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(100.36
+ ,0
+ ,100.21
+ ,100.62
+ ,0
+ ,100.36
+ ,100.78
+ ,0
+ ,100.62
+ ,100.93
+ ,0
+ ,100.78
+ ,100.70
+ ,0
+ ,100.93
+ ,100.00
+ ,0
+ ,100.70
+ ,100.20
+ ,0
+ ,100.00
+ ,99.68
+ ,0
+ ,100.20
+ ,99.56
+ ,0
+ ,99.68
+ ,100.06
+ ,0
+ ,99.56
+ ,100.50
+ ,0
+ ,100.06
+ ,99.30
+ ,0
+ ,100.50
+ ,99.37
+ ,0
+ ,99.30
+ ,99.20
+ ,0
+ ,99.37
+ ,98.11
+ ,0
+ ,99.20
+ ,97.60
+ ,0
+ ,98.11
+ ,97.76
+ ,0
+ ,97.60
+ ,98.06
+ ,0
+ ,97.76
+ ,98.25
+ ,0
+ ,98.06
+ ,98.50
+ ,0
+ ,98.25
+ ,97.39
+ ,0
+ ,98.50
+ ,98.09
+ ,0
+ ,97.39
+ ,97.78
+ ,0
+ ,98.09
+ ,98.12
+ ,0
+ ,97.78
+ ,97.50
+ ,0
+ ,98.12
+ ,97.30
+ ,0
+ ,97.50
+ ,97.64
+ ,0
+ ,97.30
+ ,96.88
+ ,0
+ ,97.64
+ ,97.40
+ ,0
+ ,96.88
+ ,98.27
+ ,0
+ ,97.40
+ ,97.94
+ ,0
+ ,98.27
+ ,98.61
+ ,0
+ ,97.94
+ ,98.72
+ ,0
+ ,98.61
+ ,98.62
+ ,0
+ ,98.72
+ ,98.56
+ ,0
+ ,98.62
+ ,98.06
+ ,0
+ ,98.56
+ ,97.40
+ ,0
+ ,98.06
+ ,97.76
+ ,0
+ ,97.40
+ ,97.05
+ ,0
+ ,97.76
+ ,97.85
+ ,0
+ ,97.05
+ ,97.40
+ ,0
+ ,97.85
+ ,97.27
+ ,0
+ ,97.40
+ ,97.93
+ ,0
+ ,97.27
+ ,98.60
+ ,0
+ ,97.93
+ ,98.70
+ ,0
+ ,98.60
+ ,98.88
+ ,0
+ ,98.70
+ ,98.27
+ ,0
+ ,98.88
+ ,97.85
+ ,0
+ ,98.27
+ ,97.70
+ ,0
+ ,97.85
+ ,96.97
+ ,0
+ ,97.70
+ ,97.72
+ ,0
+ ,96.97
+ ,97.66
+ ,0
+ ,97.72
+ ,99.00
+ ,0
+ ,97.66
+ ,98.86
+ ,0
+ ,99.00
+ ,99.56
+ ,0
+ ,98.86
+ ,100.19
+ ,0
+ ,99.56
+ ,100.37
+ ,0
+ ,100.19
+ ,100.01
+ ,0
+ ,100.37
+ ,99.68
+ ,0
+ ,100.01
+ ,99.78
+ ,0
+ ,99.68
+ ,99.36
+ ,0
+ ,99.78
+ ,99.21
+ ,0
+ ,99.36
+ ,99.26
+ ,0
+ ,99.21
+ ,99.26
+ ,0
+ ,99.26
+ ,100.43
+ ,0
+ ,99.26
+ ,101.50
+ ,0
+ ,100.43
+ ,102.27
+ ,0
+ ,101.50
+ ,102.69
+ ,0
+ ,102.27
+ ,103.47
+ ,0
+ ,102.69
+ ,104.02
+ ,0
+ ,103.47
+ ,103.55
+ ,0
+ ,104.02
+ ,103.77
+ ,0
+ ,103.55
+ ,104.19
+ ,0
+ ,103.77
+ ,103.64
+ ,0
+ ,104.19
+ ,103.68
+ ,0
+ ,103.64
+ ,105.39
+ ,0
+ ,103.68
+ ,106.61
+ ,0
+ ,105.39
+ ,108.12
+ ,0
+ ,106.61
+ ,109.22
+ ,0
+ ,108.12
+ ,110.17
+ ,0
+ ,109.22
+ ,110.31
+ ,0
+ ,110.17
+ ,111.06
+ ,0
+ ,110.31
+ ,111.14
+ ,0
+ ,111.06
+ ,111.39
+ ,0
+ ,111.14
+ ,112.51
+ ,0
+ ,111.39
+ ,111.28
+ ,0
+ ,112.51
+ ,112.22
+ ,0
+ ,111.28
+ ,113.19
+ ,0
+ ,112.22
+ ,114.32
+ ,0
+ ,113.19
+ ,115.34
+ ,0
+ ,114.32
+ ,116.61
+ ,0
+ ,115.34
+ ,117.83
+ ,0
+ ,116.61
+ ,117.70
+ ,0
+ ,117.83
+ ,118.51
+ ,0
+ ,117.70
+ ,118.82
+ ,0
+ ,118.51
+ ,119.49
+ ,0
+ ,118.82
+ ,119.57
+ ,0
+ ,119.49
+ ,120.00
+ ,0
+ ,119.57
+ ,121.96
+ ,0
+ ,120.00
+ ,121.45
+ ,0
+ ,121.96
+ ,123.41
+ ,0
+ ,121.45
+ ,124.44
+ ,0
+ ,123.41
+ ,126.25
+ ,0
+ ,124.44
+ ,127.41
+ ,0
+ ,126.25
+ ,127.63
+ ,0
+ ,127.41
+ ,129.19
+ ,0
+ ,127.63
+ ,129.82
+ ,0
+ ,129.19
+ ,130.45
+ ,0
+ ,129.82
+ ,132.02
+ ,0
+ ,130.45
+ ,132.72
+ ,0
+ ,132.02
+ ,132.96
+ ,0
+ ,132.72
+ ,135.06
+ ,0
+ ,132.96
+ ,137.04
+ ,0
+ ,135.06
+ ,137.83
+ ,0
+ ,137.04
+ ,139.17
+ ,0
+ ,137.83
+ ,140.35
+ ,0
+ ,139.17
+ ,141.01
+ ,0
+ ,140.35
+ ,141.89
+ ,0
+ ,141.01
+ ,143.28
+ ,0
+ ,141.89
+ ,142.90
+ ,0
+ ,143.28
+ ,143.37
+ ,0
+ ,142.90
+ ,145.03
+ ,0
+ ,143.37
+ ,146.05
+ ,0
+ ,145.03
+ ,147.39
+ ,0
+ ,146.05
+ ,149.58
+ ,0
+ ,147.39
+ ,151.02
+ ,0
+ ,149.58
+ ,153.57
+ ,0
+ ,151.02
+ ,155.60
+ ,0
+ ,153.57
+ ,157.18
+ ,0
+ ,155.60
+ ,158.77
+ ,0
+ ,157.18
+ ,159.95
+ ,0
+ ,158.77
+ ,161.34
+ ,0
+ ,159.95
+ ,161.95
+ ,0
+ ,161.34
+ ,163.36
+ ,0
+ ,161.95
+ ,165.00
+ ,0
+ ,163.36
+ ,166.65
+ ,0
+ ,165.00
+ ,168.65
+ ,0
+ ,166.65
+ ,170.29
+ ,0
+ ,168.65
+ ,172.70
+ ,0
+ ,170.29
+ ,173.79
+ ,0
+ ,172.70
+ ,176.45
+ ,0
+ ,173.79
+ ,177.58
+ ,0
+ ,176.45
+ ,179.19
+ ,0
+ ,177.58
+ ,181.01
+ ,0
+ ,179.19
+ ,184.08
+ ,0
+ ,181.01
+ ,185.63
+ ,0
+ ,184.08
+ ,188.51
+ ,0
+ ,185.63
+ ,190.18
+ ,0
+ ,188.51
+ ,192.19
+ ,0
+ ,190.18
+ ,193.47
+ ,0
+ ,192.19
+ ,196.73
+ ,0
+ ,193.47
+ ,200.39
+ ,0
+ ,196.73
+ ,203.24
+ ,0
+ ,200.39
+ ,205.53
+ ,0
+ ,203.24
+ ,208.21
+ ,0
+ ,205.53
+ ,208.88
+ ,0
+ ,208.21
+ ,212.85
+ ,0
+ ,208.88
+ ,216.41
+ ,0
+ ,212.85
+ ,216.23
+ ,0
+ ,216.41
+ ,219.27
+ ,0
+ ,216.23
+ ,222.02
+ ,0
+ ,219.27
+ ,224.89
+ ,0
+ ,222.02
+ ,230.37
+ ,0
+ ,224.89
+ ,232.29
+ ,0
+ ,230.37
+ ,235.53
+ ,0
+ ,232.29
+ ,236.92
+ ,0
+ ,235.53
+ ,242.37
+ ,0
+ ,236.92
+ ,242.75
+ ,0
+ ,242.37
+ ,244.19
+ ,0
+ ,242.75
+ ,247.94
+ ,0
+ ,244.19
+ ,248.80
+ ,0
+ ,247.94
+ ,250.18
+ ,0
+ ,248.80
+ ,251.55
+ ,0
+ ,250.18
+ ,254.40
+ ,0
+ ,251.55
+ ,255.72
+ ,0
+ ,254.40
+ ,257.69
+ ,0
+ ,255.72
+ ,258.37
+ ,0
+ ,257.69
+ ,258.22
+ ,0
+ ,258.37
+ ,258.59
+ ,0
+ ,258.22
+ ,257.45
+ ,0
+ ,258.59
+ ,257.45
+ ,0
+ ,257.45
+ ,256.73
+ ,0
+ ,257.45
+ ,258.82
+ ,0
+ ,256.73
+ ,257.99
+ ,0
+ ,258.82
+ ,262.85
+ ,0
+ ,257.99
+ ,262.58
+ ,0
+ ,262.85
+ ,261.55
+ ,0
+ ,262.58
+ ,261.25
+ ,0
+ ,261.55
+ ,259.78
+ ,1
+ ,261.25
+ ,256.26
+ ,1
+ ,259.78
+ ,254.29
+ ,1
+ ,256.26
+ ,248.50
+ ,1
+ ,254.29
+ ,241.88
+ ,1
+ ,248.50
+ ,238.53
+ ,1
+ ,241.88
+ ,232.24
+ ,1
+ ,238.53
+ ,232.46
+ ,1
+ ,232.24
+ ,225.79
+ ,1
+ ,232.46
+ ,221.63
+ ,1
+ ,225.79
+ ,219.62
+ ,1
+ ,221.63
+ ,215.94
+ ,1
+ ,219.62
+ ,211.81
+ ,1
+ ,215.94
+ ,205.57
+ ,1
+ ,211.81
+ ,201.25
+ ,1
+ ,205.57
+ ,194.70
+ ,1
+ ,201.25
+ ,187.94
+ ,1
+ ,194.70
+ ,185.61
+ ,1
+ ,187.94
+ ,181.15
+ ,1
+ ,185.61
+ ,186.50
+ ,1
+ ,181.15
+ ,183.21
+ ,1
+ ,186.50
+ ,182.61
+ ,1
+ ,183.21
+ ,187.09
+ ,1
+ ,182.61
+ ,189.10
+ ,1
+ ,187.09
+ ,191.25
+ ,1
+ ,189.10
+ ,190.74
+ ,1
+ ,191.25
+ ,190.79
+ ,1
+ ,190.74)
+ ,dim=c(3
+ ,215)
+ ,dimnames=list(c('Huizenprijs_Pacific'
+ ,'Dummy_Crisis'
+ ,'Y1')
+ ,1:215))
> y <- array(NA,dim=c(3,215),dimnames=list(c('Huizenprijs_Pacific','Dummy_Crisis','Y1'),1:215))
> 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 = '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
Huizenprijs_Pacific Dummy_Crisis Y1 t
1 100.36 0 100.21 1
2 100.62 0 100.36 2
3 100.78 0 100.62 3
4 100.93 0 100.78 4
