R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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(70.5
+ ,0
+ ,71.3
+ ,0
+ ,71.4
+ ,0
+ ,70.1
+ ,0
+ ,69.4
+ ,0
+ ,69.8
+ ,0
+ ,69.8
+ ,0
+ ,70.7
+ ,0
+ ,69.4
+ ,0
+ ,69.8
+ ,0
+ ,69.3
+ ,0
+ ,72.9
+ ,0
+ ,70.0
+ ,0
+ ,64.4
+ ,0
+ ,63.5
+ ,0
+ ,69.8
+ ,0
+ ,69.9
+ ,0
+ ,69.3
+ ,0
+ ,69.7
+ ,0
+ ,69.8
+ ,0
+ ,70.2
+ ,0
+ ,69.8
+ ,0
+ ,70.7
+ ,0
+ ,71.4
+ ,0
+ ,70.3
+ ,0
+ ,70.9
+ ,0
+ ,70.6
+ ,0
+ ,69.0
+ ,0
+ ,71.0
+ ,0
+ ,74.7
+ ,0
+ ,77.5
+ ,0
+ ,78.6
+ ,0
+ ,75.3
+ ,0
+ ,72.1
+ ,0
+ ,73.8
+ ,0
+ ,73.7
+ ,0
+ ,75.2
+ ,0
+ ,75.2
+ ,0
+ ,74.5
+ ,0
+ ,74.4
+ ,0
+ ,75.4
+ ,0
+ ,73.7
+ ,0
+ ,74.3
+ ,0
+ ,75.0
+ ,0
+ ,75.8
+ ,0
+ ,76.7
+ ,0
+ ,76.8
+ ,0
+ ,76.8
+ ,0
+ ,76.4
+ ,0
+ ,76.4
+ ,0
+ ,77.2
+ ,0
+ ,77.2
+ ,0
+ ,77.4
+ ,0
+ ,78.1
+ ,0
+ ,78.5
+ ,0
+ ,77.9
+ ,0
+ ,78.6
+ ,0
+ ,79.8
+ ,0
+ ,80.3
+ ,0
+ ,80.8
+ ,0
+ ,80.5
+ ,0
+ ,79.4
+ ,0
+ ,79.3
+ ,0
+ ,79.6
+ ,0
+ ,79.2
+ ,0
+ ,79.1
+ ,0
+ ,79.8
+ ,0
+ ,80.0
+ ,0
+ ,80.5
+ ,0
+ ,80.4
+ ,0
+ ,81.1
+ ,0
+ ,82.2
+ ,0
+ ,81.5
+ ,0
+ ,84.2
+ ,0
+ ,84.3
+ ,0
+ ,83.3
+ ,0
+ ,84.2
+ ,0
+ ,84.9
+ ,0
+ ,85.0
+ ,0
+ ,85.3
+ ,0
+ ,85.4
+ ,0
+ ,85.8
+ ,0
+ ,85.2
+ ,0
+ ,86.4
+ ,0
+ ,88.2
+ ,0
+ ,88.3
+ ,0
+ ,88.0
+ ,0
+ ,87.8
+ ,0
+ ,87.4
+ ,0
+ ,87.4
+ ,0
+ ,88.0
+ ,0
+ ,88.0
+ ,0
+ ,89.9
+ ,0
+ ,88.4
+ ,0
+ ,89.7
+ ,0
+ ,89.9
+ ,0
+ ,90.5
+ ,0
+ ,90.7
+ ,0
+ ,89.5
+ ,0
+ ,91.2
+ ,0
+ ,91.2
+ ,0
+ ,89.8
+ ,0
+ ,89.6
+ ,0
+ ,92.3
+ ,0
+ ,90.1
+ ,0
+ ,92.9
+ ,0
+ ,93.3
+ ,0
+ ,93.5
+ ,0
+ ,93.4
+ ,0
+ ,93.6
+ ,0
+ ,93.7
+ ,0
+ ,93.6
+ ,0
+ ,93.0
+ ,0
+ ,94.1
+ ,0
+ ,95.7
+ ,0
+ ,95.6
+ ,0
+ ,97.2
+ ,0
+ ,98.1
+ ,0
+ ,98.8
+ ,0
+ ,95.3
+ ,0
+ ,95.3
+ ,0
+ ,96.7
+ ,0
+ ,99.2
+ ,0
+ ,99.0
+ ,0
+ ,100.9
+ ,0
+ ,100.1
+ ,0
+ ,100.4
+ ,0
+ ,100.5
+ ,0
+ ,102.6
+ ,0
+ ,101.8
+ ,0
+ ,102.6
+ ,0
+ ,101.0
+ ,0
+ ,101.6
+ ,0
+ ,100.6
+ ,0
+ ,100.4
+ ,0
+ ,100.7
+ ,0
+ ,100.6
+ ,0
+ ,100.3
+ ,0
+ ,101.4
+ ,0
+ ,103.2
+ ,0
+ ,79.2
+ ,1
+ ,83.4
+ ,1
+ ,86.5
+ ,1
+ ,91.3
+ ,1
+ ,91.5
+ ,1
+ ,93.1
+ ,1
+ ,93.1
+ ,1
+ ,93.3
+ ,1
+ ,94.4
+ ,1
+ ,94.4
+ ,1
+ ,94.1
+ ,1
+ ,95.3
+ ,1
+ ,93.8
+ ,1
+ ,96.3
+ ,1
+ ,96.0
+ ,1
+ ,97.6
+ ,1
+ ,96.8
+ ,1
+ ,95.0
+ ,1
+ ,93.7
+ ,1
+ ,91.0
+ ,1
+ ,92.2
+ ,1
+ ,93.6
+ ,1
+ ,97.2
+ ,1
+ ,97.1
+ ,1
+ ,98.2
+ ,1
+ ,98.3
+ ,1
+ ,99.8
+ ,1
+ ,100.5
+ ,1
+ ,99.2
+ ,1
+ ,101.0
+ ,1
+ ,102.1
+ ,1
+ ,102.8
+ ,1
+ ,102.5
+ ,1
+ ,104.2
+ ,1
+ ,104.3
+ ,1
+ ,105.3
+ ,1
+ ,105.1
+ ,1
+ ,107.4
+ ,1
+ ,106.4
+ ,1
+ ,106.4
+ ,1
+ ,107.9
+ ,1
+ ,107.8
+ ,1
+ ,108.3
+ ,1
+ ,108.3
+ ,1
+ ,109.2
+ ,1
+ ,109.3
+ ,1
+ ,109.3
+ ,1
+ ,109.6
+ ,1
+ ,111.1
+ ,1
+ ,109.0
+ ,1
+ ,109.8
+ ,1
+ ,108.8
+ ,1
+ ,110.9
+ ,1
+ ,110.2
+ ,1
+ ,111.3
+ ,1
+ ,111.6
+ ,1
+ ,112.3
+ ,1
+ ,111.2
+ ,1
+ ,111.7
+ ,1
+ ,111.7
+ ,1
+ ,112.7
+ ,1
+ ,113.2
+ ,1
+ ,113.0
+ ,1
+ ,114.2
+ ,1
+ ,114.0
+ ,1
+ ,111.7
+ ,1
+ ,114.2
+ ,1
+ ,114.7
+ ,1
+ ,116.5
+ ,1
+ ,116.2
+ ,1
+ ,116.2
+ ,1
+ ,117.4
+ ,1
+ ,117.4
+ ,1
+ ,118.2
+ ,1
+ ,116.4
+ ,1
+ ,117.3
+ ,1
+ ,117.1
+ ,1
+ ,116.5
+ ,1
+ ,117.4
+ ,1
+ ,118.2
+ ,1
+ ,118.4
+ ,1
+ ,116.9
+ ,1
+ ,116.3
+ ,1
+ ,116.8
+ ,1
+ ,114.9
+ ,1)
+ ,dim=c(2
+ ,225)
+ ,dimnames=list(c('Y'
+ ,'D')
+ ,1:225))
> y <- array(NA,dim=c(2,225),dimnames=list(c('Y','D'),1:225))
> 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
Y D t
1 70.5 0 1
2 71.3 0 2
3 71.4 0 3
4 70.1 0 4
5 69.4 0 5
6 69.8 0 6
7 69.8 0 7
8 70.7 0 8
9 69.4 0 9
10 69.8 0 10
11 69.3 0 11
12 72.9 0 12
13 70.0 0 13
14 64.4 0 14
15 63.5 0 15
16 69.8 0 16
17 69.9 0 17
18 69.3 0 18
19 69.7 0 19
20 69.8 0 20
21 70.2 0 21
22 69.8 0 22
23 70.7 0 23
24 71.4 0 24
25 70.3 0 25
26 70.9 0 26
27 70.6 0 27
28 69.0 0 28
29 71.0 0 29
30 74.7 0 30
31 77.5 0 31
32 78.6 0 32
33 75.3 0 33
34 72.1 0 34
35 73.8 0 35
36 73.7 0 36
37 75.2 0 37
38 75.2 0 38
39 74.5 0 39
40 74.4 0 40
41 75.4 0 41
42 73.7 0 42
43 74.3 0 43
44 75.0 0 44
45 75.8 0 45
46 76.7 0 46
47 76.8 0 47
48 76.8 0 48
49 76.4 0 49
50 76.4 0 50
51 77.2 0 51
52 77.2 0 52
53 77.4 0 53
54 78.1 0 54
55 78.5 0 55
56 77.9 0 56
57 78.6 0 57
58 79.8 0 58
59 80.3 0 59
60 80.8 0 60
61 80.5 0 61
62 79.4 0 62
63 79.3 0 63
64 79.6 0 64
65 79.2 0 65
66 79.1 0 66
67 79.8 0 67
68 80.0 0 68
69 80.5 0 69
70 80.4 0 70
71 81.1 0 71
72 82.2 0 72
73 81.5 0 73
74 84.2 0 74
75 84.3 0 75
76 83.3 0 76
77 84.2 0 77
78 84.9 0 78
79 85.0 0 79
80 85.3 0 80
81 85.4 0 81
82 85.8 0 82
83 85.2 0 83
84 86.4 0 84
85 88.2 0 85
86 88.3 0 86
87 88.0 0 87
88 87.8 0 88
89 87.4 0 89
90 87.4 0 90
91 88.0 0 91
92 88.0 0 92
93 89.9 0 93
94 88.4 0 94
95 89.7 0 95
96 89.9 0 96
97 90.5 0 97
98 90.7 0 98
99 89.5 0 99
100 91.2 0 100
101 91.2 0 101
102 89.8 0 102
103 89.6 0 103
104 92.3 0 104
105 90.1 0 105
106 92.9 0 106
107 93.3 0 107
108 93.5 0 108
109 93.4 0 109
110 93.6 0 110
111 93.7 0 111
112 93.6 0 112
113 93.0 0 113
114 94.1 0 114
115 95.7 0 115
116 95.6 0 116
117 97.2 0 117
118 98.1 0 118
119 98.8 0 119
120 95.3 0 120
121 95.3 0 121
122 96.7 0 122
123 99.2 0 123
124 99.0 0 124
125 100.9 0 125
126 100.1 0 126
127 100.4 0 127
128 100.5 0 128
129 102.6 0 129
130 101.8 0 130
131 102.6 0 131
132 101.0 0 132
133 101.6 0 133
134 100.6 0 134
135 100.4 0 135
136 100.7 0 136
137 100.6 0 137
138 100.3 0 138
139 101.4 0 139
140 103.2 0 140
141 79.2 1 141
142 83.4 1 142
143 86.5 1 143
144 91.3 1 144
145 91.5 1 145
146 93.1 1 146
147 93.1 1 147
148 93.3 1 148
149 94.4 1 149
150 94.4 1 150
151 94.1 1 151
152 95.3 1 152
153 93.8 1 153
154 96.3 1 154
155 96.0 1 155
156 97.6 1 156
157 96.8 1 157
158 95.0 1 158
159 93.7 1 159
160 91.0 1 160
161 92.2 1 161
