R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(100.00
+ ,100.00
+ ,103.53
+ ,102.62
+ ,108.36
+ ,107.62
+ ,115.20
+ ,103.46
+ ,123.51
+ ,103.61
+ ,132.87
+ ,106.10
+ ,130.55
+ ,107.13
+ ,136.68
+ ,108.82
+ ,140.63
+ ,112.93
+ ,143.47
+ ,109.35
+ ,124.10
+ ,108.75
+ ,111.49
+ ,110.83
+ ,119.93
+ ,110.95
+ ,131.79
+ ,114.96
+ ,136.61
+ ,120.45
+ ,141.79
+ ,122.89
+ ,142.23
+ ,120.43
+ ,146.74
+ ,121.76
+ ,154.85
+ ,122.78
+ ,148.44
+ ,125.32
+ ,154.18
+ ,128.68
+ ,149.10
+ ,127.91
+ ,152.22
+ ,125.52
+ ,149.34
+ ,127.56
+ ,160.94
+ ,127.90
+ ,176.16
+ ,130.75
+ ,195.12
+ ,133.57
+ ,186.07
+ ,135.83
+ ,200.78
+ ,135.26
+ ,208.15
+ ,135.99
+ ,209.56
+ ,139.12
+ ,203.33
+ ,137.64
+ ,198.84
+ ,138.59
+ ,200.63
+ ,138.32
+ ,206.47
+ ,135.99
+ ,196.68
+ ,136.96
+ ,203.81
+ ,137.13
+ ,190.18
+ ,138.67
+ ,187.50
+ ,143.04
+ ,187.62
+ ,143.98
+ ,168.92
+ ,144.09
+ ,164.78
+ ,144.97
+ ,175.98
+ ,147.77
+ ,174.70
+ ,149.73
+ ,166.95
+ ,153.11
+ ,161.76
+ ,151.58
+ ,149.65
+ ,149.04
+ ,137.42
+ ,154.70
+ ,142.60
+ ,154.91
+ ,146.94
+ ,159.08
+ ,152.52
+ ,168.01
+ ,147.47
+ ,164.17
+ ,146.15
+ ,163.77
+ ,152.04
+ ,163.49
+ ,144.42
+ ,166.13
+ ,138.15
+ ,166.15
+ ,125.94
+ ,170.05
+ ,112.61
+ ,167.37
+ ,111.48
+ ,164.80
+ ,95.25
+ ,169.53
+ ,105.38
+ ,168.17
+ ,109.59
+ ,172.45
+ ,99.07
+ ,177.81
+ ,92.07
+ ,175.38
+ ,89.10
+ ,175.64
+ ,86.36
+ ,178.80
+ ,95.39
+ ,180.49
+ ,95.27
+ ,182.71
+ ,98.56
+ ,185.73
+ ,101.79
+ ,183.17
+ ,102.02
+ ,182.11
+ ,98.21
+ ,185.43
+ ,104.42
+ ,185.29
+ ,105.62
+ ,188.55
+ ,109.46
+ ,191.89
+ ,110.94
+ ,190.62
+ ,113.09
+ ,190.29
+ ,109.58
+ ,193.27
+ ,111.41
+ ,194.54
+ ,109.83
+ ,195.42
+ ,110.58
+ ,198.58
+ ,109.04
+ ,197.60
+ ,107.80
+ ,194.62
+ ,109.79
+ ,199.30
+ ,110.76
+ ,199.51
+ ,112.64
+ ,203.08
+ ,114.17
+ ,204.36
+ ,115.99
+ ,206.47
+ ,119.01
+ ,206.51
+ ,117.92
+ ,208.09
+ ,115.92
+ ,210.08
+ ,120.75
+ ,212.42
+ ,124.94
+ ,231.32
+ ,129.17
+ ,231.94
+ ,128.14
+ ,228.02
+ ,134.18
+ ,231.95
+ ,131.74
+ ,233.88
+ ,134.32
+ ,235.95
+ ,137.80
+ ,242.92
+ ,141.79
+ ,240.80
+ ,142.75
+ ,240.34
+ ,144.30
+ ,241.95
+ ,145.49
+ ,246.61
+ ,138.21
+ ,247.80
+ ,139.02
+ ,250.97
+ ,141.91
+ ,248.11
+ ,144.95
+ ,243.75
+ ,146.11
+ ,248.79
+ ,150.96
+ ,247.03
+ ,148.20
+ ,250.49
+ ,152.12
+ ,260.83
+ ,154.74
+ ,256.22
+ ,150.80
+ ,255.33
+ ,152.60
+ ,259.54
+ ,158.74
+ ,260.64
+ ,161.83
+ ,262.20
+ ,162.40
+ ,267.29
+ ,156.11
+ ,265.55
+ ,154.93
+ ,258.99
+ ,157.18
+ ,265.04
+ ,159.85
+ ,262.18
+ ,154.40
+ ,265.05
+ ,151.57
+ ,268.78
+ ,133.34
+ ,265.93
+ ,131.20
+ ,261.30
+ ,124.17
+ ,265.20
+ ,133.19
+ ,263.26
+ ,130.94
+ ,265.41
+ ,119.58
+ ,268.75
+ ,118.55
+ ,261.95
+ ,119.96
+ ,258.16
+ ,108.42
+ ,265.22
+ ,95.93
+ ,267.34
+ ,88.83
+ ,269.01
+ ,84.98
+ ,272.90
+ ,81.61
+ ,278.76
+ ,72.84
+ ,278.98
+ ,74.72
+ ,281.03
+ ,83.40
+ ,285.65
+ ,87.42
+ ,287.34
+ ,86.33
+ ,294.57
+ ,94.28
+ ,294.24
+ ,98.81
+ ,295.13
+ ,100.96
+ ,299.65
+ ,99.14
+ ,303.59)
+ ,dim=c(2
+ ,145)
+ ,dimnames=list(c('Y'
+ ,'X')
+ ,1:145))
> y <- array(NA,dim=c(2,145),dimnames=list(c('Y','X'),1:145))
> 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 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 100.00 100.00 1 0 0 0 0 0 0 0 0 0 0 1
2 103.53 102.62 0 1 0 0 0 0 0 0 0 0 0 2
3 108.36 107.62 0 0 1 0 0 0 0 0 0 0 0 3
4 115.20 103.46 0 0 0 1 0 0 0 0 0 0 0 4
5 123.51 103.61 0 0 0 0 1 0 0 0 0 0 0 5
6 132.87 106.10 0 0 0 0 0 1 0 0 0 0 0 6
7 130.55 107.13 0 0 0 0 0 0 1 0 0 0 0 7
8 136.68 108.82 0 0 0 0 0 0 0 1 0 0 0 8
9 140.63 112.93 0 0 0 0 0 0 0 0 1 0 0 9
10 143.47 109.35 0 0 0 0 0 0 0 0 0 1 0 10
11 124.10 108.75 0 0 0 0 0 0 0 0 0 0 1 11
12 111.49 110.83 0 0 0 0 0 0 0 0 0 0 0 12
13 119.93 110.95 1 0 0 0 0 0 0 0 0 0 0 13
14 131.79 114.96 0 1 0 0 0 0 0 0 0 0 0 14
15 136.61 120.45 0 0 1 0 0 0 0 0 0 0 0 15
16 141.79 122.89 0 0 0 1 0 0 0 0 0 0 0 16
17 142.23 120.43 0 0 0 0 1 0 0 0 0 0 0 17
18 146.74 121.76 0 0 0 0 0 1 0 0 0 0 0 18
19 154.85 122.78 0 0 0 0 0 0 1 0 0 0 0 19
