R version 2.15.1 (2012-06-22) -- "Roasted Marshmallows"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
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+ ,4
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+ ,0
+ ,0
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+ ,15
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+ ,0
+ ,1
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+ ,12
+ ,15
+ ,0
+ ,0
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+ ,36
+ ,11
+ ,9
+ ,11
+ ,0
+ ,0
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+ ,31
+ ,16
+ ,12
+ ,15
+ ,0
+ ,1
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+ ,35
+ ,15
+ ,10
+ ,14
+ ,0
+ ,0
+ ,27
+ ,29
+ ,12
+ ,9
+ ,13
+ ,0
+ ,1
+ ,16
+ ,22
+ ,6
+ ,6
+ ,12
+ ,0
+ ,0
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+ ,41
+ ,16
+ ,10
+ ,16
+ ,0
+ ,0
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+ ,36
+ ,10
+ ,9
+ ,16
+ ,0
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+ ,42
+ ,42
+ ,15
+ ,13
+ ,9
+ ,0
+ ,1
+ ,30
+ ,33
+ ,14
+ ,12
+ ,14)
+ ,dim=c(7
+ ,288)
+ ,dimnames=list(c('Populatie'
+ ,'Geslacht'
+ ,'Connected'
+ ,'Seperate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness')
+ ,1:288))
> y <- array(NA,dim=c(7,288),dimnames=list(c('Populatie','Geslacht','Connected','Seperate','Learning','Software','Happiness'),1:288))
> 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 = '7'
> par3 <- 'Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '7'
> #'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, 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
Happiness Populatie Geslacht Connected Seperate Learning Software t
1 14 1 1 41 38 13 12 1
2 18 1 1 39 32 16 11 2
3 11 1 1 30 35 19 15 3
4 12 1 0 31 33 15 6 4
5 16 1 1 34 37 14 13 5
6 18 1 1 35 29 13 10 6
7 14 1 1 39 31 19 12 7
8 14 1 1 34 36 15 14 8
9 15 1 1 36 35 14 12 9
10 15 1 1 37 38 15 9 10
11 17 1 0 38 31 16 10 11
12 19 1 1 36 34 16 12 12
13 10 1 0 38 35 16 12 13
14 16 1 1 39 38 16 11 14
15 18 1 1 33 37 17 15 15
16 14 1 0 32 33 15 12 16
17 14 1 0 36 32 15 10 17
18 17 1 1 38 38 20 12 18
19 14 1 0 39 38 18 11 19
20 16 1 1 32 32 16 12 20
21 18 1 0 32 33 16 11 21
22 11 1 1 31 31 16 12 22
23 14 1 1 39 38 19 13 23
24 12 1 1 37 39 16 11 24
25 17 1 0 39 32 17 12 25
26 9 1 1 41 32 17 13 26
27 16 1 0 36 35 16 10 27
28 14 1 1 33 37 15 14 28
29 15 1 1 33 33 16 12 29
30 11 1 0 34 33 14 10 30
31 16 1 1 31 31 15 12 31
32 13 1 0 27 32 12 8 32
33 17 1 1 37 31 14 10 33
34 15 1 1 34 37 16 12 34
35 14 1 0 34 30 14 12 35
36 16 1 0 32 33 10 7 36
37 9 1 0 29 31 10 9 37
38 15 1 0 36 33 14 12 38
39 17 1 1 29 31 16 10 39
40 13 1 0 35 33 16 10 40
41 15 1 0 37 32 16 10 41
42 16 1 1 34 33 14 12 42
43 16 1 0 38 32 20 15 43
44 12 1 0 35 33 14 10 44
45 15 1 1 38 28 14 10 45
46 11 1 1 37 35 11 12 46
47 15 1 1 38 39 14 13 47
48 15 1 1 33 34 15 11 48
49 17 1 1 36 38 16 11 49
50 13 1 0 38 32 14 12 50
51 16 1 1 32 38 16 14 51
52 14 1 0 32 30 14 10 52
53 11 1 0 32 33 12 12 53
54 12 1 1 34 38 16 13 54
55 12 1 0 32 32 9 5 55
56 15 1 1 37 35 14 6 56
57 16 1 1 39 34 16 12 57
58 15 1 1 29 34 16 12 58
59 12 1 0 37 36 15 11 59
60 12 1 1 35 34 16 10 60
61 8 1 0 30 28 12 7 61
62 13 1 0 38 34 16 12 62
63 11 1 1 34 35 16 14 63
64 14 1 1 31 35 14 11 64
65 15 1 1 34 31 16 12 65
66 10 1 0 35 37 17 13 66
67 11 1 1 36 35 18 14 67
68 12 1 0 30 27 18 11 68
69 15 1 1 39 40 12 12 69
70 15 1 0 35 37 16 12 70
71 14 1 0 38 36 10 8 71
72 16 1 1 31 38 14 11 72
73 15 1 1 34 39 18 14 73
74 15 1 0 38 41 18 14 74
75 13 1 0 34 27 16 12 75
76 12 1 1 39 30 17 9 76
77 17 1 1 37 37 16 13 77
78 13 1 1 34 31 16 11 78
79 15 1 0 28 31 13 12 79
80 13 1 0 37 27 16 12 80
81 15 1 0 33 36 16 12 81
82 15 1 1 35 37 16 12 82
83 16 1 0 37 33 15 12 83
84 15 1 1 32 34 15 11 84
85 14 1 1 33 31 16 10 85
86 15 1 0 38 39 14 9 86
87 14 1 1 33 34 16 12 87
88 13 1 1 29 32 16 12 88
89 7 1 1 33 33 15 12 89
90 17 1 1 31 36 12 9 90
91 13 1 1 36 32 17 15 91
92 15 1 1 35 41 16 12 92
93 14 1 1 32 28 15 12 93
94 13 1 1 29 30 13 12 94
95 16 1 1 39 36 16 10 95
96 12 1 1 37 35 16 13 96
97 14 1 1 35 31 16 9 97
98 17 1 0 37 34 16 12 98
99 15 1 0 32 36 14 10 99
100 17 1 1 38 36 16 14 100
101 12 1 0 37 35 16 11 101
102 16 1 1 36 37 20 15 102
103 11 1 0 32 28 15 11 103
104 15 1 1 33 39 16 11 104
105 9 1 0 40 32 13 12 105
106 16 1 1 38 35 17 12 106
107 15 1 0 41 39 16 12 107
108 10 1 0 36 35 16 11 108
109 10 1 1 43 42 12 7 109
110 15 1 1 30 34 16 12 110
111 11 1 1 31 33 16 14 111
112 13 1 1 32 41 17 11 112
113 18 1 1 37 34 12 10 113
114 16 1 0 37 32 18 13 114
115 14 1 1 33 40 14 13 115
116 14 1 1 34 40 14 8 116
117 14 1 1 33 35 13 11 117
118 14 1 1 38 36 16 12 118
119 12 1 0 33 37 13 11 119
120 14 1 1 31 27 16 13 120
121 15 1 1 38 39 13 12 121
122 15 1 1 37 38 16 14 122
123 15 1 1 36 31 15 13 123
124 13 1 1 31 33 16 15 124
125 17 1 0 39 32 15 10 125
126 17 1 1 44 39 17 11 126
127 19 1 1 33 36 15 9 127
128 15 1 1 35 33 12 11 128
129 13 1 0 32 33 16 10 129
130 9 1 0 28 32 10 11 130
131 15 1 1 40 37 16 8 131
132 15 1 0 27 30 12 11 132
133 15 1 0 37 38 14 12 133
134 16 1 1 32 29 15 12 134
135 11 1 0 28 22 13 9 135
136 14 1 0 34 35 15 11 136
137 11 1 1 30 35 11 10 137
138 15 1 1 35 34 12 8 138
139 13 1 0 31 35 11 9 139
140 15 1 1 32 34 16 8 140
141 16 1 0 30 37 15 9 141
142 14 1 1 30 35 17 15 142
143 15 1 0 31 23 16 11 143
144 16 1 1 40 31 10 8 144
145 16 1 1 32 27 18 13 145
146 11 1 0 36 36 13 12 146
147 12 1 0 32 31 16 12 147
148 9 1 0 35 32 13 9 148
149 16 1 1 38 39 10 7 149
150 13 1 1 42 37 15 13 150
151 16 1 0 34 38 16 9 151
152 12 1 1 35 39 16 6 152
153 9 1 1 38 34 14 8 153
154 13 1 1 33 31 10 8 154
155 14 1 1 32 37 13 6 155
156 19 1 1 33 36 15 9 156
157 13 1 1 34 32 16 11 157
158 12 1 1 32 38 12 8 158
159 10 0 0 27 26 13 10 159
160 14 0 0 31 26 12 8 160
161 16 0 0 38 33 17 14 161
162 10 0 1 34 39 15 10 162
163 11 0 0 24 30 10 8 163
164 14 0 0 30 33 14 11 164
165 12 0 1 26 25 11 12 165
166 9 0 1 34 38 13 12 166
167 9 0 0 27 37 16 12 167
168 11 0 0 37 31 12 5 168
169 16 0 1 36 37 16 12 169
170 9 0 0 41 35 12 10 170
171 13 0 1 29 25 9 7 171
172 16 0 1 36 28 12 12 172
173 13 0 0 32 35 15 11 173
174 9 0 1 37 33 12 8 174
175 12 0 0 30 30 12 9 175
176 16 0 1 31 31 14 10 176
177 11 0 1 38 37 12 9 177
178 14 0 1 36 36 16 12 178
179 13 0 0 35 30 11 6 179
180 15 0 0 31 36 19 15 180
181 14 0 0 38 32 15 12 181
182 16 0 1 22 28 8 12 182
183 13 0 1 32 36 16 12 183
184 14 0 0 36 34 17 11 184
185 15 0 1 39 31 12 7 185