162 93.6 1 162
163 97.2 1 163
164 97.1 1 164
165 98.2 1 165
166 98.3 1 166
167 99.8 1 167
168 100.5 1 168
169 99.2 1 169
170 101.0 1 170
171 102.1 1 171
172 102.8 1 172
173 102.5 1 173
174 104.2 1 174
175 104.3 1 175
176 105.3 1 176
177 105.1 1 177
178 107.4 1 178
179 106.4 1 179
180 106.4 1 180
181 107.9 1 181
182 107.8 1 182
183 108.3 1 183
184 108.3 1 184
185 109.2 1 185
186 109.3 1 186
187 109.3 1 187
188 109.6 1 188
189 111.1 1 189
190 109.0 1 190
191 109.8 1 191
192 108.8 1 192
193 110.9 1 193
194 110.2 1 194
195 111.3 1 195
196 111.6 1 196
197 112.3 1 197
198 111.2 1 198
199 111.7 1 199
200 111.7 1 200
201 112.7 1 201
202 113.2 1 202
203 113.0 1 203
204 114.2 1 204
205 114.0 1 205
206 111.7 1 206
207 114.2 1 207
208 114.7 1 208
209 116.5 1 209
210 116.2 1 210
211 116.2 1 211
212 117.4 1 212
213 117.4 1 213
214 118.2 1 214
215 116.4 1 215
216 117.3 1 216
217 117.1 1 217
218 116.5 1 218
219 117.4 1 219
220 118.2 1 220
221 118.4 1 221
222 116.9 1 222
223 116.3 1 223
224 116.8 1 224
225 114.9 1 225
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) D t
63.6322 -9.4325 0.2804
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-14.5352 -1.4343 -0.2608 1.7510 7.1070
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 63.632236 0.426025 149.36 <2e-16 ***
D -9.432540 0.683670 -13.80 <2e-16 ***
t 0.280394 0.005103 54.94 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.7 on 222 degrees of freedom
Multiple R-squared: 0.9673, Adjusted R-squared: 0.967
F-statistic: 3284 on 2 and 222 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.0337790248 6.755805e-02 9.662210e-01
[2,] 0.0093708590 1.874172e-02 9.906291e-01
[3,] 0.0070786142 1.415723e-02 9.929214e-01
[4,] 0.0021215697 4.243139e-03 9.978784e-01
[5,] 0.0006179999 1.236000e-03 9.993820e-01
[6,] 0.0001598004 3.196008e-04 9.998402e-01
[7,] 0.0124147691 2.482954e-02 9.875852e-01
[8,] 0.0060582954 1.211659e-02 9.939417e-01
[9,] 0.1757314445 3.514629e-01 8.242686e-01
[10,] 0.3878951942 7.757904e-01 6.121048e-01
[11,] 0.4024330811 8.048662e-01 5.975669e-01
[12,] 0.4003811542 8.007623e-01 5.996188e-01
[13,] 0.3552742609 7.105485e-01 6.447257e-01
[14,] 0.3235188760 6.470378e-01 6.764811e-01
[15,] 0.2904793775 5.809588e-01 7.095206e-01
[16,] 0.2680382100 5.360764e-01 7.319618e-01
[17,] 0.2278399080 4.556798e-01 7.721601e-01
[18,] 0.2145138282 4.290277e-01 7.854862e-01
[19,] 0.2195985897 4.391972e-01 7.804014e-01
[20,] 0.1819155566 3.638311e-01 8.180844e-01
[21,] 0.1577187994 3.154376e-01 8.422812e-01
[22,] 0.1279787234 2.559574e-01 8.720213e-01
[23,] 0.1000810859 2.001622e-01 8.999189e-01
[24,] 0.0830320415 1.660641e-01 9.169680e-01
[25,] 0.1965515296 3.931031e-01 8.034485e-01
[26,] 0.6009871534 7.980257e-01 3.990128e-01
[27,] 0.9060734187 1.878532e-01 9.392658e-02
[28,] 0.9230390833 1.539218e-01 7.696092e-02
[29,] 0.9052822973 1.894354e-01 9.471770e-02
[30,] 0.8911971957 2.176056e-01 1.088028e-01
[31,] 0.8726482396 2.547035e-01 1.273518e-01
[32,] 0.8725851448 2.548297e-01 1.274149e-01
[33,] 0.8675616961 2.648766e-01 1.324383e-01
[34,] 0.8485242877 3.029514e-01 1.514757e-01
[35,] 0.8248986898 3.502026e-01 1.751013e-01
[36,] 0.8102657304 3.794685e-01 1.897343e-01
[37,] 0.7813221853 4.373556e-01 2.186778e-01
[38,] 0.7480421720 5.039157e-01 2.519578e-01
[39,] 0.7146631991 5.706736e-01 2.853368e-01
[40,] 0.6882303749 6.235393e-01 3.117696e-01
[41,] 0.6783659118 6.432682e-01 3.216341e-01
[42,] 0.6640730589 6.718539e-01 3.359269e-01
[43,] 0.6431255089 7.137490e-01 3.568745e-01
[44,] 0.6091236486 7.817527e-01 3.908764e-01
[45,] 0.5714289130 8.571422e-01 4.285711e-01
[46,] 0.5428996409 9.142007e-01 4.571004e-01
[47,] 0.5095457817 9.809084e-01 4.904542e-01
[48,] 0.4756854532 9.513709e-01 5.243145e-01
[49,] 0.4524814684 9.049629e-01 5.475185e-01
[50,] 0.4339291446 8.678583e-01 5.660709e-01
[51,] 0.3978154397 7.956309e-01 6.021846e-01
[52,] 0.3706112590 7.412225e-01 6.293887e-01
[53,] 0.3728181706 7.456363e-01 6.271818e-01
[54,] 0.3845977120 7.691954e-01 6.154023e-01
[55,] 0.4068719660 8.137439e-01 5.931280e-01
[56,] 0.4048364289 8.096729e-01 5.951636e-01
[57,] 0.3706072194 7.412144e-01 6.293928e-01
[58,] 0.3345652990 6.691306e-01 6.654347e-01
[59,] 0.3000189575 6.000379e-01 6.999810e-01
[60,] 0.2664108398 5.328217e-01 7.335892e-01
[61,] 0.2362592354 4.725185e-01 7.637408e-01
[62,] 0.2054561943 4.109124e-01 7.945438e-01
[63,] 0.1769849131 3.539698e-01 8.230151e-01
[64,] 0.1512238756 3.024478e-01 8.487761e-01
[65,] 0.1279013269 2.558027e-01 8.720987e-01
[66,] 0.1076893955 2.153788e-01 8.923106e-01
[67,] 0.0954653590 1.909307e-01 9.045346e-01
[68,] 0.0790547284 1.581095e-01 9.209453e-01
[69,] 0.0908196913 1.816394e-01 9.091803e-01
[70,] 0.0986862622 1.973725e-01 9.013137e-01
[71,] 0.0870021159 1.740042e-01 9.129979e-01
[72,] 0.0831169859 1.662340e-01 9.168830e-01
[73,] 0.0853369919 1.706740e-01 9.146630e-01
[74,] 0.0842992957 1.685986e-01 9.157007e-01
[75,] 0.0832527726 1.665055e-01 9.167472e-01
[76,] 0.0794829549 1.589659e-01 9.205170e-01
[77,] 0.0771588555 1.543177e-01 9.228411e-01
[78,] 0.0665290500 1.330581e-01 9.334710e-01
[79,] 0.0647772451 1.295545e-01 9.352228e-01
[80,] 0.0897985438 1.795971e-01 9.102015e-01
[81,] 0.1136656861 2.273314e-01 8.863343e-01
[82,] 0.1236141917 2.472284e-01 8.763858e-01
[83,] 0.1227187044 2.454374e-01 8.772813e-01
[84,] 0.1111786014 2.223572e-01 8.888214e-01
[85,] 0.0976945628 1.953891e-01 9.023054e-01
[86,] 0.0881769218 1.763538e-01 9.118231e-01
[87,] 0.0770140746 1.540281e-01 9.229859e-01
[88,] 0.0866538625 1.733077e-01 9.133461e-01
