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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> x <- array(list(38
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+ ,11
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+ ,3
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+ ,52
+ ,11
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+ ,11
+ ,14
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+ ,12
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+ ,62
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+ ,11
+ ,40
+ ,32
+ ,9
+ ,14
+ ,12
+ ,72
+ ,43
+ ,11
+ ,29)
+ ,dim=c(8
+ ,264)
+ ,dimnames=list(c('Separate'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport1'
+ ,'Sport2'
+ ,'Month'
+ ,'Connected')
+ ,1:264))
> y <- array(NA,dim=c(8,264),dimnames=list(c('Separate','Software','Happiness','Depression','Sport1','Sport2','Month','Connected'),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 = '8'
> par3 <- 'Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '8'
> #'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
Connected Separate Software Happiness Depression Sport1 Sport2 Month t
1 41 38 12 14 12.0 53 32 9 1
2 39 32 11 18 11.0 83 51 9 2
3 30 35 15 11 14.0 66 42 9 3
4 31 33 6 12 12.0 67 41 9 4
5 34 37 13 16 21.0 76 46 9 5
6 35 29 10 18 12.0 78 47 9 6
7 39 31 12 14 22.0 53 37 9 7
8 34 36 14 14 11.0 80 49 9 8
9 36 35 12 15 10.0 74 45 9 9
10 37 38 9 15 13.0 76 47 9 10
11 38 31 10 17 10.0 79 49 9 11
12 36 34 12 19 8.0 54 33 9 12
13 38 35 12 10 15.0 67 42 9 13
14 39 38 11 16 14.0 54 33 9 14
15 33 37 15 18 10.0 87 53 9 15
16 32 33 12 14 14.0 58 36 9 16
17 36 32 10 14 14.0 75 45 9 17
18 38 38 12 17 11.0 88 54 9 18
19 39 38 11 14 10.0 64 41 9 19
20 32 32 12 16 13.0 57 36 9 20
21 32 33 11 18 9.5 66 41 9 21
22 31 31 12 11 14.0 68 44 9 22
23 39 38 13 14 12.0 54 33 9 23
24 37 39 11 12 14.0 56 37 9 24
25 39 32 12 17 11.0 86 52 9 25
26 41 32 13 9 9.0 80 47 9 26
27 36 35 10 16 11.0 76 43 9 27
28 33 37 14 14 15.0 69 44 9 28
29 33 33 12 15 14.0 78 45 9 29
30 34 33 10 11 13.0 67 44 9 30
31 31 31 12 16 9.0 80 49 9 31
32 27 32 8 13 15.0 54 33 9 32
33 37 31 10 17 10.0 71 43 9 33
34 34 37 12 15 11.0 84 54 9 34
35 34 30 12 14 13.0 74 42 9 35
36 32 33 7 16 8.0 71 44 9 36
37 29 31 9 9 20.0 63 37 9 37
38 36 33 12 15 12.0 71 43 9 38
39 29 31 10 17 10.0 76 46 9 39
40 35 33 10 13 10.0 69 42 9 40
41 37 32 10 15 9.0 74 45 9 41
42 34 33 12 16 14.0 75 44 9 42
43 38 32 15 16 8.0 54 33 9 43
44 35 33 10 12 14.0 52 31 9 44
45 38 28 10 15 11.0 69 42 9 45
46 37 35 12 11 13.0 68 40 9 46
47 38 39 13 15 9.0 65 43 9 47
48 33 34 11 15 11.0 75 46 9 48
49 36 38 11 17 15.0 74 42 9 49
50 38 32 12 13 11.0 75 45 9 50
51 32 38 14 16 10.0 72 44 9 51
52 32 30 10 14 14.0 67 40 9 52
53 32 33 12 11 18.0 63 37 9 53
54 34 38 13 12 14.0 62 46 9 54
55 32 32 5 12 11.0 63 36 9 55
56 37 35 6 15 14.5 76 47 9 56
57 39 34 12 16 13.0 74 45 9 57
58 29 34 12 15 9.0 67 42 9 58
59 37 36 11 12 10.0 73 43 9 59
60 35 34 10 12 15.0 70 43 9 60
61 30 28 7 8 20.0 53 32 9 61
62 38 34 12 13 12.0 77 45 9 62
63 34 35 14 11 12.0 80 48 9 63
64 31 35 11 14 14.0 52 31 9 64
65 34 31 12 15 13.0 54 33 9 65
66 35 37 13 10 11.0 80 49 10 66
67 36 35 14 11 17.0 66 42 10 67
68 30 27 11 12 12.0 73 41 10 68
69 39 40 12 15 13.0 63 38 10 69
70 35 37 12 15 14.0 69 42 10 70
71 38 36 8 14 13.0 67 44 10 71
72 31 38 11 16 15.0 54 33 10 72
73 34 39 14 15 13.0 81 48 10 73
74 38 41 14 15 10.0 69 40 10 74
75 34 27 12 13 11.0 84 50 10 75
76 39 30 9 12 19.0 80 49 10 76
77 37 37 13 17 13.0 70 43 10 77
78 34 31 11 13 17.0 69 44 10 78
79 28 31 12 15 13.0 77 47 10 79
80 37 27 12 13 9.0 54 33 10 80
81 33 36 12 15 11.0 79 46 10 81
82 35 37 12 15 9.0 71 45 10 82
83 37 33 12 16 12.0 73 43 10 83
84 32 34 11 15 12.0 72 44 10 84
85 33 31 10 14 13.0 77 47 10 85
86 38 39 9 15 13.0 75 45 10 86
87 33 34 12 14 12.0 69 42 10 87
88 29 32 12 13 15.0 54 33 10 88
89 33 33 12 7 22.0 70 43 10 89
90 31 36 9 17 13.0 73 46 10 90
91 36 32 15 13 15.0 54 33 10 91
92 35 41 12 15 13.0 77 46 10 92
93 32 28 12 14 15.0 82 48 10 93
94 29 30 12 13 12.5 80 47 10 94
95 39 36 10 16 11.0 80 47 10 95
96 37 35 13 12 16.0 69 43 10 96
97 35 31 9 14 11.0 78 46 10 97
98 37 34 12 17 11.0 81 48 10 98
99 32 36 10 15 10.0 76 46 10 99
100 38 36 14 17 10.0 76 45 10 100
101 37 35 11 12 16.0 73 45 10 101
102 36 37 15 16 12.0 85 52 10 102
103 32 28 11 11 11.0 66 42 10 103
104 33 39 11 15 16.0 79 47 10 104
105 40 32 12 9 19.0 68 41 10 105
106 38 35 12 16 11.0 76 47 10 106
107 41 39 12 15 16.0 71 43 10 107
108 36 35 11 10 15.0 54 33 10 108
109 43 42 7 10 24.0 46 30 10 109
110 30 34 12 15 14.0 85 52 10 110
111 31 33 14 11 15.0 74 44 10 111
112 32 41 11 13 11.0 88 55 10 112
113 32 33 11 14 15.0 38 11 10 113
114 37 34 10 18 12.0 76 47 10 114
115 37 32 13 16 10.0 86 53 10 115
116 33 40 13 14 14.0 54 33 10 116
117 34 40 8 14 13.0 67 44 10 117
118 33 35 11 14 9.0 69 42 10 118
119 38 36 12 14 15.0 90 55 10 119
120 33 37 11 12 15.0 54 33 10 120
121 31 27 13 14 14.0 76 46 10 121
122 38 39 12 15 11.0 89 54 10 122
123 37 38 14 15 8.0 76 47 10 123
124 36 31 13 15 11.0 73 45 10 124
125 31 33 15 13 11.0 79 47 10 125
126 39 32 10 17 8.0 90 55 10 126
127 44 39 11 17 10.0 74 44 10 127
128 33 36 9 19 11.0 81 53 10 128
129 35 33 11 15 13.0 72 44 10 129
130 32 33 10 13 11.0 71 42 10 130
131 28 32 11 9 20.0 66 40 10 131
132 40 37 8 15 10.0 77 46 10 132
133 27 30 11 15 15.0 65 40 10 133
134 37 38 12 15 12.0 74 46 10 134
135 32 29 12 16 14.0 85 53 10 135
136 28 22 9 11 23.0 54 33 10 136
137 34 35 11 14 14.0 63 42 10 137
138 30 35 10 11 16.0 54 35 10 138
139 35 34 8 15 11.0 64 40 10 139
140 31 35 9 13 12.0 69 41 10 140
141 32 34 8 15 10.0 54 33 10 141
142 30 37 9 16 14.0 84 51 10 142
143 30 35 15 14 12.0 86 53 10 143
144 31 23 11 15 12.0 77 46 10 144
145 40 31 8 16 11.0 89 55 10 145
146 32 27 13 16 12.0 76 47 10 146
147 36 36 12 11 13.0 60 38 10 147
148 32 31 12 12 11.0 75 46 10 148
149 35 32 9 9 19.0 73 46 10 149
150 38 39 7 16 12.0 85 53 10 150
151 42 37 13 13 17.0 79 47 10 151
152 34 38 9 16 9.0 71 41 10 152
153 35 39 6 12 12.0 72 44 10 153
