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(41
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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
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+ ,4
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+ ,83
+ ,27
+ ,30
+ ,15
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+ ,14
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+ ,64
+ ,34
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+ ,11
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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])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '5'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Happiness Connected Separate Learning Software Depression Belonging t
1 14 41 38 13 12 12.0 53 1
2 18 39 32 16 11 11.0 83 2
3 11 30 35 19 15 14.0 66 3
4 12 31 33 15 6 12.0 67 4
5 16 34 37 14 13 21.0 76 5
6 18 35 29 13 10 12.0 78 6
7 14 39 31 19 12 22.0 53 7
8 14 34 36 15 14 11.0 80 8
9 15 36 35 14 12 10.0 74 9
10 15 37 38 15 9 13.0 76 10
11 17 38 31 16 10 10.0 79 11
12 19 36 34 16 12 8.0 54 12
13 10 38 35 16 12 15.0 67 13
14 16 39 38 16 11 14.0 54 14
15 18 33 37 17 15 10.0 87 15
16 14 32 33 15 12 14.0 58 16
17 14 36 32 15 10 14.0 75 17
18 17 38 38 20 12 11.0 88 18
19 14 39 38 18 11 10.0 64 19
20 16 32 32 16 12 13.0 57 20
21 18 32 33 16 11 9.5 66 21
22 11 31 31 16 12 14.0 68 22
23 14 39 38 19 13 12.0 54 23
24 12 37 39 16 11 14.0 56 24
25 17 39 32 17 12 11.0 86 25
26 9 41 32 17 13 9.0 80 26
27 16 36 35 16 10 11.0 76 27
28 14 33 37 15 14 15.0 69 28
29 15 33 33 16 12 14.0 78 29
30 11 34 33 14 10 13.0 67 30
31 16 31 31 15 12 9.0 80 31
32 13 27 32 12 8 15.0 54 32
33 17 37 31 14 10 10.0 71 33
34 15 34 37 16 12 11.0 84 34
35 14 34 30 14 12 13.0 74 35
36 16 32 33 10 7 8.0 71 36
37 9 29 31 10 9 20.0 63 37
38 15 36 33 14 12 12.0 71 38
39 17 29 31 16 10 10.0 76 39
40 13 35 33 16 10 10.0 69 40
41 15 37 32 16 10 9.0 74 41
42 16 34 33 14 12 14.0 75 42
43 16 38 32 20 15 8.0 54 43
44 12 35 33 14 10 14.0 52 44
45 15 38 28 14 10 11.0 69 45
46 11 37 35 11 12 13.0 68 46
47 15 38 39 14 13 9.0 65 47
48 15 33 34 15 11 11.0 75 48
49 17 36 38 16 11 15.0 74 49
50 13 38 32 14 12 11.0 75 50
51 16 32 38 16 14 10.0 72 51
52 14 32 30 14 10 14.0 67 52
53 11 32 33 12 12 18.0 63 53
54 12 34 38 16 13 14.0 62 54
55 12 32 32 9 5 11.0 63 55
56 15 37 35 14 6 14.5 76 56
57 16 39 34 16 12 13.0 74 57
58 15 29 34 16 12 9.0 67 58
59 12 37 36 15 11 10.0 73 59
60 12 35 34 16 10 15.0 70 60
61 8 30 28 12 7 20.0 53 61
62 13 38 34 16 12 12.0 77 62
63 11 34 35 16 14 12.0 80 63
64 14 31 35 14 11 14.0 52 64
65 15 34 31 16 12 13.0 54 65
66 10 35 37 17 13 11.0 80 66
67 11 36 35 18 14 17.0 66 67
68 12 30 27 18 11 12.0 73 68
69 15 39 40 12 12 13.0 63 69
70 15 35 37 16 12 14.0 69 70
71 14 38 36 10 8 13.0 67 71
72 16 31 38 14 11 15.0 54 72
73 15 34 39 18 14 13.0 81 73
74 15 38 41 18 14 10.0 69 74
75 13 34 27 16 12 11.0 84 75
76 12 39 30 17 9 19.0 80 76
77 17 37 37 16 13 13.0 70 77
78 13 34 31 16 11 17.0 69 78
79 15 28 31 13 12 13.0 77 79
80 13 37 27 16 12 9.0 54 80
81 15 33 36 16 12 11.0 79 81
82 15 35 37 16 12 9.0 71 82
83 16 37 33 15 12 12.0 73 83
84 15 32 34 15 11 12.0 72 84
85 14 33 31 16 10 13.0 77 85
86 15 38 39 14 9 13.0 75 86
87 14 33 34 16 12 12.0 69 87
88 13 29 32 16 12 15.0 54 88
89 7 33 33 15 12 22.0 70 89
90 17 31 36 12 9 13.0 73 90
91 13 36 32 17 15 15.0 54 91
92 15 35 41 16 12 13.0 77 92
93 14 32 28 15 12 15.0 82 93
94 13 29 30 13 12 12.5 80 94
95 16 39 36 16 10 11.0 80 95
96 12 37 35 16 13 16.0 69 96
97 14 35 31 16 9 11.0 78 97
98 17 37 34 16 12 11.0 81 98
99 15 32 36 14 10 10.0 76 99
100 17 38 36 16 14 10.0 76 100
101 12 37 35 16 11 16.0 73 101
102 16 36 37 20 15 12.0 85 102
103 11 32 28 15 11 11.0 66 103
104 15 33 39 16 11 16.0 79 104
105 9 40 32 13 12 19.0 68 105
106 16 38 35 17 12 11.0 76 106
107 15 41 39 16 12 16.0 71 107
108 10 36 35 16 11 15.0 54 108
109 10 43 42 12 7 24.0 46 109
110 15 30 34 16 12 14.0 85 110
111 11 31 33 16 14 15.0 74 111
112 13 32 41 17 11 11.0 88 112
113 14 32 33 13 11 15.0 38 113
114 18 37 34 12 10 12.0 76 114
115 16 37 32 18 13 10.0 86 115
116 14 33 40 14 13 14.0 54 116
117 14 34 40 14 8 13.0 67 117
118 14 33 35 13 11 9.0 69 118
119 14 38 36 16 12 15.0 90 119
120 12 33 37 13 11 15.0 54 120
121 14 31 27 16 13 14.0 76 121
122 15 38 39 13 12 11.0 89 122
123 15 37 38 16 14 8.0 76 123
124 15 36 31 15 13 11.0 73 124
125 13 31 33 16 15 11.0 79 125
126 17 39 32 15 10 8.0 90 126
127 17 44 39 17 11 10.0 74 127
128 19 33 36 15 9 11.0 81 128
129 15 35 33 12 11 13.0 72 129
130 13 32 33 16 10 11.0 71 130
131 9 28 32 10 11 20.0 66 131
132 15 40 37 16 8 10.0 77 132
133 15 27 30 12 11 15.0 65 133
134 15 37 38 14 12 12.0 74 134
135 16 32 29 15 12 14.0 85 135
136 11 28 22 13 9 23.0 54 136
137 14 34 35 15 11 14.0 63 137
138 11 30 35 11 10 16.0 54 138
139 15 35 34 12 8 11.0 64 139
140 13 31 35 11 9 12.0 69 140
141 15 32 34 16 8 10.0 54 141
142 16 30 37 15 9 14.0 84 142
143 14 30 35 17 15 12.0 86 143
144 15 31 23 16 11 12.0 77 144
145 16 40 31 10 8 11.0 89 145
146 16 32 27 18 13 12.0 76 146
147 11 36 36 13 12 13.0 60 147
148 12 32 31 16 12 11.0 75 148
149 9 35 32 13 9 19.0 73 149
150 16 38 39 10 7 12.0 85 150
151 13 42 37 15 13 17.0 79 151
152 16 34 38 16 9 9.0 71 152
153 12 35 39 16 6 12.0 72 153
154 9 38 34 14 8 19.0 69 154
155 13 33 31 10 8 18.0 78 155
156 13 36 32 17 15 15.0 54 156
157 14 32 37 13 6 14.0 69 157
158 19 33 36 15 9 11.0 81 158
159 13 34 32 16 11 9.0 84 159
160 12 32 38 12 8 18.0 84 160
161 13 34 36 13 8 16.0 69 161
162 10 27 26 13 10 24.0 66 162
163 14 31 26 12 8 14.0 81 163
164 16 38 33 17 14 20.0 82 164
165 10 34 39 15 10 18.0 72 165
166 11 24 30 10 8 23.0 54 166
167 14 30 33 14 11 12.0 78 167
168 12 26 25 11 12 14.0 74 168
169 9 34 38 13 12 16.0 82 169
170 9 27 37 16 12 18.0 73 170
171 11 37 31 12 5 20.0 55 171
172 16 36 37 16 12 12.0 72 172
173 9 41 35 12 10 12.0 78 173
174 13 29 25 9 7 17.0 59 174
175 16 36 28 12 12 13.0 72 175
176 13 32 35 15 11 9.0 78 176
