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)
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'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(121.79
+ ,125.10
+ ,138.82
+ ,107.35
+ ,139.63
+ ,114.29
+ ,103.40
+ ,125.16
+ ,89.25
+ ,110.87
+ ,119.79
+ ,126.75
+ ,125.84
+ ,121.57
+ ,123.99
+ ,138.32
+ ,106.99
+ ,139.66
+ ,114.08
+ ,103.26
+ ,125.75
+ ,88.72
+ ,110.79
+ ,119.24
+ ,126.69
+ ,125.67
+ ,121.36
+ ,123.72
+ ,138.32
+ ,106.81
+ ,139.31
+ ,114.02
+ ,103.24
+ ,124.73
+ ,88.77
+ ,110.84
+ ,119.24
+ ,128.16
+ ,125.24
+ ,120.83
+ ,123.24
+ ,136.96
+ ,106.41
+ ,138.50
+ ,113.74
+ ,103.29
+ ,122.95
+ ,88.94
+ ,111.01
+ ,119.24
+ ,128.17
+ ,125.02
+ ,120.61
+ ,123.23
+ ,136.56
+ ,106.41
+ ,138.31
+ ,113.76
+ ,103.38
+ ,122.58
+ ,88.94
+ ,111.35
+ ,119.24
+ ,126.06
+ ,124.73
+ ,120.89
+ ,123.40
+ ,135.48
+ ,106.32
+ ,139.47
+ ,113.65
+ ,103.81
+ ,123.56
+ ,89.35
+ ,110.94
+ ,119.24
+ ,125.41
+ ,124.70
+ ,120.93
+ ,122.96
+ ,132.88
+ ,106.15
+ ,140.05
+ ,113.60
+ ,103.96
+ ,125.03
+ ,89.34
+ ,109.86
+ ,119.24
+ ,125.37
+ ,124.30
+ ,120.85
+ ,122.97
+ ,132.11
+ ,106.00
+ ,140.03
+ ,113.42
+ ,104.23
+ ,124.52
+ ,90.20
+ ,110.52
+ ,119.24
+ ,124.15
+ ,124.25
+ ,120.59
+ ,122.85
+ ,132.06
+ ,105.96
+ ,139.39
+ ,113.51
+ ,104.07
+ ,123.24
+ ,90.21
+ ,111.30
+ ,119.24
+ ,124.04
+ ,123.95
+ ,119.88
+ ,121.81
+ ,132.00
+ ,106.53
+ ,138.38
+ ,113.11
+ ,104.02
+ ,122.81
+ ,90.75
+ ,109.33
+ ,119.24
+ ,123.31
+ ,123.78
+ ,119.01
+ ,121.52
+ ,131.99
+ ,106.52
+ ,137.30
+ ,112.99
+ ,103.35
+ ,121.56
+ ,86.76
+ ,108.42
+ ,119.24
+ ,123.82
+ ,121.14
+ ,118.96
+ ,121.54
+ ,131.96
+ ,106.46
+ ,137.32
+ ,112.84
+ ,103.29
+ ,121.86
+ ,86.74
+ ,108.23
+ ,119.24
+ ,123.01
+ ,121.01
+ ,118.49
+ ,120.58
+ ,131.89
+ ,106.27
+ ,136.29
+ ,112.42
+ ,103.30
+ ,121.83
+ ,86.74
+ ,107.64
+ ,119.24
+ ,123.25
+ ,120.69
+ ,118.31
+ ,120.17
+ ,131.89
+ ,105.83
+ ,136.43
+ ,112.00
+ ,103.34
+ ,121.84
+ ,86.72
+ ,107.54
+ ,115.72
+ ,123.09
+ ,120.35
+ ,117.99
+ ,120.02
+ ,131.72
+ ,105.46
+ ,135.13
+ ,111.72
+ ,103.34
+ ,121.15
+ ,87.50
+ ,107.23
+ ,115.72
+ ,124.60
+ ,119.91
+ ,118.09
+ ,120.49
+ ,131.61
+ ,105.10
+ ,135.29
+ ,111.67
+ ,103.32
+ ,121.01
+ ,87.51
+ ,107.36
+ ,115.72
+ ,124.94
+ ,119.79
+ ,117.95
+ ,120.38
+ ,131.38
+ ,105.09
+ ,135.52
+ ,111.55
+ ,103.22
+ ,121.08
+ ,87.86
+ ,107.58
+ ,115.72
+ ,122.46
+ ,119.58
+ ,117.59
+ ,120.09
+ ,130.79
+ ,105.04
+ ,133.84
+ ,111.33
+ ,103.21
+ ,121.55
+ ,87.86
+ ,107.27
+ ,115.72
+ ,121.91
+ ,119.50
+ ,117.20
+ ,119.62
+ ,130.14
+ ,104.87
+ ,132.91
+ ,111.06
+ ,103.15
+ ,121.27
+ ,87.86
+ ,106.52
+ ,115.72
+ ,122.29
+ ,119.33
+ ,116.91
+ ,118.93
+ ,130.13
+ ,104.67
+ ,132.15
+ ,110.97
+ ,103.37
+ ,120.38
+ ,87.81
+ ,108.01
+ ,115.72
+ ,121.49
+ ,119.17
+ ,116.33
+ ,119.09
+ ,130.24
+ ,104.54
+ ,130.11
+ ,110.81
+ ,103.44
+ ,118.61
+ ,87.81
+ ,108.53
+ ,115.72
+ ,120.84
+ ,118.94
+ ,115.66
+ ,118.59
+ ,130.23
+ ,104.90
+ ,128.42
+ ,110.62
+ ,103.28
+ ,118.17
+ ,88.67
+ ,107.09
+ ,115.72
+ ,120.33
+ ,118.15
+ ,115.00
+ ,117.87
+ ,130.23
+ ,104.90
+ ,127.53
+ ,110.71
+ ,103.12
+ ,117.17
+ ,88.35
+ ,105.59
+ ,115.72
+ ,120.76
+ ,117.34
+ ,114.55
+ ,117.74
+ ,130.23
+ ,104.89
+ ,126.35
+ ,110.51
+ ,103.12
+ ,115.67
+ ,87.62
+ ,106.27
+ ,115.72
+ ,120.09
+ ,117.19
+ ,114.41
+ ,117.61
+ ,130.23
+ ,104.80
+ ,125.99
+ ,110.50
+ ,103.11
+ ,115.05
+ ,87.59
+ ,106.63
+ ,115.72
+ ,120.39
+ ,117.02
+ ,114.25
+ ,117.55
+ ,130.22
+ ,104.41
+ ,125.56
+ ,110.37
+ ,103.11
+ ,114.90
+ ,87.58
+ ,106.61
+ ,116.29
+ ,120.21
+ ,116.77
+ ,113.89
+ ,117.06
+ ,130.21
+ ,104.31
+ ,124.35
+ ,110.38
+ ,103.06
+ ,114.68
+ ,87.51
+ ,106.15
+ ,116.29
+ ,121.01
+ ,116.46
