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
+ ,13
+ ,12
+ ,14
+ ,53
+ ,1
+ ,39
+ ,16
+ ,11
+ ,18
+ ,83
+ ,2
+ ,30
+ ,19
+ ,15
+ ,11
+ ,66
+ ,3
+ ,31
+ ,15
+ ,6
+ ,12
+ ,67
+ ,4
+ ,34
+ ,14
+ ,13
+ ,16
+ ,76
+ ,5
+ ,35
+ ,13
+ ,10
+ ,18
+ ,78
+ ,6
+ ,39
+ ,19
+ ,12
+ ,14
+ ,53
+ ,7
+ ,34
+ ,15
+ ,14
+ ,14
+ ,80
+ ,8
+ ,36
+ ,14
+ ,12
+ ,15
+ ,74
+ ,9
+ ,37
+ ,15
+ ,9
+ ,15
+ ,76
+ ,10
+ ,38
+ ,16
+ ,10
+ ,17
+ ,79
+ ,11
+ ,36
+ ,16
+ ,12
+ ,19
+ ,54
+ ,12
+ ,38
+ ,16
+ ,12
+ ,10
+ ,67
+ ,13
+ ,39
+ ,16
+ ,11
+ ,16
+ ,54
+ ,14
+ ,33
+ ,17
+ ,15
+ ,18
+ ,87
+ ,15
+ ,32
+ ,15
+ ,12
+ ,14
+ ,58
+ ,16
+ ,36
+ ,15
+ ,10
+ ,14
+ ,75
+ ,17
+ ,38
+ ,20
+ ,12
+ ,17
+ ,88
+ ,18
+ ,39
+ ,18
+ ,11
+ ,14
+ ,64
+ ,19
+ ,32
+ ,16
+ ,12
+ ,16
+ ,57
+ ,20
+ ,32
+ ,16
+ ,11
+ ,18
+ ,66
+ ,21
+ ,31
+ ,16
+ ,12
+ ,11
+ ,68
+ ,22
+ ,39
+ ,19
+ ,13
+ ,14
+ ,54
+ ,23
+ ,37
+ ,16
+ ,11
+ ,12
+ ,56
+ ,24
+ ,39
+ ,17
+ ,12
+ ,17
+ ,86
+ ,25
+ ,41
+ ,17
+ ,13
+ ,9
+ ,80
+ ,26
+ ,36
+ ,16
+ ,10
+ ,16
+ ,76
+ ,27
+ ,33
+ ,15
+ ,14
+ ,14
+ ,69
+ ,28
+ ,33
+ ,16
+ ,12
+ ,15
+ ,78
+ ,29
+ ,34
+ ,14
+ ,10
+ ,11
+ ,67
+ ,30
+ ,31
+ ,15
+ ,12
+ ,16
+ ,80
+ ,31
+ ,27
+ ,12
+ ,8
+ ,13
+ ,54
+ ,32
+ ,37
+ ,14
+ ,10
+ ,17
+ ,71
+ ,33
+ ,34
+ ,16
+ ,12
+ ,15
+ ,84
+ ,34
+ ,34
+ ,14
+ ,12
+ ,14
+ ,74
+ ,35
+ ,32
+ ,10
+ ,7
+ ,16
+ ,71
+ ,36
+ ,29
+ ,10
+ ,9
+ ,9
+ ,63
+ ,37
+ ,36
+ ,14
+ ,12
+ ,15
+ ,71
+ ,38
+ ,29
+ ,16
+ ,10
+ ,17
+ ,76
+ ,39
+ ,35
+ ,16
+ ,10
+ ,13
+ ,69
+ ,40
+ ,37
+ ,16
+ ,10
+ ,15
+ ,74
+ ,41
+ ,34
+ ,14
+ ,12
+ ,16
+ ,75
+ ,42
+ ,38
+ ,20
+ ,15
+ ,16
+ ,54
+ ,43
+ ,35
+ ,14
+ ,10
+ ,12
+ ,52
+ ,44
+ ,38
+ ,14
+ ,10
+ ,15
+ ,69
+ ,45
+ ,37
+ ,11
+ ,12
+ ,11
+ ,68
+ ,46
+ ,38
+ ,14
+ ,13
+ ,15
+ ,65
+ ,47
+ ,33
+ ,15
+ ,11
+ ,15
+ ,75
+ ,48
+ ,36
+ ,16
+ ,11
+ ,17
+ ,74
+ ,49
+ ,38
+ ,14
+ ,12
+ ,13
+ ,75
+ ,50
+ ,32
+ ,16
+ ,14
+ ,16
+ ,72
+ ,51
+ ,32
+ ,14
+ ,10
+ ,14
+ ,67
+ ,52
+ ,32
+ ,12
+ ,12
+ ,11
+ ,63
+ ,53
+ ,34
+ ,16
+ ,13
+ ,12
+ ,62
+ ,54
+ ,32
+ ,9
+ ,5
+ ,12
+ ,63
+ ,55
+ ,37
+ ,14
+ ,6
+ ,15
+ ,76
+ ,56
+ ,39
+ ,16
+ ,12
+ ,16
+ ,74
+ ,57
+ ,29
+ ,16
+ ,12
+ ,15
+ ,67
+ ,58
+ ,37
+ ,15
+ ,11
+ ,12
+ ,73
+ ,59
+ ,35
+ ,16
+ ,10
+ ,12
+ ,70
+ ,60
+ ,30
+ ,12
+ ,7
+ ,8
+ ,53
+ ,61
+ ,38
+ ,16
+ ,12
+ ,13
+ ,77
+ ,62
+ ,34
+ ,16
+ ,14
+ ,11
+ ,80
+ ,63
+ ,31
+ ,14
+ ,11
+ ,14
+ ,52
+ ,64
+ ,34
+ ,16
+ ,12
+ ,15
+ ,54
+ ,65
+ ,35
+ ,17
+ ,13
+ ,10
+ ,80
+ ,66
+ ,36
+ ,18
+ ,14
+ ,11
+ ,66
+ ,67
+ ,30
+ ,18
+ ,11
+ ,12
+ ,73
+ ,68
+ ,39
+ ,12
+ ,12
+ ,15
+ ,63
+ ,69
+ ,35
+ ,16
+ ,12
+ ,15
+ ,69
+ ,70
+ ,38
+ ,10
+ ,8
+ ,14
+ ,67
+ ,71
+ ,31
+ ,14
+ ,11
+ ,16
+ ,54
+ ,72
+ ,34
+ ,18
+ ,14
+ ,15
+ ,81
+ ,73
+ ,38
+ ,18
+ ,14
+ ,15
+ ,69
+ ,74
+ ,34
+ ,16
+ ,12
+ ,13
+ ,84
+ ,75
+ ,39
+ ,17
+ ,9
+ ,12
+ ,80
+ ,76
+ ,37
+ ,16
+ ,13
+ ,17
+ ,70
+ ,77
+ ,34
+ ,16
+ ,11
+ ,13
+ ,69
+ ,78
+ ,28
+ ,13
+ ,12
+ ,15
+ ,77
+ ,79
+ ,37
+ ,16
+ ,12
+ ,13
+ ,54
+ ,80
+ ,33
+ ,16
+ ,12
+ ,15
+ ,79
+ ,81
+ ,35
+ ,16
+ ,12
+ ,15
+ ,71
+ ,82
+ ,37
+ ,15
+ ,12
+ ,16
+ ,73
+ ,83
+ ,32
+ ,15
+ ,11
+ ,15
+ ,72
+ ,84
+ ,33
+ ,16
+ ,10
+ ,14
+ ,77
+ ,85
+ ,38
+ ,14
+ ,9
+ ,15
+ ,75
+ ,86
+ ,33
+ ,16
+ ,12
+ ,14
+ ,69
+ ,87
+ ,29
+ ,16
+ ,12
+ ,13
+ ,54
+ ,88
+ ,33
+ ,15
+ ,12
+ ,7
+ ,70
+ ,89
+ ,31
+ ,12
+ ,9
+ ,17
+ ,73
+ ,90
+ ,36
+ ,17
+ ,15
+ ,13
+ ,54
+ ,91
+ ,35
+ ,16
+ ,12
+ ,15
+ ,77
+ ,92
+ ,32
+ ,15
+ ,12
+ ,14
+ ,82
+ ,93
+ ,29
+ ,13
+ ,12
+ ,13
+ ,80
+ ,94
+ ,39
+ ,16
+ ,10
+ ,16
+ ,80
+ ,95
+ ,37
+ ,16
+ ,13
+ ,12
+ ,69
+ ,96
+ ,35
+ ,16
+ ,9
+ ,14
+ ,78
+ ,97
+ ,37
+ ,16
+ ,12
+ ,17
+ ,81
+ ,98
+ ,32
+ ,14
+ ,10
+ ,15
+ ,76
+ ,99
+ ,38
+ ,16
+ ,14
+ ,17
+ ,76
+ ,100
+ ,37
+ ,16
+ ,11
+ ,12
+ ,73
+ ,101
+ ,36
+ ,20
+ ,15
+ ,16
+ ,85
+ ,102
+ ,32
+ ,15
+ ,11
+ ,11
+ ,66
+ ,103
+ ,33
+ ,16
+ ,11
+ ,15
+ ,79
+ ,104
+ ,40
+ ,13
+ ,12
+ ,9
+ ,68
+ ,105
+ ,38
+ ,17
+ ,12
+ ,16
+ ,76
+ ,106
+ ,41
+ ,16
+ ,12
+ ,15
+ ,71
+ ,107
+ ,36
+ ,16
+ ,11
+ ,10
+ ,54
+ ,108
+ ,43
+ ,12
+ ,7
+ ,10
+ ,46
+ ,109
+ ,30
+ ,16
+ ,12
+ ,15
+ ,85
+ ,110
+ ,31
+ ,16
+ ,14
+ ,11
+ ,74
+ ,111
+ ,32
+ ,17
+ ,11
+ ,13
+ ,88
+ ,112
+ ,32
+ ,13
+ ,11
+ ,14
+ ,38
+ ,113
+ ,37
+ ,12
+ ,10
+ ,18
+ ,76
+ ,114
+ ,37
+ ,18
+ ,13
+ ,16
+ ,86
+ ,115
+ ,33
+ ,14
+ ,13
+ ,14
+ ,54
+ ,116
+ ,34
+ ,14
+ ,8
+ ,14
+ ,67
+ ,117
+ ,33
+ ,13
+ ,11
+ ,14
+ ,69
+ ,118
+ ,38
+ ,16
+ ,12
+ ,14
+ ,90
+ ,119
+ ,33
+ ,13
+ ,11
+ ,12
+ ,54
+ ,120
+ ,31
+ ,16
+ ,13
+ ,14
+ ,76
+ ,121
+ ,38
+ ,13
+ ,12
+ ,15
+ ,89
+ ,122
+ ,37
+ ,16
+ ,14
+ ,15
+ ,76
+ ,123
+ ,36
+ ,15
+ ,13
+ ,15
+ ,73
+ ,124
+ ,31
+ ,16
+ ,15
+ ,13
+ ,79
+ ,125
+ ,39
+ ,15
+ ,10
+ ,17
+ ,90
+ ,126
+ ,44
+ ,17
+ ,11
+ ,17
+ ,74
+ ,127
+ ,33
+ ,15
+ ,9
+ ,19
+ ,81
+ ,128
+ ,35
+ ,12
+ ,11
+ ,15
+ ,72
+ ,129
+ ,32
+ ,16
+ ,10
+ ,13
+ ,71
+ ,130
+ ,28
+ ,10
+ ,11
+ ,9
+ ,66
+ ,131
+ ,40
+ ,16
+ ,8
+ ,15
+ ,77
+ ,132
+ ,27
+ ,12
+ ,11
+ ,15
+ ,65
+ ,133
+ ,37
+ ,14
+ ,12
+ ,15
+ ,74
+ ,134
+ ,32
+ ,15
+ ,12
+ ,16
+ ,85
+ ,135
+ ,28
+ ,13
+ ,9
+ ,11
+ ,54
+ ,136
+ ,34
+ ,15
+ ,11
+ ,14
+ ,63
+ ,137
+ ,30
+ ,11
+ ,10
+ ,11
+ ,54
+ ,138
+ ,35
+ ,12
+ ,8
+ ,15
+ ,64
+ ,139
+ ,31
+ ,11
+ ,9
+ ,13
+ ,69
+ ,140
+ ,32
+ ,16
+ ,8
+ ,15
+ ,54
+ ,141
+ ,30
+ ,15
+ ,9
+ ,16
+ ,84
+ ,142
+ ,30
+ ,17
+ ,15
+ ,14
+ ,86
+ ,143
+ ,31
+ ,16
+ ,11
+ ,15
+ ,77
+ ,144
+ ,40
+ ,10
+ ,8
+ ,16
+ ,89
+ ,145
+ ,32
+ ,18
+ ,13
+ ,16
+ ,76
+ ,146
+ ,36
+ ,13
+ ,12
+ ,11
+ ,60
+ ,147
+ ,32
+ ,16
+ ,12
+ ,12
+ ,75
+ ,148