20 148.44 125.32 0 0 0 0 0 0 0 1 0 0 0 20
21 154.18 128.68 0 0 0 0 0 0 0 0 1 0 0 21
22 149.10 127.91 0 0 0 0 0 0 0 0 0 1 0 22
23 152.22 125.52 0 0 0 0 0 0 0 0 0 0 1 23
24 149.34 127.56 0 0 0 0 0 0 0 0 0 0 0 24
25 160.94 127.90 1 0 0 0 0 0 0 0 0 0 0 25
26 176.16 130.75 0 1 0 0 0 0 0 0 0 0 0 26
27 195.12 133.57 0 0 1 0 0 0 0 0 0 0 0 27
28 186.07 135.83 0 0 0 1 0 0 0 0 0 0 0 28
29 200.78 135.26 0 0 0 0 1 0 0 0 0 0 0 29
30 208.15 135.99 0 0 0 0 0 1 0 0 0 0 0 30
31 209.56 139.12 0 0 0 0 0 0 1 0 0 0 0 31
32 203.33 137.64 0 0 0 0 0 0 0 1 0 0 0 32
33 198.84 138.59 0 0 0 0 0 0 0 0 1 0 0 33
34 200.63 138.32 0 0 0 0 0 0 0 0 0 1 0 34
35 206.47 135.99 0 0 0 0 0 0 0 0 0 0 1 35
36 196.68 136.96 0 0 0 0 0 0 0 0 0 0 0 36
37 203.81 137.13 1 0 0 0 0 0 0 0 0 0 0 37
38 190.18 138.67 0 1 0 0 0 0 0 0 0 0 0 38
39 187.50 143.04 0 0 1 0 0 0 0 0 0 0 0 39
40 187.62 143.98 0 0 0 1 0 0 0 0 0 0 0 40
41 168.92 144.09 0 0 0 0 1 0 0 0 0 0 0 41
42 164.78 144.97 0 0 0 0 0 1 0 0 0 0 0 42
43 175.98 147.77 0 0 0 0 0 0 1 0 0 0 0 43
44 174.70 149.73 0 0 0 0 0 0 0 1 0 0 0 44
45 166.95 153.11 0 0 0 0 0 0 0 0 1 0 0 45
46 161.76 151.58 0 0 0 0 0 0 0 0 0 1 0 46
47 149.65 149.04 0 0 0 0 0 0 0 0 0 0 1 47
48 137.42 154.70 0 0 0 0 0 0 0 0 0 0 0 48
49 142.60 154.91 1 0 0 0 0 0 0 0 0 0 0 49
50 146.94 159.08 0 1 0 0 0 0 0 0 0 0 0 50
51 152.52 168.01 0 0 1 0 0 0 0 0 0 0 0 51
52 147.47 164.17 0 0 0 1 0 0 0 0 0 0 0 52
53 146.15 163.77 0 0 0 0 1 0 0 0 0 0 0 53
54 152.04 163.49 0 0 0 0 0 1 0 0 0 0 0 54
55 144.42 166.13 0 0 0 0 0 0 1 0 0 0 0 55
56 138.15 166.15 0 0 0 0 0 0 0 1 0 0 0 56
57 125.94 170.05 0 0 0 0 0 0 0 0 1 0 0 57
58 112.61 167.37 0 0 0 0 0 0 0 0 0 1 0 58
59 111.48 164.80 0 0 0 0 0 0 0 0 0 0 1 59
60 95.25 169.53 0 0 0 0 0 0 0 0 0 0 0 60
61 105.38 168.17 1 0 0 0 0 0 0 0 0 0 0 61
62 109.59 172.45 0 1 0 0 0 0 0 0 0 0 0 62
63 99.07 177.81 0 0 1 0 0 0 0 0 0 0 0 63
64 92.07 175.38 0 0 0 1 0 0 0 0 0 0 0 64
65 89.10 175.64 0 0 0 0 1 0 0 0 0 0 0 65
66 86.36 178.80 0 0 0 0 0 1 0 0 0 0 0 66
67 95.39 180.49 0 0 0 0 0 0 1 0 0 0 0 67
68 95.27 182.71 0 0 0 0 0 0 0 1 0 0 0 68
69 98.56 185.73 0 0 0 0 0 0 0 0 1 0 0 69
70 101.79 183.17 0 0 0 0 0 0 0 0 0 1 0 70
71 102.02 182.11 0 0 0 0 0 0 0 0 0 0 1 71
72 98.21 185.43 0 0 0 0 0 0 0 0 0 0 0 72
73 104.42 185.29 1 0 0 0 0 0 0 0 0 0 0 73
74 105.62 188.55 0 1 0 0 0 0 0 0 0 0 0 74
75 109.46 191.89 0 0 1 0 0 0 0 0 0 0 0 75
76 110.94 190.62 0 0 0 1 0 0 0 0 0 0 0 76
77 113.09 190.29 0 0 0 0 1 0 0 0 0 0 0 77
78 109.58 193.27 0 0 0 0 0 1 0 0 0 0 0 78
79 111.41 194.54 0 0 0 0 0 0 1 0 0 0 0 79
80 109.83 195.42 0 0 0 0 0 0 0 1 0 0 0 80
81 110.58 198.58 0 0 0 0 0 0 0 0 1 0 0 81
82 109.04 197.60 0 0 0 0 0 0 0 0 0 1 0 82
83 107.80 194.62 0 0 0 0 0 0 0 0 0 0 1 83
84 109.79 199.30 0 0 0 0 0 0 0 0 0 0 0 84
85 110.76 199.51 1 0 0 0 0 0 0 0 0 0 0 85
86 112.64 203.08 0 1 0 0 0 0 0 0 0 0 0 86
87 114.17 204.36 0 0 1 0 0 0 0 0 0 0 0 87
88 115.99 206.47 0 0 0 1 0 0 0 0 0 0 0 88
89 119.01 206.51 0 0 0 0 1 0 0 0 0 0 0 89
90 117.92 208.09 0 0 0 0 0 1 0 0 0 0 0 90
91 115.92 210.08 0 0 0 0 0 0 1 0 0 0 0 91
92 120.75 212.42 0 0 0 0 0 0 0 1 0 0 0 92
93 124.94 231.32 0 0 0 0 0 0 0 0 1 0 0 93
94 129.17 231.94 0 0 0 0 0 0 0 0 0 1 0 94
95 128.14 228.02 0 0 0 0 0 0 0 0 0 0 1 95
96 134.18 231.95 0 0 0 0 0 0 0 0 0 0 0 96
97 131.74 233.88 1 0 0 0 0 0 0 0 0 0 0 97
98 134.32 235.95 0 1 0 0 0 0 0 0 0 0 0 98
99 137.80 242.92 0 0 1 0 0 0 0 0 0 0 0 99
100 141.79 240.80 0 0 0 1 0 0 0 0 0 0 0 100
101 142.75 240.34 0 0 0 0 1 0 0 0 0 0 0 101
102 144.30 241.95 0 0 0 0 0 1 0 0 0 0 0 102
103 145.49 246.61 0 0 0 0 0 0 1 0 0 0 0 103
104 138.21 247.80 0 0 0 0 0 0 0 1 0 0 0 104
105 139.02 250.97 0 0 0 0 0 0 0 0 1 0 0 105
106 141.91 248.11 0 0 0 0 0 0 0 0 0 1 0 106
107 144.95 243.75 0 0 0 0 0 0 0 0 0 0 1 107
108 146.11 248.79 0 0 0 0 0 0 0 0 0 0 0 108
109 150.96 247.03 1 0 0 0 0 0 0 0 0 0 0 109
110 148.20 250.49 0 1 0 0 0 0 0 0 0 0 0 110
111 152.12 260.83 0 0 1 0 0 0 0 0 0 0 0 111
112 154.74 256.22 0 0 0 1 0 0 0 0 0 0 0 112