+ ,113.82
+ ,117.08
+ ,130.17
+ ,103.88
+ ,124.02
+ ,110.26
+ ,103.03
+ ,114.61
+ ,87.40
+ ,106.11
+ ,116.29
+ ,121.40
+ ,116.48
+ ,113.77
+ ,117.21
+ ,130.07
+ ,103.88
+ ,124.29
+ ,110.28
+ ,103.15
+ ,114.82
+ ,87.48
+ ,106.34
+ ,116.29
+ ,118.89
+ ,116.30
+ ,113.78
+ ,117.58
+ ,130.01
+ ,103.86
+ ,124.12
+ ,110.25
+ ,103.13
+ ,114.97
+ ,86.08
+ ,106.71
+ ,116.29
+ ,119.00
+ ,115.61
+ ,113.33
+ ,117.27
+ ,128.23
+ ,103.89
+ ,123.41
+ ,110.09
+ ,103.11
+ ,114.24
+ ,86.79
+ ,105.50
+ ,116.29
+ ,118.82
+ ,115.50
+ ,112.94
+ ,117.14
+ ,127.80
+ ,103.98
+ ,122.26
+ ,110.01
+ ,103.24
+ ,112.97
+ ,86.58
+ ,106.06
+ ,116.29
+ ,118.17
+ ,115.45
+ ,112.52
+ ,116.52
+ ,127.76
+ ,103.98
+ ,120.74
+ ,109.75
+ ,103.20
+ ,111.47
+ ,86.60
+ ,107.89
+ ,116.29
+ ,118.04
+ ,115.13
+ ,112.05
+ ,116.16
+ ,127.75
+ ,104.29
+ ,120.34
+ ,109.57
+ ,103.83
+ ,111.52
+ ,86.71
+ ,105.41
+ ,116.29
+ ,117.34
+ ,114.84
+ ,111.54
+ ,114.79
+ ,127.41
+ ,104.29
+ ,119.04
+ ,109.59
+ ,103.50
+ ,110.57
+ ,86.98
+ ,105.87
+ ,116.29
+ ,118.00
+ ,114.91
+ ,111.36
+ ,114.97
+ ,125.81
+ ,104.24
+ ,118.76
+ ,109.45
+ ,103.50
+ ,110.62
+ ,86.62
+ ,105.20
+ ,116.29
+ ,117.36
+ ,114.83
+ ,111.07
+ ,114.66
+ ,125.57
+ ,103.98
+ ,118.06
+ ,109.21
+ ,103.52
+ ,109.38
+ ,89.60
+ ,105.06
+ ,116.29
+ ,117.66
+ ,114.78
+ ,111.02
+ ,114.30
+ ,125.55
+ ,103.54
+ ,117.76
+ ,109.00
+ ,103.54
+ ,110.03
+ ,89.56
+ ,105.16
+ ,111.29
+ ,117.83
+ ,114.94
+ ,111.31
+ ,114.48
+ ,125.51
+ ,103.44
+ ,119.02
+ ,108.83
+ ,103.48
+ ,110.64
+ ,89.66
+ ,104.55
+ ,111.29
+ ,118.77
+ ,114.74
+ ,110.97
+ ,114.96
+ ,125.45
+ ,103.32
+ ,117.29
+ ,108.62
+ ,103.53
+ ,109.53
+ ,89.67
+ ,104.83
+ ,111.29
+ ,118.84
+ ,114.63
+ ,111.04
+ ,115.44
+ ,125.29
+ ,103.30
+ ,117.58
+ ,108.56
+ ,104.60
+ ,109.72
+ ,89.69
+ ,105.01
+ ,111.29
+ ,116.63
+ ,114.69
+ ,111.25
+ ,116.38
+ ,125.10
+ ,103.26
+ ,119.98
+ ,108.41
+ ,104.61
+ ,108.24
+ ,89.69
+ ,104.53
+ ,111.29
+ ,116.18
+ ,114.49
+ ,111.33
+ ,116.50
+ ,124.74
+ ,103.14
+ ,121.08
+ ,108.27
+ ,104.99
+ ,108.00
+ ,90.28
+ ,103.78
+ ,111.29
+ ,116.46
+ ,114.27
+ ,111.10
+ ,116.20
+ ,123.61
+ ,103.11
+ ,121.18
+ ,108.03
+ ,104.85
+ ,107.10
+ ,90.09
+ ,104.25
+ ,111.29
+ ,115.65
+ ,114.17
+ ,111.74
+ ,116.37
+ ,122.85
+ ,102.91
+ ,124.36
+ ,107.67
+ ,105.29
+ ,107.03
+ ,90.09
+ ,105.81
+ ,111.29
+ ,115.39
+ ,113.73
+ ,111.36
+ ,116.46
+ ,122.84
+ ,103.23
+ ,125.80
+ ,107.31
+ ,105.22
+ ,105.95
+ ,88.53
+ ,103.42
+ ,111.29
+ ,114.54
+ ,113.25
+ ,111.25
+ ,115.07
+ ,122.83
+ ,103.23
+ ,125.69
+ ,107.14
+ ,103.38
+ ,106.62
+ ,89.73
+ ,104.44
+ ,111.29
+ ,115.11
+ ,112.63
+ ,111.49
+ ,115.03
+ ,122.83
+ ,103.14
+ ,126.27
+ ,107.02
+ ,103.38
+ ,108.90
+ ,89.47
+ ,103.35
+ ,111.29
+ ,114.51
+ ,112.62
+ ,112.16
+ ,115.15
+ ,122.83
+ ,102.91
+ ,127.54
+ ,106.79
+ ,103.30
+ ,112.12
+ ,89.60
+ ,103.19
+ ,111.29
+ ,114.66
+ ,112.42
+ ,112.36
+ ,114.71
+ ,122.81
+ ,102.42
+ ,128.57
+ ,106.49
+ ,103.27
+ ,114.11
+ ,88.90
+ ,102.99
+ ,109.47
+ ,113.81
+ ,112.11
+ ,112.18
+ ,114.67
+ ,122.80
+ ,102.10
+ ,127.03
+ ,106.14
+ ,103.47
+ ,114.51
+ ,89.60
+ ,102.40
+ ,109.47
+ ,115.35
+ ,111.94
+ ,112.87
+ ,115.49
+ ,122.75
+ ,102.07
+ ,128.41
+ ,105.94
+ ,103.30
+ ,116.86
+ ,89.22
+ ,102.49
+ ,109.47
+ ,115.07
+ ,111.85
+ ,112.28
+ ,114.65
+ ,122.72
+ ,102.06
+ ,127.68
+ ,105.87
+ ,103.29
+ ,116.21
+ ,89.61
+ ,102.45
+ ,109.47
+ ,112.87
+ ,110.96
+ ,111.66
+ ,114.92
+ ,122.44
+ ,101.98
+ ,125.68
+ ,105.71
+ ,103.27
+ ,114.74
+ ,89.60
+ ,102.21
+ ,109.47
+ ,111.83
+ ,110.87
+ ,110.67
+ ,114.17
+ ,121.28
+ ,101.83
+ ,123.67
+ ,105.48
+ ,103.78
+ ,112.86
+ ,89.60
+ ,100.80
+ ,109.47
+ ,111.44
+ ,110.64
+ ,110.42
+ ,112.80
+ ,119.78
