R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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+ ,35
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+ ,16
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+ ,11
+ ,31
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+ ,14
+ ,8
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+ ,11
+ ,37
+ ,34
+ ,13
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+ ,11
+ ,35
+ ,30
+ ,4
+ ,3
+ ,10
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+ ,11
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+ ,15
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+ ,14
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+ ,11
+ ,34
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+ ,11
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+ ,15
+ ,12
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+ ,11
+ ,7
+ ,7
+ ,22
+ ,62
+ ,11
+ ,29
+ ,32
+ ,14
+ ,9
+ ,14
+ ,12
+ ,72
+ ,11)
+ ,dim=c(8
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport1'
+ ,'Month')
+ ,1:264))
> y <- array(NA,dim=c(8,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Sport1','Month'),1:264))
> 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 = '5'
> par3 <- 'Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '5'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects 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 Connected Separate Learning Software Depression Sport1 Month t
1 14 41 38 13 12 12.0 53 9 1
2 18 39 32 16 11 11.0 83 9 2
3 11 30 35 19 15 14.0 66 9 3
4 12 31 33 15 6 12.0 67 9 4
5 16 34 37 14 13 21.0 76 9 5
6 18 35 29 13 10 12.0 78 9 6
7 14 39 31 19 12 22.0 53 9 7
8 14 34 36 15 14 11.0 80 9 8
9 15 36 35 14 12 10.0 74 9 9
10 15 37 38 15 9 13.0 76 9 10
11 17 38 31 16 10 10.0 79 9 11
12 19 36 34 16 12 8.0 54 9 12
13 10 38 35 16 12 15.0 67 9 13
14 16 39 38 16 11 14.0 54 9 14
15 18 33 37 17 15 10.0 87 9 15
16 14 32 33 15 12 14.0 58 9 16
17 14 36 32 15 10 14.0 75 9 17
18 17 38 38 20 12 11.0 88 9 18
19 14 39 38 18 11 10.0 64 9 19
20 16 32 32 16 12 13.0 57 9 20
21 18 32 33 16 11 9.5 66 9 21
22 11 31 31 16 12 14.0 68 9 22
23 14 39 38 19 13 12.0 54 9 23
24 12 37 39 16 11 14.0 56 9 24
25 17 39 32 17 12 11.0 86 9 25
26 9 41 32 17 13 9.0 80 9 26
27 16 36 35 16 10 11.0 76 9 27
28 14 33 37 15 14 15.0 69 9 28
29 15 33 33 16 12 14.0 78 9 29
30 11 34 33 14 10 13.0 67 9 30
31 16 31 31 15 12 9.0 80 9 31
32 13 27 32 12 8 15.0 54 9 32
33 17 37 31 14 10 10.0 71 9 33
34 15 34 37 16 12 11.0 84 9 34
35 14 34 30 14 12 13.0 74 9 35
36 16 32 33 10 7 8.0 71 9 36
37 9 29 31 10 9 20.0 63 9 37
38 15 36 33 14 12 12.0 71 9 38
39 17 29 31 16 10 10.0 76 9 39
40 13 35 33 16 10 10.0 69 9 40
41 15 37 32 16 10 9.0 74 9 41
42 16 34 33 14 12 14.0 75 9 42
43 16 38 32 20 15 8.0 54 9 43
44 12 35 33 14 10 14.0 52 9 44
45 15 38 28 14 10 11.0 69 9 45
46 11 37 35 11 12 13.0 68 9 46
47 15 38 39 14 13 9.0 65 9 47
48 15 33 34 15 11 11.0 75 9 48
49 17 36 38 16 11 15.0 74 9 49
50 13 38 32 14 12 11.0 75 9 50
51 16 32 38 16 14 10.0 72 9 51
52 14 32 30 14 10 14.0 67 9 52
53 11 32 33 12 12 18.0 63 9 53
54 12 34 38 16 13 14.0 62 9 54
55 12 32 32 9 5 11.0 63 9 55
56 15 37 35 14 6 14.5 76 9 56
57 16 39 34 16 12 13.0 74 9 57
58 15 29 34 16 12 9.0 67 9 58
59 12 37 36 15 11 10.0 73 9 59
60 12 35 34 16 10 15.0 70 9 60
61 8 30 28 12 7 20.0 53 9 61
62 13 38 34 16 12 12.0 77 9 62
63 11 34 35 16 14 12.0 80 9 63
64 14 31 35 14 11 14.0 52 9 64
65 15 34 31 16 12 13.0 54 9 65
66 10 35 37 17 13 11.0 80 10 66
67 11 36 35 18 14 17.0 66 10 67
68 12 30 27 18 11 12.0 73 10 68
69 15 39 40 12 12 13.0 63 10 69
70 15 35 37 16 12 14.0 69 10 70
71 14 38 36 10 8 13.0 67 10 71
72 16 31 38 14 11 15.0 54 10 72
73 15 34 39 18 14 13.0 81 10 73
74 15 38 41 18 14 10.0 69 10 74
75 13 34 27 16 12 11.0 84 10 75
76 12 39 30 17 9 19.0 80 10 76
77 17 37 37 16 13 13.0 70 10 77
78 13 34 31 16 11 17.0 69 10 78
79 15 28 31 13 12 13.0 77 10 79
80 13 37 27 16 12 9.0 54 10 80
81 15 33 36 16 12 11.0 79 10 81
82 15 35 37 16 12 9.0 71 10 82
83 16 37 33 15 12 12.0 73 10 83
84 15 32 34 15 11 12.0 72 10 84
85 14 33 31 16 10 13.0 77 10 85
86 15 38 39 14 9 13.0 75 10 86
87 14 33 34 16 12 12.0 69 10 87
88 13 29 32 16 12 15.0 54 10 88
89 7 33 33 15 12 22.0 70 10 89
90 17 31 36 12 9 13.0 73 10 90
91 13 36 32 17 15 15.0 54 10 91
92 15 35 41 16 12 13.0 77 10 92
93 14 32 28 15 12 15.0 82 10 93
94 13 29 30 13 12 12.5 80 10 94
95 16 39 36 16 10 11.0 80 10 95
96 12 37 35 16 13 16.0 69 10 96
97 14 35 31 16 9 11.0 78 10 97
98 17 37 34 16 12 11.0 81 10 98
99 15 32 36 14 10 10.0 76 10 99
100 17 38 36 16 14 10.0 76 10 100
101 12 37 35 16 11 16.0 73 10 101
102 16 36 37 20 15 12.0 85 10 102
103 11 32 28 15 11 11.0 66 10 103
104 15 33 39 16 11 16.0 79 10 104
105 9 40 32 13 12 19.0 68 10 105
106 16 38 35 17 12 11.0 76 10 106
107 15 41 39 16 12 16.0 71 10 107
108 10 36 35 16 11 15.0 54 10 108
109 10 43 42 12 7 24.0 46 10 109
110 15 30 34 16 12 14.0 85 10 110
111 11 31 33 16 14 15.0 74 10 111
112 13 32 41 17 11 11.0 88 10 112
113 14 32 33 13 11 15.0 38 10 113
114 18 37 34 12 10 12.0 76 10 114
115 16 37 32 18 13 10.0 86 10 115
116 14 33 40 14 13 14.0 54 10 116
117 14 34 40 14 8 13.0 67 10 117
118 14 33 35 13 11 9.0 69 10 118
119 14 38 36 16 12 15.0 90 10 119
120 12 33 37 13 11 15.0 54 10 120
121 14 31 27 16 13 14.0 76 10 121
122 15 38 39 13 12 11.0 89 10 122
123 15 37 38 16 14 8.0 76 10 123
124 15 36 31 15 13 11.0 73 10 124
125 13 31 33 16 15 11.0 79 10 125
126 17 39 32 15 10 8.0 90 10 126
127 17 44 39 17 11 10.0 74 10 127
128 19 33 36 15 9 11.0 81 10 128
129 15 35 33 12 11 13.0 72 10 129
130 13 32 33 16 10 11.0 71 10 130
131 9 28 32 10 11 20.0 66 10 131
132 15 40 37 16 8 10.0 77 10 132
133 15 27 30 12 11 15.0 65 10 133
134 15 37 38 14 12 12.0 74 10 134
135 16 32 29 15 12 14.0 85 10 135
136 11 28 22 13 9 23.0 54 10 136
137 14 34 35 15 11 14.0 63 10 137
138 11 30 35 11 10 16.0 54 10 138
139 15 35 34 12 8 11.0 64 10 139
140 13 31 35 11 9 12.0 69 10 140
141 15 32 34 16 8 10.0 54 10 141
142 16 30 37 15 9 14.0 84 10 142
143 14 30 35 17 15 12.0 86 10 143
144 15 31 23 16 11 12.0 77 10 144
145 16 40 31 10 8 11.0 89 10 145
146 16 32 27 18 13 12.0 76 10 146
147 11 36 36 13 12 13.0 60 10 147
148 12 32 31 16 12 11.0 75 10 148
149 9 35 32 13 9 19.0 73 10 149