[89,] 0.0743831957 1.487664e-01 9.256168e-01
[90,] 0.0716146802 1.432294e-01 9.283853e-01
[91,] 0.0677615125 1.355230e-01 9.322385e-01
[92,] 0.0672903278 1.345807e-01 9.327097e-01
[93,] 0.0655159343 1.310319e-01 9.344841e-01
[94,] 0.0543998267 1.087997e-01 9.456002e-01
[95,] 0.0520158325 1.040317e-01 9.479842e-01
[96,] 0.0473559280 9.471186e-02 9.526441e-01
[97,] 0.0386392575 7.727851e-02 9.613607e-01
[98,] 0.0320359510 6.407190e-02 9.679640e-01
[99,] 0.0301209774 6.024195e-02 9.698790e-01
[100,] 0.0250562738 5.011255e-02 9.749437e-01
[101,] 0.0235665480 4.713310e-02 9.764335e-01
[102,] 0.0224831869 4.496637e-02 9.775168e-01
[103,] 0.0209555012 4.191100e-02 9.790445e-01
[104,] 0.0182552553 3.651051e-02 9.817447e-01
[105,] 0.0156312443 3.126249e-02 9.843688e-01
[106,] 0.0130242827 2.604857e-02 9.869757e-01
[107,] 0.0104597033 2.091941e-02 9.895403e-01
[108,] 0.0083421581 1.668432e-02 9.916578e-01
[109,] 0.0066421688 1.328434e-02 9.933578e-01
[110,] 0.0062165895 1.243318e-02 9.937834e-01
[111,] 0.0053817142 1.076343e-02 9.946183e-01
[112,] 0.0062501754 1.250035e-02 9.937498e-01
[113,] 0.0086401225 1.728025e-02 9.913599e-01
[114,] 0.0132576361 2.651527e-02 9.867424e-01
[115,] 0.0106889897 2.137798e-02 9.893110e-01
[116,] 0.0088339598 1.766792e-02 9.911660e-01
[117,] 0.0071258183 1.425164e-02 9.928742e-01
[118,] 0.0081667005 1.633340e-02 9.918333e-01
[119,] 0.0081212071 1.624241e-02 9.918788e-01
[120,] 0.0128384814 2.567696e-02 9.871615e-01
[121,] 0.0139301402 2.786028e-02 9.860699e-01
[122,] 0.0149557327 2.991147e-02 9.850443e-01
[123,] 0.0152116276 3.042326e-02 9.847884e-01
[124,] 0.0262735401 5.254708e-02 9.737265e-01
[125,] 0.0312623281 6.252466e-02 9.687377e-01
[126,] 0.0428894418 8.577888e-02 9.571106e-01
[127,] 0.0391720019 7.834400e-02 9.608280e-01
[128,] 0.0377757614 7.555152e-02 9.622242e-01
[129,] 0.0312890828 6.257817e-02 9.687109e-01
[130,] 0.0251456238 5.029125e-02 9.748544e-01
[131,] 0.0200460166 4.009203e-02 9.799540e-01
[132,] 0.0158010854 3.160217e-02 9.841989e-01
[133,] 0.0128734339 2.574687e-02 9.871266e-01
[134,] 0.0102660163 2.053203e-02 9.897340e-01
[135,] 0.0085831798 1.716636e-02 9.914168e-01
[136,] 0.1240619027 2.481238e-01 8.759381e-01
[137,] 0.3355648538 6.711297e-01 6.644351e-01
[138,] 0.4970527344 9.941055e-01 5.029473e-01
[139,] 0.6102639870 7.794720e-01 3.897360e-01
[140,] 0.6556273868 6.887452e-01 3.443726e-01
[141,] 0.6993425734 6.013149e-01 3.006574e-01
[142,] 0.7112481444 5.775037e-01 2.887519e-01
[143,] 0.7094581565 5.810837e-01 2.905418e-01
[144,] 0.7149295608 5.701409e-01 2.850704e-01
[145,] 0.7063919214 5.872162e-01 2.936081e-01
[146,] 0.6858524686 6.282951e-01 3.141475e-01
[147,] 0.6739356167 6.521288e-01 3.260644e-01
[148,] 0.6508275419 6.983449e-01 3.491725e-01
[149,] 0.6410656172 7.178688e-01 3.589344e-01
[150,] 0.6170349922 7.659300e-01 3.829650e-01
[151,] 0.6196986555 7.606027e-01 3.803013e-01
[152,] 0.5933609928 8.132780e-01 4.066390e-01
[153,] 0.5746360201 8.507280e-01 4.253640e-01
[154,] 0.6221642101 7.556716e-01 3.778358e-01
[155,] 0.8789735877 2.420528e-01 1.210264e-01
[156,] 0.9776764354 4.464713e-02 2.232356e-02
[157,] 0.9971794824 5.641035e-03 2.820518e-03
[158,] 0.9981895328 3.620934e-03 1.810467e-03
[159,] 0.9992501467 1.499707e-03 7.498533e-04
[160,] 0.9996116776 7.766448e-04 3.883224e-04
[161,] 0.9998680087 2.639825e-04 1.319913e-04
[162,] 0.9999218345 1.563311e-04 7.816555e-05
[163,] 0.9999493825 1.012350e-04 5.061751e-05
[164,] 0.9999947131 1.057390e-05 5.286948e-06
[165,] 0.9999983414 3.317225e-06 1.658613e-06
[166,] 0.9999991642 1.671663e-06 8.358314e-07
[167,] 0.9999994979 1.004249e-06 5.021247e-07
[168,] 0.9999998616 2.767755e-07 1.383877e-07
[169,] 0.9999998881 2.238960e-07 1.119480e-07
[170,] 0.9999999232 1.535177e-07 7.675887e-08
[171,] 0.9999999227 1.546221e-07 7.731105e-08
[172,] 0.9999999417 1.165376e-07 5.826878e-08
[173,] 0.9999999381 1.237585e-07 6.187924e-08
[174,] 0.9999999245 1.510900e-07 7.554502e-08
[175,] 0.9999999178 1.643911e-07 8.219557e-08
[176,] 0.9999998858 2.283755e-07 1.141877e-07
[177,] 0.9999998305 3.390057e-07 1.695028e-07
[178,] 0.9999997416 5.168695e-07 2.584347e-07
[179,] 0.9999995932 8.136437e-07 4.068218e-07
[180,] 0.9999993875 1.225092e-06 6.125461e-07
[181,] 0.9999990191 1.961743e-06 9.808713e-07
[182,] 0.9999983392 3.321583e-06 1.660792e-06
[183,] 0.9999971582 5.683600e-06 2.841800e-06
[184,] 0.9999971011 5.797763e-06 2.898882e-06
[185,] 0.9999955167 8.966636e-06 4.483318e-06
[186,] 0.9999920826 1.583476e-05 7.917379e-06
[187,] 0.9999929343 1.413131e-05 7.065655e-06
[188,] 0.9999869769 2.604615e-05 1.302308e-05
[189,] 0.9999811280 3.774396e-05 1.887198e-05
[190,] 0.9999656470 6.870596e-05 3.435298e-05
[191,] 0.9999373980 1.252039e-04 6.260195e-05
[192,] 0.9998861153 2.277694e-04 1.138847e-04
[193,] 0.9998418316 3.163368e-04 1.581684e-04
[194,] 0.9997695866 4.608268e-04 2.304134e-04
[195,] 0.9997291935 5.416130e-04 2.708065e-04
[196,] 0.9995740305 8.519389e-04 4.259695e-04
[197,] 0.9992929738 1.414052e-03 7.070262e-04
[198,] 0.9990525331 1.894934e-03 9.474669e-04
[199,] 0.9983211752 3.357650e-03 1.678825e-03
[200,] 0.9973584301 5.283140e-03 2.641570e-03
[201,] 0.9998247640 3.504720e-04 1.752360e-04
[202,] 0.9999068036 1.863928e-04 9.319642e-05
[203,] 0.9999635105 7.297906e-05 3.648953e-05
[204,] 0.9999166694 1.666612e-04 8.333062e-05
[205,] 0.9998704073 2.591854e-04 1.295927e-04
[206,] 0.9998600883 2.798234e-04 1.399117e-04
[207,] 0.9996093229 7.813543e-04 3.906771e-04
[208,] 0.9989259810 2.148038e-03 1.074019e-03