+ ,101.75
+ ,122.87
+ ,105.31
+ ,103.69
+ ,112.38
+ ,90.13
+ ,102.99
+ ,109.47
+ ,111.20
+ ,110.32
+ ,109.62
+ ,112.28
+ ,119.78
+ ,101.56
+ ,120.13
+ ,105.09
+ ,103.60
+ ,110.76
+ ,90.10
+ ,103.75
+ ,109.47
+ ,110.44
+ ,110.02
+ ,108.84
+ ,112.05
+ ,119.78
+ ,101.66
+ ,117.44
+ ,104.88
+ ,104.36
+ ,110.78
+ ,89.98
+ ,101.69
+ ,109.47
+ ,109.57
+ ,109.68
+ ,108.40
+ ,111.03
+ ,119.77
+ ,101.65
+ ,115.65
+ ,104.76
+ ,104.10
+ ,110.76
+ ,89.97
+ ,102.39
+ ,109.47
+ ,109.74
+ ,109.20
+ ,108.10
+ ,110.40
+ ,119.77
+ ,101.61
+ ,114.97
+ ,104.62
+ ,104.03
+ ,111.69
+ ,89.96
+ ,101.30
+ ,109.47
+ ,108.81
+ ,109.10
+ ,107.10
+ ,109.08
+ ,119.73
+ ,101.52
+ ,112.47
+ ,104.49
+ ,104.00
+ ,109.55
+ ,90.14
+ ,101.33
+ ,109.47
+ ,108.81
+ ,108.99
+ ,106.54
+ ,107.89
+ ,119.67
+ ,101.31
+ ,111.55
+ ,104.29
+ ,104.32
+ ,108.65
+ ,90.03
+ ,101.22
+ ,107.35
+ ,108.81
+ ,108.88
+ ,106.44
+ ,107.26
+ ,119.67
+ ,101.19
+ ,111.07
+ ,104.22
+ ,104.31
+ ,108.39
+ ,91.22
+ ,101.09
+ ,107.35
+ ,110.56
+ ,108.94
+ ,106.57
+ ,107.76
+ ,119.50
+ ,101.11
+ ,110.73
+ ,103.69
+ ,104.30
+ ,109.02
+ ,91.63
+ ,101.23
+ ,107.35
+ ,110.69
+ ,108.92
+ ,106.12
+ ,107.32
+ ,119.39
+ ,101.10
+ ,110.43
+ ,103.11
+ ,104.23
+ ,108.43
+ ,91.98
+ ,100.87
+ ,107.35
+ ,108.76
+ ,108.65
+ ,106.13
+ ,107.15
+ ,119.28
+ ,101.07
+ ,110.71
+ ,102.95
+ ,105.02
+ ,108.12
+ ,94.09
+ ,100.82
+ ,107.35
+ ,108.29
+ ,108.58
+ ,106.26
+ ,108.04
+ ,117.00
+ ,100.98
+ ,111.21
+ ,102.75
+ ,105.18
+ ,107.90
+ ,95.02
+ ,100.28
+ ,107.35
+ ,108.20
+ ,108.45
+ ,105.78
+ ,106.52
+ ,113.14
+ ,100.93
+ ,111.02
+ ,102.61
+ ,105.33
+ ,107.01
+ ,94.78
+ ,101.27
+ ,107.35
+ ,107.58
+ ,107.79
+ ,105.77
+ ,106.62
+ ,107.46
+ ,100.92
+ ,111.92
+ ,102.43
+ ,105.39
+ ,105.68
+ ,94.69
+ ,102.68
+ ,107.35
+ ,107.35
+ ,107.16
+ ,105.20
+ ,106.47
+ ,107.41
+ ,101.02
+ ,111.08
+ ,102.15
+ ,105.26
+ ,105.16
+ ,94.67
+ ,100.84
+ ,107.35
+ ,106.42
+ ,106.98
+ ,105.15
+ ,105.46
+ ,107.39
+ ,101.01
+ ,111.26
+ ,102.03
+ ,104.64
+ ,106.52
+ ,94.79
+ ,101.03
+ ,107.35
+ ,106.38
+ ,105.66
+ ,105.01
+ ,106.13
+ ,107.31
+ ,100.97
+ ,110.75
+ ,101.94
+ ,104.62
+ ,106.25
+ ,94.51
+ ,100.11
+ ,107.35
+ ,106.30
+ ,105.61
+ ,104.75
+ ,105.15
+ ,107.27
+ ,100.89
+ ,110.58
+ ,101.73
+ ,104.59
+ ,106.15
+ ,94.49
+ ,100.11
+ ,107.35
+ ,106.32
+ ,105.46
+ ,104.96
+ ,105.39
+ ,106.90
+ ,100.62
+ ,110.93
+ ,101.63
+ ,104.57
+ ,107.20
+ ,94.29
+ ,100.05
+ ,104.85
+ ,106.58
+ ,105.28
+ ,105.26
+ ,104.57
+ ,105.54
+ ,100.53
+ ,111.45
+ ,101.49
+ ,104.49
+ ,109.21
+ ,94.96
+ ,100.04
+ ,104.85
+ ,107.77
+ ,105.09
+ ,105.13
+ ,104.29
+ ,105.17
+ ,100.48
+ ,111.33
+ ,101.42
+ ,104.45
+ ,109.09
+ ,95.02
+ ,99.98
+ ,104.85
+ ,107.63
+ ,104.99
+ ,104.77
+ ,104.09
+ ,105.17
+ ,100.48
+ ,110.71
+ ,101.36
+ ,104.74
+ ,108.49
+ ,95.08
+ ,100.18
+ ,104.85
+ ,105.87
+ ,104.47
+ ,104.79
+ ,104.51
+ ,105.16
+ ,100.47
+ ,110.59
+ ,101.30
+ ,105.08
+ ,108.50
+ ,95.23
+ ,100.16
+ ,104.85
+ ,105.20
+ ,104.36
+ ,104.40
+ ,103.39
+ ,105.16
+ ,100.52
+ ,110.25
+ ,101.12
+ ,105.01
+ ,108.03
+ ,95.35
+ ,99.94
+ ,104.85
+ ,105.25
+ ,104.10
+ ,103.89
+ ,102.71
+ ,105.16
+ ,100.49
+ ,109.43
+ ,100.88
+ ,105.06
+ ,106.61
+ ,95.46
+ ,100.30
+ ,104.85
+ ,104.51
+ ,103.98
+ ,103.93
+ ,102.62
+ ,105.16
+ ,100.47
+ ,108.62
+ ,100.89
+ ,105.06
+ ,106.35
+ ,96.15
+ ,102.01
+ ,104.85
+ ,104.35
+ ,103.87
+ ,103.48
+ ,101.94
+ ,105.16
+ ,100.44
+ ,108.42
+ ,100.76
+ ,105.01
+ ,106.34
+ ,96.84
+ ,100.17
+ ,104.85
+ ,103.75
+ ,103.51)
+ ,dim=c(13
+ ,82)
+ ,dimnames=list(c('Algemeen_indexcijfer'
+ ,'Voedingsmiddelen_en_dranken'
+ ,'Tabak'
+ ,'Kleding_en_schoeisel'
+ ,'Huisv_wat_elektr_gas_ed'
+ ,'Stoff_huish_app_&_ond_won.'
