16 37 35 13 12 16 69
98 16 35 31 9 14 11 78
99 16 37 34 12 17 11 81
100 14 32 36 10 15 10 76
101 16 38 36 14 17 10 76
102 16 37 35 11 12 16 73
103 20 36 37 15 16 12 85
104 15 32 28 11 11 11 66
105 16 33 39 11 15 16 79
106 13 40 32 12 9 19 68
107 17 38 35 12 16 11 76
108 16 41 39 12 15 16 71
109 16 36 35 11 10 15 54
110 12 43 42 7 10 24 46
111 16 30 34 12 15 14 82
112 16 31 33 14 11 15 74
113 17 32 41 11 13 11 88
114 13 32 33 11 14 15 38
115 12 37 34 10 18 12 76
116 18 37 32 13 16 10 86
117 14 33 40 13 14 14 54
118 14 34 40 8 14 13 70
119 13 33 35 11 14 9 69
120 16 38 36 12 14 15 90
121 13 33 37 11 12 15 54
122 16 31 27 13 14 14 76
123 13 38 39 12 15 11 89
124 16 37 38 14 15 8 76
125 15 33 31 13 15 11 73
126 16 31 33 15 13 11 79
127 15 39 32 10 17 8 90
128 17 44 39 11 17 10 74
129 15 33 36 9 19 11 81
130 12 35 33 11 15 13 72
131 16 32 33 10 13 11 71
132 10 28 32 11 9 20 66
133 16 40 37 8 15 10 77
134 12 27 30 11 15 15 65
135 14 37 38 12 15 12 74
136 15 32 29 12 16 14 82
137 13 28 22 9 11 23 54
138 15 34 35 11 14 14 63
139 11 30 35 10 11 16 54
140 12 35 34 8 15 11 64
141 8 31 35 9 13 12 69
142 16 32 34 8 15 10 54
143 15 30 34 9 16 14 84
144 17 30 35 15 14 12 86
145 16 31 23 11 15 12 77
146 10 40 31 8 16 11 89
147 18 32 27 13 16 12 76
148 13 36 36 12 11 13 60
149 16 32 31 12 12 11 75
150 13 35 32 9 9 19 73
151 10 38 39 7 16 12 85
152 15 42 37 13 13 17 79
153 16 34 38 9 16 9 71
154 16 35 39 6 12 12 72
155 14 35 34 8 9 19 69
156 10 33 31 8 13 18 78
157 17 36 32 15 13 15 54
158 13 32 37 6 14 14 69
159 15 33 36 9 19 11 81
160 16 34 32 11 13 9 84
161 12 32 35 8 12 18 84
Belonging_Final
1 32
2 51
3 42
4 41
5 46
6 47
7 37
8 49
9 45
10 47
11 49
12 33
13 42
14 33
15 53
16 36
17 45
18 54
19 41
20 36
21 41
22 44
23 33
24 37
25 52
26 47
27 43
28 44
29 45
30 44
31 49
32 33
33 43
34 54
35 42
36 44
37 37
38 43
39 46
40 42
41 45
42 44
43 33
44 31
45 42
46 40
47 43
48 46
49 42
50 45
51 44
52 40
53 37
54 46
55 36
56 47
57 45
58 42
59 43
60 43
61 32
62 45
63 45
64 31
65 33
66 49
67 42
68 41
69 38
70 42
71 44
72 33
73 48
74 40
75 50
76 49
77 43
78 44
79 47
80 33
81 46
82 0
83 45
84 43
85 44
86 47
87 45
88 42
89 33
90 43
91 46
92 33
93 46
94 48
95 47
96 47
97 43
98 46
99 48
100 46
101 45
102 45
103 52
104 42
105 47
106 41
107 47
108 43
109 33
110 30
111 49
112 44
113 55
114 11
115 47
116 53
117 33
118 44
119 42
120 55
121 33
122 46
123 54
124 47
125 45
126 47
127 55
128 44
129 53
130 44
131 42
132 40
133 46
134 40
135 46
136 53
137 33
138 42
139 35
140 40
141 41
142 33
143 51
144 53
145 46
146 55
147 47
148 38
149 46
150 46
151 53
152 47
153 41
154 44
155 43
156 51
157 33
158 43
159 53
160 51
161 50
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Software
5.50659 0.11421 -0.02105 0.54377
Happiness Depression Belonging Belonging_Final
0.05963 -0.07157 0.03659 -0.05355
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.8966 -1.1111 0.1426 1.1166 3.9481
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.50659 2.60516 2.114 0.0362 *
Connected 0.11421 0.04705 2.428 0.0164 *
Separate -0.02105 0.04501 -0.468 0.6406
Software 0.54377 0.06952 7.821 7.94e-13 ***
Happiness 0.05963 0.07662 0.778 0.4376
Depression -0.07157 0.05657 -1.265 0.2077
Belonging 0.03659 0.04493 0.814 0.4167
Belonging_Final -0.05355 0.06461 -0.829 0.4085
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.856 on 153 degrees of freedom
Multiple R-squared: 0.3538, Adjusted R-squared: 0.3243
