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(26
+ ,21
+ ,21
+ ,23
+ ,17
+ ,23
+ ,4
+ ,14
+ ,12
+ ,127
+ ,20
+ ,16
+ ,15
+ ,24
+ ,17
+ ,20
+ ,4
+ ,18
+ ,11
+ ,108
+ ,19
+ ,19
+ ,18
+ ,22
+ ,18
+ ,20
+ ,6
+ ,11
+ ,14
+ ,110
+ ,19
+ ,18
+ ,11
+ ,20
+ ,21
+ ,21
+ ,8
+ ,12
+ ,12
+ ,102
+ ,20
+ ,16
+ ,8
+ ,24
+ ,20
+ ,24
+ ,8
+ ,16
+ ,21
+ ,104
+ ,25
+ ,23
+ ,19
+ ,27
+ ,28
+ ,22
+ ,4
+ ,18
+ ,12
+ ,140
+ ,25
+ ,17
+ ,4
+ ,28
+ ,19
+ ,23
+ ,4
+ ,14
+ ,22
+ ,112
+ ,22
+ ,12
+ ,20
+ ,27
+ ,22
+ ,20
+ ,8
+ ,14
+ ,11
+ ,115
+ ,26
+ ,19
+ ,16
+ ,24
+ ,16
+ ,25
+ ,5
+ ,15
+ ,10
+ ,121
+ ,22
+ ,16
+ ,14
+ ,23
+ ,18
+ ,23
+ ,4
+ ,15
+ ,13
+ ,112
+ ,17
+ ,19
+ ,10
+ ,24
+ ,25
+ ,27
+ ,4
+ ,17
+ ,10
+ ,118
+ ,22
+ ,20
+ ,13
+ ,27
+ ,17
+ ,27
+ ,4
+ ,19
+ ,8
+ ,122
+ ,19
+ ,13
+ ,14
+ ,27
+ ,14
+ ,22
+ ,4
+ ,10
+ ,15
+ ,105
+ ,24
+ ,20
+ ,8
+ ,28
+ ,11
+ ,24
+ ,4
+ ,16
+ ,14
+ ,111
+ ,26
+ ,27
+ ,23
+ ,27
+ ,27
+ ,25
+ ,4
+ ,18
+ ,10
+ ,151
+ ,21
+ ,17
+ ,11
+ ,23
+ ,20
+ ,22
+ ,8
+ ,14
+ ,14
+ ,106
+ ,13
+ ,8
+ ,9
+ ,24
+ ,22
+ ,28
+ ,4
+ ,14
+ ,14
+ ,100
+ ,26
+ ,25
+ ,24
+ ,28
+ ,22
+ ,28
+ ,4
+ ,17
+ ,11
+ ,149
+ ,20
+ ,26
+ ,5
+ ,27
+ ,21
+ ,27
+ ,4
+ ,14
+ ,10
+ ,122
+ ,22
+ ,13
+ ,15
+ ,25
+ ,23
+ ,25
+ ,8
+ ,16
+ ,13
+ ,115
+ ,14
+ ,19
+ ,5
+ ,19
+ ,17
+ ,16
+ ,4
+ ,18
+ ,7
+ ,86
+ ,21
+ ,15
+ ,19
+ ,24
+ ,24
+ ,28
+ ,7
+ ,11
+ ,14
+ ,124
+ ,7
+ ,5
+ ,6
+ ,20
+ ,14
+ ,21
+ ,4
+ ,14
+ ,12
+ ,69
+ ,23
+ ,16
+ ,13
+ ,28
+ ,17
+ ,24
+ ,4
+ ,12
+ ,14
+ ,117
+ ,17
+ ,14
+ ,11
+ ,26
+ ,23
+ ,27
+ ,5
+ ,17
+ ,11
+ ,113
+ ,25
+ ,24
+ ,17
+ ,23
+ ,24
+ ,14
+ ,4
+ ,9
+ ,9
+ ,123
+ ,25
+ ,24
+ ,17
+ ,23
+ ,24
+ ,14
+ ,4
+ ,16
+ ,11
+ ,123
+ ,19
+ ,9
+ ,5
+ ,20
+ ,8
+ ,27
+ ,4
+ ,14
+ ,15
+ ,84
+ ,20
+ ,19
+ ,9
+ ,11
+ ,22
+ ,20
+ ,4
+ ,15
+ ,14
+ ,97
+ ,23
+ ,19
+ ,15
+ ,24
+ ,23
+ ,21
+ ,4
+ ,11
+ ,13
+ ,121
+ ,22
+ ,25
+ ,17
+ ,25
+ ,25
+ ,22
+ ,4
+ ,16
+ ,9
+ ,132
+ ,22
+ ,19
+ ,17
+ ,23
+ ,21
+ ,21
+ ,4
+ ,13
+ ,15
+ ,119
+ ,21
+ ,18
+ ,20
+ ,18
+ ,24
+ ,12
+ ,15
+ ,17
+ ,10
+ ,98
+ ,15
+ ,15
+ ,12
+ ,20
+ ,15
+ ,20
+ ,10
+ ,15
+ ,11
+ ,87
+ ,20
+ ,12
+ ,7
+ ,20
+ ,22
+ ,24
+ ,4
+ ,14
+ ,13
+ ,101
+ ,22
+ ,21
+ ,16
+ ,24
+ ,21
+ ,19
+ ,8
+ ,16
+ ,8
+ ,115
+ ,18
+ ,12
+ ,7
+ ,23
+ ,25
+ ,28
+ ,4
+ ,9
+ ,20
+ ,109
+ ,20
+ ,15
+ ,14
+ ,25
+ ,16
+ ,23
+ ,4
+ ,15
+ ,12
+ ,109
+ ,28
+ ,28
+ ,24
+ ,28
+ ,28
+ ,27
+ ,4
+ ,17
+ ,10
+ ,159
+ ,22
+ ,25
+ ,15
+ ,26
+ ,23
+ ,22
+ ,4
+ ,13
+ ,10
+ ,129
+ ,18
+ ,19
+ ,15
+ ,26
+ ,21
+ ,27
+ ,7
+ ,15
+ ,9
+ ,119
+ ,23
+ ,20
+ ,10
+ ,23
+ ,21
+ ,26
+ ,4
+ ,16
+ ,14
+ ,119
+ ,20
+ ,24
+ ,14
+ ,22
+ ,26
+ ,22
+ ,6
+ ,16
+ ,8
+ ,122
+ ,25
+ ,26
+ ,18
+ ,24
+ ,22
+ ,21
+ ,5
+ ,12
+ ,14
+ ,131
+ ,26
+ ,25
+ ,12
+ ,21
+ ,21
+ ,19
+ ,4
+ ,12
+ ,11
+ ,120
+ ,15
+ ,12
+ ,9
+ ,20
+ ,18
+ ,24
+ ,16
+ ,11
+ ,13
+ ,82
+ ,17
+ ,12
+ ,9
+ ,22
+ ,12
+ ,19
+ ,5
+ ,15
+ ,9
+ ,86
+ ,23
+ ,15
+ ,8
+ ,20
+ ,25
+ ,26
+ ,12
+ ,15
+ ,11
+ ,105
+ ,21
+ ,17
+ ,18
+ ,25
+ ,17
+ ,22
+ ,6
+ ,17
+ ,15
+ ,114
+ ,13
+ ,14
+ ,10
+ ,20
+ ,24
+ ,28
+ ,9
+ ,13
+ ,11
+ ,100
+ ,18
+ ,16
+ ,17
+ ,22
+ ,15
+ ,21
+ ,9
+ ,16
+ ,10
+ ,100
+ ,19
+ ,11
+ ,14
+ ,23
+ ,13
+ ,23
+ ,4
+ ,14
+ ,14
+ ,99
+ ,22
+ ,20
+ ,16
+ ,25
+ ,26
+ ,28
+ ,5
+ ,11
+ ,18
+ ,132
+ ,16
+ ,11
+ ,10
+ ,23
+ ,16
+ ,10
+ ,4
+ ,12
+ ,14
+ ,82
+ ,24
+ ,22
+ ,19
+ ,23
+ ,24
+ ,24
+ ,4
+ ,12
+ ,11
+ ,132
+ ,18
+ ,20
+ ,10
+ ,22
+ ,21
+ ,21
+ ,5
+ ,15
+ ,12
+ ,107
+ ,20
+ ,19
+ ,14
+ ,24
+ ,20
+ ,21
+ ,4
+ ,16
+ ,13
+ ,114
+ ,24
+ ,17
+ ,10
+ ,25
+ ,14
+ ,24
+ ,4
+ ,15
+ ,9
+ ,110
+ ,14
+ ,21
+ ,4
+ ,21
+ ,25
+ ,24
+ ,4
+ ,12
+ ,10
+ ,105
+ ,22
+ ,23
+ ,19
+ ,12
+ ,25
+ ,25
+ ,5
+ ,12
+ ,15
+ ,121
+ ,24
+ ,18
+ ,9
+ ,17
+ ,20
+ ,25
+ ,4
+ ,8
+ ,20
+ ,109
+ ,18
+ ,17
+ ,12
+ ,20
+ ,22
+ ,23
+ ,6
+ ,13
+ ,12
+ ,106
+ ,21
+ ,27
+ ,16
+ ,23
+ ,20
+ ,21
+ ,4
+ ,11
+ ,12
+ ,124
+ ,23
+ ,25
+ ,11
+ ,23
+ ,26
+ ,16
+ ,4
+ ,14
+ ,14
+ ,120
+ ,17
+ ,19
+ ,18
+ ,20
+ ,18
+ ,17
+ ,18
+ ,15
+ ,13
+ ,91
+ ,22
+ ,22
+ ,11
+ ,28
+ ,22
+ ,25
+ ,4
+ ,10
+ ,11
+ ,126
+ ,24
+ ,24
+ ,24
+ ,24
+ ,24
+ ,24
+ ,6
+ ,11
+ ,17
+ ,138
+ ,21
+ ,20
+ ,17
+ ,24
+ ,17
+ ,23
+ ,4
+ ,12
+ ,12
+ ,118
+ ,22
+ ,19
+ ,18
+ ,24
+ ,24
+ ,25
+ ,4
+ ,15
+ ,13
+ ,128
+ ,16
+ ,11
+ ,9
+ ,24
+ ,20
+ ,23
+ ,5
+ ,15
+ ,14
+ ,98
+ ,21
+ ,22
+ ,19
+ ,28
+ ,19
+ ,28
+ ,4
+ ,14
+ ,13
+ ,133
+ ,23
+ ,22
+ ,18
+ ,25
+ ,20
+ ,26
+ ,4
+ ,16
+ ,15
+ ,130
+ ,22
+ ,16
+ ,12
+ ,21
+ ,15
+ ,22
+ ,5
+ ,15
+ ,13
+ ,103
+ ,24
+ ,20
+ ,23
+ ,25
+ ,23
+ ,19
+ ,10
+ ,15
+ ,10
+ ,124
+ ,24
+ ,24
+ ,22
+ ,25
+ ,26
+ ,26
+ ,5
+ ,13
+ ,11
+ ,142
+ ,16
+ ,16
+ ,14
+ ,18
+ ,22
+ ,18
+ ,8
+ ,12
+ ,19
+ ,96
+ ,16
+ ,16
+ ,14
+ ,17
+ ,20
+ ,18
+ ,8
+ ,17
+ ,13
+ ,93
+ ,21
+ ,22
+ ,16
+ ,26
+ ,24
+ ,25
+ ,5
+ ,13
+ ,17
+ ,129
+ ,26
+ ,24
+ ,23
+ ,28
+ ,26
+ ,27
+ ,4
+ ,15
+ ,13
+ ,150
+ ,15
+ ,16
+ ,7
+ ,21
+ ,21
+ ,12
+ ,4
+ ,13
+ ,9
+ ,88
+ ,25
+ ,27
+ ,10
+ ,27
+ ,25
+ ,15
+ ,4
+ ,15
+ ,11
+ ,125
+ ,18
+ ,11
+ ,12
+ ,22
+ ,13
+ ,21
+ ,5
+ ,16
+ ,10
+ ,92
+ ,23
+ ,21
+ ,12
+ ,21
+ ,20
+ ,23
+ ,4
+ ,15
+ ,9
+ ,0
+ ,20
+ ,20
+ ,12
+ ,25
+ ,22
+ ,22
+ ,4
+ ,16
+ ,12
+ ,117
+ ,17
+ ,20
+ ,17
+ ,22
+ ,23
+ ,21
+ ,8
+ ,15
+ ,12
+ ,112
+ ,25
+ ,27
+ ,21
+ ,23
+ ,28
+ ,24
+ ,4
+ ,14
+ ,13
+ ,144
+ ,24
+ ,20
+ ,16
+ ,26
+ ,22
+ ,27
+ ,5
+ ,15
+ ,13
+ ,130
+ ,17
+ ,12
+ ,11
+ ,19
+ ,20
+ ,22
+ ,14
+ ,14
+ ,12
+ ,87
+ ,19
+ ,8
+ ,14
+ ,25
+ ,6
+ ,28
+ ,8
+ ,13
+ ,15
+ ,92
+ ,20
+ ,21
+ ,13
+ ,21
+ ,21
+ ,26
+ ,8
+ ,7
+ ,22
+ ,114
+ ,15