150 16 38 39 10 7 12.0 85 10 150
151 13 42 37 15 13 17.0 79 10 151
152 16 34 38 16 9 9.0 71 10 152
153 12 35 39 16 6 12.0 72 10 153
154 9 38 34 14 8 19.0 69 9 154
155 13 33 31 10 8 18.0 78 10 155
156 13 36 32 17 15 15.0 54 10 156
157 14 32 37 13 6 14.0 69 10 157
158 19 33 36 15 9 11.0 81 10 158
159 13 34 32 16 11 9.0 84 10 159
160 12 32 38 12 8 18.0 84 10 160
161 13 34 36 13 8 16.0 69 10 161
162 10 27 26 13 10 24.0 66 11 162
163 14 31 26 12 8 14.0 81 11 163
164 16 38 33 17 14 20.0 82 11 164
165 10 34 39 15 10 18.0 72 11 165
166 11 24 30 10 8 23.0 54 11 166
167 14 30 33 14 11 12.0 78 11 167
168 12 26 25 11 12 14.0 74 11 168
169 9 34 38 13 12 16.0 82 11 169
170 9 27 37 16 12 18.0 73 11 170
171 11 37 31 12 5 20.0 55 11 171
172 16 36 37 16 12 12.0 72 11 172
173 9 41 35 12 10 12.0 78 11 173
174 13 29 25 9 7 17.0 59 11 174
175 16 36 28 12 12 13.0 72 11 175
176 13 32 35 15 11 9.0 78 11 176
177 9 37 33 12 8 16.0 68 11 177
178 12 30 30 12 9 18.0 69 11 178
179 16 31 31 14 10 10.0 67 11 179
180 11 38 37 12 9 14.0 74 11 180
181 14 36 36 16 12 11.0 54 11 181
182 13 35 30 11 6 9.0 67 11 182
183 15 31 36 19 15 11.0 70 11 183
184 14 38 32 15 12 10.0 80 11 184
185 16 22 28 8 12 11.0 89 11 185
186 13 32 36 16 12 19.0 76 11 186
187 14 36 34 17 11 14.0 74 11 187
188 15 39 31 12 7 12.0 87 11 188
189 13 28 28 11 7 14.0 54 11 189
190 11 32 36 11 5 21.0 61 11 190
191 11 32 36 14 12 13.0 38 11 191
192 14 38 40 16 12 10.0 75 11 192
193 15 32 33 12 3 15.0 69 11 193
194 11 35 37 16 11 16.0 62 11 194
195 15 32 32 13 10 14.0 72 11 195
196 12 37 38 15 12 12.0 70 11 196
197 14 34 31 16 9 19.0 79 11 197
198 14 33 37 16 12 15.0 87 11 198
199 8 33 33 14 9 19.0 62 11 199
200 13 26 32 16 12 13.0 77 11 200
201 9 30 30 16 12 17.0 69 11 201
202 15 24 30 14 10 12.0 69 11 202
203 17 34 31 11 9 11.0 75 11 203
204 13 34 32 12 12 14.0 54 11 204
205 15 33 34 15 8 11.0 72 11 205
206 15 34 36 15 11 13.0 74 11 206
207 14 35 37 16 11 12.0 85 11 207
208 16 35 36 16 12 15.0 52 11 208
209 13 36 33 11 10 14.0 70 11 209
210 16 34 33 15 10 12.0 84 11 210
211 9 34 33 12 12 17.0 64 11 211
212 16 41 44 12 12 11.0 84 11 212
213 11 32 39 15 11 18.0 87 11 213
214 10 30 32 15 8 13.0 79 11 214
215 11 35 35 16 12 17.0 67 11 215
216 15 28 25 14 10 13.0 65 11 216
217 17 33 35 17 11 11.0 85 11 217
218 14 39 34 14 10 12.0 83 11 218
219 8 36 35 13 8 22.0 61 11 219
220 15 36 39 15 12 14.0 82 11 220
221 11 35 33 13 12 12.0 76 11 221
222 16 38 36 14 10 12.0 58 11 222
223 10 33 32 15 12 17.0 72 11 223
224 15 31 32 12 9 9.0 72 11 224
225 9 34 36 13 9 21.0 38 11 225
226 16 32 36 8 6 10.0 78 11 226
227 19 31 32 14 10 11.0 54 11 227
228 12 33 34 14 9 12.0 63 11 228
229 8 34 33 11 9 23.0 66 11 229
230 11 34 35 12 9 13.0 70 11 230
231 14 34 30 13 6 12.0 71 11 231
232 9 33 38 10 10 16.0 67 11 232
233 15 32 34 16 6 9.0 58 11 233
234 13 41 33 18 14 17.0 72 11 234
235 16 34 32 13 10 9.0 72 11 235
236 11 36 31 11 10 14.0 70 11 236
237 12 37 30 4 6 17.0 76 11 237
238 13 36 27 13 12 13.0 50 11 238
239 10 29 31 16 12 11.0 72 11 239
240 11 37 30 10 7 12.0 72 11 240
241 12 27 32 12 8 10.0 88 11 241
242 8 35 35 12 11 19.0 53 11 242
243 12 28 28 10 3 16.0 58 11 243
244 12 35 33 13 6 16.0 66 11 244
245 15 37 31 15 10 14.0 82 11 245
246 11 29 35 12 8 20.0 69 11 246
247 13 32 35 14 9 15.0 68 11 247
248 14 36 32 10 9 23.0 44 11 248
249 10 19 21 12 8 20.0 56 11 249
250 12 21 20 12 9 16.0 53 11 250
251 15 31 34 11 7 14.0 70 11 251
252 13 33 32 10 7 17.0 78 11 252
253 13 36 34 12 6 11.0 71 11 253
254 13 33 32 16 9 13.0 72 11 254
255 12 37 33 12 10 17.0 68 11 255
256 12 34 33 14 11 15.0 67 11 256
257 9 35 37 16 12 21.0 75 11 257
258 9 31 32 14 8 18.0 62 11 258
259 15 37 34 13 11 15.0 67 11 259
260 10 35 30 4 3 8.0 83 11 260
261 14 27 30 15 11 12.0 64 11 261
262 15 34 38 11 12 12.0 68 11 262
263 7 40 36 11 7 22.0 62 11 263
264 14 29 32 14 9 12.0 72 11 264
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Software Depression
12.806876 0.004602 0.011570 0.079783 -0.041191 -0.363249
Sport1 Month t
0.025340 0.361015 -0.007901
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9883 -1.4257 0.3156 1.3323 5.4582
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.806876 4.108033 3.118 0.00203 **
Connected 0.004602 0.037247 0.124 0.90178
Separate 0.011570 0.037943 0.305 0.76066
Learning 0.079783 0.067261 1.186 0.23665
Software -0.041191 0.069599 -0.592 0.55449
Depression -0.363249 0.039208 -9.265 < 2e-16 ***
Sport1 0.025340 0.012743 1.989 0.04782 *
Month 0.361015 0.433885 0.832 0.40616
t -0.007901 0.004616 -1.712 0.08817 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2 on 255 degrees of freedom
Multiple R-squared: 0.3787, Adjusted R-squared: 0.3592
F-statistic: 19.43 on 8 and 255 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,] 0.88324664 0.233506712 0.1167533559
[2,] 0.99151942 0.016961158 0.0084805792
[3,] 0.98993449 0.020131017 0.0100655083
[4,] 0.98966155 0.020676895 0.0103384476
[5,] 0.98208902 0.035821960 0.0179109800
[6,] 0.97874147 0.042517051 0.0212585253
[7,] 0.96698881 0.066022377 0.0330111886
[8,] 0.95513021 0.089739584 0.0448697919
[9,] 0.94284777 0.114304469 0.0571522347
[10,] 0.94006025 0.119879491 0.0599397456
[11,] 0.97581138 0.048377243 0.0241886217
[12,] 0.96482791 0.070344184 0.0351720922
[13,] 0.95549049 0.089019020 0.0445095101
[14,] 0.93929462 0.121410769 0.0607053846
[15,] 0.99903219 0.001935614 0.0009678072
[16,] 0.99863960 0.002720806 0.0013604030
[17,] 0.99784324 0.004313524 0.0021567618
[18,] 0.99678471 0.006430576 0.0032152882
[19,] 0.99742584 0.005148319 0.0025741597
[20,] 0.99629296 0.007414079 0.0037070396
[21,] 0.99441526 0.011169484 0.0055847419
[22,] 0.99430768 0.011384641 0.0056923205
[23,] 0.99164541 0.016709172 0.0083545859
[24,] 0.98800702 0.023985968 0.0119929838
[25,] 0.98334356 0.033312878 0.0166564390
[26,] 0.98499893 0.030002133 0.0150010663
[27,] 0.98052037 0.038959268 0.0194796340
[28,] 0.98015346 0.039693078 0.0198465388
[29,] 0.97724130 0.045517399 0.0227586994
[30,] 0.96952506 0.060949871 0.0304749357
[31,] 0.97185897 0.056282065 0.0281410326
[32,] 0.96510857 0.069782853 0.0348914267
[33,] 0.95579465 0.088410693 0.0442053466
[34,] 0.94356436 0.112871271 0.0564356354
[35,] 0.94512109 0.109757829 0.0548789145