+ ,'Gezondheidsuitgaven'
+ ,'Vervoer'
+ ,'Communicatie'
+ ,'Recreatie_en_cultuur'
+ ,'Onderwijs'
+ ,'Hotels_cafés_en_restaurants'
+ ,'Diverse_goederen_&_diensten')
+ ,1:82))
> y <- array(NA,dim=c(13,82),dimnames=list(c('Algemeen_indexcijfer','Voedingsmiddelen_en_dranken','Tabak','Kleding_en_schoeisel','Huisv_wat_elektr_gas_ed','Stoff_huish_app_&_ond_won.','Gezondheidsuitgaven','Vervoer','Communicatie','Recreatie_en_cultuur','Onderwijs','Hotels_cafés_en_restaurants','Diverse_goederen_&_diensten'),1:82))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> 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
Algemeen_indexcijfer Voedingsmiddelen_en_dranken Tabak Kleding_en_schoeisel
1 121.79 125.10 138.82 107.35
2 121.57 123.99 138.32 106.99
3 121.36 123.72 138.32 106.81
4 120.83 123.24 136.96 106.41
5 120.61 123.23 136.56 106.41
6 120.89 123.40 135.48 106.32
7 120.93 122.96 132.88 106.15
8 120.85 122.97 132.11 106.00
9 120.59 122.85 132.06 105.96
10 119.88 121.81 132.00 106.53
11 119.01 121.52 131.99 106.52
12 118.96 121.54 131.96 106.46
13 118.49 120.58 131.89 106.27
14 118.31 120.17 131.89 105.83
15 117.99 120.02 131.72 105.46
16 118.09 120.49 131.61 105.10
17 117.95 120.38 131.38 105.09
18 117.59 120.09 130.79 105.04
19 117.20 119.62 130.14 104.87
20 116.91 118.93 130.13 104.67
21 116.33 119.09 130.24 104.54
22 115.66 118.59 130.23 104.90
23 115.00 117.87 130.23 104.90
24 114.55 117.74 130.23 104.89
25 114.41 117.61 130.23 104.80
26 114.25 117.55 130.22 104.41
27 113.89 117.06 130.21 104.31
28 113.82 117.08 130.17 103.88
29 113.77 117.21 130.07 103.88
30 113.78 117.58 130.01 103.86
31 113.33 117.27 128.23 103.89
32 112.94 117.14 127.80 103.98
33 112.52 116.52 127.76 103.98
34 112.05 116.16 127.75 104.29
35 111.54 114.79 127.41 104.29
36 111.36 114.97 125.81 104.24
37 111.07 114.66 125.57 103.98
38 111.02 114.30 125.55 103.54
39 111.31 114.48 125.51 103.44
40 110.97 114.96 125.45 103.32
41 111.04 115.44 125.29 103.30
42 111.25 116.38 125.10 103.26
43 111.33 116.50 124.74 103.14
44 111.10 116.20 123.61 103.11
45 111.74 116.37 122.85 102.91
46 111.36 116.46 122.84 103.23
47 111.25 115.07 122.83 103.23
48 111.49 115.03 122.83 103.14
49 112.16 115.15 122.83 102.91
50 112.36 114.71 122.81 102.42
51 112.18 114.67 122.80 102.10
52 112.87 115.49 122.75 102.07
53 112.28 114.65 122.72 102.06
54 111.66 114.92 122.44 101.98
55 110.67 114.17 121.28 101.83
56 110.42 112.80 119.78 101.75
57 109.62 112.28 119.78 101.56
58 108.84 112.05 119.78 101.66
59 108.40 111.03 119.77 101.65
60 108.10 110.40 119.77 101.61
61 107.10 109.08 119.73 101.52
62 106.54 107.89 119.67 101.31
63 106.44 107.26 119.67 101.19
64 106.57 107.76 119.50 101.11
65 106.12 107.32 119.39 101.10
66 106.13 107.15 119.28 101.07
67 106.26 108.04 117.00 100.98
68 105.78 106.52 113.14 100.93
69 105.77 106.62 107.46 100.92
70 105.20 106.47 107.41 101.02
71 105.15 105.46 107.39 101.01
72 105.01 106.13 107.31 100.97
73 104.75 105.15 107.27 100.89
74 104.96 105.39 106.90 100.62
75 105.26 104.57 105.54 100.53
76 105.13 104.29 105.17 100.48
77 104.77 104.09 105.17 100.48
78 104.79 104.51 105.16 100.47
79 104.40 103.39 105.16 100.52
80 103.89 102.71 105.16 100.49
81 103.93 102.62 105.16 100.47
82 103.48 101.94 105.16 100.44
Huisv_wat_elektr_gas_ed Stoff_huish_app_&_ond_won. Gezondheidsuitgaven
1 139.63 114.29 103.40
2 139.66 114.08 103.26
3 139.31 114.02 103.24
4 138.50 113.74 103.29
5 138.31 113.76 103.38
6 139.47 113.65 103.81
7 140.05 113.60 103.96
8 140.03 113.42 104.23
9 139.39 113.51 104.07
10 138.38 113.11 104.02
11 137.30 112.99 103.35
12 137.32 112.84 103.29
13 136.29 112.42 103.30
14 136.43 112.00 103.34
15 135.13 111.72 103.34
16 135.29 111.67 103.32
17 135.52 111.55 103.22
18 133.84 111.33 103.21
19 132.91 111.06 103.15
20 132.15 110.97 103.37
21 130.11 110.81 103.44
22 128.42 110.62 103.28
23 127.53 110.71 103.12
24 126.35 110.51 103.12
25 125.99 110.50 103.11
26 125.56 110.37 103.11
27 124.35 110.38 103.06
28 124.02 110.26 103.03
29 124.29 110.28 103.15
30 124.12 110.25 103.13
31 123.41 110.09 103.11
32 122.26 110.01 103.24
33 120.74 109.75 103.20
34 120.34 109.57 103.83
35 119.04 109.59 103.50
36 118.76 109.45 103.50
37 118.06 109.21 103.52
38 117.76 109.00 103.54
39 119.02 108.83 103.48
40 117.29 108.62 103.53
41 117.58 108.56 104.60
42 119.98 108.41 104.61
43 121.08 108.27 104.99
44 121.18 108.03 104.85
45 124.36 107.67 105.29
46 125.80 107.31 105.22
47 125.69 107.14 103.38
48 126.27 107.02 103.38
49 127.54 106.79 103.30
50 128.57 106.49 103.27
51 127.03 106.14 103.47
52 128.41 105.94 103.30
53 127.68 105.87 103.29
54 125.68 105.71 103.27
55 123.67 105.48 103.78
56 122.87 105.31 103.69
57 120.13 105.09 103.60
58 117.44 104.88 104.36
59 115.65 104.76 104.10
60 114.97 104.62 104.03
61 112.47 104.49 104.00
62 111.55 104.29 104.32
63 111.07 104.22 104.31
64 110.73 103.69 104.30
65 110.43 103.11 104.23
66 110.71 102.95 105.02
67 111.21 102.75 105.18
68 111.02 102.61 105.33
69 111.92 102.43 105.39
70 111.08 102.15 105.26
71 111.26 102.03 104.64
72 110.75 101.94 104.62
73 110.58 101.73 104.59
74 110.93 101.63 104.57
75 111.45 101.49 104.49
76 111.33 101.42 104.45
77 110.71 101.36 104.74
78 110.59 101.30 105.08
79 110.25 101.12 105.01
80 109.43 100.88 105.06
81 108.62 100.89 105.06
82 108.42 100.76 105.01