+ ,35
+ ,13
+ ,9
+ ,9
+ ,73
+ ,149
+ ,38
+ ,10
+ ,7
+ ,16
+ ,85
+ ,150
+ ,42
+ ,15
+ ,13
+ ,13
+ ,79
+ ,151
+ ,34
+ ,16
+ ,9
+ ,16
+ ,71
+ ,152
+ ,35
+ ,16
+ ,6
+ ,12
+ ,72
+ ,153
+ ,38
+ ,14
+ ,8
+ ,9
+ ,69
+ ,154
+ ,33
+ ,10
+ ,8
+ ,13
+ ,78
+ ,155
+ ,36
+ ,17
+ ,15
+ ,13
+ ,54
+ ,156
+ ,32
+ ,13
+ ,6
+ ,14
+ ,69
+ ,157
+ ,33
+ ,15
+ ,9
+ ,19
+ ,81
+ ,158
+ ,34
+ ,16
+ ,11
+ ,13
+ ,84
+ ,159
+ ,32
+ ,12
+ ,8
+ ,12
+ ,84
+ ,160
+ ,34
+ ,13
+ ,8
+ ,13
+ ,69
+ ,161
+ ,27
+ ,13
+ ,10
+ ,10
+ ,66
+ ,162
+ ,31
+ ,12
+ ,8
+ ,14
+ ,81
+ ,163
+ ,38
+ ,17
+ ,14
+ ,16
+ ,82
+ ,164
+ ,34
+ ,15
+ ,10
+ ,10
+ ,72
+ ,165
+ ,24
+ ,10
+ ,8
+ ,11
+ ,54
+ ,166
+ ,30
+ ,14
+ ,11
+ ,14
+ ,78
+ ,167
+ ,26
+ ,11
+ ,12
+ ,12
+ ,74
+ ,168
+ ,34
+ ,13
+ ,12
+ ,9
+ ,82
+ ,169
+ ,27
+ ,16
+ ,12
+ ,9
+ ,73
+ ,170
+ ,37
+ ,12
+ ,5
+ ,11
+ ,55
+ ,171
+ ,36
+ ,16
+ ,12
+ ,16
+ ,72
+ ,172
+ ,41
+ ,12
+ ,10
+ ,9
+ ,78
+ ,173
+ ,29
+ ,9
+ ,7
+ ,13
+ ,59
+ ,174
+ ,36
+ ,12
+ ,12
+ ,16
+ ,72
+ ,175
+ ,32
+ ,15
+ ,11
+ ,13
+ ,78
+ ,176
+ ,37
+ ,12
+ ,8
+ ,9
+ ,68
+ ,177
+ ,30
+ ,12
+ ,9
+ ,12
+ ,69
+ ,178
+ ,31
+ ,14
+ ,10
+ ,16
+ ,67
+ ,179
+ ,38
+ ,12
+ ,9
+ ,11
+ ,74
+ ,180
+ ,36
+ ,16
+ ,12
+ ,14
+ ,54
+ ,181
+ ,35
+ ,11
+ ,6
+ ,13
+ ,67
+ ,182
+ ,31
+ ,19
+ ,15
+ ,15
+ ,70
+ ,183
+ ,38
+ ,15
+ ,12
+ ,14
+ ,80
+ ,184
+ ,22
+ ,8
+ ,12
+ ,16
+ ,89
+ ,185
+ ,32
+ ,16
+ ,12
+ ,13
+ ,76
+ ,186
+ ,36
+ ,17
+ ,11
+ ,14
+ ,74
+ ,187
+ ,39
+ ,12
+ ,7
+ ,15
+ ,87
+ ,188
+ ,28
+ ,11
+ ,7
+ ,13
+ ,54
+ ,189
+ ,32
+ ,11
+ ,5
+ ,11
+ ,61
+ ,190
+ ,32
+ ,14
+ ,12
+ ,11
+ ,38
+ ,191
+ ,38
+ ,16
+ ,12
+ ,14
+ ,75
+ ,192
+ ,32
+ ,12
+ ,3
+ ,15
+ ,69
+ ,193
+ ,35
+ ,16
+ ,11
+ ,11
+ ,62
+ ,194
+ ,32
+ ,13
+ ,10
+ ,15
+ ,72
+ ,195
+ ,37
+ ,15
+ ,12
+ ,12
+ ,70
+ ,196
+ ,34
+ ,16
+ ,9
+ ,14
+ ,79
+ ,197
+ ,33
+ ,16
+ ,12
+ ,14
+ ,87
+ ,198
+ ,33
+ ,14
+ ,9
+ ,8
+ ,62
+ ,199
+ ,26
+ ,16
+ ,12
+ ,13
+ ,77
+ ,200
+ ,30
+ ,16
+ ,12
+ ,9
+ ,69
+ ,201
+ ,24
+ ,14
+ ,10
+ ,15
+ ,69
+ ,202
+ ,34
+ ,11
+ ,9
+ ,17
+ ,75
+ ,203
+ ,34
+ ,12
+ ,12
+ ,13
+ ,54
+ ,204
+ ,33
+ ,15
+ ,8
+ ,15
+ ,72
+ ,205
+ ,34
+ ,15
+ ,11
+ ,15
+ ,74
+ ,206
+ ,35
+ ,16
+ ,11
+ ,14
+ ,85
+ ,207
+ ,35
+ ,16
+ ,12
+ ,16
+ ,52
+ ,208
+ ,36
+ ,11
+ ,10
+ ,13
+ ,70
+ ,209
+ ,34
+ ,15
+ ,10
+ ,16
+ ,84
+ ,210
+ ,34
+ ,12
+ ,12
+ ,9
+ ,64
+ ,211
+ ,41
+ ,12
+ ,12
+ ,16
+ ,84
+ ,212
+ ,32
+ ,15
+ ,11
+ ,11
+ ,87
+ ,213
+ ,30
+ ,15
+ ,8
+ ,10
+ ,79
+ ,214
+ ,35
+ ,16
+ ,12
+ ,11
+ ,67
+ ,215
+ ,28
+ ,14
+ ,10
+ ,15
+ ,65
+ ,216
+ ,33
+ ,17
+ ,11
+ ,17
+ ,85
+ ,217
+ ,39
+ ,14
+ ,10
+ ,14
+ ,83
+ ,218
+ ,36
+ ,13
+ ,8
+ ,8
+ ,61
+ ,219
+ ,36
+ ,15
+ ,12
+ ,15
+ ,82
+ ,220
+ ,35
+ ,13
+ ,12
+ ,11
+ ,76
+ ,221
+ ,38
+ ,14
+ ,10
+ ,16
+ ,58
+ ,222
+ ,33
+ ,15
+ ,12
+ ,10
+ ,72
+ ,223
+ ,31
+ ,12
+ ,9
+ ,15
+ ,72
+ ,224
+ ,34
+ ,13
+ ,9
+ ,9
+ ,38
+ ,225
+ ,32
+ ,8
+ ,6
+ ,16
+ ,78
+ ,226
+ ,31
+ ,14
+ ,10
+ ,19
+ ,54
+ ,227
+ ,33
+ ,14
+ ,9
+ ,12
+ ,63
+ ,228
+ ,34
+ ,11
+ ,9
+ ,8
+ ,66
+ ,229
+ ,34
+ ,12
+ ,9
+ ,11
+ ,70
+ ,230
+ ,34
+ ,13
+ ,6
+ ,14
+ ,71
+ ,231
+ ,33
+ ,10
+ ,10
+ ,9
+ ,67
+ ,232
+ ,32
+ ,16
+ ,6
+ ,15
+ ,58
+ ,233
+ ,41
+ ,18
+ ,14
+ ,13
+ ,72
+ ,234
+ ,34
+ ,13
+ ,10
+ ,16
+ ,72
+ ,235
+ ,36
+ ,11
+ ,10
+ ,11
+ ,70
+ ,236
+ ,37
+ ,4
+ ,6
+ ,12
+ ,76
+ ,237
+ ,36
+ ,13
+ ,12
+ ,13
+ ,50
+ ,238
+ ,29
+ ,16
+ ,12
+ ,10
+ ,72
+ ,239
+ ,37
+ ,10
+ ,7
+ ,11
+ ,72
+ ,240
+ ,27
+ ,12
+ ,8
+ ,12
+ ,88
+ ,241
+ ,35
+ ,12
+ ,11
+ ,8
+ ,53
+ ,242
+ ,28
+ ,10
+ ,3
+ ,12
+ ,58
+ ,243
+ ,35
+ ,13
+ ,6
+ ,12
+ ,66
+ ,244
+ ,37
+ ,15
+ ,10
+ ,15
+ ,82
+ ,245
+ ,29
+ ,12
+ ,8
+ ,11
+ ,69
+ ,246
+ ,32
+ ,14
+ ,9
+ ,13
+ ,68
+ ,247
+ ,36
+ ,10
+ ,9
+ ,14
+ ,44
+ ,248
+ ,19
+ ,12
+ ,8
+ ,10
+ ,56
+ ,249
+ ,21
+ ,12
+ ,9
+ ,12
+ ,53
+ ,250
+ ,31
+ ,11
+ ,7
+ ,15
+ ,70
+ ,251
+ ,33
+ ,10
+ ,7
+ ,13
+ ,78
+ ,252
+ ,36
+ ,12
+ ,6
+ ,13
+ ,71
+ ,253
+ ,33
+ ,16
+ ,9
+ ,13
+ ,72
+ ,254
+ ,37
+ ,12
+ ,10
+ ,12
+ ,68
+ ,255
+ ,34
+ ,14
+ ,11
+ ,12
+ ,67
+ ,256
+ ,35
+ ,16
+ ,12
+ ,9
+ ,75
+ ,257
+ ,31
+ ,14
+ ,8
+ ,9
+ ,62
+ ,258
+ ,37
+ ,13
+ ,11
+ ,15
+ ,67
+ ,259
+ ,35
+ ,4
+ ,3
+ ,10
+ ,83
+ ,260
+ ,27
+ ,15
+ ,11
+ ,14
+ ,64
+ ,261
+ ,34
+ ,11
+ ,12
+ ,15
+ ,68
+ ,262
+ ,40
+ ,11
+ ,7
+ ,7
+ ,62
+ ,263
+ ,29
+ ,14
+ ,9
+ ,14
+ ,72
+ ,264)
+ ,dim=c(6
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Belonging'
+ ,'t')
+ ,1:264))
> y <- array(NA,dim=c(6,264),dimnames=list(c('Connected','Learning','Software','Happiness','Belonging','t'),1:264))
> 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 = '2'
> 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 Software Happiness Belonging t
1 13 41 12 14 53 1
2 16 39 11 18 83 2
3 19 30 15 11 66 3
4 15 31 6 12 67 4
5 14 34 13 16 76 5
6 13 35 10 18 78 6
7 19 39 12 14 53 7
8 15 34 14 14 80 8
9 14 36 12 15 74 9
10 15 37 9 15 76 10
11 16 38 10 17 79 11
12 16 36 12 19 54 12
13 16 38 12 10 67 13
14 16 39 11 16 54 14
15 17 33 15 18 87 15
16 15 32 12 14 58 16
17 15 36 10 14 75 17
18 20 38 12 17 88 18
19 18 39 11 14 64 19
20 16 32 12 16 57 20
21 16 32 11 18 66 21
22 16 31 12 11 68 22
23 19 39 13 14 54 23
24 16 37 11 12 56 24
25 17 39 12 17 86 25
26 17 41 13 9 80 26
27 16 36 10 16 76 27
28 15 33 14 14 69 28
29 16 33 12 15 78 29
30 14 34 10 11 67 30
31 15 31 12 16 80 31
32 12 27 8 13 54 32
33 14 37 10 17 71 33
34 16 34 12 15 84 34
35 14 34 12 14 74 35
36 10 32 7 16 71 36
37 10 29 9 9 63 37
38 14 36 12 15 71 38
39 16 29 10 17 76 39
40 16 35 10 13 69 40