F-statistic: 11.97 on 7 and 153 DF, p-value: 3.999e-12
> 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,] 0.389151031 0.77830206 0.61084897
[2,] 0.622140028 0.75571994 0.37785997
[3,] 0.483033059 0.96606612 0.51696694
[4,] 0.456387390 0.91277478 0.54361261
[5,] 0.347784746 0.69556949 0.65221525
[6,] 0.263600614 0.52720123 0.73639939
[7,] 0.214652324 0.42930465 0.78534768
[8,] 0.414116625 0.82823325 0.58588337
[9,] 0.337270603 0.67454121 0.66272940
[10,] 0.258542229 0.51708446 0.74145777
[11,] 0.199499380 0.39899876 0.80050062
[12,] 0.181810432 0.36362086 0.81818957
[13,] 0.364158907 0.72831781 0.63584109
[14,] 0.400219057 0.80043811 0.59978094
[15,] 0.389138206 0.77827641 0.61086179
[16,] 0.351628403 0.70325681 0.64837160
[17,] 0.410639528 0.82127906 0.58936047
[18,] 0.416582672 0.83316534 0.58341733
[19,] 0.397714523 0.79542905 0.60228548
[20,] 0.439279806 0.87855961 0.56072019
[21,] 0.381431269 0.76286254 0.61856873
[22,] 0.335136234 0.67027247 0.66486377
[23,] 0.311901870 0.62380374 0.68809813
[24,] 0.279056086 0.55811217 0.72094391
[25,] 0.238671957 0.47734391 0.76132804
[26,] 0.822427491 0.35514502 0.17757251
[27,] 0.795239548 0.40952090 0.20476045
[28,] 0.788967979 0.42206404 0.21103202
[29,] 0.810757544 0.37848491 0.18924246
[30,] 0.791795061 0.41640988 0.20820494
[31,] 0.758168151 0.48366370 0.24183185
[32,] 0.732518816 0.53496237 0.26748118
[33,] 0.757936033 0.48412793 0.24206397
[34,] 0.714280356 0.57143929 0.28571964
[35,] 0.685051413 0.62989717 0.31494859
[36,] 0.854494660 0.29101068 0.14550534
[37,] 0.903951256 0.19209749 0.09604874
[38,] 0.879926283 0.24014743 0.12007372
[39,] 0.863242228 0.27351554 0.13675777
[40,] 0.862182932 0.27563414 0.13781707
[41,] 0.832815589 0.33436882 0.16718441
[42,] 0.799168663 0.40166267 0.20083134
[43,] 0.816639238 0.36672152 0.18336076
[44,] 0.803546565 0.39290687 0.19645344
[45,] 0.817396477 0.36520705 0.18260352
[46,] 0.791381927 0.41723615 0.20861807
[47,] 0.756078049 0.48784390 0.24392195
[48,] 0.732028190 0.53594362 0.26797181
[49,] 0.693329424 0.61334115 0.30667058
[50,] 0.689665087 0.62066983 0.31033491
[51,] 0.650943859 0.69811228 0.34905614
[52,] 0.607684557 0.78463089 0.39231544
[53,] 0.567620131 0.86475974 0.43237987
[54,] 0.519776502 0.96044700 0.48022350
[55,] 0.480843544 0.96168709 0.51915646
[56,] 0.451550593 0.90310119 0.54844941
[57,] 0.448713149 0.89742630 0.55128685
[58,] 0.597827976 0.80434405 0.40217202
[59,] 0.714897548 0.57020490 0.28510245
[60,] 0.679068582 0.64186284 0.32093142
[61,] 0.788914331 0.42217134 0.21108567
[62,] 0.753062784 0.49387443 0.24693722
[63,] 0.742878099 0.51424380 0.25712190
[64,] 0.722335860 0.55532828 0.27766414
[65,] 0.681603554 0.63679289 0.31839645
[66,] 0.734409257 0.53118149 0.26559074
[67,] 0.695949307 0.60810139 0.30405069
[68,] 0.682701126 0.63459775 0.31729887
[69,] 0.688375501 0.62324900 0.31162450
[70,] 0.645667743 0.70866451 0.35433226
[71,] 0.605446653 0.78910669 0.39455335
[72,] 0.729842656 0.54031469 0.27015734
[73,] 0.691288017 0.61742397 0.30871198
[74,] 0.660443711 0.67911258 0.33955629
[75,] 0.617199815 0.76560037 0.38280018
[76,] 0.610446712 0.77910658 0.38955329
[77,] 0.564709031 0.87058194 0.43529097
[78,] 0.524269687 0.95146063 0.47573031
[79,] 0.507439276 0.98512145 0.49256072
[80,] 0.473356449 0.94671290 0.52664355
[81,] 0.453806356 0.90761271 0.54619364
[82,] 0.415660272 0.83132054 0.58433973