Vervoer Communicatie Recreatie_en_cultuur Onderwijs
1 125.16 89.25 110.87 119.79
2 125.75 88.72 110.79 119.24
3 124.73 88.77 110.84 119.24
4 122.95 88.94 111.01 119.24
5 122.58 88.94 111.35 119.24
6 123.56 89.35 110.94 119.24
7 125.03 89.34 109.86 119.24
8 124.52 90.20 110.52 119.24
9 123.24 90.21 111.30 119.24
10 122.81 90.75 109.33 119.24
11 121.56 86.76 108.42 119.24
12 121.86 86.74 108.23 119.24
13 121.83 86.74 107.64 119.24
14 121.84 86.72 107.54 115.72
15 121.15 87.50 107.23 115.72
16 121.01 87.51 107.36 115.72
17 121.08 87.86 107.58 115.72
18 121.55 87.86 107.27 115.72
19 121.27 87.86 106.52 115.72
20 120.38 87.81 108.01 115.72
21 118.61 87.81 108.53 115.72
22 118.17 88.67 107.09 115.72
23 117.17 88.35 105.59 115.72
24 115.67 87.62 106.27 115.72
25 115.05 87.59 106.63 115.72
26 114.90 87.58 106.61 116.29
27 114.68 87.51 106.15 116.29
28 114.61 87.40 106.11 116.29
29 114.82 87.48 106.34 116.29
30 114.97 86.08 106.71 116.29
31 114.24 86.79 105.50 116.29
32 112.97 86.58 106.06 116.29
33 111.47 86.60 107.89 116.29
34 111.52 86.71 105.41 116.29
35 110.57 86.98 105.87 116.29
36 110.62 86.62 105.20 116.29
37 109.38 89.60 105.06 116.29
38 110.03 89.56 105.16 111.29
39 110.64 89.66 104.55 111.29
40 109.53 89.67 104.83 111.29
41 109.72 89.69 105.01 111.29
42 108.24 89.69 104.53 111.29
43 108.00 90.28 103.78 111.29
44 107.10 90.09 104.25 111.29
45 107.03 90.09 105.81 111.29
46 105.95 88.53 103.42 111.29
47 106.62 89.73 104.44 111.29
48 108.90 89.47 103.35 111.29
49 112.12 89.60 103.19 111.29
50 114.11 88.90 102.99 109.47
51 114.51 89.60 102.40 109.47
52 116.86 89.22 102.49 109.47
53 116.21 89.61 102.45 109.47
54 114.74 89.60 102.21 109.47
55 112.86 89.60 100.80 109.47
56 112.38 90.13 102.99 109.47
57 110.76 90.10 103.75 109.47
58 110.78 89.98 101.69 109.47
59 110.76 89.97 102.39 109.47
60 111.69 89.96 101.30 109.47
61 109.55 90.14 101.33 109.47
62 108.65 90.03 101.22 107.35
63 108.39 91.22 101.09 107.35
64 109.02 91.63 101.23 107.35
65 108.43 91.98 100.87 107.35
66 108.12 94.09 100.82 107.35
67 107.90 95.02 100.28 107.35
68 107.01 94.78 101.27 107.35
69 105.68 94.69 102.68 107.35
70 105.16 94.67 100.84 107.35
71 106.52 94.79 101.03 107.35
72 106.25 94.51 100.11 107.35
73 106.15 94.49 100.11 107.35
74 107.20 94.29 100.05 104.85
75 109.21 94.96 100.04 104.85
76 109.09 95.02 99.98 104.85
77 108.49 95.08 100.18 104.85
78 108.50 95.23 100.16 104.85
79 108.03 95.35 99.94 104.85
80 106.61 95.46 100.30 104.85
81 106.35 96.15 102.01 104.85
82 106.34 96.84 100.17 104.85
Hotels_caf\303\251s_en_restaurants Diverse_goederen_&_diensten
1 126.75 125.84
2 126.69 125.67
3 128.16 125.24
4 128.17 125.02
5 126.06 124.73
6 125.41 124.70
7 125.37 124.30
8 124.15 124.25
9 124.04 123.95
10 123.31 123.78
11 123.82 121.14
12 123.01 121.01
13 123.25 120.69
14 123.09 120.35
15 124.60 119.91
16 124.94 119.79
17 122.46 119.58
18 121.91 119.50
19 122.29 119.33
20 121.49 119.17
21 120.84 118.94
22 120.33 118.15
23 120.76 117.34
24 120.09 117.19
25 120.39 117.02
26 120.21 116.77
27 121.01 116.46
28 121.40 116.48
29 118.89 116.30
30 119.00 115.61
31 118.82 115.50
32 118.17 115.45
33 118.04 115.13
34 117.34 114.84
35 118.00 114.91
36 117.36 114.83
37 117.66 114.78
38 117.83 114.94
39 118.77 114.74
40 118.84 114.63
41 116.63 114.69
42 116.18 114.49
43 116.46 114.27
44 115.65 114.17
45 115.39 113.73
46 114.54 113.25
47 115.11 112.63
48 114.51 112.62
49 114.66 112.42
50 113.81 112.11
51 115.35 111.94
52 115.07 111.85
53 112.87 110.96
54 111.83 110.87
55 111.44 110.64
56 111.20 110.32
57 110.44 110.02
58 109.57 109.68
59 109.74 109.20
60 108.81 109.10
61 108.81 108.99
62 108.81 108.88
63 110.56 108.94
64 110.69 108.92
65 108.76 108.65
66 108.29 108.58
67 108.20 108.45
68 107.58 107.79
69 107.35 107.16
70 106.42 106.98
71 106.38 105.66
72 106.30 105.61
73 106.32 105.46
74 106.58 105.28
75 107.77 105.09
76 107.63 104.99
77 105.87 104.47
78 105.20 104.36
79 105.25 104.10
80 104.51 103.98
81 104.35 103.87
82 103.75 103.51
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Voedingsmiddelen_en_dranken
0.337765 0.192251
Tabak Kleding_en_schoeisel
0.009930 0.060412
Huisv_wat_elektr_gas_ed `Stoff_huish_app_&_ond_won.`
0.156811 0.073152
Gezondheidsuitgaven Vervoer
0.041061 0.156328
Communicatie Recreatie_en_cultuur
0.035877 0.123698
Onderwijs `Hotels_caf\\303\\251s_en_restaurants`
0.005539 0.069958
`Diverse_goederen_&_diensten`
0.071649
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.0082554 -0.0021369 -0.0003613 0.0025690 0.0068040
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.3377645 0.1982273 1.704 0.0929
Voedingsmiddelen_en_dranken 0.1922506 0.0006818 281.980 <2e-16
Tabak 0.0099300 0.0002985 33.266 <2e-16
Kleding_en_schoeisel 0.0604115 0.0016665 36.250 <2e-16
Huisv_wat_elektr_gas_ed 0.1568111 0.0002653 591.176 <2e-16
`Stoff_huish_app_&_ond_won.` 0.0731521 0.0017076 42.840 <2e-16
Gezondheidsuitgaven 0.0410610 0.0013197 31.114 <2e-16
Vervoer 0.1563276 0.0002465 634.316 <2e-16
Communicatie 0.0358769 0.0005986 59.935 <2e-16
Recreatie_en_cultuur 0.1236984 0.0005921 208.918 <2e-16
Onderwijs 0.0055387 0.0004682 11.831 <2e-16
`Hotels_caf\\303\\251s_en_restaurants` 0.0699584 0.0004772 146.615 <2e-16
`Diverse_goederen_&_diensten` 0.0716489 0.0010810 66.281 <2e-16
(Intercept) .