[209,] 0.9974337661 5.132468e-03 2.566234e-03
[210,] 0.9960233456 7.953309e-03 3.976654e-03
[211,] 0.9895525422 2.089492e-02 1.044746e-02
[212,] 0.9765863435 4.682731e-02 2.341366e-02
[213,] 0.9818616262 3.627675e-02 1.813837e-02
[214,] 0.9780935881 4.381282e-02 2.190641e-02
> postscript(file="/var/www/html/rcomp/tmp/12zbl1227803728.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ly8t1227803728.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ydu41227803728.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4q9cn1227803728.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5rxod1227803728.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 = 225
Frequency = 1
1 2 3 4 5
6.587370e+00 7.106977e+00 6.926583e+00 5.346189e+00 4.365795e+00
6 7 8 9 10
4.485401e+00 4.205007e+00 4.824614e+00 3.244220e+00 3.363826e+00
11 12 13 14 15
2.583432e+00 5.903038e+00 2.722645e+00 -3.157749e+00 -4.338143e+00
16 17 18 19 20
1.681463e+00 1.501069e+00 6.206754e-01 7.402816e-01 5.598878e-01
21 22 23 24 25
6.794940e-01 -8.998481e-04 6.187063e-01 1.038313e+00 -3.420813e-01
26 27 28 29 30
-2.247512e-02 -6.028689e-01 -2.483263e+00 -7.636566e-01 2.655950e+00
31 32 33 34 35
5.175556e+00 5.995162e+00 2.414768e+00 -1.065626e+00 3.539805e-01
36 37 38 39 40
-2.641329e-02 1.193193e+00 9.127991e-01 -6.759475e-02 -4.479886e-01
41 42 43 44 45
2.716176e-01 -1.708776e+00 -1.389170e+00 -9.695638e-01 -4.499577e-01
46 47 48 49 50
1.696485e-01 -1.074529e-02 -2.911391e-01 -9.715329e-01 -1.251927e+00
51 52 53 54 55
-7.323206e-01 -1.012714e+00 -1.093108e+00 -6.735020e-01 -5.538958e-01
56 57 58 59 60
-1.434290e+00 -1.014683e+00 -9.507728e-02 1.245289e-01 3.441351e-01
61 62 63 64 65
-2.362587e-01 -1.616653e+00 -1.997046e+00 -1.977440e+00 -2.657834e+00
66 67 68 69 70
-3.038228e+00 -2.618622e+00 -2.699015e+00 -2.479409e+00 -2.859803e+00
71 72 73 74 75
-2.440197e+00 -1.620591e+00 -2.600985e+00 -1.813784e-01 -3.617722e-01
76 77 78 79 80
-1.642166e+00 -1.022560e+00 -6.029536e-01 -7.833474e-01 -7.637413e-01
81 82 83 84 85
-9.441351e-01 -8.245289e-01 -1.704923e+00 -7.853165e-01 7.342896e-01
86 87 88 89 90
5.538958e-01 -2.649799e-02 -5.068918e-01 -1.187286e+00 -1.467679e+00
91 92 93 94 95
-1.148073e+00 -1.428467e+00 1.911391e-01 -1.589255e+00 -5.696485e-01
96 97 98 99 100
-6.500423e-01 -3.304362e-01 -4.108300e-01 -1.891224e+00 -4.716176e-01
101 102 103 104 105
-7.520114e-01 -2.432405e+00 -2.912799e+00 -4.931929e-01 -2.973587e+00
106 107 108 109 110
-4.539805e-01 -3.343743e-01 -4.147682e-01 -7.951620e-01 -8.755558e-01
111 112 113 114 115
-1.055950e+00 -1.436343e+00 -2.316737e+00 -1.497131e+00 -1.775249e-01
116 117 118 119 120
-5.579187e-01 7.616875e-01 1.381294e+00 1.800900e+00 -1.979494e+00
121 122 123 124 125
-2.259888e+00 -1.140282e+00 1.079325e+00 5.989308e-01 2.218537e+00
126 127 128 129 130
1.138143e+00 1.157749e+00 9.773555e-01 2.796962e+00 1.716568e+00
131 132 133 134 135
2.236174e+00 3.557802e-01 6.753864e-01 -6.050074e-01 -1.085401e+00
136 137 138 139 140
-1.065795e+00 -1.446189e+00 -2.026583e+00 -1.206977e+00 3.126297e-01
141 142 143 144 145
-1.453522e+01 -1.061562e+01 -7.796012e+00 -3.276406e+00 -3.356800e+00
146 147 148 149 150
-2.037193e+00 -2.317587e+00 -2.397981e+00 -1.578375e+00 -1.858769e+00
151 152 153 154 155
-2.439163e+00 -1.519556e+00 -3.299950e+00 -1.080344e+00 -1.660738e+00
156 157 158 159 160
-3.411316e-01 -1.421525e+00 -3.501919e+00 -5.082313e+00 -8.062707e+00
161 162 163 164 165
-7.143101e+00 -6.023495e+00 -2.703888e+00 -3.084282e+00 -2.264676e+00
166 167 168 169 170
-2.445070e+00 -1.225464e+00 -8.058574e-01 -2.386251e+00 -8.666451e-01
171 172 173 174 175
-4.703890e-02 3.725673e-01 -2.078265e-01 1.211780e+00 1.031386e+00
176 177 178 179 180
1.750992e+00 1.270598e+00 3.290204e+00 2.009811e+00 1.729417e+00
181 182 183 184 185
2.949023e+00 2.568629e+00 2.788235e+00 2.507841e+00 3.127448e+00
186 187 188 189 190
2.947054e+00 2.666660e+00 2.686266e+00 3.905872e+00 1.525479e+00
191 192 193 194 195
2.045085e+00 7.646909e-01 2.584297e+00 1.603903e+00 2.423509e+00
196 197 198 199 200
2.443116e+00 2.862722e+00 1.482328e+00 1.701934e+00 1.421540e+00
201 202 203 204 205
2.141147e+00 2.360753e+00 1.880359e+00 2.799965e+00 2.319571e+00
206 207 208 209 210
-2.608225e-01 1.958784e+00 2.178390e+00 3.697996e+00 3.117602e+00
211 212 213 214 215
2.837208e+00 3.756815e+00 3.476421e+00 3.996027e+00 1.915633e+00
216 217 218 219 220
2.535239e+00 2.054845e+00 1.174452e+00 1.794058e+00 2.313664e+00
221 222 223 224 225
2.233270e+00 4.528764e-01 -4.275174e-01 -2.079112e-01 -2.388305e+00
> postscript(file="/var/www/html/rcomp/tmp/61z1t1227803728.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 = 225
Frequency = 1
lag(myerror, k = 1) myerror
0 6.587370e+00 NA
1 7.106977e+00 6.587370e+00
2 6.926583e+00 7.106977e+00
3 5.346189e+00 6.926583e+00
4 4.365795e+00 5.346189e+00
5 4.485401e+00 4.365795e+00
6 4.205007e+00 4.485401e+00
7 4.824614e+00 4.205007e+00
8 3.244220e+00 4.824614e+00
9 3.363826e+00 3.244220e+00
10 2.583432e+00 3.363826e+00
11 5.903038e+00 2.583432e+00
12 2.722645e+00 5.903038e+00
13 -3.157749e+00 2.722645e+00
14 -4.338143e+00 -3.157749e+00
15 1.681463e+00 -4.338143e+00
16 1.501069e+00 1.681463e+00
17 6.206754e-01 1.501069e+00
18 7.402816e-01 6.206754e-01
19 5.598878e-01 7.402816e-01
20 6.794940e-01 5.598878e-01
21 -8.998481e-04 6.794940e-01
22 6.187063e-01 -8.998481e-04
23 1.038313e+00 6.187063e-01
24 -3.420813e-01 1.038313e+00
25 -2.247512e-02 -3.420813e-01
26 -6.028689e-01 -2.247512e-02
27 -2.483263e+00 -6.028689e-01