154 38 34 8 9 19.0 69 43 9 154
155 33 31 8 13 18.0 78 51 10 155
156 36 32 15 13 15.0 54 33 10 156
157 32 37 6 14 14.0 69 43 10 157
158 33 36 9 19 11.0 81 53 10 158
159 34 32 11 13 9.0 84 51 10 159
160 32 38 8 12 18.0 84 50 10 160
161 34 36 8 13 16.0 69 46 10 161
162 27 26 10 10 24.0 66 43 11 162
163 31 26 8 14 14.0 81 47 11 163
164 38 33 14 16 20.0 82 50 11 164
165 34 39 10 10 18.0 72 43 11 165
166 24 30 8 11 23.0 54 33 11 166
167 30 33 11 14 12.0 78 48 11 167
168 26 25 12 12 14.0 74 44 11 168
169 34 38 12 9 16.0 82 50 11 169
170 27 37 12 9 18.0 73 41 11 170
171 37 31 5 11 20.0 55 34 11 171
172 36 37 12 16 12.0 72 44 11 172
173 41 35 10 9 12.0 78 47 11 173
174 29 25 7 13 17.0 59 35 11 174
175 36 28 12 16 13.0 72 44 11 175
176 32 35 11 13 9.0 78 44 11 176
177 37 33 8 9 16.0 68 43 11 177
178 30 30 9 12 18.0 69 41 11 178
179 31 31 10 16 10.0 67 41 11 179
180 38 37 9 11 14.0 74 42 11 180
181 36 36 12 14 11.0 54 33 11 181
182 35 30 6 13 9.0 67 41 11 182
183 31 36 15 15 11.0 70 44 11 183
184 38 32 12 14 10.0 80 48 11 184
185 22 28 12 16 11.0 89 55 11 185
186 32 36 12 13 19.0 76 44 11 186
187 36 34 11 14 14.0 74 43 11 187
188 39 31 7 15 12.0 87 52 11 188
189 28 28 7 13 14.0 54 30 11 189
190 32 36 5 11 21.0 61 39 11 190
191 32 36 12 11 13.0 38 11 11 191
192 38 40 12 14 10.0 75 44 11 192
193 32 33 3 15 15.0 69 42 11 193
194 35 37 11 11 16.0 62 41 11 194
195 32 32 10 15 14.0 72 44 11 195
196 37 38 12 12 12.0 70 44 11 196
197 34 31 9 14 19.0 79 48 11 197
198 33 37 12 14 15.0 87 53 11 198
199 33 33 9 8 19.0 62 37 11 199
200 26 32 12 13 13.0 77 44 11 200
201 30 30 12 9 17.0 69 44 11 201
202 24 30 10 15 12.0 69 40 11 202
203 34 31 9 17 11.0 75 42 11 203
204 34 32 12 13 14.0 54 35 11 204
205 33 34 8 15 11.0 72 43 11 205
206 34 36 11 15 13.0 74 45 11 206
207 35 37 11 14 12.0 85 55 11 207
208 35 36 12 16 15.0 52 31 11 208
209 36 33 10 13 14.0 70 44 11 209
210 34 33 10 16 12.0 84 50 11 210
211 34 33 12 9 17.0 64 40 11 211
212 41 44 12 16 11.0 84 53 11 212
213 32 39 11 11 18.0 87 54 11 213
214 30 32 8 10 13.0 79 49 11 214
215 35 35 12 11 17.0 67 40 11 215
216 28 25 10 15 13.0 65 41 11 216
217 33 35 11 17 11.0 85 52 11 217
218 39 34 10 14 12.0 83 52 11 218
219 36 35 8 8 22.0 61 36 11 219
220 36 39 12 15 14.0 82 52 11 220
221 35 33 12 11 12.0 76 46 11 221
222 38 36 10 16 12.0 58 31 11 222
223 33 32 12 10 17.0 72 44 11 223
224 31 32 9 15 9.0 72 44 11 224
225 34 36 9 9 21.0 38 11 11 225
226 32 36 6 16 10.0 78 46 11 226
227 31 32 10 19 11.0 54 33 11 227
228 33 34 9 12 12.0 63 34 11 228
229 34 33 9 8 23.0 66 42 11 229
230 34 35 9 11 13.0 70 43 11 230
231 34 30 6 14 12.0 71 43 11 231
232 33 38 10 9 16.0 67 44 11 232
233 32 34 6 15 9.0 58 36 11 233
234 41 33 14 13 17.0 72 46 11 234
235 34 32 10 16 9.0 72 44 11 235
236 36 31 10 11 14.0 70 43 11 236
237 37 30 6 12 17.0 76 50 11 237
238 36 27 12 13 13.0 50 33 11 238
239 29 31 12 10 11.0 72 43 11 239
240 37 30 7 11 12.0 72 44 11 240
241 27 32 8 12 10.0 88 53 11 241
242 35 35 11 8 19.0 53 34 11 242
243 28 28 3 12 16.0 58 35 11 243
244 35 33 6 12 16.0 66 40 11 244
245 37 31 10 15 14.0 82 53 11 245
246 29 35 8 11 20.0 69 42 11 246
247 32 35 9 13 15.0 68 43 11 247
248 36 32 9 14 23.0 44 29 11 248
249 19 21 8 10 20.0 56 36 11 249
250 21 20 9 12 16.0 53 30 11 250
251 31 34 7 15 14.0 70 42 11 251
252 33 32 7 13 17.0 78 47 11 252
253 36 34 6 13 11.0 71 44 11 253
254 33 32 9 13 13.0 72 45 11 254
255 37 33 10 12 17.0 68 44 11 255
256 34 33 11 12 15.0 67 43 11 256
257 35 37 12 9 21.0 75 43 11 257
258 31 32 8 9 18.0 62 40 11 258
259 37 34 11 15 15.0 67 41 11 259
260 35 30 3 10 8.0 83 52 11 260
261 27 30 11 14 12.0 64 38 11 261
262 34 38 12 15 12.0 68 41 11 262
263 40 36 7 7 22.0 62 39 11 263
264 29 32 9 14 12.0 72 43 11 264
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Separate Software Happiness Depression Sport1
23.288026 0.435274 -0.000997 0.017094 -0.045887 -0.052220
Sport2 Month t
0.119644 -0.464043 -0.001944
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.7013 -2.2855 0.1273 2.3199 7.7352
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.288026 6.879237 3.385 0.000823 ***
Separate 0.435274 0.057423 7.580 6.41e-13 ***
Software -0.000997 0.098717 -0.010 0.991950
Happiness 0.017094 0.104927 0.163 0.870715
Depression -0.045887 0.076621 -0.599 0.549780
Sport1 -0.052220 0.068512 -0.762 0.446645
Sport2 0.119644 0.101617 1.177 0.240137
Month -0.464043 0.737611 -0.629 0.529836
t -0.001943 0.007823 -0.248 0.804003
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.359 on 255 degrees of freedom
Multiple R-squared: 0.2408, Adjusted R-squared: 0.217
F-statistic: 10.11 on 8 and 255 DF, p-value: 3.038e-12
> 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,] 0.73579376 0.52841249 0.26420624
[2,] 0.90842878 0.18314245 0.09157122
[3,] 0.84468277 0.31063447 0.15531723
[4,] 0.81916080 0.36167840 0.18083920
[5,] 0.78799900 0.42400200 0.21200100
[6,] 0.75976915 0.48046170 0.24023085
[7,] 0.68838708 0.62322584 0.31161292
[8,] 0.60548305 0.78903391 0.39451695
[9,] 0.61699718 0.76600563 0.38300282
[10,] 0.63279812 0.73440377 0.36720188
[11,] 0.56078125 0.87843750 0.43921875
[12,] 0.57442330 0.85115341 0.42557670
[13,] 0.50139871 0.99720258 0.49860129
[14,] 0.60266006 0.79467989 0.39733994
[15,] 0.78772646 0.42454708 0.21227354
[16,] 0.75201717 0.49596567 0.24798283
[17,] 0.73706200 0.52587600 0.26293800
[18,] 0.71152573 0.57694854 0.28847427
[19,] 0.65853557 0.68292885 0.34146443
[20,] 0.65123151 0.69753699 0.34876849
[21,] 0.78302134 0.43395731 0.21697866
[22,] 0.78145146 0.43709708 0.21854854
[23,] 0.74371175 0.51257650 0.25628825
[24,] 0.69756454 0.60487093 0.30243546
[25,] 0.66426481 0.67147038 0.33573519
[26,] 0.63979969 0.72040061 0.36020031
[27,] 0.61244425 0.77511150 0.38755575
[28,] 0.63175068 0.73649863 0.36824932
[29,] 0.59213953 0.81572094 0.40786047
[30,] 0.59601245 0.80797510 0.40398755
[31,] 0.54617916 0.90764168 0.45382084
[32,] 0.53861687 0.92276625 0.46138313
[33,] 0.49913216 0.99826432 0.50086784
[34,] 0.57700909 0.84598181 0.42299091
[35,] 0.55029731 0.89940537 0.44970269
[36,] 0.50837918 0.98324163 0.49162082
