R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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+ ,16
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+ ,31
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+ ,14
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+ ,62
+ ,37
+ ,34
+ ,13
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+ ,15
+ ,67
+ ,35
+ ,30
+ ,4
+ ,3
+ ,10
+ ,8
+ ,83
+ ,27
+ ,30
+ ,15
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+ ,14
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+ ,34
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+ ,11
+ ,12
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+ ,12
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+ ,11
+ ,7
+ ,7
+ ,22
+ ,62
+ ,29
+ ,32
+ ,14
+ ,9
+ ,14
+ ,12
+ ,72)
+ ,dim=c(7
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging')
+ ,1:264))
> y <- array(NA,dim=c(7,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Belonging'),1:264))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '3'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Learning Connected Separate Software Happiness Depression Belonging t
1 13 41 38 12 14 12.0 53 1
2 16 39 32 11 18 11.0 83 2
3 19 30 35 15 11 14.0 66 3
4 15 31 33 6 12 12.0 67 4
5 14 34 37 13 16 21.0 76 5
6 13 35 29 10 18 12.0 78 6
7 19 39 31 12 14 22.0 53 7
8 15 34 36 14 14 11.0 80 8
9 14 36 35 12 15 10.0 74 9
10 15 37 38 9 15 13.0 76 10
11 16 38 31 10 17 10.0 79 11
12 16 36 34 12 19 8.0 54 12
13 16 38 35 12 10 15.0 67 13
14 16 39 38 11 16 14.0 54 14
15 17 33 37 15 18 10.0 87 15
16 15 32 33 12 14 14.0 58 16
17 15 36 32 10 14 14.0 75 17
18 20 38 38 12 17 11.0 88 18
19 18 39 38 11 14 10.0 64 19
20 16 32 32 12 16 13.0 57 20
21 16 32 33 11 18 9.5 66 21
22 16 31 31 12 11 14.0 68 22
23 19 39 38 13 14 12.0 54 23
24 16 37 39 11 12 14.0 56 24
25 17 39 32 12 17 11.0 86 25
26 17 41 32 13 9 9.0 80 26
27 16 36 35 10 16 11.0 76 27
28 15 33 37 14 14 15.0 69 28
29 16 33 33 12 15 14.0 78 29
30 14 34 33 10 11 13.0 67 30
31 15 31 31 12 16 9.0 80 31
32 12 27 32 8 13 15.0 54 32
33 14 37 31 10 17 10.0 71 33
34 16 34 37 12 15 11.0 84 34
35 14 34 30 12 14 13.0 74 35
36 10 32 33 7 16 8.0 71 36
37 10 29 31 9 9 20.0 63 37
38 14 36 33 12 15 12.0 71 38
39 16 29 31 10 17 10.0 76 39
40 16 35 33 10 13 10.0 69 40
41 16 37 32 10 15 9.0 74 41
42 14 34 33 12 16 14.0 75 42
43 20 38 32 15 16 8.0 54 43
44 14 35 33 10 12 14.0 52 44
45 14 38 28 10 15 11.0 69 45
46 11 37 35 12 11 13.0 68 46
47 14 38 39 13 15 9.0 65 47
48 15 33 34 11 15 11.0 75 48
49 16 36 38 11 17 15.0 74 49
50 14 38 32 12 13 11.0 75 50
51 16 32 38 14 16 10.0 72 51
52 14 32 30 10 14 14.0 67 52
53 12 32 33 12 11 18.0 63 53
54 16 34 38 13 12 14.0 62 54
55 9 32 32 5 12 11.0 63 55
56 14 37 35 6 15 14.5 76 56
57 16 39 34 12 16 13.0 74 57
58 16 29 34 12 15 9.0 67 58
59 15 37 36 11 12 10.0 73 59
60 16 35 34 10 12 15.0 70 60
61 12 30 28 7 8 20.0 53 61
62 16 38 34 12 13 12.0 77 62
63 16 34 35 14 11 12.0 80 63
64 14 31 35 11 14 14.0 52 64
65 16 34 31 12 15 13.0 54 65
66 17 35 37 13 10 11.0 80 66
67 18 36 35 14 11 17.0 66 67
68 18 30 27 11 12 12.0 73 68
69 12 39 40 12 15 13.0 63 69
70 16 35 37 12 15 14.0 69 70
71 10 38 36 8 14 13.0 67 71
72 14 31 38 11 16 15.0 54 72
73 18 34 39 14 15 13.0 81 73
74 18 38 41 14 15 10.0 69 74
75 16 34 27 12 13 11.0 84 75
76 17 39 30 9 12 19.0 80 76
77 16 37 37 13 17 13.0 70 77
78 16 34 31 11 13 17.0 69 78
79 13 28 31 12 15 13.0 77 79
80 16 37 27 12 13 9.0 54 80
81 16 33 36 12 15 11.0 79 81
82 16 35 37 12 15 9.0 71 82
83 15 37 33 12 16 12.0 73 83
84 15 32 34 11 15 12.0 72 84
85 16 33 31 10 14 13.0 77 85
86 14 38 39 9 15 13.0 75 86
87 16 33 34 12 14 12.0 69 87
88 16 29 32 12 13 15.0 54 88
89 15 33 33 12 7 22.0 70 89
90 12 31 36 9 17 13.0 73 90
91 17 36 32 15 13 15.0 54 91
92 16 35 41 12 15 13.0 77 92
93 15 32 28 12 14 15.0 82 93
94 13 29 30 12 13 12.5 80 94
95 16 39 36 10 16 11.0 80 95
96 16 37 35 13 12 16.0 69 96
97 16 35 31 9 14 11.0 78 97
98 16 37 34 12 17 11.0 81 98
99 14 32 36 10 15 10.0 76 99
100 16 38 36 14 17 10.0 76 100
101 16 37 35 11 12 16.0 73 101
102 20 36 37 15 16 12.0 85 102
103 15 32 28 11 11 11.0 66 103
104 16 33 39 11 15 16.0 79 104
105 13 40 32 12 9 19.0 68 105
106 17 38 35 12 16 11.0 76 106
107 16 41 39 12 15 16.0 71 107
108 16 36 35 11 10 15.0 54 108
109 12 43 42 7 10 24.0 46 109
110 16 30 34 12 15 14.0 85 110
111 16 31 33 14 11 15.0 74 111
112 17 32 41 11 13 11.0 88 112
113 13 32 33 11 14 15.0 38 113
114 12 37 34 10 18 12.0 76 114
115 18 37 32 13 16 10.0 86 115
116 14 33 40 13 14 14.0 54 116
117 14 34 40 8 14 13.0 67 117
118 13 33 35 11 14 9.0 69 118
119 16 38 36 12 14 15.0 90 119
120 13 33 37 11 12 15.0 54 120
121 16 31 27 13 14 14.0 76 121
122 13 38 39 12 15 11.0 89 122
123 16 37 38 14 15 8.0 76 123
124 15 36 31 13 15 11.0 73 124
125 16 31 33 15 13 11.0 79 125
126 15 39 32 10 17 8.0 90 126
127 17 44 39 11 17 10.0 74 127
128 15 33 36 9 19 11.0 81 128
129 12 35 33 11 15 13.0 72 129
130 16 32 33 10 13 11.0 71 130
131 10 28 32 11 9 20.0 66 131
132 16 40 37 8 15 10.0 77 132
133 12 27 30 11 15 15.0 65 133
134 14 37 38 12 15 12.0 74 134
135 15 32 29 12 16 14.0 85 135
136 13 28 22 9 11 23.0 54 136
137 15 34 35 11 14 14.0 63 137
138 11 30 35 10 11 16.0 54 138
139 12 35 34 8 15 11.0 64 139
140 11 31 35 9 13 12.0 69 140
141 16 32 34 8 15 10.0 54 141
142 15 30 37 9 16 14.0 84 142
143 17 30 35 15 14 12.0 86 143
144 16 31 23 11 15 12.0 77 144
145 10 40 31 8 16 11.0 89 145
146 18 32 27 13 16 12.0 76 146
147 13 36 36 12 11 13.0 60 147
148 16 32 31 12 12 11.0 75 148
149 13 35 32 9 9 19.0 73 149
150 10 38 39 7 16 12.0 85 150
151 15 42 37 13 13 17.0 79 151
152 16 34 38 9 16 9.0 71 152
153 16 35 39 6 12 12.0 72 153
154 14 38 34 8 9 19.0 69 154
155 10 33 31 8 13 18.0 78 155
156 17 36 32 15 13 15.0 54 156
157 13 32 37 6 14 14.0 69 157
158 15 33 36 9 19 11.0 81 158
159 16 34 32 11 13 9.0 84 159
160 12 32 38 8 12 18.0 84 160
161 13 34 36 8 13 16.0 69 161
162 13 27 26 10 10 24.0 66 162
163 12 31 26 8 14 14.0 81 163
164 17 38 33 14 16 20.0 82 164
165 15 34 39 10 10 18.0 72 165
166 10 24 30 8 11 23.0 54 166
167 14 30 33 11 14 12.0 78 167
168 11 26 25 12 12 14.0 74 168
169 13 34 38 12 9 16.0 82 169
170 16 27 37 12 9 18.0 73 170
171 12 37 31 5 11 20.0 55 171
172 16 36 37 12 16 12.0 72 172