[36,] 0.93311075 0.133778501 0.0668892504
[37,] 0.91792104 0.164157919 0.0820789596
[38,] 0.94875439 0.102491210 0.0512456051
[39,] 0.94211171 0.115776581 0.0578882906
[40,] 0.93177664 0.136446719 0.0682233594
[41,] 0.91683183 0.166336332 0.0831681658
[42,] 0.89994270 0.200114607 0.1000573037
[43,] 0.88569997 0.228600064 0.1143000321
[44,] 0.87478200 0.250435994 0.1252179969
[45,] 0.86167054 0.276658922 0.1383294611
[46,] 0.85740875 0.285182503 0.1425912515
[47,] 0.83117828 0.337643442 0.1688217209
[48,] 0.85182321 0.296353572 0.1481767862
[49,] 0.83555426 0.328891490 0.1644457448
[50,] 0.84496084 0.310078316 0.1550391578
[51,] 0.82810999 0.343780023 0.1718900115
[52,] 0.86364510 0.272709801 0.1363549004
[53,] 0.86060289 0.278794221 0.1393971104
[54,] 0.86204242 0.275915168 0.1379575841
[55,] 0.88015044 0.239699117 0.1198495586
[56,] 0.88106814 0.237863719 0.1189318595
[57,] 0.87335517 0.253289651 0.1266448256
[58,] 0.91242215 0.175155704 0.0875778519
[59,] 0.91989584 0.160208317 0.0801041587
[60,] 0.90939559 0.181208812 0.0906044060
[61,] 0.93755070 0.124898604 0.0624493020
[62,] 0.92655428 0.146891441 0.0734457205
[63,] 0.91183195 0.176336105 0.0881680525
[64,] 0.90417488 0.191650234 0.0958251169
[65,] 0.88685821 0.226283584 0.1131417920
[66,] 0.91043277 0.179134453 0.0895672267
[67,] 0.89520578 0.209588445 0.1047942223
[68,] 0.88516376 0.229672485 0.1148362426
[69,] 0.87837134 0.243257321 0.1216286606
[70,] 0.85805772 0.283884552 0.1419422759
[71,] 0.83636752 0.327264968 0.1636324840
[72,] 0.83259091 0.334818172 0.1674090862
[73,] 0.81128966 0.377420673 0.1887103365
[74,] 0.78517874 0.429642515 0.2148212573
[75,] 0.75946515 0.481069702 0.2405348510
[76,] 0.72945044 0.541099115 0.2705495577
[77,] 0.69797389 0.604052225 0.3020261124
[78,] 0.77054783 0.458904331 0.2294521653
[79,] 0.80568595 0.388628098 0.1943140491
[80,] 0.78196385 0.436072305 0.2180361523
[81,] 0.75580784 0.488384313 0.2441921566
[82,] 0.73380526 0.532389481 0.2661947403
[83,] 0.70954266 0.580914687 0.2904573433
[84,] 0.68511567 0.629768666 0.3148843329
[85,] 0.65630715 0.687385699 0.3436928497
[86,] 0.62874086 0.742518283 0.3712591414
[87,] 0.63552546 0.728949085 0.3644745424
[88,] 0.60003706 0.799925878 0.3999629388
[89,] 0.59691538 0.806169242 0.4030846211
[90,] 0.56940264 0.861194718 0.4305973588
[91,] 0.54468844 0.910623120 0.4553115598
[92,] 0.59953079 0.800938421 0.4004692104
[93,] 0.58839802 0.823203953 0.4116019767
[94,] 0.60352524 0.792949518 0.3964747589
[95,] 0.58100657 0.837986867 0.4189934335
[96,] 0.57654846 0.846903076 0.4234515382
[97,] 0.61031360 0.779372793 0.3896863964
[98,] 0.57635705 0.847285894 0.4236429469
[99,] 0.55213628 0.895727433 0.4478637166
[100,] 0.55367552 0.892648970 0.4463244848
[101,] 0.57216169 0.855676615 0.4278383074
[102,] 0.57939273 0.841214540 0.4206072702
[103,] 0.67096112 0.658077756 0.3290388779
[104,] 0.64442876 0.711142480 0.3555712399
[105,] 0.61838576 0.763228478 0.3816142390
[106,] 0.58373837 0.832523266 0.4162616331
[107,] 0.55823526 0.883529480 0.4417647401
[108,] 0.52289675 0.954206501 0.4771032507
[109,] 0.48909486 0.978189718 0.5109051412
[110,] 0.46628382 0.932567647 0.5337161767
[111,] 0.43098822 0.861976439 0.5690117805
[112,] 0.40125650 0.802513008 0.5987434960
[113,] 0.37305671 0.746113415 0.6269432924
[114,] 0.36137472 0.722749437 0.6386252817
[115,] 0.33570203 0.671404059 0.6642979706
[116,] 0.32364473 0.647289453 0.6763552735
[117,] 0.43158609 0.863172171 0.5684139144
[118,] 0.41329506 0.826590127 0.5867049366
[119,] 0.40066211 0.801324224 0.5993378880
[120,] 0.38797555 0.775951097 0.6120244517
[121,] 0.35553588 0.711071755 0.6444641227
[122,] 0.37817853 0.756357066 0.6218214669
[123,] 0.34937729 0.698754588 0.6506227061
[124,] 0.36355962 0.727119237 0.6364403815
[125,] 0.35538498 0.710769951 0.6446150244
[126,] 0.32695738 0.653914758 0.6730426208
[127,] 0.30229588 0.604591754 0.6977041232
[128,] 0.27568404 0.551368073 0.7243159633
[129,] 0.25156389 0.503127773 0.7484361134
[130,] 0.22511465 0.450229299 0.7748853505
[131,] 0.23020594 0.460411884 0.7697940581
[132,] 0.20464229 0.409284587 0.7953577066
[133,] 0.18571168 0.371423369 0.8142883156
[134,] 0.17165822 0.343316432 0.8283417840
[135,] 0.16595642 0.331912837 0.8340435813
[136,] 0.17156515 0.343130290 0.8284348549
[137,] 0.18663006 0.373260127 0.8133699365
[138,] 0.20367607 0.407352137 0.7963239315
[139,] 0.19916774 0.398335474 0.8008322632
[140,] 0.17654801 0.353096023 0.8234519886
[141,] 0.15665341 0.313306828 0.8433465859
[142,] 0.16536656 0.330733119 0.8346334403
[143,] 0.18224483 0.364489652 0.8177551742
[144,] 0.16386577 0.327731530 0.8361342348
[145,] 0.14855859 0.297117185 0.8514414074
[146,] 0.12919183 0.258383652 0.8708081742
[147,] 0.20491219 0.409824371 0.7950878146
[148,] 0.22395373 0.447907459 0.7760462706
[149,] 0.19826930 0.396538599 0.8017307003
[150,] 0.17386100 0.347722008 0.8261389958
[151,] 0.15164386 0.303287720 0.8483561401
[152,] 0.13145654 0.262913080 0.8685434598
[153,] 0.21666423 0.433328456 0.7833357718
[154,] 0.21892571 0.437851429 0.7810742853
[155,] 0.21173383 0.423467657 0.7882661716
[156,] 0.18694899 0.373897974 0.8130510132
[157,] 0.16943778 0.338875560 0.8305622198
[158,] 0.22667836 0.453356712 0.7733216442
[159,] 0.25768259 0.515365172 0.7423174138
[160,] 0.22955047 0.459100932 0.7704495340
[161,] 0.22354519 0.447090376 0.7764548121
[162,] 0.39336466 0.786729327 0.6066353363
[163,] 0.37295378 0.745907558 0.6270462208
[164,] 0.39018506 0.780370121 0.6098149394
[165,] 0.40408791 0.808175829 0.5959120856
[166,] 0.46989230 0.939784597 0.5301077015
[167,] 0.43272969 0.865459390 0.5672703051
[168,] 0.40917210 0.818344193 0.5908279034
[169,] 0.41789614 0.835792276 0.5821038622
[170,] 0.38254088 0.765081765 0.6174591177
[171,] 0.38729760 0.774595190 0.6127024048
[172,] 0.35141917 0.702838347 0.6485808264
[173,] 0.32977813 0.659556256 0.6702218719
[174,] 0.32975271 0.659505427 0.6702472863
[175,] 0.31486183 0.629723653 0.6851381735
[176,] 0.28102959 0.562059188 0.7189704062
[177,] 0.25041188 0.500823754 0.7495881230
[178,] 0.22029193 0.440583861 0.7797080693
[179,] 0.19635201 0.392704022 0.8036479892
[180,] 0.20843219 0.416864385 0.7915678077
[181,] 0.19220797 0.384415945 0.8077920275