[83,] 0.374058193 0.74811639 0.62594181
[84,] 0.332724002 0.66544800 0.66727600
[85,] 0.348396702 0.69679340 0.65160330
[86,] 0.314133713 0.62826743 0.68586629
[87,] 0.277525301 0.55505060 0.72247470
[88,] 0.275793340 0.55158668 0.72420666
[89,] 0.237431288 0.47486258 0.76256871
[90,] 0.203484227 0.40696845 0.79651577
[91,] 0.183032885 0.36606577 0.81696712
[92,] 0.169138322 0.33827664 0.83086168
[93,] 0.209156844 0.41831369 0.79084316
[94,] 0.177024659 0.35404932 0.82297534
[95,] 0.170892394 0.34178479 0.82910761
[96,] 0.182198546 0.36439709 0.81780145
[97,] 0.163441085 0.32688217 0.83655892
[98,] 0.144488549 0.28897710 0.85551145
[99,] 0.138613156 0.27722631 0.86138684
[100,] 0.131131324 0.26226265 0.86886868
[101,] 0.114623916 0.22924783 0.88537608
[102,] 0.094356591 0.18871318 0.90564341
[103,] 0.110763204 0.22152641 0.88923680
[104,] 0.103391747 0.20678349 0.89660825
[105,] 0.132559781 0.26511956 0.86744022
[106,] 0.125713269 0.25142654 0.87428673
[107,] 0.108119916 0.21623983 0.89188008
[108,] 0.093906518 0.18781304 0.90609348
[109,] 0.096207981 0.19241596 0.90379202
[110,] 0.089020070 0.17804014 0.91097993
[111,] 0.074376992 0.14875398 0.92562301
[112,] 0.058320188 0.11664038 0.94167981
[113,] 0.066296044 0.13259209 0.93370396
[114,] 0.052282286 0.10456457 0.94771771
[115,] 0.041646808 0.08329362 0.95835319
[116,] 0.031496727 0.06299345 0.96850327
[117,] 0.023339234 0.04667847 0.97666077
[118,] 0.018037772 0.03607554 0.98196223
[119,] 0.014576429 0.02915286 0.98542357
[120,] 0.020569593 0.04113919 0.97943041
[121,] 0.016659392 0.03331878 0.98334061
[122,] 0.026310932 0.05262186 0.97368907
[123,] 0.026869840 0.05373968 0.97313016
[124,] 0.036396292 0.07279258 0.96360371
[125,] 0.028247857 0.05649571 0.97175214
[126,] 0.019004510 0.03800902 0.98099549
[127,] 0.012436596 0.02487319 0.98756340
[128,] 0.008880797 0.01776159 0.99111920
[129,] 0.013122676 0.02624535 0.98687732
[130,] 0.010573701 0.02114740 0.98942630
[131,] 0.554083709 0.89183258 0.44591629
[132,] 0.487503328 0.97500666 0.51249667
[133,] 0.401658079 0.80331616 0.59834192
[134,] 0.321939186 0.64387837 0.67806081
[135,] 0.239550171 0.47910034 0.76044983
[136,] 0.294878452 0.58975690 0.70512155
[137,] 0.237363938 0.47472788 0.76263606
[138,] 0.738241860 0.52351628 0.26175814
[139,] 0.781658180 0.43668364 0.21834182
[140,] 0.624799516 0.75040097 0.37520048
> postscript(file="/var/wessaorg/rcomp/tmp/12ipo1352150119.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2jh961352150119.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/31s641352150119.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4qrzm1352150119.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/56a1r1352150119.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 = 161
Frequency = 1
1 2 3 4 5 6
-3.11637378 0.02920411 2.82725850 3.27196896 -1.44884429 -1.88321678
7 8 9 10 11 12
3.94810617 -1.59584553 -1.88360899 2.57658996 0.80325061 -0.19703448
13 14 15 16 17 18
0.63950141 0.69664690 -0.35642108 -0.01938934 0.45009567 3.87305260
19 20 21 22 23 24
2.59206963 0.80531204 0.75984121 1.29405231 2.58522867 1.32565192
25 26 27 28 29 30
2.22992569 0.11216971 1.03559481 -1.03950779 0.55682709 0.04609385
31 32 33 34 35 36
-0.59651237 -0.17736923 -1.11107281 0.57447646 -1.64675004 -5.89656195
37 38 39 40 41 42
-0.85806456 -1.77988902 1.78028404 1.41762791 0.95499033 -1.56078434
43 44 45 46 47 48