173 12 41 35 10 9 12.0 78 173
174 9 29 25 7 13 17.0 59 174
175 12 36 28 12 16 13.0 72 175
176 15 32 35 11 13 9.0 78 176
177 12 37 33 8 9 16.0 68 177
178 12 30 30 9 12 18.0 69 178
179 14 31 31 10 16 10.0 67 179
180 12 38 37 9 11 14.0 74 180
181 16 36 36 12 14 11.0 54 181
182 11 35 30 6 13 9.0 67 182
183 19 31 36 15 15 11.0 70 183
184 15 38 32 12 14 10.0 80 184
185 8 22 28 12 16 11.0 89 185
186 16 32 36 12 13 19.0 76 186
187 17 36 34 11 14 14.0 74 187
188 12 39 31 7 15 12.0 87 188
189 11 28 28 7 13 14.0 54 189
190 11 32 36 5 11 21.0 61 190
191 14 32 36 12 11 13.0 38 191
192 16 38 40 12 14 10.0 75 192
193 12 32 33 3 15 15.0 69 193
194 16 35 37 11 11 16.0 62 194
195 13 32 32 10 15 14.0 72 195
196 15 37 38 12 12 12.0 70 196
197 16 34 31 9 14 19.0 79 197
198 16 33 37 12 14 15.0 87 198
199 14 33 33 9 8 19.0 62 199
200 16 26 32 12 13 13.0 77 200
201 16 30 30 12 9 17.0 69 201
202 14 24 30 10 15 12.0 69 202
203 11 34 31 9 17 11.0 75 203
204 12 34 32 12 13 14.0 54 204
205 15 33 34 8 15 11.0 72 205
206 15 34 36 11 15 13.0 74 206
207 16 35 37 11 14 12.0 85 207
208 16 35 36 12 16 15.0 52 208
209 11 36 33 10 13 14.0 70 209
210 15 34 33 10 16 12.0 84 210
211 12 34 33 12 9 17.0 64 211
212 12 41 44 12 16 11.0 84 212
213 15 32 39 11 11 18.0 87 213
214 15 30 32 8 10 13.0 79 214
215 16 35 35 12 11 17.0 67 215
216 14 28 25 10 15 13.0 65 216
217 17 33 35 11 17 11.0 85 217
218 14 39 34 10 14 12.0 83 218
219 13 36 35 8 8 22.0 61 219
220 15 36 39 12 15 14.0 82 220
221 13 35 33 12 11 12.0 76 221
222 14 38 36 10 16 12.0 58 222
223 15 33 32 12 10 17.0 72 223
224 12 31 32 9 15 9.0 72 224
225 13 34 36 9 9 21.0 38 225
226 8 32 36 6 16 10.0 78 226
227 14 31 32 10 19 11.0 54 227
228 14 33 34 9 12 12.0 63 228
229 11 34 33 9 8 23.0 66 229
230 12 34 35 9 11 13.0 70 230
231 13 34 30 6 14 12.0 71 231
232 10 33 38 10 9 16.0 67 232
233 16 32 34 6 15 9.0 58 233
234 18 41 33 14 13 17.0 72 234
235 13 34 32 10 16 9.0 72 235
236 11 36 31 10 11 14.0 70 236
237 4 37 30 6 12 17.0 76 237
238 13 36 27 12 13 13.0 50 238
239 16 29 31 12 10 11.0 72 239
240 10 37 30 7 11 12.0 72 240
241 12 27 32 8 12 10.0 88 241
242 12 35 35 11 8 19.0 53 242
243 10 28 28 3 12 16.0 58 243
244 13 35 33 6 12 16.0 66 244
245 15 37 31 10 15 14.0 82 245
246 12 29 35 8 11 20.0 69 246
247 14 32 35 9 13 15.0 68 247
248 10 36 32 9 14 23.0 44 248
249 12 19 21 8 10 20.0 56 249
250 12 21 20 9 12 16.0 53 250
251 11 31 34 7 15 14.0 70 251
252 10 33 32 7 13 17.0 78 252
253 12 36 34 6 13 11.0 71 253
254 16 33 32 9 13 13.0 72 254
255 12 37 33 10 12 17.0 68 255
256 14 34 33 11 12 15.0 67 256
257 16 35 37 12 9 21.0 75 257
258 14 31 32 8 9 18.0 62 258
259 13 37 34 11 15 15.0 67 259
260 4 35 30 3 10 8.0 83 260
261 15 27 30 11 14 12.0 64 261
262 11 34 38 12 15 12.0 68 262
263 11 40 36 7 7 22.0 62 263
264 14 29 32 9 14 12.0 72 264
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Software Happiness Depression
5.451533 0.032167 0.042765 0.558490 0.070233 -0.031126
Belonging t
0.007479 -0.004911
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9938 -1.0551 0.2896 1.2547 4.6512
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.451533 1.942794 2.806 0.00540 **
Connected 0.032167 0.034424 0.934 0.35097
Separate 0.042765 0.035076 1.219 0.22389
Software 0.558490 0.053726 10.395 < 2e-16 ***
Happiness 0.070233 0.057809 1.215 0.22552
Depression -0.031126 0.041759 -0.745 0.45672
Belonging 0.007479 0.011869 0.630 0.52919
t -0.004911 0.001680 -2.923 0.00378 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.854 on 256 degrees of freedom
Multiple R-squared: 0.4452, Adjusted R-squared: 0.43
F-statistic: 29.34 on 7 and 256 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,] 0.402475229 0.804950459 0.5975248
[2,] 0.799813020 0.400373961 0.2001870
[3,] 0.760826665 0.478346670 0.2391733
[4,] 0.673552621 0.652894758 0.3264474
[5,] 0.618114700 0.763770600 0.3818853
[6,] 0.680833201 0.638333598 0.3191668
[7,] 0.608142628 0.783714744 0.3918574
[8,] 0.857694380 0.284611239 0.1423056
[9,] 0.830980289 0.338039422 0.1690197
[10,] 0.780467389 0.439065223 0.2195326
[11,] 0.716435091 0.567129819 0.2835649
[12,] 0.677924860 0.644150281 0.3220751
[13,] 0.640080624 0.719838752 0.3599194
[14,] 0.608002819 0.783994361 0.3919972
[15,] 0.544602520 0.910794959 0.4553975
[16,] 0.495118874 0.990237748 0.5048811
[17,] 0.435695067 0.871390133 0.5643049
[18,] 0.489428728 0.978857455 0.5105713
[19,] 0.429531193 0.859062387 0.5704688
[20,] 0.439110691 0.878221383 0.5608893
[21,] 0.390777631 0.781555263 0.6092224
[22,] 0.386536251 0.773072502 0.6134637
[23,] 0.367573744 0.735147487 0.6324263
[24,] 0.311946112 0.623892223 0.6880539
[25,] 0.300790076 0.601580151 0.6992099
[26,] 0.399446956 0.798893912 0.6005530
[27,] 0.480233641 0.960467281 0.5197664
[28,] 0.456414494 0.912828988 0.5435855
[29,] 0.504036782 0.991926437 0.4959632
[30,] 0.484662564 0.969325129 0.5153374
[31,] 0.448981539 0.897963078 0.5510185
[32,] 0.419330929 0.838661858 0.5806691
[33,] 0.435488787 0.870977574 0.5645112
[34,] 0.388357019 0.776714038 0.6116430
[35,] 0.349784442 0.699568883 0.6502156
[36,] 0.574490259 0.851019482 0.4255097
[37,] 0.592513782 0.814972435 0.4074862
[38,] 0.554065144 0.891869713 0.4459349
[39,] 0.541344030 0.917311940 0.4586560
[40,] 0.514186099 0.971627801 0.4858139
[41,] 0.469970782 0.939941563 0.5300292
[42,] 0.428427599 0.856855199 0.5715724
[43,] 0.435661290 0.871322581 0.5643387
[44,] 0.406008878 0.812017756 0.5939911
[45,] 0.404277689 0.808555378 0.5957223
[46,] 0.420734697 0.841469394 0.5792653
[47,] 0.380141521 0.760283041 0.6198585
[48,] 0.365628948 0.731257895 0.6343711
[49,] 0.326508006 0.653016011 0.6734920
[50,] 0.353372037 0.706744074 0.6466280
[51,] 0.327220431 0.654440861 0.6727796
[52,] 0.293131978 0.586263956 0.7068680
[53,] 0.259117537 0.518235075 0.7408825
[54,] 0.226514477 0.453028954 0.7734855
[55,] 0.202909489 0.405818977 0.7970905
[56,] 0.193131795 0.386263589 0.8068682
[57,] 0.195831245 0.391662490 0.8041688
[58,] 0.309302991 0.618605982 0.6906970
[59,] 0.422218465 0.844436930 0.5777815
[60,] 0.390197657 0.780395315 0.6098023
[61,] 0.462670997 0.925341994 0.5373290
[62,] 0.427799440 0.855598881 0.5722006
[63,] 0.421024396 0.842048791 0.5789756
[64,] 0.399631794 0.799263587 0.6003682