Voedingsmiddelen_en_dranken ***
Tabak ***
Kleding_en_schoeisel ***
Huisv_wat_elektr_gas_ed ***
`Stoff_huish_app_&_ond_won.` ***
Gezondheidsuitgaven ***
Vervoer ***
Communicatie ***
Recreatie_en_cultuur ***
Onderwijs ***
`Hotels_caf\\303\\251s_en_restaurants` ***
`Diverse_goederen_&_diensten` ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.003397 on 69 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 1.659e+07 on 12 and 69 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,] 0.4172944 0.8345888 0.5827056
[2,] 0.3314260 0.6628520 0.6685740
[3,] 0.7681963 0.4636075 0.2318037
[4,] 0.6843461 0.6313078 0.3156539
[5,] 0.6724935 0.6550129 0.3275065
[6,] 0.5839805 0.8320390 0.4160195
[7,] 0.5726899 0.8546201 0.4273101
[8,] 0.5332934 0.9334131 0.4667066
[9,] 0.6172415 0.7655169 0.3827585
[10,] 0.5475186 0.9049628 0.4524814
[11,] 0.5175566 0.9648867 0.4824434
[12,] 0.5155811 0.9688378 0.4844189
[13,] 0.4297630 0.8595260 0.5702370
[14,] 0.3642101 0.7284201 0.6357899
[15,] 0.4166056 0.8332113 0.5833944
[16,] 0.3616328 0.7232656 0.6383672
[17,] 0.3378065 0.6756129 0.6621935
[18,] 0.3891117 0.7782233 0.6108883
[19,] 0.5445883 0.9108234 0.4554117
[20,] 0.5236908 0.9526183 0.4763092
[21,] 0.5560829 0.8878341 0.4439171
[22,] 0.5059162 0.9881675 0.4940838
[23,] 0.4454672 0.8909344 0.5545328
[24,] 0.3903531 0.7807062 0.6096469
[25,] 0.4586507 0.9173013 0.5413493
[26,] 0.4497181 0.8994361 0.5502819
[27,] 0.4974406 0.9948812 0.5025594
[28,] 0.4825060 0.9650120 0.5174940
[29,] 0.4361057 0.8722115 0.5638943
[30,] 0.4886094 0.9772188 0.5113906
[31,] 0.5229719 0.9540563 0.4770281
[32,] 0.5391505 0.9216990 0.4608495
[33,] 0.4673061 0.9346122 0.5326939
[34,] 0.6282907 0.7434187 0.3717093
[35,] 0.6336156 0.7327688 0.3663844
[36,] 0.5981786 0.8036428 0.4018214
[37,] 0.5200539 0.9598923 0.4799461
[38,] 0.5149630 0.9700740 0.4850370
[39,] 0.4615583 0.9231167 0.5384417
[40,] 0.3865156 0.7730313 0.6134844
[41,] 0.3195201 0.6390402 0.6804799
[42,] 0.5072632 0.9854736 0.4927368
[43,] 0.4318509 0.8637018 0.5681491
[44,] 0.4693710 0.9387421 0.5306290
[45,] 0.4141106 0.8282212 0.5858894
[46,] 0.4237890 0.8475780 0.5762110
[47,] 0.5547369 0.8905262 0.4452631
[48,] 0.5893024 0.8213953 0.4106976
[49,] 0.6834619 0.6330763 0.3165381
[50,] 0.5635203 0.8729593 0.4364797
[51,] 0.6694148 0.6611705 0.3305852
> postscript(file="/var/wessaorg/rcomp/tmp/1vl0j1353250279.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/25hig1353250279.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/31n271353250279.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4j7l41353250279.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5aqki1353250279.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 82
Frequency = 1
1 2 3 4 5
6.804047e-03 -5.770696e-04 -8.255430e-03 3.339418e-03 -1.956403e-03
6 7 8 9 10
4.401887e-04 3.275742e-03 -5.581210e-04 -1.783938e-03 -1.205971e-03
11 12 13 14 15
-4.506440e-03 -8.260456e-04 1.546013e-03 6.520560e-04 1.984000e-03
16 17 18 19 20
4.114078e-03 2.796945e-03 -3.542866e-03 3.730090e-03 -6.633124e-04
21 22 23 24 25
-1.608531e-03 -3.315646e-03 -4.037978e-03 5.412413e-03 -1.903005e-03
26 27 28 29 30
3.859915e-03 -4.684710e-03 7.227814e-04 2.336530e-03 -4.565996e-03
31 32 33 34 35
3.551692e-03 4.078706e-03 2.114285e-03 -2.522271e-03 9.140409e-04
36 37 38 39 40
-2.151554e-03 -1.114879e-03 -1.751841e-03 2.476321e-03 4.147008e-03
41 42 43 44 45
-2.734062e-03 1.620319e-03 -3.180210e-03 -1.643391e-03 2.599951e-03
46 47 48 49 50
3.763102e-03 -3.090108e-03 -1.711549e-03 -4.338453e-03 2.231805e-03
51 52 53 54 55
-1.985471e-03 -9.434553e-04 1.697182e-03 2.603727e-03 5.200756e-04
56 57 58 59 60
5.228136e-05 -4.068507e-03 1.307109e-03 -2.352900e-03 2.959961e-03
61 62 63 64 65
-2.411955e-03 3.277177e-03 -2.486215e-04 -5.531193e-03 1.673934e-03
66 67 68 69 70
-5.377693e-04 -4.738859e-04 2.916861e-03 -1.768542e-03 -3.362784e-03
71 72 73 74 75
4.584426e-03 1.601597e-03 4.187410e-03 2.927215e-04 2.208188e-03
76 77 78 79 80
-6.988831e-04 -5.256030e-03 -5.769507e-03 -2.598826e-03 -2.093097e-03
81 82
6.143772e-03 3.793340e-03
> postscript(file="/var/wessaorg/rcomp/tmp/6snib1353250279.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 = 82
Frequency = 1
lag(myerror, k = 1) myerror
0 6.804047e-03 NA
1 -5.770696e-04 6.804047e-03
2 -8.255430e-03 -5.770696e-04
3 3.339418e-03 -8.255430e-03
4 -1.956403e-03 3.339418e-03
5 4.401887e-04 -1.956403e-03
6 3.275742e-03 4.401887e-04
7 -5.581210e-04 3.275742e-03
8 -1.783938e-03 -5.581210e-04
9 -1.205971e-03 -1.783938e-03
10 -4.506440e-03 -1.205971e-03