+ ,18
+ ,9
+ ,13
+ ,20
+ ,10
+ ,4
+ ,17
+ ,13
+ ,81
+ ,27
+ ,24
+ ,19
+ ,24
+ ,18
+ ,19
+ ,4
+ ,13
+ ,15
+ ,127
+ ,22
+ ,16
+ ,13
+ ,25
+ ,23
+ ,22
+ ,6
+ ,15
+ ,13
+ ,115
+ ,23
+ ,18
+ ,19
+ ,26
+ ,20
+ ,21
+ ,4
+ ,14
+ ,15
+ ,123
+ ,16
+ ,20
+ ,13
+ ,25
+ ,24
+ ,24
+ ,7
+ ,13
+ ,10
+ ,115
+ ,19
+ ,20
+ ,13
+ ,25
+ ,22
+ ,25
+ ,7
+ ,16
+ ,11
+ ,117
+ ,25
+ ,19
+ ,13
+ ,22
+ ,21
+ ,21
+ ,4
+ ,12
+ ,16
+ ,117
+ ,19
+ ,17
+ ,14
+ ,21
+ ,18
+ ,20
+ ,6
+ ,14
+ ,11
+ ,103
+ ,19
+ ,16
+ ,12
+ ,23
+ ,21
+ ,21
+ ,4
+ ,17
+ ,11
+ ,108
+ ,26
+ ,26
+ ,22
+ ,25
+ ,23
+ ,24
+ ,7
+ ,15
+ ,10
+ ,139
+ ,21
+ ,15
+ ,11
+ ,24
+ ,23
+ ,23
+ ,4
+ ,17
+ ,10
+ ,113
+ ,20
+ ,22
+ ,5
+ ,21
+ ,15
+ ,18
+ ,4
+ ,12
+ ,16
+ ,97
+ ,24
+ ,17
+ ,18
+ ,21
+ ,21
+ ,24
+ ,8
+ ,16
+ ,12
+ ,117
+ ,22
+ ,23
+ ,19
+ ,25
+ ,24
+ ,24
+ ,4
+ ,11
+ ,11
+ ,133
+ ,20
+ ,21
+ ,14
+ ,22
+ ,23
+ ,19
+ ,4
+ ,15
+ ,16
+ ,115
+ ,18
+ ,19
+ ,15
+ ,20
+ ,21
+ ,20
+ ,10
+ ,9
+ ,19
+ ,103
+ ,18
+ ,14
+ ,12
+ ,20
+ ,21
+ ,18
+ ,8
+ ,16
+ ,11
+ ,95
+ ,24
+ ,17
+ ,19
+ ,23
+ ,20
+ ,20
+ ,6
+ ,15
+ ,16
+ ,117
+ ,24
+ ,12
+ ,15
+ ,28
+ ,11
+ ,27
+ ,4
+ ,10
+ ,15
+ ,113
+ ,22
+ ,24
+ ,17
+ ,23
+ ,22
+ ,23
+ ,4
+ ,10
+ ,24
+ ,127
+ ,23
+ ,18
+ ,8
+ ,28
+ ,27
+ ,26
+ ,4
+ ,15
+ ,14
+ ,126
+ ,22
+ ,20
+ ,10
+ ,24
+ ,25
+ ,23
+ ,5
+ ,11
+ ,15
+ ,119
+ ,20
+ ,16
+ ,12
+ ,18
+ ,18
+ ,17
+ ,4
+ ,13
+ ,11
+ ,97
+ ,18
+ ,20
+ ,12
+ ,20
+ ,20
+ ,21
+ ,6
+ ,14
+ ,15
+ ,105
+ ,25
+ ,22
+ ,20
+ ,28
+ ,24
+ ,25
+ ,4
+ ,18
+ ,12
+ ,140
+ ,18
+ ,12
+ ,12
+ ,21
+ ,10
+ ,23
+ ,5
+ ,16
+ ,10
+ ,91
+ ,16
+ ,16
+ ,12
+ ,21
+ ,27
+ ,27
+ ,7
+ ,14
+ ,14
+ ,112
+ ,20
+ ,17
+ ,14
+ ,25
+ ,21
+ ,24
+ ,8
+ ,14
+ ,13
+ ,113
+ ,19
+ ,22
+ ,6
+ ,19
+ ,21
+ ,20
+ ,5
+ ,14
+ ,9
+ ,102
+ ,15
+ ,12
+ ,10
+ ,18
+ ,18
+ ,27
+ ,8
+ ,14
+ ,15
+ ,92
+ ,19
+ ,14
+ ,18
+ ,21
+ ,15
+ ,21
+ ,10
+ ,12
+ ,15
+ ,98
+ ,19
+ ,23
+ ,18
+ ,22
+ ,24
+ ,24
+ ,8
+ ,14
+ ,14
+ ,122
+ ,16
+ ,15
+ ,7
+ ,24
+ ,22
+ ,21
+ ,5
+ ,15
+ ,11
+ ,100
+ ,17
+ ,17
+ ,18
+ ,15
+ ,14
+ ,15
+ ,12
+ ,15
+ ,8
+ ,84
+ ,28
+ ,28
+ ,9
+ ,28
+ ,28
+ ,25
+ ,4
+ ,15
+ ,11
+ ,142
+ ,23
+ ,20
+ ,17
+ ,26
+ ,18
+ ,25
+ ,5
+ ,13
+ ,11
+ ,124
+ ,25
+ ,23
+ ,22
+ ,23
+ ,26
+ ,22
+ ,4
+ ,17
+ ,8
+ ,137
+ ,20
+ ,13
+ ,11
+ ,26
+ ,17
+ ,24
+ ,6
+ ,17
+ ,10
+ ,105
+ ,17
+ ,18
+ ,15
+ ,20
+ ,19
+ ,21
+ ,4
+ ,19
+ ,11
+ ,106
+ ,23
+ ,23
+ ,17
+ ,22
+ ,22
+ ,22
+ ,4
+ ,15
+ ,13
+ ,125
+ ,16
+ ,19
+ ,15
+ ,20
+ ,18
+ ,23
+ ,7
+ ,13
+ ,11
+ ,104
+ ,23
+ ,23
+ ,22
+ ,23
+ ,24
+ ,22
+ ,7
+ ,9
+ ,20
+ ,130
+ ,11
+ ,12
+ ,9
+ ,22
+ ,15
+ ,20
+ ,10
+ ,15
+ ,10
+ ,79
+ ,18
+ ,16
+ ,13
+ ,24
+ ,18
+ ,23
+ ,4
+ ,15
+ ,15
+ ,108
+ ,24
+ ,23
+ ,20
+ ,23
+ ,26
+ ,25
+ ,5
+ ,15
+ ,12
+ ,136
+ ,23
+ ,13
+ ,14
+ ,22
+ ,11
+ ,23
+ ,8
+ ,16
+ ,14
+ ,98
+ ,21
+ ,22
+ ,14
+ ,26
+ ,26
+ ,22
+ ,11
+ ,11
+ ,23
+ ,120
+ ,16
+ ,18
+ ,12
+ ,23
+ ,21
+ ,25
+ ,7
+ ,14
+ ,14
+ ,108
+ ,24
+ ,23
+ ,20
+ ,27
+ ,23
+ ,26
+ ,4
+ ,11
+ ,16
+ ,139
+ ,23
+ ,20
+ ,20
+ ,23
+ ,23
+ ,22
+ ,8
+ ,15
+ ,11
+ ,123
+ ,18
+ ,10
+ ,8
+ ,21
+ ,15
+ ,24
+ ,6
+ ,13
+ ,12
+ ,90
+ ,20
+ ,17
+ ,17
+ ,26
+ ,22
+ ,24
+ ,7
+ ,15
+ ,10
+ ,119
+ ,9
+ ,18
+ ,9
+ ,23
+ ,26
+ ,25
+ ,5
+ ,16
+ ,14
+ ,105
+ ,24
+ ,15
+ ,18
+ ,21
+ ,16
+ ,20
+ ,4
+ ,14
+ ,12
+ ,110
+ ,25
+ ,23
+ ,22
+ ,27
+ ,20
+ ,26
+ ,8
+ ,15
+ ,12
+ ,135
+ ,20
+ ,17
+ ,10
+ ,19
+ ,18
+ ,21
+ ,4
+ ,16
+ ,11
+ ,101
+ ,21
+ ,17
+ ,13
+ ,23
+ ,22
+ ,26
+ ,8
+ ,16
+ ,12
+ ,114
+ ,25
+ ,22
+ ,15
+ ,25
+ ,16
+ ,21
+ ,6
+ ,11
+ ,13
+ ,118
+ ,22
+ ,20
+ ,18
+ ,23
+ ,19
+ ,22
+ ,4
+ ,12
+ ,11
+ ,120
+ ,21
+ ,20
+ ,18
+ ,22
+ ,20
+ ,16
+ ,9
+ ,9
+ ,19
+ ,108
+ ,21
+ ,19
+ ,12
+ ,22
+ ,19
+ ,26
+ ,5
+ ,16
+ ,12
+ ,114
+ ,22
+ ,18
+ ,12
+ ,25
+ ,23
+ ,28
+ ,6
+ ,13
+ ,17
+ ,122
+ ,27
+ ,22
+ ,20
+ ,25
+ ,24
+ ,18
+ ,4
+ ,16
+ ,9
+ ,132
+ ,24
+ ,20
+ ,12
+ ,28
+ ,25
+ ,25
+ ,4
+ ,12
+ ,12
+ ,130
+ ,24
+ ,22
+ ,16
+ ,28
+ ,21
+ ,23
+ ,4
+ ,9
+ ,19
+ ,130
+ ,21
+ ,18
+ ,16
+ ,20
+ ,21
+ ,21
+ ,5
+ ,13
+ ,18
+ ,112
+ ,18
+ ,16
+ ,18
+ ,25
+ ,23
+ ,20
+ ,6
+ ,13
+ ,15
+ ,114
+ ,16
+ ,16
+ ,16
+ ,19
+ ,27
+ ,25
+ ,16
+ ,14
+ ,14
+ ,103
+ ,22
+ ,16
+ ,13
+ ,25
+ ,23
+ ,22
+ ,6
+ ,19
+ ,11
+ ,115
+ ,20
+ ,16
+ ,17
+ ,22
+ ,18
+ ,21
+ ,6
+ ,13
+ ,9
+ ,108
+ ,18
+ ,17
+ ,13
+ ,18
+ ,16
+ ,16
+ ,4
+ ,12
+ ,18
+ ,94
+ ,20
+ ,18
+ ,17
+ ,20
+ ,16
+ ,18
+ ,4
+ ,13
+ ,16
+ ,105)
+ ,dim=c(10
+ ,162)
+ ,dimnames=list(c('I1'
+ ,'I2'
+ ,'I3'
+ ,'E1'
+ ,'E2'
+ ,'E3'
+ ,'A'
+ ,'Happiness'
+ ,'Depression'
+ ,'Motivatie
')
+ ,1:162))
> y <- array(NA,dim=c(10,162),dimnames=list(c('I1','I2','I3','E1','E2','E3','A','Happiness','Depression','Motivatie
'),1:162))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '10'
> 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
Motivatie\r I1 I2 I3 E1 E2 E3 A Happiness Depression t
1 127 26 21 21 23 17 23 4 14 12 1
2 108 20 16 15 24 17 20 4 18 11 2
3 110 19 19 18 22 18 20 6 11 14 3
4 102 19 18 11 20 21 21 8 12 12 4
5 104 20 16 8 24 20 24 8 16 21 5
6 140 25 23 19 27 28 22 4 18 12 6
7 112 25 17 4 28 19 23 4 14 22 7
8 115 22 12 20 27 22 20 8 14 11 8
9 121 26 19 16 24 16 25 5 15 10 9
10 112 22 16 14 23 18 23 4 15 13 10
11 118 17 19 10 24 25 27 4 17 10 11
12 122 22 20 13 27 17 27 4 19 8 12
13 105 19 13 14 27 14 22 4 10 15 13
14 111 24 20 8 28 11 24 4 16 14 14
15 151 26 27 23 27 27 25 4 18 10 15
16 106 21 17 11 23 20 22 8 14 14 16
17 100 13 8 9 24 22 28 4 14 14 17
18 149 26 25 24 28 22 28 4 17 11 18
19 122 20 26 5 27 21 27 4 14 10 19