186 13 0 0 28 28 11 7 186
187 11 0 0 32 36 11 5 187
188 11 0 1 32 36 14 12 188
189 14 0 1 38 40 16 12 189
190 15 0 1 32 33 12 3 190
191 11 0 1 35 37 16 11 191
192 15 0 1 32 32 13 10 192
193 12 0 0 37 38 15 12 193
194 14 0 1 34 31 16 9 194
195 14 0 1 33 37 16 12 195
196 8 0 0 33 33 14 9 196
197 9 0 0 30 30 16 12 197
198 15 0 0 24 30 14 10 198
199 17 0 0 34 31 11 9 199
200 13 0 0 34 32 12 12 200
201 15 0 1 33 34 15 8 201
202 15 0 1 34 36 15 11 202
203 14 0 1 35 37 16 11 203
204 16 0 0 35 36 16 12 204
205 13 0 0 36 33 11 10 205
206 16 0 0 34 33 15 10 206
207 9 0 1 34 33 12 12 207
208 16 0 0 41 44 12 12 208
209 11 0 0 32 39 15 11 209
210 10 0 0 30 32 15 8 210
211 11 0 1 35 35 16 12 211
212 15 0 0 28 25 14 10 212
213 17 0 1 33 35 17 11 213
214 14 0 1 39 34 14 10 214
215 8 0 0 36 35 13 8 215
216 15 0 1 36 39 15 12 216
217 11 0 0 35 33 13 12 217
218 16 0 0 38 36 14 10 218
219 10 0 1 33 32 15 12 219
220 15 0 0 31 32 12 9 220
221 16 0 1 32 36 8 6 221
222 19 0 0 31 32 14 10 222
223 12 0 0 33 34 14 9 223
224 8 0 0 34 33 11 9 224
225 11 0 0 34 35 12 9 225
226 14 0 1 34 30 13 6 226
227 9 0 0 33 38 10 10 227
228 15 0 0 32 34 16 6 228
229 13 0 1 41 33 18 14 229
230 16 0 1 34 32 13 10 230
231 11 0 0 36 31 11 10 231
232 12 0 0 37 30 4 6 232
233 13 0 0 36 27 13 12 233
234 10 0 1 29 31 16 12 234
235 11 0 0 37 30 10 7 235
236 12 0 0 27 32 12 8 236
237 8 0 0 35 35 12 11 237
238 12 0 0 28 28 10 3 238
239 12 0 0 35 33 13 6 239
240 11 0 0 29 35 12 8 240
241 13 0 0 32 35 14 9 241
242 14 0 1 36 32 10 9 242
243 10 0 1 19 21 12 8 243
244 12 0 1 21 20 12 9 244
245 15 0 0 31 34 11 7 245
246 13 0 0 33 32 10 7 246
247 13 0 1 36 34 12 6 247
248 13 0 1 33 32 16 9 248
249 12 0 0 37 33 12 10 249
250 12 0 0 34 33 14 11 250
251 9 0 0 35 37 16 12 251
252 9 0 1 31 32 14 8 252
253 15 0 1 37 34 13 11 253
254 10 0 1 35 30 4 3 254
255 14 0 1 27 30 15 11 255
256 15 0 0 34 38 11 12 256
257 7 0 0 40 36 11 7 257
258 14 0 0 29 32 14 9 258
259 8 0 0 38 34 15 12 259
260 10 0 1 34 33 14 8 260
261 13 0 0 21 27 13 11 261
262 13 0 0 36 32 11 8 262
263 13 0 1 38 34 15 10 263
264 8 0 0 30 29 11 8 264
265 12 0 0 35 35 13 7 265
266 13 0 1 30 27 13 8 266
267 12 0 1 36 33 16 10 267
268 10 0 0 34 38 13 8 268
269 13 0 1 35 36 16 12 269
270 12 0 0 34 33 16 14 270
271 9 0 0 32 39 12 7 271
272 15 0 1 33 29 7 6 272
273 13 0 0 33 32 16 11 273
274 13 0 1 26 34 5 4 274
275 13 0 0 35 38 16 9 275
276 15 0 0 21 17 4 5 276
277 15 0 0 38 35 12 9 277
278 14 0 0 35 32 15 11 278
279 15 0 1 33 34 14 12 279
280 11 0 0 37 36 11 9 280
281 15 0 0 38 31 16 12 281
282 14 0 1 34 35 15 10 282
283 13 0 0 27 29 12 9 283
284 12 0 1 16 22 6 6 284
285 16 0 0 40 41 16 10 285
286 16 0 0 36 36 10 9 286
287 9 0 1 42 42 15 13 287
288 14 0 1 30 33 14 12 288
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Populatie Geslacht Connected Seperate Learning
11.37897 0.56595 0.79407 0.02548 -0.01293 0.11259
Software t
-0.02314 -0.00315
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.2840 -1.3996 0.4132 1.6151 6.5993
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.37897 1.67468 6.795 6.52e-11 ***
Populatie 0.56595 0.55792 1.014 0.31127
Geslacht 0.79407 0.28880 2.750 0.00636 **
Connected 0.02548 0.04136 0.616 0.53829
Seperate -0.01293 0.04309 -0.300 0.76430
Learning 0.11259 0.07439 1.513 0.13130
Software -0.02314 0.07977 -0.290 0.77195
t -0.00315 0.00339 -0.929 0.35362
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.369 on 280 degrees of freedom
Multiple R-squared: 0.1195, Adjusted R-squared: 0.09745
F-statistic: 5.427 on 7 and 280 DF, p-value: 7.455e-06
> 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,] 0.55461768 0.89076464 0.44538232
[2,] 0.58964810 0.82070380 0.41035190
[3,] 0.71366240 0.57267520 0.28633760
[4,] 0.60303450 0.79393101 0.39696550
[5,] 0.65752349 0.68495302 0.34247651
[6,] 0.55973345 0.88053310 0.44026655
[7,] 0.49467611 0.98935222 0.50532389
[8,] 0.42324180 0.84648360 0.57675820
[9,] 0.35675434 0.71350868 0.64324566
[10,] 0.39670511 0.79341022 0.60329489
[11,] 0.46757818 0.93515636 0.53242182
[12,] 0.85813698 0.28372603 0.14186302
[13,] 0.84022141 0.31955718 0.15977859
[14,] 0.86779123 0.26441755 0.13220877
[15,] 0.84225658 0.31548684 0.15774342
[16,] 0.97507807 0.04984385 0.02492193
[17,] 0.96977469 0.06045061 0.03022531
[18,] 0.95751667 0.08496666 0.04248333
[19,] 0.94228790 0.11542420 0.05771210
[20,] 0.94894691 0.10210618 0.05105309
[21,] 0.93868025 0.12263950 0.06131975
[22,] 0.92092925 0.15814150 0.07907075
[23,] 0.91274958 0.17450083 0.08725042
[24,] 0.89023808 0.21952385 0.10976192
[25,] 0.86190583 0.27618835 0.13809417
[26,] 0.84540274 0.30919453 0.15459726
[27,] 0.91144943 0.17710114 0.08855057
[28,] 0.89638613 0.20722775 0.10361387
[29,] 0.89149046 0.21701909 0.10850954
[30,] 0.86912152 0.26175696 0.13087848
[31,] 0.84374852 0.31250295 0.15625148
[32,] 0.82066532 0.35866937 0.17933468
[33,] 0.80476101 0.39047798 0.19523899
[34,] 0.78977054 0.42045893 0.21022946
[35,] 0.75786557 0.48426886 0.24213443
[36,] 0.76959828 0.46080345 0.23040172
[37,] 0.74276593 0.51446813 0.25723407
[38,] 0.70429794 0.59140413 0.29570206
[39,] 0.70174133 0.59651733 0.29825867
[40,] 0.66160079 0.67679842 0.33839921
[41,] 0.63905738 0.72188524 0.36094262
[42,] 0.59549985 0.80900030 0.40450015
[43,] 0.57555274 0.84889451 0.42444726
[44,] 0.57528468 0.84943064 0.42471532
[45,] 0.53846750 0.92306500 0.46153250
[46,] 0.49519017 0.99038034 0.50480983
[47,] 0.46140297 0.92280594 0.53859703
[48,] 0.41919745 0.83839491 0.58080255
[49,] 0.38841201 0.77682401 0.61158799
[50,] 0.40703889 0.81407778 0.59296111
[51,] 0.55505252 0.88989496 0.44494748
[52,] 0.51276649 0.97446702 0.48723351
[53,] 0.53311629 0.93376742 0.46688371
[54,] 0.49294362 0.98588723 0.50705638
[55,] 0.45548834 0.91097668 0.54451166
[56,] 0.47157264 0.94314528 0.52842736
[57,] 0.49720196 0.99440392 0.50279804
[58,] 0.46645698 0.93291397 0.53354302
[59,] 0.45194837 0.90389674 0.54805163
[60,] 0.45799882 0.91599763 0.54200118
[61,] 0.43331579 0.86663159 0.56668421
[62,] 0.43818320 0.87636639 0.56181680
[63,] 0.40636621 0.81273241 0.59363379
[64,] 0.38977334 0.77954667 0.61022666
[65,] 0.35391449 0.70782899 0.64608551
[66,] 0.35647698 0.71295397 0.64352302
[67,] 0.37663038 0.75326075 0.62336962
[68,] 0.34552115 0.69104230 0.65447885
[69,] 0.35629834 0.71259668 0.64370166
[70,] 0.32229130 0.64458261 0.67770870
[71,] 0.30930266 0.61860532 0.69069734