28 -7.636566e-01 -2.483263e+00
29 2.655950e+00 -7.636566e-01
30 5.175556e+00 2.655950e+00
31 5.995162e+00 5.175556e+00
32 2.414768e+00 5.995162e+00
33 -1.065626e+00 2.414768e+00
34 3.539805e-01 -1.065626e+00
35 -2.641329e-02 3.539805e-01
36 1.193193e+00 -2.641329e-02
37 9.127991e-01 1.193193e+00
38 -6.759475e-02 9.127991e-01
39 -4.479886e-01 -6.759475e-02
40 2.716176e-01 -4.479886e-01
41 -1.708776e+00 2.716176e-01
42 -1.389170e+00 -1.708776e+00
43 -9.695638e-01 -1.389170e+00
44 -4.499577e-01 -9.695638e-01
45 1.696485e-01 -4.499577e-01
46 -1.074529e-02 1.696485e-01
47 -2.911391e-01 -1.074529e-02
48 -9.715329e-01 -2.911391e-01
49 -1.251927e+00 -9.715329e-01
50 -7.323206e-01 -1.251927e+00
51 -1.012714e+00 -7.323206e-01
52 -1.093108e+00 -1.012714e+00
53 -6.735020e-01 -1.093108e+00
54 -5.538958e-01 -6.735020e-01
55 -1.434290e+00 -5.538958e-01
56 -1.014683e+00 -1.434290e+00
57 -9.507728e-02 -1.014683e+00
58 1.245289e-01 -9.507728e-02
59 3.441351e-01 1.245289e-01
60 -2.362587e-01 3.441351e-01
61 -1.616653e+00 -2.362587e-01
62 -1.997046e+00 -1.616653e+00
63 -1.977440e+00 -1.997046e+00
64 -2.657834e+00 -1.977440e+00
65 -3.038228e+00 -2.657834e+00
66 -2.618622e+00 -3.038228e+00
67 -2.699015e+00 -2.618622e+00
68 -2.479409e+00 -2.699015e+00
69 -2.859803e+00 -2.479409e+00
70 -2.440197e+00 -2.859803e+00
71 -1.620591e+00 -2.440197e+00
72 -2.600985e+00 -1.620591e+00
73 -1.813784e-01 -2.600985e+00
74 -3.617722e-01 -1.813784e-01
75 -1.642166e+00 -3.617722e-01
76 -1.022560e+00 -1.642166e+00
77 -6.029536e-01 -1.022560e+00
78 -7.833474e-01 -6.029536e-01
79 -7.637413e-01 -7.833474e-01
80 -9.441351e-01 -7.637413e-01
81 -8.245289e-01 -9.441351e-01
82 -1.704923e+00 -8.245289e-01
83 -7.853165e-01 -1.704923e+00
84 7.342896e-01 -7.853165e-01
85 5.538958e-01 7.342896e-01
86 -2.649799e-02 5.538958e-01
87 -5.068918e-01 -2.649799e-02
88 -1.187286e+00 -5.068918e-01
89 -1.467679e+00 -1.187286e+00
90 -1.148073e+00 -1.467679e+00
91 -1.428467e+00 -1.148073e+00
92 1.911391e-01 -1.428467e+00
93 -1.589255e+00 1.911391e-01
94 -5.696485e-01 -1.589255e+00
95 -6.500423e-01 -5.696485e-01
96 -3.304362e-01 -6.500423e-01
97 -4.108300e-01 -3.304362e-01
98 -1.891224e+00 -4.108300e-01
99 -4.716176e-01 -1.891224e+00
100 -7.520114e-01 -4.716176e-01
101 -2.432405e+00 -7.520114e-01
102 -2.912799e+00 -2.432405e+00
103 -4.931929e-01 -2.912799e+00
104 -2.973587e+00 -4.931929e-01
105 -4.539805e-01 -2.973587e+00
106 -3.343743e-01 -4.539805e-01
107 -4.147682e-01 -3.343743e-01
108 -7.951620e-01 -4.147682e-01
109 -8.755558e-01 -7.951620e-01
110 -1.055950e+00 -8.755558e-01
111 -1.436343e+00 -1.055950e+00
112 -2.316737e+00 -1.436343e+00
113 -1.497131e+00 -2.316737e+00
114 -1.775249e-01 -1.497131e+00
115 -5.579187e-01 -1.775249e-01
116 7.616875e-01 -5.579187e-01
117 1.381294e+00 7.616875e-01
118 1.800900e+00 1.381294e+00
119 -1.979494e+00 1.800900e+00
120 -2.259888e+00 -1.979494e+00
121 -1.140282e+00 -2.259888e+00
122 1.079325e+00 -1.140282e+00
123 5.989308e-01 1.079325e+00
124 2.218537e+00 5.989308e-01
125 1.138143e+00 2.218537e+00
126 1.157749e+00 1.138143e+00
127 9.773555e-01 1.157749e+00
128 2.796962e+00 9.773555e-01
129 1.716568e+00 2.796962e+00
130 2.236174e+00 1.716568e+00
131 3.557802e-01 2.236174e+00
132 6.753864e-01 3.557802e-01
133 -6.050074e-01 6.753864e-01
134 -1.085401e+00 -6.050074e-01
135 -1.065795e+00 -1.085401e+00
136 -1.446189e+00 -1.065795e+00
137 -2.026583e+00 -1.446189e+00
138 -1.206977e+00 -2.026583e+00
139 3.126297e-01 -1.206977e+00
140 -1.453522e+01 3.126297e-01
141 -1.061562e+01 -1.453522e+01
142 -7.796012e+00 -1.061562e+01
143 -3.276406e+00 -7.796012e+00
144 -3.356800e+00 -3.276406e+00
145 -2.037193e+00 -3.356800e+00
146 -2.317587e+00 -2.037193e+00
147 -2.397981e+00 -2.317587e+00
148 -1.578375e+00 -2.397981e+00
149 -1.858769e+00 -1.578375e+00
150 -2.439163e+00 -1.858769e+00
151 -1.519556e+00 -2.439163e+00
152 -3.299950e+00 -1.519556e+00
153 -1.080344e+00 -3.299950e+00
154 -1.660738e+00 -1.080344e+00
155 -3.411316e-01 -1.660738e+00
156 -1.421525e+00 -3.411316e-01
157 -3.501919e+00 -1.421525e+00
158 -5.082313e+00 -3.501919e+00
159 -8.062707e+00 -5.082313e+00
160 -7.143101e+00 -8.062707e+00
161 -6.023495e+00 -7.143101e+00
162 -2.703888e+00 -6.023495e+00
163 -3.084282e+00 -2.703888e+00
164 -2.264676e+00 -3.084282e+00
165 -2.445070e+00 -2.264676e+00
166 -1.225464e+00 -2.445070e+00
167 -8.058574e-01 -1.225464e+00
168 -2.386251e+00 -8.058574e-01
169 -8.666451e-01 -2.386251e+00
170 -4.703890e-02 -8.666451e-01
171 3.725673e-01 -4.703890e-02
172 -2.078265e-01 3.725673e-01
173 1.211780e+00 -2.078265e-01
174 1.031386e+00 1.211780e+00
175 1.750992e+00 1.031386e+00
176 1.270598e+00 1.750992e+00
177 3.290204e+00 1.270598e+00
178 2.009811e+00 3.290204e+00
179 1.729417e+00 2.009811e+00
180 2.949023e+00 1.729417e+00
181 2.568629e+00 2.949023e+00
182 2.788235e+00 2.568629e+00
183 2.507841e+00 2.788235e+00
184 3.127448e+00 2.507841e+00
185 2.947054e+00 3.127448e+00
186 2.666660e+00 2.947054e+00
187 2.686266e+00 2.666660e+00
188 3.905872e+00 2.686266e+00
189 1.525479e+00 3.905872e+00
190 2.045085e+00 1.525479e+00
191 7.646909e-01 2.045085e+00
192 2.584297e+00 7.646909e-01
193 1.603903e+00 2.584297e+00
194 2.423509e+00 1.603903e+00
195 2.443116e+00 2.423509e+00
196 2.862722e+00 2.443116e+00
197 1.482328e+00 2.862722e+00
198 1.701934e+00 1.482328e+00
199 1.421540e+00 1.701934e+00
200 2.141147e+00 1.421540e+00
201 2.360753e+00 2.141147e+00
202 1.880359e+00 2.360753e+00
203 2.799965e+00 1.880359e+00
204 2.319571e+00 2.799965e+00
205 -2.608225e-01 2.319571e+00
206 1.958784e+00 -2.608225e-01