20 115 22 13 15 25 23 25 8 16 13 20
21 86 14 19 5 19 17 16 4 18 7 21
22 124 21 15 19 24 24 28 7 11 14 22
23 69 7 5 6 20 14 21 4 14 12 23
24 117 23 16 13 28 17 24 4 12 14 24
25 113 17 14 11 26 23 27 5 17 11 25
26 123 25 24 17 23 24 14 4 9 9 26
27 123 25 24 17 23 24 14 4 16 11 27
28 84 19 9 5 20 8 27 4 14 15 28
29 97 20 19 9 11 22 20 4 15 14 29
30 121 23 19 15 24 23 21 4 11 13 30
31 132 22 25 17 25 25 22 4 16 9 31
32 119 22 19 17 23 21 21 4 13 15 32
33 98 21 18 20 18 24 12 15 17 10 33
34 87 15 15 12 20 15 20 10 15 11 34
35 101 20 12 7 20 22 24 4 14 13 35
36 115 22 21 16 24 21 19 8 16 8 36
37 109 18 12 7 23 25 28 4 9 20 37
38 109 20 15 14 25 16 23 4 15 12 38
39 159 28 28 24 28 28 27 4 17 10 39
40 129 22 25 15 26 23 22 4 13 10 40
41 119 18 19 15 26 21 27 7 15 9 41
42 119 23 20 10 23 21 26 4 16 14 42
43 122 20 24 14 22 26 22 6 16 8 43
44 131 25 26 18 24 22 21 5 12 14 44
45 120 26 25 12 21 21 19 4 12 11 45
46 82 15 12 9 20 18 24 16 11 13 46
47 86 17 12 9 22 12 19 5 15 9 47
48 105 23 15 8 20 25 26 12 15 11 48
49 114 21 17 18 25 17 22 6 17 15 49
50 100 13 14 10 20 24 28 9 13 11 50
51 100 18 16 17 22 15 21 9 16 10 51
52 99 19 11 14 23 13 23 4 14 14 52
53 132 22 20 16 25 26 28 5 11 18 53
54 82 16 11 10 23 16 10 4 12 14 54
55 132 24 22 19 23 24 24 4 12 11 55
56 107 18 20 10 22 21 21 5 15 12 56
57 114 20 19 14 24 20 21 4 16 13 57
58 110 24 17 10 25 14 24 4 15 9 58
59 105 14 21 4 21 25 24 4 12 10 59
60 121 22 23 19 12 25 25 5 12 15 60
61 109 24 18 9 17 20 25 4 8 20 61
62 106 18 17 12 20 22 23 6 13 12 62
63 124 21 27 16 23 20 21 4 11 12 63
64 120 23 25 11 23 26 16 4 14 14 64
65 91 17 19 18 20 18 17 18 15 13 65
66 126 22 22 11 28 22 25 4 10 11 66
67 138 24 24 24 24 24 24 6 11 17 67
68 118 21 20 17 24 17 23 4 12 12 68
69 128 22 19 18 24 24 25 4 15 13 69
70 98 16 11 9 24 20 23 5 15 14 70
71 133 21 22 19 28 19 28 4 14 13 71
72 130 23 22 18 25 20 26 4 16 15 72
73 103 22 16 12 21 15 22 5 15 13 73
74 124 24 20 23 25 23 19 10 15 10 74
75 142 24 24 22 25 26 26 5 13 11 75
76 96 16 16 14 18 22 18 8 12 19 76
77 93 16 16 14 17 20 18 8 17 13 77
78 129 21 22 16 26 24 25 5 13 17 78
79 150 26 24 23 28 26 27 4 15 13 79
80 88 15 16 7 21 21 12 4 13 9 80
81 125 25 27 10 27 25 15 4 15 11 81
82 92 18 11 12 22 13 21 5 16 10 82
83 0 23 21 12 21 20 23 4 15 9 83
84 117 20 20 12 25 22 22 4 16 12 84
85 112 17 20 17 22 23 21 8 15 12 85
86 144 25 27 21 23 28 24 4 14 13 86
87 130 24 20 16 26 22 27 5 15 13 87
88 87 17 12 11 19 20 22 14 14 12 88
89 92 19 8 14 25 6 28 8 13 15 89
90 114 20 21 13 21 21 26 8 7 22 90
91 81 15 18 9 13 20 10 4 17 13 91
92 127 27 24 19 24 18 19 4 13 15 92
93 115 22 16 13 25 23 22 6 15 13 93
94 123 23 18 19 26 20 21 4 14 15 94
95 115 16 20 13 25 24 24 7 13 10 95
96 117 19 20 13 25 22 25 7 16 11 96
97 117 25 19 13 22 21 21 4 12 16 97
98 103 19 17 14 21 18 20 6 14 11 98
99 108 19 16 12 23 21 21 4 17 11 99
100 139 26 26 22 25 23 24 7 15 10 100
101 113 21 15 11 24 23 23 4 17 10 101
102 97 20 22 5 21 15 18 4 12 16 102
103 117 24 17 18 21 21 24 8 16 12 103
104 133 22 23 19 25 24 24 4 11 11 104
105 115 20 21 14 22 23 19 4 15 16 105
106 103 18 19 15 20 21 20 10 9 19 106
107 95 18 14 12 20 21 18 8 16 11 107
108 117 24 17 19 23 20 20 6 15 16 108
109 113 24 12 15 28 11 27 4 10 15 109
110 127 22 24 17 23 22 23 4 10 24 110
111 126 23 18 8 28 27 26 4 15 14 111
112 119 22 20 10 24 25 23 5 11 15 112
113 97 20 16 12 18 18 17 4 13 11 113
114 105 18 20 12 20 20 21 6 14 15 114
115 140 25 22 20 28 24 25 4 18 12 115
116 91 18 12 12 21 10 23 5 16 10 116
117 112 16 16 12 21 27 27 7 14 14 117
118 113 20 17 14 25 21 24 8 14 13 118
119 102 19 22 6 19 21 20 5 14 9 119
120 92 15 12 10 18 18 27 8 14 15 120
121 98 19 14 18 21 15 21 10 12 15 121
122 122 19 23 18 22 24 24 8 14 14 122
123 100 16 15 7 24 22 21 5 15 11 123
124 84 17 17 18 15 14 15 12 15 8 124
125 142 28 28 9 28 28 25 4 15 11 125
126 124 23 20 17 26 18 25 5 13 11 126
127 137 25 23 22 23 26 22 4 17 8 127
128 105 20 13 11 26 17 24 6 17 10 128
129 106 17 18 15 20 19 21 4 19 11 129
130 125 23 23 17 22 22 22 4 15 13 130
131 104 16 19 15 20 18 23 7 13 11 131
132 130 23 23 22 23 24 22 7 9 20 132
133 79 11 12 9 22 15 20 10 15 10 133
134 108 18 16 13 24 18 23 4 15 15 134
135 136 24 23 20 23 26 25 5 15 12 135
136 98 23 13 14 22 11 23 8 16 14 136
137 120 21 22 14 26 26 22 11 11 23 137
138 108 16 18 12 23 21 25 7 14 14 138
139 139 24 23 20 27 23 26 4 11 16 139
140 123 23 20 20 23 23 22 8 15 11 140
141 90 18 10 8 21 15 24 6 13 12 141
142 119 20 17 17 26 22 24 7 15 10 142
143 105 9 18 9 23 26 25 5 16 14 143
144 110 24 15 18 21 16 20 4 14 12 144
145 135 25 23 22 27 20 26 8 15 12 145
146 101 20 17 10 19 18 21 4 16 11 146
147 114 21 17 13 23 22 26 8 16 12 147
148 118 25 22 15 25 16 21 6 11 13 148
149 120 22 20 18 23 19 22 4 12 11 149
150 108 21 20 18 22 20 16 9 9 19 150
151 114 21 19 12 22 19 26 5 16 12 151
152 122 22 18 12 25 23 28 6 13 17 152
153 132 27 22 20 25 24 18 4 16 9 153
154 130 24 20 12 28 25 25 4 12 12 154
155 130 24 22 16 28 21 23 4 9 19 155
156 112 21 18 16 20 21 21 5 13 18 156
157 114 18 16 18 25 23 20 6 13 15 157
158 103 16 16 16 19 27 25 16 14 14 158
159 115 22 16 13 25 23 22 6 19 11 159
160 108 20 16 17 22 18 21 6 13 9 160
161 94 18 17 13 18 16 16 4 12 18 161
162 105 20 18 17 20 16 18 4 13 16 162
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) I1 I2 I3 E1 E2
-7.232458 0.692849 0.902424 1.179034 1.377666 1.069563
E3 A Happiness Depression t
0.818835 -0.894605 0.078944 0.367180 -0.005065