11 -8.260456e-04 -4.506440e-03
12 1.546013e-03 -8.260456e-04
13 6.520560e-04 1.546013e-03
14 1.984000e-03 6.520560e-04
15 4.114078e-03 1.984000e-03
16 2.796945e-03 4.114078e-03
17 -3.542866e-03 2.796945e-03
18 3.730090e-03 -3.542866e-03
19 -6.633124e-04 3.730090e-03
20 -1.608531e-03 -6.633124e-04
21 -3.315646e-03 -1.608531e-03
22 -4.037978e-03 -3.315646e-03
23 5.412413e-03 -4.037978e-03
24 -1.903005e-03 5.412413e-03
25 3.859915e-03 -1.903005e-03
26 -4.684710e-03 3.859915e-03
27 7.227814e-04 -4.684710e-03
28 2.336530e-03 7.227814e-04
29 -4.565996e-03 2.336530e-03
30 3.551692e-03 -4.565996e-03
31 4.078706e-03 3.551692e-03
32 2.114285e-03 4.078706e-03
33 -2.522271e-03 2.114285e-03
34 9.140409e-04 -2.522271e-03
35 -2.151554e-03 9.140409e-04
36 -1.114879e-03 -2.151554e-03
37 -1.751841e-03 -1.114879e-03
38 2.476321e-03 -1.751841e-03
39 4.147008e-03 2.476321e-03
40 -2.734062e-03 4.147008e-03
41 1.620319e-03 -2.734062e-03
42 -3.180210e-03 1.620319e-03
43 -1.643391e-03 -3.180210e-03
44 2.599951e-03 -1.643391e-03
45 3.763102e-03 2.599951e-03
46 -3.090108e-03 3.763102e-03
47 -1.711549e-03 -3.090108e-03
48 -4.338453e-03 -1.711549e-03
49 2.231805e-03 -4.338453e-03
50 -1.985471e-03 2.231805e-03
51 -9.434553e-04 -1.985471e-03
52 1.697182e-03 -9.434553e-04
53 2.603727e-03 1.697182e-03
54 5.200756e-04 2.603727e-03
55 5.228136e-05 5.200756e-04
56 -4.068507e-03 5.228136e-05
57 1.307109e-03 -4.068507e-03
58 -2.352900e-03 1.307109e-03
59 2.959961e-03 -2.352900e-03
60 -2.411955e-03 2.959961e-03
61 3.277177e-03 -2.411955e-03
62 -2.486215e-04 3.277177e-03
63 -5.531193e-03 -2.486215e-04
64 1.673934e-03 -5.531193e-03
65 -5.377693e-04 1.673934e-03
66 -4.738859e-04 -5.377693e-04
67 2.916861e-03 -4.738859e-04
68 -1.768542e-03 2.916861e-03
69 -3.362784e-03 -1.768542e-03
70 4.584426e-03 -3.362784e-03
71 1.601597e-03 4.584426e-03
72 4.187410e-03 1.601597e-03
73 2.927215e-04 4.187410e-03
74 2.208188e-03 2.927215e-04
75 -6.988831e-04 2.208188e-03
76 -5.256030e-03 -6.988831e-04
77 -5.769507e-03 -5.256030e-03
78 -2.598826e-03 -5.769507e-03
79 -2.093097e-03 -2.598826e-03
80 6.143772e-03 -2.093097e-03
81 3.793340e-03 6.143772e-03
82 NA 3.793340e-03
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.770696e-04 6.804047e-03
[2,] -8.255430e-03 -5.770696e-04
[3,] 3.339418e-03 -8.255430e-03
[4,] -1.956403e-03 3.339418e-03
[5,] 4.401887e-04 -1.956403e-03
[6,] 3.275742e-03 4.401887e-04
[7,] -5.581210e-04 3.275742e-03
[8,] -1.783938e-03 -5.581210e-04
[9,] -1.205971e-03 -1.783938e-03
[10,] -4.506440e-03 -1.205971e-03
[11,] -8.260456e-04 -4.506440e-03
[12,] 1.546013e-03 -8.260456e-04
[13,] 6.520560e-04 1.546013e-03
[14,] 1.984000e-03 6.520560e-04
[15,] 4.114078e-03 1.984000e-03
[16,] 2.796945e-03 4.114078e-03
[17,] -3.542866e-03 2.796945e-03
[18,] 3.730090e-03 -3.542866e-03
[19,] -6.633124e-04 3.730090e-03
[20,] -1.608531e-03 -6.633124e-04
[21,] -3.315646e-03 -1.608531e-03
[22,] -4.037978e-03 -3.315646e-03
[23,] 5.412413e-03 -4.037978e-03
[24,] -1.903005e-03 5.412413e-03
[25,] 3.859915e-03 -1.903005e-03
[26,] -4.684710e-03 3.859915e-03
[27,] 7.227814e-04 -4.684710e-03
[28,] 2.336530e-03 7.227814e-04
[29,] -4.565996e-03 2.336530e-03
[30,] 3.551692e-03 -4.565996e-03
[31,] 4.078706e-03 3.551692e-03
[32,] 2.114285e-03 4.078706e-03
[33,] -2.522271e-03 2.114285e-03
[34,] 9.140409e-04 -2.522271e-03
[35,] -2.151554e-03 9.140409e-04
[36,] -1.114879e-03 -2.151554e-03
[37,] -1.751841e-03 -1.114879e-03
[38,] 2.476321e-03 -1.751841e-03
[39,] 4.147008e-03 2.476321e-03
[40,] -2.734062e-03 4.147008e-03
[41,] 1.620319e-03 -2.734062e-03
[42,] -3.180210e-03 1.620319e-03
[43,] -1.643391e-03 -3.180210e-03
[44,] 2.599951e-03 -1.643391e-03
[45,] 3.763102e-03 2.599951e-03
[46,] -3.090108e-03 3.763102e-03
[47,] -1.711549e-03 -3.090108e-03
[48,] -4.338453e-03 -1.711549e-03
[49,] 2.231805e-03 -4.338453e-03
[50,] -1.985471e-03 2.231805e-03
[51,] -9.434553e-04 -1.985471e-03
[52,] 1.697182e-03 -9.434553e-04
[53,] 2.603727e-03 1.697182e-03
[54,] 5.200756e-04 2.603727e-03
[55,] 5.228136e-05 5.200756e-04
[56,] -4.068507e-03 5.228136e-05
[57,] 1.307109e-03 -4.068507e-03
[58,] -2.352900e-03 1.307109e-03
[59,] 2.959961e-03 -2.352900e-03
[60,] -2.411955e-03 2.959961e-03
[61,] 3.277177e-03 -2.411955e-03
[62,] -2.486215e-04 3.277177e-03
[63,] -5.531193e-03 -2.486215e-04
[64,] 1.673934e-03 -5.531193e-03
[65,] -5.377693e-04 1.673934e-03
[66,] -4.738859e-04 -5.377693e-04
[67,] 2.916861e-03 -4.738859e-04
[68,] -1.768542e-03 2.916861e-03
[69,] -3.362784e-03 -1.768542e-03
[70,] 4.584426e-03 -3.362784e-03
[71,] 1.601597e-03 4.584426e-03