2.08002211 -0.20338452 -0.89906931 -4.59274728 -2.42091382 0.07026332
49 50 51 52 53 54
0.80127552 -2.02094723 -0.49114193 -0.11015915 -2.68362357 0.82204229
55 56 57 58 59 60
-2.51245333 1.37869473 -0.09221445 0.91874922 -0.32459341 1.87312273
61 62 63 64 65 66
0.57862192 0.01954659 -0.47083450 -0.36747273 0.56461101 1.09330208
67 68 69 70 71 72
1.90048060 3.32144264 -3.87855870 0.58131875 -3.43894196 -0.31809962
73 74 75 76 77 78
1.46068696 0.84198623 0.26901918 3.11746871 -0.36475725 1.55404653
79 80 81 82 83 84
-1.84211623 -0.02925351 0.42222972 3.20622521 0.31091632 -1.02691766
85 86 87 88 89 90
0.25873082 1.73400799 -0.21837926 0.66306857 1.41912473 0.79210855
91 92 93 94 95 96
-1.47460451 -0.01166158 0.51540633 -0.28876197 -2.18260481 0.78183051
97 98 99 100 101 102
0.14260558 1.81607857 -0.16207964 -0.33781038 -1.37096972 1.19086940
103 104 105 106 107 108
2.58299544 0.41183217 1.44056346 -2.39630302 0.93381444 0.06166729
109 110 111 112 113 114
1.40548538 -0.29528151 0.98832472 0.10063307 2.45732632 -2.01083890
115 116 117 118 119 120
-2.93318196 1.32486891 -1.54425949 0.99233778 -1.98681872 0.27648803
121 122 123 124 125 126
-1.32904180 0.30151610 -3.02321968 -1.13143533 -1.06080211 -0.87101385
127 128 129 130 131 132
-0.51429148 0.65788400 1.11664628 -3.03338330 1.75864459 -3.39079748
133 134 135 136 137 138
1.82051121 -1.99775594 -1.73796960 -0.19081635 0.64594197 0.47638099
139 140 141 142 143 144
-2.24645208 -1.44559924 -5.45005530 2.81656653 1.59391195 0.36236868
145 146 147 148 149 150
1.06547507 -4.25107475 1.97844347 -2.27183366 0.75643952 -0.10917159
151 152 153 154 155 156
-3.19960054 -1.52614309 1.80367864 3.91912628 1.46244100 -2.58337130
157 158 159 160 161
-0.01166158 1.29975670 1.11664628 0.82846476 -0.59841292
> postscript(file="/var/wessaorg/rcomp/tmp/61sc51352150119.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 = 161
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.11637378 NA
1 0.02920411 -3.11637378
2 2.82725850 0.02920411
3 3.27196896 2.82725850
4 -1.44884429 3.27196896
5 -1.88321678 -1.44884429
6 3.94810617 -1.88321678
7 -1.59584553 3.94810617
8 -1.88360899 -1.59584553
9 2.57658996 -1.88360899
10 0.80325061 2.57658996
11 -0.19703448 0.80325061
12 0.63950141 -0.19703448
13 0.69664690 0.63950141
14 -0.35642108 0.69664690
15 -0.01938934 -0.35642108
16 0.45009567 -0.01938934
17 3.87305260 0.45009567
18 2.59206963 3.87305260
19 0.80531204 2.59206963
20 0.75984121 0.80531204
21 1.29405231 0.75984121
22 2.58522867 1.29405231
23 1.32565192 2.58522867
24 2.22992569 1.32565192
25 0.11216971 2.22992569
26 1.03559481 0.11216971
27 -1.03950779 1.03559481
28 0.55682709 -1.03950779
29 0.04609385 0.55682709
30 -0.59651237 0.04609385
31 -0.17736923 -0.59651237
32 -1.11107281 -0.17736923
33 0.57447646 -1.11107281
34 -1.64675004 0.57447646
35 -5.89656195 -1.64675004
36 -0.85806456 -5.89656195
37 -1.77988902 -0.85806456
38 1.78028404 -1.77988902
39 1.41762791 1.78028404
40 0.95499033 1.41762791
41 -1.56078434 0.95499033
42 2.08002211 -1.56078434
43 -0.20338452 2.08002211
44 -0.89906931 -0.20338452
45 -4.59274728 -0.89906931
46 -2.42091382 -4.59274728
47 0.07026332 -2.42091382
48 0.80127552 0.07026332
49 -2.02094723 0.80127552
50 -0.49114193 -2.02094723
51 -0.11015915 -0.49114193
52 -2.68362357 -0.11015915
53 0.82204229 -2.68362357
54 -2.51245333 0.82204229
55 1.37869473 -2.51245333
56 -0.09221445 1.37869473