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-111.448 -0.362 0.723 1.735 4.957
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.232458 10.797282 -0.670 0.503982
I1 0.692849 0.300188 2.308 0.022354 *
I2 0.902424 0.259679 3.475 0.000667 ***
I3 1.179034 0.202501 5.822 3.37e-08 ***
E1 1.377666 0.289646 4.756 4.56e-06 ***
E2 1.069563 0.221456 4.830 3.32e-06 ***
E3 0.818835 0.228401 3.585 0.000454 ***
A -0.894605 0.317894 -2.814 0.005542 **
Happiness 0.078944 0.376672 0.210 0.834275
Depression 0.367180 0.282124 1.301 0.195075
t -0.005065 0.015988 -0.317 0.751847
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.253 on 151 degrees of freedom
Multiple R-squared: 0.7629, Adjusted R-squared: 0.7472
F-statistic: 48.59 on 10 and 151 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,] 4.379716e-46 8.759431e-46 1.000000e+00
[2,] 2.401802e-61 4.803603e-61 1.000000e+00
[3,] 8.093106e-76 1.618621e-75 1.000000e+00
[4,] 9.336955e-95 1.867391e-94 1.000000e+00
[5,] 6.977239e-111 1.395448e-110 1.000000e+00
[6,] 1.437772e-119 2.875545e-119 1.000000e+00
[7,] 2.394665e-134 4.789331e-134 1.000000e+00
[8,] 9.074254e-153 1.814851e-152 1.000000e+00
[9,] 2.019333e-171 4.038667e-171 1.000000e+00
[10,] 4.592650e-178 9.185300e-178 1.000000e+00
[11,] 3.150352e-194 6.300705e-194 1.000000e+00
[12,] 6.556079e-217 1.311216e-216 1.000000e+00
[13,] 5.723299e-230 1.144660e-229 1.000000e+00
[14,] 2.973983e-244 5.947966e-244 1.000000e+00
[15,] 1.472343e-261 2.944687e-261 1.000000e+00
[16,] 1.639901e-266 3.279802e-266 1.000000e+00
[17,] 1.158135e-294 2.316270e-294 1.000000e+00
[18,] 1.633267e-308 3.266534e-308 1.000000e+00
[19,] 4.940656e-324 9.881313e-324 1.000000e+00
[20,] 0.000000e+00 0.000000e+00 1.000000e+00
[21,] 0.000000e+00 0.000000e+00 1.000000e+00
[22,] 0.000000e+00 0.000000e+00 1.000000e+00
[23,] 0.000000e+00 0.000000e+00 1.000000e+00
[24,] 0.000000e+00 0.000000e+00 1.000000e+00
[25,] 0.000000e+00 0.000000e+00 1.000000e+00
[26,] 0.000000e+00 0.000000e+00 1.000000e+00
[27,] 0.000000e+00 0.000000e+00 1.000000e+00
[28,] 0.000000e+00 0.000000e+00 1.000000e+00
[29,] 0.000000e+00 0.000000e+00 1.000000e+00
[30,] 0.000000e+00 0.000000e+00 1.000000e+00
[31,] 0.000000e+00 0.000000e+00 1.000000e+00
[32,] 0.000000e+00 0.000000e+00 1.000000e+00
[33,] 0.000000e+00 0.000000e+00 1.000000e+00
[34,] 0.000000e+00 0.000000e+00 1.000000e+00
[35,] 0.000000e+00 0.000000e+00 1.000000e+00
[36,] 0.000000e+00 0.000000e+00 1.000000e+00
[37,] 0.000000e+00 0.000000e+00 1.000000e+00
[38,] 0.000000e+00 0.000000e+00 1.000000e+00
[39,] 0.000000e+00 0.000000e+00 1.000000e+00
[40,] 0.000000e+00 0.000000e+00 1.000000e+00
[41,] 0.000000e+00 0.000000e+00 1.000000e+00
[42,] 0.000000e+00 0.000000e+00 1.000000e+00
[43,] 0.000000e+00 0.000000e+00 1.000000e+00
[44,] 0.000000e+00 0.000000e+00 1.000000e+00
[45,] 0.000000e+00 0.000000e+00 1.000000e+00
[46,] 0.000000e+00 0.000000e+00 1.000000e+00
[47,] 0.000000e+00 0.000000e+00 1.000000e+00
[48,] 0.000000e+00 0.000000e+00 1.000000e+00
[49,] 0.000000e+00 0.000000e+00 1.000000e+00
[50,] 0.000000e+00 0.000000e+00 1.000000e+00
[51,] 0.000000e+00 0.000000e+00 1.000000e+00
[52,] 0.000000e+00 0.000000e+00 1.000000e+00
[53,] 0.000000e+00 0.000000e+00 1.000000e+00
[54,] 0.000000e+00 0.000000e+00 1.000000e+00
[55,] 0.000000e+00 0.000000e+00 1.000000e+00
[56,] 0.000000e+00 0.000000e+00 1.000000e+00
[57,] 0.000000e+00 0.000000e+00 1.000000e+00
[58,] 0.000000e+00 0.000000e+00 1.000000e+00
[59,] 0.000000e+00 0.000000e+00 1.000000e+00
[60,] 0.000000e+00 0.000000e+00 1.000000e+00
[61,] 0.000000e+00 0.000000e+00 1.000000e+00
[62,] 0.000000e+00 0.000000e+00 1.000000e+00
[63,] 0.000000e+00 0.000000e+00 1.000000e+00
[64,] 0.000000e+00 0.000000e+00 1.000000e+00
[65,] 0.000000e+00 0.000000e+00 1.000000e+00
[66,] 0.000000e+00 0.000000e+00 1.000000e+00
[67,] 0.000000e+00 0.000000e+00 1.000000e+00
[68,] 0.000000e+00 0.000000e+00 1.000000e+00
[69,] 0.000000e+00 0.000000e+00 1.000000e+00
[70,] 1.000000e+00 0.000000e+00 0.000000e+00
[71,] 1.000000e+00 0.000000e+00 0.000000e+00
[72,] 1.000000e+00 0.000000e+00 0.000000e+00
[73,] 1.000000e+00 0.000000e+00 0.000000e+00
[74,] 1.000000e+00 0.000000e+00 0.000000e+00
[75,] 1.000000e+00 0.000000e+00 0.000000e+00
[76,] 1.000000e+00 0.000000e+00 0.000000e+00
[77,] 1.000000e+00 0.000000e+00 0.000000e+00
[78,] 1.000000e+00 0.000000e+00 0.000000e+00
[79,] 1.000000e+00 0.000000e+00 0.000000e+00
[80,] 1.000000e+00 0.000000e+00 0.000000e+00
[81,] 1.000000e+00 0.000000e+00 0.000000e+00
[82,] 1.000000e+00 0.000000e+00 0.000000e+00
[83,] 1.000000e+00 0.000000e+00 0.000000e+00
[84,] 1.000000e+00 0.000000e+00 0.000000e+00
[85,] 1.000000e+00 0.000000e+00 0.000000e+00
[86,] 1.000000e+00 0.000000e+00 0.000000e+00
[87,] 1.000000e+00 0.000000e+00 0.000000e+00
[88,] 1.000000e+00 0.000000e+00 0.000000e+00
[89,] 1.000000e+00 0.000000e+00 0.000000e+00
[90,] 1.000000e+00 0.000000e+00 0.000000e+00
[91,] 1.000000e+00 0.000000e+00 0.000000e+00
[92,] 1.000000e+00 0.000000e+00 0.000000e+00
[93,] 1.000000e+00 0.000000e+00 0.000000e+00
[94,] 1.000000e+00 0.000000e+00 0.000000e+00
[95,] 1.000000e+00 0.000000e+00 0.000000e+00
[96,] 1.000000e+00 0.000000e+00 0.000000e+00
[97,] 1.000000e+00 0.000000e+00 0.000000e+00
[98,] 1.000000e+00 0.000000e+00 0.000000e+00
[99,] 1.000000e+00 0.000000e+00 0.000000e+00
[100,] 1.000000e+00 0.000000e+00 0.000000e+00
[101,] 1.000000e+00 0.000000e+00 0.000000e+00
[102,] 1.000000e+00 0.000000e+00 0.000000e+00