[72,] 0.27982943 0.55965885 0.72017057
[73,] 0.29321931 0.58643862 0.70678069
[74,] 0.26680299 0.53360599 0.73319701
[75,] 0.23623575 0.47247149 0.76376425
[76,] 0.21873437 0.43746875 0.78126563
[77,] 0.19152463 0.38304926 0.80847537
[78,] 0.16982546 0.33965092 0.83017454
[79,] 0.39679577 0.79359155 0.60320423
[80,] 0.43347448 0.86694895 0.56652552
[81,] 0.40329201 0.80658402 0.59670799
[82,] 0.37012911 0.74025823 0.62987089
[83,] 0.33969624 0.67939248 0.66030376
[84,] 0.30835476 0.61670953 0.69164524
[85,] 0.28993018 0.57986036 0.71006982
[86,] 0.28364356 0.56728713 0.71635644
[87,] 0.25394114 0.50788228 0.74605886
[88,] 0.29582782 0.59165563 0.70417218
[89,] 0.28129709 0.56259418 0.71870291
[90,] 0.29222165 0.58444331 0.70777835
[91,] 0.27599577 0.55199155 0.72400423
[92,] 0.25619092 0.51238185 0.74380908
[93,] 0.24763793 0.49527585 0.75236207
[94,] 0.22186920 0.44373841 0.77813080
[95,] 0.27825210 0.55650419 0.72174790
[96,] 0.26190905 0.52381809 0.73809095
[97,] 0.24008904 0.48017808 0.75991096
[98,] 0.27479725 0.54959450 0.72520275
[99,] 0.35004300 0.70008600 0.64995700
[100,] 0.32434488 0.64868977 0.67565512
[101,] 0.33914585 0.67829170 0.66085415
[102,] 0.31797350 0.63594700 0.68202650
[103,] 0.39942500 0.79884999 0.60057500
[104,] 0.40400597 0.80801193 0.59599403
[105,] 0.37030482 0.74060965 0.62969518
[106,] 0.33758878 0.67517756 0.66241122
[107,] 0.30700637 0.61401274 0.69299363
[108,] 0.27736400 0.55472800 0.72263600
[109,] 0.25350707 0.50701413 0.74649293
[110,] 0.22949379 0.45898758 0.77050621
[111,] 0.20895394 0.41790788 0.79104606
[112,] 0.18733216 0.37466431 0.81266784
[113,] 0.16979784 0.33959568 0.83020216
[114,] 0.15225528 0.30451056 0.84774472
[115,] 0.18017006 0.36034012 0.81982994
[116,] 0.17673146 0.35346293 0.82326854
[117,] 0.25743751 0.51487502 0.74256249
[118,] 0.23793096 0.47586192 0.76206904
[119,] 0.21254338 0.42508676 0.78745662
[120,] 0.23795270 0.47590540 0.76204730
[121,] 0.21390603 0.42781206 0.78609397
[122,] 0.21817521 0.43635041 0.78182479
[123,] 0.20619607 0.41239213 0.79380393
[124,] 0.20068686 0.40137371 0.79931314
[125,] 0.19326417 0.38652834 0.80673583
[126,] 0.17220993 0.34441987 0.82779007
[127,] 0.17387173 0.34774346 0.82612827
[128,] 0.15727931 0.31455862 0.84272069
[129,] 0.13772569 0.27545138 0.86227431
[130,] 0.12140300 0.24280600 0.87859700
[131,] 0.12736546 0.25473093 0.87263454
[132,] 0.11027363 0.22054726 0.88972637
[133,] 0.10362506 0.20725012 0.89637494
[134,] 0.10269555 0.20539111 0.89730445
[135,] 0.09631925 0.19263849 0.90368075
[136,] 0.09153483 0.18306967 0.90846517
[137,] 0.08160326 0.16320652 0.91839674
[138,] 0.11223091 0.22446182 0.88776909
[139,] 0.11180942 0.22361884 0.88819058
[140,] 0.10081091 0.20162183 0.89918909
[141,] 0.10359417 0.20718833 0.89640583
[142,] 0.10588794 0.21177588 0.89411206
[143,] 0.17610757 0.35221514 0.82389243
[144,] 0.15802614 0.31605228 0.84197386
[145,] 0.13821467 0.27642934 0.86178533
[146,] 0.21424266 0.42848532 0.78575734
[147,] 0.19283793 0.38567586 0.80716207
[148,] 0.17685831 0.35371661 0.82314169
[149,] 0.17257198 0.34514396 0.82742802
[150,] 0.16642263 0.33284526 0.83357737
[151,] 0.17043430 0.34086861 0.82956570
[152,] 0.19915903 0.39831805 0.80084097
[153,] 0.17988588 0.35977177 0.82011412
[154,] 0.16690639 0.33381278 0.83309361
[155,] 0.15085762 0.30171525 0.84914238
[156,] 0.19243028 0.38486056 0.80756972
[157,] 0.22109969 0.44219939 0.77890031
[158,] 0.20235186 0.40470373 0.79764814
[159,] 0.20882959 0.41765918 0.79117041
[160,] 0.23393403 0.46786807 0.76606597
[161,] 0.21301352 0.42602704 0.78698648
[162,] 0.22574420 0.45148841 0.77425580
[163,] 0.20201425 0.40402849 0.79798575
[164,] 0.25296347 0.50592693 0.74703653
[165,] 0.22973986 0.45947971 0.77026014
[166,] 0.23882346 0.47764692 0.76117654
[167,] 0.23318869 0.46637739 0.76681131
[168,] 0.20837306 0.41674612 0.79162694
[169,] 0.18671599 0.37343198 0.81328401
[170,] 0.18013643 0.36027286 0.81986357
[171,] 0.16315007 0.32630014 0.83684993
[172,] 0.18943474 0.37886948 0.81056526
[173,] 0.16682263 0.33364527 0.83317737
[174,] 0.14951653 0.29903305 0.85048347
[175,] 0.13919479 0.27838958 0.86080521
[176,] 0.12224707 0.24449415 0.87775293
[177,] 0.11027432 0.22054863 0.88972568
[178,] 0.10747531 0.21495061 0.89252469
[179,] 0.09251915 0.18503831 0.90748085
[180,] 0.08637878 0.17275755 0.91362122
[181,] 0.08639396 0.17278791 0.91360604
[182,] 0.08016793 0.16033585 0.91983207
[183,] 0.06841036 0.13682072 0.93158964
[184,] 0.05784989 0.11569978 0.94215011
[185,] 0.04864940 0.09729880 0.95135060
[186,] 0.07696292 0.15392584 0.92303708
[187,] 0.09989446 0.19978893 0.90010554
[188,] 0.09921590 0.19843180 0.90078410
[189,] 0.14624022 0.29248043 0.85375978
[190,] 0.12722020 0.25444040 0.87277980
[191,] 0.11776632 0.23553265 0.88223368
[192,] 0.10964541 0.21929081 0.89035459
[193,] 0.09495249 0.18990498 0.90504751
[194,] 0.10970306 0.21940612 0.89029694
[195,] 0.09457285 0.18914570 0.90542715
[196,] 0.11319165 0.22638330 0.88680835
[197,] 0.14263125 0.28526249 0.85736875
[198,] 0.17995441 0.35990883 0.82004559
[199,] 0.16134730 0.32269461 0.83865270
[200,] 0.16075038 0.32150076 0.83924962
[201,] 0.15572534 0.31145069 0.84427466
[202,] 0.15656476 0.31312953 0.84343524
[203,] 0.19686632 0.39373264 0.80313368
[204,] 0.17819280 0.35638559 0.82180720
[205,] 0.23120976 0.46241953 0.76879024
[206,] 0.23233567 0.46467135 0.76766433
[207,] 0.20873604 0.41747208 0.79126396
[208,] 0.25721876 0.51443753 0.74278124
[209,] 0.26418505 0.52837009 0.73581495
[210,] 0.28341742 0.56683483 0.71658258
[211,] 0.36507695 0.73015391 0.63492305
[212,] 0.71060512 0.57878975 0.28939488
[213,] 0.67899588 0.64200824 0.32100412
[214,] 0.71769529 0.56460942 0.28230471
[215,] 0.68245743 0.63508514 0.31754257
[216,] 0.66692793 0.66614414 0.33307207
[217,] 0.65661792 0.68676416 0.34338208
[218,] 0.72242149 0.55515703 0.27757851
[219,] 0.69094914 0.61810173 0.30905086
[220,] 0.78406117 0.43187765 0.21593883
[221,] 0.75176434 0.49647133 0.24823566
[222,] 0.71580692 0.56838615 0.28419308
[223,] 0.68486914 0.63026171 0.31513086
[224,] 0.66856604 0.66286791 0.33143396
[225,] 0.62772219 0.74455562 0.37227781
[226,] 0.58872539 0.82254921 0.41127461
[227,] 0.65333146 0.69333707 0.34666854
[228,] 0.61703087 0.76593827 0.38296913
[229,] 0.58131342 0.83737316 0.41868658
[230,] 0.53464548 0.93070904 0.46535452
[231,] 0.51364163 0.97271675 0.48635837
[232,] 0.49753200 0.99506399 0.50246800