[182,] 0.19733487 0.394669741 0.8026651295
[183,] 0.18861639 0.377232780 0.8113836100
[184,] 0.18488722 0.369774432 0.8151127839
[185,] 0.19675259 0.393505173 0.8032474136
[186,] 0.23322784 0.466455689 0.7667721555
[187,] 0.21504086 0.430081726 0.7849591370
[188,] 0.24906561 0.498131230 0.7509343851
[189,] 0.21852122 0.437042432 0.7814787838
[190,] 0.25684317 0.513686348 0.7431568259
[191,] 0.23236946 0.464738913 0.7676305434
[192,] 0.26744465 0.534889293 0.7325553537
[193,] 0.23656909 0.473138174 0.7634309131
[194,] 0.20552714 0.411054282 0.7944728590
[195,] 0.18649603 0.372992064 0.8135039681
[196,] 0.15830263 0.316605250 0.8416973749
[197,] 0.18217936 0.364358717 0.8178206413
[198,] 0.15427568 0.308551365 0.8457243177
[199,] 0.16288871 0.325777422 0.8371112892
[200,] 0.18021541 0.360430814 0.8197845932
[201,] 0.17138543 0.342770863 0.8286145684
[202,] 0.14743477 0.294869549 0.8525652254
[203,] 0.18331175 0.366623500 0.8166882502
[204,] 0.16155540 0.323110804 0.8384445981
[205,] 0.15119110 0.302382198 0.8488089008
[206,] 0.17881992 0.357639840 0.8211800801
[207,] 0.15287846 0.305756923 0.8471215386
[208,] 0.14005001 0.280100023 0.8599499887
[209,] 0.15555814 0.311116271 0.8444418644
[210,] 0.15869363 0.317387255 0.8413063725
[211,] 0.15647488 0.312949754 0.8435251232
[212,] 0.14074280 0.281485591 0.8592572045
[213,] 0.11539680 0.230793593 0.8846032037
[214,] 0.11265601 0.225312020 0.8873439900
[215,] 0.13940802 0.278816046 0.8605919772
[216,] 0.35039121 0.700782412 0.6496087938
[217,] 0.30961090 0.619221799 0.6903891007
[218,] 0.27184900 0.543697999 0.7281510004
[219,] 0.24656800 0.493136003 0.7534319983
[220,] 0.21263319 0.425266377 0.7873668116
[221,] 0.23758771 0.475175421 0.7624122893
[222,] 0.19791794 0.395835888 0.8020820562
[223,] 0.16351020 0.327020407 0.8364897965
[224,] 0.16894405 0.337888091 0.8310559546
[225,] 0.14318427 0.286368535 0.8568157323
[226,] 0.12154923 0.243098462 0.8784507690
[227,] 0.09143349 0.182866979 0.9085665104
[228,] 0.18113612 0.362272230 0.8188638848
[229,] 0.18788917 0.375778340 0.8121108301
[230,] 0.18877180 0.377543592 0.8112282041
[231,] 0.80585956 0.388280881 0.1941404404
[232,] 0.74428626 0.511427472 0.2557137358
[233,] 0.67939871 0.641202584 0.3206012922
[234,] 0.61677746 0.766445073 0.3832225364
[235,] 0.53899539 0.922009221 0.4610046106
[236,] 0.54782934 0.904341325 0.4521706623
[237,] 0.54533651 0.909326979 0.4546634894
[238,] 0.43040533 0.860810652 0.5695946739
[239,] 0.34034101 0.680682022 0.6596589889
[240,] 0.27599742 0.551994838 0.7240025812
[241,] 0.79563332 0.408733367 0.2043666836
> postscript(file="/var/wessaorg/rcomp/tmp/1aqte1384794492.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/22h8t1384794492.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ciog1384794492.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4fpsj1384794492.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ogje1384794492.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 = 264
Frequency = 1
1 2 3 4 5 6
-0.20337538 2.47914904 -3.06029642 -2.83728300 4.51983463 3.25198056
7 8 9 10 11 12
3.08802064 -1.21733754 -0.42087552 0.38342457 1.26335402 3.23514100
13 14 15 16 17 18
-3.56441059 2.32916317 2.17199652 0.45464038 -0.05746296 1.13610862
19 20 21 22 23 24
-1.49729708 2.08012384 2.53582822 -2.80339642 -0.48319032 -1.64487082
25 26 27 28 29 30
1.54626728 -6.98829983 0.79196902 0.66546234 0.96616613 -3.03785539
31 32 33 34 35 36
0.22716822 0.15483591 1.80407478 -0.28699906 -0.05863856 0.29670729
37 38 39 40 41 42
-2.01434975 0.63392385 1.60202764 -2.26343824 -0.74312108 2.29986963
43 44 45 46 47 48
0.29846105 -1.18848320 0.34292983 -2.65198722 -0.27010221 0.12958716
49 50 51 52 53 54
3.47595773 -1.73350363 0.86817452 0.54313736 -0.68736440 -1.45211495
55 56 57 58 59 60
-2.25172508 1.28268123 1.88633677 -0.33536052 -3.13761299 -1.32607499
61 62 63 64 65 66
-2.78315331 -1.50882519 -3.48772635 1.00600139 1.51407315 -5.33700285
67 68 69 70 71 72
-1.81489413 -2.80402480 1.14859390 1.10168646 0.10871892 2.98605535
73 74 75 76 77 78
0.36233431 -0.45697444 -2.20835748 -0.45417517 2.80039429 0.28747625
79 80 81 82 83 84
0.94780773 -2.14894321 -0.13377674 -0.67042435 1.49340448 0.49689297
85 86 87 88 89 90
-0.34952379 0.71186473 -0.44657548 0.07272574 -3.73226698 3.02063866
91 92 93 94 95 96
0.10800889 0.66326389 0.51496118 -1.18434906 0.84151013 -0.91125141
97 98 99 100 101 102
-1.05694070 1.95459950 -0.19699478 1.78849523 -1.05548830 1.02242492
103 104 105 106 107 108
-3.49476771 1.78829964 -2.50598711 1.04855737 2.01910497 -2.87735959
109 110 111 112 113 114
0.64367449 1.07001347 -2.19074115 -2.29112088 1.84848902 3.80772273
115 116 117 118 119 120
0.50373660 1.02050491 0.12517504 -1.10479287 0.31772025 -0.55252919
121 122 123 124 125 126
0.50257189 0.11840671 -0.77481059 0.52304398 -1.61862990 0.86936443
127 128 129 130 131 132
1.78683634 4.14311635 1.45281833 -1.58695746 -1.63324419 -0.25192160
133 134 135 136 137 138
2.45982794 0.89296631 2.39597795 1.59406405 0.84945374 -0.89173522
139 140 141 142 143 144
0.87291278 -0.75482780 0.47354203 2.26969691 -0.38886035 0.89636240
145 146 147 148 149 150
1.45808052 1.80943949 -2.17877670 -2.44057192 -2.38559436 1.83764939
151 152 153 154 155 156
0.66680421 0.75212916 -2.31530706 -2.04163013 1.39079433 0.62159748
157 158 159 160 161 162
0.79511248 4.38015724 -2.37018317 0.04230363 0.63796567 0.49716157
163 164 165 166 167 168
0.47145912 4.36854507 -2.15286093 1.59409366 -0.25979488 -1.03252578
169 170 171 172 173 174
-3.84763901 -3.08074399 0.16398170 1.73948898 -5.16776929 1.42454152
175 176 177 178 179 180
2.54970579 -2.39055701 -3.47059876 0.34657341 1.36461433 -2.33512626
181 182 183 184 185 186
-0.08495145 -1.90718225 0.43263744 -0.96648571 1.85498966 1.32146812
187 188 189 190 191 192
0.44756328 0.65459807 0.39034092 0.57025539 -1.69602078 -0.94891689
193 194 195 196 197 198
2.08428572 -1.41686980 1.88094294 -1.95658613 2.25743591 0.66837166
199 200 201 202 203 204
-3.15494752 -0.69885893 -3.03050373 1.26594480 2.89912696 0.56114392
205 206 207 208 209 210
0.60051836 1.38006919 -0.34997754 3.63666482 0.17183256 1.78854089
211 212 213 214 215 216
-2.55877237 1.60334383 -1.10330660 -3.74230834 -0.95006238 1.88061869