5 100.70 0 100.93 5
6 100.00 0 100.70 6
7 100.20 0 100.00 7
8 99.68 0 100.20 8
9 99.56 0 99.68 9
10 100.06 0 99.56 10
11 100.50 0 100.06 11
12 99.30 0 100.50 12
13 99.37 0 99.30 13
14 99.20 0 99.37 14
15 98.11 0 99.20 15
16 97.60 0 98.11 16
17 97.76 0 97.60 17
18 98.06 0 97.76 18
19 98.25 0 98.06 19
20 98.50 0 98.25 20
21 97.39 0 98.50 21
22 98.09 0 97.39 22
23 97.78 0 98.09 23
24 98.12 0 97.78 24
25 97.50 0 98.12 25
26 97.30 0 97.50 26
27 97.64 0 97.30 27
28 96.88 0 97.64 28
29 97.40 0 96.88 29
30 98.27 0 97.40 30
31 97.94 0 98.27 31
32 98.61 0 97.94 32
33 98.72 0 98.61 33
34 98.62 0 98.72 34
35 98.56 0 98.62 35
36 98.06 0 98.56 36
37 97.40 0 98.06 37
38 97.76 0 97.40 38
39 97.05 0 97.76 39
40 97.85 0 97.05 40
41 97.40 0 97.85 41
42 97.27 0 97.40 42
43 97.93 0 97.27 43
44 98.60 0 97.93 44
45 98.70 0 98.60 45
46 98.88 0 98.70 46
47 98.27 0 98.88 47
48 97.85 0 98.27 48
49 97.70 0 97.85 49
50 96.97 0 97.70 50
51 97.72 0 96.97 51
52 97.66 0 97.72 52
53 99.00 0 97.66 53
54 98.86 0 99.00 54
55 99.56 0 98.86 55
56 100.19 0 99.56 56
57 100.37 0 100.19 57
58 100.01 0 100.37 58
59 99.68 0 100.01 59
60 99.78 0 99.68 60
61 99.36 0 99.78 61
62 99.21 0 99.36 62
63 99.26 0 99.21 63
64 99.26 0 99.26 64
65 100.43 0 99.26 65
66 101.50 0 100.43 66
67 102.27 0 101.50 67
68 102.69 0 102.27 68
69 103.47 0 102.69 69
70 104.02 0 103.47 70
71 103.55 0 104.02 71
72 103.77 0 103.55 72
73 104.19 0 103.77 73
74 103.64 0 104.19 74
75 103.68 0 103.64 75
76 105.39 0 103.68 76
77 106.61 0 105.39 77
78 108.12 0 106.61 78
79 109.22 0 108.12 79
80 110.17 0 109.22 80
81 110.31 0 110.17 81
82 111.06 0 110.31 82
83 111.14 0 111.06 83
84 111.39 0 111.14 84
85 112.51 0 111.39 85
86 111.28 0 112.51 86
87 112.22 0 111.28 87
88 113.19 0 112.22 88
89 114.32 0 113.19 89
90 115.34 0 114.32 90
91 116.61 0 115.34 91
92 117.83 0 116.61 92
93 117.70 0 117.83 93
94 118.51 0 117.70 94
95 118.82 0 118.51 95
96 119.49 0 118.82 96
97 119.57 0 119.49 97
98 120.00 0 119.57 98
99 121.96 0 120.00 99
100 121.45 0 121.96 100
101 123.41 0 121.45 101
102 124.44 0 123.41 102
103 126.25 0 124.44 103
104 127.41 0 126.25 104
105 127.63 0 127.41 105
106 129.19 0 127.63 106
107 129.82 0 129.19 107
108 130.45 0 129.82 108
109 132.02 0 130.45 109
110 132.72 0 132.02 110
111 132.96 0 132.72 111
112 135.06 0 132.96 112
113 137.04 0 135.06 113
114 137.83 0 137.04 114
115 139.17 0 137.83 115
116 140.35 0 139.17 116
117 141.01 0 140.35 117
118 141.89 0 141.01 118
119 143.28 0 141.89 119
120 142.90 0 143.28 120
121 143.37 0 142.90 121
122 145.03 0 143.37 122
123 146.05 0 145.03 123
124 147.39 0 146.05 124
125 149.58 0 147.39 125
126 151.02 0 149.58 126
127 153.57 0 151.02 127
128 155.60 0 153.57 128
129 157.18 0 155.60 129
130 158.77 0 157.18 130
131 159.95 0 158.77 131
132 161.34 0 159.95 132
133 161.95 0 161.34 133
134 163.36 0 161.95 134
135 165.00 0 163.36 135
136 166.65 0 165.00 136
137 168.65 0 166.65 137
138 170.29 0 168.65 138
139 172.70 0 170.29 139
140 173.79 0 172.70 140
141 176.45 0 173.79 141
142 177.58 0 176.45 142
143 179.19 0 177.58 143
144 181.01 0 179.19 144
145 184.08 0 181.01 145
146 185.63 0 184.08 146
147 188.51 0 185.63 147
148 190.18 0 188.51 148
149 192.19 0 190.18 149
150 193.47 0 192.19 150
151 196.73 0 193.47 151
152 200.39 0 196.73 152
153 203.24 0 200.39 153
154 205.53 0 203.24 154
155 208.21 0 205.53 155
156 208.88 0 208.21 156
157 212.85 0 208.88 157
158 216.41 0 212.85 158
159 216.23 0 216.41 159
160 219.27 0 216.23 160
161 222.02 0 219.27 161
162 224.89 0 222.02 162
163 230.37 0 224.89 163
164 232.29 0 230.37 164
165 235.53 0 232.29 165
166 236.92 0 235.53 166
167 242.37 0 236.92 167
168 242.75 0 242.37 168
169 244.19 0 242.75 169
170 247.94 0 244.19 170
171 248.80 0 247.94 171
172 250.18 0 248.80 172
173 251.55 0 250.18 173
174 254.40 0 251.55 174
175 255.72 0 254.40 175
176 257.69 0 255.72 176
177 258.37 0 257.69 177
178 258.22 0 258.37 178
179 258.59 0 258.22 179
180 257.45 0 258.59 180
181 257.45 0 257.45 181
182 256.73 0 257.45 182
183 258.82 0 256.73 183
184 257.99 0 258.82 184
185 262.85 0 257.99 185
186 262.58 0 262.85 186
187 261.55 0 262.58 187
188 261.25 0 261.55 188
189 259.78 1 261.25 189
190 256.26 1 259.78 190
191 254.29 1 256.26 191
192 248.50 1 254.29 192
193 241.88 1 248.50 193
194 238.53 1 241.88 194
195 232.24 1 238.53 195
196 232.46 1 232.24 196
197 225.79 1 232.46 197
198 221.63 1 225.79 198
199 219.62 1 221.63 199
200 215.94 1 219.62 200
201 211.81 1 215.94 201
202 205.57 1 211.81 202
203 201.25 1 205.57 203
204 194.70 1 201.25 204
205 187.94 1 194.70 205
206 185.61 1 187.94 206
207 181.15 1 185.61 207
208 186.50 1 181.15 208
209 183.21 1 186.50 209
210 182.61 1 183.21 210
211 187.09 1 182.61 211
212 189.10 1 187.09 212
213 191.25 1 189.10 213
214 190.74 1 191.25 214
215 190.79 1 190.74 215
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummy_Crisis Y1 t
0.41155 -5.21336 0.98647 0.02521
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.49156 -0.70916 -0.01912 0.63361 7.35943
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.411545 0.325567 1.264 0.208
Dummy_Crisis -5.213361 0.383016 -13.611 < 2e-16 ***
Y1 0.986467 0.004117 239.621 < 2e-16 ***
t 0.025211 0.004152 6.073 5.78e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.472 on 211 degrees of freedom
Multiple R-squared: 0.9993, Adjusted R-squared: 0.9993
F-statistic: 1.051e+05 on 3 and 211 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,] 1.890904e-02 3.781809e-02 0.9810910
[2,] 5.316838e-03 1.063368e-02 0.9946832
[3,] 1.060670e-03 2.121340e-03 0.9989393
[4,] 7.546597e-04 1.509319e-03 0.9992453
[5,] 5.652010e-04 1.130402e-03 0.9994348
[6,] 4.919006e-04 9.838013e-04 0.9995081
[7,] 1.349836e-04 2.699672e-04 0.9998650
[8,] 3.653447e-05 7.306894e-05 0.9999635
[9,] 7.794070e-05 1.558814e-04 0.9999221