[233,] 0.48956163 0.97912325 0.51043837
[234,] 0.44500548 0.89001096 0.55499452
[235,] 0.54068486 0.91863028 0.45931514
[236,] 0.52875212 0.94249577 0.47124788
[237,] 0.53398274 0.93203451 0.46601726
[238,] 0.52491296 0.95017409 0.47508704
[239,] 0.47641592 0.95283185 0.52358408
[240,] 0.42796789 0.85593579 0.57203211
[241,] 0.42126414 0.84252829 0.57873586
[242,] 0.41690878 0.83381755 0.58309122
[243,] 0.46188595 0.92377190 0.53811405
[244,] 0.41035206 0.82070413 0.58964794
[245,] 0.40082572 0.80165143 0.59917428
[246,] 0.62646573 0.74706854 0.37353427
[247,] 0.70135078 0.59729843 0.29864922
[248,] 0.76562350 0.46875299 0.23437650
[249,] 0.81243190 0.37513619 0.18756810
[250,] 0.79509897 0.40980207 0.20490103
[251,] 0.85027376 0.29945249 0.14972624
[252,] 0.81691655 0.36616690 0.18308345
[253,] 0.76875733 0.46248533 0.23124267
[254,] 0.86434422 0.27131155 0.13565578
[255,] 0.81660326 0.36679349 0.18339674
[256,] 0.76345082 0.47309836 0.23654918
[257,] 0.74147280 0.51705441 0.25852720
[258,] 0.70117447 0.59765107 0.29882553
[259,] 0.63159965 0.73680070 0.36840035
[260,] 0.56540789 0.86918423 0.43459211
[261,] 0.58866036 0.82267927 0.41133964
[262,] 0.49541214 0.99082428 0.50458786
[263,] 0.39960876 0.79921752 0.60039124
[264,] 0.31580406 0.63160811 0.68419594
[265,] 0.24357059 0.48714117 0.75642941
[266,] 0.16651664 0.33303328 0.83348336
[267,] 0.10633604 0.21267208 0.89366396
> postscript(file="/var/wessaorg/rcomp/tmp/1kmte1351542721.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2nkgx1351542721.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3ayzm1351542721.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/40dis1351542721.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/517901351542721.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 288
Frequency = 1
1 2 3 4 5 6
-0.47520068 3.14041472 -3.83348615 -1.84552933 1.61340430 3.53077596
7 8 9 10 11 12
-1.17139345 -0.47952612 0.52603001 0.36048084 2.95224521 4.29736965
13 14 15 16 17 18
-3.94344302 1.25580657 3.37890241 0.30563214 0.14763230 1.86667603
19 20 21 22 23 24
-0.15954935 1.39863775 4.18565112 -3.58251143 -1.00732877 -2.64879586
25 26 27 28 29 30
2.91748485 -5.90126387 2.10533870 -0.37811438 0.41443455 -2.63493212
31 32 33 34 35 36
1.55842528 -0.28428460 2.47812897 0.45642944 0.38830215 2.81586467
37 38 39 40 41 42
-4.08411681 1.38558140 2.47571964 -0.85409526 1.08515480 1.65507539
43 44 45 46 47 48
1.73132251 -1.61631881 0.45164549 -3.04514567 0.66962540 0.57666022
49 50 51 52 53 54
2.44249999 -0.64052092 1.62015807 0.44653322 -2.24005988 -2.44450119
55 56 57 58 59 60
-1.07091576 0.50973848 1.36265894 0.62064533 -1.67068952 -2.57223964
61 62 63 64 65 66
-5.34426276 -0.80203601 -3.43180907 -0.19645485 0.47647830 -3.76363604
67 68 69 70 71 72
-3.69535457 -1.91811347 0.92840542 1.33841046 0.83514365 1.86754064
73 74 75 76 77 78
0.42624109 1.14739351 -0.74968103 -2.81123731 2.53856045 -1.50571580
79 80 81 82 83 84
1.80531567 -0.81038309 1.41109300 0.58213541 2.38924936 0.71553610
85 86 87 88 89 90
-0.48132494 1.49397429 -0.38994558 -1.31072596 -7.28398976 3.07726668
91 92 93 94 95 96
-1.52282703 0.66536287 -0.31056900 -0.97992610 1.46193297 -2.42745847
97 98 99 100 101 102
-0.51763614 3.33683987 1.67216751 2.59573070 -1.66391966 1.23871666
103 104 105 106 107 108
-2.50813998 0.70512150 -4.40566147 1.44282577 1.32791532 -3.61638751
109 110 111 112 113 114
-4.13737949 0.75895044 -3.23003327 -1.33092041 3.99408622 2.15933557
115 116 117 118 119 120
0.02416142 -0.11387889 0.03210503 -0.39385576 -1.14165812 -0.30242095
121 122 123 124 125 126
0.99215678 0.71637442 0.74392839 -1.16594479 3.41135798 2.38150782
127 128 129 130 131 132
4.80507539 1.10250918 -0.49731350 -3.70648826 0.51649137 2.07425284
133 134 135 136 137 138
1.72398925 1.83150455 -2.20411195 0.63536242 -2.62641228 1.07751556
139 140 141 142 143 144
0.12533481 0.70991149 2.73262822 -0.17048886 1.46608435 2.15537605
145 146 147 148 149 150
1.52566268 -2.12285605 -1.42019963 -4.21222632 2.30241027 -1.24633497
151 152 153 154 155 156
2.56253517 -2.31036263 -5.17686597 -0.63474048 0.08743886 4.89641913
157 158 159 160 161 162
-1.24395046 -1.73130815 -2.46221372 1.50530751 2.99650296 -3.48227848
163 164 165 166 167 168
-1.02995057 1.47816370 -0.95337634 -4.21115186 -3.58624236 -1.62715801
169 170 171 172 173 174
2.39663179 -3.55535702 0.09854226 2.74004411 0.36882059 -4.30704310
175 176 177 178 179 180
-0.34709116 2.64739888 -2.24820649 0.41204744 0.58125447 2.07149567
181 182 183 184 185 186
1.22546097 3.57866782 -0.47026900 1.06342385 1.62763110 0.77896506
187 188 189 190 191 192
-1.26264312 -2.22934276 0.44745737 1.75506531 -2.53173059 1.79783283
193 194 195 196 197 198
-0.63366352 0.37932541 0.55497716 -4.54377677 -3.65872668 2.67621981
199 200 201 202 203 204
4.75209008 0.72500747 1.55510215 1.62805695 0.50606682 3.31349795
205 206 207 208 209 210
0.76902756 3.37279004 -4.03408410 3.72700901 -1.46605756 -2.57189116
211 212 213 214 215 216
-2.47145843 2.55372011 3.45007851 0.60201861 -4.44506967 1.68312511
217 218 219 220 221 222
-1.34658611 3.46003868 -3.32150141 2.79503027 3.41128432 6.59929382
223 224 225 226 227 228
-0.44580020 -4.14330051 -1.22687457 0.73552820 -2.90797552 2.30083121
229 230 231 232 233 234
-0.77242384 2.86655730 -1.17494287 0.48534629 0.60073233 -3.29784107
235 236 237 238 239 240
-1.15759501 -0.07577983 -4.16827825 -0.03722267 -0.41613859 -1.07535079
241 242 243 244 245 246
0.64931198 1.16801206 -2.78619100 -0.82379966 2.96594577 1.00485207
247 248 249 250 251 252
-0.08497538 -0.41217019 -0.23045426 -0.35288952 -3.52552980 -4.14656771
253 254 255 256 257 258
1.91155745 -2.25788747 0.89578668 3.09157847 -5.19974496 1.74051207
259 260 261 262 263 264
-4.50299107 -3.18488801 1.04803990 0.88935026 -0.33074698 -3.99024542
265 266 267 268 269 270
-0.28523810 -0.02906045 -1.39270158 -2.18836658 -0.27583856 -0.44564730
271 272 273 274 275 276
-3.02557013 2.56850145 0.50692945 0.99674342 0.49357356 3.84041278
277 278 279 280 281 282
2.83497955 1.58429970 2.00593910 -1.00456637 2.41491830 0.84396597
283 284 285 286 287 288
1.05660408 0.06158290 3.45959161 4.15240465 -4.18420369 1.09780573
> postscript(file="/var/wessaorg/rcomp/tmp/6vey31351542722.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 288
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.47520068 NA
1 3.14041472 -0.47520068