217 218 219 220 221 222
2.31834703 -0.07770278 -1.88018500 1.64849233 -2.68447427 2.56887311
223 224 225 226 227 228
-1.88985666 0.33702963 -0.57437358 1.70871671 5.42498363 -1.50546348
229 230 231 232 233 234
-1.33152308 -2.16039938 0.31340625 -2.80817896 0.29245918 0.99170828
235 236 237 238 239 240
1.37154782 -1.59169046 0.75460261 0.53676653 -3.99273726 -2.37408606
241 242 243 244 245 246
-2.59362842 -2.37752049 0.35717028 -0.04348895 1.85160329 0.51592466
247 248 249 250 251 252
0.60073999 5.45824080 0.07705072 0.75153404 2.39155430 1.38020078
253 254 255 256 257 258
-0.85171347 -0.30126811 0.59133909 -0.20648813 -1.39107106 -2.07243425
259 260 261 262 263 264
2.87162422 -4.62466237 0.80643021 1.94852193 -2.46946671 0.59246891
> postscript(file="/var/wessaorg/rcomp/tmp/6b41e1384794492.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 = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.20337538 NA
1 2.47914904 -0.20337538
2 -3.06029642 2.47914904
3 -2.83728300 -3.06029642
4 4.51983463 -2.83728300
5 3.25198056 4.51983463
6 3.08802064 3.25198056
7 -1.21733754 3.08802064
8 -0.42087552 -1.21733754
9 0.38342457 -0.42087552
10 1.26335402 0.38342457
11 3.23514100 1.26335402
12 -3.56441059 3.23514100
13 2.32916317 -3.56441059
14 2.17199652 2.32916317
15 0.45464038 2.17199652
16 -0.05746296 0.45464038
17 1.13610862 -0.05746296
18 -1.49729708 1.13610862
19 2.08012384 -1.49729708
20 2.53582822 2.08012384
21 -2.80339642 2.53582822
22 -0.48319032 -2.80339642
23 -1.64487082 -0.48319032
24 1.54626728 -1.64487082
25 -6.98829983 1.54626728
26 0.79196902 -6.98829983
27 0.66546234 0.79196902
28 0.96616613 0.66546234
29 -3.03785539 0.96616613
30 0.22716822 -3.03785539
31 0.15483591 0.22716822
32 1.80407478 0.15483591
33 -0.28699906 1.80407478
34 -0.05863856 -0.28699906
35 0.29670729 -0.05863856
36 -2.01434975 0.29670729
37 0.63392385 -2.01434975
38 1.60202764 0.63392385
39 -2.26343824 1.60202764
40 -0.74312108 -2.26343824
41 2.29986963 -0.74312108
42 0.29846105 2.29986963
43 -1.18848320 0.29846105
44 0.34292983 -1.18848320
45 -2.65198722 0.34292983
46 -0.27010221 -2.65198722
47 0.12958716 -0.27010221
48 3.47595773 0.12958716
49 -1.73350363 3.47595773
50 0.86817452 -1.73350363
51 0.54313736 0.86817452
52 -0.68736440 0.54313736
53 -1.45211495 -0.68736440
54 -2.25172508 -1.45211495
55 1.28268123 -2.25172508
56 1.88633677 1.28268123
57 -0.33536052 1.88633677
58 -3.13761299 -0.33536052
59 -1.32607499 -3.13761299
60 -2.78315331 -1.32607499
61 -1.50882519 -2.78315331
62 -3.48772635 -1.50882519
63 1.00600139 -3.48772635
64 1.51407315 1.00600139
65 -5.33700285 1.51407315
66 -1.81489413 -5.33700285
67 -2.80402480 -1.81489413
68 1.14859390 -2.80402480
69 1.10168646 1.14859390
70 0.10871892 1.10168646
71 2.98605535 0.10871892
72 0.36233431 2.98605535
73 -0.45697444 0.36233431
74 -2.20835748 -0.45697444
75 -0.45417517 -2.20835748
76 2.80039429 -0.45417517
77 0.28747625 2.80039429
78 0.94780773 0.28747625
79 -2.14894321 0.94780773
80 -0.13377674 -2.14894321
81 -0.67042435 -0.13377674
82 1.49340448 -0.67042435
83 0.49689297 1.49340448
84 -0.34952379 0.49689297
85 0.71186473 -0.34952379
86 -0.44657548 0.71186473
87 0.07272574 -0.44657548
88 -3.73226698 0.07272574
89 3.02063866 -3.73226698
90 0.10800889 3.02063866
91 0.66326389 0.10800889
92 0.51496118 0.66326389
93 -1.18434906 0.51496118
94 0.84151013 -1.18434906
95 -0.91125141 0.84151013
96 -1.05694070 -0.91125141
97 1.95459950 -1.05694070
98 -0.19699478 1.95459950
99 1.78849523 -0.19699478
100 -1.05548830 1.78849523
101 1.02242492 -1.05548830
102 -3.49476771 1.02242492
103 1.78829964 -3.49476771
104 -2.50598711 1.78829964
105 1.04855737 -2.50598711
106 2.01910497 1.04855737
107 -2.87735959 2.01910497
108 0.64367449 -2.87735959
109 1.07001347 0.64367449
110 -2.19074115 1.07001347
111 -2.29112088 -2.19074115
112 1.84848902 -2.29112088
113 3.80772273 1.84848902
114 0.50373660 3.80772273
115 1.02050491 0.50373660
116 0.12517504 1.02050491
117 -1.10479287 0.12517504
118 0.31772025 -1.10479287
119 -0.55252919 0.31772025
120 0.50257189 -0.55252919
121 0.11840671 0.50257189
122 -0.77481059 0.11840671
123 0.52304398 -0.77481059
124 -1.61862990 0.52304398
125 0.86936443 -1.61862990
126 1.78683634 0.86936443
127 4.14311635 1.78683634
128 1.45281833 4.14311635
129 -1.58695746 1.45281833
130 -1.63324419 -1.58695746
131 -0.25192160 -1.63324419
132 2.45982794 -0.25192160
133 0.89296631 2.45982794
134 2.39597795 0.89296631
135 1.59406405 2.39597795
136 0.84945374 1.59406405
137 -0.89173522 0.84945374
138 0.87291278 -0.89173522
139 -0.75482780 0.87291278
140 0.47354203 -0.75482780
141 2.26969691 0.47354203
142 -0.38886035 2.26969691
143 0.89636240 -0.38886035
144 1.45808052 0.89636240
145 1.80943949 1.45808052
146 -2.17877670 1.80943949
147 -2.44057192 -2.17877670
148 -2.38559436 -2.44057192
149 1.83764939 -2.38559436
150 0.66680421 1.83764939
151 0.75212916 0.66680421
152 -2.31530706 0.75212916
153 -2.04163013 -2.31530706
154 1.39079433 -2.04163013
155 0.62159748 1.39079433
156 0.79511248 0.62159748
157 4.38015724 0.79511248
158 -2.37018317 4.38015724
159 0.04230363 -2.37018317
160 0.63796567 0.04230363
161 0.49716157 0.63796567
162 0.47145912 0.49716157
163 4.36854507 0.47145912
164 -2.15286093 4.36854507
165 1.59409366 -2.15286093
166 -0.25979488 1.59409366
167 -1.03252578 -0.25979488
168 -3.84763901 -1.03252578
169 -3.08074399 -3.84763901
170 0.16398170 -3.08074399
171 1.73948898 0.16398170
172 -5.16776929 1.73948898
173 1.42454152 -5.16776929
174 2.54970579 1.42454152
175 -2.39055701 2.54970579
176 -3.47059876 -2.39055701
177 0.34657341 -3.47059876
178 1.36461433 0.34657341
179 -2.33512626 1.36461433
180 -0.08495145 -2.33512626
181 -1.90718225 -0.08495145
182 0.43263744 -1.90718225
183 -0.96648571 0.43263744
184 1.85498966 -0.96648571
185 1.32146812 1.85498966
186 0.44756328 1.32146812
187 0.65459807 0.44756328
188 0.39034092 0.65459807
189 0.57025539 0.39034092
190 -1.69602078 0.57025539
191 -0.94891689 -1.69602078
192 2.08428572 -0.94891689
193 -1.41686980 2.08428572
194 1.88094294 -1.41686980
195 -1.95658613 1.88094294
196 2.25743591 -1.95658613
197 0.66837166 2.25743591
198 -3.15494752 0.66837166
199 -0.69885893 -3.15494752
200 -3.03050373 -0.69885893