[37,] 0.46876156 0.93752313 0.53123844
[38,] 0.42383142 0.84766284 0.57616858
[39,] 0.43203059 0.86406119 0.56796941
[40,] 0.46827589 0.93655178 0.53172411
[41,] 0.42341755 0.84683510 0.57658245
[42,] 0.38257111 0.76514222 0.61742889
[43,] 0.34648768 0.69297537 0.65351232
[44,] 0.30746647 0.61493295 0.69253353
[45,] 0.32820127 0.65640254 0.67179873
[46,] 0.36466319 0.72932638 0.63533681
[47,] 0.45802794 0.91605588 0.54197206
[48,] 0.42653275 0.85306549 0.57346725
[49,] 0.38955021 0.77910043 0.61044979
[50,] 0.35039160 0.70078320 0.64960840
[51,] 0.34909098 0.69818197 0.65090902
[52,] 0.31367079 0.62734157 0.68632921
[53,] 0.31175317 0.62350634 0.68824683
[54,] 0.27653365 0.55306731 0.72346635
[55,] 0.24190772 0.48381544 0.75809228
[56,] 0.21797212 0.43594423 0.78202788
[57,] 0.20568891 0.41137783 0.79431109
[58,] 0.19506146 0.39012293 0.80493854
[59,] 0.16769208 0.33538416 0.83230792
[60,] 0.16209635 0.32419270 0.83790365
[61,] 0.18738062 0.37476125 0.81261938
[62,] 0.17280121 0.34560242 0.82719879
[63,] 0.14948001 0.29896002 0.85051999
[64,] 0.13239805 0.26479610 0.86760195
[65,] 0.19901717 0.39803433 0.80098283
[66,] 0.17458490 0.34916980 0.82541510
[67,] 0.15017575 0.30035151 0.84982425
[68,] 0.20781352 0.41562705 0.79218648
[69,] 0.23776056 0.47552112 0.76223944
[70,] 0.22314116 0.44628232 0.77685884
[71,] 0.19634655 0.39269309 0.80365345
[72,] 0.18799938 0.37599876 0.81200062
[73,] 0.17726856 0.35453711 0.82273144
[74,] 0.15349874 0.30699748 0.84650126
[75,] 0.14108260 0.28216521 0.85891740
[76,] 0.12397275 0.24794549 0.87602725
[77,] 0.13531192 0.27062384 0.86468808
[78,] 0.11532904 0.23065809 0.88467096
[79,] 0.12175411 0.24350821 0.87824589
[80,] 0.11680937 0.23361875 0.88319063
[81,] 0.10305368 0.20610735 0.89694632
[82,] 0.08733247 0.17466494 0.91266753
[83,] 0.09306538 0.18613077 0.90693462
[84,] 0.10335140 0.20670279 0.89664860
[85,] 0.09950558 0.19901116 0.90049442
[86,] 0.08851737 0.17703474 0.91148263
[87,] 0.08260788 0.16521577 0.91739212
[88,] 0.08030529 0.16061058 0.91969471
[89,] 0.07621045 0.15242090 0.92378955
[90,] 0.07293496 0.14586993 0.92706504
[91,] 0.06079950 0.12159900 0.93920050
[92,] 0.05054225 0.10108450 0.94945775
[93,] 0.04725612 0.09451225 0.95274388
[94,] 0.09241120 0.18482239 0.90758880
[95,] 0.09134051 0.18268102 0.90865949
[96,] 0.11510407 0.23020814 0.88489593
[97,] 0.10336937 0.20673875 0.89663063
[98,] 0.16061927 0.32123853 0.83938073
[99,] 0.17877240 0.35754481 0.82122760
[100,] 0.17137138 0.34274276 0.82862862
[101,] 0.20343796 0.40687593 0.79656204
[102,] 0.18785353 0.37570706 0.81214647
[103,] 0.17844797 0.35689594 0.82155203
[104,] 0.17623085 0.35246170 0.82376915
[105,] 0.17402227 0.34804453 0.82597773
[106,] 0.16591293 0.33182587 0.83408707
[107,] 0.14858032 0.29716064 0.85141968
[108,] 0.14333045 0.28666091 0.85666955
[109,] 0.12982791 0.25965583 0.87017209
[110,] 0.11354018 0.22708035 0.88645982
[111,] 0.10136222 0.20272444 0.89863778
[112,] 0.08782582 0.17565164 0.91217418
[113,] 0.08402640 0.16805280 0.91597360
[114,] 0.07978763 0.15957525 0.92021237
[115,] 0.09892940 0.19785881 0.90107060
[116,] 0.18386579 0.36773159 0.81613421
[117,] 0.17766999 0.35533998 0.82233001
[118,] 0.15851341 0.31702682 0.84148659
[119,] 0.14262500 0.28525000 0.85737500
[120,] 0.16202207 0.32404414 0.83797793
[121,] 0.18092264 0.36184528 0.81907736
[122,] 0.21685844 0.43371688 0.78314156
[123,] 0.19406546 0.38813091 0.80593454
[124,] 0.17085336 0.34170672 0.82914664
[125,] 0.14993550 0.29987100 0.85006450
[126,] 0.13069917 0.26139835 0.86930083
[127,] 0.14106888 0.28213776 0.85893112
[128,] 0.12291916 0.24583832 0.87708084
[129,] 0.12199166 0.24398331 0.87800834
[130,] 0.11085905 0.22171811 0.88914095
[131,] 0.14002109 0.28004217 0.85997891
[132,] 0.16145421 0.32290841 0.83854579
[133,] 0.14652963 0.29305926 0.85347037
[134,] 0.22269384 0.44538768 0.77730616
[135,] 0.19947489 0.39894977 0.80052511
[136,] 0.18053276 0.36106552 0.81946724
[137,] 0.15870041 0.31740083 0.84129959
[138,] 0.14661489 0.29322979 0.85338511
[139,] 0.13003069 0.26006138 0.86996931
[140,] 0.21104462 0.42208924 0.78895538
[141,] 0.19104601 0.38209202 0.80895399
[142,] 0.17172107 0.34344214 0.82827893
[143,] 0.19553749 0.39107499 0.80446251
[144,] 0.17417976 0.34835951 0.82582024
[145,] 0.19309838 0.38619676 0.80690162
[146,] 0.18299372 0.36598745 0.81700628
[147,] 0.16744895 0.33489790 0.83255105
[148,] 0.15946753 0.31893505 0.84053247
[149,] 0.14803501 0.29607003 0.85196499
[150,] 0.12777449 0.25554897 0.87222551
[151,] 0.12034665 0.24069330 0.87965335
[152,] 0.11034950 0.22069899 0.88965050
[153,] 0.14358409 0.28716818 0.85641591
[154,] 0.12930490 0.25860979 0.87069510
[155,] 0.21135235 0.42270469 0.78864765
[156,] 0.21045614 0.42091229 0.78954386
[157,] 0.20164488 0.40328976 0.79835512
[158,] 0.18253972 0.36507944 0.81746028
[159,] 0.28954548 0.57909096 0.71045452
[160,] 0.32303237 0.64606475 0.67696763
[161,] 0.29202023 0.58404046 0.70797977
[162,] 0.39181899 0.78363799 0.60818101
[163,] 0.35668361 0.71336722 0.64331639
[164,] 0.42911490 0.85822980 0.57088510
[165,] 0.40069699 0.80139397 0.59930301
[166,] 0.40466134 0.80932267 0.59533866
[167,] 0.37098016 0.74196033 0.62901984
[168,] 0.33945976 0.67891953 0.66054024
[169,] 0.33859708 0.67719415 0.66140292
[170,] 0.30846699 0.61693399 0.69153301
[171,] 0.30733150 0.61466301 0.69266850
[172,] 0.31884376 0.63768751 0.68115624
[173,] 0.39705030 0.79410060 0.60294970
[174,] 0.59428742 0.81142516 0.40571258
[175,] 0.57041575 0.85916849 0.42958425
[176,] 0.55988864 0.88022273 0.44011136
[177,] 0.72540773 0.54918454 0.27459227
[178,] 0.69792098 0.60415804 0.30207902
[179,] 0.69512769 0.60974462 0.30487231
[180,] 0.66073783 0.67852433 0.33926217
[181,] 0.63135037 0.73729926 0.36864963
[182,] 0.59639190 0.80721620 0.40360810
[183,] 0.56916006 0.86167987 0.43083994
[184,] 0.52809610 0.94380780 0.47190390
[185,] 0.48972465 0.97944929 0.51027535
[186,] 0.47330349 0.94660697 0.52669651
[187,] 0.44345419 0.88690837 0.55654581
[188,] 0.40216217 0.80432433 0.59783783