2 -3.83348615 3.14041472
3 -1.84552933 -3.83348615
4 1.61340430 -1.84552933
5 3.53077596 1.61340430
6 -1.17139345 3.53077596
7 -0.47952612 -1.17139345
8 0.52603001 -0.47952612
9 0.36048084 0.52603001
10 2.95224521 0.36048084
11 4.29736965 2.95224521
12 -3.94344302 4.29736965
13 1.25580657 -3.94344302
14 3.37890241 1.25580657
15 0.30563214 3.37890241
16 0.14763230 0.30563214
17 1.86667603 0.14763230
18 -0.15954935 1.86667603
19 1.39863775 -0.15954935
20 4.18565112 1.39863775
21 -3.58251143 4.18565112
22 -1.00732877 -3.58251143
23 -2.64879586 -1.00732877
24 2.91748485 -2.64879586
25 -5.90126387 2.91748485
26 2.10533870 -5.90126387
27 -0.37811438 2.10533870
28 0.41443455 -0.37811438
29 -2.63493212 0.41443455
30 1.55842528 -2.63493212
31 -0.28428460 1.55842528
32 2.47812897 -0.28428460
33 0.45642944 2.47812897
34 0.38830215 0.45642944
35 2.81586467 0.38830215
36 -4.08411681 2.81586467
37 1.38558140 -4.08411681
38 2.47571964 1.38558140
39 -0.85409526 2.47571964
40 1.08515480 -0.85409526
41 1.65507539 1.08515480
42 1.73132251 1.65507539
43 -1.61631881 1.73132251
44 0.45164549 -1.61631881
45 -3.04514567 0.45164549
46 0.66962540 -3.04514567
47 0.57666022 0.66962540
48 2.44249999 0.57666022
49 -0.64052092 2.44249999
50 1.62015807 -0.64052092
51 0.44653322 1.62015807
52 -2.24005988 0.44653322
53 -2.44450119 -2.24005988
54 -1.07091576 -2.44450119
55 0.50973848 -1.07091576
56 1.36265894 0.50973848
57 0.62064533 1.36265894
58 -1.67068952 0.62064533
59 -2.57223964 -1.67068952
60 -5.34426276 -2.57223964
61 -0.80203601 -5.34426276
62 -3.43180907 -0.80203601
63 -0.19645485 -3.43180907
64 0.47647830 -0.19645485
65 -3.76363604 0.47647830
66 -3.69535457 -3.76363604
67 -1.91811347 -3.69535457
68 0.92840542 -1.91811347
69 1.33841046 0.92840542
70 0.83514365 1.33841046
71 1.86754064 0.83514365
72 0.42624109 1.86754064
73 1.14739351 0.42624109
74 -0.74968103 1.14739351
75 -2.81123731 -0.74968103
76 2.53856045 -2.81123731
77 -1.50571580 2.53856045
78 1.80531567 -1.50571580
79 -0.81038309 1.80531567
80 1.41109300 -0.81038309
81 0.58213541 1.41109300
82 2.38924936 0.58213541
83 0.71553610 2.38924936
84 -0.48132494 0.71553610
85 1.49397429 -0.48132494
86 -0.38994558 1.49397429
87 -1.31072596 -0.38994558
88 -7.28398976 -1.31072596
89 3.07726668 -7.28398976
90 -1.52282703 3.07726668
91 0.66536287 -1.52282703
92 -0.31056900 0.66536287
93 -0.97992610 -0.31056900
94 1.46193297 -0.97992610
95 -2.42745847 1.46193297
96 -0.51763614 -2.42745847
97 3.33683987 -0.51763614
98 1.67216751 3.33683987
99 2.59573070 1.67216751
100 -1.66391966 2.59573070
101 1.23871666 -1.66391966
102 -2.50813998 1.23871666
103 0.70512150 -2.50813998
104 -4.40566147 0.70512150
105 1.44282577 -4.40566147
106 1.32791532 1.44282577
107 -3.61638751 1.32791532
108 -4.13737949 -3.61638751
109 0.75895044 -4.13737949
110 -3.23003327 0.75895044
111 -1.33092041 -3.23003327
112 3.99408622 -1.33092041
113 2.15933557 3.99408622
114 0.02416142 2.15933557
115 -0.11387889 0.02416142
116 0.03210503 -0.11387889
117 -0.39385576 0.03210503
118 -1.14165812 -0.39385576
119 -0.30242095 -1.14165812
120 0.99215678 -0.30242095
121 0.71637442 0.99215678
122 0.74392839 0.71637442
123 -1.16594479 0.74392839
124 3.41135798 -1.16594479
125 2.38150782 3.41135798
126 4.80507539 2.38150782
127 1.10250918 4.80507539
128 -0.49731350 1.10250918
129 -3.70648826 -0.49731350
130 0.51649137 -3.70648826
131 2.07425284 0.51649137
132 1.72398925 2.07425284
133 1.83150455 1.72398925
134 -2.20411195 1.83150455
135 0.63536242 -2.20411195
136 -2.62641228 0.63536242
137 1.07751556 -2.62641228
138 0.12533481 1.07751556
139 0.70991149 0.12533481
140 2.73262822 0.70991149
141 -0.17048886 2.73262822
142 1.46608435 -0.17048886
143 2.15537605 1.46608435
144 1.52566268 2.15537605
145 -2.12285605 1.52566268
146 -1.42019963 -2.12285605
147 -4.21222632 -1.42019963
148 2.30241027 -4.21222632
149 -1.24633497 2.30241027
150 2.56253517 -1.24633497
151 -2.31036263 2.56253517
152 -5.17686597 -2.31036263
153 -0.63474048 -5.17686597
154 0.08743886 -0.63474048
155 4.89641913 0.08743886
156 -1.24395046 4.89641913
157 -1.73130815 -1.24395046
158 -2.46221372 -1.73130815
159 1.50530751 -2.46221372
160 2.99650296 1.50530751
161 -3.48227848 2.99650296
162 -1.02995057 -3.48227848
163 1.47816370 -1.02995057
164 -0.95337634 1.47816370
165 -4.21115186 -0.95337634
166 -3.58624236 -4.21115186
167 -1.62715801 -3.58624236
168 2.39663179 -1.62715801
169 -3.55535702 2.39663179
170 0.09854226 -3.55535702
171 2.74004411 0.09854226
172 0.36882059 2.74004411
173 -4.30704310 0.36882059
174 -0.34709116 -4.30704310
175 2.64739888 -0.34709116
176 -2.24820649 2.64739888
177 0.41204744 -2.24820649
178 0.58125447 0.41204744
179 2.07149567 0.58125447
180 1.22546097 2.07149567
181 3.57866782 1.22546097
182 -0.47026900 3.57866782
183 1.06342385 -0.47026900
184 1.62763110 1.06342385
185 0.77896506 1.62763110
186 -1.26264312 0.77896506
187 -2.22934276 -1.26264312
188 0.44745737 -2.22934276
189 1.75506531 0.44745737
190 -2.53173059 1.75506531
191 1.79783283 -2.53173059
192 -0.63366352 1.79783283
193 0.37932541 -0.63366352
194 0.55497716 0.37932541
195 -4.54377677 0.55497716
196 -3.65872668 -4.54377677
197 2.67621981 -3.65872668
198 4.75209008 2.67621981
199 0.72500747 4.75209008
200 1.55510215 0.72500747
201 1.62805695 1.55510215
202 0.50606682 1.62805695
203 3.31349795 0.50606682
204 0.76902756 3.31349795
205 3.37279004 0.76902756
206 -4.03408410 3.37279004
207 3.72700901 -4.03408410
208 -1.46605756 3.72700901
209 -2.57189116 -1.46605756
210 -2.47145843 -2.57189116
211 2.55372011 -2.47145843
212 3.45007851 2.55372011
213 0.60201861 3.45007851
214 -4.44506967 0.60201861
215 1.68312511 -4.44506967
216 -1.34658611 1.68312511
217 3.46003868 -1.34658611
218 -3.32150141 3.46003868
219 2.79503027 -3.32150141
220 3.41128432 2.79503027
221 6.59929382 3.41128432
222 -0.44580020 6.59929382
223 -4.14330051 -0.44580020
224 -1.22687457 -4.14330051
225 0.73552820 -1.22687457
226 -2.90797552 0.73552820
227 2.30083121 -2.90797552
228 -0.77242384 2.30083121
229 2.86655730 -0.77242384
230 -1.17494287 2.86655730
231 0.48534629 -1.17494287
232 0.60073233 0.48534629
233 -3.29784107 0.60073233
234 -1.15759501 -3.29784107
235 -0.07577983 -1.15759501
236 -4.16827825 -0.07577983
237 -0.03722267 -4.16827825
238 -0.41613859 -0.03722267
239 -1.07535079 -0.41613859
240 0.64931198 -1.07535079
241 1.16801206 0.64931198
242 -2.78619100 1.16801206