[65,] 0.363591795 0.727183591 0.6364082
[66,] 0.441561639 0.883123277 0.5584384
[67,] 0.403416720 0.806833441 0.5965833
[68,] 0.382623080 0.765246159 0.6173769
[69,] 0.392051967 0.784103934 0.6079480
[70,] 0.357774231 0.715548461 0.6422258
[71,] 0.326049221 0.652098443 0.6739508
[72,] 0.294041160 0.588082321 0.7059588
[73,] 0.267059647 0.534119293 0.7329404
[74,] 0.237094036 0.474188071 0.7629060
[75,] 0.234219608 0.468439215 0.7657804
[76,] 0.205457332 0.410914664 0.7945427
[77,] 0.182360085 0.364720170 0.8176399
[78,] 0.168667247 0.337334494 0.8313328
[79,] 0.146035405 0.292070810 0.8539646
[80,] 0.141401399 0.282802797 0.8585986
[81,] 0.121453403 0.242906806 0.8785466
[82,] 0.105538335 0.211076671 0.8944617
[83,] 0.090473150 0.180946301 0.9095268
[84,] 0.094451705 0.188903409 0.9055483
[85,] 0.085145288 0.170290577 0.9148547
[86,] 0.071343760 0.142687519 0.9286562
[87,] 0.076539042 0.153078084 0.9234610
[88,] 0.063917296 0.127834591 0.9360827
[89,] 0.053256054 0.106512108 0.9467439
[90,] 0.047196464 0.094392929 0.9528035
[91,] 0.041823771 0.083647542 0.9581762
[92,] 0.049564664 0.099129328 0.9504353
[93,] 0.041815933 0.083631865 0.9581841
[94,] 0.037154625 0.074309249 0.9628454
[95,] 0.044379965 0.088759929 0.9556200
[96,] 0.039246724 0.078493448 0.9607533
[97,] 0.032101541 0.064203081 0.9678985
[98,] 0.030638962 0.061277924 0.9693610
[99,] 0.024976651 0.049953302 0.9750233
[100,] 0.020680288 0.041360576 0.9793197
[101,] 0.016536702 0.033073404 0.9834633
[102,] 0.017461097 0.034922194 0.9825389
[103,] 0.015186805 0.030373610 0.9848132
[104,] 0.020342784 0.040685568 0.9796572
[105,] 0.019554579 0.039109158 0.9804454
[106,] 0.019156583 0.038313166 0.9808434
[107,] 0.016189542 0.032379083 0.9838105
[108,] 0.016142880 0.032285760 0.9838571
[109,] 0.013111597 0.026223193 0.9868884
[110,] 0.011851286 0.023702573 0.9881487
[111,] 0.009611389 0.019222778 0.9903886
[112,] 0.014297613 0.028595226 0.9857024
[113,] 0.011918249 0.023836497 0.9880818
[114,] 0.010041723 0.020083446 0.9899583
[115,] 0.008203863 0.016407727 0.9917961
[116,] 0.006524021 0.013048043 0.9934760
[117,] 0.005924995 0.011849989 0.9940750
[118,] 0.004989580 0.009979161 0.9950104
[119,] 0.006796768 0.013593536 0.9932032
[120,] 0.007378965 0.014757929 0.9926210
[121,] 0.015038004 0.030076007 0.9849620
[122,] 0.017902001 0.035804002 0.9820980
[123,] 0.019001428 0.038002856 0.9809986
[124,] 0.017939951 0.035879902 0.9820600
[125,] 0.014328083 0.028656166 0.9856719
[126,] 0.012230943 0.024461885 0.9877691
[127,] 0.009906487 0.019812975 0.9900935
[128,] 0.012538421 0.025076842 0.9874616
[129,] 0.010761822 0.021523643 0.9892382
[130,] 0.012905296 0.025810591 0.9870947
[131,] 0.020156017 0.040312033 0.9798440
[132,] 0.018524281 0.037048563 0.9814757
[133,] 0.015020531 0.030041062 0.9849795
[134,] 0.015723601 0.031447203 0.9842764
[135,] 0.026697455 0.053394910 0.9733025
[136,] 0.032899284 0.065798568 0.9671007
[137,] 0.034710663 0.069421325 0.9652893
[138,] 0.030826816 0.061653632 0.9691732
[139,] 0.025169449 0.050338898 0.9748306
[140,] 0.034743167 0.069486334 0.9652568
[141,] 0.029564965 0.059129929 0.9704350
[142,] 0.031211900 0.062423800 0.9687881
[143,] 0.061492165 0.122984329 0.9385078
[144,] 0.059269348 0.118538697 0.9407307
[145,] 0.066851467 0.133702935 0.9331485
[146,] 0.056882837 0.113765674 0.9431172
[147,] 0.050726351 0.101452702 0.9492736
[148,] 0.044306946 0.088613892 0.9556931
[149,] 0.042896371 0.085792741 0.9571036
[150,] 0.036828929 0.073657858 0.9631711
[151,] 0.030149405 0.060298810 0.9698506
[152,] 0.024688251 0.049376502 0.9753117
[153,] 0.020455692 0.040911385 0.9795443
[154,] 0.017466334 0.034932668 0.9825337
[155,] 0.015289971 0.030579942 0.9847100
[156,] 0.016215857 0.032431714 0.9837841
[157,] 0.012951919 0.025903838 0.9870481
[158,] 0.019009794 0.038019589 0.9809902
[159,] 0.019314506 0.038629012 0.9806855
[160,] 0.017801931 0.035603863 0.9821981
[161,] 0.016327214 0.032654427 0.9836728
[162,] 0.013177650 0.026355300 0.9868223
[163,] 0.012812807 0.025625613 0.9871872
[164,] 0.014685951 0.029371902 0.9853140
[165,] 0.018892991 0.037785982 0.9811070
[166,] 0.015145406 0.030290813 0.9848546
[167,] 0.011980872 0.023961744 0.9880191
[168,] 0.010078151 0.020156301 0.9899218
[169,] 0.007837987 0.015675975 0.9921620
[170,] 0.006892284 0.013784568 0.9931077
[171,] 0.005579292 0.011158585 0.9944207
[172,] 0.004301702 0.008603405 0.9956983
[173,] 0.004542725 0.009085450 0.9954573
[174,] 0.003463408 0.006926816 0.9965366
[175,] 0.094548553 0.189097106 0.9054514
[176,] 0.083767329 0.167534657 0.9162327
[177,] 0.093731987 0.187463975 0.9062680
[178,] 0.079142243 0.158284486 0.9208578
[179,] 0.070541865 0.141083731 0.9294581
[180,] 0.059670561 0.119341123 0.9403294
[181,] 0.051688800 0.103377601 0.9483112
[182,] 0.043010977 0.086021953 0.9569890
[183,] 0.043312076 0.086624153 0.9566879
[184,] 0.041069668 0.082139337 0.9589303
[185,] 0.035702774 0.071405548 0.9642972
[186,] 0.028455014 0.056910028 0.9715450
[187,] 0.037322240 0.074644480 0.9626778
[188,] 0.030519500 0.061038999 0.9694805
[189,] 0.027405782 0.054811564 0.9725942
[190,] 0.023276539 0.046553078 0.9767235
[191,] 0.021311356 0.042622711 0.9786886
[192,] 0.017816554 0.035633109 0.9821834
[193,] 0.019846911 0.039693822 0.9801531
[194,] 0.026367508 0.052735016 0.9736325
[195,] 0.027823707 0.055647413 0.9721763
[196,] 0.021750328 0.043500656 0.9782497
[197,] 0.019386927 0.038773854 0.9806131
[198,] 0.015490372 0.030980744 0.9845096
[199,] 0.018077899 0.036155799 0.9819221
[200,] 0.014963694 0.029927388 0.9850363
[201,] 0.018395839 0.036791678 0.9816042
[202,] 0.032471761 0.064943522 0.9675282
[203,] 0.025371095 0.050742189 0.9746289
[204,] 0.031479117 0.062958234 0.9685209
[205,] 0.026761250 0.053522501 0.9732387
[206,] 0.020729367 0.041458734 0.9792706
[207,] 0.023137066 0.046274133 0.9768629
[208,] 0.019606755 0.039213511 0.9803932
[209,] 0.018294390 0.036588781 0.9817056
[210,] 0.013741904 0.027483808 0.9862581
[211,] 0.011402514 0.022805027 0.9885975
[212,] 0.008311548 0.016623096 0.9916885
[213,] 0.006416314 0.012832628 0.9935837
[214,] 0.004916912 0.009833823 0.9950831
[215,] 0.003439601 0.006879202 0.9965604
[216,] 0.007101991 0.014203982 0.9928980