177 9 37 33 12 8 16.0 68 177
178 12 30 30 12 9 18.0 69 178
179 16 31 31 14 10 10.0 67 179
180 11 38 37 12 9 14.0 74 180
181 14 36 36 16 12 11.0 54 181
182 13 35 30 11 6 9.0 67 182
183 15 31 36 19 15 11.0 70 183
184 14 38 32 15 12 10.0 80 184
185 16 22 28 8 12 11.0 89 185
186 13 32 36 16 12 19.0 76 186
187 14 36 34 17 11 14.0 74 187
188 15 39 31 12 7 12.0 87 188
189 13 28 28 11 7 14.0 54 189
190 11 32 36 11 5 21.0 61 190
191 11 32 36 14 12 13.0 38 191
192 14 38 40 16 12 10.0 75 192
193 15 32 33 12 3 15.0 69 193
194 11 35 37 16 11 16.0 62 194
195 15 32 32 13 10 14.0 72 195
196 12 37 38 15 12 12.0 70 196
197 14 34 31 16 9 19.0 79 197
198 14 33 37 16 12 15.0 87 198
199 8 33 33 14 9 19.0 62 199
200 13 26 32 16 12 13.0 77 200
201 9 30 30 16 12 17.0 69 201
202 15 24 30 14 10 12.0 69 202
203 17 34 31 11 9 11.0 75 203
204 13 34 32 12 12 14.0 54 204
205 15 33 34 15 8 11.0 72 205
206 15 34 36 15 11 13.0 74 206
207 14 35 37 16 11 12.0 85 207
208 16 35 36 16 12 15.0 52 208
209 13 36 33 11 10 14.0 70 209
210 16 34 33 15 10 12.0 84 210
211 9 34 33 12 12 17.0 64 211
212 16 41 44 12 12 11.0 84 212
213 11 32 39 15 11 18.0 87 213
214 10 30 32 15 8 13.0 79 214
215 11 35 35 16 12 17.0 67 215
216 15 28 25 14 10 13.0 65 216
217 17 33 35 17 11 11.0 85 217
218 14 39 34 14 10 12.0 83 218
219 8 36 35 13 8 22.0 61 219
220 15 36 39 15 12 14.0 82 220
221 11 35 33 13 12 12.0 76 221
222 16 38 36 14 10 12.0 58 222
223 10 33 32 15 12 17.0 72 223
224 15 31 32 12 9 9.0 72 224
225 9 34 36 13 9 21.0 38 225
226 16 32 36 8 6 10.0 78 226
227 19 31 32 14 10 11.0 54 227
228 12 33 34 14 9 12.0 63 228
229 8 34 33 11 9 23.0 66 229
230 11 34 35 12 9 13.0 70 230
231 14 34 30 13 6 12.0 71 231
232 9 33 38 10 10 16.0 67 232
233 15 32 34 16 6 9.0 58 233
234 13 41 33 18 14 17.0 72 234
235 16 34 32 13 10 9.0 72 235
236 11 36 31 11 10 14.0 70 236
237 12 37 30 4 6 17.0 76 237
238 13 36 27 13 12 13.0 50 238
239 10 29 31 16 12 11.0 72 239
240 11 37 30 10 7 12.0 72 240
241 12 27 32 12 8 10.0 88 241
242 8 35 35 12 11 19.0 53 242
243 12 28 28 10 3 16.0 58 243
244 12 35 33 13 6 16.0 66 244
245 15 37 31 15 10 14.0 82 245
246 11 29 35 12 8 20.0 69 246
247 13 32 35 14 9 15.0 68 247
248 14 36 32 10 9 23.0 44 248
249 10 19 21 12 8 20.0 56 249
250 12 21 20 12 9 16.0 53 250
251 15 31 34 11 7 14.0 70 251
252 13 33 32 10 7 17.0 78 252
253 13 36 34 12 6 11.0 71 253
254 13 33 32 16 9 13.0 72 254
255 12 37 33 12 10 17.0 68 255
256 12 34 33 14 11 15.0 67 256
257 9 35 37 16 12 21.0 75 257
258 9 31 32 14 8 18.0 62 258
259 15 37 34 13 11 15.0 67 259
260 10 35 30 4 3 8.0 83 260
261 14 27 30 15 11 12.0 64 261
262 15 34 38 11 12 12.0 68 262
263 7 40 36 11 7 22.0 62 263
264 14 29 32 14 9 12.0 72 264
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Software Depression
15.844832 0.002969 0.012037 0.081622 -0.034113 -0.360392
Belonging t
0.026126 -0.004372
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.0287 -1.5008 0.3255 1.3270 5.4224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.844832 1.881681 8.421 2.7e-15 ***
Connected 0.002969 0.037173 0.080 0.9364
Separate 0.012037 0.037916 0.317 0.7512
Learning 0.081622 0.067184 1.215 0.2255
Software -0.034113 0.069036 -0.494 0.6216
Depression -0.360392 0.039034 -9.233 < 2e-16 ***
Belonging 0.026126 0.012700 2.057 0.0407 *
t -0.004372 0.001821 -2.401 0.0171 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.999 on 256 degrees of freedom
Multiple R-squared: 0.377, Adjusted R-squared: 0.36
F-statistic: 22.13 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,] 0.04250684 0.085013688 0.957493156
[2,] 0.79470084 0.410598324 0.205299162
[3,] 0.98241287 0.035174250 0.017587125
[4,] 0.98072028 0.038559436 0.019279718
[5,] 0.98119498 0.037610048 0.018805024
[6,] 0.96972521 0.060549588 0.030274794
[7,] 0.96554938 0.068901234 0.034450617
[8,] 0.94924744 0.101505121 0.050752560
[9,] 0.93375586 0.132488276 0.066244138
[10,] 0.91835834 0.163283328 0.081641664
[11,] 0.91589899 0.168202018 0.084101009
[12,] 0.96482112 0.070357757 0.035178879
[13,] 0.95050543 0.098989133 0.049494567
[14,] 0.93888648 0.122227036 0.061113518
[15,] 0.91880496 0.162390078 0.081195039
[16,] 0.99850602 0.002987966 0.001493983
[17,] 0.99794541 0.004109171 0.002054585
[18,] 0.99681827 0.006363461 0.003181730
[19,] 0.99535314 0.009293711 0.004646856
[20,] 0.99630233 0.007395340 0.003697670
[21,] 0.99477665 0.010446692 0.005223346
[22,] 0.99228876 0.015422471 0.007711236
[23,] 0.99219220 0.015615608 0.007807804
[24,] 0.98875817 0.022483669 0.011241835
[25,] 0.98414458 0.031710837 0.015855419
[26,] 0.97833582 0.043328367 0.021664184
[27,] 0.98060091 0.038798173 0.019399086
[28,] 0.97511623 0.049767537 0.024883769
[29,] 0.97470707 0.050585854 0.025292927
[30,] 0.97136755 0.057264902 0.028632451
[31,] 0.96222741 0.075545181 0.037772590
[32,] 0.96496248 0.070075035 0.035037518
[33,] 0.95700584 0.085988314 0.042994157
[34,] 0.94628149 0.107437025 0.053718513
[35,] 0.93215802 0.135683960 0.067841980
[36,] 0.93458955 0.130820903 0.065410452
[37,] 0.92110899 0.157782011 0.078891005
[38,] 0.90411163 0.191776737 0.095888369
[39,] 0.93870630 0.122587398 0.061293699
[40,] 0.93163698 0.136726050 0.068363025
[41,] 0.91980300 0.160394009 0.080197004
[42,] 0.90291522 0.194169561 0.097084780
[43,] 0.88450013 0.230999743 0.115499871
[44,] 0.86965694 0.260686129 0.130343065
[45,] 0.85870662 0.282586760 0.141293380
[46,] 0.84424898 0.311502040 0.155751020
[47,] 0.83881568 0.322368634 0.161184317
[48,] 0.81101283 0.377974341 0.188987171
[49,] 0.83545139 0.329097217 0.164548609
[50,] 0.81894176 0.362116472 0.181058236
[51,] 0.83027813 0.339443747 0.169721873
[52,] 0.81295621 0.374087585 0.187043792
[53,] 0.85055958 0.298880846 0.149440423
[54,] 0.84773630 0.304527405 0.152263703
[55,] 0.84981309 0.300373822 0.150186911
[56,] 0.92284701 0.154305986 0.077152993
[57,] 0.91187976 0.176240480 0.088120240
[58,] 0.91177740 0.176445207 0.088222604
[59,] 0.91514099 0.169718022 0.084859011
[60,] 0.91508347 0.169833057 0.084916529