113 150.80 255.33 0 0 0 0 1 0 0 0 0 0 0 113
114 152.60 259.54 0 0 0 0 0 1 0 0 0 0 0 114
115 158.74 260.64 0 0 0 0 0 0 1 0 0 0 0 115
116 161.83 262.20 0 0 0 0 0 0 0 1 0 0 0 116
117 162.40 267.29 0 0 0 0 0 0 0 0 1 0 0 117
118 156.11 265.55 0 0 0 0 0 0 0 0 0 1 0 118
119 154.93 258.99 0 0 0 0 0 0 0 0 0 0 1 119
120 157.18 265.04 0 0 0 0 0 0 0 0 0 0 0 120
121 159.85 262.18 1 0 0 0 0 0 0 0 0 0 0 121
122 154.40 265.05 0 1 0 0 0 0 0 0 0 0 0 122
123 151.57 268.78 0 0 1 0 0 0 0 0 0 0 0 123
124 133.34 265.93 0 0 0 1 0 0 0 0 0 0 0 124
125 131.20 261.30 0 0 0 0 1 0 0 0 0 0 0 125
126 124.17 265.20 0 0 0 0 0 1 0 0 0 0 0 126
127 133.19 263.26 0 0 0 0 0 0 1 0 0 0 0 127
128 130.94 265.41 0 0 0 0 0 0 0 1 0 0 0 128
129 119.58 268.75 0 0 0 0 0 0 0 0 1 0 0 129
130 118.55 261.95 0 0 0 0 0 0 0 0 0 1 0 130
131 119.96 258.16 0 0 0 0 0 0 0 0 0 0 1 131
132 108.42 265.22 0 0 0 0 0 0 0 0 0 0 0 132
133 95.93 267.34 1 0 0 0 0 0 0 0 0 0 0 133
134 88.83 269.01 0 1 0 0 0 0 0 0 0 0 0 134
135 84.98 272.90 0 0 1 0 0 0 0 0 0 0 0 135
136 81.61 278.76 0 0 0 1 0 0 0 0 0 0 0 136
137 72.84 278.98 0 0 0 0 1 0 0 0 0 0 0 137
138 74.72 281.03 0 0 0 0 0 1 0 0 0 0 0 138
139 83.40 285.65 0 0 0 0 0 0 1 0 0 0 0 139
140 87.42 287.34 0 0 0 0 0 0 0 1 0 0 0 140
141 86.33 294.57 0 0 0 0 0 0 0 0 1 0 0 141
142 94.28 294.24 0 0 0 0 0 0 0 0 0 1 0 142
143 98.81 295.13 0 0 0 0 0 0 0 0 0 0 1 143
144 100.96 299.65 0 0 0 0 0 0 0 0 0 0 0 144
145 99.14 303.59 1 0 0 0 0 0 0 0 0 0 0 145
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
32.6921 1.2898 0.1449 1.7366 -0.5505 0.6218
M5 M6 M7 M8 M9 M10
2.9661 3.5406 6.7511 5.9059 0.1008 3.8991
M11 t
7.9391 -2.0687
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-59.750 -19.658 -1.705 17.104 70.644
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.6921 35.6211 0.918 0.360424
X 1.2898 0.3745 3.444 0.000770 ***
M1 0.1449 11.6640 0.012 0.990108
M2 1.7366 11.9039 0.146 0.884237
M3 -0.5505 11.9899 -0.046 0.963451
M4 0.6218 11.9192 0.052 0.958472
M5 2.9661 11.8970 0.249 0.803505
M6 3.5406 11.8957 0.298 0.766452
M7 6.7511 11.8993 0.567 0.571445
M8 5.9059 11.8982 0.496 0.620466
M9 0.1008 12.0161 0.008 0.993318
M10 3.8991 11.8999 0.328 0.743691
M11 7.9391 11.9356 0.665 0.507117
t -2.0687 0.5215 -3.967 0.000119 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 29.12 on 131 degrees of freedom
Multiple R-squared: 0.2205, Adjusted R-squared: 0.1431
F-statistic: 2.85 on 13 and 131 DF, p-value: 0.001222
> 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,] 4.666094e-03 9.332188e-03 9.953339e-01
[2,] 2.350127e-03 4.700255e-03 9.976499e-01
[3,] 3.975032e-04 7.950063e-04 9.996025e-01
[4,] 2.193737e-04 4.387473e-04 9.997806e-01
[5,] 6.851592e-05 1.370318e-04 9.999315e-01
[6,] 4.541543e-05 9.083087e-05 9.999546e-01
[7,] 2.271132e-05 4.542263e-05 9.999773e-01
[8,] 4.548538e-05 9.097076e-05 9.999545e-01
[9,] 6.293020e-05 1.258604e-04 9.999371e-01
[10,] 1.108236e-04 2.216473e-04 9.998892e-01
[11,] 2.802151e-04 5.604302e-04 9.997198e-01
[12,] 1.680255e-04 3.360510e-04 9.998320e-01
[13,] 2.615432e-04 5.230864e-04 9.997385e-01
[14,] 2.469207e-04 4.938415e-04 9.997531e-01
[15,] 2.624308e-04 5.248616e-04 9.997376e-01
[16,] 1.293097e-04 2.586194e-04 9.998707e-01
[17,] 8.721869e-05 1.744374e-04 9.999128e-01
[18,] 4.693674e-05 9.387347e-05 9.999531e-01
[19,] 3.444596e-05 6.889192e-05 9.999656e-01
[20,] 2.349151e-05 4.698303e-05 9.999765e-01
[21,] 2.001782e-05 4.003564e-05 9.999800e-01
[22,] 9.220433e-05 1.844087e-04 9.999078e-01
[23,] 5.455184e-04 1.091037e-03 9.994545e-01
[24,] 1.494414e-03 2.988829e-03 9.985056e-01
[25,] 1.714861e-02 3.429722e-02 9.828514e-01
[26,] 9.592137e-02 1.918427e-01 9.040786e-01
[27,] 1.912589e-01 3.825178e-01 8.087411e-01
[28,] 3.066501e-01 6.133002e-01 6.933499e-01
[29,] 5.004591e-01 9.990818e-01 4.995409e-01
[30,] 6.823488e-01 6.353025e-01 3.176512e-01
[31,] 8.153050e-01 3.693900e-01 1.846950e-01
[32,] 9.151589e-01 1.696821e-01 8.484105e-02
[33,] 9.550603e-01 8.987949e-02 4.493975e-02