[189,] 0.46221371 0.92442741 0.53778629
[190,] 0.43091640 0.86183280 0.56908360
[191,] 0.57462381 0.85075239 0.42537619
[192,] 0.56623754 0.86752492 0.43376246
[193,] 0.52604581 0.94790838 0.47395419
[194,] 0.48277387 0.96554773 0.51722613
[195,] 0.44391891 0.88783782 0.55608109
[196,] 0.41328156 0.82656311 0.58671844
[197,] 0.37678314 0.75356629 0.62321686
[198,] 0.34986560 0.69973120 0.65013440
[199,] 0.31427082 0.62854163 0.68572918
[200,] 0.27637694 0.55275388 0.72362306
[201,] 0.24508528 0.49017056 0.75491472
[202,] 0.30844599 0.61689199 0.69155401
[203,] 0.30891117 0.61782234 0.69108883
[204,] 0.27136048 0.54272096 0.72863952
[205,] 0.23867095 0.47734191 0.76132905
[206,] 0.22058748 0.44117495 0.77941252
[207,] 0.22980300 0.45960599 0.77019700
[208,] 0.20013453 0.40026906 0.79986547
[209,] 0.19391807 0.38783615 0.80608193
[210,] 0.16409388 0.32818775 0.83590612
[211,] 0.17194595 0.34389190 0.82805405
[212,] 0.14225507 0.28451014 0.85774493
[213,] 0.12347900 0.24695799 0.87652100
[214,] 0.18453273 0.36906547 0.81546727
[215,] 0.16898159 0.33796318 0.83101841
[216,] 0.15193852 0.30387703 0.84806148
[217,] 0.14860080 0.29720159 0.85139920
[218,] 0.12112161 0.24224323 0.87887839
[219,] 0.09587433 0.19174866 0.90412567
[220,] 0.08592022 0.17184044 0.91407978
[221,] 0.19712735 0.39425471 0.80287265
[222,] 0.19620200 0.39240400 0.80379800
[223,] 0.24008721 0.48017442 0.75991279
[224,] 0.19386012 0.38772024 0.80613988
[225,] 0.20045174 0.40090348 0.79954826
[226,] 0.17519439 0.35038879 0.82480561
[227,] 0.25813434 0.51626868 0.74186566
[228,] 0.20898279 0.41796558 0.79101721
[229,] 0.45869004 0.91738009 0.54130996
[230,] 0.45040189 0.90080379 0.54959811
[231,] 0.40215615 0.80431231 0.59784385
[232,] 0.32768237 0.65536475 0.67231763
[233,] 0.33839010 0.67678020 0.66160990
[234,] 0.36814475 0.73628950 0.63185525
[235,] 0.46118018 0.92236037 0.53881982
[236,] 0.45682738 0.91365476 0.54317262
[237,] 0.41847834 0.83695667 0.58152166
[238,] 0.59347909 0.81304182 0.40652091
[239,] 0.61375575 0.77248850 0.38624425
[240,] 0.54189863 0.91620275 0.45810137
[241,] 0.53301099 0.93397803 0.46698901
> postscript(file="/var/wessaorg/rcomp/tmp/1p1pu1384709342.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2ply61384709342.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3rj511384709342.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/47wwg1384709342.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5a9ms1384709342.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
4.61222109 4.40391136 -5.44960081 -3.52308719 -2.03889246 1.97987534
7 8 9 10 11 12
5.53145928 -2.17152908 0.36596965 0.06191349 3.85729580 1.03825298
13 14 15 16 17 18
2.68204497 2.62665282 -3.81950246 -2.30795505 1.93821094 0.74362042
19 20 21 22 23 24
2.05205659 -1.99720200 -2.75456527 -2.80941490 2.58855207 -0.09494489
25 26 27 28 29 30
4.50371950 6.83653847 0.77147962 -3.36058121 -1.33218188 -0.76451654
31 32 33 34 35 36
-3.07841292 -6.63253815 3.20015815 -2.96469912 1.10656142 -2.86187551
37 38 39 40 41 42
-3.89733231 1.46728436 -4.88601306 0.42679503 2.68610472 -0.36102546
43 44 45 46 47 48
4.02332506 1.06355862 5.62458386 1.92881994 0.42314611 -2.14549128
49 50 51 52 53 54
-0.30892859 3.88377360 -4.85812059 -0.94275558 -1.85975664 -3.36284518
55 56 57 58 59 60
-1.64623696 1.52297985 4.01510239 -6.15601681 1.26522557 0.20949907
61 62 63 64 65 66
-1.45374274 3.18687428 -1.41254618 -3.80131060 0.74489792 -0.96265532
67 68 69 70 71 72
1.27549442 -1.00470766 2.17100468 -0.64059758 2.42011213 -4.75069116
73 74 75 76 77 78
-2.64043467 0.68381056 2.44453620 6.43262430 1.22650447 0.91816340
79 80 81 82 83 84
-5.23780753 5.82983006 -2.27798416 -1.10120410 3.10613214 -2.48296499
85 86 87 88 89 90
-0.21104613 1.42545911 -1.37641498 -4.05566868 -0.42614366 -4.51921503
91 92 93 94 95 96
2.95315296 -2.44564164 0.34554929 -3.60547623 3.66271331 2.30489402
97 98 99 100 101 102
1.89136788 2.45656859 -3.44754106 2.64384590 2.28220924 -0.04520367
103 104 105 106 107 108
0.11406682 -3.43030916 7.00322464 2.91248175 4.63733545 1.72766447
109 110 111 112 113 114
6.03285984 -4.61795406 -2.68174542 -5.96773074 0.33620022 2.37300965
115 116 117 118 119 120
2.99524140 -3.54542920 -3.23158243 -1.89009896 2.49413821 -2.15375009
121 122 123 124 125 126
-0.28367855 1.06092560 0.52112456 2.78928246 -2.96910949 4.87435177
127 128 129 130 131 132
7.40271164 -2.99107174 1.08565632 -1.78391610 -4.88614852 4.23154720
133 134 135 136 137 138
-5.39593468 0.73926368 -0.52973141 -0.21133763 -0.93705264 -4.42552032
139 140 141 142 143 144
0.63586838 -3.57493473 -2.09082336 -5.81424503 -5.12820529 1.44347711
145 146 147 148 149 150
6.44709632 0.51930306 0.97541602 -1.12899129 1.74862965 1.04991790
151 152 153 154 155 156
6.61365593 -1.94194079 -1.47893500 3.57281076 -0.25681567 3.07948327
157 158 159 160 161 162
-3.58004089 -2.93276544 0.21900623 -3.84396242 -1.38506034 -2.94368170
163 164 165 166 167 168
0.83373761 4.72916762 -1.55842457 -7.17218041 -3.57049577 -3.68970096
169 170 171 172 173 174
-1.50337144 -7.36756277 5.19418551 0.83018954 6.77473418 -0.26895904
175 176 177 178 179 180
4.79937713 -2.06554610 3.79099055 -1.56824611 -1.54049582 3.36371947
181 182 183 184 185 186
1.64738227 2.90201616 -3.84339887 4.91148037 -9.70130774 -2.12595276
187 188 189 190 191 192
2.51421527 6.31118800 -2.34617160 -2.18427178 -0.39347902 1.66230979
193 194 195 196 197 198
-1.15948674 -0.02229503 -0.84186193 1.40549734 1.72979736 -2.24092500
199 200 201 202 203 204
0.39404594 -6.58075072 -1.87408974 -7.72756767 1.83205968 1.34865048
205 206 207 208 209 210
-0.71298800 -0.62167522 -0.70581942 0.98406749 2.67982213 0.55192226
211 212 213 214 215 216
1.05699871 2.36496649 -4.01401646 -3.00002575 1.31669499 -1.80662128
217 218 219 220 221 222
-1.55407402 4.87487737 2.76645743 -0.27315240 1.72158299 4.18493905
223 224 225 226 227 228
0.43768448 -2.01593356 2.07089855 -2.65331074 -1.60958196 0.03669622
229 230 231 232 233 234
1.24656253 -0.04296470 2.08740956 -2.44835594 -1.64590803 7.73521320
235 236 237 238 239 240