201 1.26594480 -3.03050373
202 2.89912696 1.26594480
203 0.56114392 2.89912696
204 0.60051836 0.56114392
205 1.38006919 0.60051836
206 -0.34997754 1.38006919
207 3.63666482 -0.34997754
208 0.17183256 3.63666482
209 1.78854089 0.17183256
210 -2.55877237 1.78854089
211 1.60334383 -2.55877237
212 -1.10330660 1.60334383
213 -3.74230834 -1.10330660
214 -0.95006238 -3.74230834
215 1.88061869 -0.95006238
216 2.31834703 1.88061869
217 -0.07770278 2.31834703
218 -1.88018500 -0.07770278
219 1.64849233 -1.88018500
220 -2.68447427 1.64849233
221 2.56887311 -2.68447427
222 -1.88985666 2.56887311
223 0.33702963 -1.88985666
224 -0.57437358 0.33702963
225 1.70871671 -0.57437358
226 5.42498363 1.70871671
227 -1.50546348 5.42498363
228 -1.33152308 -1.50546348
229 -2.16039938 -1.33152308
230 0.31340625 -2.16039938
231 -2.80817896 0.31340625
232 0.29245918 -2.80817896
233 0.99170828 0.29245918
234 1.37154782 0.99170828
235 -1.59169046 1.37154782
236 0.75460261 -1.59169046
237 0.53676653 0.75460261
238 -3.99273726 0.53676653
239 -2.37408606 -3.99273726
240 -2.59362842 -2.37408606
241 -2.37752049 -2.59362842
242 0.35717028 -2.37752049
243 -0.04348895 0.35717028
244 1.85160329 -0.04348895
245 0.51592466 1.85160329
246 0.60073999 0.51592466
247 5.45824080 0.60073999
248 0.07705072 5.45824080
249 0.75153404 0.07705072
250 2.39155430 0.75153404
251 1.38020078 2.39155430
252 -0.85171347 1.38020078
253 -0.30126811 -0.85171347
254 0.59133909 -0.30126811
255 -0.20648813 0.59133909
256 -1.39107106 -0.20648813
257 -2.07243425 -1.39107106
258 2.87162422 -2.07243425
259 -4.62466237 2.87162422
260 0.80643021 -4.62466237
261 1.94852193 0.80643021
262 -2.46946671 1.94852193
263 0.59246891 -2.46946671
264 NA 0.59246891
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.47914904 -0.20337538
[2,] -3.06029642 2.47914904
[3,] -2.83728300 -3.06029642
[4,] 4.51983463 -2.83728300
[5,] 3.25198056 4.51983463
[6,] 3.08802064 3.25198056
[7,] -1.21733754 3.08802064
[8,] -0.42087552 -1.21733754
[9,] 0.38342457 -0.42087552
[10,] 1.26335402 0.38342457
[11,] 3.23514100 1.26335402
[12,] -3.56441059 3.23514100
[13,] 2.32916317 -3.56441059
[14,] 2.17199652 2.32916317
[15,] 0.45464038 2.17199652
[16,] -0.05746296 0.45464038
[17,] 1.13610862 -0.05746296
[18,] -1.49729708 1.13610862
[19,] 2.08012384 -1.49729708
[20,] 2.53582822 2.08012384
[21,] -2.80339642 2.53582822
[22,] -0.48319032 -2.80339642
[23,] -1.64487082 -0.48319032
[24,] 1.54626728 -1.64487082
[25,] -6.98829983 1.54626728
[26,] 0.79196902 -6.98829983
[27,] 0.66546234 0.79196902
[28,] 0.96616613 0.66546234
[29,] -3.03785539 0.96616613
[30,] 0.22716822 -3.03785539
[31,] 0.15483591 0.22716822
[32,] 1.80407478 0.15483591
[33,] -0.28699906 1.80407478
[34,] -0.05863856 -0.28699906
[35,] 0.29670729 -0.05863856
[36,] -2.01434975 0.29670729
[37,] 0.63392385 -2.01434975
[38,] 1.60202764 0.63392385
[39,] -2.26343824 1.60202764
[40,] -0.74312108 -2.26343824
[41,] 2.29986963 -0.74312108
[42,] 0.29846105 2.29986963
[43,] -1.18848320 0.29846105
[44,] 0.34292983 -1.18848320
[45,] -2.65198722 0.34292983
[46,] -0.27010221 -2.65198722
[47,] 0.12958716 -0.27010221
[48,] 3.47595773 0.12958716
[49,] -1.73350363 3.47595773
[50,] 0.86817452 -1.73350363
[51,] 0.54313736 0.86817452
[52,] -0.68736440 0.54313736
[53,] -1.45211495 -0.68736440
[54,] -2.25172508 -1.45211495
[55,] 1.28268123 -2.25172508
[56,] 1.88633677 1.28268123
[57,] -0.33536052 1.88633677
[58,] -3.13761299 -0.33536052
[59,] -1.32607499 -3.13761299
[60,] -2.78315331 -1.32607499
[61,] -1.50882519 -2.78315331
[62,] -3.48772635 -1.50882519
[63,] 1.00600139 -3.48772635
[64,] 1.51407315 1.00600139
[65,] -5.33700285 1.51407315
[66,] -1.81489413 -5.33700285
[67,] -2.80402480 -1.81489413
[68,] 1.14859390 -2.80402480
[69,] 1.10168646 1.14859390
[70,] 0.10871892 1.10168646
[71,] 2.98605535 0.10871892
[72,] 0.36233431 2.98605535
[73,] -0.45697444 0.36233431
[74,] -2.20835748 -0.45697444
[75,] -0.45417517 -2.20835748
[76,] 2.80039429 -0.45417517
[77,] 0.28747625 2.80039429
[78,] 0.94780773 0.28747625
[79,] -2.14894321 0.94780773
[80,] -0.13377674 -2.14894321
[81,] -0.67042435 -0.13377674
[82,] 1.49340448 -0.67042435
[83,] 0.49689297 1.49340448
[84,] -0.34952379 0.49689297
[85,] 0.71186473 -0.34952379
[86,] -0.44657548 0.71186473
[87,] 0.07272574 -0.44657548
[88,] -3.73226698 0.07272574
[89,] 3.02063866 -3.73226698
[90,] 0.10800889 3.02063866
[91,] 0.66326389 0.10800889
[92,] 0.51496118 0.66326389
[93,] -1.18434906 0.51496118
[94,] 0.84151013 -1.18434906
[95,] -0.91125141 0.84151013
[96,] -1.05694070 -0.91125141
[97,] 1.95459950 -1.05694070
[98,] -0.19699478 1.95459950
[99,] 1.78849523 -0.19699478
[100,] -1.05548830 1.78849523
[101,] 1.02242492 -1.05548830
[102,] -3.49476771 1.02242492
[103,] 1.78829964 -3.49476771
[104,] -2.50598711 1.78829964
[105,] 1.04855737 -2.50598711
[106,] 2.01910497 1.04855737
[107,] -2.87735959 2.01910497
[108,] 0.64367449 -2.87735959
[109,] 1.07001347 0.64367449
[110,] -2.19074115 1.07001347
[111,] -2.29112088 -2.19074115
[112,] 1.84848902 -2.29112088
[113,] 3.80772273 1.84848902
[114,] 0.50373660 3.80772273
[115,] 1.02050491 0.50373660
[116,] 0.12517504 1.02050491
[117,] -1.10479287 0.12517504
[118,] 0.31772025 -1.10479287
[119,] -0.55252919 0.31772025
[120,] 0.50257189 -0.55252919
[121,] 0.11840671 0.50257189
[122,] -0.77481059 0.11840671
[123,] 0.52304398 -0.77481059
[124,] -1.61862990 0.52304398
[125,] 0.86936443 -1.61862990
[126,] 1.78683634 0.86936443
[127,] 4.14311635 1.78683634
[128,] 1.45281833 4.14311635
[129,] -1.58695746 1.45281833
[130,] -1.63324419 -1.58695746
[131,] -0.25192160 -1.63324419
[132,] 2.45982794 -0.25192160
[133,] 0.89296631 2.45982794
[134,] 2.39597795 0.89296631
[135,] 1.59406405 2.39597795
[136,] 0.84945374 1.59406405
[137,] -0.89173522 0.84945374
[138,] 0.87291278 -0.89173522
[139,] -0.75482780 0.87291278
[140,] 0.47354203 -0.75482780
[141,] 2.26969691 0.47354203
[142,] -0.38886035 2.26969691
[143,] 0.89636240 -0.38886035
[144,] 1.45808052 0.89636240
[145,] 1.80943949 1.45808052
[146,] -2.17877670 1.80943949
[147,] -2.44057192 -2.17877670
[148,] -2.38559436 -2.44057192
[149,] 1.83764939 -2.38559436
[150,] 0.66680421 1.83764939
[151,] 0.75212916 0.66680421
[152,] -2.31530706 0.75212916