[61,] 0.90225306 0.195493875 0.097746937
[62,] 0.93321766 0.133564676 0.066782338
[63,] 0.92270295 0.154594107 0.077297054
[64,] 0.90777435 0.184451292 0.092225646
[65,] 0.89909666 0.201806689 0.100903345
[66,] 0.88160201 0.236795979 0.118397989
[67,] 0.90977070 0.180458595 0.090229298
[68,] 0.89593088 0.208138249 0.104069124
[69,] 0.88998357 0.220032866 0.110016433
[70,] 0.88201290 0.235974201 0.117987100
[71,] 0.86306696 0.273866078 0.136933039
[72,] 0.84218895 0.315622099 0.157811049
[73,] 0.84231754 0.315364910 0.157682455
[74,] 0.82410024 0.351799524 0.175899762
[75,] 0.79988114 0.400237711 0.200118855
[76,] 0.77649118 0.447017634 0.223508817
[77,] 0.74800168 0.503996646 0.251998323
[78,] 0.71897145 0.562057100 0.281028550
[79,] 0.78723327 0.425533456 0.212766728
[80,] 0.82559178 0.348816431 0.174408215
[81,] 0.80405796 0.391884081 0.195942040
[82,] 0.77948368 0.441032633 0.220516316
[83,] 0.75952229 0.480955425 0.240477713
[84,] 0.73590815 0.528183697 0.264091849
[85,] 0.71248615 0.575027704 0.287513852
[86,] 0.68526939 0.629461224 0.314730612
[87,] 0.65865358 0.682692830 0.341346415
[88,] 0.66388821 0.672223586 0.336111793
[89,] 0.62948286 0.741034288 0.370517144
[90,] 0.62484594 0.750308122 0.375154061
[91,] 0.59911878 0.801762450 0.400881225
[92,] 0.57169290 0.856614204 0.428307102
[93,] 0.62479033 0.750419346 0.375209673
[94,] 0.61154672 0.776906561 0.388453281
[95,] 0.62831245 0.743375109 0.371687555
[96,] 0.60436185 0.791276294 0.395638147
[97,] 0.59704234 0.805915319 0.402957659
[98,] 0.63233692 0.735326164 0.367663082
[99,] 0.59904374 0.801912527 0.400956263
[100,] 0.57306018 0.853879645 0.426939822
[101,] 0.57762785 0.844744304 0.422372152
[102,] 0.60167903 0.796641943 0.398320971
[103,] 0.60830382 0.783392368 0.391696184
[104,] 0.69416417 0.611671653 0.305835826
[105,] 0.66627609 0.667447819 0.333723909
[106,] 0.64008738 0.719825234 0.359912617
[107,] 0.60641062 0.787178752 0.393589376
[108,] 0.58205746 0.835885077 0.417942539
[109,] 0.54654809 0.906903820 0.453451910
[110,] 0.51360901 0.972781988 0.486390994
[111,] 0.48794354 0.975887081 0.512056459
[112,] 0.45320256 0.906405119 0.546797440
[113,] 0.42467004 0.849340078 0.575329961
[114,] 0.39446365 0.788927307 0.605536346
[115,] 0.38493013 0.769860264 0.615069868
[116,] 0.35696921 0.713938418 0.643030791
[117,] 0.34192469 0.683849374 0.658075313
[118,] 0.44541188 0.890823770 0.554588115
[119,] 0.42565972 0.851319433 0.574340284
[120,] 0.41500139 0.830002780 0.584998610
[121,] 0.40412859 0.808257175 0.595871413
[122,] 0.37237572 0.744751447 0.627624277
[123,] 0.39214001 0.784280022 0.607859989
[124,] 0.36238199 0.724763987 0.637618007
[125,] 0.37261944 0.745238874 0.627380563
[126,] 0.36207117 0.724142349 0.637928825
[127,] 0.33262305 0.665246094 0.667376953
[128,] 0.30896523 0.617930452 0.691034774
[129,] 0.28166476 0.563329513 0.718335243
[130,] 0.25819345 0.516386896 0.741806552
[131,] 0.23105260 0.462105207 0.768947396
[132,] 0.23372246 0.467444917 0.766277542
[133,] 0.20886141 0.417722818 0.791138591
[134,] 0.18899981 0.377999626 0.811000187
[135,] 0.17381117 0.347622342 0.826188829
[136,] 0.16579210 0.331584200 0.834207900
[137,] 0.17474749 0.349494987 0.825252506
[138,] 0.19319746 0.386394924 0.806802538
[139,] 0.21305272 0.426105436 0.786947282
[140,] 0.20834294 0.416685887 0.791657057
[141,] 0.18488037 0.369760731 0.815119635
[142,] 0.16393571 0.327871427 0.836064287
[143,] 0.17571160 0.351423194 0.824288403
[144,] 0.19303455 0.386069104 0.806965448
[145,] 0.17704300 0.354085994 0.822957003
[146,] 0.15651683 0.313033669 0.843483165
[147,] 0.13816292 0.276325845 0.861837078
[148,] 0.22071326 0.441426520 0.779286740
[149,] 0.23922208 0.478444158 0.760777921
[150,] 0.21568418 0.431368366 0.784315817
[151,] 0.19252913 0.385058267 0.807470867
[152,] 0.17037820 0.340756410 0.829621795
[153,] 0.14957339 0.299146771 0.850426614
[154,] 0.24922837 0.498456745 0.750771627
[155,] 0.24462846 0.489256916 0.755371542
[156,] 0.24087390 0.481747794 0.759126103
[157,] 0.21310932 0.426218631 0.786890684
[158,] 0.19234409 0.384688186 0.807655907
[159,] 0.24677030 0.493540597 0.753229702
[160,] 0.27630074 0.552601483 0.723699259
[161,] 0.24718142 0.494362848 0.752818576
[162,] 0.24320536 0.486410721 0.756794640
[163,] 0.41068913 0.821378266 0.589310867
[164,] 0.39222023 0.784440453 0.607779774
[165,] 0.41277023 0.825540465 0.587229767
[166,] 0.42467391 0.849347829 0.575326085
[167,] 0.48824194 0.976483882 0.511758059
[168,] 0.45135167 0.902703330 0.548648335
[169,] 0.42910663 0.858213260 0.570893370
[170,] 0.43663479 0.873269579 0.563365210
[171,] 0.40125244 0.802504878 0.598747561
[172,] 0.40530157 0.810603135 0.594698433
[173,] 0.36924877 0.738497536 0.630751232
[174,] 0.34722266 0.694445324 0.652777338
[175,] 0.34800924 0.696018484 0.651990758
[176,] 0.33325613 0.666512253 0.666743873
[177,] 0.29886435 0.597728709 0.701135645
[178,] 0.26771747 0.535434944 0.732282528
[179,] 0.23684557 0.473691145 0.763154428
[180,] 0.21216901 0.424338019 0.787830991
[181,] 0.22496493 0.449929856 0.775035072
[182,] 0.20807725 0.416154501 0.791922749
[183,] 0.21443690 0.428873804 0.785563098
[184,] 0.20542601 0.410852021 0.794573990
[185,] 0.20195309 0.403906174 0.798046913
[186,] 0.21468750 0.429374996 0.785312502
[187,] 0.25349816 0.506996324 0.746501838
[188,] 0.23468265 0.469365308 0.765317346
[189,] 0.27076549 0.541530978 0.729234511
[190,] 0.23901547 0.478030932 0.760984534
[191,] 0.28002796 0.560055912 0.719972044
[192,] 0.25465351 0.509307020 0.745346490
[193,] 0.29212434 0.584248689 0.707875656
[194,] 0.25996925 0.519938506 0.740030747
[195,] 0.22745739 0.454914772 0.772542614
[196,] 0.20739744 0.414794889 0.792602555
[197,] 0.17743623 0.354872451 0.822563775
[198,] 0.20302779 0.406055570 0.796972215
[199,] 0.17329675 0.346593510 0.826703245
[200,] 0.18287996 0.365759928 0.817120036
[201,] 0.20237114 0.404742285 0.797628858
[202,] 0.19313818 0.386276361 0.806861819