57 0.91874922 -0.09221445
58 -0.32459341 0.91874922
59 1.87312273 -0.32459341
60 0.57862192 1.87312273
61 0.01954659 0.57862192
62 -0.47083450 0.01954659
63 -0.36747273 -0.47083450
64 0.56461101 -0.36747273
65 1.09330208 0.56461101
66 1.90048060 1.09330208
67 3.32144264 1.90048060
68 -3.87855870 3.32144264
69 0.58131875 -3.87855870
70 -3.43894196 0.58131875
71 -0.31809962 -3.43894196
72 1.46068696 -0.31809962
73 0.84198623 1.46068696
74 0.26901918 0.84198623
75 3.11746871 0.26901918
76 -0.36475725 3.11746871
77 1.55404653 -0.36475725
78 -1.84211623 1.55404653
79 -0.02925351 -1.84211623
80 0.42222972 -0.02925351
81 3.20622521 0.42222972
82 0.31091632 3.20622521
83 -1.02691766 0.31091632
84 0.25873082 -1.02691766
85 1.73400799 0.25873082
86 -0.21837926 1.73400799
87 0.66306857 -0.21837926
88 1.41912473 0.66306857
89 0.79210855 1.41912473
90 -1.47460451 0.79210855
91 -0.01166158 -1.47460451
92 0.51540633 -0.01166158
93 -0.28876197 0.51540633
94 -2.18260481 -0.28876197
95 0.78183051 -2.18260481
96 0.14260558 0.78183051
97 1.81607857 0.14260558
98 -0.16207964 1.81607857
99 -0.33781038 -0.16207964
100 -1.37096972 -0.33781038
101 1.19086940 -1.37096972
102 2.58299544 1.19086940
103 0.41183217 2.58299544
104 1.44056346 0.41183217
105 -2.39630302 1.44056346
106 0.93381444 -2.39630302
107 0.06166729 0.93381444
108 1.40548538 0.06166729
109 -0.29528151 1.40548538
110 0.98832472 -0.29528151
111 0.10063307 0.98832472
112 2.45732632 0.10063307
113 -2.01083890 2.45732632
114 -2.93318196 -2.01083890
115 1.32486891 -2.93318196
116 -1.54425949 1.32486891
117 0.99233778 -1.54425949
118 -1.98681872 0.99233778
119 0.27648803 -1.98681872
120 -1.32904180 0.27648803
121 0.30151610 -1.32904180
122 -3.02321968 0.30151610
123 -1.13143533 -3.02321968
124 -1.06080211 -1.13143533
125 -0.87101385 -1.06080211
126 -0.51429148 -0.87101385
127 0.65788400 -0.51429148
128 1.11664628 0.65788400
129 -3.03338330 1.11664628
130 1.75864459 -3.03338330
131 -3.39079748 1.75864459
132 1.82051121 -3.39079748
133 -1.99775594 1.82051121
134 -1.73796960 -1.99775594
135 -0.19081635 -1.73796960
136 0.64594197 -0.19081635
137 0.47638099 0.64594197
138 -2.24645208 0.47638099
139 -1.44559924 -2.24645208
140 -5.45005530 -1.44559924
141 2.81656653 -5.45005530
142 1.59391195 2.81656653
143 0.36236868 1.59391195
144 1.06547507 0.36236868
145 -4.25107475 1.06547507
146 1.97844347 -4.25107475
147 -2.27183366 1.97844347
148 0.75643952 -2.27183366
149 -0.10917159 0.75643952
150 -3.19960054 -0.10917159
151 -1.52614309 -3.19960054
152 1.80367864 -1.52614309
153 3.91912628 1.80367864
154 1.46244100 3.91912628
155 -2.58337130 1.46244100
156 -0.01166158 -2.58337130
157 1.29975670 -0.01166158
158 1.11664628 1.29975670
159 0.82846476 1.11664628
160 -0.59841292 0.82846476
161 NA -0.59841292
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.02920411 -3.11637378
[2,] 2.82725850 0.02920411
[3,] 3.27196896 2.82725850
[4,] -1.44884429 3.27196896
[5,] -1.88321678 -1.44884429
[6,] 3.94810617 -1.88321678
[7,] -1.59584553 3.94810617
[8,] -1.88360899 -1.59584553
[9,] 2.57658996 -1.88360899
[10,] 0.80325061 2.57658996
[11,] -0.19703448 0.80325061
[12,] 0.63950141 -0.19703448
[13,] 0.69664690 0.63950141
[14,] -0.35642108 0.69664690
[15,] -0.01938934 -0.35642108
[16,] 0.45009567 -0.01938934
[17,] 3.87305260 0.45009567
[18,] 2.59206963 3.87305260
[19,] 0.80531204 2.59206963
[20,] 0.75984121 0.80531204