243 -0.82379966 -2.78619100
244 2.96594577 -0.82379966
245 1.00485207 2.96594577
246 -0.08497538 1.00485207
247 -0.41217019 -0.08497538
248 -0.23045426 -0.41217019
249 -0.35288952 -0.23045426
250 -3.52552980 -0.35288952
251 -4.14656771 -3.52552980
252 1.91155745 -4.14656771
253 -2.25788747 1.91155745
254 0.89578668 -2.25788747
255 3.09157847 0.89578668
256 -5.19974496 3.09157847
257 1.74051207 -5.19974496
258 -4.50299107 1.74051207
259 -3.18488801 -4.50299107
260 1.04803990 -3.18488801
261 0.88935026 1.04803990
262 -0.33074698 0.88935026
263 -3.99024542 -0.33074698
264 -0.28523810 -3.99024542
265 -0.02906045 -0.28523810
266 -1.39270158 -0.02906045
267 -2.18836658 -1.39270158
268 -0.27583856 -2.18836658
269 -0.44564730 -0.27583856
270 -3.02557013 -0.44564730
271 2.56850145 -3.02557013
272 0.50692945 2.56850145
273 0.99674342 0.50692945
274 0.49357356 0.99674342
275 3.84041278 0.49357356
276 2.83497955 3.84041278
277 1.58429970 2.83497955
278 2.00593910 1.58429970
279 -1.00456637 2.00593910
280 2.41491830 -1.00456637
281 0.84396597 2.41491830
282 1.05660408 0.84396597
283 0.06158290 1.05660408
284 3.45959161 0.06158290
285 4.15240465 3.45959161
286 -4.18420369 4.15240465
287 1.09780573 -4.18420369
288 NA 1.09780573
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.14041472 -0.47520068
[2,] -3.83348615 3.14041472
[3,] -1.84552933 -3.83348615
[4,] 1.61340430 -1.84552933
[5,] 3.53077596 1.61340430
[6,] -1.17139345 3.53077596
[7,] -0.47952612 -1.17139345
[8,] 0.52603001 -0.47952612
[9,] 0.36048084 0.52603001
[10,] 2.95224521 0.36048084
[11,] 4.29736965 2.95224521
[12,] -3.94344302 4.29736965
[13,] 1.25580657 -3.94344302
[14,] 3.37890241 1.25580657
[15,] 0.30563214 3.37890241
[16,] 0.14763230 0.30563214
[17,] 1.86667603 0.14763230
[18,] -0.15954935 1.86667603
[19,] 1.39863775 -0.15954935
[20,] 4.18565112 1.39863775
[21,] -3.58251143 4.18565112
[22,] -1.00732877 -3.58251143
[23,] -2.64879586 -1.00732877
[24,] 2.91748485 -2.64879586
[25,] -5.90126387 2.91748485
[26,] 2.10533870 -5.90126387
[27,] -0.37811438 2.10533870
[28,] 0.41443455 -0.37811438
[29,] -2.63493212 0.41443455
[30,] 1.55842528 -2.63493212
[31,] -0.28428460 1.55842528
[32,] 2.47812897 -0.28428460
[33,] 0.45642944 2.47812897
[34,] 0.38830215 0.45642944
[35,] 2.81586467 0.38830215
[36,] -4.08411681 2.81586467
[37,] 1.38558140 -4.08411681
[38,] 2.47571964 1.38558140
[39,] -0.85409526 2.47571964
[40,] 1.08515480 -0.85409526
[41,] 1.65507539 1.08515480
[42,] 1.73132251 1.65507539
[43,] -1.61631881 1.73132251
[44,] 0.45164549 -1.61631881
[45,] -3.04514567 0.45164549
[46,] 0.66962540 -3.04514567
[47,] 0.57666022 0.66962540
[48,] 2.44249999 0.57666022
[49,] -0.64052092 2.44249999
[50,] 1.62015807 -0.64052092
[51,] 0.44653322 1.62015807
[52,] -2.24005988 0.44653322
[53,] -2.44450119 -2.24005988
[54,] -1.07091576 -2.44450119
[55,] 0.50973848 -1.07091576
[56,] 1.36265894 0.50973848
[57,] 0.62064533 1.36265894
[58,] -1.67068952 0.62064533
[59,] -2.57223964 -1.67068952
[60,] -5.34426276 -2.57223964
[61,] -0.80203601 -5.34426276
[62,] -3.43180907 -0.80203601
[63,] -0.19645485 -3.43180907
[64,] 0.47647830 -0.19645485
[65,] -3.76363604 0.47647830
[66,] -3.69535457 -3.76363604
[67,] -1.91811347 -3.69535457
[68,] 0.92840542 -1.91811347
[69,] 1.33841046 0.92840542
[70,] 0.83514365 1.33841046
[71,] 1.86754064 0.83514365
[72,] 0.42624109 1.86754064
[73,] 1.14739351 0.42624109
[74,] -0.74968103 1.14739351
[75,] -2.81123731 -0.74968103
[76,] 2.53856045 -2.81123731
[77,] -1.50571580 2.53856045
[78,] 1.80531567 -1.50571580
[79,] -0.81038309 1.80531567
[80,] 1.41109300 -0.81038309
[81,] 0.58213541 1.41109300
[82,] 2.38924936 0.58213541
[83,] 0.71553610 2.38924936
[84,] -0.48132494 0.71553610
[85,] 1.49397429 -0.48132494
[86,] -0.38994558 1.49397429
[87,] -1.31072596 -0.38994558
[88,] -7.28398976 -1.31072596
[89,] 3.07726668 -7.28398976
[90,] -1.52282703 3.07726668
[91,] 0.66536287 -1.52282703
[92,] -0.31056900 0.66536287
[93,] -0.97992610 -0.31056900
[94,] 1.46193297 -0.97992610
[95,] -2.42745847 1.46193297
[96,] -0.51763614 -2.42745847
[97,] 3.33683987 -0.51763614
[98,] 1.67216751 3.33683987
[99,] 2.59573070 1.67216751
[100,] -1.66391966 2.59573070
[101,] 1.23871666 -1.66391966
[102,] -2.50813998 1.23871666
[103,] 0.70512150 -2.50813998
[104,] -4.40566147 0.70512150
[105,] 1.44282577 -4.40566147
[106,] 1.32791532 1.44282577
[107,] -3.61638751 1.32791532
[108,] -4.13737949 -3.61638751
[109,] 0.75895044 -4.13737949
[110,] -3.23003327 0.75895044
[111,] -1.33092041 -3.23003327
[112,] 3.99408622 -1.33092041
[113,] 2.15933557 3.99408622
[114,] 0.02416142 2.15933557
[115,] -0.11387889 0.02416142
[116,] 0.03210503 -0.11387889
[117,] -0.39385576 0.03210503
[118,] -1.14165812 -0.39385576
[119,] -0.30242095 -1.14165812
[120,] 0.99215678 -0.30242095
[121,] 0.71637442 0.99215678
[122,] 0.74392839 0.71637442
[123,] -1.16594479 0.74392839
[124,] 3.41135798 -1.16594479
[125,] 2.38150782 3.41135798
[126,] 4.80507539 2.38150782
[127,] 1.10250918 4.80507539
[128,] -0.49731350 1.10250918
[129,] -3.70648826 -0.49731350
[130,] 0.51649137 -3.70648826
[131,] 2.07425284 0.51649137
[132,] 1.72398925 2.07425284
[133,] 1.83150455 1.72398925
[134,] -2.20411195 1.83150455
[135,] 0.63536242 -2.20411195
[136,] -2.62641228 0.63536242
[137,] 1.07751556 -2.62641228
[138,] 0.12533481 1.07751556
[139,] 0.70991149 0.12533481
[140,] 2.73262822 0.70991149
[141,] -0.17048886 2.73262822
[142,] 1.46608435 -0.17048886
[143,] 2.15537605 1.46608435
[144,] 1.52566268 2.15537605
[145,] -2.12285605 1.52566268
[146,] -1.42019963 -2.12285605
[147,] -4.21222632 -1.42019963
[148,] 2.30241027 -4.21222632
[149,] -1.24633497 2.30241027
[150,] 2.56253517 -1.24633497
[151,] -2.31036263 2.56253517
[152,] -5.17686597 -2.31036263
[153,] -0.63474048 -5.17686597
[154,] 0.08743886 -0.63474048
[155,] 4.89641913 0.08743886
[156,] -1.24395046 4.89641913
[157,] -1.73130815 -1.24395046
[158,] -2.46221372 -1.73130815
[159,] 1.50530751 -2.46221372
[160,] 2.99650296 1.50530751
[161,] -3.48227848 2.99650296
[162,] -1.02995057 -3.48227848
[163,] 1.47816370 -1.02995057
[164,] -0.95337634 1.47816370
[165,] -4.21115186 -0.95337634
[166,] -3.58624236 -4.21115186
[167,] -1.62715801 -3.58624236
[168,] 2.39663179 -1.62715801
[169,] -3.55535702 2.39663179
[170,] 0.09854226 -3.55535702
[171,] 2.74004411 0.09854226
[172,] 0.36882059 2.74004411
[173,] -4.30704310 0.36882059
[174,] -0.34709116 -4.30704310