[203,] 0.16770281 0.335405623 0.832297189
[204,] 0.20749715 0.414994304 0.792502848
[205,] 0.18442829 0.368856587 0.815571707
[206,] 0.17317035 0.346340692 0.826829654
[207,] 0.20351330 0.407026595 0.796486703
[208,] 0.17563491 0.351269818 0.824365091
[209,] 0.16220895 0.324417894 0.837791053
[210,] 0.17958551 0.359171021 0.820414490
[211,] 0.18403628 0.368072556 0.815963722
[212,] 0.18174671 0.363493413 0.818253293
[213,] 0.16519740 0.330394794 0.834802603
[214,] 0.13712234 0.274244687 0.862877656
[215,] 0.13454836 0.269096722 0.865451639
[216,] 0.16534663 0.330693258 0.834653371
[217,] 0.39366990 0.787339798 0.606330101
[218,] 0.35206973 0.704139462 0.647930269
[219,] 0.31315072 0.626301440 0.686849280
[220,] 0.28728399 0.574567982 0.712716009
[221,] 0.25107315 0.502146306 0.748926847
[222,] 0.28032045 0.560640897 0.719679551
[223,] 0.23743780 0.474875597 0.762562201
[224,] 0.19922122 0.398442449 0.800778776
[225,] 0.20631517 0.412630341 0.793684830
[226,] 0.17808352 0.356167034 0.821916483
[227,] 0.15370596 0.307411923 0.846294039
[228,] 0.11845595 0.236911908 0.881544046
[229,] 0.22705721 0.454114426 0.772942787
[230,] 0.23702893 0.474057854 0.762971073
[231,] 0.24069177 0.481383545 0.759308228
[232,] 0.85848587 0.283028266 0.141514133
[233,] 0.80880261 0.382394774 0.191197387
[234,] 0.75498620 0.490027591 0.245013796
[235,] 0.70143716 0.597125671 0.298562835
[236,] 0.63253937 0.734921258 0.367460629
[237,] 0.64681654 0.706366923 0.353183462
[238,] 0.65039021 0.699219587 0.349609794
[239,] 0.54494112 0.910117765 0.455058883
[240,] 0.45956411 0.919128225 0.540435887
[241,] 0.39993887 0.799877736 0.600061132
[242,] 0.90559464 0.188810726 0.094405363
[243,] 0.81758706 0.364825876 0.182412938
> postscript(file="/var/fisher/rcomp/tmp/1pzpt1384681942.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/2hae41384681942.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3ms5b1384681942.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/49h6a1384681942.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5qfr51384681942.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 264
Frequency = 1
1 2 3 4 5 6
-0.13129622 2.52809378 -3.06002248 -2.76198338 4.51414435 3.29534576
7 8 9 10 11 12
3.09931925 -1.21663381 -0.39640532 0.41385011 1.29244833 3.26723256
13 14 15 16 17 18
-3.56326038 2.34716048 2.13250153 0.44810812 -0.05972274 1.10579419
19 20 21 22 23 24
-1.49704740 2.06174535 2.52346494 -2.84149398 -0.51091400 -1.66746571
25 26 27 28 29 30
1.50277046 -7.02871310 0.75896580 0.59069506 0.89784314 -3.07874501
31 32 33 34 35 36
0.16401146 0.11825872 1.76385864 -0.36934189 -0.13542648 0.27111579
37 38 39 40 41 42
-2.08959474 0.55362611 1.51997477 -2.33466359 -0.81521360 2.19333557
43 44 45 46 47 48
0.19675813 -1.26822538 0.26211060 -2.75480216 -0.37549186 0.01359052
49 50 51 52 53 54
3.34697842 -1.85270289 0.72023135 0.41988701 -0.83430948 -1.60387919
55 56 57 58 59 60
-2.33019607 1.17095784 1.73452489 -0.49009683 -3.28240616 -1.48342000
61 62 63 64 65 66
-2.92173295 -1.67941309 -3.68535067 0.84113792 1.34297401 -5.17540433
67 68 69 70 71 72
-1.66932617 -2.63802149 1.32864374 1.25815243 0.31079507 3.14814662
73 74 75 76 77 78
0.48124655 -0.31800064 -2.06970926 -0.31261772 2.93041513 0.41538407
79 80 81 82 83 84
1.06598028 -1.99377043 -0.01820902 -0.54359115 1.61353665 0.61273191
85 86 87 88 89 90
-0.23572660 0.83888721 -0.34625305 0.16713138 -3.66605492 3.12876915
91 92 93 94 95 96
0.18017899 0.73679870 0.57833603 -1.11794071 0.93083485 -0.85513582
97 98 99 100 101 102
-0.97022050 2.01606475 -0.12353432 1.83622872 -1.00600305 1.03215170
103 104 105 106 107 108
-3.43561149 1.81409045 -2.47052700 1.08504275 2.04657018 -2.83643206
109 110 111 112 113 114
0.70546723 1.06599182 -2.20456819 -2.29074857 1.88425868 3.83530562
115 116 117 118 119 120
0.49431545 1.01834973 0.14916177 -1.09317002 0.28727719 -0.55426231
121 122 123 124 125 126
0.46461937 0.09370877 -0.80509641 0.49356532 -1.68143906 0.85371354
127 128 129 130 131 132
1.76864356 4.11432130 1.41787355 -1.62410709 -1.69781620 -0.27263725
133 134 135 136 137 138
2.39889224 0.83183700 2.31116711 1.52600369 0.78240328 -0.95305502
139 140 141 142 143 144
0.83544183 -0.81484567 0.42746943 2.17520408 -0.52795179 0.79819387
145 146 147 148 149 150
1.39304253 1.68692867 -2.27650637 -2.55760844 -2.49626585 1.75532998
151 152 153 154 155 156
0.52717811 0.65106366 -2.40685918 -2.51861917 1.26767774 0.46437886
157 158 159 160 161 162
0.68764031 4.24549047 -2.51751668 -0.11174781 0.50023759 0.67550306
163 164 165 166 167 168
0.68558927 4.51771022 -1.97099539 1.78351045 -0.08152249 -0.86471090
169 170 171 172 173 174
-3.69203879 -2.94379617 0.38184674 1.90199724 -4.98289421 1.61835497
175 176 177 178 179 180
2.71032730 -2.23498266 -3.29485528 0.49518490 1.52453450 -2.17627986
181 182 183 184 185 186
0.06325456 -1.71416639 0.52630772 -0.83945665 1.95719225 1.40536538
187 188 189 190 191 192
0.55648939 0.79930640 0.53700592 0.70484421 -1.57910529 -0.85176689
193 194 195 196 197 198
2.23286580 -1.33013119 1.97204730 -1.87419990 2.32698976 0.71387667
199 200 201 202 203 204
-3.07798879 -0.65593607 -2.98879471 1.32645306 2.98270262 0.62556953
205 206 207 208 209 210
0.67608026 1.42428139 -0.31574919 3.67809551 0.22484126 1.82211946
211 212 213 214 215 216
-2.53594170 1.63037465 -1.11295757 -3.71368265 -0.95036235 1.90086625
217 218 219 220 221 222
2.31597191 -0.06203797 -1.86871359 1.62894420 -2.69227832 2.58748839
223 224 225 226 227 228
-1.92234040 0.34736305 -0.57396660 1.73277971 5.42239383 -1.51209831
229 230 231 232 233 234
-1.36785631 -2.17760259 0.31647445 -2.84508805 0.29660081 0.91332140
235 236 237 238 239 240
1.33904071 -1.63303287 0.73973385 0.49096126 -4.07244336 -2.40022830
241 242 243 244 245 246
-2.65816154 -2.45339019 0.33456198 -0.09356923 1.76335075 0.42195798
247 248 249 250 251 252
0.51245583 5.37770226 -0.02708191 0.65431041 2.30894926 1.28524952
253 254 255 256 257 258
