R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(61
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+ ,10064)
+ ,dim=c(5
+ ,240)
+ ,dimnames=list(c('Temp'
+ ,'Max'
+ ,'Min'
+ ,'Neerslag'
+ ,'Luchtdruk')
+ ,1:240))
> y <- array(NA,dim=c(5,240),dimnames=list(c('Temp','Max','Min','Neerslag','Luchtdruk'),1:240))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
Temp Max Min Neerslag Luchtdruk
1 61 80 41 568 10173
2 81 111 50 1110 10083
3 87 122 46 338 10258
4 87 131 36 555 10154
5 136 192 67 281 10207
6 147 188 104 571 10133
7 168 216 115 322 10197
8 185 238 125 503 10184
9 137 173 97 1078 10163
10 125 160 88 582 10104
11 64 93 27 926 10127
12 45 67 19 491 10164
13 35 60 9 504 10219
14 -4 32 -47 314 10177
15 88 126 46 269 10138
16 85 131 36 252 10164
17 95 134 51 342 10223
18 128 162 90 1464 10122
19 186 230 142 921 10161
20 182 232 119 115 10199
21 151 200 92 789 10160
22 106 143 70 495 10157
23 60 85 30 1279 10113
24 44 66 19 391 10276
25 30 54 2 352 10303
26 54 81 21 340 10232
27 72 100 41 715 10140
28 88 126 46 425 10121
29 153 204 94 413 10188
30 168 218 114 935 10164
31 181 227 130 680 10165
32 180 220 141 1472 10121
33 149 220 109 767 10166
34 84 120 46 1215 10091
35 85 110 58 1113 10121
36 42 67 17 711 10180
37 54 81 22 742 10197
38 30 52 5 225 10289
39 96 106 17 107 10220
40 110 156 57 457 10106
41 141 187 91 448 10141
42 159 204 106 385 10165
43 164 204 125 1518 10152
44 155 196 111 495 10188
45 135 204 99 1283 10110
46 93 124 63 751 10144
47 28 53 3 674 10206
48 56 77 30 1705 10045
49 56 77 30 894 10100
50 22 50 -9 309 10147
51 76 105 42 745 10149
52 83 125 38 806 10116
53 121 165 73 423 10138
54 151 194 102 506 10183
55 208 263 149 148 10174
56 179 225 132 494 10143
57 139 263 108 1794 10120
58 99 140 58 1329 10143
59 103 127 66 289 10181
60 57 86 24 1213 10161
61 44 71 15 1263 10121
62 70 95 43 910 10095
63 58 95 17 934 10114
64 91 133 47 228 10173
65 126 178 63 366 10164
66 146 160 101 45 10174
67 199 250 142 459 10155
68 194 251 131 253 10182
69 145 250 107 999 10109
70 131 173 85 182 10198
71 74 103 38 483 10167
72 -3 21 -36 401 10178
73 7 29 -15 47 10143
74 10 39 -21 665 10127
75 34 71 -3 102 10183
76 94 148 35 22 10178
77 105 144 62 445 10142
78 151 199 91 378 10207
79 162 206 110 419 10176
80 175 224 127 1046 10145
81 128 206 79 531 10172
82 115 152 76 809 10157
83 62 88 32 1416 10086
84 11 35 -18 369 10151
85 -7 23 -39 20 10236
86 64 92 30 882 10160
87 80 117 43 262 10233
88 77 120 26 186 10212
89 127 173 74 763 10150
90 158 202 111 1038 10100
91 173 217 123 558 10178
92 206 256 151 335 10161
93 147 217 95 242 10217
94 103 143 59 898 10173
95 73 95 47 498 10067
96 52 77 21 757 10117
97 52 76 23 843 10149
98 68 100 33 133 10238
99 77 108 39 1035 10211
100 94 132 61 1117 10030
101 147 195 91 341 10165
102 160 198 123 1304 10142
103 166 204 124 566 10126
104 167 212 112 756 10176
105 155 204 122 1761 10095
106 104 129 70 1469 10105
107 44 73 11 1370 10172
108 53 77 29 795 10180
109 56 80 26 920 10126
110 36 64 2 754 10154
111 76 109 38 1034 10107
112 99 138 58 617 10133
113 142 185 92 706 10158
114 150 198 97 832 10173
115 190 237 138 393 10171
116 176 223 127 1551 10130
117 175 237 136 675 10105
118 112 146 80 1225 10154
119 73 102 38 737 10206