[21,] 1.29405231 0.75984121
[22,] 2.58522867 1.29405231
[23,] 1.32565192 2.58522867
[24,] 2.22992569 1.32565192
[25,] 0.11216971 2.22992569
[26,] 1.03559481 0.11216971
[27,] -1.03950779 1.03559481
[28,] 0.55682709 -1.03950779
[29,] 0.04609385 0.55682709
[30,] -0.59651237 0.04609385
[31,] -0.17736923 -0.59651237
[32,] -1.11107281 -0.17736923
[33,] 0.57447646 -1.11107281
[34,] -1.64675004 0.57447646
[35,] -5.89656195 -1.64675004
[36,] -0.85806456 -5.89656195
[37,] -1.77988902 -0.85806456
[38,] 1.78028404 -1.77988902
[39,] 1.41762791 1.78028404
[40,] 0.95499033 1.41762791
[41,] -1.56078434 0.95499033
[42,] 2.08002211 -1.56078434
[43,] -0.20338452 2.08002211
[44,] -0.89906931 -0.20338452
[45,] -4.59274728 -0.89906931
[46,] -2.42091382 -4.59274728
[47,] 0.07026332 -2.42091382
[48,] 0.80127552 0.07026332
[49,] -2.02094723 0.80127552
[50,] -0.49114193 -2.02094723
[51,] -0.11015915 -0.49114193
[52,] -2.68362357 -0.11015915
[53,] 0.82204229 -2.68362357
[54,] -2.51245333 0.82204229
[55,] 1.37869473 -2.51245333
[56,] -0.09221445 1.37869473
[57,] 0.91874922 -0.09221445
[58,] -0.32459341 0.91874922
[59,] 1.87312273 -0.32459341
[60,] 0.57862192 1.87312273
[61,] 0.01954659 0.57862192
[62,] -0.47083450 0.01954659
[63,] -0.36747273 -0.47083450
[64,] 0.56461101 -0.36747273
[65,] 1.09330208 0.56461101
[66,] 1.90048060 1.09330208
[67,] 3.32144264 1.90048060
[68,] -3.87855870 3.32144264
[69,] 0.58131875 -3.87855870
[70,] -3.43894196 0.58131875
[71,] -0.31809962 -3.43894196
[72,] 1.46068696 -0.31809962
[73,] 0.84198623 1.46068696
[74,] 0.26901918 0.84198623
[75,] 3.11746871 0.26901918
[76,] -0.36475725 3.11746871
[77,] 1.55404653 -0.36475725
[78,] -1.84211623 1.55404653
[79,] -0.02925351 -1.84211623
[80,] 0.42222972 -0.02925351
[81,] 3.20622521 0.42222972
[82,] 0.31091632 3.20622521
[83,] -1.02691766 0.31091632
[84,] 0.25873082 -1.02691766
[85,] 1.73400799 0.25873082
[86,] -0.21837926 1.73400799
[87,] 0.66306857 -0.21837926
[88,] 1.41912473 0.66306857
[89,] 0.79210855 1.41912473
[90,] -1.47460451 0.79210855
[91,] -0.01166158 -1.47460451
[92,] 0.51540633 -0.01166158
[93,] -0.28876197 0.51540633
[94,] -2.18260481 -0.28876197
[95,] 0.78183051 -2.18260481
[96,] 0.14260558 0.78183051
[97,] 1.81607857 0.14260558
[98,] -0.16207964 1.81607857
[99,] -0.33781038 -0.16207964
[100,] -1.37096972 -0.33781038
[101,] 1.19086940 -1.37096972
[102,] 2.58299544 1.19086940
[103,] 0.41183217 2.58299544
[104,] 1.44056346 0.41183217
[105,] -2.39630302 1.44056346
[106,] 0.93381444 -2.39630302
[107,] 0.06166729 0.93381444
[108,] 1.40548538 0.06166729
[109,] -0.29528151 1.40548538
[110,] 0.98832472 -0.29528151
[111,] 0.10063307 0.98832472
[112,] 2.45732632 0.10063307
[113,] -2.01083890 2.45732632
[114,] -2.93318196 -2.01083890
[115,] 1.32486891 -2.93318196
[116,] -1.54425949 1.32486891
[117,] 0.99233778 -1.54425949
[118,] -1.98681872 0.99233778
[119,] 0.27648803 -1.98681872
[120,] -1.32904180 0.27648803
[121,] 0.30151610 -1.32904180
[122,] -3.02321968 0.30151610
[123,] -1.13143533 -3.02321968
[124,] -1.06080211 -1.13143533
[125,] -0.87101385 -1.06080211
[126,] -0.51429148 -0.87101385
[127,] 0.65788400 -0.51429148
[128,] 1.11664628 0.65788400
[129,] -3.03338330 1.11664628
[130,] 1.75864459 -3.03338330
[131,] -3.39079748 1.75864459
[132,] 1.82051121 -3.39079748
[133,] -1.99775594 1.82051121
[134,] -1.73796960 -1.99775594
[135,] -0.19081635 -1.73796960
[136,] 0.64594197 -0.19081635