41 16 37 10 15 74 41
42 14 34 12 16 75 42
43 20 38 15 16 54 43
44 14 35 10 12 52 44
45 14 38 10 15 69 45
46 11 37 12 11 68 46
47 14 38 13 15 65 47
48 15 33 11 15 75 48
49 16 36 11 17 74 49
50 14 38 12 13 75 50
51 16 32 14 16 72 51
52 14 32 10 14 67 52
53 12 32 12 11 63 53
54 16 34 13 12 62 54
55 9 32 5 12 63 55
56 14 37 6 15 76 56
57 16 39 12 16 74 57
58 16 29 12 15 67 58
59 15 37 11 12 73 59
60 16 35 10 12 70 60
61 12 30 7 8 53 61
62 16 38 12 13 77 62
63 16 34 14 11 80 63
64 14 31 11 14 52 64
65 16 34 12 15 54 65
66 17 35 13 10 80 66
67 18 36 14 11 66 67
68 18 30 11 12 73 68
69 12 39 12 15 63 69
70 16 35 12 15 69 70
71 10 38 8 14 67 71
72 14 31 11 16 54 72
73 18 34 14 15 81 73
74 18 38 14 15 69 74
75 16 34 12 13 84 75
76 17 39 9 12 80 76
77 16 37 13 17 70 77
78 16 34 11 13 69 78
79 13 28 12 15 77 79
80 16 37 12 13 54 80
81 16 33 12 15 79 81
82 16 35 12 15 71 82
83 15 37 12 16 73 83
84 15 32 11 15 72 84
85 16 33 10 14 77 85
86 14 38 9 15 75 86
87 16 33 12 14 69 87
88 16 29 12 13 54 88
89 15 33 12 7 70 89
90 12 31 9 17 73 90
91 17 36 15 13 54 91
92 16 35 12 15 77 92
93 15 32 12 14 82 93
94 13 29 12 13 80 94
95 16 39 10 16 80 95
96 16 37 13 12 69 96
97 16 35 9 14 78 97
98 16 37 12 17 81 98
99 14 32 10 15 76 99
100 16 38 14 17 76 100
101 16 37 11 12 73 101
102 20 36 15 16 85 102
103 15 32 11 11 66 103
104 16 33 11 15 79 104
105 13 40 12 9 68 105
106 17 38 12 16 76 106
107 16 41 12 15 71 107
108 16 36 11 10 54 108
109 12 43 7 10 46 109
110 16 30 12 15 85 110
111 16 31 14 11 74 111
112 17 32 11 13 88 112
113 13 32 11 14 38 113
114 12 37 10 18 76 114
115 18 37 13 16 86 115
116 14 33 13 14 54 116
117 14 34 8 14 67 117
118 13 33 11 14 69 118
119 16 38 12 14 90 119
120 13 33 11 12 54 120
121 16 31 13 14 76 121
122 13 38 12 15 89 122
123 16 37 14 15 76 123
124 15 36 13 15 73 124
125 16 31 15 13 79 125
126 15 39 10 17 90 126
127 17 44 11 17 74 127
128 15 33 9 19 81 128
129 12 35 11 15 72 129
130 16 32 10 13 71 130
131 10 28 11 9 66 131
132 16 40 8 15 77 132
133 12 27 11 15 65 133
134 14 37 12 15 74 134
135 15 32 12 16 85 135
136 13 28 9 11 54 136
137 15 34 11 14 63 137
138 11 30 10 11 54 138
139 12 35 8 15 64 139
140 11 31 9 13 69 140
141 16 32 8 15 54 141
142 15 30 9 16 84 142
143 17 30 15 14 86 143
144 16 31 11 15 77 144
145 10 40 8 16 89 145
146 18 32 13 16 76 146
147 13 36 12 11 60 147
148 16 32 12 12 75 148
149 13 35 9 9 73 149
150 10 38 7 16 85 150
151 15 42 13 13 79 151
152 16 34 9 16 71 152
153 16 35 6 12 72 153
154 14 38 8 9 69 154
155 10 33 8 13 78 155
156 17 36 15 13 54 156
157 13 32 6 14 69 157
158 15 33 9 19 81 158
159 16 34 11 13 84 159
160 12 32 8 12 84 160
161 13 34 8 13 69 161
162 13 27 10 10 66 162
163 12 31 8 14 81 163
164 17 38 14 16 82 164
165 15 34 10 10 72 165
166 10 24 8 11 54 166
167 14 30 11 14 78 167
168 11 26 12 12 74 168
169 13 34 12 9 82 169
170 16 27 12 9 73 170
171 12 37 5 11 55 171
172 16 36 12 16 72 172
173 12 41 10 9 78 173
174 9 29 7 13 59 174
175 12 36 12 16 72 175
176 15 32 11 13 78 176
177 12 37 8 9 68 177
178 12 30 9 12 69 178
179 14 31 10 16 67 179
180 12 38 9 11 74 180
181 16 36 12 14 54 181
182 11 35 6 13 67 182
183 19 31 15 15 70 183
184 15 38 12 14 80 184
185 8 22 12 16 89 185
186 16 32 12 13 76 186
187 17 36 11 14 74 187
188 12 39 7 15 87 188
189 11 28 7 13 54 189
190 11 32 5 11 61 190
191 14 32 12 11 38 191
192 16 38 12 14 75 192
193 12 32 3 15 69 193
194 16 35 11 11 62 194
195 13 32 10 15 72 195
196 15 37 12 12 70 196
197 16 34 9 14 79 197
198 16 33 12 14 87 198
199 14 33 9 8 62 199
200 16 26 12 13 77 200
201 16 30 12 9 69 201
202 14 24 10 15 69 202
203 11 34 9 17 75 203
204 12 34 12 13 54 204
205 15 33 8 15 72 205
206 15 34 11 15 74 206
207 16 35 11 14 85 207
208 16 35 12 16 52 208
209 11 36 10 13 70 209
210 15 34 10 16 84 210
211 12 34 12 9 64 211
212 12 41 12 16 84 212
213 15 32 11 11 87 213
214 15 30 8 10 79 214
215 16 35 12 11 67 215
216 14 28 10 15 65 216
217 17 33 11 17 85 217
218 14 39 10 14 83 218
219 13 36 8 8 61 219
220 15 36 12 15 82 220
221 13 35 12 11 76 221
222 14 38 10 16 58 222
223 15 33 12 10 72 223
224 12 31 9 15 72 224
225 13 34 9 9 38 225
226 8 32 6 16 78 226
227 14 31 10 19 54 227
228 14 33 9 12 63 228
229 11 34 9 8 66 229
230 12 34 9 11 70 230
231 13 34 6 14 71 231
232 10 33 10 9 67 232
233 16 32 6 15 58 233
234 18 41 14 13 72 234
235 13 34 10 16 72 235
236 11 36 10 11 70 236
237 4 37 6 12 76 237
238 13 36 12 13 50 238
239 16 29 12 10 72 239
240 10 37 7 11 72 240
241 12 27 8 12 88 241
242 12 35 11 8 53 242
243 10 28 3 12 58 243
244 13 35 6 12 66 244
245 15 37 10 15 82 245
246 12 29 8 11 69 246
247 14 32 9 13 68 247
248 10 36 9 14 44 248
249 12 19 8 10 56 249
250 12 21 9 12 53 250
251 11 31 7 15 70 251
252 10 33 7 13 78 252
253 12 36 6 13 71 253
254 16 33 9 13 72 254
255 12 37 10 12 68 255
256 14 34 11 12 67 256
257 16 35 12 9 75 257
258 14 31 8 9 62 258
259 13 37 11 15 67 259
260 4 35 3 10 83 260
261 15 27 11 14 64 261
262 11 34 12 15 68 262
263 11 40 7 7 62 263
264 14 29 9 14 72 264
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Software Happiness Belonging t
5.256380 0.051069 0.568670 0.093704 0.009676 -0.004994
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.2342 -1.0573 0.2475 1.2285 4.8942
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.256380 1.443252 3.642 0.000327 ***
Connected 0.051069 0.031050 1.645 0.101246
Software 0.568670 0.053215 10.686 < 2e-16 ***
Happiness 0.093704 0.049869 1.879 0.061374 .