[34,] 9.712068e-01 5.758637e-02 2.879319e-02
[35,] 9.758854e-01 4.822913e-02 2.411456e-02
[36,] 9.818353e-01 3.632938e-02 1.816469e-02
[37,] 9.854702e-01 2.905952e-02 1.452976e-02
[38,] 9.904356e-01 1.912890e-02 9.564449e-03
[39,] 9.935293e-01 1.294143e-02 6.470714e-03
[40,] 9.959535e-01 8.093008e-03 4.046504e-03
[41,] 9.976423e-01 4.715490e-03 2.357745e-03
[42,] 9.989814e-01 2.037219e-03 1.018610e-03
[43,] 9.994342e-01 1.131548e-03 5.657738e-04
[44,] 9.996846e-01 6.308040e-04 3.154020e-04
[45,] 9.997556e-01 4.887563e-04 2.443782e-04
[46,] 9.997710e-01 4.580420e-04 2.290210e-04
[47,] 9.998686e-01 2.627243e-04 1.313622e-04
[48,] 9.999460e-01 1.080592e-04 5.402961e-05
[49,] 9.999775e-01 4.502519e-05 2.251259e-05
[50,] 9.999903e-01 1.934895e-05 9.674477e-06
[51,] 9.999940e-01 1.190035e-05 5.950176e-06
[52,] 9.999958e-01 8.471105e-06 4.235553e-06
[53,] 9.999952e-01 9.605330e-06 4.802665e-06
[54,] 9.999939e-01 1.224760e-05 6.123801e-06
[55,] 9.999926e-01 1.481107e-05 7.405535e-06
[56,] 9.999911e-01 1.781910e-05 8.909549e-06
[57,] 9.999867e-01 2.662676e-05 1.331338e-05
[58,] 9.999827e-01 3.467993e-05 1.733996e-05
[59,] 9.999752e-01 4.967374e-05 2.483687e-05
[60,] 9.999612e-01 7.766052e-05 3.883026e-05
[61,] 9.999371e-01 1.257997e-04 6.289983e-05
[62,] 9.999034e-01 1.932873e-04 9.664363e-05
[63,] 9.998606e-01 2.787680e-04 1.393840e-04
[64,] 9.998050e-01 3.899036e-04 1.949518e-04
[65,] 9.996905e-01 6.189184e-04 3.094592e-04
[66,] 9.995384e-01 9.231612e-04 4.615806e-04
[67,] 9.993759e-01 1.248274e-03 6.241368e-04
[68,] 9.991483e-01 1.703491e-03 8.517456e-04
[69,] 9.988175e-01 2.364903e-03 1.182451e-03
[70,] 9.984884e-01 3.023172e-03 1.511586e-03
[71,] 9.978542e-01 4.291640e-03 2.145820e-03
[72,] 9.969844e-01 6.031141e-03 3.015571e-03
[73,] 9.957252e-01 8.549607e-03 4.274803e-03
[74,] 9.939743e-01 1.205135e-02 6.025677e-03
[75,] 9.927909e-01 1.441811e-02 7.209055e-03
[76,] 9.917164e-01 1.656727e-02 8.283633e-03
[77,] 9.957909e-01 8.418284e-03 4.209142e-03
[78,] 9.972424e-01 5.515195e-03 2.757597e-03
[79,] 9.983023e-01 3.395431e-03 1.697716e-03
[80,] 9.987915e-01 2.417042e-03 1.208521e-03
[81,] 9.992803e-01 1.439479e-03 7.197395e-04
[82,] 9.994752e-01 1.049508e-03 5.247541e-04
[83,] 9.995887e-01 8.225778e-04 4.112889e-04
[84,] 9.995388e-01 9.223792e-04 4.611896e-04
[85,] 9.994101e-01 1.179834e-03 5.899172e-04
[86,] 9.991872e-01 1.625633e-03 8.128164e-04
[87,] 9.991925e-01 1.615022e-03 8.075110e-04
[88,] 9.995631e-01 8.737738e-04 4.368869e-04
[89,] 9.997466e-01 5.067265e-04 2.533633e-04
[90,] 9.998780e-01 2.440509e-04 1.220254e-04
[91,] 9.999585e-01 8.305164e-05 4.152582e-05
[92,] 9.999901e-01 1.979516e-05 9.897582e-06
[93,] 9.999964e-01 7.112762e-06 3.556381e-06
[94,] 9.999981e-01 3.873436e-06 1.936718e-06
[95,] 9.999991e-01 1.860149e-06 9.300746e-07
[96,] 9.999980e-01 3.921139e-06 1.960570e-06
[97,] 9.999959e-01 8.120194e-06 4.060097e-06
[98,] 9.999905e-01 1.899522e-05 9.497612e-06
[99,] 9.999825e-01 3.503930e-05 1.751965e-05
[100,] 9.999666e-01 6.683665e-05 3.341832e-05
[101,] 9.999176e-01 1.648457e-04 8.242285e-05
[102,] 9.999319e-01 1.362069e-04 6.810343e-05
[103,] 9.999746e-01 5.076014e-05 2.538007e-05
[104,] 9.999968e-01 6.436043e-06 3.218021e-06
[105,] 9.999948e-01 1.034304e-05 5.171519e-06
[106,] 9.999814e-01 3.724081e-05 1.862041e-05
[107,] 9.999455e-01 1.090173e-04 5.450864e-05
[108,] 9.998638e-01 2.723105e-04 1.361552e-04
[109,] 9.994400e-01 1.119990e-03 5.599951e-04
[110,] 9.982434e-01 3.513181e-03 1.756591e-03
[111,] 9.931411e-01 1.371772e-02 6.858861e-03
[112,] 9.700804e-01 5.983915e-02 2.991958e-02
> postscript(file="/var/www/html/rcomp/tmp/1m0yd1260702743.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2bkw81260702743.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3pk7i1260702743.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/47jvs1260702743.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5tl7j1260702743.ps",horizontal=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 = 145