0.98934827 3.75667859 4.78628921 5.57562390 -3.25162559 5.08975694
241 242 243 244 245 246
-6.12799806 1.49801708 -2.52567879 2.12242314 4.13599356 -4.62422871
247 248 249 250 251 252
-2.05677728 4.02273343 -8.46845676 -5.68677698 -2.47171534 0.39216702
253 254 255 256 257 258
2.24063294 0.14046724 3.81954224 0.79813181 0.80433997 -1.47892037
259 260 261 262 263 264
3.55669301 2.57545142 -4.61661870 -1.26301960 6.12608470 -3.66379154
> postscript(file="/var/wessaorg/rcomp/tmp/61g7a1384709342.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 4.61222109 NA
1 4.40391136 4.61222109
2 -5.44960081 4.40391136
3 -3.52308719 -5.44960081
4 -2.03889246 -3.52308719
5 1.97987534 -2.03889246
6 5.53145928 1.97987534
7 -2.17152908 5.53145928
8 0.36596965 -2.17152908
9 0.06191349 0.36596965
10 3.85729580 0.06191349
11 1.03825298 3.85729580
12 2.68204497 1.03825298
13 2.62665282 2.68204497
14 -3.81950246 2.62665282
15 -2.30795505 -3.81950246
16 1.93821094 -2.30795505
17 0.74362042 1.93821094
18 2.05205659 0.74362042
19 -1.99720200 2.05205659
20 -2.75456527 -1.99720200
21 -2.80941490 -2.75456527
22 2.58855207 -2.80941490
23 -0.09494489 2.58855207
24 4.50371950 -0.09494489
25 6.83653847 4.50371950
26 0.77147962 6.83653847
27 -3.36058121 0.77147962
28 -1.33218188 -3.36058121
29 -0.76451654 -1.33218188
30 -3.07841292 -0.76451654
31 -6.63253815 -3.07841292
32 3.20015815 -6.63253815
33 -2.96469912 3.20015815
34 1.10656142 -2.96469912
35 -2.86187551 1.10656142
36 -3.89733231 -2.86187551
37 1.46728436 -3.89733231
38 -4.88601306 1.46728436
39 0.42679503 -4.88601306
40 2.68610472 0.42679503
41 -0.36102546 2.68610472
42 4.02332506 -0.36102546
43 1.06355862 4.02332506
44 5.62458386 1.06355862
45 1.92881994 5.62458386
46 0.42314611 1.92881994
47 -2.14549128 0.42314611
48 -0.30892859 -2.14549128
49 3.88377360 -0.30892859
50 -4.85812059 3.88377360
51 -0.94275558 -4.85812059
52 -1.85975664 -0.94275558
53 -3.36284518 -1.85975664
54 -1.64623696 -3.36284518
55 1.52297985 -1.64623696
56 4.01510239 1.52297985
57 -6.15601681 4.01510239
58 1.26522557 -6.15601681
59 0.20949907 1.26522557
60 -1.45374274 0.20949907
61 3.18687428 -1.45374274
62 -1.41254618 3.18687428
63 -3.80131060 -1.41254618
64 0.74489792 -3.80131060
65 -0.96265532 0.74489792
66 1.27549442 -0.96265532
67 -1.00470766 1.27549442
68 2.17100468 -1.00470766
69 -0.64059758 2.17100468
70 2.42011213 -0.64059758
71 -4.75069116 2.42011213
72 -2.64043467 -4.75069116
73 0.68381056 -2.64043467
74 2.44453620 0.68381056
75 6.43262430 2.44453620
76 1.22650447 6.43262430
77 0.91816340 1.22650447
78 -5.23780753 0.91816340
79 5.82983006 -5.23780753
80 -2.27798416 5.82983006
81 -1.10120410 -2.27798416
82 3.10613214 -1.10120410
83 -2.48296499 3.10613214
84 -0.21104613 -2.48296499
85 1.42545911 -0.21104613
86 -1.37641498 1.42545911
87 -4.05566868 -1.37641498
88 -0.42614366 -4.05566868
89 -4.51921503 -0.42614366
90 2.95315296 -4.51921503
91 -2.44564164 2.95315296
92 0.34554929 -2.44564164
93 -3.60547623 0.34554929
94 3.66271331 -3.60547623
95 2.30489402 3.66271331
96 1.89136788 2.30489402
97 2.45656859 1.89136788
98 -3.44754106 2.45656859
99 2.64384590 -3.44754106
100 2.28220924 2.64384590
101 -0.04520367 2.28220924
102 0.11406682 -0.04520367
103 -3.43030916 0.11406682
104 7.00322464 -3.43030916
105 2.91248175 7.00322464
106 4.63733545 2.91248175
107 1.72766447 4.63733545
108 6.03285984 1.72766447
109 -4.61795406 6.03285984
110 -2.68174542 -4.61795406
111 -5.96773074 -2.68174542
112 0.33620022 -5.96773074
113 2.37300965 0.33620022
114 2.99524140 2.37300965
115 -3.54542920 2.99524140
116 -3.23158243 -3.54542920
117 -1.89009896 -3.23158243
118 2.49413821 -1.89009896
119 -2.15375009 2.49413821
120 -0.28367855 -2.15375009
121 1.06092560 -0.28367855
122 0.52112456 1.06092560
123 2.78928246 0.52112456
124 -2.96910949 2.78928246
125 4.87435177 -2.96910949
126 7.40271164 4.87435177
127 -2.99107174 7.40271164
128 1.08565632 -2.99107174
129 -1.78391610 1.08565632
130 -4.88614852 -1.78391610
131 4.23154720 -4.88614852
132 -5.39593468 4.23154720
133 0.73926368 -5.39593468
134 -0.52973141 0.73926368
135 -0.21133763 -0.52973141
136 -0.93705264 -0.21133763
137 -4.42552032 -0.93705264
138 0.63586838 -4.42552032
139 -3.57493473 0.63586838
140 -2.09082336 -3.57493473
141 -5.81424503 -2.09082336
142 -5.12820529 -5.81424503
143 1.44347711 -5.12820529
144 6.44709632 1.44347711
145 0.51930306 6.44709632
146 0.97541602 0.51930306
147 -1.12899129 0.97541602
148 1.74862965 -1.12899129
149 1.04991790 1.74862965
150 6.61365593 1.04991790
151 -1.94194079 6.61365593
152 -1.47893500 -1.94194079
153 3.57281076 -1.47893500
154 -0.25681567 3.57281076
155 3.07948327 -0.25681567
156 -3.58004089 3.07948327
157 -2.93276544 -3.58004089
158 0.21900623 -2.93276544
159 -3.84396242 0.21900623
160 -1.38506034 -3.84396242
161 -2.94368170 -1.38506034
162 0.83373761 -2.94368170
163 4.72916762 0.83373761
164 -1.55842457 4.72916762
165 -7.17218041 -1.55842457
166 -3.57049577 -7.17218041
167 -3.68970096 -3.57049577
168 -1.50337144 -3.68970096
169 -7.36756277 -1.50337144
170 5.19418551 -7.36756277
171 0.83018954 5.19418551
172 6.77473418 0.83018954
173 -0.26895904 6.77473418
174 4.79937713 -0.26895904
175 -2.06554610 4.79937713
176 3.79099055 -2.06554610
177 -1.56824611 3.79099055
178 -1.54049582 -1.56824611
179 3.36371947 -1.54049582
180 1.64738227 3.36371947
181 2.90201616 1.64738227
182 -3.84339887 2.90201616
183 4.91148037 -3.84339887
184 -9.70130774 4.91148037
185 -2.12595276 -9.70130774
186 2.51421527 -2.12595276
187 6.31118800 2.51421527
188 -2.34617160 6.31118800
189 -2.18427178 -2.34617160
190 -0.39347902 -2.18427178
191 1.66230979 -0.39347902
192 -1.15948674 1.66230979
193 -0.02229503 -1.15948674
194 -0.84186193 -0.02229503
195 1.40549734 -0.84186193
196 1.72979736 1.40549734
197 -2.24092500 1.72979736
198 0.39404594 -2.24092500
199 -6.58075072 0.39404594
200 -1.87408974 -6.58075072
201 -7.72756767 -1.87408974
202 1.83205968 -7.72756767
203 1.34865048 1.83205968
204 -0.71298800 1.34865048
205 -0.62167522 -0.71298800
206 -0.70581942 -0.62167522
207 0.98406749 -0.70581942