[217,] 0.005651567 0.011303135 0.9943484
[218,] 0.004122618 0.008245236 0.9958774
[219,] 0.002931620 0.005863240 0.9970684
[220,] 0.002075990 0.004151981 0.9979240
[221,] 0.002268510 0.004537020 0.9977315
[222,] 0.007798519 0.015597038 0.9922015
[223,] 0.029369840 0.058739681 0.9706302
[224,] 0.044950747 0.089901494 0.9550493
[225,] 0.033091411 0.066182821 0.9669086
[226,] 0.027101151 0.054202303 0.9728988
[227,] 0.238959016 0.477918032 0.7610410
[228,] 0.192949272 0.385898544 0.8070507
[229,] 0.163289925 0.326579850 0.8367101
[230,] 0.130447621 0.260895241 0.8695524
[231,] 0.116817471 0.233634942 0.8831825
[232,] 0.177907984 0.355815968 0.8220920
[233,] 0.150453950 0.300907900 0.8495460
[234,] 0.151452823 0.302905645 0.8485472
[235,] 0.132852249 0.265704499 0.8671478
[236,] 0.116475821 0.232951642 0.8835242
[237,] 0.079256297 0.158512595 0.9207437
[238,] 0.070429088 0.140858177 0.9295709
[239,] 0.053497790 0.106995580 0.9465022
[240,] 0.130779813 0.261559626 0.8692202
[241,] 0.107472862 0.214945723 0.8925271
[242,] 0.543978390 0.912043221 0.4560216
[243,] 0.382288727 0.764577455 0.6177113
> postscript(file="/var/fisher/rcomp/tmp/14ygs1353428416.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/fisher/rcomp/tmp/2yxqy1353428416.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3pzf11353428416.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4ojou1353428416.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/57tcs1353428416.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-3.098538785 0.249371924 1.893678110 2.838394759 -2.401787347 -1.847013370
7 8 9 10 11 12
3.605880460 -2.103491161 -2.059658512 0.538680280 0.996010221 -0.195778066
13 14 15 16 17 18
0.454799205 0.502429577 -1.002615235 -0.496690760 0.412162340 3.577867081
19 20 21 22 23 24
2.468158708 0.401602040 0.605520846 0.786385822 2.507859621 0.839080777
25 26 27 28 29 30
0.851623609 0.778196929 1.091647210 -1.809108735 0.315176248 -0.263029559
31 32 33 34 35 36
-0.765960661 -0.849288952 -0.803962023 -0.001749614 -1.490209881 -3.030476921
37 38 39 40 41 42
-3.035534565 -1.747032491 1.445444080 1.505106075 1.279462294 -1.700951059
43 44 45 46 47 48
2.512878443 -0.153377764 -0.462355329 -4.490949562 -2.630761274 -0.146742166
49 50 51 52 53 54
0.582123384 -1.630247367 -1.025298496 -0.141940881 -3.017185579 -0.036186091
55 56 57 58 59 60
-2.343289800 1.615021855 0.145459891 0.470116771 -0.112393030 1.778939435
61 62 63 64 65 66
0.440446592 0.359318244 -0.548816853 -0.710986002 0.693678842 0.945813967
67 68 69 70 71 72
1.666822920 3.604111813 -3.899708374 0.348421327 -3.412380609 -0.924295106
73 74 75 76 77 78
1.071941325 0.859016376 0.767705125 3.508113010 -0.419099992 1.468801152
79 80 81 82 83 84
-2.216577334 0.857859743 0.341372549 0.236759169 -0.643413724 0.115767618
85 86 87 88 89 90
1.839264528 -0.155569285 0.632511822 1.127411151 0.480518610 -1.907972516
91 92 93 94 95 96
0.241505625 0.194438081 -0.053106791 -2.029851850 1.256386663 0.211757119
97 98 99 100 101 102
2.322616035 0.226291624 -0.429781644 -0.992298111 1.323375337 2.545781557
103 104 105 106 107 108
0.760339627 1.040141556 -1.842204209 1.298270035 0.298876541 1.641348531
109 110 111 112 113 114
-0.304342299 0.714319104 0.007172851 1.943590631 -1.281178077 -2.579874261
115 116 117 118 119 120
1.838527156 -1.865732844 0.771112171 -1.792915136 0.479615794 -1.429221584
121 122 123 124 125 126
0.614576732 -2.821209317 -0.854504464 -0.843764797 -0.784933914 0.341278399
127 128 129 130 131 132
1.509416094 0.951747888 -2.685865073 2.059728520 -3.723953244 2.541667846
133 134 135 136 137 138
-2.165989022 -1.544046009 -0.083656696 0.887887265 0.468722193 -2.498950057
139 140 141 142 143 144
-1.006480416 -2.339956434 3.143500085 1.355875574 0.158636042 1.875596153
145 146 147 148 149 150
-3.266751430 2.502454533 -1.945751239 1.156989895 0.172772330 -2.900457717
151 152 153 154 155 156
-0.878418291 2.175136908 4.147418985 1.603697482 -2.481628296 0.560693436
157 158 159 160 161 162
1.293314145 1.099065340 1.462603867 -0.698903764 0.306895079 0.329795885
163 164 165 166 167 168
-0.381357631 0.786901982 1.331779722 -1.619761147 -0.344192471 -3.194346992
169 170 171 172 173 174
-1.789596334 1.612807316 1.518467982 0.662211895 -1.844438422 -2.333612434
175 176 177 178 179 180
-2.907041435 0.526992426 -0.294327621 -0.650368444 0.206131805 -1.288904893
181 182 183 184 185 186
0.993126239 -0.451505878 2.298423231 -0.110981881 -6.596986586 1.301064443
187 188 189 190 191 192
2.610419292 -0.348622911 -0.412069714 0.545031937 -0.436490890 0.623572202
193 194 195 196 197 198
2.277518581 1.911359536 -0.632884513 0.001024589 3.087373988 1.008056355
199 200 201 202 203 204
1.592366903 1.539638452 1.966679997 0.704538164 -2.312960385 -2.494922264
205 206 207 208 209 210
2.322123701 0.581163430 1.467985216 1.156874554 -2.580145319 1.111446590
211 212 213 214 215 216
-2.203785389 -3.722425549 0.890920641 2.909421188 1.535256743 0.919486133
217 218 219 220 221 222
2.425128675 0.095076893 1.167894530 -0.129909691 -1.572687684 0.107851037
223 224 225 226 227 228
0.800012311 -1.055453201 0.731082508 -3.657369597 0.316720867 1.185710905
229 230 231 232 233 234
-1.197890874 -0.830389846 1.814511262 -3.218905829 4.651212489 2.226248327
235 236 237 238 239 240
-0.726660655 -2.221561835 -6.993819348 -1.179681722 1.863256037 -1.593061865
241 242 243 244 245 246
-0.162645268 -1.396015419 1.189632700 2.020251001 1.419790411 0.192867082
247 248 249 250 251 252
1.254166697 -2.383032043 1.295437316 0.477752654 -0.720829792 -1.520704011
253 254 255 256 257 258
0.726255791 3.292503160 -1.207855665 0.280291587 1.861115030 2.446320148
259 260 261 262 263 264
-1.054943238 -5.333093544 1.446898172 -3.774119819 -0.166228177 1.368916629
> postscript(file="/var/fisher/rcomp/tmp/60und1353428416.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 -3.098538785 NA
1 0.249371924 -3.098538785
2 1.893678110 0.249371924
3 2.838394759 1.893678110
4 -2.401787347 2.838394759
5 -1.847013370 -2.401787347
6 3.605880460 -1.847013370
7 -2.103491161 3.605880460
8 -2.059658512 -2.103491161
9 0.538680280 -2.059658512
10 0.996010221 0.538680280
11 -0.195778066 0.996010221
12 0.454799205 -0.195778066
13 0.502429577 0.454799205