[175,] 2.64739888 -0.34709116
[176,] -2.24820649 2.64739888
[177,] 0.41204744 -2.24820649
[178,] 0.58125447 0.41204744
[179,] 2.07149567 0.58125447
[180,] 1.22546097 2.07149567
[181,] 3.57866782 1.22546097
[182,] -0.47026900 3.57866782
[183,] 1.06342385 -0.47026900
[184,] 1.62763110 1.06342385
[185,] 0.77896506 1.62763110
[186,] -1.26264312 0.77896506
[187,] -2.22934276 -1.26264312
[188,] 0.44745737 -2.22934276
[189,] 1.75506531 0.44745737
[190,] -2.53173059 1.75506531
[191,] 1.79783283 -2.53173059
[192,] -0.63366352 1.79783283
[193,] 0.37932541 -0.63366352
[194,] 0.55497716 0.37932541
[195,] -4.54377677 0.55497716
[196,] -3.65872668 -4.54377677
[197,] 2.67621981 -3.65872668
[198,] 4.75209008 2.67621981
[199,] 0.72500747 4.75209008
[200,] 1.55510215 0.72500747
[201,] 1.62805695 1.55510215
[202,] 0.50606682 1.62805695
[203,] 3.31349795 0.50606682
[204,] 0.76902756 3.31349795
[205,] 3.37279004 0.76902756
[206,] -4.03408410 3.37279004
[207,] 3.72700901 -4.03408410
[208,] -1.46605756 3.72700901
[209,] -2.57189116 -1.46605756
[210,] -2.47145843 -2.57189116
[211,] 2.55372011 -2.47145843
[212,] 3.45007851 2.55372011
[213,] 0.60201861 3.45007851
[214,] -4.44506967 0.60201861
[215,] 1.68312511 -4.44506967
[216,] -1.34658611 1.68312511
[217,] 3.46003868 -1.34658611
[218,] -3.32150141 3.46003868
[219,] 2.79503027 -3.32150141
[220,] 3.41128432 2.79503027
[221,] 6.59929382 3.41128432
[222,] -0.44580020 6.59929382
[223,] -4.14330051 -0.44580020
[224,] -1.22687457 -4.14330051
[225,] 0.73552820 -1.22687457
[226,] -2.90797552 0.73552820
[227,] 2.30083121 -2.90797552
[228,] -0.77242384 2.30083121
[229,] 2.86655730 -0.77242384
[230,] -1.17494287 2.86655730
[231,] 0.48534629 -1.17494287
[232,] 0.60073233 0.48534629
[233,] -3.29784107 0.60073233
[234,] -1.15759501 -3.29784107
[235,] -0.07577983 -1.15759501
[236,] -4.16827825 -0.07577983
[237,] -0.03722267 -4.16827825
[238,] -0.41613859 -0.03722267
[239,] -1.07535079 -0.41613859
[240,] 0.64931198 -1.07535079
[241,] 1.16801206 0.64931198
[242,] -2.78619100 1.16801206
[243,] -0.82379966 -2.78619100
[244,] 2.96594577 -0.82379966
[245,] 1.00485207 2.96594577
[246,] -0.08497538 1.00485207
[247,] -0.41217019 -0.08497538
[248,] -0.23045426 -0.41217019
[249,] -0.35288952 -0.23045426
[250,] -3.52552980 -0.35288952
[251,] -4.14656771 -3.52552980
[252,] 1.91155745 -4.14656771
[253,] -2.25788747 1.91155745
[254,] 0.89578668 -2.25788747
[255,] 3.09157847 0.89578668
[256,] -5.19974496 3.09157847
[257,] 1.74051207 -5.19974496
[258,] -4.50299107 1.74051207
[259,] -3.18488801 -4.50299107
[260,] 1.04803990 -3.18488801
[261,] 0.88935026 1.04803990
[262,] -0.33074698 0.88935026
[263,] -3.99024542 -0.33074698
[264,] -0.28523810 -3.99024542
[265,] -0.02906045 -0.28523810
[266,] -1.39270158 -0.02906045
[267,] -2.18836658 -1.39270158
[268,] -0.27583856 -2.18836658
[269,] -0.44564730 -0.27583856
[270,] -3.02557013 -0.44564730
[271,] 2.56850145 -3.02557013
[272,] 0.50692945 2.56850145
[273,] 0.99674342 0.50692945
[274,] 0.49357356 0.99674342
[275,] 3.84041278 0.49357356
[276,] 2.83497955 3.84041278
[277,] 1.58429970 2.83497955
[278,] 2.00593910 1.58429970
[279,] -1.00456637 2.00593910
[280,] 2.41491830 -1.00456637
[281,] 0.84396597 2.41491830
[282,] 1.05660408 0.84396597
[283,] 0.06158290 1.05660408
[284,] 3.45959161 0.06158290
[285,] 4.15240465 3.45959161
[286,] -4.18420369 4.15240465
[287,] 1.09780573 -4.18420369
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.14041472 -0.47520068
2 -3.83348615 3.14041472
3 -1.84552933 -3.83348615
4 1.61340430 -1.84552933
5 3.53077596 1.61340430
6 -1.17139345 3.53077596
7 -0.47952612 -1.17139345
8 0.52603001 -0.47952612
9 0.36048084 0.52603001
10 2.95224521 0.36048084
11 4.29736965 2.95224521
12 -3.94344302 4.29736965
13 1.25580657 -3.94344302
14 3.37890241 1.25580657
15 0.30563214 3.37890241
16 0.14763230 0.30563214
17 1.86667603 0.14763230
18 -0.15954935 1.86667603
19 1.39863775 -0.15954935
20 4.18565112 1.39863775
21 -3.58251143 4.18565112
22 -1.00732877 -3.58251143
23 -2.64879586 -1.00732877
24 2.91748485 -2.64879586
25 -5.90126387 2.91748485
26 2.10533870 -5.90126387
27 -0.37811438 2.10533870
28 0.41443455 -0.37811438
29 -2.63493212 0.41443455
30 1.55842528 -2.63493212
31 -0.28428460 1.55842528
32 2.47812897 -0.28428460
33 0.45642944 2.47812897
34 0.38830215 0.45642944
35 2.81586467 0.38830215
36 -4.08411681 2.81586467
37 1.38558140 -4.08411681
38 2.47571964 1.38558140
39 -0.85409526 2.47571964
40 1.08515480 -0.85409526
41 1.65507539 1.08515480
42 1.73132251 1.65507539
43 -1.61631881 1.73132251
44 0.45164549 -1.61631881
45 -3.04514567 0.45164549
46 0.66962540 -3.04514567
47 0.57666022 0.66962540
48 2.44249999 0.57666022
49 -0.64052092 2.44249999
50 1.62015807 -0.64052092
51 0.44653322 1.62015807
52 -2.24005988 0.44653322
53 -2.44450119 -2.24005988
54 -1.07091576 -2.44450119
55 0.50973848 -1.07091576
56 1.36265894 0.50973848
57 0.62064533 1.36265894
58 -1.67068952 0.62064533
59 -2.57223964 -1.67068952
60 -5.34426276 -2.57223964
61 -0.80203601 -5.34426276
62 -3.43180907 -0.80203601
63 -0.19645485 -3.43180907
64 0.47647830 -0.19645485
65 -3.76363604 0.47647830
66 -3.69535457 -3.76363604
67 -1.91811347 -3.69535457
68 0.92840542 -1.91811347
69 1.33841046 0.92840542
70 0.83514365 1.33841046
71 1.86754064 0.83514365
72 0.42624109 1.86754064
73 1.14739351 0.42624109
74 -0.74968103 1.14739351
75 -2.81123731 -0.74968103
76 2.53856045 -2.81123731
77 -1.50571580 2.53856045
78 1.80531567 -1.50571580
79 -0.81038309 1.80531567
80 1.41109300 -0.81038309
81 0.58213541 1.41109300
82 2.38924936 0.58213541
83 0.71553610 2.38924936
84 -0.48132494 0.71553610
85 1.49397429 -0.48132494
86 -0.38994558 1.49397429
87 -1.31072596 -0.38994558
88 -7.28398976 -1.31072596
89 3.07726668 -7.28398976
90 -1.52282703 3.07726668
91 0.66536287 -1.52282703
92 -0.31056900 0.66536287
93 -0.97992610 -0.31056900
94 1.46193297 -0.97992610
95 -2.42745847 1.46193297
96 -0.51763614 -2.42745847
97 3.33683987 -0.51763614
98 1.67216751 3.33683987
99 2.59573070 1.67216751
100 -1.66391966 2.59573070
101 1.23871666 -1.66391966
102 -2.50813998 1.23871666
103 0.70512150 -2.50813998
104 -4.40566147 0.70512150
105 1.44282577 -4.40566147
106 1.32791532 1.44282577
107 -3.61638751 1.32791532
108 -4.13737949 -3.61638751
109 0.75895044 -4.13737949
110 -3.23003327 0.75895044
111 -1.33092041 -3.23003327
112 3.99408622 -1.33092041
113 2.15933557 3.99408622