Belonging 0.009676 0.011610 0.833 0.405386
t -0.004994 0.001675 -2.982 0.003139 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.854 on 258 degrees of freedom
Multiple R-squared: 0.4408, Adjusted R-squared: 0.4299
F-statistic: 40.67 on 5 and 258 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,] 0.963173082 0.073653835 0.03682692
[2,] 0.927602218 0.144795563 0.07239778
[3,] 0.900332340 0.199335320 0.09966766
[4,] 0.839716769 0.320566462 0.16028323
[5,] 0.781053041 0.437893917 0.21894696
[6,] 0.696809095 0.606381811 0.30319091
[7,] 0.621418054 0.757163891 0.37858195
[8,] 0.615672783 0.768654435 0.38432722
[9,] 0.530032969 0.939934062 0.46996703
[10,] 0.766704983 0.466590034 0.23329502
[11,] 0.726517299 0.546965402 0.27348270
[12,] 0.663587481 0.672825037 0.33641252
[13,] 0.591545772 0.816908456 0.40845423
[14,] 0.549911471 0.900177057 0.45008853
[15,] 0.512629038 0.974741925 0.48737096
[16,] 0.480631404 0.961262807 0.51936860
[17,] 0.418104565 0.836209129 0.58189544
[18,] 0.385641439 0.771282877 0.61435856
[19,] 0.329965665 0.659931330 0.67003433
[20,] 0.371389629 0.742779258 0.62861037
[21,] 0.317258749 0.634517498 0.68274125
[22,] 0.329318403 0.658636805 0.67068160
[23,] 0.289193348 0.578386696 0.71080665
[24,] 0.286544688 0.573089377 0.71345531
[25,] 0.275599506 0.551199012 0.72440049
[26,] 0.228389237 0.456778475 0.77161076
[27,] 0.230336231 0.460672461 0.76966377
[28,] 0.312836799 0.625673597 0.68716320
[29,] 0.402432372 0.804864744 0.59756763
[30,] 0.385026943 0.770053886 0.61497306
[31,] 0.433595221 0.867190442 0.56640478
[32,] 0.420620315 0.841240631 0.57937968
[33,] 0.387052488 0.774104975 0.61294751
[34,] 0.369146963 0.738293927 0.63085304
[35,] 0.395749612 0.791499224 0.60425039
[36,] 0.352794476 0.705588953 0.64720552
[37,] 0.322502723 0.645005446 0.67749728
[38,] 0.547692665 0.904614671 0.45230734
[39,] 0.559962811 0.880074377 0.44003719
[40,] 0.519638008 0.960723983 0.48036199
[41,] 0.487005303 0.974010606 0.51299470
[42,] 0.461966251 0.923932502 0.53803375
[43,] 0.418507689 0.837015378 0.58149231
[44,] 0.376422473 0.752844947 0.62357753
[45,] 0.400453269 0.800906538 0.59954673
[46,] 0.371035052 0.742070104 0.62896495
[47,] 0.362818383 0.725636766 0.63718162
[48,] 0.366694852 0.733389705 0.63330515
[49,] 0.326632621 0.653265243 0.67336738
[50,] 0.321846978 0.643693955 0.67815302
[51,] 0.287605481 0.575210962 0.71239452
[52,] 0.306380206 0.612760411 0.69361979
[53,] 0.276057515 0.552115031 0.72394248
[54,] 0.244680910 0.489361821 0.75531909
[55,] 0.214226555 0.428453110 0.78577345
[56,] 0.185001035 0.370002070 0.81499896
[57,] 0.162955743 0.325911485 0.83704426
[58,] 0.158828484 0.317656968 0.84117152
[59,] 0.157210843 0.314421685 0.84278916
[60,] 0.258210479 0.516420959 0.74178952
[61,] 0.379005806 0.758011612 0.62099419
[62,] 0.344414138 0.688828276 0.65558586
[63,] 0.424794807 0.849589614 0.57520519
[64,] 0.388305921 0.776611841 0.61169408
[65,] 0.373707366 0.747414733 0.62629263
[66,] 0.352692980 0.705385960 0.64730702
[67,] 0.319908109 0.639816217 0.68009189
[68,] 0.380905507 0.761811014 0.61909449
[69,] 0.344924254 0.689848508 0.65507575
[70,] 0.325747256 0.651494512 0.67425274
[71,] 0.332356094 0.664712188 0.66764391
[72,] 0.301849258 0.603698517 0.69815074
[73,] 0.271483259 0.542966518 0.72851674
[74,] 0.241878366 0.483756732 0.75812163
[75,] 0.217546159 0.435092317 0.78245384
[76,] 0.190942394 0.381884788 0.80905761
[77,] 0.188345617 0.376691233 0.81165438
[78,] 0.163492822 0.326985645 0.83650718
[79,] 0.143984918 0.287969836 0.85601508
[80,] 0.133443939 0.266887878 0.86655606
[81,] 0.114082221 0.228164441 0.88591778
[82,] 0.111033366 0.222066732 0.88896663
[83,] 0.093906268 0.187812535 0.90609373
[84,] 0.079441278 0.158882556 0.92055872
[85,] 0.066736081 0.133472162 0.93326392
[86,] 0.069051772 0.138103544 0.93094823
[87,] 0.061392582 0.122785164 0.93860742
[88,] 0.050784141 0.101568282 0.94921586
[89,] 0.054649434 0.109298867 0.94535057
[90,] 0.044977656 0.089955312 0.95502234
[91,] 0.036838421 0.073676842 0.96316158
[92,] 0.032308712 0.064617424 0.96769129
[93,] 0.027989332 0.055978664 0.97201067
[94,] 0.033223323 0.066446647 0.96677668
[95,] 0.027540214 0.055080427 0.97245979
[96,] 0.023902656 0.047805312 0.97609734
[97,] 0.029677858 0.059355715 0.97032214
[98,] 0.025886787 0.051773574 0.97411321
[99,] 0.020957361 0.041914723 0.97904264
[100,] 0.019913601 0.039827201 0.98008640
[101,] 0.016465315 0.032930630 0.98353469
[102,] 0.013437707 0.026875414 0.98656229
[103,] 0.010606185 0.021212371 0.98939381
[104,] 0.011784338 0.023568676 0.98821566
[105,] 0.010106890 0.020213780 0.98989311
[106,] 0.014014411 0.028028823 0.98598559
[107,] 0.013097438 0.026194875 0.98690256
[108,] 0.012139415 0.024278831 0.98786058
[109,] 0.010358336 0.020716672 0.98964166
[110,] 0.009832907 0.019665813 0.99016709
[111,] 0.007859649 0.015719297 0.99214035
[112,] 0.006789236 0.013578472 0.99321076
[113,] 0.005294124 0.010588248 0.99470588
[114,] 0.007887180 0.015774359 0.99211282
[115,] 0.006357179 0.012714358 0.99364282
[116,] 0.005292650 0.010585301 0.99470735
[117,] 0.004233604 0.008467209 0.99576640
[118,] 0.003247620 0.006495240 0.99675238
[119,] 0.002946929 0.005893859 0.99705307
[120,] 0.002472652 0.004945303 0.99752735
[121,] 0.003485322 0.006970643 0.99651468
[122,] 0.003929130 0.007858261 0.99607087
[123,] 0.008565492 0.017130984 0.99143451
[124,] 0.010668388 0.021336777 0.98933161
[125,] 0.011457613 0.022915225 0.98854239
[126,] 0.010469786 0.020939572 0.98953021
[127,] 0.008289432 0.016578864 0.99171057
[128,] 0.006772412 0.013544824 0.99322759
[129,] 0.005413036 0.010826072 0.99458696
[130,] 0.006325339 0.012650678 0.99367466
[131,] 0.005259941 0.010519881 0.99474006
[132,] 0.005798953 0.011597907 0.99420105
[133,] 0.010635237 0.021270474 0.98936476
[134,] 0.009813404 0.019626807 0.99018660
[135,] 0.007795920 0.015591840 0.99220408
[136,] 0.007131646 0.014263292 0.99286835
[137,] 0.014116633 0.028233267 0.98588337
[138,] 0.015529748 0.031059497 0.98447025
[139,] 0.015631119 0.031262239 0.98436888
[140,] 0.013559317 0.027118635 0.98644068
[141,] 0.010741945 0.021483889 0.98925806
[142,] 0.014303178 0.028606356 0.98569682
[143,] 0.012323524 0.024647048 0.98767648
[144,] 0.014435602 0.028871203 0.98556440
[145,] 0.037893009 0.075786018 0.96210699
[146,] 0.035077469 0.070154938 0.96492253
[147,] 0.043322289 0.086644577 0.95667771
[148,] 0.036103464 0.072206928 0.96389654
[149,] 0.033453298 0.066906596 0.96654670
[150,] 0.029666033 0.059332066 0.97033397
[151,] 0.027694052 0.055388104 0.97230595
[152,] 0.022914360 0.045828721 0.97708564
[153,] 0.018618271 0.037236542 0.98138173
[154,] 0.014902952 0.029805904 0.98509705
[155,] 0.012131147 0.024262294 0.98786885
[156,] 0.009668089 0.019336178 0.99033191
[157,] 0.008857139 0.017714278 0.99114286
[158,] 0.008870572 0.017741144 0.99112943
[159,] 0.006937117 0.013874234 0.99306288
[160,] 0.012949844 0.025899689 0.98705016
[161,] 0.012429260 0.024858520 0.98757074
[162,] 0.011814436 0.023628873 0.98818556
[163,] 0.010413314 0.020826627 0.98958669
[164,] 0.008452345 0.016904690 0.99154765
[165,] 0.008198055 0.016396110 0.99180195
[166,] 0.011027124 0.022054248 0.98897288
[167,] 0.017747258 0.035494516 0.98225274
[168,] 0.014445350 0.028890701 0.98555465
[169,] 0.011494929 0.022989858 0.98850507
[170,] 0.009827644 0.019655289 0.99017236
[171,] 0.007729173 0.015458346 0.99227083
[172,] 0.006699198 0.013398395 0.99330080
[173,] 0.005526709 0.011053417 0.99447329
[174,] 0.004316163 0.008632326 0.99568384
[175,] 0.004780232 0.009560465 0.99521977
[176,] 0.003628137 0.007256274 0.99637186
[177,] 0.100002509 0.200005017 0.89999749
[178,] 0.087755080 0.175510160 0.91224492
[179,] 0.095981245 0.191962490 0.90401875
[180,] 0.081463128 0.162926256 0.91853687
[181,] 0.075047597 0.150095194 0.92495240
[182,] 0.062682371 0.125364742 0.93731763
[183,] 0.054381550 0.108763100 0.94561845
[184,] 0.045932620 0.091865241 0.95406738
[185,] 0.048220067 0.096440134 0.95177993
[186,] 0.047138463 0.094276926 0.95286154
[187,] 0.041345866 0.082691732 0.95865413
[188,] 0.033193608 0.066387217 0.96680639