120 52 77 23 1444 10078
121 48 70 22 452 10233
122 61 86 30 1157 10179
123 68 98 32 718 10197
124 97 141 55 419 10075
125 146 195 96 898 10147
126 160 205 110 417 10195
127 155 191 117 1207 10129
128 175 226 125 163 10175
129 163 191 127 643 10128
130 117 147 86 1333 10099
131 82 100 62 1625 10015
132 55 74 33 970 10079
133 32 56 6 787 10112
134 48 77 17 995 10170
135 53 80 24 669 10048
136 82 120 44 861 10119
137 139 186 85 247 10180
138 150 196 95 349 10168
139 184 229 140 994 10141
140 185 229 139 1213 10149
141 138 229 104 2540 10117
142 147 176 117 388 10140
143 77 104 42 907 10216
144 32 61 -4 778 10227
145 48 72 23 729 10209
146 72 99 42 1428 10097
147 76 113 34 462 10176
148 94 140 44 528 10158
149 133 174 89 325 10132
150 164 209 116 777 10154
151 174 205 133 686 10145
152 187 229 141 1464 10153
153 149 215 104 438 10199
154 102 136 63 792 10111
155 86 113 52 1089 10071
156 35 57 13 920 10151
157 31 55 2 680 10148
158 28 66 -10 206 10206
159 75 125 23 177 10235
160 102 149 45 438 10170
161 133 176 83 800 10164
162 178 230 114 278 10161
163 190 238 137 396 10155
164 190 245 132 101 10181
165 147 238 87 785 10200
166 83 124 39 724 10133
167 83 111 52 556 10139
168 46 72 18 905 10169
169 40 63 12 1199 10080
170 50 78 19 688 10191
171 61 100 18 443 10202
172 102 149 49 710 10128
173 117 166 61 273 10160
174 158 201 105 752 10170
175 170 214 123 852 10158
176 190 231 150 1838 10110
177 155 214 113 765 10181
178 117 151 84 453 10093
179 68 97 33 792 10206
180 40 68 7 490 10180
181 56 81 30 562 10202
182 28 55 -2 731 10193
183 66 99 28 315 10158
184 103 146 57 623 10139
185 122 170 68 423 10167
186 166 218 111 726 10188
187 176 218 132 1137 10147
188 164 207 115 773 10173
189 160 218 114 971 10180
190 139 178 102 547 10166
191 75 105 40 1004 10149
192 44 67 16 538 10167
193 22 47 -7 149 10243
194 32 55 11 504 10148
195 42 73 7 619 10105
196 86 124 47 176 10144
197 140 185 93 908 10136
198 163 213 104 290 10208
199 222 278 159 155 10192
200 166 205 129 2681 10111
201 183 278 140 179 10139
202 140 171 100 1243 10112
203 98 125 67 973 10147
204 69 92 44 860 10205
205 75 96 49 1029 10154
206 63 92 32 772 10087
207 81 118 40 805 10151
208 126 185 63 3 10217
209 139 183 92 1237 10106
210 171 215 133 939 10117
211 170 207 134 1799 10115
212 173 214 126 534 10148
213 144 207 102 1042 10191
214 105 142 64 270 10238
215 75 102 43 724 10183
216 41 66 16 783 10206
217 68 87 43 648 10138
218 53 90 11 465 10238
219 61 90 26 1292 10052
220 87 133 40 318 10110
221 155 205 94 747 10156
222 159 201 113 298 10160
223 180 220 137 1145 10141
224 175 210 140 1456 10116
225 138 220 93 612 10176
226 105 136 67 1136 10146
227 73 95 44 903 1125
228 26 52 -3 609 10180
229 12 40 -14 532 10133
230 35 60 4 672 10141
231 64 100 25 568 10141
232 115 169 57 234 10140
233 138 184 87 778 10187
234 138 202 107 436 10169
235 182 226 140 795 10128
236 191 239 136 298 10164
237 155 226 112 284 10208
238 113 149 70 852 10165
239 98 121 72 1307 10036
240 29 50 3 1166 10064
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Max Min Neerslag Luchtdruk
1.566e+01 3.389e-01 6.906e-01 -5.990e-03 2.398e-05
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-29.865 -2.192 0.532 3.066 33.076