-0.92019047 -0.41232856 0.47480269 -0.33570688 -1.55823640 -2.19655213
259 260 261 262 263 264
2.73808738 -4.68251093 0.65862966 1.80202175 -2.59724049 0.44612518
> postscript(file="/var/fisher/rcomp/tmp/6brt41384681942.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.13129622 NA
1 2.52809378 -0.13129622
2 -3.06002248 2.52809378
3 -2.76198338 -3.06002248
4 4.51414435 -2.76198338
5 3.29534576 4.51414435
6 3.09931925 3.29534576
7 -1.21663381 3.09931925
8 -0.39640532 -1.21663381
9 0.41385011 -0.39640532
10 1.29244833 0.41385011
11 3.26723256 1.29244833
12 -3.56326038 3.26723256
13 2.34716048 -3.56326038
14 2.13250153 2.34716048
15 0.44810812 2.13250153
16 -0.05972274 0.44810812
17 1.10579419 -0.05972274
18 -1.49704740 1.10579419
19 2.06174535 -1.49704740
20 2.52346494 2.06174535
21 -2.84149398 2.52346494
22 -0.51091400 -2.84149398
23 -1.66746571 -0.51091400
24 1.50277046 -1.66746571
25 -7.02871310 1.50277046
26 0.75896580 -7.02871310
27 0.59069506 0.75896580
28 0.89784314 0.59069506
29 -3.07874501 0.89784314
30 0.16401146 -3.07874501
31 0.11825872 0.16401146
32 1.76385864 0.11825872
33 -0.36934189 1.76385864
34 -0.13542648 -0.36934189
35 0.27111579 -0.13542648
36 -2.08959474 0.27111579
37 0.55362611 -2.08959474
38 1.51997477 0.55362611
39 -2.33466359 1.51997477
40 -0.81521360 -2.33466359
41 2.19333557 -0.81521360
42 0.19675813 2.19333557
43 -1.26822538 0.19675813
44 0.26211060 -1.26822538
45 -2.75480216 0.26211060
46 -0.37549186 -2.75480216
47 0.01359052 -0.37549186
48 3.34697842 0.01359052
49 -1.85270289 3.34697842
50 0.72023135 -1.85270289
51 0.41988701 0.72023135
52 -0.83430948 0.41988701
53 -1.60387919 -0.83430948
54 -2.33019607 -1.60387919
55 1.17095784 -2.33019607
56 1.73452489 1.17095784
57 -0.49009683 1.73452489
58 -3.28240616 -0.49009683
59 -1.48342000 -3.28240616
60 -2.92173295 -1.48342000
61 -1.67941309 -2.92173295
62 -3.68535067 -1.67941309
63 0.84113792 -3.68535067
64 1.34297401 0.84113792
65 -5.17540433 1.34297401
66 -1.66932617 -5.17540433
67 -2.63802149 -1.66932617
68 1.32864374 -2.63802149
69 1.25815243 1.32864374
70 0.31079507 1.25815243
71 3.14814662 0.31079507
72 0.48124655 3.14814662
73 -0.31800064 0.48124655
74 -2.06970926 -0.31800064
75 -0.31261772 -2.06970926
76 2.93041513 -0.31261772
77 0.41538407 2.93041513
78 1.06598028 0.41538407
79 -1.99377043 1.06598028
80 -0.01820902 -1.99377043
81 -0.54359115 -0.01820902
82 1.61353665 -0.54359115
83 0.61273191 1.61353665
84 -0.23572660 0.61273191
85 0.83888721 -0.23572660
86 -0.34625305 0.83888721
87 0.16713138 -0.34625305
88 -3.66605492 0.16713138
89 3.12876915 -3.66605492
90 0.18017899 3.12876915
91 0.73679870 0.18017899
92 0.57833603 0.73679870
93 -1.11794071 0.57833603
94 0.93083485 -1.11794071
95 -0.85513582 0.93083485
96 -0.97022050 -0.85513582
97 2.01606475 -0.97022050
98 -0.12353432 2.01606475
99 1.83622872 -0.12353432
100 -1.00600305 1.83622872
101 1.03215170 -1.00600305
102 -3.43561149 1.03215170
103 1.81409045 -3.43561149
104 -2.47052700 1.81409045
105 1.08504275 -2.47052700
106 2.04657018 1.08504275
107 -2.83643206 2.04657018
108 0.70546723 -2.83643206
109 1.06599182 0.70546723
110 -2.20456819 1.06599182
111 -2.29074857 -2.20456819
112 1.88425868 -2.29074857
113 3.83530562 1.88425868
114 0.49431545 3.83530562
115 1.01834973 0.49431545
116 0.14916177 1.01834973
117 -1.09317002 0.14916177
118 0.28727719 -1.09317002
119 -0.55426231 0.28727719
120 0.46461937 -0.55426231
121 0.09370877 0.46461937
122 -0.80509641 0.09370877
123 0.49356532 -0.80509641
124 -1.68143906 0.49356532
125 0.85371354 -1.68143906
126 1.76864356 0.85371354
127 4.11432130 1.76864356
128 1.41787355 4.11432130
129 -1.62410709 1.41787355
130 -1.69781620 -1.62410709
131 -0.27263725 -1.69781620
132 2.39889224 -0.27263725
133 0.83183700 2.39889224
134 2.31116711 0.83183700
135 1.52600369 2.31116711
136 0.78240328 1.52600369
137 -0.95305502 0.78240328
138 0.83544183 -0.95305502
139 -0.81484567 0.83544183
140 0.42746943 -0.81484567
141 2.17520408 0.42746943
142 -0.52795179 2.17520408
143 0.79819387 -0.52795179
144 1.39304253 0.79819387
145 1.68692867 1.39304253
146 -2.27650637 1.68692867
147 -2.55760844 -2.27650637
148 -2.49626585 -2.55760844
149 1.75532998 -2.49626585
150 0.52717811 1.75532998
151 0.65106366 0.52717811
152 -2.40685918 0.65106366
153 -2.51861917 -2.40685918
154 1.26767774 -2.51861917
155 0.46437886 1.26767774
156 0.68764031 0.46437886
157 4.24549047 0.68764031
158 -2.51751668 4.24549047
159 -0.11174781 -2.51751668
160 0.50023759 -0.11174781
161 0.67550306 0.50023759
162 0.68558927 0.67550306
163 4.51771022 0.68558927
164 -1.97099539 4.51771022
165 1.78351045 -1.97099539
166 -0.08152249 1.78351045
167 -0.86471090 -0.08152249
168 -3.69203879 -0.86471090
169 -2.94379617 -3.69203879
170 0.38184674 -2.94379617
171 1.90199724 0.38184674
172 -4.98289421 1.90199724
173 1.61835497 -4.98289421
174 2.71032730 1.61835497
175 -2.23498266 2.71032730
176 -3.29485528 -2.23498266
177 0.49518490 -3.29485528
178 1.52453450 0.49518490
179 -2.17627986 1.52453450
180 0.06325456 -2.17627986
181 -1.71416639 0.06325456
182 0.52630772 -1.71416639
183 -0.83945665 0.52630772
184 1.95719225 -0.83945665
185 1.40536538 1.95719225
186 0.55648939 1.40536538
187 0.79930640 0.55648939
188 0.53700592 0.79930640
189 0.70484421 0.53700592
190 -1.57910529 0.70484421
191 -0.85176689 -1.57910529
192 2.23286580 -0.85176689
193 -1.33013119 2.23286580
194 1.97204730 -1.33013119
195 -1.87419990 1.97204730
196 2.32698976 -1.87419990
197 0.71387667 2.32698976
198 -3.07798879 0.71387667
199 -0.65593607 -3.07798879
200 -2.98879471 -0.65593607
201 1.32645306 -2.98879471
202 2.98270262 1.32645306
203 0.62556953 2.98270262
204 0.67608026 0.62556953
205 1.42428139 0.67608026
206 -0.31574919 1.42428139
207 3.67809551 -0.31574919
208 0.22484126 3.67809551
209 1.82211946 0.22484126
210 -2.53594170 1.82211946
211 1.63037465 -2.53594170
212 -1.11295757 1.63037465
213 -3.71368265 -1.11295757
214 -0.95036235 -3.71368265
215 1.90086625 -0.95036235