[103,] 1.000000e+00 0.000000e+00 0.000000e+00
[104,] 1.000000e+00 0.000000e+00 0.000000e+00
[105,] 1.000000e+00 0.000000e+00 0.000000e+00
[106,] 1.000000e+00 0.000000e+00 0.000000e+00
[107,] 1.000000e+00 0.000000e+00 0.000000e+00
[108,] 1.000000e+00 0.000000e+00 0.000000e+00
[109,] 1.000000e+00 0.000000e+00 0.000000e+00
[110,] 1.000000e+00 0.000000e+00 0.000000e+00
[111,] 1.000000e+00 0.000000e+00 0.000000e+00
[112,] 1.000000e+00 0.000000e+00 0.000000e+00
[113,] 1.000000e+00 0.000000e+00 0.000000e+00
[114,] 1.000000e+00 0.000000e+00 0.000000e+00
[115,] 1.000000e+00 0.000000e+00 0.000000e+00
[116,] 1.000000e+00 0.000000e+00 0.000000e+00
[117,] 1.000000e+00 1.131555e-314 5.657775e-315
[118,] 1.000000e+00 1.041841e-303 5.209206e-304
[119,] 1.000000e+00 6.876388e-286 3.438194e-286
[120,] 1.000000e+00 3.337249e-265 1.668624e-265
[121,] 1.000000e+00 5.752831e-267 2.876416e-267
[122,] 1.000000e+00 6.910654e-251 3.455327e-251
[123,] 1.000000e+00 7.024794e-226 3.512397e-226
[124,] 1.000000e+00 1.997487e-208 9.987435e-209
[125,] 1.000000e+00 3.239113e-200 1.619556e-200
[126,] 1.000000e+00 5.989013e-183 2.994506e-183
[127,] 1.000000e+00 3.648691e-164 1.824345e-164
[128,] 1.000000e+00 6.186090e-149 3.093045e-149
[129,] 1.000000e+00 3.008593e-139 1.504296e-139
[130,] 1.000000e+00 1.977733e-126 9.888666e-127
[131,] 1.000000e+00 1.345267e-109 6.726335e-110
[132,] 1.000000e+00 3.473220e-96 1.736610e-96
[133,] 1.000000e+00 4.902209e-76 2.451105e-76
[134,] 1.000000e+00 2.402673e-60 1.201336e-60
[135,] 1.000000e+00 6.606407e-49 3.303203e-49
> postscript(file="/var/wessaorg/rcomp/tmp/1v7z31353431275.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/295d81353431275.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/37lbd1353431275.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4op8j1353431275.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/54gtq1353431275.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 162
Frequency = 1
1 2 3 4 5
1.877809e+00 -2.434692e-01 -8.638794e-01 1.469283e+00 -2.394553e+00
6 7 8 9 10
-6.235901e-01 -1.444932e+00 -2.470865e+00 3.222593e+00 9.444576e-01
11 12 13 14 15
1.226340e+00 2.327610e+00 -2.007747e+00 1.377180e+00 7.507316e-01
16 17 18 19 20
2.722693e-01 -1.708216e+00 6.171461e-01 3.148603e+00 -7.179939e-01
21 22 23 24 25
2.725313e+00 -5.514644e-01 -1.742994e+00 -2.663969e-01 -4.584486e-01
26 27 28 29 30
2.084982e+00 8.030766e-01 2.940311e+00 4.957349e+00 6.945694e-01
31 32 33 34 35
1.358246e+00 -3.359781e-01 -8.628078e-01 7.662803e-02 2.434566e+00
36 37 38 39 40
1.230320e+00 -9.621691e-01 1.253684e-01 1.399179e+00 1.393010e+00
41 42 43 44 45
5.220697e-01 2.408809e+00 2.464072e+00 1.043755e+00 4.379764e+00
46 47 48 49 50
-1.531988e-01 1.534672e+00 3.501458e+00 -1.753696e+00 8.903966e-01
51 52 53 54 55
1.060544e-01 -1.925770e-01 -8.370398e-01 -2.793710e+00 2.211949e+00
56 57 58 59 60
1.123711e+00 -9.713295e-02 3.788389e+00 2.801580e+00 4.412357e+00
61 62 63 64 65
3.378940e+00 1.603895e+00 1.802517e+00 8.275085e-01 -2.165659e+00
66 67 68 69 70
1.675483e+00 -1.139881e+00 1.080181e+00 3.871661e-01 -1.176635e+00
71 72 73 74 75
-3.365708e-01 2.706862e-01 2.299071e+00 -6.967237e-01 1.254831e+00
76 77 78 79 80
-2.247743e+00 8.247122e-02 -1.395162e+00 -2.832664e-02 5.826132e-01
81 82 83 84 85
1.302431e+00 1.231058e+00 -1.114478e+02 5.268170e-01 -7.451076e-01
86 87 88 89 90
1.635425e+00 1.189010e+00 5.331063e-01 -3.702890e-01 -6.022461e-01
91 92 93 94 95
1.419472e+00 1.102174e+00 4.877603e-02 -1.312829e+00 5.310520e-02
96 97 98 99 100
6.959011e-01 1.720257e+00 1.380638e+00 8.372976e-01 2.035767e+00
101 102 103 104 105
1.755907e+00 2.186373e+00 2.005697e+00 1.267013e+00 -4.971494e-01
106 107 108 109 110
-1.665195e+00 6.223795e-01 -7.374282e-01 4.744725e-01 -2.228057e+00
111 112 113 114 115
6.944407e-01 1.178933e+00 2.903489e+00 7.563076e-01 9.594839e-02
116 117 118 119 120
2.449522e+00 2.510670e-01 -1.507527e-01 4.793553e+00 1.213358e+00
121 122 123 124 125
-8.542932e-01 -1.119341e-02 4.402602e-01 2.210531e+00 3.948646e+00
126 127 128 129 130
1.708522e+00 2.649607e+00 1.022774e+00 1.147225e+00 1.923574e+00
131 132 133 134 135
1.537154e+00 -1.891036e+00 -7.943344e-01 -5.890969e-01 1.868312e+00
136 137 138 139 140
1.593951e+00 -4.098447e+00 -1.477048e-01 -2.797866e-01 1.009944e+00
141 142 143 144 145
2.327558e+00 1.144637e-01 -2.030249e+00 2.220823e+00 6.397369e-01
146 147 148 149 150
3.636436e+00 1.739704e+00 2.097736e+00 2.043106e+00 -2.265448e+00
151 152 153 154 155
3.036687e+00 4.979486e-01 2.026985e+00 1.627550e+00 -1.305876e+00
156 157 158 159 160
-7.567325e-03 -2.689650e+00 -8.129619e-01 8.016392e-01 1.983935e+00
161 162
-8.251348e-02 1.807084e-01
> postscript(file="/var/wessaorg/rcomp/tmp/6gtam1353431275.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 1.877809e+00 NA
1 -2.434692e-01 1.877809e+00
2 -8.638794e-01 -2.434692e-01
3 1.469283e+00 -8.638794e-01
4 -2.394553e+00 1.469283e+00
5 -6.235901e-01 -2.394553e+00
6 -1.444932e+00 -6.235901e-01
7 -2.470865e+00 -1.444932e+00
8 3.222593e+00 -2.470865e+00
9 9.444576e-01 3.222593e+00
10 1.226340e+00 9.444576e-01
11 2.327610e+00 1.226340e+00
12 -2.007747e+00 2.327610e+00
13 1.377180e+00 -2.007747e+00
14 7.507316e-01 1.377180e+00
15 2.722693e-01 7.507316e-01
16 -1.708216e+00 2.722693e-01
17 6.171461e-01 -1.708216e+00
18 3.148603e+00 6.171461e-01
19 -7.179939e-01 3.148603e+00
20 2.725313e+00 -7.179939e-01
21 -5.514644e-01 2.725313e+00