14 -1.002615235 0.502429577
15 -0.496690760 -1.002615235
16 0.412162340 -0.496690760
17 3.577867081 0.412162340
18 2.468158708 3.577867081
19 0.401602040 2.468158708
20 0.605520846 0.401602040
21 0.786385822 0.605520846
22 2.507859621 0.786385822
23 0.839080777 2.507859621
24 0.851623609 0.839080777
25 0.778196929 0.851623609
26 1.091647210 0.778196929
27 -1.809108735 1.091647210
28 0.315176248 -1.809108735
29 -0.263029559 0.315176248
30 -0.765960661 -0.263029559
31 -0.849288952 -0.765960661
32 -0.803962023 -0.849288952
33 -0.001749614 -0.803962023
34 -1.490209881 -0.001749614
35 -3.030476921 -1.490209881
36 -3.035534565 -3.030476921
37 -1.747032491 -3.035534565
38 1.445444080 -1.747032491
39 1.505106075 1.445444080
40 1.279462294 1.505106075
41 -1.700951059 1.279462294
42 2.512878443 -1.700951059
43 -0.153377764 2.512878443
44 -0.462355329 -0.153377764
45 -4.490949562 -0.462355329
46 -2.630761274 -4.490949562
47 -0.146742166 -2.630761274
48 0.582123384 -0.146742166
49 -1.630247367 0.582123384
50 -1.025298496 -1.630247367
51 -0.141940881 -1.025298496
52 -3.017185579 -0.141940881
53 -0.036186091 -3.017185579
54 -2.343289800 -0.036186091
55 1.615021855 -2.343289800
56 0.145459891 1.615021855
57 0.470116771 0.145459891
58 -0.112393030 0.470116771
59 1.778939435 -0.112393030
60 0.440446592 1.778939435
61 0.359318244 0.440446592
62 -0.548816853 0.359318244
63 -0.710986002 -0.548816853
64 0.693678842 -0.710986002
65 0.945813967 0.693678842
66 1.666822920 0.945813967
67 3.604111813 1.666822920
68 -3.899708374 3.604111813
69 0.348421327 -3.899708374
70 -3.412380609 0.348421327
71 -0.924295106 -3.412380609
72 1.071941325 -0.924295106
73 0.859016376 1.071941325
74 0.767705125 0.859016376
75 3.508113010 0.767705125
76 -0.419099992 3.508113010
77 1.468801152 -0.419099992
78 -2.216577334 1.468801152
79 0.857859743 -2.216577334
80 0.341372549 0.857859743
81 0.236759169 0.341372549
82 -0.643413724 0.236759169
83 0.115767618 -0.643413724
84 1.839264528 0.115767618
85 -0.155569285 1.839264528
86 0.632511822 -0.155569285
87 1.127411151 0.632511822
88 0.480518610 1.127411151
89 -1.907972516 0.480518610
90 0.241505625 -1.907972516
91 0.194438081 0.241505625
92 -0.053106791 0.194438081
93 -2.029851850 -0.053106791
94 1.256386663 -2.029851850
95 0.211757119 1.256386663
96 2.322616035 0.211757119
97 0.226291624 2.322616035
98 -0.429781644 0.226291624
99 -0.992298111 -0.429781644
100 1.323375337 -0.992298111
101 2.545781557 1.323375337
102 0.760339627 2.545781557
103 1.040141556 0.760339627
104 -1.842204209 1.040141556
105 1.298270035 -1.842204209
106 0.298876541 1.298270035
107 1.641348531 0.298876541
108 -0.304342299 1.641348531
109 0.714319104 -0.304342299
110 0.007172851 0.714319104
111 1.943590631 0.007172851
112 -1.281178077 1.943590631
113 -2.579874261 -1.281178077
114 1.838527156 -2.579874261
115 -1.865732844 1.838527156
116 0.771112171 -1.865732844
117 -1.792915136 0.771112171
118 0.479615794 -1.792915136
119 -1.429221584 0.479615794
120 0.614576732 -1.429221584
121 -2.821209317 0.614576732
122 -0.854504464 -2.821209317
123 -0.843764797 -0.854504464
124 -0.784933914 -0.843764797
125 0.341278399 -0.784933914
126 1.509416094 0.341278399
127 0.951747888 1.509416094
128 -2.685865073 0.951747888
129 2.059728520 -2.685865073
130 -3.723953244 2.059728520
131 2.541667846 -3.723953244
132 -2.165989022 2.541667846
133 -1.544046009 -2.165989022
134 -0.083656696 -1.544046009
135 0.887887265 -0.083656696
136 0.468722193 0.887887265
137 -2.498950057 0.468722193
138 -1.006480416 -2.498950057
139 -2.339956434 -1.006480416
140 3.143500085 -2.339956434
141 1.355875574 3.143500085
142 0.158636042 1.355875574
143 1.875596153 0.158636042
144 -3.266751430 1.875596153
145 2.502454533 -3.266751430
146 -1.945751239 2.502454533
147 1.156989895 -1.945751239
148 0.172772330 1.156989895
149 -2.900457717 0.172772330
150 -0.878418291 -2.900457717
151 2.175136908 -0.878418291
152 4.147418985 2.175136908
153 1.603697482 4.147418985
154 -2.481628296 1.603697482
155 0.560693436 -2.481628296
156 1.293314145 0.560693436
157 1.099065340 1.293314145
158 1.462603867 1.099065340
159 -0.698903764 1.462603867
160 0.306895079 -0.698903764
161 0.329795885 0.306895079
162 -0.381357631 0.329795885
163 0.786901982 -0.381357631
164 1.331779722 0.786901982
165 -1.619761147 1.331779722
166 -0.344192471 -1.619761147
167 -3.194346992 -0.344192471
168 -1.789596334 -3.194346992
169 1.612807316 -1.789596334
170 1.518467982 1.612807316
171 0.662211895 1.518467982
172 -1.844438422 0.662211895
173 -2.333612434 -1.844438422
174 -2.907041435 -2.333612434
175 0.526992426 -2.907041435
176 -0.294327621 0.526992426
177 -0.650368444 -0.294327621
178 0.206131805 -0.650368444
179 -1.288904893 0.206131805
180 0.993126239 -1.288904893
181 -0.451505878 0.993126239
182 2.298423231 -0.451505878
183 -0.110981881 2.298423231
184 -6.596986586 -0.110981881
185 1.301064443 -6.596986586
186 2.610419292 1.301064443
187 -0.348622911 2.610419292
188 -0.412069714 -0.348622911
189 0.545031937 -0.412069714
190 -0.436490890 0.545031937
191 0.623572202 -0.436490890
192 2.277518581 0.623572202
193 1.911359536 2.277518581
194 -0.632884513 1.911359536
195 0.001024589 -0.632884513
196 3.087373988 0.001024589
197 1.008056355 3.087373988
198 1.592366903 1.008056355
199 1.539638452 1.592366903
200 1.966679997 1.539638452
201 0.704538164 1.966679997
202 -2.312960385 0.704538164
203 -2.494922264 -2.312960385
204 2.322123701 -2.494922264
205 0.581163430 2.322123701
206 1.467985216 0.581163430
207 1.156874554 1.467985216
208 -2.580145319 1.156874554
209 1.111446590 -2.580145319
210 -2.203785389 1.111446590
211 -3.722425549 -2.203785389
212 0.890920641 -3.722425549
213 2.909421188 0.890920641
214 1.535256743 2.909421188
215 0.919486133 1.535256743
216 2.425128675 0.919486133
217 0.095076893 2.425128675
218 1.167894530 0.095076893
219 -0.129909691 1.167894530
220 -1.572687684 -0.129909691
221 0.107851037 -1.572687684
222 0.800012311 0.107851037
223 -1.055453201 0.800012311
224 0.731082508 -1.055453201
225 -3.657369597 0.731082508
226 0.316720867 -3.657369597
227 1.185710905 0.316720867
228 -1.197890874 1.185710905
229 -0.830389846 -1.197890874
230 1.814511262 -0.830389846
231 -3.218905829 1.814511262