[10,] 4.212037e-05 8.424075e-05 0.9999579
[11,] 1.309853e-05 2.619707e-05 0.9999869
[12,] 5.028742e-06 1.005748e-05 0.9999950
[13,] 2.037183e-06 4.074367e-06 0.9999980
[14,] 1.120366e-06 2.240732e-06 0.9999989
[15,] 7.299925e-07 1.459985e-06 0.9999993
[16,] 6.259179e-07 1.251836e-06 0.9999994
[17,] 1.944982e-07 3.889965e-07 0.9999998
[18,] 1.284096e-07 2.568192e-07 0.9999999
[19,] 3.950499e-08 7.900998e-08 1.0000000
[20,] 1.170006e-08 2.340012e-08 1.0000000
[21,] 6.313783e-09 1.262757e-08 1.0000000
[22,] 2.273199e-09 4.546397e-09 1.0000000
[23,] 1.473049e-09 2.946098e-09 1.0000000
[24,] 7.539821e-09 1.507964e-08 1.0000000
[25,] 2.978807e-09 5.957614e-09 1.0000000
[26,] 6.622411e-09 1.324482e-08 1.0000000
[27,] 3.587450e-09 7.174900e-09 1.0000000
[28,] 1.343997e-09 2.687994e-09 1.0000000
[29,] 4.875997e-10 9.751994e-10 1.0000000
[30,] 1.725797e-10 3.451594e-10 1.0000000
[31,] 7.546820e-11 1.509364e-10 1.0000000
[32,] 3.605287e-11 7.210574e-11 1.0000000
[33,] 1.766131e-11 3.532262e-11 1.0000000
[34,] 1.993788e-11 3.987575e-11 1.0000000
[35,] 6.978990e-12 1.395798e-11 1.0000000
[36,] 2.254262e-12 4.508524e-12 1.0000000
[37,] 2.028418e-12 4.056836e-12 1.0000000
[38,] 2.347795e-12 4.695589e-12 1.0000000
[39,] 9.639211e-13 1.927842e-12 1.0000000
[40,] 4.138812e-13 8.277625e-13 1.0000000
[41,] 1.651451e-13 3.302902e-13 1.0000000
[42,] 5.777336e-14 1.155467e-13 1.0000000
[43,] 1.834823e-14 3.669646e-14 1.0000000
[44,] 1.062195e-14 2.124390e-14 1.0000000
[45,] 8.942500e-15 1.788500e-14 1.0000000
[46,] 2.853994e-15 5.707989e-15 1.0000000
[47,] 2.410416e-14 4.820832e-14 1.0000000
[48,] 8.174455e-15 1.634891e-14 1.0000000
[49,] 8.672894e-15 1.734579e-14 1.0000000
[50,] 7.090965e-15 1.418193e-14 1.0000000
[51,] 2.698817e-15 5.397634e-15 1.0000000
[52,] 9.835384e-16 1.967077e-15 1.0000000
[53,] 3.466779e-16 6.933557e-16 1.0000000
[54,] 1.188571e-16 2.377143e-16 1.0000000
[55,] 4.495788e-17 8.991576e-17 1.0000000
[56,] 1.443048e-17 2.886095e-17 1.0000000
[57,] 4.605479e-18 9.210958e-18 1.0000000
[58,] 1.433474e-18 2.866948e-18 1.0000000
[59,] 4.250790e-18 8.501581e-18 1.0000000
[60,] 8.144363e-18 1.628873e-17 1.0000000
[61,] 5.806601e-18 1.161320e-17 1.0000000
[62,] 2.195437e-18 4.390874e-18 1.0000000
[63,] 1.220739e-18 2.441478e-18 1.0000000
[64,] 4.509666e-19 9.019332e-19 1.0000000
[65,] 3.002119e-19 6.004237e-19 1.0000000
[66,] 9.633348e-20 1.926670e-19 1.0000000
[67,] 3.288244e-20 6.576487e-20 1.0000000
[68,] 2.280419e-20 4.560838e-20 1.0000000
[69,] 7.265117e-21 1.453023e-20 1.0000000
[70,] 6.378323e-20 1.275665e-19 1.0000000
[71,] 6.414923e-20 1.282985e-19 1.0000000
[72,] 9.235516e-20 1.847103e-19 1.0000000
[73,] 4.220221e-20 8.440442e-20 1.0000000
[74,] 1.542365e-20 3.084731e-20 1.0000000
[75,] 8.362607e-21 1.672521e-20 1.0000000
[76,] 2.815294e-21 5.630589e-21 1.0000000
[77,] 1.493345e-21 2.986690e-21 1.0000000
[78,] 5.794476e-22 1.158895e-21 1.0000000
[79,] 2.655052e-22 5.310104e-22 1.0000000
[80,] 5.940471e-21 1.188094e-20 1.0000000
[81,] 2.574201e-21 5.148402e-21 1.0000000
[82,] 1.085613e-21 2.171225e-21 1.0000000
[83,] 5.159635e-22 1.031927e-21 1.0000000
[84,] 2.011924e-22 4.023849e-22 1.0000000
[85,] 9.684304e-23 1.936861e-22 1.0000000
[86,] 3.987166e-23 7.974332e-23 1.0000000
[87,] 3.940892e-23 7.881783e-23 1.0000000
[88,] 1.287277e-23 2.574554e-23 1.0000000
[89,] 5.353018e-24 1.070604e-23 1.0000000
[90,] 1.721374e-24 3.442747e-24 1.0000000
[91,] 9.448262e-25 1.889652e-24 1.0000000
[92,] 3.294167e-25 6.588333e-25 1.0000000
[93,] 6.925550e-25 1.385110e-24 1.0000000
[94,] 1.711713e-24 3.423426e-24 1.0000000
[95,] 3.047402e-24 6.094805e-24 1.0000000
[96,] 1.035999e-24 2.071999e-24 1.0000000
[97,] 8.762686e-25 1.752537e-24 1.0000000
[98,] 2.940201e-25 5.880402e-25 1.0000000
[99,] 1.910322e-25 3.820645e-25 1.0000000
[100,] 9.030528e-26 1.806106e-25 1.0000000
[101,] 3.481848e-26 6.963696e-26 1.0000000
[102,] 1.321446e-26 2.642892e-26 1.0000000
[103,] 5.893092e-27 1.178618e-26 1.0000000
[104,] 2.189093e-27 4.378185e-27 1.0000000
[105,] 1.523636e-27 3.047272e-27 1.0000000
[106,] 1.725407e-27 3.450814e-27 1.0000000
[107,] 1.181476e-27 2.362952e-27 1.0000000
[108,] 4.592912e-28 9.185824e-28 1.0000000
[109,] 1.447552e-28 2.895104e-28 1.0000000
[110,] 4.484307e-29 8.968614e-29 1.0000000
[111,] 2.123555e-29 4.247109e-29 1.0000000
[112,] 7.666289e-30 1.533258e-29 1.0000000
[113,] 2.392441e-30 4.784881e-30 1.0000000
[114,] 1.775954e-29 3.551909e-29 1.0000000
[115,] 1.069239e-29 2.138478e-29 1.0000000
[116,] 4.546485e-30 9.092970e-30 1.0000000
[117,] 1.595886e-30 3.191773e-30 1.0000000
[118,] 5.359709e-31 1.071942e-30 1.0000000
[119,] 4.389082e-31 8.778165e-31 1.0000000
[120,] 1.430877e-31 2.861753e-31 1.0000000
[121,] 1.941118e-31 3.882236e-31 1.0000000
[122,] 8.047777e-32 1.609555e-31 1.0000000
[123,] 2.471500e-32 4.943000e-32 1.0000000
[124,] 7.537879e-33 1.507576e-32 1.0000000
[125,] 2.968366e-33 5.936732e-33 1.0000000
[126,] 9.882455e-34 1.976491e-33 1.0000000
[127,] 1.140604e-33 2.281208e-33 1.0000000
[128,] 3.960690e-34 7.921380e-34 1.0000000
[129,] 1.300778e-34 2.601555e-34 1.0000000
[130,] 4.292752e-35 8.585503e-35 1.0000000
[131,] 1.522206e-35 3.044413e-35 1.0000000
[132,] 5.099965e-36 1.019993e-35 1.0000000
[133,] 2.470554e-36 4.941109e-36 1.0000000
[134,] 1.749430e-36 3.498860e-36 1.0000000
[135,] 1.142359e-36 2.284717e-36 1.0000000
[136,] 8.852085e-37 1.770417e-36 1.0000000
[137,] 3.586775e-37 7.173550e-37 1.0000000
[138,] 1.323234e-37 2.646467e-37 1.0000000
[139,] 1.604795e-37 3.209591e-37 1.0000000
[140,] 8.266004e-38 1.653201e-37 1.0000000
[141,] 5.125796e-38 1.025159e-37 1.0000000
[142,] 2.535941e-38 5.071882e-38 1.0000000
[143,] 9.441971e-39 1.888394e-38 1.0000000
[144,] 1.217740e-38 2.435481e-38 1.0000000
[145,] 1.321097e-38 2.642193e-38 1.0000000