[189,] 0.040990294 0.081980589 0.95900971
[190,] 0.034046660 0.068093320 0.96595334
[191,] 0.030720755 0.061441510 0.96927925
[192,] 0.026450562 0.052901124 0.97354944
[193,] 0.023519148 0.047038296 0.97648085
[194,] 0.019665416 0.039330833 0.98033458
[195,] 0.023578976 0.047157952 0.97642102
[196,] 0.032700969 0.065401938 0.96729903
[197,] 0.035160694 0.070321388 0.96483931
[198,] 0.027949801 0.055899602 0.97205020
[199,] 0.025640395 0.051280790 0.97435961
[200,] 0.020955497 0.041910994 0.97904450
[201,] 0.025271637 0.050543274 0.97472836
[202,] 0.020859855 0.041719710 0.97914014
[203,] 0.026051122 0.052102244 0.97394888
[204,] 0.038196417 0.076392835 0.96180358
[205,] 0.030463880 0.060927759 0.96953612
[206,] 0.038635455 0.077270909 0.96136455
[207,] 0.033420841 0.066841683 0.96657916
[208,] 0.026289916 0.052579833 0.97371008
[209,] 0.029652132 0.059304265 0.97034787
[210,] 0.024475687 0.048951374 0.97552431
[211,] 0.022653385 0.045306771 0.97734661
[212,] 0.017383338 0.034766677 0.98261666
[213,] 0.014529190 0.029058380 0.98547081
[214,] 0.010891805 0.021783610 0.98910820
[215,] 0.008264248 0.016528496 0.99173575
[216,] 0.006298378 0.012596756 0.99370162
[217,] 0.004632345 0.009264690 0.99536765
[218,] 0.007819712 0.015639424 0.99218029
[219,] 0.005960404 0.011920807 0.99403960
[220,] 0.004807608 0.009615217 0.99519239
[221,] 0.003550270 0.007100541 0.99644973
[222,] 0.002439321 0.004878643 0.99756068
[223,] 0.002522844 0.005045687 0.99747716
[224,] 0.003684042 0.007368085 0.99631596
[225,] 0.037323210 0.074646420 0.96267679
[226,] 0.047896266 0.095792531 0.95210373
[227,] 0.036350688 0.072701376 0.96364931
[228,] 0.031464727 0.062929454 0.96853527
[229,] 0.286129074 0.572258149 0.71387093
[230,] 0.256200447 0.512400894 0.74379955
[231,] 0.213542055 0.427084110 0.78645794
[232,] 0.192774895 0.385549790 0.80722510
[233,] 0.181407592 0.362815183 0.81859241
[234,] 0.233541261 0.467082521 0.76645874
[235,] 0.211711868 0.423423737 0.78828813
[236,] 0.239410547 0.478821095 0.76058945
[237,] 0.193651835 0.387303671 0.80634816
[238,] 0.149928678 0.299857357 0.85007132
[239,] 0.118724943 0.237449885 0.88127506
[240,] 0.109877927 0.219755853 0.89012207
[241,] 0.085820431 0.171640862 0.91417957
[242,] 0.270261343 0.540522686 0.72973866
[243,] 0.234792086 0.469584171 0.76520791
[244,] 0.216131885 0.432263770 0.78386812
[245,] 0.172536714 0.345073427 0.82746329
[246,] 0.417502449 0.835004899 0.58249755
[247,] 0.271043407 0.542086814 0.72895659
> postscript(file="/var/wessaorg/rcomp/tmp/157wc1352122093.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/2e2iy1352122093.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3g5001352122093.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4g3u81352122093.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/54toi1352122093.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 264
Frequency = 1
1 2 3 4 5 6
-2.993913501 0.016804164 2.027151406 2.995725307 -2.595072605 -2.141897200
7 8 9 10 11 12
3.138186711 -2.000056132 -1.995510768 0.645072278 0.813892694 -0.161832553
13 14 15 16 17 18
0.458574597 0.544729299 -0.925245616 -0.507765215 0.265806840 3.624428454
19 20 21 22 23 24
2.660349338 0.334477621 0.633652977 0.757621321 2.639740689 1.052268418
25 26 27 28 29 30
0.627666732 0.769536955 1.118660729 -1.742681077 0.218867943 -0.208620540
31 32 33 34 35 36
-0.782061761 -0.765435307 -0.947771675 0.134713850 -1.669832437 -2.877732375
37 38 39 40 41 42
-3.123539774 -1.821666183 1.442364216 1.583488771 1.250558833 -1.831959692
43 44 45 46 47 48
2.465936621 -0.138347047 -0.732157180 -4.428943288 -2.389476807 -0.088554778
49 50 51 52 53 54
0.585500029 -1.715174355 -0.793191168 -0.277732352 -3.090264389 0.159893358
55 56 57 58 59 60
-2.193291880 1.580792693 -0.002723157 0.674393315 0.062562939 1.767391273
61 62 63 64 65 66
0.273040107 0.325398988 -0.444290349 -0.590274389 0.579787544 1.181995498
67 68 69 70 71 72
1.609005236 3.464989102 -3.742662962 0.408552891 -3.351925850 -0.757083546
73 74 75 76 77 78
1.221155899 1.137981182 0.526863803 3.114928532 -0.424382129 1.255649056
79 80 81 82 83 84
-2.266425888 0.688894025 0.468865501 0.449126235 -0.761073029 0.171314773
85 86 87 88 89 90
1.739235102 -0.016798732 0.689287788 1.137395434 0.345526346 -1.807397550
91 92 93 94 95 96
0.088884322 0.441009678 -0.355463991 -2.084208337 1.266323989 0.148693176
97 98 99 100 101 102
2.256015984 0.142723932 -0.223812131 -0.987319196 1.272298966 2.562759575
103 104 105 106 107 108
0.699064347 1.152390786 -2.100113350 1.273686600 0.267555340 1.729568310
109 110 111 112 113 114
-0.270836391 0.708836411 0.006668557 2.343736549 -1.261192671 -2.685362676
115 116 117 118 119 120
1.704272975 -1.589430057 1.082060881 -1.587237207 0.390553887 -1.244707870
121 122 123 124 125 126
0.324812851 -2.678493171 -0.633987150 -0.980227778 -0.727875007 0.230669153
127 128 129 130 131 132
1.566458103 1.015413111 -2.757174939 2.166778762 -3.769428149 2.660092839
133 134 135 136 137 138
-2.260919066 -1.422365365 -0.362162460 0.321579882 0.514628122 -2.339240454
139 140 141 142 143 144
-0.923823320 -2.144194050 3.336127171 1.490616613 0.251647677 1.473628541
145 146 147 148 149 150
-3.484800100 2.211179121 -1.796104179 1.174327295 0.032586352 -2.750321290
151 152 153 154 155 156
-1.022457307 2.462060948 4.487134895 1.511720357 -2.689836965 0.413475792
157 158 159 160 161 162
1.501935850 1.165224558 1.515005699 -0.578149315 0.376136923 -0.088588004
163 164 165 166 167 168
-0.670479997 0.367928673 1.510856706 -1.755662983 -0.276418852 -3.409709022
169 170 171 172 173 174
-1.609560476 1.839996527 1.301743006 0.744112177 -1.771026188 -2.638173958
175 176 177 178 179 180
-3.240906678 0.760090481 -0.312679621 -0.809660101 0.220130762 -1.162898629
181 182 183 184 185 186
1.150624372 -0.413373104 2.461433375 -0.188098466 -6.640489573 1.260709023
187 188 189 190 191 192
2.555744150 -0.537276499 -0.463821184 0.593914689 -0.159241288 0.900229342
193 194 195 196 197 198
2.294015295 2.038987991 -0.705713028 0.207058893 2.796780819 1.069429025
199 200 201 202 203 204
1.584545636 1.627359181 1.880297276 0.766821519 -2.415665884 -2.538678442
205 206 207 208 209 210
2.430494845 0.659058835 1.600255615 1.168467230 -2.633317987 1.057243539
211 212 213 214 215 216
-2.225663331 -3.427591445 1.045185015 3.029434943 1.526808073 0.671160154
217 218 219 220 221 222
2.471219224 0.038931712 1.109558511 -0.019241863 -1.530310197 0.164458334
223 224 225 226 227 228
0.714221447 -0.941156492 0.801824267 -3.427986022 0.304500645 1.344872467
229 230 231 232 233 234
-1.355414349 -0.670234567 1.749981596 -2.961413524 4.894186157 1.942149473
235 236 237 238 239 240
-0.701806175 -2.311080043 -7.234233445 -1.432327586 1.998396730 -1.655515851
241 242 243 244 245 246
0.042983929 -1.353121671 1.135520143 1.999616659 1.191871734 0.243355138
247 248 249 250 251 252
1.348740103 -2.712031023 0.988512746 0.164317907 -0.649635268 -1.636776776
253 254 255 256 257 258
0.851409222 3.293924675 -1.341621006 0.257585367 1.846546799 2.456278760
259 260 261 262 263 264
-1.161751834 -5.191552233 1.481655882 -3.571909266 -0.222295831 1.454433848
> postscript(file="/var/wessaorg/rcomp/tmp/6mcls1352122093.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 = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.993913501 NA
1 0.016804164 -2.993913501
2 2.027151406 0.016804164
3 2.995725307 2.027151406
4 -2.595072605 2.995725307
5 -2.141897200 -2.595072605
6 3.138186711 -2.141897200
7 -2.000056132 3.138186711
8 -1.995510768 -2.000056132
9 0.645072278 -1.995510768
10 0.813892694 0.645072278
11 -0.161832553 0.813892694
12 0.458574597 -0.161832553
13 0.544729299 0.458574597
14 -0.925245616 0.544729299
15 -0.507765215 -0.925245616
16 0.265806840 -0.507765215
17 3.624428454 0.265806840
18 2.660349338 3.624428454
19 0.334477621 2.660349338
20 0.633652977 0.334477621
21 0.757621321 0.633652977
22 2.639740689 0.757621321
23 1.052268418 2.639740689
24 0.627666732 1.052268418
25 0.769536955 0.627666732
26 1.118660729 0.769536955
27 -1.742681077 1.118660729
28 0.218867943 -1.742681077
29 -0.208620540 0.218867943