22 -1.742994e+00 -5.514644e-01
23 -2.663969e-01 -1.742994e+00
24 -4.584486e-01 -2.663969e-01
25 2.084982e+00 -4.584486e-01
26 8.030766e-01 2.084982e+00
27 2.940311e+00 8.030766e-01
28 4.957349e+00 2.940311e+00
29 6.945694e-01 4.957349e+00
30 1.358246e+00 6.945694e-01
31 -3.359781e-01 1.358246e+00
32 -8.628078e-01 -3.359781e-01
33 7.662803e-02 -8.628078e-01
34 2.434566e+00 7.662803e-02
35 1.230320e+00 2.434566e+00
36 -9.621691e-01 1.230320e+00
37 1.253684e-01 -9.621691e-01
38 1.399179e+00 1.253684e-01
39 1.393010e+00 1.399179e+00
40 5.220697e-01 1.393010e+00
41 2.408809e+00 5.220697e-01
42 2.464072e+00 2.408809e+00
43 1.043755e+00 2.464072e+00
44 4.379764e+00 1.043755e+00
45 -1.531988e-01 4.379764e+00
46 1.534672e+00 -1.531988e-01
47 3.501458e+00 1.534672e+00
48 -1.753696e+00 3.501458e+00
49 8.903966e-01 -1.753696e+00
50 1.060544e-01 8.903966e-01
51 -1.925770e-01 1.060544e-01
52 -8.370398e-01 -1.925770e-01
53 -2.793710e+00 -8.370398e-01
54 2.211949e+00 -2.793710e+00
55 1.123711e+00 2.211949e+00
56 -9.713295e-02 1.123711e+00
57 3.788389e+00 -9.713295e-02
58 2.801580e+00 3.788389e+00
59 4.412357e+00 2.801580e+00
60 3.378940e+00 4.412357e+00
61 1.603895e+00 3.378940e+00
62 1.802517e+00 1.603895e+00
63 8.275085e-01 1.802517e+00
64 -2.165659e+00 8.275085e-01
65 1.675483e+00 -2.165659e+00
66 -1.139881e+00 1.675483e+00
67 1.080181e+00 -1.139881e+00
68 3.871661e-01 1.080181e+00
69 -1.176635e+00 3.871661e-01
70 -3.365708e-01 -1.176635e+00
71 2.706862e-01 -3.365708e-01
72 2.299071e+00 2.706862e-01
73 -6.967237e-01 2.299071e+00
74 1.254831e+00 -6.967237e-01
75 -2.247743e+00 1.254831e+00
76 8.247122e-02 -2.247743e+00
77 -1.395162e+00 8.247122e-02
78 -2.832664e-02 -1.395162e+00
79 5.826132e-01 -2.832664e-02
80 1.302431e+00 5.826132e-01
81 1.231058e+00 1.302431e+00
82 -1.114478e+02 1.231058e+00
83 5.268170e-01 -1.114478e+02
84 -7.451076e-01 5.268170e-01
85 1.635425e+00 -7.451076e-01
86 1.189010e+00 1.635425e+00
87 5.331063e-01 1.189010e+00
88 -3.702890e-01 5.331063e-01
89 -6.022461e-01 -3.702890e-01
90 1.419472e+00 -6.022461e-01
91 1.102174e+00 1.419472e+00
92 4.877603e-02 1.102174e+00
93 -1.312829e+00 4.877603e-02
94 5.310520e-02 -1.312829e+00
95 6.959011e-01 5.310520e-02
96 1.720257e+00 6.959011e-01
97 1.380638e+00 1.720257e+00
98 8.372976e-01 1.380638e+00
99 2.035767e+00 8.372976e-01
100 1.755907e+00 2.035767e+00
101 2.186373e+00 1.755907e+00
102 2.005697e+00 2.186373e+00
103 1.267013e+00 2.005697e+00
104 -4.971494e-01 1.267013e+00
105 -1.665195e+00 -4.971494e-01
106 6.223795e-01 -1.665195e+00
107 -7.374282e-01 6.223795e-01
108 4.744725e-01 -7.374282e-01
109 -2.228057e+00 4.744725e-01
110 6.944407e-01 -2.228057e+00
111 1.178933e+00 6.944407e-01
112 2.903489e+00 1.178933e+00
113 7.563076e-01 2.903489e+00
114 9.594839e-02 7.563076e-01
115 2.449522e+00 9.594839e-02
116 2.510670e-01 2.449522e+00
117 -1.507527e-01 2.510670e-01
118 4.793553e+00 -1.507527e-01
119 1.213358e+00 4.793553e+00
120 -8.542932e-01 1.213358e+00
121 -1.119341e-02 -8.542932e-01
122 4.402602e-01 -1.119341e-02
123 2.210531e+00 4.402602e-01
124 3.948646e+00 2.210531e+00
125 1.708522e+00 3.948646e+00
126 2.649607e+00 1.708522e+00
127 1.022774e+00 2.649607e+00
128 1.147225e+00 1.022774e+00
129 1.923574e+00 1.147225e+00
130 1.537154e+00 1.923574e+00
131 -1.891036e+00 1.537154e+00
132 -7.943344e-01 -1.891036e+00
133 -5.890969e-01 -7.943344e-01
134 1.868312e+00 -5.890969e-01
135 1.593951e+00 1.868312e+00
136 -4.098447e+00 1.593951e+00
137 -1.477048e-01 -4.098447e+00
138 -2.797866e-01 -1.477048e-01
139 1.009944e+00 -2.797866e-01
140 2.327558e+00 1.009944e+00
141 1.144637e-01 2.327558e+00
142 -2.030249e+00 1.144637e-01
143 2.220823e+00 -2.030249e+00
144 6.397369e-01 2.220823e+00
145 3.636436e+00 6.397369e-01
146 1.739704e+00 3.636436e+00
147 2.097736e+00 1.739704e+00
148 2.043106e+00 2.097736e+00
149 -2.265448e+00 2.043106e+00
150 3.036687e+00 -2.265448e+00
151 4.979486e-01 3.036687e+00
152 2.026985e+00 4.979486e-01
153 1.627550e+00 2.026985e+00
154 -1.305876e+00 1.627550e+00
155 -7.567325e-03 -1.305876e+00
156 -2.689650e+00 -7.567325e-03
157 -8.129619e-01 -2.689650e+00
158 8.016392e-01 -8.129619e-01
159 1.983935e+00 8.016392e-01
160 -8.251348e-02 1.983935e+00
161 1.807084e-01 -8.251348e-02
162 NA 1.807084e-01
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.434692e-01 1.877809e+00
[2,] -8.638794e-01 -2.434692e-01
[3,] 1.469283e+00 -8.638794e-01
[4,] -2.394553e+00 1.469283e+00
[5,] -6.235901e-01 -2.394553e+00
[6,] -1.444932e+00 -6.235901e-01
[7,] -2.470865e+00 -1.444932e+00
[8,] 3.222593e+00 -2.470865e+00
[9,] 9.444576e-01 3.222593e+00
[10,] 1.226340e+00 9.444576e-01
[11,] 2.327610e+00 1.226340e+00
[12,] -2.007747e+00 2.327610e+00
[13,] 1.377180e+00 -2.007747e+00
[14,] 7.507316e-01 1.377180e+00
[15,] 2.722693e-01 7.507316e-01
[16,] -1.708216e+00 2.722693e-01
[17,] 6.171461e-01 -1.708216e+00
[18,] 3.148603e+00 6.171461e-01
[19,] -7.179939e-01 3.148603e+00
[20,] 2.725313e+00 -7.179939e-01
[21,] -5.514644e-01 2.725313e+00
[22,] -1.742994e+00 -5.514644e-01
[23,] -2.663969e-01 -1.742994e+00
[24,] -4.584486e-01 -2.663969e-01
[25,] 2.084982e+00 -4.584486e-01
[26,] 8.030766e-01 2.084982e+00
[27,] 2.940311e+00 8.030766e-01
[28,] 4.957349e+00 2.940311e+00
[29,] 6.945694e-01 4.957349e+00
[30,] 1.358246e+00 6.945694e-01
[31,] -3.359781e-01 1.358246e+00
[32,] -8.628078e-01 -3.359781e-01
[33,] 7.662803e-02 -8.628078e-01
[34,] 2.434566e+00 7.662803e-02