[137,] 0.47638099 0.64594197
[138,] -2.24645208 0.47638099
[139,] -1.44559924 -2.24645208
[140,] -5.45005530 -1.44559924
[141,] 2.81656653 -5.45005530
[142,] 1.59391195 2.81656653
[143,] 0.36236868 1.59391195
[144,] 1.06547507 0.36236868
[145,] -4.25107475 1.06547507
[146,] 1.97844347 -4.25107475
[147,] -2.27183366 1.97844347
[148,] 0.75643952 -2.27183366
[149,] -0.10917159 0.75643952
[150,] -3.19960054 -0.10917159
[151,] -1.52614309 -3.19960054
[152,] 1.80367864 -1.52614309
[153,] 3.91912628 1.80367864
[154,] 1.46244100 3.91912628
[155,] -2.58337130 1.46244100
[156,] -0.01166158 -2.58337130
[157,] 1.29975670 -0.01166158
[158,] 1.11664628 1.29975670
[159,] 0.82846476 1.11664628
[160,] -0.59841292 0.82846476
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.02920411 -3.11637378
2 2.82725850 0.02920411
3 3.27196896 2.82725850
4 -1.44884429 3.27196896
5 -1.88321678 -1.44884429
6 3.94810617 -1.88321678
7 -1.59584553 3.94810617
8 -1.88360899 -1.59584553
9 2.57658996 -1.88360899
10 0.80325061 2.57658996
11 -0.19703448 0.80325061
12 0.63950141 -0.19703448
13 0.69664690 0.63950141
14 -0.35642108 0.69664690
15 -0.01938934 -0.35642108
16 0.45009567 -0.01938934
17 3.87305260 0.45009567
18 2.59206963 3.87305260
19 0.80531204 2.59206963
20 0.75984121 0.80531204
21 1.29405231 0.75984121
22 2.58522867 1.29405231
23 1.32565192 2.58522867
24 2.22992569 1.32565192
25 0.11216971 2.22992569
26 1.03559481 0.11216971
27 -1.03950779 1.03559481
28 0.55682709 -1.03950779
29 0.04609385 0.55682709
30 -0.59651237 0.04609385
31 -0.17736923 -0.59651237
32 -1.11107281 -0.17736923
33 0.57447646 -1.11107281
34 -1.64675004 0.57447646
35 -5.89656195 -1.64675004
36 -0.85806456 -5.89656195
37 -1.77988902 -0.85806456
38 1.78028404 -1.77988902
39 1.41762791 1.78028404
40 0.95499033 1.41762791
41 -1.56078434 0.95499033
42 2.08002211 -1.56078434
43 -0.20338452 2.08002211
44 -0.89906931 -0.20338452
45 -4.59274728 -0.89906931
46 -2.42091382 -4.59274728
47 0.07026332 -2.42091382
48 0.80127552 0.07026332
49 -2.02094723 0.80127552
50 -0.49114193 -2.02094723
51 -0.11015915 -0.49114193
52 -2.68362357 -0.11015915
53 0.82204229 -2.68362357
54 -2.51245333 0.82204229
55 1.37869473 -2.51245333
56 -0.09221445 1.37869473
57 0.91874922 -0.09221445
58 -0.32459341 0.91874922
59 1.87312273 -0.32459341
60 0.57862192 1.87312273
61 0.01954659 0.57862192
62 -0.47083450 0.01954659
63 -0.36747273 -0.47083450
64 0.56461101 -0.36747273
65 1.09330208 0.56461101
66 1.90048060 1.09330208
67 3.32144264 1.90048060
68 -3.87855870 3.32144264
69 0.58131875 -3.87855870
70 -3.43894196 0.58131875
71 -0.31809962 -3.43894196
72 1.46068696 -0.31809962
73 0.84198623 1.46068696
74 0.26901918 0.84198623
75 3.11746871 0.26901918
76 -0.36475725 3.11746871
77 1.55404653 -0.36475725
78 -1.84211623 1.55404653
79 -0.02925351 -1.84211623
80 0.42222972 -0.02925351
81 3.20622521 0.42222972
82 0.31091632 3.20622521
83 -1.02691766 0.31091632
84 0.25873082 -1.02691766
85 1.73400799 0.25873082
86 -0.21837926 1.73400799
87 0.66306857 -0.21837926
88 1.41912473 0.66306857
89 0.79210855 1.41912473
90 -1.47460451 0.79210855
91 -0.01166158 -1.47460451
92 0.51540633 -0.01166158
93 -0.28876197 0.51540633
94 -2.18260481 -0.28876197
95 0.78183051 -2.18260481
96 0.14260558 0.78183051
97 1.81607857 0.14260558
98 -0.16207964 1.81607857
99 -0.33781038 -0.16207964
100 -1.37096972 -0.33781038
101 1.19086940 -1.37096972
102 2.58299544 1.19086940