232 4.651212489 -3.218905829
233 2.226248327 4.651212489
234 -0.726660655 2.226248327
235 -2.221561835 -0.726660655
236 -6.993819348 -2.221561835
237 -1.179681722 -6.993819348
238 1.863256037 -1.179681722
239 -1.593061865 1.863256037
240 -0.162645268 -1.593061865
241 -1.396015419 -0.162645268
242 1.189632700 -1.396015419
243 2.020251001 1.189632700
244 1.419790411 2.020251001
245 0.192867082 1.419790411
246 1.254166697 0.192867082
247 -2.383032043 1.254166697
248 1.295437316 -2.383032043
249 0.477752654 1.295437316
250 -0.720829792 0.477752654
251 -1.520704011 -0.720829792
252 0.726255791 -1.520704011
253 3.292503160 0.726255791
254 -1.207855665 3.292503160
255 0.280291587 -1.207855665
256 1.861115030 0.280291587
257 2.446320148 1.861115030
258 -1.054943238 2.446320148
259 -5.333093544 -1.054943238
260 1.446898172 -5.333093544
261 -3.774119819 1.446898172
262 -0.166228177 -3.774119819
263 1.368916629 -0.166228177
264 NA 1.368916629
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.249371924 -3.098538785
[2,] 1.893678110 0.249371924
[3,] 2.838394759 1.893678110
[4,] -2.401787347 2.838394759
[5,] -1.847013370 -2.401787347
[6,] 3.605880460 -1.847013370
[7,] -2.103491161 3.605880460
[8,] -2.059658512 -2.103491161
[9,] 0.538680280 -2.059658512
[10,] 0.996010221 0.538680280
[11,] -0.195778066 0.996010221
[12,] 0.454799205 -0.195778066
[13,] 0.502429577 0.454799205
[14,] -1.002615235 0.502429577
[15,] -0.496690760 -1.002615235
[16,] 0.412162340 -0.496690760
[17,] 3.577867081 0.412162340
[18,] 2.468158708 3.577867081
[19,] 0.401602040 2.468158708
[20,] 0.605520846 0.401602040
[21,] 0.786385822 0.605520846
[22,] 2.507859621 0.786385822
[23,] 0.839080777 2.507859621
[24,] 0.851623609 0.839080777
[25,] 0.778196929 0.851623609
[26,] 1.091647210 0.778196929
[27,] -1.809108735 1.091647210
[28,] 0.315176248 -1.809108735
[29,] -0.263029559 0.315176248
[30,] -0.765960661 -0.263029559
[31,] -0.849288952 -0.765960661
[32,] -0.803962023 -0.849288952
[33,] -0.001749614 -0.803962023
[34,] -1.490209881 -0.001749614
[35,] -3.030476921 -1.490209881
[36,] -3.035534565 -3.030476921
[37,] -1.747032491 -3.035534565
[38,] 1.445444080 -1.747032491
[39,] 1.505106075 1.445444080
[40,] 1.279462294 1.505106075
[41,] -1.700951059 1.279462294
[42,] 2.512878443 -1.700951059
[43,] -0.153377764 2.512878443
[44,] -0.462355329 -0.153377764
[45,] -4.490949562 -0.462355329
[46,] -2.630761274 -4.490949562
[47,] -0.146742166 -2.630761274
[48,] 0.582123384 -0.146742166
[49,] -1.630247367 0.582123384
[50,] -1.025298496 -1.630247367
[51,] -0.141940881 -1.025298496
[52,] -3.017185579 -0.141940881
[53,] -0.036186091 -3.017185579
[54,] -2.343289800 -0.036186091
[55,] 1.615021855 -2.343289800
[56,] 0.145459891 1.615021855
[57,] 0.470116771 0.145459891
[58,] -0.112393030 0.470116771
[59,] 1.778939435 -0.112393030
[60,] 0.440446592 1.778939435
[61,] 0.359318244 0.440446592
[62,] -0.548816853 0.359318244
[63,] -0.710986002 -0.548816853
[64,] 0.693678842 -0.710986002
[65,] 0.945813967 0.693678842
[66,] 1.666822920 0.945813967
[67,] 3.604111813 1.666822920
[68,] -3.899708374 3.604111813
[69,] 0.348421327 -3.899708374
[70,] -3.412380609 0.348421327
[71,] -0.924295106 -3.412380609
[72,] 1.071941325 -0.924295106
[73,] 0.859016376 1.071941325
[74,] 0.767705125 0.859016376
[75,] 3.508113010 0.767705125
[76,] -0.419099992 3.508113010
[77,] 1.468801152 -0.419099992
[78,] -2.216577334 1.468801152
[79,] 0.857859743 -2.216577334
[80,] 0.341372549 0.857859743
[81,] 0.236759169 0.341372549
[82,] -0.643413724 0.236759169
[83,] 0.115767618 -0.643413724
[84,] 1.839264528 0.115767618
[85,] -0.155569285 1.839264528
[86,] 0.632511822 -0.155569285
[87,] 1.127411151 0.632511822
[88,] 0.480518610 1.127411151
[89,] -1.907972516 0.480518610
[90,] 0.241505625 -1.907972516
[91,] 0.194438081 0.241505625
[92,] -0.053106791 0.194438081
[93,] -2.029851850 -0.053106791
[94,] 1.256386663 -2.029851850
[95,] 0.211757119 1.256386663
[96,] 2.322616035 0.211757119
[97,] 0.226291624 2.322616035
[98,] -0.429781644 0.226291624
[99,] -0.992298111 -0.429781644
[100,] 1.323375337 -0.992298111
[101,] 2.545781557 1.323375337
[102,] 0.760339627 2.545781557
[103,] 1.040141556 0.760339627
[104,] -1.842204209 1.040141556
[105,] 1.298270035 -1.842204209
[106,] 0.298876541 1.298270035
[107,] 1.641348531 0.298876541
[108,] -0.304342299 1.641348531
[109,] 0.714319104 -0.304342299
[110,] 0.007172851 0.714319104
[111,] 1.943590631 0.007172851
[112,] -1.281178077 1.943590631
[113,] -2.579874261 -1.281178077
[114,] 1.838527156 -2.579874261
[115,] -1.865732844 1.838527156
[116,] 0.771112171 -1.865732844
[117,] -1.792915136 0.771112171
[118,] 0.479615794 -1.792915136
[119,] -1.429221584 0.479615794
[120,] 0.614576732 -1.429221584
[121,] -2.821209317 0.614576732
[122,] -0.854504464 -2.821209317
[123,] -0.843764797 -0.854504464
[124,] -0.784933914 -0.843764797
[125,] 0.341278399 -0.784933914
[126,] 1.509416094 0.341278399
[127,] 0.951747888 1.509416094
[128,] -2.685865073 0.951747888
[129,] 2.059728520 -2.685865073
[130,] -3.723953244 2.059728520
[131,] 2.541667846 -3.723953244
[132,] -2.165989022 2.541667846
[133,] -1.544046009 -2.165989022
[134,] -0.083656696 -1.544046009
[135,] 0.887887265 -0.083656696
[136,] 0.468722193 0.887887265
[137,] -2.498950057 0.468722193
[138,] -1.006480416 -2.498950057
[139,] -2.339956434 -1.006480416
[140,] 3.143500085 -2.339956434
[141,] 1.355875574 3.143500085
[142,] 0.158636042 1.355875574
[143,] 1.875596153 0.158636042
[144,] -3.266751430 1.875596153
[145,] 2.502454533 -3.266751430
[146,] -1.945751239 2.502454533
[147,] 1.156989895 -1.945751239
[148,] 0.172772330 1.156989895
[149,] -2.900457717 0.172772330
[150,] -0.878418291 -2.900457717
[151,] 2.175136908 -0.878418291
[152,] 4.147418985 2.175136908
[153,] 1.603697482 4.147418985
[154,] -2.481628296 1.603697482
[155,] 0.560693436 -2.481628296
[156,] 1.293314145 0.560693436
[157,] 1.099065340 1.293314145
[158,] 1.462603867 1.099065340
[159,] -0.698903764 1.462603867
[160,] 0.306895079 -0.698903764
[161,] 0.329795885 0.306895079
[162,] -0.381357631 0.329795885
[163,] 0.786901982 -0.381357631
[164,] 1.331779722 0.786901982
[165,] -1.619761147 1.331779722
[166,] -0.344192471 -1.619761147