[153,] -2.04163013 -2.31530706
[154,] 1.39079433 -2.04163013
[155,] 0.62159748 1.39079433
[156,] 0.79511248 0.62159748
[157,] 4.38015724 0.79511248
[158,] -2.37018317 4.38015724
[159,] 0.04230363 -2.37018317
[160,] 0.63796567 0.04230363
[161,] 0.49716157 0.63796567
[162,] 0.47145912 0.49716157
[163,] 4.36854507 0.47145912
[164,] -2.15286093 4.36854507
[165,] 1.59409366 -2.15286093
[166,] -0.25979488 1.59409366
[167,] -1.03252578 -0.25979488
[168,] -3.84763901 -1.03252578
[169,] -3.08074399 -3.84763901
[170,] 0.16398170 -3.08074399
[171,] 1.73948898 0.16398170
[172,] -5.16776929 1.73948898
[173,] 1.42454152 -5.16776929
[174,] 2.54970579 1.42454152
[175,] -2.39055701 2.54970579
[176,] -3.47059876 -2.39055701
[177,] 0.34657341 -3.47059876
[178,] 1.36461433 0.34657341
[179,] -2.33512626 1.36461433
[180,] -0.08495145 -2.33512626
[181,] -1.90718225 -0.08495145
[182,] 0.43263744 -1.90718225
[183,] -0.96648571 0.43263744
[184,] 1.85498966 -0.96648571
[185,] 1.32146812 1.85498966
[186,] 0.44756328 1.32146812
[187,] 0.65459807 0.44756328
[188,] 0.39034092 0.65459807
[189,] 0.57025539 0.39034092
[190,] -1.69602078 0.57025539
[191,] -0.94891689 -1.69602078
[192,] 2.08428572 -0.94891689
[193,] -1.41686980 2.08428572
[194,] 1.88094294 -1.41686980
[195,] -1.95658613 1.88094294
[196,] 2.25743591 -1.95658613
[197,] 0.66837166 2.25743591
[198,] -3.15494752 0.66837166
[199,] -0.69885893 -3.15494752
[200,] -3.03050373 -0.69885893
[201,] 1.26594480 -3.03050373
[202,] 2.89912696 1.26594480
[203,] 0.56114392 2.89912696
[204,] 0.60051836 0.56114392
[205,] 1.38006919 0.60051836
[206,] -0.34997754 1.38006919
[207,] 3.63666482 -0.34997754
[208,] 0.17183256 3.63666482
[209,] 1.78854089 0.17183256
[210,] -2.55877237 1.78854089
[211,] 1.60334383 -2.55877237
[212,] -1.10330660 1.60334383
[213,] -3.74230834 -1.10330660
[214,] -0.95006238 -3.74230834
[215,] 1.88061869 -0.95006238
[216,] 2.31834703 1.88061869
[217,] -0.07770278 2.31834703
[218,] -1.88018500 -0.07770278
[219,] 1.64849233 -1.88018500
[220,] -2.68447427 1.64849233
[221,] 2.56887311 -2.68447427
[222,] -1.88985666 2.56887311
[223,] 0.33702963 -1.88985666
[224,] -0.57437358 0.33702963
[225,] 1.70871671 -0.57437358
[226,] 5.42498363 1.70871671
[227,] -1.50546348 5.42498363
[228,] -1.33152308 -1.50546348
[229,] -2.16039938 -1.33152308
[230,] 0.31340625 -2.16039938
[231,] -2.80817896 0.31340625
[232,] 0.29245918 -2.80817896
[233,] 0.99170828 0.29245918
[234,] 1.37154782 0.99170828
[235,] -1.59169046 1.37154782
[236,] 0.75460261 -1.59169046
[237,] 0.53676653 0.75460261
[238,] -3.99273726 0.53676653
[239,] -2.37408606 -3.99273726
[240,] -2.59362842 -2.37408606
[241,] -2.37752049 -2.59362842
[242,] 0.35717028 -2.37752049
[243,] -0.04348895 0.35717028
[244,] 1.85160329 -0.04348895
[245,] 0.51592466 1.85160329
[246,] 0.60073999 0.51592466
[247,] 5.45824080 0.60073999
[248,] 0.07705072 5.45824080
[249,] 0.75153404 0.07705072
[250,] 2.39155430 0.75153404
[251,] 1.38020078 2.39155430
[252,] -0.85171347 1.38020078
[253,] -0.30126811 -0.85171347
[254,] 0.59133909 -0.30126811
[255,] -0.20648813 0.59133909
[256,] -1.39107106 -0.20648813
[257,] -2.07243425 -1.39107106
[258,] 2.87162422 -2.07243425
[259,] -4.62466237 2.87162422
[260,] 0.80643021 -4.62466237
[261,] 1.94852193 0.80643021
[262,] -2.46946671 1.94852193
[263,] 0.59246891 -2.46946671
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.47914904 -0.20337538
2 -3.06029642 2.47914904
3 -2.83728300 -3.06029642
4 4.51983463 -2.83728300
5 3.25198056 4.51983463
6 3.08802064 3.25198056
7 -1.21733754 3.08802064
8 -0.42087552 -1.21733754
9 0.38342457 -0.42087552
10 1.26335402 0.38342457
11 3.23514100 1.26335402
12 -3.56441059 3.23514100
13 2.32916317 -3.56441059
14 2.17199652 2.32916317
15 0.45464038 2.17199652
16 -0.05746296 0.45464038
17 1.13610862 -0.05746296
18 -1.49729708 1.13610862
19 2.08012384 -1.49729708
20 2.53582822 2.08012384
21 -2.80339642 2.53582822
22 -0.48319032 -2.80339642
23 -1.64487082 -0.48319032
24 1.54626728 -1.64487082
25 -6.98829983 1.54626728
26 0.79196902 -6.98829983
27 0.66546234 0.79196902
28 0.96616613 0.66546234
29 -3.03785539 0.96616613
30 0.22716822 -3.03785539
31 0.15483591 0.22716822
32 1.80407478 0.15483591
33 -0.28699906 1.80407478
34 -0.05863856 -0.28699906
35 0.29670729 -0.05863856
36 -2.01434975 0.29670729
37 0.63392385 -2.01434975
38 1.60202764 0.63392385
39 -2.26343824 1.60202764
40 -0.74312108 -2.26343824
41 2.29986963 -0.74312108
42 0.29846105 2.29986963
43 -1.18848320 0.29846105
44 0.34292983 -1.18848320
45 -2.65198722 0.34292983
46 -0.27010221 -2.65198722
47 0.12958716 -0.27010221
48 3.47595773 0.12958716
49 -1.73350363 3.47595773
50 0.86817452 -1.73350363
51 0.54313736 0.86817452
52 -0.68736440 0.54313736
53 -1.45211495 -0.68736440
54 -2.25172508 -1.45211495
55 1.28268123 -2.25172508
56 1.88633677 1.28268123
57 -0.33536052 1.88633677
58 -3.13761299 -0.33536052
59 -1.32607499 -3.13761299
60 -2.78315331 -1.32607499
61 -1.50882519 -2.78315331
62 -3.48772635 -1.50882519
63 1.00600139 -3.48772635
64 1.51407315 1.00600139
65 -5.33700285 1.51407315
66 -1.81489413 -5.33700285
67 -2.80402480 -1.81489413
68 1.14859390 -2.80402480
69 1.10168646 1.14859390
70 0.10871892 1.10168646
71 2.98605535 0.10871892
72 0.36233431 2.98605535
73 -0.45697444 0.36233431
74 -2.20835748 -0.45697444
75 -0.45417517 -2.20835748
76 2.80039429 -0.45417517
77 0.28747625 2.80039429
78 0.94780773 0.28747625
79 -2.14894321 0.94780773
80 -0.13377674 -2.14894321
81 -0.67042435 -0.13377674
82 1.49340448 -0.67042435
83 0.49689297 1.49340448
84 -0.34952379 0.49689297
85 0.71186473 -0.34952379
86 -0.44657548 0.71186473
87 0.07272574 -0.44657548
88 -3.73226698 0.07272574
89 3.02063866 -3.73226698
90 0.10800889 3.02063866
91 0.66326389 0.10800889
92 0.51496118 0.66326389
93 -1.18434906 0.51496118
94 0.84151013 -1.18434906
95 -0.91125141 0.84151013
96 -1.05694070 -0.91125141
97 1.95459950 -1.05694070
98 -0.19699478 1.95459950
99 1.78849523 -0.19699478
100 -1.05548830 1.78849523
101 1.02242492 -1.05548830
102 -3.49476771 1.02242492
103 1.78829964 -3.49476771
104 -2.50598711 1.78829964
105 1.04855737 -2.50598711
106 2.01910497 1.04855737
107 -2.87735959 2.01910497
108 0.64367449 -2.87735959
109 1.07001347 0.64367449
110 -2.19074115 1.07001347