103 0.41183217 2.58299544
104 1.44056346 0.41183217
105 -2.39630302 1.44056346
106 0.93381444 -2.39630302
107 0.06166729 0.93381444
108 1.40548538 0.06166729
109 -0.29528151 1.40548538
110 0.98832472 -0.29528151
111 0.10063307 0.98832472
112 2.45732632 0.10063307
113 -2.01083890 2.45732632
114 -2.93318196 -2.01083890
115 1.32486891 -2.93318196
116 -1.54425949 1.32486891
117 0.99233778 -1.54425949
118 -1.98681872 0.99233778
119 0.27648803 -1.98681872
120 -1.32904180 0.27648803
121 0.30151610 -1.32904180
122 -3.02321968 0.30151610
123 -1.13143533 -3.02321968
124 -1.06080211 -1.13143533
125 -0.87101385 -1.06080211
126 -0.51429148 -0.87101385
127 0.65788400 -0.51429148
128 1.11664628 0.65788400
129 -3.03338330 1.11664628
130 1.75864459 -3.03338330
131 -3.39079748 1.75864459
132 1.82051121 -3.39079748
133 -1.99775594 1.82051121
134 -1.73796960 -1.99775594
135 -0.19081635 -1.73796960
136 0.64594197 -0.19081635
137 0.47638099 0.64594197
138 -2.24645208 0.47638099
139 -1.44559924 -2.24645208
140 -5.45005530 -1.44559924
141 2.81656653 -5.45005530
142 1.59391195 2.81656653
143 0.36236868 1.59391195
144 1.06547507 0.36236868
145 -4.25107475 1.06547507
146 1.97844347 -4.25107475
147 -2.27183366 1.97844347
148 0.75643952 -2.27183366
149 -0.10917159 0.75643952
150 -3.19960054 -0.10917159
151 -1.52614309 -3.19960054
152 1.80367864 -1.52614309
153 3.91912628 1.80367864
154 1.46244100 3.91912628
155 -2.58337130 1.46244100
156 -0.01166158 -2.58337130
157 1.29975670 -0.01166158
158 1.11664628 1.29975670
159 0.82846476 1.11664628
160 -0.59841292 0.82846476
> 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/wessaorg/rcomp/tmp/7qkmm1352150119.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8neq51352150119.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/90uk81352150119.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10h2tw1352150119.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/1185nc1352150119.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/wessaorg/rcomp/tmp/12u38s1352150119.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/wessaorg/rcomp/tmp/13y9gl1352150119.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/wessaorg/rcomp/tmp/14l4d31352150119.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/wessaorg/rcomp/tmp/1531dk1352150119.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/wessaorg/rcomp/tmp/16f5fq1352150119.tab")
+ }
>
> try(system("convert tmp/12ipo1352150119.ps tmp/12ipo1352150119.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jh961352150119.ps tmp/2jh961352150119.png",intern=TRUE))
character(0)
> try(system("convert tmp/31s641352150119.ps tmp/31s641352150119.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qrzm1352150119.ps tmp/4qrzm1352150119.png",intern=TRUE))
character(0)
> try(system("convert tmp/56a1r1352150119.ps tmp/56a1r1352150119.png",intern=TRUE))
character(0)
> try(system("convert tmp/61sc51352150119.ps tmp/61sc51352150119.png",intern=TRUE))
character(0)
> try(system("convert tmp/7qkmm1352150119.ps tmp/7qkmm1352150119.png",intern=TRUE))
character(0)
> try(system("convert tmp/8neq51352150119.ps tmp/8neq51352150119.png",intern=TRUE))
character(0)
> try(system("convert tmp/90uk81352150119.ps tmp/90uk81352150119.png",intern=TRUE))
character(0)
> try(system("convert tmp/10h2tw1352150119.ps tmp/10h2tw1352150119.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.920 0.940 8.882