[146,] 2.658495e-38 5.316991e-38 1.0000000
[147,] 9.138171e-39 1.827634e-38 1.0000000
[148,] 2.986395e-39 5.972790e-39 1.0000000
[149,] 8.667854e-40 1.733571e-39 1.0000000
[150,] 2.445381e-38 4.890761e-38 1.0000000
[151,] 5.508743e-38 1.101749e-37 1.0000000
[152,] 3.264981e-38 6.529962e-38 1.0000000
[153,] 8.034404e-35 1.606881e-34 1.0000000
[154,] 2.981990e-35 5.963981e-35 1.0000000
[155,] 9.663172e-36 1.932634e-35 1.0000000
[156,] 2.985208e-36 5.970415e-36 1.0000000
[157,] 5.359013e-34 1.071803e-33 1.0000000
[158,] 3.677493e-34 7.354985e-34 1.0000000
[159,] 1.167041e-34 2.334083e-34 1.0000000
[160,] 2.337396e-34 4.674792e-34 1.0000000
[161,] 3.252735e-32 6.505470e-32 1.0000000
[162,] 1.098202e-30 2.196404e-30 1.0000000
[163,] 1.217014e-30 2.434029e-30 1.0000000
[164,] 1.642699e-30 3.285398e-30 1.0000000
[165,] 5.492944e-30 1.098589e-29 1.0000000
[166,] 5.537471e-30 1.107494e-29 1.0000000
[167,] 5.185273e-30 1.037055e-29 1.0000000
[168,] 5.966102e-30 1.193220e-29 1.0000000
[169,] 8.312302e-30 1.662460e-29 1.0000000
[170,] 1.264187e-29 2.528373e-29 1.0000000
[171,] 4.282246e-29 8.564492e-29 1.0000000
[172,] 4.168171e-28 8.336341e-28 1.0000000
[173,] 1.110387e-27 2.220774e-27 1.0000000
[174,] 4.236628e-26 8.473256e-26 1.0000000
[175,] 8.180313e-26 1.636063e-25 1.0000000
[176,] 4.361009e-25 8.722017e-25 1.0000000
[177,] 3.766592e-25 7.533183e-25 1.0000000
[178,] 1.848757e-24 3.697513e-24 1.0000000
[179,] 2.242445e-21 4.484891e-21 1.0000000
[180,] 3.574267e-21 7.148534e-21 1.0000000
[181,] 1.193811e-20 2.387622e-20 1.0000000
[182,] 1.258095e-20 2.516190e-20 1.0000000
[183,] 1.177211e-19 2.354423e-19 1.0000000
[184,] 1.709210e-19 3.418420e-19 1.0000000
[185,] 2.248015e-18 4.496030e-18 1.0000000
[186,] 5.318114e-18 1.063623e-17 1.0000000
[187,] 1.338748e-17 2.677496e-17 1.0000000
[188,] 1.611732e-17 3.223465e-17 1.0000000
[189,] 1.624492e-17 3.248983e-17 1.0000000
[190,] 3.621238e-13 7.242476e-13 1.0000000
[191,] 4.396047e-13 8.792093e-13 1.0000000
[192,] 3.186420e-13 6.372840e-13 1.0000000
[193,] 1.477019e-11 2.954039e-11 1.0000000
[194,] 1.271554e-10 2.543109e-10 1.0000000
[195,] 2.851300e-09 5.702600e-09 1.0000000
[196,] 9.073200e-09 1.814640e-08 1.0000000
[197,] 1.405781e-06 2.811562e-06 0.9999986
[198,] 3.273476e-05 6.546952e-05 0.9999673
[199,] 7.074199e-05 1.414840e-04 0.9999293
[200,] 1.393485e-03 2.786970e-03 0.9986065
[201,] 6.449473e-04 1.289895e-03 0.9993551
[202,] 5.741210e-02 1.148242e-01 0.9425879
> postscript(file="/var/www/html/rcomp/tmp/1ju101261250737.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/24m8k1261250737.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3dohb1261250737.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43dch1261250737.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5uxab1261250737.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 = 215
Frequency = 1
1 2 3 4 5
1.0694106546 1.1562294405 1.0345368843 1.0014910028 0.5983097887
6 7 8 9 10
0.0999859383 0.9653014588 0.2227969073 0.5905484135 1.1837132209
11 12 13 14 15
1.1052686455 -0.5539879251 0.6745609687 0.4102970943 -0.5372147608
16 17 18 19 20
0.0028227908 0.6407096294 0.7576637479 0.6265125218 0.6638726378
21 22 23 24 25
-0.7179552509 1.0518116357 0.0260737110 0.6466672003 -0.3339426956
26 27 28 29 30
0.0524554851 0.5645376323 -0.5560722636 0.6884312617 1.0202573513
31 32 33 34 35
-0.1931799203 0.7771429040 0.2009989817 -0.0327235625 -0.0192880900
36 37 38 39 40
-0.4853112873 -0.6772891161 0.3085677345 -0.7817714964 0.6934086916
41 42 43 44 45
-0.5709759078 -0.2822770739 0.4807524010 0.4744731462 -0.1116707761
46 47 48 49 50
-0.0555286529 -0.8683038694 -0.7117703561 -0.4726655247 -1.0799067148
51 52 53 54 55
0.3650028081 -0.4600584539 0.9139183488 -0.5731582937 0.2397358487
56 57 58 59 60
0.1539979240 -0.3126873285 -0.8754625450 -0.8755457183 -0.4752228940
61 62 63 64 65
-1.0190807708 -0.7799759394 -0.6072171295 -0.6817516689 0.4630371290
66 67 68 69 70
0.3536598334 0.0429292125 -0.3218613845 0.0186113798 -0.2260438846
71 72 73 74 75
-1.2638117973 -0.6053836285 -0.4276175149 -1.4171447505 -0.8597992420
76 77 78 79 80
0.7855308860 0.2934615473 0.5747609144 0.1599849250 -0.0003396984
81 82 83 84 85
-0.8226943097 -0.2360108563 -0.9210721184 -0.7752006602 0.0729714511
86 87 88 89 90
-2.2870825072 -0.1589396110 -0.1414295549 0.0064864988 -0.1134321269
91 92 93 94 95
0.1251605895 0.0671366193 -1.2915640136 -0.3785345387 -0.8927838055
96 97 98 99 100
-0.5537996991 -1.1599436214 -0.8340721632 0.6765359337 -1.7921500917
101 102 103 104 105
0.6457367469 -0.2829492784 0.4857787705 -0.1649372429 -1.1144498710
106 107 108 109 110
0.2033162427 -0.7307830841 -0.7474683365 0.1758464110 -0.6981175832
111 112 113 114 115
-1.1738555079 0.6641812709 0.5473899010 -0.6410254593 -0.1055453912
116 117 118 119 120
-0.2726220337 -0.8018639968 -0.5981432516 -0.1014451907 -1.8778451705
121 122 123 124 125
-1.0581990089 0.1129504181 -0.5297955833 -0.2212028669 0.6217204906
126 127 128 129 130
-0.1238528865 0.9804237964 0.4697223905 0.0219836929 0.0281550312
131 132 133 134 135
-0.3855382979 -0.1847802609 -0.9711802408 -0.1881361583 0.0357345270
136 137 138 139 140
0.0427178605 0.3898365266 0.0316918313 0.7986751649 -0.5139208964
141 142 143 144 145
1.0456191477 -0.4735936002 -0.0035122260 0.2030651100 1.4524844291
146 147 148 149 150
-0.0511796849 1.2745856558 0.0783502236 0.4157395547 -0.3122698080
151 152 153 154 155
1.6598415543 2.0787487584 1.2930692639 0.7464278341 1.1422077824
156 157 158 159 160
-0.8567343005 2.4271217772 2.0456375913 -1.6713952286 1.5209575837
161 162 163 164 165
1.2468874720 1.3788927169 4.0025219521 0.4914729789 1.8122456234
166 167 168 169 170
-0.0191178376 4.0344821826 -0.9869727883 0.0529586459 2.3572353288
171 172 173 174 175