207 2.178390e+00 1.958784e+00
208 3.697996e+00 2.178390e+00
209 3.117602e+00 3.697996e+00
210 2.837208e+00 3.117602e+00
211 3.756815e+00 2.837208e+00
212 3.476421e+00 3.756815e+00
213 3.996027e+00 3.476421e+00
214 1.915633e+00 3.996027e+00
215 2.535239e+00 1.915633e+00
216 2.054845e+00 2.535239e+00
217 1.174452e+00 2.054845e+00
218 1.794058e+00 1.174452e+00
219 2.313664e+00 1.794058e+00
220 2.233270e+00 2.313664e+00
221 4.528764e-01 2.233270e+00
222 -4.275174e-01 4.528764e-01
223 -2.079112e-01 -4.275174e-01
224 -2.388305e+00 -2.079112e-01
225 NA -2.388305e+00
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 7.106977e+00 6.587370e+00
[2,] 6.926583e+00 7.106977e+00
[3,] 5.346189e+00 6.926583e+00
[4,] 4.365795e+00 5.346189e+00
[5,] 4.485401e+00 4.365795e+00
[6,] 4.205007e+00 4.485401e+00
[7,] 4.824614e+00 4.205007e+00
[8,] 3.244220e+00 4.824614e+00
[9,] 3.363826e+00 3.244220e+00
[10,] 2.583432e+00 3.363826e+00
[11,] 5.903038e+00 2.583432e+00
[12,] 2.722645e+00 5.903038e+00
[13,] -3.157749e+00 2.722645e+00
[14,] -4.338143e+00 -3.157749e+00
[15,] 1.681463e+00 -4.338143e+00
[16,] 1.501069e+00 1.681463e+00
[17,] 6.206754e-01 1.501069e+00
[18,] 7.402816e-01 6.206754e-01
[19,] 5.598878e-01 7.402816e-01
[20,] 6.794940e-01 5.598878e-01
[21,] -8.998481e-04 6.794940e-01
[22,] 6.187063e-01 -8.998481e-04
[23,] 1.038313e+00 6.187063e-01
[24,] -3.420813e-01 1.038313e+00
[25,] -2.247512e-02 -3.420813e-01
[26,] -6.028689e-01 -2.247512e-02
[27,] -2.483263e+00 -6.028689e-01
[28,] -7.636566e-01 -2.483263e+00
[29,] 2.655950e+00 -7.636566e-01
[30,] 5.175556e+00 2.655950e+00
[31,] 5.995162e+00 5.175556e+00
[32,] 2.414768e+00 5.995162e+00
[33,] -1.065626e+00 2.414768e+00
[34,] 3.539805e-01 -1.065626e+00
[35,] -2.641329e-02 3.539805e-01
[36,] 1.193193e+00 -2.641329e-02
[37,] 9.127991e-01 1.193193e+00
[38,] -6.759475e-02 9.127991e-01
[39,] -4.479886e-01 -6.759475e-02
[40,] 2.716176e-01 -4.479886e-01
[41,] -1.708776e+00 2.716176e-01
[42,] -1.389170e+00 -1.708776e+00
[43,] -9.695638e-01 -1.389170e+00
[44,] -4.499577e-01 -9.695638e-01
[45,] 1.696485e-01 -4.499577e-01
[46,] -1.074529e-02 1.696485e-01
[47,] -2.911391e-01 -1.074529e-02
[48,] -9.715329e-01 -2.911391e-01
[49,] -1.251927e+00 -9.715329e-01
[50,] -7.323206e-01 -1.251927e+00
[51,] -1.012714e+00 -7.323206e-01
[52,] -1.093108e+00 -1.012714e+00
[53,] -6.735020e-01 -1.093108e+00
[54,] -5.538958e-01 -6.735020e-01
[55,] -1.434290e+00 -5.538958e-01
[56,] -1.014683e+00 -1.434290e+00
[57,] -9.507728e-02 -1.014683e+00
[58,] 1.245289e-01 -9.507728e-02
[59,] 3.441351e-01 1.245289e-01
[60,] -2.362587e-01 3.441351e-01
[61,] -1.616653e+00 -2.362587e-01
[62,] -1.997046e+00 -1.616653e+00
[63,] -1.977440e+00 -1.997046e+00
[64,] -2.657834e+00 -1.977440e+00
[65,] -3.038228e+00 -2.657834e+00
[66,] -2.618622e+00 -3.038228e+00
[67,] -2.699015e+00 -2.618622e+00
[68,] -2.479409e+00 -2.699015e+00
[69,] -2.859803e+00 -2.479409e+00
[70,] -2.440197e+00 -2.859803e+00
[71,] -1.620591e+00 -2.440197e+00
[72,] -2.600985e+00 -1.620591e+00
[73,] -1.813784e-01 -2.600985e+00
[74,] -3.617722e-01 -1.813784e-01
[75,] -1.642166e+00 -3.617722e-01
[76,] -1.022560e+00 -1.642166e+00
[77,] -6.029536e-01 -1.022560e+00
[78,] -7.833474e-01 -6.029536e-01
[79,] -7.637413e-01 -7.833474e-01
[80,] -9.441351e-01 -7.637413e-01
[81,] -8.245289e-01 -9.441351e-01
[82,] -1.704923e+00 -8.245289e-01
[83,] -7.853165e-01 -1.704923e+00
[84,] 7.342896e-01 -7.853165e-01
[85,] 5.538958e-01 7.342896e-01
[86,] -2.649799e-02 5.538958e-01
[87,] -5.068918e-01 -2.649799e-02
[88,] -1.187286e+00 -5.068918e-01
[89,] -1.467679e+00 -1.187286e+00
[90,] -1.148073e+00 -1.467679e+00
[91,] -1.428467e+00 -1.148073e+00
[92,] 1.911391e-01 -1.428467e+00
[93,] -1.589255e+00 1.911391e-01
[94,] -5.696485e-01 -1.589255e+00
[95,] -6.500423e-01 -5.696485e-01
[96,] -3.304362e-01 -6.500423e-01
[97,] -4.108300e-01 -3.304362e-01
[98,] -1.891224e+00 -4.108300e-01
[99,] -4.716176e-01 -1.891224e+00
[100,] -7.520114e-01 -4.716176e-01
[101,] -2.432405e+00 -7.520114e-01
[102,] -2.912799e+00 -2.432405e+00
[103,] -4.931929e-01 -2.912799e+00
[104,] -2.973587e+00 -4.931929e-01
[105,] -4.539805e-01 -2.973587e+00
[106,] -3.343743e-01 -4.539805e-01
[107,] -4.147682e-01 -3.343743e-01
[108,] -7.951620e-01 -4.147682e-01
[109,] -8.755558e-01 -7.951620e-01
[110,] -1.055950e+00 -8.755558e-01
[111,] -1.436343e+00 -1.055950e+00
[112,] -2.316737e+00 -1.436343e+00
[113,] -1.497131e+00 -2.316737e+00
[114,] -1.775249e-01 -1.497131e+00
[115,] -5.579187e-01 -1.775249e-01
[116,] 7.616875e-01 -5.579187e-01
[117,] 1.381294e+00 7.616875e-01
[118,] 1.800900e+00 1.381294e+00
[119,] -1.979494e+00 1.800900e+00
[120,] -2.259888e+00 -1.979494e+00
[121,] -1.140282e+00 -2.259888e+00
[122,] 1.079325e+00 -1.140282e+00
[123,] 5.989308e-01 1.079325e+00
[124,] 2.218537e+00 5.989308e-01
[125,] 1.138143e+00 2.218537e+00
[126,] 1.157749e+00 1.138143e+00
[127,] 9.773555e-01 1.157749e+00
[128,] 2.796962e+00 9.773555e-01
[129,] 1.716568e+00 2.796962e+00
[130,] 2.236174e+00 1.716568e+00
[131,] 3.557802e-01 2.236174e+00
[132,] 6.753864e-01 3.557802e-01
[133,] -6.050074e-01 6.753864e-01
[134,] -1.085401e+00 -6.050074e-01
[135,] -1.065795e+00 -1.085401e+00
[136,] -1.446189e+00 -1.065795e+00
[137,] -2.026583e+00 -1.446189e+00
[138,] -1.206977e+00 -2.026583e+00
[139,] 3.126297e-01 -1.206977e+00
[140,] -1.453522e+01 3.126297e-01
[141,] -1.061562e+01 -1.453522e+01
[142,] -7.796012e+00 -1.061562e+01
[143,] -3.276406e+00 -7.796012e+00
[144,] -3.356800e+00 -3.276406e+00
[145,] -2.037193e+00 -3.356800e+00