Frequency = 1
1 2 3 4 5 6
-59.7497825 -59.1220647 -56.3853548 -43.2832986 -35.4423325 -27.7997183
7 8 9 10 11 12
-32.5900231 -25.7258688 -19.2031859 -13.4752199 -34.0425756 -39.3275633
13 14 15 16 17 18
-29.1185042 -21.9536290 -19.8589284 -16.9296498 -13.5922671 -9.3034677
19 20 21 22 23 24
-3.6508744 -10.4230626 -1.1430187 -6.9594322 -2.7280194 1.7685855
25 26 27 28 29 30
14.8538852 26.8749456 46.5534517 35.4848970 50.6545297 58.5772180
31 32 33 34 35 36
54.8083021 53.4011693 55.5596669 55.9683460 62.8423699 61.8090766
37 38 39 40 41 42
70.6436449 55.5043627 51.5436559 51.3476567 32.2302153 28.4494313
43 44 45 46 47 48
34.8961544 34.0020587 29.7663063 24.8201520 14.0150371 4.4925124
49 50 51 52 53 54
11.3254881 10.7639929 9.1817307 9.9810462 8.9014104 16.6468116
55 56 57 58 59 60
4.4799050 1.0980500 -8.2684060 -19.8712733 -19.6576939 -31.9806908
61 62 63 64 65 66
-18.1727058 -19.0060806 -32.0837041 -35.0530274 -38.6339410 -43.9555027
67 68 69 70 71 72
-38.2470853 -38.3165328 -31.0479518 -26.2455969 -26.6196377 -24.7039958
73 74 75 76 77 78
-16.3895849 -18.9173486 -15.0295462 -11.0150547 -8.7149775 -14.5743725
79 80 81 82 83 84
-15.5242329 -15.3253286 -10.7773217 -12.7828741 -12.1504706 -6.1889767
85 86 87 88 89 90
-3.5660010 -5.8136073 -1.5787864 -1.5838689 1.1089768 -0.5246776
91 92 93 94 95 96
-6.2332046 -1.5074299 -13.8211078 -12.1203639 -10.0655345 0.9133204
97 98 99 100 101 102
-2.0921853 -1.7050694 -2.8592947 4.7615394 6.0392925 7.0069437
103 104 105 106 107 108
1.0446112 -4.8563271 -0.2612183 4.5880811 11.2804290 15.9475895
109 110 111 112 113 114
24.9915004 18.2457737 13.1848726 22.6473455 19.5797190 17.4438517
115 116 117 118 119 120
21.0232599 25.0150901 26.8937545 21.1184613 26.4284018 30.8828494
121 122 123 124 125 126
39.1655565 30.4908205 27.2055952 13.5479941 17.1042749 6.5382502
127 128 129 130 131 132
16.9186953 14.8095349 7.0153751 13.0265448 17.3536983 6.7154329
133 134 135 136 137 138
-6.5851376 -15.3620958 -19.8736915 -29.9055795 -39.2349005 -38.5047678
139 140 141 142 143 144
-36.9255077 -32.1713533 -34.7128926 -28.0668247 -26.6560044 -20.3281402
145
-25.3061737
> postscript(file="/var/www/html/rcomp/tmp/6i6c01260702744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 145
Frequency = 1
lag(myerror, k = 1) myerror
0 -59.7497825 NA
1 -59.1220647 -59.7497825
2 -56.3853548 -59.1220647
3 -43.2832986 -56.3853548
4 -35.4423325 -43.2832986
5 -27.7997183 -35.4423325
6 -32.5900231 -27.7997183
7 -25.7258688 -32.5900231
8 -19.2031859 -25.7258688
9 -13.4752199 -19.2031859
10 -34.0425756 -13.4752199
11 -39.3275633 -34.0425756
12 -29.1185042 -39.3275633
13 -21.9536290 -29.1185042
14 -19.8589284 -21.9536290
15 -16.9296498 -19.8589284
16 -13.5922671 -16.9296498
17 -9.3034677 -13.5922671
18 -3.6508744 -9.3034677
19 -10.4230626 -3.6508744
20 -1.1430187 -10.4230626
21 -6.9594322 -1.1430187
22 -2.7280194 -6.9594322
23 1.7685855 -2.7280194
24 14.8538852 1.7685855
25 26.8749456 14.8538852
26 46.5534517 26.8749456
27 35.4848970 46.5534517
28 50.6545297 35.4848970
29 58.5772180 50.6545297
30 54.8083021 58.5772180
31 53.4011693 54.8083021
32 55.5596669 53.4011693
33 55.9683460 55.5596669
34 62.8423699 55.9683460
35 61.8090766 62.8423699
36 70.6436449 61.8090766
37 55.5043627 70.6436449
38 51.5436559 55.5043627
39 51.3476567 51.5436559
40 32.2302153 51.3476567
41 28.4494313 32.2302153
42 34.8961544 28.4494313
43 34.0020587 34.8961544
44 29.7663063 34.0020587
45 24.8201520 29.7663063
46 14.0150371 24.8201520
47 4.4925124 14.0150371
48 11.3254881 4.4925124
49 10.7639929 11.3254881
50 9.1817307 10.7639929
51 9.9810462 9.1817307
52 8.9014104 9.9810462
53 16.6468116 8.9014104
54 4.4799050 16.6468116
55 1.0980500 4.4799050
56 -8.2684060 1.0980500
57 -19.8712733 -8.2684060
58 -19.6576939 -19.8712733
59 -31.9806908 -19.6576939
60 -18.1727058 -31.9806908
61 -19.0060806 -18.1727058
62 -32.0837041 -19.0060806
63 -35.0530274 -32.0837041