[167,] -3.194346992 -0.344192471
[168,] -1.789596334 -3.194346992
[169,] 1.612807316 -1.789596334
[170,] 1.518467982 1.612807316
[171,] 0.662211895 1.518467982
[172,] -1.844438422 0.662211895
[173,] -2.333612434 -1.844438422
[174,] -2.907041435 -2.333612434
[175,] 0.526992426 -2.907041435
[176,] -0.294327621 0.526992426
[177,] -0.650368444 -0.294327621
[178,] 0.206131805 -0.650368444
[179,] -1.288904893 0.206131805
[180,] 0.993126239 -1.288904893
[181,] -0.451505878 0.993126239
[182,] 2.298423231 -0.451505878
[183,] -0.110981881 2.298423231
[184,] -6.596986586 -0.110981881
[185,] 1.301064443 -6.596986586
[186,] 2.610419292 1.301064443
[187,] -0.348622911 2.610419292
[188,] -0.412069714 -0.348622911
[189,] 0.545031937 -0.412069714
[190,] -0.436490890 0.545031937
[191,] 0.623572202 -0.436490890
[192,] 2.277518581 0.623572202
[193,] 1.911359536 2.277518581
[194,] -0.632884513 1.911359536
[195,] 0.001024589 -0.632884513
[196,] 3.087373988 0.001024589
[197,] 1.008056355 3.087373988
[198,] 1.592366903 1.008056355
[199,] 1.539638452 1.592366903
[200,] 1.966679997 1.539638452
[201,] 0.704538164 1.966679997
[202,] -2.312960385 0.704538164
[203,] -2.494922264 -2.312960385
[204,] 2.322123701 -2.494922264
[205,] 0.581163430 2.322123701
[206,] 1.467985216 0.581163430
[207,] 1.156874554 1.467985216
[208,] -2.580145319 1.156874554
[209,] 1.111446590 -2.580145319
[210,] -2.203785389 1.111446590
[211,] -3.722425549 -2.203785389
[212,] 0.890920641 -3.722425549
[213,] 2.909421188 0.890920641
[214,] 1.535256743 2.909421188
[215,] 0.919486133 1.535256743
[216,] 2.425128675 0.919486133
[217,] 0.095076893 2.425128675
[218,] 1.167894530 0.095076893
[219,] -0.129909691 1.167894530
[220,] -1.572687684 -0.129909691
[221,] 0.107851037 -1.572687684
[222,] 0.800012311 0.107851037
[223,] -1.055453201 0.800012311
[224,] 0.731082508 -1.055453201
[225,] -3.657369597 0.731082508
[226,] 0.316720867 -3.657369597
[227,] 1.185710905 0.316720867
[228,] -1.197890874 1.185710905
[229,] -0.830389846 -1.197890874
[230,] 1.814511262 -0.830389846
[231,] -3.218905829 1.814511262
[232,] 4.651212489 -3.218905829
[233,] 2.226248327 4.651212489
[234,] -0.726660655 2.226248327
[235,] -2.221561835 -0.726660655
[236,] -6.993819348 -2.221561835
[237,] -1.179681722 -6.993819348
[238,] 1.863256037 -1.179681722
[239,] -1.593061865 1.863256037
[240,] -0.162645268 -1.593061865
[241,] -1.396015419 -0.162645268
[242,] 1.189632700 -1.396015419
[243,] 2.020251001 1.189632700
[244,] 1.419790411 2.020251001
[245,] 0.192867082 1.419790411
[246,] 1.254166697 0.192867082
[247,] -2.383032043 1.254166697
[248,] 1.295437316 -2.383032043
[249,] 0.477752654 1.295437316
[250,] -0.720829792 0.477752654
[251,] -1.520704011 -0.720829792
[252,] 0.726255791 -1.520704011
[253,] 3.292503160 0.726255791
[254,] -1.207855665 3.292503160
[255,] 0.280291587 -1.207855665
[256,] 1.861115030 0.280291587
[257,] 2.446320148 1.861115030
[258,] -1.054943238 2.446320148
[259,] -5.333093544 -1.054943238
[260,] 1.446898172 -5.333093544
[261,] -3.774119819 1.446898172
[262,] -0.166228177 -3.774119819
[263,] 1.368916629 -0.166228177
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.249371924 -3.098538785
2 1.893678110 0.249371924
3 2.838394759 1.893678110
4 -2.401787347 2.838394759
5 -1.847013370 -2.401787347
6 3.605880460 -1.847013370
7 -2.103491161 3.605880460
8 -2.059658512 -2.103491161
9 0.538680280 -2.059658512
10 0.996010221 0.538680280
11 -0.195778066 0.996010221
12 0.454799205 -0.195778066
13 0.502429577 0.454799205
14 -1.002615235 0.502429577
15 -0.496690760 -1.002615235
16 0.412162340 -0.496690760
17 3.577867081 0.412162340
18 2.468158708 3.577867081
19 0.401602040 2.468158708
20 0.605520846 0.401602040
21 0.786385822 0.605520846
22 2.507859621 0.786385822
23 0.839080777 2.507859621
24 0.851623609 0.839080777
25 0.778196929 0.851623609
26 1.091647210 0.778196929
27 -1.809108735 1.091647210
28 0.315176248 -1.809108735
29 -0.263029559 0.315176248
30 -0.765960661 -0.263029559
31 -0.849288952 -0.765960661
32 -0.803962023 -0.849288952
33 -0.001749614 -0.803962023
34 -1.490209881 -0.001749614
35 -3.030476921 -1.490209881
36 -3.035534565 -3.030476921
37 -1.747032491 -3.035534565
38 1.445444080 -1.747032491
39 1.505106075 1.445444080
40 1.279462294 1.505106075
41 -1.700951059 1.279462294
42 2.512878443 -1.700951059
43 -0.153377764 2.512878443
44 -0.462355329 -0.153377764
45 -4.490949562 -0.462355329
46 -2.630761274 -4.490949562
47 -0.146742166 -2.630761274
48 0.582123384 -0.146742166
49 -1.630247367 0.582123384
50 -1.025298496 -1.630247367
51 -0.141940881 -1.025298496
52 -3.017185579 -0.141940881
53 -0.036186091 -3.017185579
54 -2.343289800 -0.036186091
55 1.615021855 -2.343289800
56 0.145459891 1.615021855
57 0.470116771 0.145459891
58 -0.112393030 0.470116771
59 1.778939435 -0.112393030
60 0.440446592 1.778939435
61 0.359318244 0.440446592
62 -0.548816853 0.359318244
63 -0.710986002 -0.548816853
64 0.693678842 -0.710986002
65 0.945813967 0.693678842
66 1.666822920 0.945813967
67 3.604111813 1.666822920
68 -3.899708374 3.604111813
69 0.348421327 -3.899708374
70 -3.412380609 0.348421327
71 -0.924295106 -3.412380609
72 1.071941325 -0.924295106
73 0.859016376 1.071941325
74 0.767705125 0.859016376
75 3.508113010 0.767705125
76 -0.419099992 3.508113010
77 1.468801152 -0.419099992
78 -2.216577334 1.468801152
79 0.857859743 -2.216577334
80 0.341372549 0.857859743
81 0.236759169 0.341372549
82 -0.643413724 0.236759169
83 0.115767618 -0.643413724
84 1.839264528 0.115767618
85 -0.155569285 1.839264528
86 0.632511822 -0.155569285
87 1.127411151 0.632511822
88 0.480518610 1.127411151
89 -1.907972516 0.480518610
90 0.241505625 -1.907972516
91 0.194438081 0.241505625
92 -0.053106791 0.194438081
93 -2.029851850 -0.053106791
94 1.256386663 -2.029851850
95 0.211757119 1.256386663
96 2.322616035 0.211757119
97 0.226291624 2.322616035
98 -0.429781644 0.226291624
99 -0.992298111 -0.429781644
100 1.323375337 -0.992298111
101 2.545781557 1.323375337
102 0.760339627 2.545781557
103 1.040141556 0.760339627
104 -1.842204209 1.040141556
105 1.298270035 -1.842204209
106 0.298876541 1.298270035
107 1.641348531 0.298876541
108 -0.304342299 1.641348531
109 0.714319104 -0.304342299
110 0.007172851 0.714319104