30 -0.782061761 -0.208620540
31 -0.765435307 -0.782061761
32 -0.947771675 -0.765435307
33 0.134713850 -0.947771675
34 -1.669832437 0.134713850
35 -2.877732375 -1.669832437
36 -3.123539774 -2.877732375
37 -1.821666183 -3.123539774
38 1.442364216 -1.821666183
39 1.583488771 1.442364216
40 1.250558833 1.583488771
41 -1.831959692 1.250558833
42 2.465936621 -1.831959692
43 -0.138347047 2.465936621
44 -0.732157180 -0.138347047
45 -4.428943288 -0.732157180
46 -2.389476807 -4.428943288
47 -0.088554778 -2.389476807
48 0.585500029 -0.088554778
49 -1.715174355 0.585500029
50 -0.793191168 -1.715174355
51 -0.277732352 -0.793191168
52 -3.090264389 -0.277732352
53 0.159893358 -3.090264389
54 -2.193291880 0.159893358
55 1.580792693 -2.193291880
56 -0.002723157 1.580792693
57 0.674393315 -0.002723157
58 0.062562939 0.674393315
59 1.767391273 0.062562939
60 0.273040107 1.767391273
61 0.325398988 0.273040107
62 -0.444290349 0.325398988
63 -0.590274389 -0.444290349
64 0.579787544 -0.590274389
65 1.181995498 0.579787544
66 1.609005236 1.181995498
67 3.464989102 1.609005236
68 -3.742662962 3.464989102
69 0.408552891 -3.742662962
70 -3.351925850 0.408552891
71 -0.757083546 -3.351925850
72 1.221155899 -0.757083546
73 1.137981182 1.221155899
74 0.526863803 1.137981182
75 3.114928532 0.526863803
76 -0.424382129 3.114928532
77 1.255649056 -0.424382129
78 -2.266425888 1.255649056
79 0.688894025 -2.266425888
80 0.468865501 0.688894025
81 0.449126235 0.468865501
82 -0.761073029 0.449126235
83 0.171314773 -0.761073029
84 1.739235102 0.171314773
85 -0.016798732 1.739235102
86 0.689287788 -0.016798732
87 1.137395434 0.689287788
88 0.345526346 1.137395434
89 -1.807397550 0.345526346
90 0.088884322 -1.807397550
91 0.441009678 0.088884322
92 -0.355463991 0.441009678
93 -2.084208337 -0.355463991
94 1.266323989 -2.084208337
95 0.148693176 1.266323989
96 2.256015984 0.148693176
97 0.142723932 2.256015984
98 -0.223812131 0.142723932
99 -0.987319196 -0.223812131
100 1.272298966 -0.987319196
101 2.562759575 1.272298966
102 0.699064347 2.562759575
103 1.152390786 0.699064347
104 -2.100113350 1.152390786
105 1.273686600 -2.100113350
106 0.267555340 1.273686600
107 1.729568310 0.267555340
108 -0.270836391 1.729568310
109 0.708836411 -0.270836391
110 0.006668557 0.708836411
111 2.343736549 0.006668557
112 -1.261192671 2.343736549
113 -2.685362676 -1.261192671
114 1.704272975 -2.685362676
115 -1.589430057 1.704272975
116 1.082060881 -1.589430057
117 -1.587237207 1.082060881
118 0.390553887 -1.587237207
119 -1.244707870 0.390553887
120 0.324812851 -1.244707870
121 -2.678493171 0.324812851
122 -0.633987150 -2.678493171
123 -0.980227778 -0.633987150
124 -0.727875007 -0.980227778
125 0.230669153 -0.727875007
126 1.566458103 0.230669153
127 1.015413111 1.566458103
128 -2.757174939 1.015413111
129 2.166778762 -2.757174939
130 -3.769428149 2.166778762
131 2.660092839 -3.769428149
132 -2.260919066 2.660092839
133 -1.422365365 -2.260919066
134 -0.362162460 -1.422365365
135 0.321579882 -0.362162460
136 0.514628122 0.321579882
137 -2.339240454 0.514628122
138 -0.923823320 -2.339240454
139 -2.144194050 -0.923823320
140 3.336127171 -2.144194050
141 1.490616613 3.336127171
142 0.251647677 1.490616613
143 1.473628541 0.251647677
144 -3.484800100 1.473628541
145 2.211179121 -3.484800100
146 -1.796104179 2.211179121
147 1.174327295 -1.796104179
148 0.032586352 1.174327295
149 -2.750321290 0.032586352
150 -1.022457307 -2.750321290
151 2.462060948 -1.022457307
152 4.487134895 2.462060948
153 1.511720357 4.487134895
154 -2.689836965 1.511720357
155 0.413475792 -2.689836965
156 1.501935850 0.413475792
157 1.165224558 1.501935850
158 1.515005699 1.165224558
159 -0.578149315 1.515005699
160 0.376136923 -0.578149315
161 -0.088588004 0.376136923
162 -0.670479997 -0.088588004
163 0.367928673 -0.670479997
164 1.510856706 0.367928673
165 -1.755662983 1.510856706
166 -0.276418852 -1.755662983
167 -3.409709022 -0.276418852
168 -1.609560476 -3.409709022
169 1.839996527 -1.609560476
170 1.301743006 1.839996527
171 0.744112177 1.301743006
172 -1.771026188 0.744112177
173 -2.638173958 -1.771026188
174 -3.240906678 -2.638173958
175 0.760090481 -3.240906678
176 -0.312679621 0.760090481
177 -0.809660101 -0.312679621
178 0.220130762 -0.809660101
179 -1.162898629 0.220130762
180 1.150624372 -1.162898629
181 -0.413373104 1.150624372
182 2.461433375 -0.413373104
183 -0.188098466 2.461433375
184 -6.640489573 -0.188098466
185 1.260709023 -6.640489573
186 2.555744150 1.260709023
187 -0.537276499 2.555744150
188 -0.463821184 -0.537276499
189 0.593914689 -0.463821184
190 -0.159241288 0.593914689
191 0.900229342 -0.159241288
192 2.294015295 0.900229342
193 2.038987991 2.294015295
194 -0.705713028 2.038987991
195 0.207058893 -0.705713028
196 2.796780819 0.207058893
197 1.069429025 2.796780819
198 1.584545636 1.069429025
199 1.627359181 1.584545636
200 1.880297276 1.627359181
201 0.766821519 1.880297276
202 -2.415665884 0.766821519
203 -2.538678442 -2.415665884
204 2.430494845 -2.538678442
205 0.659058835 2.430494845
206 1.600255615 0.659058835
207 1.168467230 1.600255615
208 -2.633317987 1.168467230
209 1.057243539 -2.633317987
210 -2.225663331 1.057243539
211 -3.427591445 -2.225663331
212 1.045185015 -3.427591445
213 3.029434943 1.045185015
214 1.526808073 3.029434943
215 0.671160154 1.526808073
216 2.471219224 0.671160154
217 0.038931712 2.471219224
218 1.109558511 0.038931712
219 -0.019241863 1.109558511
220 -1.530310197 -0.019241863
221 0.164458334 -1.530310197
222 0.714221447 0.164458334
223 -0.941156492 0.714221447
224 0.801824267 -0.941156492
225 -3.427986022 0.801824267
226 0.304500645 -3.427986022
227 1.344872467 0.304500645
228 -1.355414349 1.344872467
229 -0.670234567 -1.355414349
230 1.749981596 -0.670234567
231 -2.961413524 1.749981596
232 4.894186157 -2.961413524
233 1.942149473 4.894186157
234 -0.701806175 1.942149473
235 -2.311080043 -0.701806175
236 -7.234233445 -2.311080043
237 -1.432327586 -7.234233445
238 1.998396730 -1.432327586
239 -1.655515851 1.998396730
240 0.042983929 -1.655515851
241 -1.353121671 0.042983929
242 1.135520143 -1.353121671
243 1.999616659 1.135520143
244 1.191871734 1.999616659
245 0.243355138 1.191871734
246 1.348740103 0.243355138
247 -2.712031023 1.348740103
248 0.988512746 -2.712031023
249 0.164317907 0.988512746
250 -0.649635268 0.164317907
251 -1.636776776 -0.649635268
252 0.851409222 -1.636776776
253 3.293924675 0.851409222
254 -1.341621006 3.293924675
255 0.257585367 -1.341621006
256 1.846546799 0.257585367
257 2.456278760 1.846546799
258 -1.161751834 2.456278760
259 -5.191552233 -1.161751834
260 1.481655882 -5.191552233
261 -3.571909266 1.481655882
262 -0.222295831 -3.571909266
263 1.454433848 -0.222295831
264 NA 1.454433848
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.016804164 -2.993913501
[2,] 2.027151406 0.016804164
[3,] 2.995725307 2.027151406
[4,] -2.595072605 2.995725307
[5,] -2.141897200 -2.595072605
[6,] 3.138186711 -2.141897200
[7,] -2.000056132 3.138186711
[8,] -1.995510768 -2.000056132
[9,] 0.645072278 -1.995510768
[10,] 0.813892694 0.645072278
[11,] -0.161832553 0.813892694
[12,] 0.458574597 -0.161832553
[13,] 0.544729299 0.458574597
[14,] -0.925245616 0.544729299
[15,] -0.507765215 -0.925245616
[16,] 0.265806840 -0.507765215
[17,] 3.624428454 0.265806840
[18,] 2.660349338 3.624428454
[19,] 0.334477621 2.660349338
[20,] 0.633652977 0.334477621
[21,] 0.757621321 0.633652977
[22,] 2.639740689 0.757621321
[23,] 1.052268418 2.639740689
[24,] 0.627666732 1.052268418
[25,] 0.769536955 0.627666732
[26,] 1.118660729 0.769536955
[27,] -1.742681077 1.118660729
[28,] 0.218867943 -1.742681077
[29,] -0.208620540 0.218867943
[30,] -0.782061761 -0.208620540
[31,] -0.765435307 -0.782061761
[32,] -0.947771675 -0.765435307
[33,] 0.134713850 -0.947771675
[34,] -1.669832437 0.134713850
[35,] -2.877732375 -1.669832437
[36,] -3.123539774 -2.877732375
[37,] -1.821666183 -3.123539774
[38,] 1.442364216 -1.821666183
[39,] 1.583488771 1.442364216
[40,] 1.250558833 1.583488771
[41,] -1.831959692 1.250558833
[42,] 2.465936621 -1.831959692
[43,] -0.138347047 2.465936621
[44,] -0.732157180 -0.138347047