216 2.31597191 1.90086625
217 -0.06203797 2.31597191
218 -1.86871359 -0.06203797
219 1.62894420 -1.86871359
220 -2.69227832 1.62894420
221 2.58748839 -2.69227832
222 -1.92234040 2.58748839
223 0.34736305 -1.92234040
224 -0.57396660 0.34736305
225 1.73277971 -0.57396660
226 5.42239383 1.73277971
227 -1.51209831 5.42239383
228 -1.36785631 -1.51209831
229 -2.17760259 -1.36785631
230 0.31647445 -2.17760259
231 -2.84508805 0.31647445
232 0.29660081 -2.84508805
233 0.91332140 0.29660081
234 1.33904071 0.91332140
235 -1.63303287 1.33904071
236 0.73973385 -1.63303287
237 0.49096126 0.73973385
238 -4.07244336 0.49096126
239 -2.40022830 -4.07244336
240 -2.65816154 -2.40022830
241 -2.45339019 -2.65816154
242 0.33456198 -2.45339019
243 -0.09356923 0.33456198
244 1.76335075 -0.09356923
245 0.42195798 1.76335075
246 0.51245583 0.42195798
247 5.37770226 0.51245583
248 -0.02708191 5.37770226
249 0.65431041 -0.02708191
250 2.30894926 0.65431041
251 1.28524952 2.30894926
252 -0.92019047 1.28524952
253 -0.41232856 -0.92019047
254 0.47480269 -0.41232856
255 -0.33570688 0.47480269
256 -1.55823640 -0.33570688
257 -2.19655213 -1.55823640
258 2.73808738 -2.19655213
259 -4.68251093 2.73808738
260 0.65862966 -4.68251093
261 1.80202175 0.65862966
262 -2.59724049 1.80202175
263 0.44612518 -2.59724049
264 NA 0.44612518
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.52809378 -0.13129622
[2,] -3.06002248 2.52809378
[3,] -2.76198338 -3.06002248
[4,] 4.51414435 -2.76198338
[5,] 3.29534576 4.51414435
[6,] 3.09931925 3.29534576
[7,] -1.21663381 3.09931925
[8,] -0.39640532 -1.21663381
[9,] 0.41385011 -0.39640532
[10,] 1.29244833 0.41385011
[11,] 3.26723256 1.29244833
[12,] -3.56326038 3.26723256
[13,] 2.34716048 -3.56326038
[14,] 2.13250153 2.34716048
[15,] 0.44810812 2.13250153
[16,] -0.05972274 0.44810812
[17,] 1.10579419 -0.05972274
[18,] -1.49704740 1.10579419
[19,] 2.06174535 -1.49704740
[20,] 2.52346494 2.06174535
[21,] -2.84149398 2.52346494
[22,] -0.51091400 -2.84149398
[23,] -1.66746571 -0.51091400
[24,] 1.50277046 -1.66746571
[25,] -7.02871310 1.50277046
[26,] 0.75896580 -7.02871310
[27,] 0.59069506 0.75896580
[28,] 0.89784314 0.59069506
[29,] -3.07874501 0.89784314
[30,] 0.16401146 -3.07874501
[31,] 0.11825872 0.16401146
[32,] 1.76385864 0.11825872
[33,] -0.36934189 1.76385864
[34,] -0.13542648 -0.36934189
[35,] 0.27111579 -0.13542648
[36,] -2.08959474 0.27111579
[37,] 0.55362611 -2.08959474
[38,] 1.51997477 0.55362611
[39,] -2.33466359 1.51997477
[40,] -0.81521360 -2.33466359
[41,] 2.19333557 -0.81521360
[42,] 0.19675813 2.19333557
[43,] -1.26822538 0.19675813
[44,] 0.26211060 -1.26822538
[45,] -2.75480216 0.26211060
[46,] -0.37549186 -2.75480216
[47,] 0.01359052 -0.37549186
[48,] 3.34697842 0.01359052
[49,] -1.85270289 3.34697842
[50,] 0.72023135 -1.85270289
[51,] 0.41988701 0.72023135
[52,] -0.83430948 0.41988701
[53,] -1.60387919 -0.83430948
[54,] -2.33019607 -1.60387919
[55,] 1.17095784 -2.33019607
[56,] 1.73452489 1.17095784
[57,] -0.49009683 1.73452489
[58,] -3.28240616 -0.49009683
[59,] -1.48342000 -3.28240616
[60,] -2.92173295 -1.48342000
[61,] -1.67941309 -2.92173295
[62,] -3.68535067 -1.67941309
[63,] 0.84113792 -3.68535067
[64,] 1.34297401 0.84113792
[65,] -5.17540433 1.34297401
[66,] -1.66932617 -5.17540433
[67,] -2.63802149 -1.66932617
[68,] 1.32864374 -2.63802149
[69,] 1.25815243 1.32864374
[70,] 0.31079507 1.25815243
[71,] 3.14814662 0.31079507
[72,] 0.48124655 3.14814662
[73,] -0.31800064 0.48124655
[74,] -2.06970926 -0.31800064
[75,] -0.31261772 -2.06970926
[76,] 2.93041513 -0.31261772
[77,] 0.41538407 2.93041513
[78,] 1.06598028 0.41538407
[79,] -1.99377043 1.06598028
[80,] -0.01820902 -1.99377043
[81,] -0.54359115 -0.01820902
[82,] 1.61353665 -0.54359115
[83,] 0.61273191 1.61353665
[84,] -0.23572660 0.61273191
[85,] 0.83888721 -0.23572660
[86,] -0.34625305 0.83888721
[87,] 0.16713138 -0.34625305
[88,] -3.66605492 0.16713138
[89,] 3.12876915 -3.66605492
[90,] 0.18017899 3.12876915
[91,] 0.73679870 0.18017899
[92,] 0.57833603 0.73679870
[93,] -1.11794071 0.57833603
[94,] 0.93083485 -1.11794071
[95,] -0.85513582 0.93083485
[96,] -0.97022050 -0.85513582
[97,] 2.01606475 -0.97022050
[98,] -0.12353432 2.01606475
[99,] 1.83622872 -0.12353432
[100,] -1.00600305 1.83622872
[101,] 1.03215170 -1.00600305
[102,] -3.43561149 1.03215170
[103,] 1.81409045 -3.43561149
[104,] -2.47052700 1.81409045
[105,] 1.08504275 -2.47052700
[106,] 2.04657018 1.08504275
[107,] -2.83643206 2.04657018
[108,] 0.70546723 -2.83643206
[109,] 1.06599182 0.70546723
[110,] -2.20456819 1.06599182
[111,] -2.29074857 -2.20456819
[112,] 1.88425868 -2.29074857
[113,] 3.83530562 1.88425868
[114,] 0.49431545 3.83530562
[115,] 1.01834973 0.49431545
[116,] 0.14916177 1.01834973
[117,] -1.09317002 0.14916177
[118,] 0.28727719 -1.09317002
[119,] -0.55426231 0.28727719
[120,] 0.46461937 -0.55426231
[121,] 0.09370877 0.46461937
[122,] -0.80509641 0.09370877
[123,] 0.49356532 -0.80509641
[124,] -1.68143906 0.49356532
[125,] 0.85371354 -1.68143906
[126,] 1.76864356 0.85371354
[127,] 4.11432130 1.76864356
[128,] 1.41787355 4.11432130
[129,] -1.62410709 1.41787355
[130,] -1.69781620 -1.62410709
[131,] -0.27263725 -1.69781620
[132,] 2.39889224 -0.27263725
[133,] 0.83183700 2.39889224
[134,] 2.31116711 0.83183700
[135,] 1.52600369 2.31116711
[136,] 0.78240328 1.52600369
[137,] -0.95305502 0.78240328
[138,] 0.83544183 -0.95305502
[139,] -0.81484567 0.83544183
[140,] 0.42746943 -0.81484567
[141,] 2.17520408 0.42746943
[142,] -0.52795179 2.17520408
[143,] 0.79819387 -0.52795179
[144,] 1.39304253 0.79819387
[145,] 1.68692867 1.39304253
[146,] -2.27650637 1.68692867
[147,] -2.55760844 -2.27650637
[148,] -2.49626585 -2.55760844
[149,] 1.75532998 -2.49626585
[150,] 0.52717811 1.75532998
[151,] 0.65106366 0.52717811
[152,] -2.40685918 0.65106366
[153,] -2.51861917 -2.40685918
[154,] 1.26767774 -2.51861917
[155,] 0.46437886 1.26767774
[156,] 0.68764031 0.46437886
[157,] 4.24549047 0.68764031