64 -38.6339410 -35.0530274
65 -43.9555027 -38.6339410
66 -38.2470853 -43.9555027
67 -38.3165328 -38.2470853
68 -31.0479518 -38.3165328
69 -26.2455969 -31.0479518
70 -26.6196377 -26.2455969
71 -24.7039958 -26.6196377
72 -16.3895849 -24.7039958
73 -18.9173486 -16.3895849
74 -15.0295462 -18.9173486
75 -11.0150547 -15.0295462
76 -8.7149775 -11.0150547
77 -14.5743725 -8.7149775
78 -15.5242329 -14.5743725
79 -15.3253286 -15.5242329
80 -10.7773217 -15.3253286
81 -12.7828741 -10.7773217
82 -12.1504706 -12.7828741
83 -6.1889767 -12.1504706
84 -3.5660010 -6.1889767
85 -5.8136073 -3.5660010
86 -1.5787864 -5.8136073
87 -1.5838689 -1.5787864
88 1.1089768 -1.5838689
89 -0.5246776 1.1089768
90 -6.2332046 -0.5246776
91 -1.5074299 -6.2332046
92 -13.8211078 -1.5074299
93 -12.1203639 -13.8211078
94 -10.0655345 -12.1203639
95 0.9133204 -10.0655345
96 -2.0921853 0.9133204
97 -1.7050694 -2.0921853
98 -2.8592947 -1.7050694
99 4.7615394 -2.8592947
100 6.0392925 4.7615394
101 7.0069437 6.0392925
102 1.0446112 7.0069437
103 -4.8563271 1.0446112
104 -0.2612183 -4.8563271
105 4.5880811 -0.2612183
106 11.2804290 4.5880811
107 15.9475895 11.2804290
108 24.9915004 15.9475895
109 18.2457737 24.9915004
110 13.1848726 18.2457737
111 22.6473455 13.1848726
112 19.5797190 22.6473455
113 17.4438517 19.5797190
114 21.0232599 17.4438517
115 25.0150901 21.0232599
116 26.8937545 25.0150901
117 21.1184613 26.8937545
118 26.4284018 21.1184613
119 30.8828494 26.4284018
120 39.1655565 30.8828494
121 30.4908205 39.1655565
122 27.2055952 30.4908205
123 13.5479941 27.2055952
124 17.1042749 13.5479941
125 6.5382502 17.1042749
126 16.9186953 6.5382502
127 14.8095349 16.9186953
128 7.0153751 14.8095349
129 13.0265448 7.0153751
130 17.3536983 13.0265448
131 6.7154329 17.3536983
132 -6.5851376 6.7154329
133 -15.3620958 -6.5851376
134 -19.8736915 -15.3620958
135 -29.9055795 -19.8736915
136 -39.2349005 -29.9055795
137 -38.5047678 -39.2349005
138 -36.9255077 -38.5047678
139 -32.1713533 -36.9255077
140 -34.7128926 -32.1713533
141 -28.0668247 -34.7128926
142 -26.6560044 -28.0668247
143 -20.3281402 -26.6560044
144 -25.3061737 -20.3281402
145 NA -25.3061737
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -59.1220647 -59.7497825
[2,] -56.3853548 -59.1220647
[3,] -43.2832986 -56.3853548
[4,] -35.4423325 -43.2832986
[5,] -27.7997183 -35.4423325
[6,] -32.5900231 -27.7997183
[7,] -25.7258688 -32.5900231
[8,] -19.2031859 -25.7258688
[9,] -13.4752199 -19.2031859
[10,] -34.0425756 -13.4752199
[11,] -39.3275633 -34.0425756
[12,] -29.1185042 -39.3275633
[13,] -21.9536290 -29.1185042
[14,] -19.8589284 -21.9536290
[15,] -16.9296498 -19.8589284
[16,] -13.5922671 -16.9296498
[17,] -9.3034677 -13.5922671
[18,] -3.6508744 -9.3034677
[19,] -10.4230626 -3.6508744
[20,] -1.1430187 -10.4230626
[21,] -6.9594322 -1.1430187
[22,] -2.7280194 -6.9594322
[23,] 1.7685855 -2.7280194
[24,] 14.8538852 1.7685855
[25,] 26.8749456 14.8538852
[26,] 46.5534517 26.8749456
[27,] 35.4848970 46.5534517
[28,] 50.6545297 35.4848970
[29,] 58.5772180 50.6545297
[30,] 54.8083021 58.5772180
[31,] 53.4011693 54.8083021
[32,] 55.5596669 53.4011693
[33,] 55.9683460 55.5596669
[34,] 62.8423699 55.9683460
[35,] 61.8090766 62.8423699
[36,] 70.6436449 61.8090766
[37,] 55.5043627 70.6436449
[38,] 51.5436559 55.5043627
[39,] 51.3476567 51.5436559
[40,] 32.2302153 51.3476567
[41,] 28.4494313 32.2302153
[42,] 34.8961544 28.4494313
[43,] 34.0020587 34.8961544
[44,] 29.7663063 34.0020587
[45,] 24.8201520 29.7663063
[46,] 14.0150371 24.8201520
[47,] 4.4925124 14.0150371
[48,] 11.3254881 4.4925124
[49,] 10.7639929 11.3254881
[50,] 9.1817307 10.7639929
[51,] 9.9810462 9.1817307
[52,] 8.9014104 9.9810462
[53,] 16.6468116 8.9014104
[54,] 4.4799050 16.6468116
[55,] 1.0980500 4.4799050
[56,] -8.2684060 1.0980500
[57,] -19.8712733 -8.2684060
[58,] -19.6576939 -19.8712733
[59,] -31.9806908 -19.6576939
[60,] -18.1727058 -31.9806908
[61,] -19.0060806 -18.1727058
[62,] -32.0837041 -19.0060806