[35,] 1.230320e+00 2.434566e+00
[36,] -9.621691e-01 1.230320e+00
[37,] 1.253684e-01 -9.621691e-01
[38,] 1.399179e+00 1.253684e-01
[39,] 1.393010e+00 1.399179e+00
[40,] 5.220697e-01 1.393010e+00
[41,] 2.408809e+00 5.220697e-01
[42,] 2.464072e+00 2.408809e+00
[43,] 1.043755e+00 2.464072e+00
[44,] 4.379764e+00 1.043755e+00
[45,] -1.531988e-01 4.379764e+00
[46,] 1.534672e+00 -1.531988e-01
[47,] 3.501458e+00 1.534672e+00
[48,] -1.753696e+00 3.501458e+00
[49,] 8.903966e-01 -1.753696e+00
[50,] 1.060544e-01 8.903966e-01
[51,] -1.925770e-01 1.060544e-01
[52,] -8.370398e-01 -1.925770e-01
[53,] -2.793710e+00 -8.370398e-01
[54,] 2.211949e+00 -2.793710e+00
[55,] 1.123711e+00 2.211949e+00
[56,] -9.713295e-02 1.123711e+00
[57,] 3.788389e+00 -9.713295e-02
[58,] 2.801580e+00 3.788389e+00
[59,] 4.412357e+00 2.801580e+00
[60,] 3.378940e+00 4.412357e+00
[61,] 1.603895e+00 3.378940e+00
[62,] 1.802517e+00 1.603895e+00
[63,] 8.275085e-01 1.802517e+00
[64,] -2.165659e+00 8.275085e-01
[65,] 1.675483e+00 -2.165659e+00
[66,] -1.139881e+00 1.675483e+00
[67,] 1.080181e+00 -1.139881e+00
[68,] 3.871661e-01 1.080181e+00
[69,] -1.176635e+00 3.871661e-01
[70,] -3.365708e-01 -1.176635e+00
[71,] 2.706862e-01 -3.365708e-01
[72,] 2.299071e+00 2.706862e-01
[73,] -6.967237e-01 2.299071e+00
[74,] 1.254831e+00 -6.967237e-01
[75,] -2.247743e+00 1.254831e+00
[76,] 8.247122e-02 -2.247743e+00
[77,] -1.395162e+00 8.247122e-02
[78,] -2.832664e-02 -1.395162e+00
[79,] 5.826132e-01 -2.832664e-02
[80,] 1.302431e+00 5.826132e-01
[81,] 1.231058e+00 1.302431e+00
[82,] -1.114478e+02 1.231058e+00
[83,] 5.268170e-01 -1.114478e+02
[84,] -7.451076e-01 5.268170e-01
[85,] 1.635425e+00 -7.451076e-01
[86,] 1.189010e+00 1.635425e+00
[87,] 5.331063e-01 1.189010e+00
[88,] -3.702890e-01 5.331063e-01
[89,] -6.022461e-01 -3.702890e-01
[90,] 1.419472e+00 -6.022461e-01
[91,] 1.102174e+00 1.419472e+00
[92,] 4.877603e-02 1.102174e+00
[93,] -1.312829e+00 4.877603e-02
[94,] 5.310520e-02 -1.312829e+00
[95,] 6.959011e-01 5.310520e-02
[96,] 1.720257e+00 6.959011e-01
[97,] 1.380638e+00 1.720257e+00
[98,] 8.372976e-01 1.380638e+00
[99,] 2.035767e+00 8.372976e-01
[100,] 1.755907e+00 2.035767e+00
[101,] 2.186373e+00 1.755907e+00
[102,] 2.005697e+00 2.186373e+00
[103,] 1.267013e+00 2.005697e+00
[104,] -4.971494e-01 1.267013e+00
[105,] -1.665195e+00 -4.971494e-01
[106,] 6.223795e-01 -1.665195e+00
[107,] -7.374282e-01 6.223795e-01
[108,] 4.744725e-01 -7.374282e-01
[109,] -2.228057e+00 4.744725e-01
[110,] 6.944407e-01 -2.228057e+00
[111,] 1.178933e+00 6.944407e-01
[112,] 2.903489e+00 1.178933e+00
[113,] 7.563076e-01 2.903489e+00
[114,] 9.594839e-02 7.563076e-01
[115,] 2.449522e+00 9.594839e-02
[116,] 2.510670e-01 2.449522e+00
[117,] -1.507527e-01 2.510670e-01
[118,] 4.793553e+00 -1.507527e-01
[119,] 1.213358e+00 4.793553e+00
[120,] -8.542932e-01 1.213358e+00
[121,] -1.119341e-02 -8.542932e-01
[122,] 4.402602e-01 -1.119341e-02
[123,] 2.210531e+00 4.402602e-01
[124,] 3.948646e+00 2.210531e+00
[125,] 1.708522e+00 3.948646e+00
[126,] 2.649607e+00 1.708522e+00
[127,] 1.022774e+00 2.649607e+00
[128,] 1.147225e+00 1.022774e+00
[129,] 1.923574e+00 1.147225e+00
[130,] 1.537154e+00 1.923574e+00
[131,] -1.891036e+00 1.537154e+00
[132,] -7.943344e-01 -1.891036e+00
[133,] -5.890969e-01 -7.943344e-01
[134,] 1.868312e+00 -5.890969e-01
[135,] 1.593951e+00 1.868312e+00
[136,] -4.098447e+00 1.593951e+00
[137,] -1.477048e-01 -4.098447e+00
[138,] -2.797866e-01 -1.477048e-01
[139,] 1.009944e+00 -2.797866e-01
[140,] 2.327558e+00 1.009944e+00
[141,] 1.144637e-01 2.327558e+00
[142,] -2.030249e+00 1.144637e-01
[143,] 2.220823e+00 -2.030249e+00
[144,] 6.397369e-01 2.220823e+00
[145,] 3.636436e+00 6.397369e-01
[146,] 1.739704e+00 3.636436e+00
[147,] 2.097736e+00 1.739704e+00
[148,] 2.043106e+00 2.097736e+00
[149,] -2.265448e+00 2.043106e+00
[150,] 3.036687e+00 -2.265448e+00
[151,] 4.979486e-01 3.036687e+00
[152,] 2.026985e+00 4.979486e-01
[153,] 1.627550e+00 2.026985e+00
[154,] -1.305876e+00 1.627550e+00
[155,] -7.567325e-03 -1.305876e+00
[156,] -2.689650e+00 -7.567325e-03
[157,] -8.129619e-01 -2.689650e+00
[158,] 8.016392e-01 -8.129619e-01
[159,] 1.983935e+00 8.016392e-01
[160,] -8.251348e-02 1.983935e+00
[161,] 1.807084e-01 -8.251348e-02
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.434692e-01 1.877809e+00
2 -8.638794e-01 -2.434692e-01
3 1.469283e+00 -8.638794e-01
4 -2.394553e+00 1.469283e+00
5 -6.235901e-01 -2.394553e+00
6 -1.444932e+00 -6.235901e-01
7 -2.470865e+00 -1.444932e+00
8 3.222593e+00 -2.470865e+00
9 9.444576e-01 3.222593e+00
10 1.226340e+00 9.444576e-01
11 2.327610e+00 1.226340e+00
12 -2.007747e+00 2.327610e+00
13 1.377180e+00 -2.007747e+00
14 7.507316e-01 1.377180e+00
15 2.722693e-01 7.507316e-01
16 -1.708216e+00 2.722693e-01
17 6.171461e-01 -1.708216e+00
18 3.148603e+00 6.171461e-01
19 -7.179939e-01 3.148603e+00
20 2.725313e+00 -7.179939e-01
21 -5.514644e-01 2.725313e+00
22 -1.742994e+00 -5.514644e-01
23 -2.663969e-01 -1.742994e+00
24 -4.584486e-01 -2.663969e-01
25 2.084982e+00 -4.584486e-01
26 8.030766e-01 2.084982e+00
27 2.940311e+00 8.030766e-01
28 4.957349e+00 2.940311e+00
29 6.945694e-01 4.957349e+00
30 1.358246e+00 6.945694e-01
31 -3.359781e-01 1.358246e+00
32 -8.628078e-01 -3.359781e-01
33 7.662803e-02 -8.628078e-01
34 2.434566e+00 7.662803e-02
35 1.230320e+00 2.434566e+00
36 -9.621691e-01 1.230320e+00
37 1.253684e-01 -9.621691e-01
38 1.399179e+00 1.253684e-01
39 1.393010e+00 1.399179e+00