[45,] -4.428943288 -0.732157180
[46,] -2.389476807 -4.428943288
[47,] -0.088554778 -2.389476807
[48,] 0.585500029 -0.088554778
[49,] -1.715174355 0.585500029
[50,] -0.793191168 -1.715174355
[51,] -0.277732352 -0.793191168
[52,] -3.090264389 -0.277732352
[53,] 0.159893358 -3.090264389
[54,] -2.193291880 0.159893358
[55,] 1.580792693 -2.193291880
[56,] -0.002723157 1.580792693
[57,] 0.674393315 -0.002723157
[58,] 0.062562939 0.674393315
[59,] 1.767391273 0.062562939
[60,] 0.273040107 1.767391273
[61,] 0.325398988 0.273040107
[62,] -0.444290349 0.325398988
[63,] -0.590274389 -0.444290349
[64,] 0.579787544 -0.590274389
[65,] 1.181995498 0.579787544
[66,] 1.609005236 1.181995498
[67,] 3.464989102 1.609005236
[68,] -3.742662962 3.464989102
[69,] 0.408552891 -3.742662962
[70,] -3.351925850 0.408552891
[71,] -0.757083546 -3.351925850
[72,] 1.221155899 -0.757083546
[73,] 1.137981182 1.221155899
[74,] 0.526863803 1.137981182
[75,] 3.114928532 0.526863803
[76,] -0.424382129 3.114928532
[77,] 1.255649056 -0.424382129
[78,] -2.266425888 1.255649056
[79,] 0.688894025 -2.266425888
[80,] 0.468865501 0.688894025
[81,] 0.449126235 0.468865501
[82,] -0.761073029 0.449126235
[83,] 0.171314773 -0.761073029
[84,] 1.739235102 0.171314773
[85,] -0.016798732 1.739235102
[86,] 0.689287788 -0.016798732
[87,] 1.137395434 0.689287788
[88,] 0.345526346 1.137395434
[89,] -1.807397550 0.345526346
[90,] 0.088884322 -1.807397550
[91,] 0.441009678 0.088884322
[92,] -0.355463991 0.441009678
[93,] -2.084208337 -0.355463991
[94,] 1.266323989 -2.084208337
[95,] 0.148693176 1.266323989
[96,] 2.256015984 0.148693176
[97,] 0.142723932 2.256015984
[98,] -0.223812131 0.142723932
[99,] -0.987319196 -0.223812131
[100,] 1.272298966 -0.987319196
[101,] 2.562759575 1.272298966
[102,] 0.699064347 2.562759575
[103,] 1.152390786 0.699064347
[104,] -2.100113350 1.152390786
[105,] 1.273686600 -2.100113350
[106,] 0.267555340 1.273686600
[107,] 1.729568310 0.267555340
[108,] -0.270836391 1.729568310
[109,] 0.708836411 -0.270836391
[110,] 0.006668557 0.708836411
[111,] 2.343736549 0.006668557
[112,] -1.261192671 2.343736549
[113,] -2.685362676 -1.261192671
[114,] 1.704272975 -2.685362676
[115,] -1.589430057 1.704272975
[116,] 1.082060881 -1.589430057
[117,] -1.587237207 1.082060881
[118,] 0.390553887 -1.587237207
[119,] -1.244707870 0.390553887
[120,] 0.324812851 -1.244707870
[121,] -2.678493171 0.324812851
[122,] -0.633987150 -2.678493171
[123,] -0.980227778 -0.633987150
[124,] -0.727875007 -0.980227778
[125,] 0.230669153 -0.727875007
[126,] 1.566458103 0.230669153
[127,] 1.015413111 1.566458103
[128,] -2.757174939 1.015413111
[129,] 2.166778762 -2.757174939
[130,] -3.769428149 2.166778762
[131,] 2.660092839 -3.769428149
[132,] -2.260919066 2.660092839
[133,] -1.422365365 -2.260919066
[134,] -0.362162460 -1.422365365
[135,] 0.321579882 -0.362162460
[136,] 0.514628122 0.321579882
[137,] -2.339240454 0.514628122
[138,] -0.923823320 -2.339240454
[139,] -2.144194050 -0.923823320
[140,] 3.336127171 -2.144194050
[141,] 1.490616613 3.336127171
[142,] 0.251647677 1.490616613
[143,] 1.473628541 0.251647677
[144,] -3.484800100 1.473628541
[145,] 2.211179121 -3.484800100
[146,] -1.796104179 2.211179121
[147,] 1.174327295 -1.796104179
[148,] 0.032586352 1.174327295
[149,] -2.750321290 0.032586352
[150,] -1.022457307 -2.750321290
[151,] 2.462060948 -1.022457307
[152,] 4.487134895 2.462060948
[153,] 1.511720357 4.487134895
[154,] -2.689836965 1.511720357
[155,] 0.413475792 -2.689836965
[156,] 1.501935850 0.413475792
[157,] 1.165224558 1.501935850
[158,] 1.515005699 1.165224558
[159,] -0.578149315 1.515005699
[160,] 0.376136923 -0.578149315
[161,] -0.088588004 0.376136923
[162,] -0.670479997 -0.088588004
[163,] 0.367928673 -0.670479997
[164,] 1.510856706 0.367928673
[165,] -1.755662983 1.510856706
[166,] -0.276418852 -1.755662983
[167,] -3.409709022 -0.276418852
[168,] -1.609560476 -3.409709022
[169,] 1.839996527 -1.609560476
[170,] 1.301743006 1.839996527
[171,] 0.744112177 1.301743006
[172,] -1.771026188 0.744112177
[173,] -2.638173958 -1.771026188
[174,] -3.240906678 -2.638173958
[175,] 0.760090481 -3.240906678
[176,] -0.312679621 0.760090481
[177,] -0.809660101 -0.312679621
[178,] 0.220130762 -0.809660101
[179,] -1.162898629 0.220130762
[180,] 1.150624372 -1.162898629
[181,] -0.413373104 1.150624372
[182,] 2.461433375 -0.413373104
[183,] -0.188098466 2.461433375
[184,] -6.640489573 -0.188098466
[185,] 1.260709023 -6.640489573
[186,] 2.555744150 1.260709023
[187,] -0.537276499 2.555744150
[188,] -0.463821184 -0.537276499
[189,] 0.593914689 -0.463821184
[190,] -0.159241288 0.593914689
[191,] 0.900229342 -0.159241288
[192,] 2.294015295 0.900229342
[193,] 2.038987991 2.294015295
[194,] -0.705713028 2.038987991
[195,] 0.207058893 -0.705713028
[196,] 2.796780819 0.207058893
[197,] 1.069429025 2.796780819
[198,] 1.584545636 1.069429025
[199,] 1.627359181 1.584545636
[200,] 1.880297276 1.627359181
[201,] 0.766821519 1.880297276
[202,] -2.415665884 0.766821519
[203,] -2.538678442 -2.415665884
[204,] 2.430494845 -2.538678442
[205,] 0.659058835 2.430494845
[206,] 1.600255615 0.659058835
[207,] 1.168467230 1.600255615
[208,] -2.633317987 1.168467230
[209,] 1.057243539 -2.633317987
[210,] -2.225663331 1.057243539
[211,] -3.427591445 -2.225663331
[212,] 1.045185015 -3.427591445
[213,] 3.029434943 1.045185015
[214,] 1.526808073 3.029434943
[215,] 0.671160154 1.526808073
[216,] 2.471219224 0.671160154
[217,] 0.038931712 2.471219224
[218,] 1.109558511 0.038931712
[219,] -0.019241863 1.109558511
[220,] -1.530310197 -0.019241863
[221,] 0.164458334 -1.530310197
[222,] 0.714221447 0.164458334
[223,] -0.941156492 0.714221447
[224,] 0.801824267 -0.941156492
[225,] -3.427986022 0.801824267
[226,] 0.304500645 -3.427986022
[227,] 1.344872467 0.304500645
[228,] -1.355414349 1.344872467
[229,] -0.670234567 -1.355414349
[230,] 1.749981596 -0.670234567
[231,] -2.961413524 1.749981596
[232,] 4.894186157 -2.961413524
[233,] 1.942149473 4.894186157
[234,] -0.701806175 1.942149473
[235,] -2.311080043 -0.701806175
[236,] -7.234233445 -2.311080043
[237,] -1.432327586 -7.234233445
[238,] 1.998396730 -1.432327586
[239,] -1.655515851 1.998396730
[240,] 0.042983929 -1.655515851
[241,] -1.353121671 0.042983929
[242,] 1.135520143 -1.353121671
[243,] 1.999616659 1.135520143
[244,] 1.191871734 1.999616659
[245,] 0.243355138 1.191871734
[246,] 1.348740103 0.243355138
[247,] -2.712031023 1.348740103
[248,] 0.988512746 -2.712031023
[249,] 0.164317907 0.988512746
[250,] -0.649635268 0.164317907
[251,] -1.636776776 -0.649635268
[252,] 0.851409222 -1.636776776
[253,] 3.293924675 0.851409222
[254,] -1.341621006 3.293924675
[255,] 0.257585367 -1.341621006
[256,] 1.846546799 0.257585367
[257,] 2.456278760 1.846546799
[258,] -1.161751834 2.456278760
[259,] -5.191552233 -1.161751834
[260,] 1.481655882 -5.191552233
[261,] -3.571909266 1.481655882
[262,] -0.222295831 -3.571909266
[263,] 1.454433848 -0.222295831
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.016804164 -2.993913501
2 2.027151406 0.016804164
3 2.995725307 2.027151406
4 -2.595072605 2.995725307
5 -2.141897200 -2.595072605
6 3.138186711 -2.141897200
7 -2.000056132 3.138186711
8 -1.995510768 -2.000056132
9 0.645072278 -1.995510768
10 0.813892694 0.645072278
11 -0.161832553 0.813892694
12 0.458574597 -0.161832553
13 0.544729299 0.458574597
14 -0.925245616 0.544729299
15 -0.507765215 -0.925245616
16 0.265806840 -0.507765215
17 3.624428454 0.265806840
18 2.660349338 3.624428454
19 0.334477621 2.660349338
20 0.633652977 0.334477621
21 0.757621321 0.633652977
22 2.639740689 0.757621321
23 1.052268418 2.639740689
24 0.627666732 1.052268418
25 0.769536955 0.627666732
26 1.118660729 0.769536955
27 -1.742681077 1.118660729
28 0.218867943 -1.742681077
29 -0.208620540 0.218867943
30 -0.782061761 -0.208620540
31 -0.765435307 -0.782061761
32 -0.947771675 -0.765435307
33 0.134713850 -0.947771675
34 -1.669832437 0.134713850
35 -2.877732375 -1.669832437
36 -3.123539774 -2.877732375
37 -1.821666183 -3.123539774
38 1.442364216 -1.821666183
39 1.583488771 1.442364216
40 1.250558833 1.583488771