[63,] -35.0530274 -32.0837041
[64,] -38.6339410 -35.0530274
[65,] -43.9555027 -38.6339410
[66,] -38.2470853 -43.9555027
[67,] -38.3165328 -38.2470853
[68,] -31.0479518 -38.3165328
[69,] -26.2455969 -31.0479518
[70,] -26.6196377 -26.2455969
[71,] -24.7039958 -26.6196377
[72,] -16.3895849 -24.7039958
[73,] -18.9173486 -16.3895849
[74,] -15.0295462 -18.9173486
[75,] -11.0150547 -15.0295462
[76,] -8.7149775 -11.0150547
[77,] -14.5743725 -8.7149775
[78,] -15.5242329 -14.5743725
[79,] -15.3253286 -15.5242329
[80,] -10.7773217 -15.3253286
[81,] -12.7828741 -10.7773217
[82,] -12.1504706 -12.7828741
[83,] -6.1889767 -12.1504706
[84,] -3.5660010 -6.1889767
[85,] -5.8136073 -3.5660010
[86,] -1.5787864 -5.8136073
[87,] -1.5838689 -1.5787864
[88,] 1.1089768 -1.5838689
[89,] -0.5246776 1.1089768
[90,] -6.2332046 -0.5246776
[91,] -1.5074299 -6.2332046
[92,] -13.8211078 -1.5074299
[93,] -12.1203639 -13.8211078
[94,] -10.0655345 -12.1203639
[95,] 0.9133204 -10.0655345
[96,] -2.0921853 0.9133204
[97,] -1.7050694 -2.0921853
[98,] -2.8592947 -1.7050694
[99,] 4.7615394 -2.8592947
[100,] 6.0392925 4.7615394
[101,] 7.0069437 6.0392925
[102,] 1.0446112 7.0069437
[103,] -4.8563271 1.0446112
[104,] -0.2612183 -4.8563271
[105,] 4.5880811 -0.2612183
[106,] 11.2804290 4.5880811
[107,] 15.9475895 11.2804290
[108,] 24.9915004 15.9475895
[109,] 18.2457737 24.9915004
[110,] 13.1848726 18.2457737
[111,] 22.6473455 13.1848726
[112,] 19.5797190 22.6473455
[113,] 17.4438517 19.5797190
[114,] 21.0232599 17.4438517
[115,] 25.0150901 21.0232599
[116,] 26.8937545 25.0150901
[117,] 21.1184613 26.8937545
[118,] 26.4284018 21.1184613
[119,] 30.8828494 26.4284018
[120,] 39.1655565 30.8828494
[121,] 30.4908205 39.1655565
[122,] 27.2055952 30.4908205
[123,] 13.5479941 27.2055952
[124,] 17.1042749 13.5479941
[125,] 6.5382502 17.1042749
[126,] 16.9186953 6.5382502
[127,] 14.8095349 16.9186953
[128,] 7.0153751 14.8095349
[129,] 13.0265448 7.0153751
[130,] 17.3536983 13.0265448
[131,] 6.7154329 17.3536983
[132,] -6.5851376 6.7154329
[133,] -15.3620958 -6.5851376
[134,] -19.8736915 -15.3620958
[135,] -29.9055795 -19.8736915
[136,] -39.2349005 -29.9055795
[137,] -38.5047678 -39.2349005
[138,] -36.9255077 -38.5047678
[139,] -32.1713533 -36.9255077
[140,] -34.7128926 -32.1713533
[141,] -28.0668247 -34.7128926
[142,] -26.6560044 -28.0668247
[143,] -20.3281402 -26.6560044
[144,] -25.3061737 -20.3281402
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -59.1220647 -59.7497825
2 -56.3853548 -59.1220647
3 -43.2832986 -56.3853548
4 -35.4423325 -43.2832986
5 -27.7997183 -35.4423325
6 -32.5900231 -27.7997183
7 -25.7258688 -32.5900231
8 -19.2031859 -25.7258688
9 -13.4752199 -19.2031859
10 -34.0425756 -13.4752199
11 -39.3275633 -34.0425756
12 -29.1185042 -39.3275633
13 -21.9536290 -29.1185042
14 -19.8589284 -21.9536290
15 -16.9296498 -19.8589284
16 -13.5922671 -16.9296498
17 -9.3034677 -13.5922671
18 -3.6508744 -9.3034677
19 -10.4230626 -3.6508744
20 -1.1430187 -10.4230626
21 -6.9594322 -1.1430187
22 -2.7280194 -6.9594322
23 1.7685855 -2.7280194
24 14.8538852 1.7685855
25 26.8749456 14.8538852
26 46.5534517 26.8749456
27 35.4848970 46.5534517
28 50.6545297 35.4848970
29 58.5772180 50.6545297
30 54.8083021 58.5772180
31 53.4011693 54.8083021
32 55.5596669 53.4011693
33 55.9683460 55.5596669
34 62.8423699 55.9683460
35 61.8090766 62.8423699
36 70.6436449 61.8090766
37 55.5043627 70.6436449
38 51.5436559 55.5043627
39 51.3476567 51.5436559
40 32.2302153 51.3476567
41 28.4494313 32.2302153
42 34.8961544 28.4494313
43 34.0020587 34.8961544
44 29.7663063 34.0020587
45 24.8201520 29.7663063
46 14.0150371 24.8201520
47 4.4925124 14.0150371
48 11.3254881 4.4925124
49 10.7639929 11.3254881
50 9.1817307 10.7639929
51 9.9810462 9.1817307
52 8.9014104 9.9810462
53 16.6468116 8.9014104
54 4.4799050 16.6468116
55 1.0980500 4.4799050
56 -8.2684060 1.0980500
57 -19.8712733 -8.2684060
58 -19.6576939 -19.8712733
59 -31.9806908 -19.6576939