-0.5072261729 -0.0007987771 -0.0173340894 1.4559952657 -0.0606461641
176 177 178 179 180
0.5820065283 -0.7065441645 -1.5525527543 -1.0597939444 -2.5899978427
181 182 183 184 185
-1.4906369538 -2.2358481559 0.5391966995 -2.3777300029 3.2758261947
186 187 188 189 190
-1.8136133957 -2.6024785763 -1.9116290294 2.1024607671 0.0073556824
191 192 193 194 195
1.4845074282 -2.3873642832 -3.3209330227 -0.1657343626 -3.1762819637
196 197 198 199 200
3.2233826700 -3.6888512163 -1.2943292189 0.7741612447 -0.9482517968
201 202 203 204 205
-1.4732653716 -3.6643689104 -1.8540276140 -4.1677024709 -4.4915564831
206 207 208 209 210
-0.1782524785 -2.3649961611 7.3594343265 -1.2333739697 1.3868904243
211 212 213 214 215
6.4335592702 3.9989770435 4.1409676808 1.4848529736 2.0127398122
> postscript(file="/var/www/html/rcomp/tmp/6b8031261250737.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 = 215
Frequency = 1
lag(myerror, k = 1) myerror
0 1.0694106546 NA
1 1.1562294405 1.0694106546
2 1.0345368843 1.1562294405
3 1.0014910028 1.0345368843
4 0.5983097887 1.0014910028
5 0.0999859383 0.5983097887
6 0.9653014588 0.0999859383
7 0.2227969073 0.9653014588
8 0.5905484135 0.2227969073
9 1.1837132209 0.5905484135
10 1.1052686455 1.1837132209
11 -0.5539879251 1.1052686455
12 0.6745609687 -0.5539879251
13 0.4102970943 0.6745609687
14 -0.5372147608 0.4102970943
15 0.0028227908 -0.5372147608
16 0.6407096294 0.0028227908
17 0.7576637479 0.6407096294
18 0.6265125218 0.7576637479
19 0.6638726378 0.6265125218
20 -0.7179552509 0.6638726378
21 1.0518116357 -0.7179552509
22 0.0260737110 1.0518116357
23 0.6466672003 0.0260737110
24 -0.3339426956 0.6466672003
25 0.0524554851 -0.3339426956
26 0.5645376323 0.0524554851
27 -0.5560722636 0.5645376323
28 0.6884312617 -0.5560722636
29 1.0202573513 0.6884312617
30 -0.1931799203 1.0202573513
31 0.7771429040 -0.1931799203
32 0.2009989817 0.7771429040
33 -0.0327235625 0.2009989817
34 -0.0192880900 -0.0327235625
35 -0.4853112873 -0.0192880900
36 -0.6772891161 -0.4853112873
37 0.3085677345 -0.6772891161
38 -0.7817714964 0.3085677345
39 0.6934086916 -0.7817714964
40 -0.5709759078 0.6934086916
41 -0.2822770739 -0.5709759078
42 0.4807524010 -0.2822770739
43 0.4744731462 0.4807524010
44 -0.1116707761 0.4744731462
45 -0.0555286529 -0.1116707761
46 -0.8683038694 -0.0555286529
47 -0.7117703561 -0.8683038694
48 -0.4726655247 -0.7117703561
49 -1.0799067148 -0.4726655247
50 0.3650028081 -1.0799067148
51 -0.4600584539 0.3650028081
52 0.9139183488 -0.4600584539
53 -0.5731582937 0.9139183488
54 0.2397358487 -0.5731582937
55 0.1539979240 0.2397358487
56 -0.3126873285 0.1539979240
57 -0.8754625450 -0.3126873285
58 -0.8755457183 -0.8754625450
59 -0.4752228940 -0.8755457183
60 -1.0190807708 -0.4752228940
61 -0.7799759394 -1.0190807708
62 -0.6072171295 -0.7799759394
63 -0.6817516689 -0.6072171295
64 0.4630371290 -0.6817516689
65 0.3536598334 0.4630371290
66 0.0429292125 0.3536598334
67 -0.3218613845 0.0429292125
68 0.0186113798 -0.3218613845
69 -0.2260438846 0.0186113798
70 -1.2638117973 -0.2260438846
71 -0.6053836285 -1.2638117973
72 -0.4276175149 -0.6053836285
73 -1.4171447505 -0.4276175149
74 -0.8597992420 -1.4171447505
75 0.7855308860 -0.8597992420
76 0.2934615473 0.7855308860
77 0.5747609144 0.2934615473
78 0.1599849250 0.5747609144
79 -0.0003396984 0.1599849250
80 -0.8226943097 -0.0003396984
81 -0.2360108563 -0.8226943097
82 -0.9210721184 -0.2360108563
83 -0.7752006602 -0.9210721184
84 0.0729714511 -0.7752006602
85 -2.2870825072 0.0729714511
86 -0.1589396110 -2.2870825072
87 -0.1414295549 -0.1589396110
88 0.0064864988 -0.1414295549
89 -0.1134321269 0.0064864988
90 0.1251605895 -0.1134321269
91 0.0671366193 0.1251605895
92 -1.2915640136 0.0671366193
93 -0.3785345387 -1.2915640136
94 -0.8927838055 -0.3785345387
95 -0.5537996991 -0.8927838055
96 -1.1599436214 -0.5537996991
97 -0.8340721632 -1.1599436214
98 0.6765359337 -0.8340721632
99 -1.7921500917 0.6765359337
100 0.6457367469 -1.7921500917
101 -0.2829492784 0.6457367469
102 0.4857787705 -0.2829492784
103 -0.1649372429 0.4857787705
104 -1.1144498710 -0.1649372429
105 0.2033162427 -1.1144498710
106 -0.7307830841 0.2033162427
107 -0.7474683365 -0.7307830841
108 0.1758464110 -0.7474683365
109 -0.6981175832 0.1758464110
110 -1.1738555079 -0.6981175832
111 0.6641812709 -1.1738555079
112 0.5473899010 0.6641812709
113 -0.6410254593 0.5473899010
114 -0.1055453912 -0.6410254593
115 -0.2726220337 -0.1055453912
116 -0.8018639968 -0.2726220337
117 -0.5981432516 -0.8018639968
118 -0.1014451907 -0.5981432516
119 -1.8778451705 -0.1014451907
120 -1.0581990089 -1.8778451705
121 0.1129504181 -1.0581990089
122 -0.5297955833 0.1129504181
123 -0.2212028669 -0.5297955833
124 0.6217204906 -0.2212028669
125 -0.1238528865 0.6217204906
126 0.9804237964 -0.1238528865
127 0.4697223905 0.9804237964
128 0.0219836929 0.4697223905
129 0.0281550312 0.0219836929
130 -0.3855382979 0.0281550312
131 -0.1847802609 -0.3855382979
132 -0.9711802408 -0.1847802609
133 -0.1881361583 -0.9711802408
134 0.0357345270 -0.1881361583
135 0.0427178605 0.0357345270
136 0.3898365266 0.0427178605
137 0.0316918313 0.3898365266
138 0.7986751649 0.0316918313
139 -0.5139208964 0.7986751649
140 1.0456191477 -0.5139208964
141 -0.4735936002 1.0456191477
142 -0.0035122260 -0.4735936002
143 0.2030651100 -0.0035122260
144 1.4524844291 0.2030651100
145 -0.0511796849 1.4524844291
146 1.2745856558 -0.0511796849
147 0.0783502236 1.2745856558
148 0.4157395547 0.0783502236
149 -0.3122698080 0.4157395547
150 1.6598415543 -0.3122698080
151 2.0787487584 1.6598415543
152 1.2930692639 2.0787487584
153 0.7464278341 1.2930692639
154 1.1422077824 0.7464278341
155 -0.8567343005 1.1422077824
156 2.4271217772 -0.8567343005
157 2.0456375913 2.4271217772
158 -1.6713952286 2.0456375913
159 1.5209575837 -1.6713952286