111 1.943590631 0.007172851
112 -1.281178077 1.943590631
113 -2.579874261 -1.281178077
114 1.838527156 -2.579874261
115 -1.865732844 1.838527156
116 0.771112171 -1.865732844
117 -1.792915136 0.771112171
118 0.479615794 -1.792915136
119 -1.429221584 0.479615794
120 0.614576732 -1.429221584
121 -2.821209317 0.614576732
122 -0.854504464 -2.821209317
123 -0.843764797 -0.854504464
124 -0.784933914 -0.843764797
125 0.341278399 -0.784933914
126 1.509416094 0.341278399
127 0.951747888 1.509416094
128 -2.685865073 0.951747888
129 2.059728520 -2.685865073
130 -3.723953244 2.059728520
131 2.541667846 -3.723953244
132 -2.165989022 2.541667846
133 -1.544046009 -2.165989022
134 -0.083656696 -1.544046009
135 0.887887265 -0.083656696
136 0.468722193 0.887887265
137 -2.498950057 0.468722193
138 -1.006480416 -2.498950057
139 -2.339956434 -1.006480416
140 3.143500085 -2.339956434
141 1.355875574 3.143500085
142 0.158636042 1.355875574
143 1.875596153 0.158636042
144 -3.266751430 1.875596153
145 2.502454533 -3.266751430
146 -1.945751239 2.502454533
147 1.156989895 -1.945751239
148 0.172772330 1.156989895
149 -2.900457717 0.172772330
150 -0.878418291 -2.900457717
151 2.175136908 -0.878418291
152 4.147418985 2.175136908
153 1.603697482 4.147418985
154 -2.481628296 1.603697482
155 0.560693436 -2.481628296
156 1.293314145 0.560693436
157 1.099065340 1.293314145
158 1.462603867 1.099065340
159 -0.698903764 1.462603867
160 0.306895079 -0.698903764
161 0.329795885 0.306895079
162 -0.381357631 0.329795885
163 0.786901982 -0.381357631
164 1.331779722 0.786901982
165 -1.619761147 1.331779722
166 -0.344192471 -1.619761147
167 -3.194346992 -0.344192471
168 -1.789596334 -3.194346992
169 1.612807316 -1.789596334
170 1.518467982 1.612807316
171 0.662211895 1.518467982
172 -1.844438422 0.662211895
173 -2.333612434 -1.844438422
174 -2.907041435 -2.333612434
175 0.526992426 -2.907041435
176 -0.294327621 0.526992426
177 -0.650368444 -0.294327621
178 0.206131805 -0.650368444
179 -1.288904893 0.206131805
180 0.993126239 -1.288904893
181 -0.451505878 0.993126239
182 2.298423231 -0.451505878
183 -0.110981881 2.298423231
184 -6.596986586 -0.110981881
185 1.301064443 -6.596986586
186 2.610419292 1.301064443
187 -0.348622911 2.610419292
188 -0.412069714 -0.348622911
189 0.545031937 -0.412069714
190 -0.436490890 0.545031937
191 0.623572202 -0.436490890
192 2.277518581 0.623572202
193 1.911359536 2.277518581
194 -0.632884513 1.911359536
195 0.001024589 -0.632884513
196 3.087373988 0.001024589
197 1.008056355 3.087373988
198 1.592366903 1.008056355
199 1.539638452 1.592366903
200 1.966679997 1.539638452
201 0.704538164 1.966679997
202 -2.312960385 0.704538164
203 -2.494922264 -2.312960385
204 2.322123701 -2.494922264
205 0.581163430 2.322123701
206 1.467985216 0.581163430
207 1.156874554 1.467985216
208 -2.580145319 1.156874554
209 1.111446590 -2.580145319
210 -2.203785389 1.111446590
211 -3.722425549 -2.203785389
212 0.890920641 -3.722425549
213 2.909421188 0.890920641
214 1.535256743 2.909421188
215 0.919486133 1.535256743
216 2.425128675 0.919486133
217 0.095076893 2.425128675
218 1.167894530 0.095076893
219 -0.129909691 1.167894530
220 -1.572687684 -0.129909691
221 0.107851037 -1.572687684
222 0.800012311 0.107851037
223 -1.055453201 0.800012311
224 0.731082508 -1.055453201
225 -3.657369597 0.731082508
226 0.316720867 -3.657369597
227 1.185710905 0.316720867
228 -1.197890874 1.185710905
229 -0.830389846 -1.197890874
230 1.814511262 -0.830389846
231 -3.218905829 1.814511262
232 4.651212489 -3.218905829
233 2.226248327 4.651212489
234 -0.726660655 2.226248327
235 -2.221561835 -0.726660655
236 -6.993819348 -2.221561835
237 -1.179681722 -6.993819348
238 1.863256037 -1.179681722
239 -1.593061865 1.863256037
240 -0.162645268 -1.593061865
241 -1.396015419 -0.162645268
242 1.189632700 -1.396015419
243 2.020251001 1.189632700
244 1.419790411 2.020251001
245 0.192867082 1.419790411
246 1.254166697 0.192867082
247 -2.383032043 1.254166697
248 1.295437316 -2.383032043
249 0.477752654 1.295437316
250 -0.720829792 0.477752654
251 -1.520704011 -0.720829792
252 0.726255791 -1.520704011
253 3.292503160 0.726255791
254 -1.207855665 3.292503160
255 0.280291587 -1.207855665
256 1.861115030 0.280291587
257 2.446320148 1.861115030
258 -1.054943238 2.446320148
259 -5.333093544 -1.054943238
260 1.446898172 -5.333093544
261 -3.774119819 1.446898172
262 -0.166228177 -3.774119819
263 1.368916629 -0.166228177
> 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/fisher/rcomp/tmp/7bd0r1353428416.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/fisher/rcomp/tmp/8pt2n1353428416.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/fisher/rcomp/tmp/93qxl1353428416.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/fisher/rcomp/tmp/10b5zr1353428416.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11o4sy1353428416.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/128dyc1353428417.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13zu3t1353428417.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14xg4i1353428417.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15ixa81353428417.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/166vjc1353428417.tab")
+ }
>
> try(system("convert tmp/14ygs1353428416.ps tmp/14ygs1353428416.png",intern=TRUE))
character(0)
> try(system("convert tmp/2yxqy1353428416.ps tmp/2yxqy1353428416.png",intern=TRUE))
character(0)
> try(system("convert tmp/3pzf11353428416.ps tmp/3pzf11353428416.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ojou1353428416.ps tmp/4ojou1353428416.png",intern=TRUE))
character(0)
> try(system("convert tmp/57tcs1353428416.ps tmp/57tcs1353428416.png",intern=TRUE))
character(0)
> try(system("convert tmp/60und1353428416.ps tmp/60und1353428416.png",intern=TRUE))
character(0)
> try(system("convert tmp/7bd0r1353428416.ps tmp/7bd0r1353428416.png",intern=TRUE))
character(0)
> try(system("convert tmp/8pt2n1353428416.ps tmp/8pt2n1353428416.png",intern=TRUE))
character(0)
> try(system("convert tmp/93qxl1353428416.ps tmp/93qxl1353428416.png",intern=TRUE))
character(0)
> try(system("convert tmp/10b5zr1353428416.ps tmp/10b5zr1353428416.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.568 1.392 12.956