60 -18.1727058 -31.9806908
61 -19.0060806 -18.1727058
62 -32.0837041 -19.0060806
63 -35.0530274 -32.0837041
64 -38.6339410 -35.0530274
65 -43.9555027 -38.6339410
66 -38.2470853 -43.9555027
67 -38.3165328 -38.2470853
68 -31.0479518 -38.3165328
69 -26.2455969 -31.0479518
70 -26.6196377 -26.2455969
71 -24.7039958 -26.6196377
72 -16.3895849 -24.7039958
73 -18.9173486 -16.3895849
74 -15.0295462 -18.9173486
75 -11.0150547 -15.0295462
76 -8.7149775 -11.0150547
77 -14.5743725 -8.7149775
78 -15.5242329 -14.5743725
79 -15.3253286 -15.5242329
80 -10.7773217 -15.3253286
81 -12.7828741 -10.7773217
82 -12.1504706 -12.7828741
83 -6.1889767 -12.1504706
84 -3.5660010 -6.1889767
85 -5.8136073 -3.5660010
86 -1.5787864 -5.8136073
87 -1.5838689 -1.5787864
88 1.1089768 -1.5838689
89 -0.5246776 1.1089768
90 -6.2332046 -0.5246776
91 -1.5074299 -6.2332046
92 -13.8211078 -1.5074299
93 -12.1203639 -13.8211078
94 -10.0655345 -12.1203639
95 0.9133204 -10.0655345
96 -2.0921853 0.9133204
97 -1.7050694 -2.0921853
98 -2.8592947 -1.7050694
99 4.7615394 -2.8592947
100 6.0392925 4.7615394
101 7.0069437 6.0392925
102 1.0446112 7.0069437
103 -4.8563271 1.0446112
104 -0.2612183 -4.8563271
105 4.5880811 -0.2612183
106 11.2804290 4.5880811
107 15.9475895 11.2804290
108 24.9915004 15.9475895
109 18.2457737 24.9915004
110 13.1848726 18.2457737
111 22.6473455 13.1848726
112 19.5797190 22.6473455
113 17.4438517 19.5797190
114 21.0232599 17.4438517
115 25.0150901 21.0232599
116 26.8937545 25.0150901
117 21.1184613 26.8937545
118 26.4284018 21.1184613
119 30.8828494 26.4284018
120 39.1655565 30.8828494
121 30.4908205 39.1655565
122 27.2055952 30.4908205
123 13.5479941 27.2055952
124 17.1042749 13.5479941
125 6.5382502 17.1042749
126 16.9186953 6.5382502
127 14.8095349 16.9186953
128 7.0153751 14.8095349
129 13.0265448 7.0153751
130 17.3536983 13.0265448
131 6.7154329 17.3536983
132 -6.5851376 6.7154329
133 -15.3620958 -6.5851376
134 -19.8736915 -15.3620958
135 -29.9055795 -19.8736915
136 -39.2349005 -29.9055795
137 -38.5047678 -39.2349005
138 -36.9255077 -38.5047678
139 -32.1713533 -36.9255077
140 -34.7128926 -32.1713533
141 -28.0668247 -34.7128926
142 -26.6560044 -28.0668247
143 -20.3281402 -26.6560044
144 -25.3061737 -20.3281402
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7hh1b1260702744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8eg6s1260702744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9w5951260702744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10106t1260702744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11o8cq1260702744.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12xcj91260702744.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/131x0j1260702744.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14pocf1260702744.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15xnq51260702744.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16ufur1260702744.tab")
+ }
>
> try(system("convert tmp/1m0yd1260702743.ps tmp/1m0yd1260702743.png",intern=TRUE))
character(0)
> try(system("convert tmp/2bkw81260702743.ps tmp/2bkw81260702743.png",intern=TRUE))
character(0)
> try(system("convert tmp/3pk7i1260702743.ps tmp/3pk7i1260702743.png",intern=TRUE))
character(0)
> try(system("convert tmp/47jvs1260702743.ps tmp/47jvs1260702743.png",intern=TRUE))
character(0)
> try(system("convert tmp/5tl7j1260702743.ps tmp/5tl7j1260702743.png",intern=TRUE))
character(0)
> try(system("convert tmp/6i6c01260702744.ps tmp/6i6c01260702744.png",intern=TRUE))
character(0)
> try(system("convert tmp/7hh1b1260702744.ps tmp/7hh1b1260702744.png",intern=TRUE))
character(0)
> try(system("convert tmp/8eg6s1260702744.ps tmp/8eg6s1260702744.png",intern=TRUE))
character(0)
> try(system("convert tmp/9w5951260702744.ps tmp/9w5951260702744.png",intern=TRUE))
character(0)
> try(system("convert tmp/10106t1260702744.ps tmp/10106t1260702744.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.610 1.752 4.765