160 1.2468874720 1.5209575837
161 1.3788927169 1.2468874720
162 4.0025219521 1.3788927169
163 0.4914729789 4.0025219521
164 1.8122456234 0.4914729789
165 -0.0191178376 1.8122456234
166 4.0344821826 -0.0191178376
167 -0.9869727883 4.0344821826
168 0.0529586459 -0.9869727883
169 2.3572353288 0.0529586459
170 -0.5072261729 2.3572353288
171 -0.0007987771 -0.5072261729
172 -0.0173340894 -0.0007987771
173 1.4559952657 -0.0173340894
174 -0.0606461641 1.4559952657
175 0.5820065283 -0.0606461641
176 -0.7065441645 0.5820065283
177 -1.5525527543 -0.7065441645
178 -1.0597939444 -1.5525527543
179 -2.5899978427 -1.0597939444
180 -1.4906369538 -2.5899978427
181 -2.2358481559 -1.4906369538
182 0.5391966995 -2.2358481559
183 -2.3777300029 0.5391966995
184 3.2758261947 -2.3777300029
185 -1.8136133957 3.2758261947
186 -2.6024785763 -1.8136133957
187 -1.9116290294 -2.6024785763
188 2.1024607671 -1.9116290294
189 0.0073556824 2.1024607671
190 1.4845074282 0.0073556824
191 -2.3873642832 1.4845074282
192 -3.3209330227 -2.3873642832
193 -0.1657343626 -3.3209330227
194 -3.1762819637 -0.1657343626
195 3.2233826700 -3.1762819637
196 -3.6888512163 3.2233826700
197 -1.2943292189 -3.6888512163
198 0.7741612447 -1.2943292189
199 -0.9482517968 0.7741612447
200 -1.4732653716 -0.9482517968
201 -3.6643689104 -1.4732653716
202 -1.8540276140 -3.6643689104
203 -4.1677024709 -1.8540276140
204 -4.4915564831 -4.1677024709
205 -0.1782524785 -4.4915564831
206 -2.3649961611 -0.1782524785
207 7.3594343265 -2.3649961611
208 -1.2333739697 7.3594343265
209 1.3868904243 -1.2333739697
210 6.4335592702 1.3868904243
211 3.9989770435 6.4335592702
212 4.1409676808 3.9989770435
213 1.4848529736 4.1409676808
214 2.0127398122 1.4848529736
215 NA 2.0127398122
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.1562294405 1.0694106546
[2,] 1.0345368843 1.1562294405
[3,] 1.0014910028 1.0345368843
[4,] 0.5983097887 1.0014910028
[5,] 0.0999859383 0.5983097887
[6,] 0.9653014588 0.0999859383
[7,] 0.2227969073 0.9653014588
[8,] 0.5905484135 0.2227969073
[9,] 1.1837132209 0.5905484135
[10,] 1.1052686455 1.1837132209
[11,] -0.5539879251 1.1052686455
[12,] 0.6745609687 -0.5539879251
[13,] 0.4102970943 0.6745609687
[14,] -0.5372147608 0.4102970943
[15,] 0.0028227908 -0.5372147608
[16,] 0.6407096294 0.0028227908
[17,] 0.7576637479 0.6407096294
[18,] 0.6265125218 0.7576637479
[19,] 0.6638726378 0.6265125218
[20,] -0.7179552509 0.6638726378
[21,] 1.0518116357 -0.7179552509
[22,] 0.0260737110 1.0518116357
[23,] 0.6466672003 0.0260737110
[24,] -0.3339426956 0.6466672003
[25,] 0.0524554851 -0.3339426956
[26,] 0.5645376323 0.0524554851
[27,] -0.5560722636 0.5645376323
[28,] 0.6884312617 -0.5560722636
[29,] 1.0202573513 0.6884312617
[30,] -0.1931799203 1.0202573513
[31,] 0.7771429040 -0.1931799203
[32,] 0.2009989817 0.7771429040
[33,] -0.0327235625 0.2009989817
[34,] -0.0192880900 -0.0327235625
[35,] -0.4853112873 -0.0192880900
[36,] -0.6772891161 -0.4853112873
[37,] 0.3085677345 -0.6772891161
[38,] -0.7817714964 0.3085677345
[39,] 0.6934086916 -0.7817714964
[40,] -0.5709759078 0.6934086916
[41,] -0.2822770739 -0.5709759078
[42,] 0.4807524010 -0.2822770739
[43,] 0.4744731462 0.4807524010
[44,] -0.1116707761 0.4744731462
[45,] -0.0555286529 -0.1116707761
[46,] -0.8683038694 -0.0555286529
[47,] -0.7117703561 -0.8683038694
[48,] -0.4726655247 -0.7117703561
[49,] -1.0799067148 -0.4726655247
[50,] 0.3650028081 -1.0799067148
[51,] -0.4600584539 0.3650028081
[52,] 0.9139183488 -0.4600584539
[53,] -0.5731582937 0.9139183488
[54,] 0.2397358487 -0.5731582937
[55,] 0.1539979240 0.2397358487
[56,] -0.3126873285 0.1539979240
[57,] -0.8754625450 -0.3126873285
[58,] -0.8755457183 -0.8754625450
[59,] -0.4752228940 -0.8755457183
[60,] -1.0190807708 -0.4752228940
[61,] -0.7799759394 -1.0190807708
[62,] -0.6072171295 -0.7799759394
[63,] -0.6817516689 -0.6072171295
[64,] 0.4630371290 -0.6817516689
[65,] 0.3536598334 0.4630371290
[66,] 0.0429292125 0.3536598334
[67,] -0.3218613845 0.0429292125
[68,] 0.0186113798 -0.3218613845
[69,] -0.2260438846 0.0186113798
[70,] -1.2638117973 -0.2260438846
[71,] -0.6053836285 -1.2638117973
[72,] -0.4276175149 -0.6053836285
[73,] -1.4171447505 -0.4276175149
[74,] -0.8597992420 -1.4171447505
[75,] 0.7855308860 -0.8597992420
[76,] 0.2934615473 0.7855308860
[77,] 0.5747609144 0.2934615473
[78,] 0.1599849250 0.5747609144
[79,] -0.0003396984 0.1599849250
[80,] -0.8226943097 -0.0003396984
[81,] -0.2360108563 -0.8226943097
[82,] -0.9210721184 -0.2360108563
[83,] -0.7752006602 -0.9210721184
[84,] 0.0729714511 -0.7752006602
[85,] -2.2870825072 0.0729714511
[86,] -0.1589396110 -2.2870825072
[87,] -0.1414295549 -0.1589396110
[88,] 0.0064864988 -0.1414295549
[89,] -0.1134321269 0.0064864988
[90,] 0.1251605895 -0.1134321269
[91,] 0.0671366193 0.1251605895
[92,] -1.2915640136 0.0671366193
[93,] -0.3785345387 -1.2915640136
[94,] -0.8927838055 -0.3785345387
[95,] -0.5537996991 -0.8927838055
[96,] -1.1599436214 -0.5537996991
[97,] -0.8340721632 -1.1599436214
[98,] 0.6765359337 -0.8340721632
[99,] -1.7921500917 0.6765359337
[100,] 0.6457367469 -1.7921500917
[101,] -0.2829492784 0.6457367469
[102,] 0.4857787705 -0.2829492784
[103,] -0.1649372429 0.4857787705
[104,] -1.1144498710 -0.1649372429
[105,] 0.2033162427 -1.1144498710
[106,] -0.7307830841 0.2033162427
[107,] -0.7474683365 -0.7307830841
[108,] 0.1758464110 -0.7474683365
[109,] -0.6981175832 0.1758464110
[110,] -1.1738555079 -0.6981175832
[111,] 0.6641812709 -1.1738555079
[112,] 0.5473899010 0.6641812709
[113,] -0.6410254593 0.5473899010
[114,] -0.1055453912 -0.6410254593
[115,] -0.2726220337 -0.1055453912
[116,] -0.8018639968 -0.2726220337
[117,] -0.5981432516 -0.8018639968
[118,] -0.1014451907 -0.5981432516
[119,] -1.8778451705 -0.1014451907
[120,] -1.0581990089 -1.8778451705