[158,] -2.51751668 4.24549047
[159,] -0.11174781 -2.51751668
[160,] 0.50023759 -0.11174781
[161,] 0.67550306 0.50023759
[162,] 0.68558927 0.67550306
[163,] 4.51771022 0.68558927
[164,] -1.97099539 4.51771022
[165,] 1.78351045 -1.97099539
[166,] -0.08152249 1.78351045
[167,] -0.86471090 -0.08152249
[168,] -3.69203879 -0.86471090
[169,] -2.94379617 -3.69203879
[170,] 0.38184674 -2.94379617
[171,] 1.90199724 0.38184674
[172,] -4.98289421 1.90199724
[173,] 1.61835497 -4.98289421
[174,] 2.71032730 1.61835497
[175,] -2.23498266 2.71032730
[176,] -3.29485528 -2.23498266
[177,] 0.49518490 -3.29485528
[178,] 1.52453450 0.49518490
[179,] -2.17627986 1.52453450
[180,] 0.06325456 -2.17627986
[181,] -1.71416639 0.06325456
[182,] 0.52630772 -1.71416639
[183,] -0.83945665 0.52630772
[184,] 1.95719225 -0.83945665
[185,] 1.40536538 1.95719225
[186,] 0.55648939 1.40536538
[187,] 0.79930640 0.55648939
[188,] 0.53700592 0.79930640
[189,] 0.70484421 0.53700592
[190,] -1.57910529 0.70484421
[191,] -0.85176689 -1.57910529
[192,] 2.23286580 -0.85176689
[193,] -1.33013119 2.23286580
[194,] 1.97204730 -1.33013119
[195,] -1.87419990 1.97204730
[196,] 2.32698976 -1.87419990
[197,] 0.71387667 2.32698976
[198,] -3.07798879 0.71387667
[199,] -0.65593607 -3.07798879
[200,] -2.98879471 -0.65593607
[201,] 1.32645306 -2.98879471
[202,] 2.98270262 1.32645306
[203,] 0.62556953 2.98270262
[204,] 0.67608026 0.62556953
[205,] 1.42428139 0.67608026
[206,] -0.31574919 1.42428139
[207,] 3.67809551 -0.31574919
[208,] 0.22484126 3.67809551
[209,] 1.82211946 0.22484126
[210,] -2.53594170 1.82211946
[211,] 1.63037465 -2.53594170
[212,] -1.11295757 1.63037465
[213,] -3.71368265 -1.11295757
[214,] -0.95036235 -3.71368265
[215,] 1.90086625 -0.95036235
[216,] 2.31597191 1.90086625
[217,] -0.06203797 2.31597191
[218,] -1.86871359 -0.06203797
[219,] 1.62894420 -1.86871359
[220,] -2.69227832 1.62894420
[221,] 2.58748839 -2.69227832
[222,] -1.92234040 2.58748839
[223,] 0.34736305 -1.92234040
[224,] -0.57396660 0.34736305
[225,] 1.73277971 -0.57396660
[226,] 5.42239383 1.73277971
[227,] -1.51209831 5.42239383
[228,] -1.36785631 -1.51209831
[229,] -2.17760259 -1.36785631
[230,] 0.31647445 -2.17760259
[231,] -2.84508805 0.31647445
[232,] 0.29660081 -2.84508805
[233,] 0.91332140 0.29660081
[234,] 1.33904071 0.91332140
[235,] -1.63303287 1.33904071
[236,] 0.73973385 -1.63303287
[237,] 0.49096126 0.73973385
[238,] -4.07244336 0.49096126
[239,] -2.40022830 -4.07244336
[240,] -2.65816154 -2.40022830
[241,] -2.45339019 -2.65816154
[242,] 0.33456198 -2.45339019
[243,] -0.09356923 0.33456198
[244,] 1.76335075 -0.09356923
[245,] 0.42195798 1.76335075
[246,] 0.51245583 0.42195798
[247,] 5.37770226 0.51245583
[248,] -0.02708191 5.37770226
[249,] 0.65431041 -0.02708191
[250,] 2.30894926 0.65431041
[251,] 1.28524952 2.30894926
[252,] -0.92019047 1.28524952
[253,] -0.41232856 -0.92019047
[254,] 0.47480269 -0.41232856
[255,] -0.33570688 0.47480269
[256,] -1.55823640 -0.33570688
[257,] -2.19655213 -1.55823640
[258,] 2.73808738 -2.19655213
[259,] -4.68251093 2.73808738
[260,] 0.65862966 -4.68251093
[261,] 1.80202175 0.65862966
[262,] -2.59724049 1.80202175
[263,] 0.44612518 -2.59724049
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.52809378 -0.13129622
2 -3.06002248 2.52809378
3 -2.76198338 -3.06002248
4 4.51414435 -2.76198338
5 3.29534576 4.51414435
6 3.09931925 3.29534576
7 -1.21663381 3.09931925
8 -0.39640532 -1.21663381
9 0.41385011 -0.39640532
10 1.29244833 0.41385011
11 3.26723256 1.29244833
12 -3.56326038 3.26723256
13 2.34716048 -3.56326038
14 2.13250153 2.34716048
15 0.44810812 2.13250153
16 -0.05972274 0.44810812
17 1.10579419 -0.05972274
18 -1.49704740 1.10579419
19 2.06174535 -1.49704740
20 2.52346494 2.06174535
21 -2.84149398 2.52346494
22 -0.51091400 -2.84149398
23 -1.66746571 -0.51091400
24 1.50277046 -1.66746571
25 -7.02871310 1.50277046
26 0.75896580 -7.02871310
27 0.59069506 0.75896580
28 0.89784314 0.59069506
29 -3.07874501 0.89784314
30 0.16401146 -3.07874501
31 0.11825872 0.16401146
32 1.76385864 0.11825872
33 -0.36934189 1.76385864
34 -0.13542648 -0.36934189
35 0.27111579 -0.13542648
36 -2.08959474 0.27111579
37 0.55362611 -2.08959474
38 1.51997477 0.55362611
39 -2.33466359 1.51997477
40 -0.81521360 -2.33466359
41 2.19333557 -0.81521360
42 0.19675813 2.19333557
43 -1.26822538 0.19675813
44 0.26211060 -1.26822538
45 -2.75480216 0.26211060
46 -0.37549186 -2.75480216
47 0.01359052 -0.37549186
48 3.34697842 0.01359052
49 -1.85270289 3.34697842
50 0.72023135 -1.85270289
51 0.41988701 0.72023135
52 -0.83430948 0.41988701
53 -1.60387919 -0.83430948
54 -2.33019607 -1.60387919
55 1.17095784 -2.33019607
56 1.73452489 1.17095784
57 -0.49009683 1.73452489
58 -3.28240616 -0.49009683
59 -1.48342000 -3.28240616
60 -2.92173295 -1.48342000
61 -1.67941309 -2.92173295
62 -3.68535067 -1.67941309
63 0.84113792 -3.68535067
64 1.34297401 0.84113792
65 -5.17540433 1.34297401
66 -1.66932617 -5.17540433
67 -2.63802149 -1.66932617
68 1.32864374 -2.63802149
69 1.25815243 1.32864374
70 0.31079507 1.25815243
71 3.14814662 0.31079507
72 0.48124655 3.14814662
73 -0.31800064 0.48124655
74 -2.06970926 -0.31800064
75 -0.31261772 -2.06970926
76 2.93041513 -0.31261772
77 0.41538407 2.93041513
78 1.06598028 0.41538407
79 -1.99377043 1.06598028
80 -0.01820902 -1.99377043
81 -0.54359115 -0.01820902
82 1.61353665 -0.54359115
83 0.61273191 1.61353665
84 -0.23572660 0.61273191
85 0.83888721 -0.23572660
86 -0.34625305 0.83888721
87 0.16713138 -0.34625305
88 -3.66605492 0.16713138
89 3.12876915 -3.66605492
90 0.18017899 3.12876915
91 0.73679870 0.18017899
92 0.57833603 0.73679870
93 -1.11794071 0.57833603
94 0.93083485 -1.11794071
95 -0.85513582 0.93083485
96 -0.97022050 -0.85513582
97 2.01606475 -0.97022050
98 -0.12353432 2.01606475
99 1.83622872 -0.12353432
100 -1.00600305 1.83622872
101 1.03215170 -1.00600305
102 -3.43561149 1.03215170
103 1.81409045 -3.43561149
104 -2.47052700 1.81409045
105 1.08504275 -2.47052700