[146,] -2.317587e+00 -2.037193e+00
[147,] -2.397981e+00 -2.317587e+00
[148,] -1.578375e+00 -2.397981e+00
[149,] -1.858769e+00 -1.578375e+00
[150,] -2.439163e+00 -1.858769e+00
[151,] -1.519556e+00 -2.439163e+00
[152,] -3.299950e+00 -1.519556e+00
[153,] -1.080344e+00 -3.299950e+00
[154,] -1.660738e+00 -1.080344e+00
[155,] -3.411316e-01 -1.660738e+00
[156,] -1.421525e+00 -3.411316e-01
[157,] -3.501919e+00 -1.421525e+00
[158,] -5.082313e+00 -3.501919e+00
[159,] -8.062707e+00 -5.082313e+00
[160,] -7.143101e+00 -8.062707e+00
[161,] -6.023495e+00 -7.143101e+00
[162,] -2.703888e+00 -6.023495e+00
[163,] -3.084282e+00 -2.703888e+00
[164,] -2.264676e+00 -3.084282e+00
[165,] -2.445070e+00 -2.264676e+00
[166,] -1.225464e+00 -2.445070e+00
[167,] -8.058574e-01 -1.225464e+00
[168,] -2.386251e+00 -8.058574e-01
[169,] -8.666451e-01 -2.386251e+00
[170,] -4.703890e-02 -8.666451e-01
[171,] 3.725673e-01 -4.703890e-02
[172,] -2.078265e-01 3.725673e-01
[173,] 1.211780e+00 -2.078265e-01
[174,] 1.031386e+00 1.211780e+00
[175,] 1.750992e+00 1.031386e+00
[176,] 1.270598e+00 1.750992e+00
[177,] 3.290204e+00 1.270598e+00
[178,] 2.009811e+00 3.290204e+00
[179,] 1.729417e+00 2.009811e+00
[180,] 2.949023e+00 1.729417e+00
[181,] 2.568629e+00 2.949023e+00
[182,] 2.788235e+00 2.568629e+00
[183,] 2.507841e+00 2.788235e+00
[184,] 3.127448e+00 2.507841e+00
[185,] 2.947054e+00 3.127448e+00
[186,] 2.666660e+00 2.947054e+00
[187,] 2.686266e+00 2.666660e+00
[188,] 3.905872e+00 2.686266e+00
[189,] 1.525479e+00 3.905872e+00
[190,] 2.045085e+00 1.525479e+00
[191,] 7.646909e-01 2.045085e+00
[192,] 2.584297e+00 7.646909e-01
[193,] 1.603903e+00 2.584297e+00
[194,] 2.423509e+00 1.603903e+00
[195,] 2.443116e+00 2.423509e+00
[196,] 2.862722e+00 2.443116e+00
[197,] 1.482328e+00 2.862722e+00
[198,] 1.701934e+00 1.482328e+00
[199,] 1.421540e+00 1.701934e+00
[200,] 2.141147e+00 1.421540e+00
[201,] 2.360753e+00 2.141147e+00
[202,] 1.880359e+00 2.360753e+00
[203,] 2.799965e+00 1.880359e+00
[204,] 2.319571e+00 2.799965e+00
[205,] -2.608225e-01 2.319571e+00
[206,] 1.958784e+00 -2.608225e-01
[207,] 2.178390e+00 1.958784e+00
[208,] 3.697996e+00 2.178390e+00
[209,] 3.117602e+00 3.697996e+00
[210,] 2.837208e+00 3.117602e+00
[211,] 3.756815e+00 2.837208e+00
[212,] 3.476421e+00 3.756815e+00
[213,] 3.996027e+00 3.476421e+00
[214,] 1.915633e+00 3.996027e+00
[215,] 2.535239e+00 1.915633e+00
[216,] 2.054845e+00 2.535239e+00
[217,] 1.174452e+00 2.054845e+00
[218,] 1.794058e+00 1.174452e+00
[219,] 2.313664e+00 1.794058e+00
[220,] 2.233270e+00 2.313664e+00
[221,] 4.528764e-01 2.233270e+00
[222,] -4.275174e-01 4.528764e-01
[223,] -2.079112e-01 -4.275174e-01
[224,] -2.388305e+00 -2.079112e-01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 7.106977e+00 6.587370e+00
2 6.926583e+00 7.106977e+00
3 5.346189e+00 6.926583e+00
4 4.365795e+00 5.346189e+00
5 4.485401e+00 4.365795e+00
6 4.205007e+00 4.485401e+00
7 4.824614e+00 4.205007e+00
8 3.244220e+00 4.824614e+00
9 3.363826e+00 3.244220e+00
10 2.583432e+00 3.363826e+00
11 5.903038e+00 2.583432e+00
12 2.722645e+00 5.903038e+00
13 -3.157749e+00 2.722645e+00
14 -4.338143e+00 -3.157749e+00
15 1.681463e+00 -4.338143e+00
16 1.501069e+00 1.681463e+00
17 6.206754e-01 1.501069e+00
18 7.402816e-01 6.206754e-01
19 5.598878e-01 7.402816e-01
20 6.794940e-01 5.598878e-01
21 -8.998481e-04 6.794940e-01
22 6.187063e-01 -8.998481e-04
23 1.038313e+00 6.187063e-01
24 -3.420813e-01 1.038313e+00
25 -2.247512e-02 -3.420813e-01
26 -6.028689e-01 -2.247512e-02
27 -2.483263e+00 -6.028689e-01
28 -7.636566e-01 -2.483263e+00
29 2.655950e+00 -7.636566e-01
30 5.175556e+00 2.655950e+00
31 5.995162e+00 5.175556e+00
32 2.414768e+00 5.995162e+00
33 -1.065626e+00 2.414768e+00
34 3.539805e-01 -1.065626e+00
35 -2.641329e-02 3.539805e-01
36 1.193193e+00 -2.641329e-02
37 9.127991e-01 1.193193e+00
38 -6.759475e-02 9.127991e-01
39 -4.479886e-01 -6.759475e-02
40 2.716176e-01 -4.479886e-01
41 -1.708776e+00 2.716176e-01
42 -1.389170e+00 -1.708776e+00
43 -9.695638e-01 -1.389170e+00
44 -4.499577e-01 -9.695638e-01
45 1.696485e-01 -4.499577e-01
46 -1.074529e-02 1.696485e-01
47 -2.911391e-01 -1.074529e-02
48 -9.715329e-01 -2.911391e-01
49 -1.251927e+00 -9.715329e-01
50 -7.323206e-01 -1.251927e+00
51 -1.012714e+00 -7.323206e-01
52 -1.093108e+00 -1.012714e+00
53 -6.735020e-01 -1.093108e+00
54 -5.538958e-01 -6.735020e-01
55 -1.434290e+00 -5.538958e-01
56 -1.014683e+00 -1.434290e+00
57 -9.507728e-02 -1.014683e+00
58 1.245289e-01 -9.507728e-02
59 3.441351e-01 1.245289e-01
60 -2.362587e-01 3.441351e-01
61 -1.616653e+00 -2.362587e-01
62 -1.997046e+00 -1.616653e+00
63 -1.977440e+00 -1.997046e+00
64 -2.657834e+00 -1.977440e+00
65 -3.038228e+00 -2.657834e+00
66 -2.618622e+00 -3.038228e+00
67 -2.699015e+00 -2.618622e+00
68 -2.479409e+00 -2.699015e+00
69 -2.859803e+00 -2.479409e+00
70 -2.440197e+00 -2.859803e+00
71 -1.620591e+00 -2.440197e+00
72 -2.600985e+00 -1.620591e+00
73 -1.813784e-01 -2.600985e+00
74 -3.617722e-01 -1.813784e-01
75 -1.642166e+00 -3.617722e-01
76 -1.022560e+00 -1.642166e+00
77 -6.029536e-01 -1.022560e+00
78 -7.833474e-01 -6.029536e-01
79 -7.637413e-01 -7.833474e-01
80 -9.441351e-01 -7.637413e-01
81 -8.245289e-01 -9.441351e-01
82 -1.704923e+00 -8.245289e-01
83 -7.853165e-01 -1.704923e+00
84 7.342896e-01 -7.853165e-01
85 5.538958e-01 7.342896e-01
86 -2.649799e-02 5.538958e-01
87 -5.068918e-01 -2.649799e-02
88 -1.187286e+00 -5.068918e-01
89 -1.467679e+00 -1.187286e+00
90 -1.148073e+00 -1.467679e+00
91 -1.428467e+00 -1.148073e+00
92 1.911391e-01 -1.428467e+00
93 -1.589255e+00 1.911391e-01