[121,] 0.1129504181 -1.0581990089
[122,] -0.5297955833 0.1129504181
[123,] -0.2212028669 -0.5297955833
[124,] 0.6217204906 -0.2212028669
[125,] -0.1238528865 0.6217204906
[126,] 0.9804237964 -0.1238528865
[127,] 0.4697223905 0.9804237964
[128,] 0.0219836929 0.4697223905
[129,] 0.0281550312 0.0219836929
[130,] -0.3855382979 0.0281550312
[131,] -0.1847802609 -0.3855382979
[132,] -0.9711802408 -0.1847802609
[133,] -0.1881361583 -0.9711802408
[134,] 0.0357345270 -0.1881361583
[135,] 0.0427178605 0.0357345270
[136,] 0.3898365266 0.0427178605
[137,] 0.0316918313 0.3898365266
[138,] 0.7986751649 0.0316918313
[139,] -0.5139208964 0.7986751649
[140,] 1.0456191477 -0.5139208964
[141,] -0.4735936002 1.0456191477
[142,] -0.0035122260 -0.4735936002
[143,] 0.2030651100 -0.0035122260
[144,] 1.4524844291 0.2030651100
[145,] -0.0511796849 1.4524844291
[146,] 1.2745856558 -0.0511796849
[147,] 0.0783502236 1.2745856558
[148,] 0.4157395547 0.0783502236
[149,] -0.3122698080 0.4157395547
[150,] 1.6598415543 -0.3122698080
[151,] 2.0787487584 1.6598415543
[152,] 1.2930692639 2.0787487584
[153,] 0.7464278341 1.2930692639
[154,] 1.1422077824 0.7464278341
[155,] -0.8567343005 1.1422077824
[156,] 2.4271217772 -0.8567343005
[157,] 2.0456375913 2.4271217772
[158,] -1.6713952286 2.0456375913
[159,] 1.5209575837 -1.6713952286
[160,] 1.2468874720 1.5209575837
[161,] 1.3788927169 1.2468874720
[162,] 4.0025219521 1.3788927169
[163,] 0.4914729789 4.0025219521
[164,] 1.8122456234 0.4914729789
[165,] -0.0191178376 1.8122456234
[166,] 4.0344821826 -0.0191178376
[167,] -0.9869727883 4.0344821826
[168,] 0.0529586459 -0.9869727883
[169,] 2.3572353288 0.0529586459
[170,] -0.5072261729 2.3572353288
[171,] -0.0007987771 -0.5072261729
[172,] -0.0173340894 -0.0007987771
[173,] 1.4559952657 -0.0173340894
[174,] -0.0606461641 1.4559952657
[175,] 0.5820065283 -0.0606461641
[176,] -0.7065441645 0.5820065283
[177,] -1.5525527543 -0.7065441645
[178,] -1.0597939444 -1.5525527543
[179,] -2.5899978427 -1.0597939444
[180,] -1.4906369538 -2.5899978427
[181,] -2.2358481559 -1.4906369538
[182,] 0.5391966995 -2.2358481559
[183,] -2.3777300029 0.5391966995
[184,] 3.2758261947 -2.3777300029
[185,] -1.8136133957 3.2758261947
[186,] -2.6024785763 -1.8136133957
[187,] -1.9116290294 -2.6024785763
[188,] 2.1024607671 -1.9116290294
[189,] 0.0073556824 2.1024607671
[190,] 1.4845074282 0.0073556824
[191,] -2.3873642832 1.4845074282
[192,] -3.3209330227 -2.3873642832
[193,] -0.1657343626 -3.3209330227
[194,] -3.1762819637 -0.1657343626
[195,] 3.2233826700 -3.1762819637
[196,] -3.6888512163 3.2233826700
[197,] -1.2943292189 -3.6888512163
[198,] 0.7741612447 -1.2943292189
[199,] -0.9482517968 0.7741612447
[200,] -1.4732653716 -0.9482517968
[201,] -3.6643689104 -1.4732653716
[202,] -1.8540276140 -3.6643689104
[203,] -4.1677024709 -1.8540276140
[204,] -4.4915564831 -4.1677024709
[205,] -0.1782524785 -4.4915564831
[206,] -2.3649961611 -0.1782524785
[207,] 7.3594343265 -2.3649961611
[208,] -1.2333739697 7.3594343265
[209,] 1.3868904243 -1.2333739697
[210,] 6.4335592702 1.3868904243
[211,] 3.9989770435 6.4335592702
[212,] 4.1409676808 3.9989770435
[213,] 1.4848529736 4.1409676808
[214,] 2.0127398122 1.4848529736
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.1562294405 1.0694106546
2 1.0345368843 1.1562294405
3 1.0014910028 1.0345368843
4 0.5983097887 1.0014910028
5 0.0999859383 0.5983097887
6 0.9653014588 0.0999859383
7 0.2227969073 0.9653014588
8 0.5905484135 0.2227969073
9 1.1837132209 0.5905484135
10 1.1052686455 1.1837132209
11 -0.5539879251 1.1052686455
12 0.6745609687 -0.5539879251
13 0.4102970943 0.6745609687
14 -0.5372147608 0.4102970943
15 0.0028227908 -0.5372147608
16 0.6407096294 0.0028227908
17 0.7576637479 0.6407096294
18 0.6265125218 0.7576637479
19 0.6638726378 0.6265125218
20 -0.7179552509 0.6638726378
21 1.0518116357 -0.7179552509
22 0.0260737110 1.0518116357
23 0.6466672003 0.0260737110
24 -0.3339426956 0.6466672003
25 0.0524554851 -0.3339426956
26 0.5645376323 0.0524554851
27 -0.5560722636 0.5645376323
28 0.6884312617 -0.5560722636
29 1.0202573513 0.6884312617
30 -0.1931799203 1.0202573513
31 0.7771429040 -0.1931799203
32 0.2009989817 0.7771429040
33 -0.0327235625 0.2009989817
34 -0.0192880900 -0.0327235625
35 -0.4853112873 -0.0192880900
36 -0.6772891161 -0.4853112873
37 0.3085677345 -0.6772891161
38 -0.7817714964 0.3085677345
39 0.6934086916 -0.7817714964
40 -0.5709759078 0.6934086916
41 -0.2822770739 -0.5709759078
42 0.4807524010 -0.2822770739
43 0.4744731462 0.4807524010
44 -0.1116707761 0.4744731462
45 -0.0555286529 -0.1116707761
46 -0.8683038694 -0.0555286529
47 -0.7117703561 -0.8683038694
48 -0.4726655247 -0.7117703561
49 -1.0799067148 -0.4726655247
50 0.3650028081 -1.0799067148
51 -0.4600584539 0.3650028081
52 0.9139183488 -0.4600584539
53 -0.5731582937 0.9139183488
54 0.2397358487 -0.5731582937
55 0.1539979240 0.2397358487
56 -0.3126873285 0.1539979240
57 -0.8754625450 -0.3126873285
58 -0.8755457183 -0.8754625450
59 -0.4752228940 -0.8755457183
60 -1.0190807708 -0.4752228940
61 -0.7799759394 -1.0190807708
62 -0.6072171295 -0.7799759394
63 -0.6817516689 -0.6072171295
64 0.4630371290 -0.6817516689
65 0.3536598334 0.4630371290
66 0.0429292125 0.3536598334
67 -0.3218613845 0.0429292125
68 0.0186113798 -0.3218613845
69 -0.2260438846 0.0186113798
70 -1.2638117973 -0.2260438846
71 -0.6053836285 -1.2638117973
72 -0.4276175149 -0.6053836285
73 -1.4171447505 -0.4276175149
74 -0.8597992420 -1.4171447505
75 0.7855308860 -0.8597992420
76 0.2934615473 0.7855308860
77 0.5747609144 0.2934615473
78 0.1599849250 0.5747609144
79 -0.0003396984 0.1599849250
80 -0.8226943097 -0.0003396984
81 -0.2360108563 -0.8226943097
82 -0.9210721184 -0.2360108563
83 -0.7752006602 -0.9210721184
84 0.0729714511 -0.7752006602