208 2.67982213 0.98406749
209 0.55192226 2.67982213
210 1.05699871 0.55192226
211 2.36496649 1.05699871
212 -4.01401646 2.36496649
213 -3.00002575 -4.01401646
214 1.31669499 -3.00002575
215 -1.80662128 1.31669499
216 -1.55407402 -1.80662128
217 4.87487737 -1.55407402
218 2.76645743 4.87487737
219 -0.27315240 2.76645743
220 1.72158299 -0.27315240
221 4.18493905 1.72158299
222 0.43768448 4.18493905
223 -2.01593356 0.43768448
224 2.07089855 -2.01593356
225 -2.65331074 2.07089855
226 -1.60958196 -2.65331074
227 0.03669622 -1.60958196
228 1.24656253 0.03669622
229 -0.04296470 1.24656253
230 2.08740956 -0.04296470
231 -2.44835594 2.08740956
232 -1.64590803 -2.44835594
233 7.73521320 -1.64590803
234 0.98934827 7.73521320
235 3.75667859 0.98934827
236 4.78628921 3.75667859
237 5.57562390 4.78628921
238 -3.25162559 5.57562390
239 5.08975694 -3.25162559
240 -6.12799806 5.08975694
241 1.49801708 -6.12799806
242 -2.52567879 1.49801708
243 2.12242314 -2.52567879
244 4.13599356 2.12242314
245 -4.62422871 4.13599356
246 -2.05677728 -4.62422871
247 4.02273343 -2.05677728
248 -8.46845676 4.02273343
249 -5.68677698 -8.46845676
250 -2.47171534 -5.68677698
251 0.39216702 -2.47171534
252 2.24063294 0.39216702
253 0.14046724 2.24063294
254 3.81954224 0.14046724
255 0.79813181 3.81954224
256 0.80433997 0.79813181
257 -1.47892037 0.80433997
258 3.55669301 -1.47892037
259 2.57545142 3.55669301
260 -4.61661870 2.57545142
261 -1.26301960 -4.61661870
262 6.12608470 -1.26301960
263 -3.66379154 6.12608470
264 NA -3.66379154
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.40391136 4.61222109
[2,] -5.44960081 4.40391136
[3,] -3.52308719 -5.44960081
[4,] -2.03889246 -3.52308719
[5,] 1.97987534 -2.03889246
[6,] 5.53145928 1.97987534
[7,] -2.17152908 5.53145928
[8,] 0.36596965 -2.17152908
[9,] 0.06191349 0.36596965
[10,] 3.85729580 0.06191349
[11,] 1.03825298 3.85729580
[12,] 2.68204497 1.03825298
[13,] 2.62665282 2.68204497
[14,] -3.81950246 2.62665282
[15,] -2.30795505 -3.81950246
[16,] 1.93821094 -2.30795505
[17,] 0.74362042 1.93821094
[18,] 2.05205659 0.74362042
[19,] -1.99720200 2.05205659
[20,] -2.75456527 -1.99720200
[21,] -2.80941490 -2.75456527
[22,] 2.58855207 -2.80941490
[23,] -0.09494489 2.58855207
[24,] 4.50371950 -0.09494489
[25,] 6.83653847 4.50371950
[26,] 0.77147962 6.83653847
[27,] -3.36058121 0.77147962
[28,] -1.33218188 -3.36058121
[29,] -0.76451654 -1.33218188
[30,] -3.07841292 -0.76451654
[31,] -6.63253815 -3.07841292
[32,] 3.20015815 -6.63253815
[33,] -2.96469912 3.20015815
[34,] 1.10656142 -2.96469912
[35,] -2.86187551 1.10656142
[36,] -3.89733231 -2.86187551
[37,] 1.46728436 -3.89733231
[38,] -4.88601306 1.46728436
[39,] 0.42679503 -4.88601306
[40,] 2.68610472 0.42679503
[41,] -0.36102546 2.68610472
[42,] 4.02332506 -0.36102546
[43,] 1.06355862 4.02332506
[44,] 5.62458386 1.06355862
[45,] 1.92881994 5.62458386
[46,] 0.42314611 1.92881994
[47,] -2.14549128 0.42314611
[48,] -0.30892859 -2.14549128
[49,] 3.88377360 -0.30892859
[50,] -4.85812059 3.88377360
[51,] -0.94275558 -4.85812059
[52,] -1.85975664 -0.94275558
[53,] -3.36284518 -1.85975664
[54,] -1.64623696 -3.36284518
[55,] 1.52297985 -1.64623696
[56,] 4.01510239 1.52297985
[57,] -6.15601681 4.01510239
[58,] 1.26522557 -6.15601681
[59,] 0.20949907 1.26522557
[60,] -1.45374274 0.20949907
[61,] 3.18687428 -1.45374274
[62,] -1.41254618 3.18687428
[63,] -3.80131060 -1.41254618
[64,] 0.74489792 -3.80131060
[65,] -0.96265532 0.74489792
[66,] 1.27549442 -0.96265532
[67,] -1.00470766 1.27549442
[68,] 2.17100468 -1.00470766
[69,] -0.64059758 2.17100468
[70,] 2.42011213 -0.64059758
[71,] -4.75069116 2.42011213
[72,] -2.64043467 -4.75069116
[73,] 0.68381056 -2.64043467
[74,] 2.44453620 0.68381056
[75,] 6.43262430 2.44453620
[76,] 1.22650447 6.43262430
[77,] 0.91816340 1.22650447
[78,] -5.23780753 0.91816340
[79,] 5.82983006 -5.23780753
[80,] -2.27798416 5.82983006
[81,] -1.10120410 -2.27798416
[82,] 3.10613214 -1.10120410
[83,] -2.48296499 3.10613214
[84,] -0.21104613 -2.48296499
[85,] 1.42545911 -0.21104613
[86,] -1.37641498 1.42545911
[87,] -4.05566868 -1.37641498
[88,] -0.42614366 -4.05566868
[89,] -4.51921503 -0.42614366
[90,] 2.95315296 -4.51921503
[91,] -2.44564164 2.95315296
[92,] 0.34554929 -2.44564164
[93,] -3.60547623 0.34554929
[94,] 3.66271331 -3.60547623
[95,] 2.30489402 3.66271331
[96,] 1.89136788 2.30489402
[97,] 2.45656859 1.89136788
[98,] -3.44754106 2.45656859
[99,] 2.64384590 -3.44754106
[100,] 2.28220924 2.64384590
[101,] -0.04520367 2.28220924
[102,] 0.11406682 -0.04520367
[103,] -3.43030916 0.11406682
[104,] 7.00322464 -3.43030916
[105,] 2.91248175 7.00322464
[106,] 4.63733545 2.91248175
[107,] 1.72766447 4.63733545
[108,] 6.03285984 1.72766447
[109,] -4.61795406 6.03285984
[110,] -2.68174542 -4.61795406
[111,] -5.96773074 -2.68174542
[112,] 0.33620022 -5.96773074
[113,] 2.37300965 0.33620022
[114,] 2.99524140 2.37300965
[115,] -3.54542920 2.99524140
[116,] -3.23158243 -3.54542920
[117,] -1.89009896 -3.23158243
[118,] 2.49413821 -1.89009896
[119,] -2.15375009 2.49413821
[120,] -0.28367855 -2.15375009
[121,] 1.06092560 -0.28367855
[122,] 0.52112456 1.06092560
[123,] 2.78928246 0.52112456
[124,] -2.96910949 2.78928246
[125,] 4.87435177 -2.96910949
[126,] 7.40271164 4.87435177
[127,] -2.99107174 7.40271164
[128,] 1.08565632 -2.99107174
[129,] -1.78391610 1.08565632
[130,] -4.88614852 -1.78391610
[131,] 4.23154720 -4.88614852
[132,] -5.39593468 4.23154720
[133,] 0.73926368 -5.39593468
[134,] -0.52973141 0.73926368
[135,] -0.21133763 -0.52973141
[136,] -0.93705264 -0.21133763
[137,] -4.42552032 -0.93705264
[138,] 0.63586838 -4.42552032
[139,] -3.57493473 0.63586838
[140,] -2.09082336 -3.57493473
[141,] -5.81424503 -2.09082336
[142,] -5.12820529 -5.81424503
[143,] 1.44347711 -5.12820529
[144,] 6.44709632 1.44347711
[145,] 0.51930306 6.44709632
[146,] 0.97541602 0.51930306
[147,] -1.12899129 0.97541602
[148,] 1.74862965 -1.12899129
[149,] 1.04991790 1.74862965
[150,] 6.61365593 1.04991790
[151,] -1.94194079 6.61365593
[152,] -1.47893500 -1.94194079