111 -2.29112088 -2.19074115
112 1.84848902 -2.29112088
113 3.80772273 1.84848902
114 0.50373660 3.80772273
115 1.02050491 0.50373660
116 0.12517504 1.02050491
117 -1.10479287 0.12517504
118 0.31772025 -1.10479287
119 -0.55252919 0.31772025
120 0.50257189 -0.55252919
121 0.11840671 0.50257189
122 -0.77481059 0.11840671
123 0.52304398 -0.77481059
124 -1.61862990 0.52304398
125 0.86936443 -1.61862990
126 1.78683634 0.86936443
127 4.14311635 1.78683634
128 1.45281833 4.14311635
129 -1.58695746 1.45281833
130 -1.63324419 -1.58695746
131 -0.25192160 -1.63324419
132 2.45982794 -0.25192160
133 0.89296631 2.45982794
134 2.39597795 0.89296631
135 1.59406405 2.39597795
136 0.84945374 1.59406405
137 -0.89173522 0.84945374
138 0.87291278 -0.89173522
139 -0.75482780 0.87291278
140 0.47354203 -0.75482780
141 2.26969691 0.47354203
142 -0.38886035 2.26969691
143 0.89636240 -0.38886035
144 1.45808052 0.89636240
145 1.80943949 1.45808052
146 -2.17877670 1.80943949
147 -2.44057192 -2.17877670
148 -2.38559436 -2.44057192
149 1.83764939 -2.38559436
150 0.66680421 1.83764939
151 0.75212916 0.66680421
152 -2.31530706 0.75212916
153 -2.04163013 -2.31530706
154 1.39079433 -2.04163013
155 0.62159748 1.39079433
156 0.79511248 0.62159748
157 4.38015724 0.79511248
158 -2.37018317 4.38015724
159 0.04230363 -2.37018317
160 0.63796567 0.04230363
161 0.49716157 0.63796567
162 0.47145912 0.49716157
163 4.36854507 0.47145912
164 -2.15286093 4.36854507
165 1.59409366 -2.15286093
166 -0.25979488 1.59409366
167 -1.03252578 -0.25979488
168 -3.84763901 -1.03252578
169 -3.08074399 -3.84763901
170 0.16398170 -3.08074399
171 1.73948898 0.16398170
172 -5.16776929 1.73948898
173 1.42454152 -5.16776929
174 2.54970579 1.42454152
175 -2.39055701 2.54970579
176 -3.47059876 -2.39055701
177 0.34657341 -3.47059876
178 1.36461433 0.34657341
179 -2.33512626 1.36461433
180 -0.08495145 -2.33512626
181 -1.90718225 -0.08495145
182 0.43263744 -1.90718225
183 -0.96648571 0.43263744
184 1.85498966 -0.96648571
185 1.32146812 1.85498966
186 0.44756328 1.32146812
187 0.65459807 0.44756328
188 0.39034092 0.65459807
189 0.57025539 0.39034092
190 -1.69602078 0.57025539
191 -0.94891689 -1.69602078
192 2.08428572 -0.94891689
193 -1.41686980 2.08428572
194 1.88094294 -1.41686980
195 -1.95658613 1.88094294
196 2.25743591 -1.95658613
197 0.66837166 2.25743591
198 -3.15494752 0.66837166
199 -0.69885893 -3.15494752
200 -3.03050373 -0.69885893
201 1.26594480 -3.03050373
202 2.89912696 1.26594480
203 0.56114392 2.89912696
204 0.60051836 0.56114392
205 1.38006919 0.60051836
206 -0.34997754 1.38006919
207 3.63666482 -0.34997754
208 0.17183256 3.63666482
209 1.78854089 0.17183256
210 -2.55877237 1.78854089
211 1.60334383 -2.55877237
212 -1.10330660 1.60334383
213 -3.74230834 -1.10330660
214 -0.95006238 -3.74230834
215 1.88061869 -0.95006238
216 2.31834703 1.88061869
217 -0.07770278 2.31834703
218 -1.88018500 -0.07770278
219 1.64849233 -1.88018500
220 -2.68447427 1.64849233
221 2.56887311 -2.68447427
222 -1.88985666 2.56887311
223 0.33702963 -1.88985666
224 -0.57437358 0.33702963
225 1.70871671 -0.57437358
226 5.42498363 1.70871671
227 -1.50546348 5.42498363
228 -1.33152308 -1.50546348
229 -2.16039938 -1.33152308
230 0.31340625 -2.16039938
231 -2.80817896 0.31340625
232 0.29245918 -2.80817896
233 0.99170828 0.29245918
234 1.37154782 0.99170828
235 -1.59169046 1.37154782
236 0.75460261 -1.59169046
237 0.53676653 0.75460261
238 -3.99273726 0.53676653
239 -2.37408606 -3.99273726
240 -2.59362842 -2.37408606
241 -2.37752049 -2.59362842
242 0.35717028 -2.37752049
243 -0.04348895 0.35717028
244 1.85160329 -0.04348895
245 0.51592466 1.85160329
246 0.60073999 0.51592466
247 5.45824080 0.60073999
248 0.07705072 5.45824080
249 0.75153404 0.07705072
250 2.39155430 0.75153404
251 1.38020078 2.39155430
252 -0.85171347 1.38020078
253 -0.30126811 -0.85171347
254 0.59133909 -0.30126811
255 -0.20648813 0.59133909
256 -1.39107106 -0.20648813
257 -2.07243425 -1.39107106
258 2.87162422 -2.07243425
259 -4.62466237 2.87162422
260 0.80643021 -4.62466237
261 1.94852193 0.80643021
262 -2.46946671 1.94852193
263 0.59246891 -2.46946671
> 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/7tkoc1384794492.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/81ruy1384794492.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/95edd1384794492.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10b79u1384794492.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$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/11hbhb1384794492.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$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/128yox1384794492.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, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13qlno1384794492.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,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/147r821384794492.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,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15ki6u1384794492.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,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ 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/16fuqy1384794492.tab")
+ }
>
> try(system("convert tmp/1aqte1384794492.ps tmp/1aqte1384794492.png",intern=TRUE))
character(0)
> try(system("convert tmp/22h8t1384794492.ps tmp/22h8t1384794492.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ciog1384794492.ps tmp/3ciog1384794492.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fpsj1384794492.ps tmp/4fpsj1384794492.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ogje1384794492.ps tmp/5ogje1384794492.png",intern=TRUE))
character(0)
> try(system("convert tmp/6b41e1384794492.ps tmp/6b41e1384794492.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tkoc1384794492.ps tmp/7tkoc1384794492.png",intern=TRUE))
character(0)
> try(system("convert tmp/81ruy1384794492.ps tmp/81ruy1384794492.png",intern=TRUE))
character(0)
> try(system("convert tmp/95edd1384794492.ps tmp/95edd1384794492.png",intern=TRUE))
character(0)
> try(system("convert tmp/10b79u1384794492.ps tmp/10b79u1384794492.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.865 2.039 13.898