40 5.220697e-01 1.393010e+00
41 2.408809e+00 5.220697e-01
42 2.464072e+00 2.408809e+00
43 1.043755e+00 2.464072e+00
44 4.379764e+00 1.043755e+00
45 -1.531988e-01 4.379764e+00
46 1.534672e+00 -1.531988e-01
47 3.501458e+00 1.534672e+00
48 -1.753696e+00 3.501458e+00
49 8.903966e-01 -1.753696e+00
50 1.060544e-01 8.903966e-01
51 -1.925770e-01 1.060544e-01
52 -8.370398e-01 -1.925770e-01
53 -2.793710e+00 -8.370398e-01
54 2.211949e+00 -2.793710e+00
55 1.123711e+00 2.211949e+00
56 -9.713295e-02 1.123711e+00
57 3.788389e+00 -9.713295e-02
58 2.801580e+00 3.788389e+00
59 4.412357e+00 2.801580e+00
60 3.378940e+00 4.412357e+00
61 1.603895e+00 3.378940e+00
62 1.802517e+00 1.603895e+00
63 8.275085e-01 1.802517e+00
64 -2.165659e+00 8.275085e-01
65 1.675483e+00 -2.165659e+00
66 -1.139881e+00 1.675483e+00
67 1.080181e+00 -1.139881e+00
68 3.871661e-01 1.080181e+00
69 -1.176635e+00 3.871661e-01
70 -3.365708e-01 -1.176635e+00
71 2.706862e-01 -3.365708e-01
72 2.299071e+00 2.706862e-01
73 -6.967237e-01 2.299071e+00
74 1.254831e+00 -6.967237e-01
75 -2.247743e+00 1.254831e+00
76 8.247122e-02 -2.247743e+00
77 -1.395162e+00 8.247122e-02
78 -2.832664e-02 -1.395162e+00
79 5.826132e-01 -2.832664e-02
80 1.302431e+00 5.826132e-01
81 1.231058e+00 1.302431e+00
82 -1.114478e+02 1.231058e+00
83 5.268170e-01 -1.114478e+02
84 -7.451076e-01 5.268170e-01
85 1.635425e+00 -7.451076e-01
86 1.189010e+00 1.635425e+00
87 5.331063e-01 1.189010e+00
88 -3.702890e-01 5.331063e-01
89 -6.022461e-01 -3.702890e-01
90 1.419472e+00 -6.022461e-01
91 1.102174e+00 1.419472e+00
92 4.877603e-02 1.102174e+00
93 -1.312829e+00 4.877603e-02
94 5.310520e-02 -1.312829e+00
95 6.959011e-01 5.310520e-02
96 1.720257e+00 6.959011e-01
97 1.380638e+00 1.720257e+00
98 8.372976e-01 1.380638e+00
99 2.035767e+00 8.372976e-01
100 1.755907e+00 2.035767e+00
101 2.186373e+00 1.755907e+00
102 2.005697e+00 2.186373e+00
103 1.267013e+00 2.005697e+00
104 -4.971494e-01 1.267013e+00
105 -1.665195e+00 -4.971494e-01
106 6.223795e-01 -1.665195e+00
107 -7.374282e-01 6.223795e-01
108 4.744725e-01 -7.374282e-01
109 -2.228057e+00 4.744725e-01
110 6.944407e-01 -2.228057e+00
111 1.178933e+00 6.944407e-01
112 2.903489e+00 1.178933e+00
113 7.563076e-01 2.903489e+00
114 9.594839e-02 7.563076e-01
115 2.449522e+00 9.594839e-02
116 2.510670e-01 2.449522e+00
117 -1.507527e-01 2.510670e-01
118 4.793553e+00 -1.507527e-01
119 1.213358e+00 4.793553e+00
120 -8.542932e-01 1.213358e+00
121 -1.119341e-02 -8.542932e-01
122 4.402602e-01 -1.119341e-02
123 2.210531e+00 4.402602e-01
124 3.948646e+00 2.210531e+00
125 1.708522e+00 3.948646e+00
126 2.649607e+00 1.708522e+00
127 1.022774e+00 2.649607e+00
128 1.147225e+00 1.022774e+00
129 1.923574e+00 1.147225e+00
130 1.537154e+00 1.923574e+00
131 -1.891036e+00 1.537154e+00
132 -7.943344e-01 -1.891036e+00
133 -5.890969e-01 -7.943344e-01
134 1.868312e+00 -5.890969e-01
135 1.593951e+00 1.868312e+00
136 -4.098447e+00 1.593951e+00
137 -1.477048e-01 -4.098447e+00
138 -2.797866e-01 -1.477048e-01
139 1.009944e+00 -2.797866e-01
140 2.327558e+00 1.009944e+00
141 1.144637e-01 2.327558e+00
142 -2.030249e+00 1.144637e-01
143 2.220823e+00 -2.030249e+00
144 6.397369e-01 2.220823e+00
145 3.636436e+00 6.397369e-01
146 1.739704e+00 3.636436e+00
147 2.097736e+00 1.739704e+00
148 2.043106e+00 2.097736e+00
149 -2.265448e+00 2.043106e+00
150 3.036687e+00 -2.265448e+00
151 4.979486e-01 3.036687e+00
152 2.026985e+00 4.979486e-01
153 1.627550e+00 2.026985e+00
154 -1.305876e+00 1.627550e+00
155 -7.567325e-03 -1.305876e+00
156 -2.689650e+00 -7.567325e-03
157 -8.129619e-01 -2.689650e+00
158 8.016392e-01 -8.129619e-01
159 1.983935e+00 8.016392e-01
160 -8.251348e-02 1.983935e+00
161 1.807084e-01 -8.251348e-02
> 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/7zfrm1353431275.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/8a3c01353431275.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/9zib81353431275.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/108lgz1353431275.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11dou61353431275.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1238p51353431275.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13gp7v1353431276.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14tiaz1353431276.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/15126p1353431276.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/16i0dn1353431276.tab")
+ }
>
> try(system("convert tmp/1v7z31353431275.ps tmp/1v7z31353431275.png",intern=TRUE))
character(0)
> try(system("convert tmp/295d81353431275.ps tmp/295d81353431275.png",intern=TRUE))
character(0)
> try(system("convert tmp/37lbd1353431275.ps tmp/37lbd1353431275.png",intern=TRUE))
character(0)
> try(system("convert tmp/4op8j1353431275.ps tmp/4op8j1353431275.png",intern=TRUE))
character(0)
> try(system("convert tmp/54gtq1353431275.ps tmp/54gtq1353431275.png",intern=TRUE))
character(0)
> try(system("convert tmp/6gtam1353431275.ps tmp/6gtam1353431275.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zfrm1353431275.ps tmp/7zfrm1353431275.png",intern=TRUE))
character(0)
> try(system("convert tmp/8a3c01353431275.ps tmp/8a3c01353431275.png",intern=TRUE))
character(0)
> try(system("convert tmp/9zib81353431275.ps tmp/9zib81353431275.png",intern=TRUE))
character(0)
> try(system("convert tmp/108lgz1353431275.ps tmp/108lgz1353431275.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
16.021 1.783 17.821