[153,] 3.57281076 -1.47893500
[154,] -0.25681567 3.57281076
[155,] 3.07948327 -0.25681567
[156,] -3.58004089 3.07948327
[157,] -2.93276544 -3.58004089
[158,] 0.21900623 -2.93276544
[159,] -3.84396242 0.21900623
[160,] -1.38506034 -3.84396242
[161,] -2.94368170 -1.38506034
[162,] 0.83373761 -2.94368170
[163,] 4.72916762 0.83373761
[164,] -1.55842457 4.72916762
[165,] -7.17218041 -1.55842457
[166,] -3.57049577 -7.17218041
[167,] -3.68970096 -3.57049577
[168,] -1.50337144 -3.68970096
[169,] -7.36756277 -1.50337144
[170,] 5.19418551 -7.36756277
[171,] 0.83018954 5.19418551
[172,] 6.77473418 0.83018954
[173,] -0.26895904 6.77473418
[174,] 4.79937713 -0.26895904
[175,] -2.06554610 4.79937713
[176,] 3.79099055 -2.06554610
[177,] -1.56824611 3.79099055
[178,] -1.54049582 -1.56824611
[179,] 3.36371947 -1.54049582
[180,] 1.64738227 3.36371947
[181,] 2.90201616 1.64738227
[182,] -3.84339887 2.90201616
[183,] 4.91148037 -3.84339887
[184,] -9.70130774 4.91148037
[185,] -2.12595276 -9.70130774
[186,] 2.51421527 -2.12595276
[187,] 6.31118800 2.51421527
[188,] -2.34617160 6.31118800
[189,] -2.18427178 -2.34617160
[190,] -0.39347902 -2.18427178
[191,] 1.66230979 -0.39347902
[192,] -1.15948674 1.66230979
[193,] -0.02229503 -1.15948674
[194,] -0.84186193 -0.02229503
[195,] 1.40549734 -0.84186193
[196,] 1.72979736 1.40549734
[197,] -2.24092500 1.72979736
[198,] 0.39404594 -2.24092500
[199,] -6.58075072 0.39404594
[200,] -1.87408974 -6.58075072
[201,] -7.72756767 -1.87408974
[202,] 1.83205968 -7.72756767
[203,] 1.34865048 1.83205968
[204,] -0.71298800 1.34865048
[205,] -0.62167522 -0.71298800
[206,] -0.70581942 -0.62167522
[207,] 0.98406749 -0.70581942
[208,] 2.67982213 0.98406749
[209,] 0.55192226 2.67982213
[210,] 1.05699871 0.55192226
[211,] 2.36496649 1.05699871
[212,] -4.01401646 2.36496649
[213,] -3.00002575 -4.01401646
[214,] 1.31669499 -3.00002575
[215,] -1.80662128 1.31669499
[216,] -1.55407402 -1.80662128
[217,] 4.87487737 -1.55407402
[218,] 2.76645743 4.87487737
[219,] -0.27315240 2.76645743
[220,] 1.72158299 -0.27315240
[221,] 4.18493905 1.72158299
[222,] 0.43768448 4.18493905
[223,] -2.01593356 0.43768448
[224,] 2.07089855 -2.01593356
[225,] -2.65331074 2.07089855
[226,] -1.60958196 -2.65331074
[227,] 0.03669622 -1.60958196
[228,] 1.24656253 0.03669622
[229,] -0.04296470 1.24656253
[230,] 2.08740956 -0.04296470
[231,] -2.44835594 2.08740956
[232,] -1.64590803 -2.44835594
[233,] 7.73521320 -1.64590803
[234,] 0.98934827 7.73521320
[235,] 3.75667859 0.98934827
[236,] 4.78628921 3.75667859
[237,] 5.57562390 4.78628921
[238,] -3.25162559 5.57562390
[239,] 5.08975694 -3.25162559
[240,] -6.12799806 5.08975694
[241,] 1.49801708 -6.12799806
[242,] -2.52567879 1.49801708
[243,] 2.12242314 -2.52567879
[244,] 4.13599356 2.12242314
[245,] -4.62422871 4.13599356
[246,] -2.05677728 -4.62422871
[247,] 4.02273343 -2.05677728
[248,] -8.46845676 4.02273343
[249,] -5.68677698 -8.46845676
[250,] -2.47171534 -5.68677698
[251,] 0.39216702 -2.47171534
[252,] 2.24063294 0.39216702
[253,] 0.14046724 2.24063294
[254,] 3.81954224 0.14046724
[255,] 0.79813181 3.81954224
[256,] 0.80433997 0.79813181
[257,] -1.47892037 0.80433997
[258,] 3.55669301 -1.47892037
[259,] 2.57545142 3.55669301
[260,] -4.61661870 2.57545142
[261,] -1.26301960 -4.61661870
[262,] 6.12608470 -1.26301960
[263,] -3.66379154 6.12608470
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.40391136 4.61222109
2 -5.44960081 4.40391136
3 -3.52308719 -5.44960081
4 -2.03889246 -3.52308719
5 1.97987534 -2.03889246
6 5.53145928 1.97987534
7 -2.17152908 5.53145928
8 0.36596965 -2.17152908
9 0.06191349 0.36596965
10 3.85729580 0.06191349
11 1.03825298 3.85729580
12 2.68204497 1.03825298
13 2.62665282 2.68204497
14 -3.81950246 2.62665282
15 -2.30795505 -3.81950246
16 1.93821094 -2.30795505
17 0.74362042 1.93821094
18 2.05205659 0.74362042
19 -1.99720200 2.05205659
20 -2.75456527 -1.99720200
21 -2.80941490 -2.75456527
22 2.58855207 -2.80941490
23 -0.09494489 2.58855207
24 4.50371950 -0.09494489
25 6.83653847 4.50371950
26 0.77147962 6.83653847
27 -3.36058121 0.77147962
28 -1.33218188 -3.36058121
29 -0.76451654 -1.33218188
30 -3.07841292 -0.76451654
31 -6.63253815 -3.07841292
32 3.20015815 -6.63253815
33 -2.96469912 3.20015815
34 1.10656142 -2.96469912
35 -2.86187551 1.10656142
36 -3.89733231 -2.86187551
37 1.46728436 -3.89733231
38 -4.88601306 1.46728436
39 0.42679503 -4.88601306
40 2.68610472 0.42679503
41 -0.36102546 2.68610472
42 4.02332506 -0.36102546
43 1.06355862 4.02332506
44 5.62458386 1.06355862
45 1.92881994 5.62458386
46 0.42314611 1.92881994
47 -2.14549128 0.42314611
48 -0.30892859 -2.14549128
49 3.88377360 -0.30892859
50 -4.85812059 3.88377360
51 -0.94275558 -4.85812059
52 -1.85975664 -0.94275558
53 -3.36284518 -1.85975664
54 -1.64623696 -3.36284518
55 1.52297985 -1.64623696
56 4.01510239 1.52297985
57 -6.15601681 4.01510239
58 1.26522557 -6.15601681
59 0.20949907 1.26522557
60 -1.45374274 0.20949907
61 3.18687428 -1.45374274
62 -1.41254618 3.18687428
63 -3.80131060 -1.41254618
64 0.74489792 -3.80131060
65 -0.96265532 0.74489792
66 1.27549442 -0.96265532
67 -1.00470766 1.27549442
68 2.17100468 -1.00470766
69 -0.64059758 2.17100468
70 2.42011213 -0.64059758
71 -4.75069116 2.42011213
72 -2.64043467 -4.75069116
73 0.68381056 -2.64043467
74 2.44453620 0.68381056
75 6.43262430 2.44453620
76 1.22650447 6.43262430
77 0.91816340 1.22650447
78 -5.23780753 0.91816340
79 5.82983006 -5.23780753
80 -2.27798416 5.82983006
81 -1.10120410 -2.27798416
82 3.10613214 -1.10120410
83 -2.48296499 3.10613214
84 -0.21104613 -2.48296499
85 1.42545911 -0.21104613
86 -1.37641498 1.42545911
87 -4.05566868 -1.37641498
88 -0.42614366 -4.05566868
89 -4.51921503 -0.42614366
90 2.95315296 -4.51921503
91 -2.44564164 2.95315296
92 0.34554929 -2.44564164
93 -3.60547623 0.34554929
94 3.66271331 -3.60547623
95 2.30489402 3.66271331
96 1.89136788 2.30489402
97 2.45656859 1.89136788
98 -3.44754106 2.45656859
99 2.64384590 -3.44754106
100 2.28220924 2.64384590
101 -0.04520367 2.28220924
102 0.11406682 -0.04520367
103 -3.43030916 0.11406682