[72,] 4.187410e-03 1.601597e-03
[73,] 2.927215e-04 4.187410e-03
[74,] 2.208188e-03 2.927215e-04
[75,] -6.988831e-04 2.208188e-03
[76,] -5.256030e-03 -6.988831e-04
[77,] -5.769507e-03 -5.256030e-03
[78,] -2.598826e-03 -5.769507e-03
[79,] -2.093097e-03 -2.598826e-03
[80,] 6.143772e-03 -2.093097e-03
[81,] 3.793340e-03 6.143772e-03
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.770696e-04 6.804047e-03
2 -8.255430e-03 -5.770696e-04
3 3.339418e-03 -8.255430e-03
4 -1.956403e-03 3.339418e-03
5 4.401887e-04 -1.956403e-03
6 3.275742e-03 4.401887e-04
7 -5.581210e-04 3.275742e-03
8 -1.783938e-03 -5.581210e-04
9 -1.205971e-03 -1.783938e-03
10 -4.506440e-03 -1.205971e-03
11 -8.260456e-04 -4.506440e-03
12 1.546013e-03 -8.260456e-04
13 6.520560e-04 1.546013e-03
14 1.984000e-03 6.520560e-04
15 4.114078e-03 1.984000e-03
16 2.796945e-03 4.114078e-03
17 -3.542866e-03 2.796945e-03
18 3.730090e-03 -3.542866e-03
19 -6.633124e-04 3.730090e-03
20 -1.608531e-03 -6.633124e-04
21 -3.315646e-03 -1.608531e-03
22 -4.037978e-03 -3.315646e-03
23 5.412413e-03 -4.037978e-03
24 -1.903005e-03 5.412413e-03
25 3.859915e-03 -1.903005e-03
26 -4.684710e-03 3.859915e-03
27 7.227814e-04 -4.684710e-03
28 2.336530e-03 7.227814e-04
29 -4.565996e-03 2.336530e-03
30 3.551692e-03 -4.565996e-03
31 4.078706e-03 3.551692e-03
32 2.114285e-03 4.078706e-03
33 -2.522271e-03 2.114285e-03
34 9.140409e-04 -2.522271e-03
35 -2.151554e-03 9.140409e-04
36 -1.114879e-03 -2.151554e-03
37 -1.751841e-03 -1.114879e-03
38 2.476321e-03 -1.751841e-03
39 4.147008e-03 2.476321e-03
40 -2.734062e-03 4.147008e-03
41 1.620319e-03 -2.734062e-03
42 -3.180210e-03 1.620319e-03
43 -1.643391e-03 -3.180210e-03
44 2.599951e-03 -1.643391e-03
45 3.763102e-03 2.599951e-03
46 -3.090108e-03 3.763102e-03
47 -1.711549e-03 -3.090108e-03
48 -4.338453e-03 -1.711549e-03
49 2.231805e-03 -4.338453e-03
50 -1.985471e-03 2.231805e-03
51 -9.434553e-04 -1.985471e-03
52 1.697182e-03 -9.434553e-04
53 2.603727e-03 1.697182e-03
54 5.200756e-04 2.603727e-03
55 5.228136e-05 5.200756e-04
56 -4.068507e-03 5.228136e-05
57 1.307109e-03 -4.068507e-03
58 -2.352900e-03 1.307109e-03
59 2.959961e-03 -2.352900e-03
60 -2.411955e-03 2.959961e-03
61 3.277177e-03 -2.411955e-03
62 -2.486215e-04 3.277177e-03
63 -5.531193e-03 -2.486215e-04
64 1.673934e-03 -5.531193e-03
65 -5.377693e-04 1.673934e-03
66 -4.738859e-04 -5.377693e-04
67 2.916861e-03 -4.738859e-04
68 -1.768542e-03 2.916861e-03
69 -3.362784e-03 -1.768542e-03
70 4.584426e-03 -3.362784e-03
71 1.601597e-03 4.584426e-03
72 4.187410e-03 1.601597e-03
73 2.927215e-04 4.187410e-03
74 2.208188e-03 2.927215e-04
75 -6.988831e-04 2.208188e-03
76 -5.256030e-03 -6.988831e-04
77 -5.769507e-03 -5.256030e-03
78 -2.598826e-03 -5.769507e-03
79 -2.093097e-03 -2.598826e-03
80 6.143772e-03 -2.093097e-03
81 3.793340e-03 6.143772e-03
> 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/7xcpb1353250279.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/8xtya1353250279.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/9gstb1353250279.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/10dbrv1353250279.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11xsz11353250279.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12k3ow1353250279.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13far61353250279.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14enfb1353250279.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/1598mt1353250279.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/16tyfm1353250279.tab")
+ }
>
> try(system("convert tmp/1vl0j1353250279.ps tmp/1vl0j1353250279.png",intern=TRUE))
character(0)
> try(system("convert tmp/25hig1353250279.ps tmp/25hig1353250279.png",intern=TRUE))
character(0)
> try(system("convert tmp/31n271353250279.ps tmp/31n271353250279.png",intern=TRUE))
character(0)
> try(system("convert tmp/4j7l41353250279.ps tmp/4j7l41353250279.png",intern=TRUE))
character(0)
> try(system("convert tmp/5aqki1353250279.ps tmp/5aqki1353250279.png",intern=TRUE))
character(0)
> try(system("convert tmp/6snib1353250279.ps tmp/6snib1353250279.png",intern=TRUE))
character(0)
> try(system("convert tmp/7xcpb1353250279.ps tmp/7xcpb1353250279.png",intern=TRUE))
character(0)
> try(system("convert tmp/8xtya1353250279.ps tmp/8xtya1353250279.png",intern=TRUE))
character(0)
> try(system("convert tmp/9gstb1353250279.ps tmp/9gstb1353250279.png",intern=TRUE))
character(0)
> try(system("convert tmp/10dbrv1353250279.ps tmp/10dbrv1353250279.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.883 0.885 7.794