94 -5.696485e-01 -1.589255e+00
95 -6.500423e-01 -5.696485e-01
96 -3.304362e-01 -6.500423e-01
97 -4.108300e-01 -3.304362e-01
98 -1.891224e+00 -4.108300e-01
99 -4.716176e-01 -1.891224e+00
100 -7.520114e-01 -4.716176e-01
101 -2.432405e+00 -7.520114e-01
102 -2.912799e+00 -2.432405e+00
103 -4.931929e-01 -2.912799e+00
104 -2.973587e+00 -4.931929e-01
105 -4.539805e-01 -2.973587e+00
106 -3.343743e-01 -4.539805e-01
107 -4.147682e-01 -3.343743e-01
108 -7.951620e-01 -4.147682e-01
109 -8.755558e-01 -7.951620e-01
110 -1.055950e+00 -8.755558e-01
111 -1.436343e+00 -1.055950e+00
112 -2.316737e+00 -1.436343e+00
113 -1.497131e+00 -2.316737e+00
114 -1.775249e-01 -1.497131e+00
115 -5.579187e-01 -1.775249e-01
116 7.616875e-01 -5.579187e-01
117 1.381294e+00 7.616875e-01
118 1.800900e+00 1.381294e+00
119 -1.979494e+00 1.800900e+00
120 -2.259888e+00 -1.979494e+00
121 -1.140282e+00 -2.259888e+00
122 1.079325e+00 -1.140282e+00
123 5.989308e-01 1.079325e+00
124 2.218537e+00 5.989308e-01
125 1.138143e+00 2.218537e+00
126 1.157749e+00 1.138143e+00
127 9.773555e-01 1.157749e+00
128 2.796962e+00 9.773555e-01
129 1.716568e+00 2.796962e+00
130 2.236174e+00 1.716568e+00
131 3.557802e-01 2.236174e+00
132 6.753864e-01 3.557802e-01
133 -6.050074e-01 6.753864e-01
134 -1.085401e+00 -6.050074e-01
135 -1.065795e+00 -1.085401e+00
136 -1.446189e+00 -1.065795e+00
137 -2.026583e+00 -1.446189e+00
138 -1.206977e+00 -2.026583e+00
139 3.126297e-01 -1.206977e+00
140 -1.453522e+01 3.126297e-01
141 -1.061562e+01 -1.453522e+01
142 -7.796012e+00 -1.061562e+01
143 -3.276406e+00 -7.796012e+00
144 -3.356800e+00 -3.276406e+00
145 -2.037193e+00 -3.356800e+00
146 -2.317587e+00 -2.037193e+00
147 -2.397981e+00 -2.317587e+00
148 -1.578375e+00 -2.397981e+00
149 -1.858769e+00 -1.578375e+00
150 -2.439163e+00 -1.858769e+00
151 -1.519556e+00 -2.439163e+00
152 -3.299950e+00 -1.519556e+00
153 -1.080344e+00 -3.299950e+00
154 -1.660738e+00 -1.080344e+00
155 -3.411316e-01 -1.660738e+00
156 -1.421525e+00 -3.411316e-01
157 -3.501919e+00 -1.421525e+00
158 -5.082313e+00 -3.501919e+00
159 -8.062707e+00 -5.082313e+00
160 -7.143101e+00 -8.062707e+00
161 -6.023495e+00 -7.143101e+00
162 -2.703888e+00 -6.023495e+00
163 -3.084282e+00 -2.703888e+00
164 -2.264676e+00 -3.084282e+00
165 -2.445070e+00 -2.264676e+00
166 -1.225464e+00 -2.445070e+00
167 -8.058574e-01 -1.225464e+00
168 -2.386251e+00 -8.058574e-01
169 -8.666451e-01 -2.386251e+00
170 -4.703890e-02 -8.666451e-01
171 3.725673e-01 -4.703890e-02
172 -2.078265e-01 3.725673e-01
173 1.211780e+00 -2.078265e-01
174 1.031386e+00 1.211780e+00
175 1.750992e+00 1.031386e+00
176 1.270598e+00 1.750992e+00
177 3.290204e+00 1.270598e+00
178 2.009811e+00 3.290204e+00
179 1.729417e+00 2.009811e+00
180 2.949023e+00 1.729417e+00
181 2.568629e+00 2.949023e+00
182 2.788235e+00 2.568629e+00
183 2.507841e+00 2.788235e+00
184 3.127448e+00 2.507841e+00
185 2.947054e+00 3.127448e+00
186 2.666660e+00 2.947054e+00
187 2.686266e+00 2.666660e+00
188 3.905872e+00 2.686266e+00
189 1.525479e+00 3.905872e+00
190 2.045085e+00 1.525479e+00
191 7.646909e-01 2.045085e+00
192 2.584297e+00 7.646909e-01
193 1.603903e+00 2.584297e+00
194 2.423509e+00 1.603903e+00
195 2.443116e+00 2.423509e+00
196 2.862722e+00 2.443116e+00
197 1.482328e+00 2.862722e+00
198 1.701934e+00 1.482328e+00
199 1.421540e+00 1.701934e+00
200 2.141147e+00 1.421540e+00
201 2.360753e+00 2.141147e+00
202 1.880359e+00 2.360753e+00
203 2.799965e+00 1.880359e+00
204 2.319571e+00 2.799965e+00
205 -2.608225e-01 2.319571e+00
206 1.958784e+00 -2.608225e-01
207 2.178390e+00 1.958784e+00
208 3.697996e+00 2.178390e+00
209 3.117602e+00 3.697996e+00
210 2.837208e+00 3.117602e+00
211 3.756815e+00 2.837208e+00
212 3.476421e+00 3.756815e+00
213 3.996027e+00 3.476421e+00
214 1.915633e+00 3.996027e+00
215 2.535239e+00 1.915633e+00
216 2.054845e+00 2.535239e+00
217 1.174452e+00 2.054845e+00
218 1.794058e+00 1.174452e+00
219 2.313664e+00 1.794058e+00
220 2.233270e+00 2.313664e+00
221 4.528764e-01 2.233270e+00
222 -4.275174e-01 4.528764e-01
223 -2.079112e-01 -4.275174e-01
224 -2.388305e+00 -2.079112e-01
> 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/7h78v1227803728.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87d9r1227803728.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/983561227803728.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10rdy21227803728.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11zhcj1227803728.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/12ibti1227803728.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/1315eb1227803728.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/14g8gc1227803728.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/15don71227803728.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/167exw1227803728.tab")
+ }
>
> system("convert tmp/12zbl1227803728.ps tmp/12zbl1227803728.png")
> system("convert tmp/2ly8t1227803728.ps tmp/2ly8t1227803728.png")
> system("convert tmp/3ydu41227803728.ps tmp/3ydu41227803728.png")
> system("convert tmp/4q9cn1227803728.ps tmp/4q9cn1227803728.png")
> system("convert tmp/5rxod1227803728.ps tmp/5rxod1227803728.png")
> system("convert tmp/61z1t1227803728.ps tmp/61z1t1227803728.png")
> system("convert tmp/7h78v1227803728.ps tmp/7h78v1227803728.png")
> system("convert tmp/87d9r1227803728.ps tmp/87d9r1227803728.png")
> system("convert tmp/983561227803728.ps tmp/983561227803728.png")
> system("convert tmp/10rdy21227803728.ps tmp/10rdy21227803728.png")
>
>
> proc.time()
user system elapsed
5.056 1.732 5.503