104 7.00322464 -3.43030916
105 2.91248175 7.00322464
106 4.63733545 2.91248175
107 1.72766447 4.63733545
108 6.03285984 1.72766447
109 -4.61795406 6.03285984
110 -2.68174542 -4.61795406
111 -5.96773074 -2.68174542
112 0.33620022 -5.96773074
113 2.37300965 0.33620022
114 2.99524140 2.37300965
115 -3.54542920 2.99524140
116 -3.23158243 -3.54542920
117 -1.89009896 -3.23158243
118 2.49413821 -1.89009896
119 -2.15375009 2.49413821
120 -0.28367855 -2.15375009
121 1.06092560 -0.28367855
122 0.52112456 1.06092560
123 2.78928246 0.52112456
124 -2.96910949 2.78928246
125 4.87435177 -2.96910949
126 7.40271164 4.87435177
127 -2.99107174 7.40271164
128 1.08565632 -2.99107174
129 -1.78391610 1.08565632
130 -4.88614852 -1.78391610
131 4.23154720 -4.88614852
132 -5.39593468 4.23154720
133 0.73926368 -5.39593468
134 -0.52973141 0.73926368
135 -0.21133763 -0.52973141
136 -0.93705264 -0.21133763
137 -4.42552032 -0.93705264
138 0.63586838 -4.42552032
139 -3.57493473 0.63586838
140 -2.09082336 -3.57493473
141 -5.81424503 -2.09082336
142 -5.12820529 -5.81424503
143 1.44347711 -5.12820529
144 6.44709632 1.44347711
145 0.51930306 6.44709632
146 0.97541602 0.51930306
147 -1.12899129 0.97541602
148 1.74862965 -1.12899129
149 1.04991790 1.74862965
150 6.61365593 1.04991790
151 -1.94194079 6.61365593
152 -1.47893500 -1.94194079
153 3.57281076 -1.47893500
154 -0.25681567 3.57281076
155 3.07948327 -0.25681567
156 -3.58004089 3.07948327
157 -2.93276544 -3.58004089
158 0.21900623 -2.93276544
159 -3.84396242 0.21900623
160 -1.38506034 -3.84396242
161 -2.94368170 -1.38506034
162 0.83373761 -2.94368170
163 4.72916762 0.83373761
164 -1.55842457 4.72916762
165 -7.17218041 -1.55842457
166 -3.57049577 -7.17218041
167 -3.68970096 -3.57049577
168 -1.50337144 -3.68970096
169 -7.36756277 -1.50337144
170 5.19418551 -7.36756277
171 0.83018954 5.19418551
172 6.77473418 0.83018954
173 -0.26895904 6.77473418
174 4.79937713 -0.26895904
175 -2.06554610 4.79937713
176 3.79099055 -2.06554610
177 -1.56824611 3.79099055
178 -1.54049582 -1.56824611
179 3.36371947 -1.54049582
180 1.64738227 3.36371947
181 2.90201616 1.64738227
182 -3.84339887 2.90201616
183 4.91148037 -3.84339887
184 -9.70130774 4.91148037
185 -2.12595276 -9.70130774
186 2.51421527 -2.12595276
187 6.31118800 2.51421527
188 -2.34617160 6.31118800
189 -2.18427178 -2.34617160
190 -0.39347902 -2.18427178
191 1.66230979 -0.39347902
192 -1.15948674 1.66230979
193 -0.02229503 -1.15948674
194 -0.84186193 -0.02229503
195 1.40549734 -0.84186193
196 1.72979736 1.40549734
197 -2.24092500 1.72979736
198 0.39404594 -2.24092500
199 -6.58075072 0.39404594
200 -1.87408974 -6.58075072
201 -7.72756767 -1.87408974
202 1.83205968 -7.72756767
203 1.34865048 1.83205968
204 -0.71298800 1.34865048
205 -0.62167522 -0.71298800
206 -0.70581942 -0.62167522
207 0.98406749 -0.70581942
208 2.67982213 0.98406749
209 0.55192226 2.67982213
210 1.05699871 0.55192226
211 2.36496649 1.05699871
212 -4.01401646 2.36496649
213 -3.00002575 -4.01401646
214 1.31669499 -3.00002575
215 -1.80662128 1.31669499
216 -1.55407402 -1.80662128
217 4.87487737 -1.55407402
218 2.76645743 4.87487737
219 -0.27315240 2.76645743
220 1.72158299 -0.27315240
221 4.18493905 1.72158299
222 0.43768448 4.18493905
223 -2.01593356 0.43768448
224 2.07089855 -2.01593356
225 -2.65331074 2.07089855
226 -1.60958196 -2.65331074
227 0.03669622 -1.60958196
228 1.24656253 0.03669622
229 -0.04296470 1.24656253
230 2.08740956 -0.04296470
231 -2.44835594 2.08740956
232 -1.64590803 -2.44835594
233 7.73521320 -1.64590803
234 0.98934827 7.73521320
235 3.75667859 0.98934827
236 4.78628921 3.75667859
237 5.57562390 4.78628921
238 -3.25162559 5.57562390
239 5.08975694 -3.25162559
240 -6.12799806 5.08975694
241 1.49801708 -6.12799806
242 -2.52567879 1.49801708
243 2.12242314 -2.52567879
244 4.13599356 2.12242314
245 -4.62422871 4.13599356
246 -2.05677728 -4.62422871
247 4.02273343 -2.05677728
248 -8.46845676 4.02273343
249 -5.68677698 -8.46845676
250 -2.47171534 -5.68677698
251 0.39216702 -2.47171534
252 2.24063294 0.39216702
253 0.14046724 2.24063294
254 3.81954224 0.14046724
255 0.79813181 3.81954224
256 0.80433997 0.79813181
257 -1.47892037 0.80433997
258 3.55669301 -1.47892037
259 2.57545142 3.55669301
260 -4.61661870 2.57545142
261 -1.26301960 -4.61661870
262 6.12608470 -1.26301960
263 -3.66379154 6.12608470
> 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/7rgj31384709342.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/89o8u1384709342.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9byte1384709342.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10dvrp1384709342.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11t0t41384709342.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/12ez271384709342.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/13lokw1384709342.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/143mz21384709342.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/156c5t1384709342.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/161rog1384709342.tab")
+ }
>
> try(system("convert tmp/1p1pu1384709342.ps tmp/1p1pu1384709342.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ply61384709342.ps tmp/2ply61384709342.png",intern=TRUE))
character(0)
> try(system("convert tmp/3rj511384709342.ps tmp/3rj511384709342.png",intern=TRUE))
character(0)
> try(system("convert tmp/47wwg1384709342.ps tmp/47wwg1384709342.png",intern=TRUE))
character(0)
> try(system("convert tmp/5a9ms1384709342.ps tmp/5a9ms1384709342.png",intern=TRUE))
character(0)
> try(system("convert tmp/61g7a1384709342.ps tmp/61g7a1384709342.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rgj31384709342.ps tmp/7rgj31384709342.png",intern=TRUE))
character(0)
> try(system("convert tmp/89o8u1384709342.ps tmp/89o8u1384709342.png",intern=TRUE))
character(0)
> try(system("convert tmp/9byte1384709342.ps tmp/9byte1384709342.png",intern=TRUE))
character(0)
> try(system("convert tmp/10dvrp1384709342.ps tmp/10dvrp1384709342.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
14.449 2.557 17.015