41 -1.831959692 1.250558833
42 2.465936621 -1.831959692
43 -0.138347047 2.465936621
44 -0.732157180 -0.138347047
45 -4.428943288 -0.732157180
46 -2.389476807 -4.428943288
47 -0.088554778 -2.389476807
48 0.585500029 -0.088554778
49 -1.715174355 0.585500029
50 -0.793191168 -1.715174355
51 -0.277732352 -0.793191168
52 -3.090264389 -0.277732352
53 0.159893358 -3.090264389
54 -2.193291880 0.159893358
55 1.580792693 -2.193291880
56 -0.002723157 1.580792693
57 0.674393315 -0.002723157
58 0.062562939 0.674393315
59 1.767391273 0.062562939
60 0.273040107 1.767391273
61 0.325398988 0.273040107
62 -0.444290349 0.325398988
63 -0.590274389 -0.444290349
64 0.579787544 -0.590274389
65 1.181995498 0.579787544
66 1.609005236 1.181995498
67 3.464989102 1.609005236
68 -3.742662962 3.464989102
69 0.408552891 -3.742662962
70 -3.351925850 0.408552891
71 -0.757083546 -3.351925850
72 1.221155899 -0.757083546
73 1.137981182 1.221155899
74 0.526863803 1.137981182
75 3.114928532 0.526863803
76 -0.424382129 3.114928532
77 1.255649056 -0.424382129
78 -2.266425888 1.255649056
79 0.688894025 -2.266425888
80 0.468865501 0.688894025
81 0.449126235 0.468865501
82 -0.761073029 0.449126235
83 0.171314773 -0.761073029
84 1.739235102 0.171314773
85 -0.016798732 1.739235102
86 0.689287788 -0.016798732
87 1.137395434 0.689287788
88 0.345526346 1.137395434
89 -1.807397550 0.345526346
90 0.088884322 -1.807397550
91 0.441009678 0.088884322
92 -0.355463991 0.441009678
93 -2.084208337 -0.355463991
94 1.266323989 -2.084208337
95 0.148693176 1.266323989
96 2.256015984 0.148693176
97 0.142723932 2.256015984
98 -0.223812131 0.142723932
99 -0.987319196 -0.223812131
100 1.272298966 -0.987319196
101 2.562759575 1.272298966
102 0.699064347 2.562759575
103 1.152390786 0.699064347
104 -2.100113350 1.152390786
105 1.273686600 -2.100113350
106 0.267555340 1.273686600
107 1.729568310 0.267555340
108 -0.270836391 1.729568310
109 0.708836411 -0.270836391
110 0.006668557 0.708836411
111 2.343736549 0.006668557
112 -1.261192671 2.343736549
113 -2.685362676 -1.261192671
114 1.704272975 -2.685362676
115 -1.589430057 1.704272975
116 1.082060881 -1.589430057
117 -1.587237207 1.082060881
118 0.390553887 -1.587237207
119 -1.244707870 0.390553887
120 0.324812851 -1.244707870
121 -2.678493171 0.324812851
122 -0.633987150 -2.678493171
123 -0.980227778 -0.633987150
124 -0.727875007 -0.980227778
125 0.230669153 -0.727875007
126 1.566458103 0.230669153
127 1.015413111 1.566458103
128 -2.757174939 1.015413111
129 2.166778762 -2.757174939
130 -3.769428149 2.166778762
131 2.660092839 -3.769428149
132 -2.260919066 2.660092839
133 -1.422365365 -2.260919066
134 -0.362162460 -1.422365365
135 0.321579882 -0.362162460
136 0.514628122 0.321579882
137 -2.339240454 0.514628122
138 -0.923823320 -2.339240454
139 -2.144194050 -0.923823320
140 3.336127171 -2.144194050
141 1.490616613 3.336127171
142 0.251647677 1.490616613
143 1.473628541 0.251647677
144 -3.484800100 1.473628541
145 2.211179121 -3.484800100
146 -1.796104179 2.211179121
147 1.174327295 -1.796104179
148 0.032586352 1.174327295
149 -2.750321290 0.032586352
150 -1.022457307 -2.750321290
151 2.462060948 -1.022457307
152 4.487134895 2.462060948
153 1.511720357 4.487134895
154 -2.689836965 1.511720357
155 0.413475792 -2.689836965
156 1.501935850 0.413475792
157 1.165224558 1.501935850
158 1.515005699 1.165224558
159 -0.578149315 1.515005699
160 0.376136923 -0.578149315
161 -0.088588004 0.376136923
162 -0.670479997 -0.088588004
163 0.367928673 -0.670479997
164 1.510856706 0.367928673
165 -1.755662983 1.510856706
166 -0.276418852 -1.755662983
167 -3.409709022 -0.276418852
168 -1.609560476 -3.409709022
169 1.839996527 -1.609560476
170 1.301743006 1.839996527
171 0.744112177 1.301743006
172 -1.771026188 0.744112177
173 -2.638173958 -1.771026188
174 -3.240906678 -2.638173958
175 0.760090481 -3.240906678
176 -0.312679621 0.760090481
177 -0.809660101 -0.312679621
178 0.220130762 -0.809660101
179 -1.162898629 0.220130762
180 1.150624372 -1.162898629
181 -0.413373104 1.150624372
182 2.461433375 -0.413373104
183 -0.188098466 2.461433375
184 -6.640489573 -0.188098466
185 1.260709023 -6.640489573
186 2.555744150 1.260709023
187 -0.537276499 2.555744150
188 -0.463821184 -0.537276499
189 0.593914689 -0.463821184
190 -0.159241288 0.593914689
191 0.900229342 -0.159241288
192 2.294015295 0.900229342
193 2.038987991 2.294015295
194 -0.705713028 2.038987991
195 0.207058893 -0.705713028
196 2.796780819 0.207058893
197 1.069429025 2.796780819
198 1.584545636 1.069429025
199 1.627359181 1.584545636
200 1.880297276 1.627359181
201 0.766821519 1.880297276
202 -2.415665884 0.766821519
203 -2.538678442 -2.415665884
204 2.430494845 -2.538678442
205 0.659058835 2.430494845
206 1.600255615 0.659058835
207 1.168467230 1.600255615
208 -2.633317987 1.168467230
209 1.057243539 -2.633317987
210 -2.225663331 1.057243539
211 -3.427591445 -2.225663331
212 1.045185015 -3.427591445
213 3.029434943 1.045185015
214 1.526808073 3.029434943
215 0.671160154 1.526808073
216 2.471219224 0.671160154
217 0.038931712 2.471219224
218 1.109558511 0.038931712
219 -0.019241863 1.109558511
220 -1.530310197 -0.019241863
221 0.164458334 -1.530310197
222 0.714221447 0.164458334
223 -0.941156492 0.714221447
224 0.801824267 -0.941156492
225 -3.427986022 0.801824267
226 0.304500645 -3.427986022
227 1.344872467 0.304500645
228 -1.355414349 1.344872467
229 -0.670234567 -1.355414349
230 1.749981596 -0.670234567
231 -2.961413524 1.749981596
232 4.894186157 -2.961413524
233 1.942149473 4.894186157
234 -0.701806175 1.942149473
235 -2.311080043 -0.701806175
236 -7.234233445 -2.311080043
237 -1.432327586 -7.234233445
238 1.998396730 -1.432327586
239 -1.655515851 1.998396730
240 0.042983929 -1.655515851
241 -1.353121671 0.042983929
242 1.135520143 -1.353121671
243 1.999616659 1.135520143
244 1.191871734 1.999616659
245 0.243355138 1.191871734
246 1.348740103 0.243355138
247 -2.712031023 1.348740103
248 0.988512746 -2.712031023
249 0.164317907 0.988512746
250 -0.649635268 0.164317907
251 -1.636776776 -0.649635268
252 0.851409222 -1.636776776
253 3.293924675 0.851409222
254 -1.341621006 3.293924675
255 0.257585367 -1.341621006
256 1.846546799 0.257585367
257 2.456278760 1.846546799
258 -1.161751834 2.456278760
259 -5.191552233 -1.161751834
260 1.481655882 -5.191552233
261 -3.571909266 1.481655882
262 -0.222295831 -3.571909266
263 1.454433848 -0.222295831
> 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/7t3xa1352122093.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/8m2sm1352122093.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/9qphx1352122093.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/10slgd1352122093.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1198m31352122093.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12j6cy1352122093.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1330sl1352122093.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/148amp1352122093.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/15k11p1352122093.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/166ck71352122093.tab")
+ }
>
> try(system("convert tmp/157wc1352122093.ps tmp/157wc1352122093.png",intern=TRUE))
character(0)
> try(system("convert tmp/2e2iy1352122093.ps tmp/2e2iy1352122093.png",intern=TRUE))
character(0)
> try(system("convert tmp/3g5001352122093.ps tmp/3g5001352122093.png",intern=TRUE))
character(0)
> try(system("convert tmp/4g3u81352122093.ps tmp/4g3u81352122093.png",intern=TRUE))
character(0)
> try(system("convert tmp/54toi1352122093.ps tmp/54toi1352122093.png",intern=TRUE))
character(0)
> try(system("convert tmp/6mcls1352122093.ps tmp/6mcls1352122093.png",intern=TRUE))
character(0)
> try(system("convert tmp/7t3xa1352122093.ps tmp/7t3xa1352122093.png",intern=TRUE))
character(0)
> try(system("convert tmp/8m2sm1352122093.ps tmp/8m2sm1352122093.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qphx1352122093.ps tmp/9qphx1352122093.png",intern=TRUE))
character(0)
> try(system("convert tmp/10slgd1352122093.ps tmp/10slgd1352122093.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.677 0.939 12.775