106 2.04657018 1.08504275
107 -2.83643206 2.04657018
108 0.70546723 -2.83643206
109 1.06599182 0.70546723
110 -2.20456819 1.06599182
111 -2.29074857 -2.20456819
112 1.88425868 -2.29074857
113 3.83530562 1.88425868
114 0.49431545 3.83530562
115 1.01834973 0.49431545
116 0.14916177 1.01834973
117 -1.09317002 0.14916177
118 0.28727719 -1.09317002
119 -0.55426231 0.28727719
120 0.46461937 -0.55426231
121 0.09370877 0.46461937
122 -0.80509641 0.09370877
123 0.49356532 -0.80509641
124 -1.68143906 0.49356532
125 0.85371354 -1.68143906
126 1.76864356 0.85371354
127 4.11432130 1.76864356
128 1.41787355 4.11432130
129 -1.62410709 1.41787355
130 -1.69781620 -1.62410709
131 -0.27263725 -1.69781620
132 2.39889224 -0.27263725
133 0.83183700 2.39889224
134 2.31116711 0.83183700
135 1.52600369 2.31116711
136 0.78240328 1.52600369
137 -0.95305502 0.78240328
138 0.83544183 -0.95305502
139 -0.81484567 0.83544183
140 0.42746943 -0.81484567
141 2.17520408 0.42746943
142 -0.52795179 2.17520408
143 0.79819387 -0.52795179
144 1.39304253 0.79819387
145 1.68692867 1.39304253
146 -2.27650637 1.68692867
147 -2.55760844 -2.27650637
148 -2.49626585 -2.55760844
149 1.75532998 -2.49626585
150 0.52717811 1.75532998
151 0.65106366 0.52717811
152 -2.40685918 0.65106366
153 -2.51861917 -2.40685918
154 1.26767774 -2.51861917
155 0.46437886 1.26767774
156 0.68764031 0.46437886
157 4.24549047 0.68764031
158 -2.51751668 4.24549047
159 -0.11174781 -2.51751668
160 0.50023759 -0.11174781
161 0.67550306 0.50023759
162 0.68558927 0.67550306
163 4.51771022 0.68558927
164 -1.97099539 4.51771022
165 1.78351045 -1.97099539
166 -0.08152249 1.78351045
167 -0.86471090 -0.08152249
168 -3.69203879 -0.86471090
169 -2.94379617 -3.69203879
170 0.38184674 -2.94379617
171 1.90199724 0.38184674
172 -4.98289421 1.90199724
173 1.61835497 -4.98289421
174 2.71032730 1.61835497
175 -2.23498266 2.71032730
176 -3.29485528 -2.23498266
177 0.49518490 -3.29485528
178 1.52453450 0.49518490
179 -2.17627986 1.52453450
180 0.06325456 -2.17627986
181 -1.71416639 0.06325456
182 0.52630772 -1.71416639
183 -0.83945665 0.52630772
184 1.95719225 -0.83945665
185 1.40536538 1.95719225
186 0.55648939 1.40536538
187 0.79930640 0.55648939
188 0.53700592 0.79930640
189 0.70484421 0.53700592
190 -1.57910529 0.70484421
191 -0.85176689 -1.57910529
192 2.23286580 -0.85176689
193 -1.33013119 2.23286580
194 1.97204730 -1.33013119
195 -1.87419990 1.97204730
196 2.32698976 -1.87419990
197 0.71387667 2.32698976
198 -3.07798879 0.71387667
199 -0.65593607 -3.07798879
200 -2.98879471 -0.65593607
201 1.32645306 -2.98879471
202 2.98270262 1.32645306
203 0.62556953 2.98270262
204 0.67608026 0.62556953
205 1.42428139 0.67608026
206 -0.31574919 1.42428139
207 3.67809551 -0.31574919
208 0.22484126 3.67809551
209 1.82211946 0.22484126
210 -2.53594170 1.82211946
211 1.63037465 -2.53594170
212 -1.11295757 1.63037465
213 -3.71368265 -1.11295757
214 -0.95036235 -3.71368265
215 1.90086625 -0.95036235
216 2.31597191 1.90086625
217 -0.06203797 2.31597191
218 -1.86871359 -0.06203797
219 1.62894420 -1.86871359
220 -2.69227832 1.62894420
221 2.58748839 -2.69227832
222 -1.92234040 2.58748839
223 0.34736305 -1.92234040
224 -0.57396660 0.34736305
225 1.73277971 -0.57396660
226 5.42239383 1.73277971
227 -1.51209831 5.42239383
228 -1.36785631 -1.51209831
229 -2.17760259 -1.36785631
230 0.31647445 -2.17760259
231 -2.84508805 0.31647445
232 0.29660081 -2.84508805
233 0.91332140 0.29660081
234 1.33904071 0.91332140
235 -1.63303287 1.33904071
236 0.73973385 -1.63303287
237 0.49096126 0.73973385
238 -4.07244336 0.49096126
239 -2.40022830 -4.07244336
240 -2.65816154 -2.40022830
241 -2.45339019 -2.65816154
242 0.33456198 -2.45339019
243 -0.09356923 0.33456198
244 1.76335075 -0.09356923
245 0.42195798 1.76335075
246 0.51245583 0.42195798
247 5.37770226 0.51245583
248 -0.02708191 5.37770226
249 0.65431041 -0.02708191
250 2.30894926 0.65431041
251 1.28524952 2.30894926
252 -0.92019047 1.28524952
253 -0.41232856 -0.92019047
254 0.47480269 -0.41232856
255 -0.33570688 0.47480269
256 -1.55823640 -0.33570688
257 -2.19655213 -1.55823640
258 2.73808738 -2.19655213
259 -4.68251093 2.73808738
260 0.65862966 -4.68251093
261 1.80202175 0.65862966
262 -2.59724049 1.80202175
263 0.44612518 -2.59724049
> 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/7px6w1384681942.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/8ua591384681942.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/9ndp71384681942.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/10atvt1384681942.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11ljdt1384681942.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12tmx21384681942.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13icfr1384681942.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14w7sc1384681942.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/15aoyp1384681942.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/16zzs11384681942.tab")
+ }
>
> try(system("convert tmp/1pzpt1384681942.ps tmp/1pzpt1384681942.png",intern=TRUE))
character(0)
> try(system("convert tmp/2hae41384681942.ps tmp/2hae41384681942.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ms5b1384681942.ps tmp/3ms5b1384681942.png",intern=TRUE))
character(0)
> try(system("convert tmp/49h6a1384681942.ps tmp/49h6a1384681942.png",intern=TRUE))
character(0)
> try(system("convert tmp/5qfr51384681942.ps tmp/5qfr51384681942.png",intern=TRUE))
character(0)
> try(system("convert tmp/6brt41384681942.ps tmp/6brt41384681942.png",intern=TRUE))
character(0)
> try(system("convert tmp/7px6w1384681942.ps tmp/7px6w1384681942.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ua591384681942.ps tmp/8ua591384681942.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ndp71384681942.ps tmp/9ndp71384681942.png",intern=TRUE))
character(0)
> try(system("convert tmp/10atvt1384681942.ps tmp/10atvt1384681942.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.760 1.650 12.405