114 0.02416142 2.15933557
115 -0.11387889 0.02416142
116 0.03210503 -0.11387889
117 -0.39385576 0.03210503
118 -1.14165812 -0.39385576
119 -0.30242095 -1.14165812
120 0.99215678 -0.30242095
121 0.71637442 0.99215678
122 0.74392839 0.71637442
123 -1.16594479 0.74392839
124 3.41135798 -1.16594479
125 2.38150782 3.41135798
126 4.80507539 2.38150782
127 1.10250918 4.80507539
128 -0.49731350 1.10250918
129 -3.70648826 -0.49731350
130 0.51649137 -3.70648826
131 2.07425284 0.51649137
132 1.72398925 2.07425284
133 1.83150455 1.72398925
134 -2.20411195 1.83150455
135 0.63536242 -2.20411195
136 -2.62641228 0.63536242
137 1.07751556 -2.62641228
138 0.12533481 1.07751556
139 0.70991149 0.12533481
140 2.73262822 0.70991149
141 -0.17048886 2.73262822
142 1.46608435 -0.17048886
143 2.15537605 1.46608435
144 1.52566268 2.15537605
145 -2.12285605 1.52566268
146 -1.42019963 -2.12285605
147 -4.21222632 -1.42019963
148 2.30241027 -4.21222632
149 -1.24633497 2.30241027
150 2.56253517 -1.24633497
151 -2.31036263 2.56253517
152 -5.17686597 -2.31036263
153 -0.63474048 -5.17686597
154 0.08743886 -0.63474048
155 4.89641913 0.08743886
156 -1.24395046 4.89641913
157 -1.73130815 -1.24395046
158 -2.46221372 -1.73130815
159 1.50530751 -2.46221372
160 2.99650296 1.50530751
161 -3.48227848 2.99650296
162 -1.02995057 -3.48227848
163 1.47816370 -1.02995057
164 -0.95337634 1.47816370
165 -4.21115186 -0.95337634
166 -3.58624236 -4.21115186
167 -1.62715801 -3.58624236
168 2.39663179 -1.62715801
169 -3.55535702 2.39663179
170 0.09854226 -3.55535702
171 2.74004411 0.09854226
172 0.36882059 2.74004411
173 -4.30704310 0.36882059
174 -0.34709116 -4.30704310
175 2.64739888 -0.34709116
176 -2.24820649 2.64739888
177 0.41204744 -2.24820649
178 0.58125447 0.41204744
179 2.07149567 0.58125447
180 1.22546097 2.07149567
181 3.57866782 1.22546097
182 -0.47026900 3.57866782
183 1.06342385 -0.47026900
184 1.62763110 1.06342385
185 0.77896506 1.62763110
186 -1.26264312 0.77896506
187 -2.22934276 -1.26264312
188 0.44745737 -2.22934276
189 1.75506531 0.44745737
190 -2.53173059 1.75506531
191 1.79783283 -2.53173059
192 -0.63366352 1.79783283
193 0.37932541 -0.63366352
194 0.55497716 0.37932541
195 -4.54377677 0.55497716
196 -3.65872668 -4.54377677
197 2.67621981 -3.65872668
198 4.75209008 2.67621981
199 0.72500747 4.75209008
200 1.55510215 0.72500747
201 1.62805695 1.55510215
202 0.50606682 1.62805695
203 3.31349795 0.50606682
204 0.76902756 3.31349795
205 3.37279004 0.76902756
206 -4.03408410 3.37279004
207 3.72700901 -4.03408410
208 -1.46605756 3.72700901
209 -2.57189116 -1.46605756
210 -2.47145843 -2.57189116
211 2.55372011 -2.47145843
212 3.45007851 2.55372011
213 0.60201861 3.45007851
214 -4.44506967 0.60201861
215 1.68312511 -4.44506967
216 -1.34658611 1.68312511
217 3.46003868 -1.34658611
218 -3.32150141 3.46003868
219 2.79503027 -3.32150141
220 3.41128432 2.79503027
221 6.59929382 3.41128432
222 -0.44580020 6.59929382
223 -4.14330051 -0.44580020
224 -1.22687457 -4.14330051
225 0.73552820 -1.22687457
226 -2.90797552 0.73552820
227 2.30083121 -2.90797552
228 -0.77242384 2.30083121
229 2.86655730 -0.77242384
230 -1.17494287 2.86655730
231 0.48534629 -1.17494287
232 0.60073233 0.48534629
233 -3.29784107 0.60073233
234 -1.15759501 -3.29784107
235 -0.07577983 -1.15759501
236 -4.16827825 -0.07577983
237 -0.03722267 -4.16827825
238 -0.41613859 -0.03722267
239 -1.07535079 -0.41613859
240 0.64931198 -1.07535079
241 1.16801206 0.64931198
242 -2.78619100 1.16801206
243 -0.82379966 -2.78619100
244 2.96594577 -0.82379966
245 1.00485207 2.96594577
246 -0.08497538 1.00485207
247 -0.41217019 -0.08497538
248 -0.23045426 -0.41217019
249 -0.35288952 -0.23045426
250 -3.52552980 -0.35288952
251 -4.14656771 -3.52552980
252 1.91155745 -4.14656771
253 -2.25788747 1.91155745
254 0.89578668 -2.25788747
255 3.09157847 0.89578668
256 -5.19974496 3.09157847
257 1.74051207 -5.19974496
258 -4.50299107 1.74051207
259 -3.18488801 -4.50299107
260 1.04803990 -3.18488801
261 0.88935026 1.04803990
262 -0.33074698 0.88935026
263 -3.99024542 -0.33074698
264 -0.28523810 -3.99024542
265 -0.02906045 -0.28523810
266 -1.39270158 -0.02906045
267 -2.18836658 -1.39270158
268 -0.27583856 -2.18836658
269 -0.44564730 -0.27583856
270 -3.02557013 -0.44564730
271 2.56850145 -3.02557013
272 0.50692945 2.56850145
273 0.99674342 0.50692945
274 0.49357356 0.99674342
275 3.84041278 0.49357356
276 2.83497955 3.84041278
277 1.58429970 2.83497955
278 2.00593910 1.58429970
279 -1.00456637 2.00593910
280 2.41491830 -1.00456637
281 0.84396597 2.41491830
282 1.05660408 0.84396597
283 0.06158290 1.05660408
284 3.45959161 0.06158290
285 4.15240465 3.45959161
286 -4.18420369 4.15240465
287 1.09780573 -4.18420369
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7ob2l1351542722.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/89h4x1351542722.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9sk2v1351542722.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/107y021351542722.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11c3oa1351542722.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12c7nw1351542722.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13lc9j1351542722.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14wvs51351542722.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15lsn11351542722.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/169zka1351542722.tab")
+ }
>
> try(system("convert tmp/1kmte1351542721.ps tmp/1kmte1351542721.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nkgx1351542721.ps tmp/2nkgx1351542721.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ayzm1351542721.ps tmp/3ayzm1351542721.png",intern=TRUE))
character(0)
> try(system("convert tmp/40dis1351542721.ps tmp/40dis1351542721.png",intern=TRUE))
character(0)
> try(system("convert tmp/517901351542721.ps tmp/517901351542721.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vey31351542722.ps tmp/6vey31351542722.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ob2l1351542722.ps tmp/7ob2l1351542722.png",intern=TRUE))
character(0)
> try(system("convert tmp/89h4x1351542722.ps tmp/89h4x1351542722.png",intern=TRUE))
character(0)
> try(system("convert tmp/9sk2v1351542722.ps tmp/9sk2v1351542722.png",intern=TRUE))
character(0)
> try(system("convert tmp/107y021351542722.ps tmp/107y021351542722.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
15.011 1.430 16.517