85 -2.2870825072 0.0729714511
86 -0.1589396110 -2.2870825072
87 -0.1414295549 -0.1589396110
88 0.0064864988 -0.1414295549
89 -0.1134321269 0.0064864988
90 0.1251605895 -0.1134321269
91 0.0671366193 0.1251605895
92 -1.2915640136 0.0671366193
93 -0.3785345387 -1.2915640136
94 -0.8927838055 -0.3785345387
95 -0.5537996991 -0.8927838055
96 -1.1599436214 -0.5537996991
97 -0.8340721632 -1.1599436214
98 0.6765359337 -0.8340721632
99 -1.7921500917 0.6765359337
100 0.6457367469 -1.7921500917
101 -0.2829492784 0.6457367469
102 0.4857787705 -0.2829492784
103 -0.1649372429 0.4857787705
104 -1.1144498710 -0.1649372429
105 0.2033162427 -1.1144498710
106 -0.7307830841 0.2033162427
107 -0.7474683365 -0.7307830841
108 0.1758464110 -0.7474683365
109 -0.6981175832 0.1758464110
110 -1.1738555079 -0.6981175832
111 0.6641812709 -1.1738555079
112 0.5473899010 0.6641812709
113 -0.6410254593 0.5473899010
114 -0.1055453912 -0.6410254593
115 -0.2726220337 -0.1055453912
116 -0.8018639968 -0.2726220337
117 -0.5981432516 -0.8018639968
118 -0.1014451907 -0.5981432516
119 -1.8778451705 -0.1014451907
120 -1.0581990089 -1.8778451705
121 0.1129504181 -1.0581990089
122 -0.5297955833 0.1129504181
123 -0.2212028669 -0.5297955833
124 0.6217204906 -0.2212028669
125 -0.1238528865 0.6217204906
126 0.9804237964 -0.1238528865
127 0.4697223905 0.9804237964
128 0.0219836929 0.4697223905
129 0.0281550312 0.0219836929
130 -0.3855382979 0.0281550312
131 -0.1847802609 -0.3855382979
132 -0.9711802408 -0.1847802609
133 -0.1881361583 -0.9711802408
134 0.0357345270 -0.1881361583
135 0.0427178605 0.0357345270
136 0.3898365266 0.0427178605
137 0.0316918313 0.3898365266
138 0.7986751649 0.0316918313
139 -0.5139208964 0.7986751649
140 1.0456191477 -0.5139208964
141 -0.4735936002 1.0456191477
142 -0.0035122260 -0.4735936002
143 0.2030651100 -0.0035122260
144 1.4524844291 0.2030651100
145 -0.0511796849 1.4524844291
146 1.2745856558 -0.0511796849
147 0.0783502236 1.2745856558
148 0.4157395547 0.0783502236
149 -0.3122698080 0.4157395547
150 1.6598415543 -0.3122698080
151 2.0787487584 1.6598415543
152 1.2930692639 2.0787487584
153 0.7464278341 1.2930692639
154 1.1422077824 0.7464278341
155 -0.8567343005 1.1422077824
156 2.4271217772 -0.8567343005
157 2.0456375913 2.4271217772
158 -1.6713952286 2.0456375913
159 1.5209575837 -1.6713952286
160 1.2468874720 1.5209575837
161 1.3788927169 1.2468874720
162 4.0025219521 1.3788927169
163 0.4914729789 4.0025219521
164 1.8122456234 0.4914729789
165 -0.0191178376 1.8122456234
166 4.0344821826 -0.0191178376
167 -0.9869727883 4.0344821826
168 0.0529586459 -0.9869727883
169 2.3572353288 0.0529586459
170 -0.5072261729 2.3572353288
171 -0.0007987771 -0.5072261729
172 -0.0173340894 -0.0007987771
173 1.4559952657 -0.0173340894
174 -0.0606461641 1.4559952657
175 0.5820065283 -0.0606461641
176 -0.7065441645 0.5820065283
177 -1.5525527543 -0.7065441645
178 -1.0597939444 -1.5525527543
179 -2.5899978427 -1.0597939444
180 -1.4906369538 -2.5899978427
181 -2.2358481559 -1.4906369538
182 0.5391966995 -2.2358481559
183 -2.3777300029 0.5391966995
184 3.2758261947 -2.3777300029
185 -1.8136133957 3.2758261947
186 -2.6024785763 -1.8136133957
187 -1.9116290294 -2.6024785763
188 2.1024607671 -1.9116290294
189 0.0073556824 2.1024607671
190 1.4845074282 0.0073556824
191 -2.3873642832 1.4845074282
192 -3.3209330227 -2.3873642832
193 -0.1657343626 -3.3209330227
194 -3.1762819637 -0.1657343626
195 3.2233826700 -3.1762819637
196 -3.6888512163 3.2233826700
197 -1.2943292189 -3.6888512163
198 0.7741612447 -1.2943292189
199 -0.9482517968 0.7741612447
200 -1.4732653716 -0.9482517968
201 -3.6643689104 -1.4732653716
202 -1.8540276140 -3.6643689104
203 -4.1677024709 -1.8540276140
204 -4.4915564831 -4.1677024709
205 -0.1782524785 -4.4915564831
206 -2.3649961611 -0.1782524785
207 7.3594343265 -2.3649961611
208 -1.2333739697 7.3594343265
209 1.3868904243 -1.2333739697
210 6.4335592702 1.3868904243
211 3.9989770435 6.4335592702
212 4.1409676808 3.9989770435
213 1.4848529736 4.1409676808
214 2.0127398122 1.4848529736
> 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/7yxrs1261250737.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8wtwi1261250737.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9cstz1261250737.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10mt9q1261250737.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/110mb61261250737.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/12m8pk1261250737.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/13z4tq1261250737.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/14v7f21261250737.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/15je4r1261250737.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/16owy21261250737.tab")
+ }
>
> try(system("convert tmp/1ju101261250737.ps tmp/1ju101261250737.png",intern=TRUE))
character(0)
> try(system("convert tmp/24m8k1261250737.ps tmp/24m8k1261250737.png",intern=TRUE))
character(0)
> try(system("convert tmp/3dohb1261250737.ps tmp/3dohb1261250737.png",intern=TRUE))
character(0)
> try(system("convert tmp/43dch1261250737.ps tmp/43dch1261250737.png",intern=TRUE))
character(0)
> try(system("convert tmp/5uxab1261250737.ps tmp/5uxab1261250737.png",intern=TRUE))
character(0)
> try(system("convert tmp/6b8031261250737.ps tmp/6b8031261250737.png",intern=TRUE))
character(0)
> try(system("convert tmp/7yxrs1261250737.ps tmp/7yxrs1261250737.png",intern=TRUE))
character(0)
> try(system("convert tmp/8wtwi1261250737.ps tmp/8wtwi1261250737.png",intern=TRUE))
character(0)
> try(system("convert tmp/9cstz1261250737.ps tmp/9cstz1261250737.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mt9q1261250737.ps tmp/10mt9q1261250737.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.971 1.789 6.409