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.566e+01 6.353e+00 2.464 0.0144 *
Max 3.389e-01 2.112e-02 16.048 < 2e-16 ***
Min 6.906e-01 2.923e-02 23.622 < 2e-16 ***
Neerslag -5.990e-03 9.053e-04 -6.616 2.47e-10 ***
Luchtdruk 2.398e-05 6.145e-04 0.039 0.9689
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.521 on 235 degrees of freedom
Multiple R-squared: 0.9894, Adjusted R-squared: 0.9892
F-statistic: 5469 on 4 and 235 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,] 2.082859e-03 4.165718e-03 9.979171e-01
[2,] 2.585245e-04 5.170490e-04 9.997415e-01
[3,] 3.960520e-05 7.921039e-05 9.999604e-01
[4,] 5.348016e-05 1.069603e-04 9.999465e-01
[5,] 8.706605e-06 1.741321e-05 9.999913e-01
[6,] 9.563347e-06 1.912669e-05 9.999904e-01
[7,] 3.147198e-06 6.294395e-06 9.999969e-01
[8,] 5.259783e-07 1.051957e-06 9.999995e-01
[9,] 4.547605e-07 9.095211e-07 9.999995e-01
[10,] 8.596841e-08 1.719368e-07 9.999999e-01
[11,] 1.647886e-08 3.295771e-08 1.000000e+00
[12,] 1.064473e-08 2.128947e-08 1.000000e+00
[13,] 7.370404e-08 1.474081e-07 9.999999e-01
[14,] 2.377807e-08 4.755615e-08 1.000000e+00
[15,] 1.695667e-08 3.391334e-08 1.000000e+00
[16,] 5.421219e-09 1.084244e-08 1.000000e+00
[17,] 1.288822e-09 2.577644e-09 1.000000e+00
[18,] 2.973596e-10 5.947192e-10 1.000000e+00
[19,] 9.388377e-11 1.877675e-10 1.000000e+00
[20,] 2.114770e-11 4.229539e-11 1.000000e+00
[21,] 4.466819e-12 8.933639e-12 1.000000e+00
[22,] 9.816125e-13 1.963225e-12 1.000000e+00
[23,] 4.699933e-13 9.399866e-13 1.000000e+00
[24,] 9.842923e-14 1.968585e-13 1.000000e+00
[25,] 4.451448e-14 8.902895e-14 1.000000e+00
[26,] 3.427664e-03 6.855328e-03 9.965723e-01
[27,] 2.122560e-03 4.245119e-03 9.978774e-01
[28,] 1.289302e-03 2.578604e-03 9.987107e-01
[29,] 8.428382e-04 1.685676e-03 9.991572e-01
[30,] 5.000715e-04 1.000143e-03 9.994999e-01
[31,] 3.137258e-04 6.274516e-04 9.996863e-01
[32,] 8.986690e-01 2.026620e-01 1.013310e-01
[33,] 8.801943e-01 2.396115e-01 1.198057e-01
[34,] 8.545254e-01 2.909492e-01 1.454746e-01
[35,] 8.282219e-01 3.435562e-01 1.717781e-01
[36,] 7.983596e-01 4.032807e-01 2.016404e-01
[37,] 7.621770e-01 4.756459e-01 2.378230e-01
[38,] 9.324607e-01 1.350785e-01 6.753925e-02
[39,] 9.189634e-01 1.620731e-01 8.103657e-02
[40,] 9.058506e-01 1.882988e-01 9.414938e-02
[41,] 9.023856e-01 1.952287e-01 9.761436e-02
[42,] 8.822969e-01 2.354062e-01 1.177031e-01
[43,] 8.678262e-01 2.643477e-01 1.321738e-01
[44,] 8.419475e-01 3.161050e-01 1.580525e-01
[45,] 8.173372e-01 3.653257e-01 1.826628e-01
[46,] 7.876341e-01 4.247318e-01 2.123659e-01
[47,] 7.557525e-01 4.884949e-01 2.442475e-01
[48,] 7.204244e-01 5.591512e-01 2.795756e-01
[49,] 6.824249e-01 6.351502e-01 3.175751e-01
[50,] 9.998833e-01 2.333190e-04 1.166595e-04
[51,] 9.998879e-01 2.241803e-04 1.120901e-04
[52,] 9.998376e-01 3.247205e-04 1.623603e-04
[53,] 9.998126e-01 3.747278e-04 1.873639e-04
[54,] 9.997444e-01 5.112114e-04 2.556057e-04
[55,] 9.996542e-01 6.916455e-04 3.458227e-04
[56,] 9.995596e-01 8.808656e-04 4.404328e-04
[57,] 9.994336e-01 1.132881e-03 5.664406e-04
[58,] 9.995442e-01 9.115874e-04 4.557937e-04
[59,] 9.995312e-01 9.376534e-04 4.688267e-04
[60,] 9.993785e-01 1.243007e-03 6.215036e-04
[61,] 9.992294e-01 1.541116e-03 7.705582e-04
[62,] 9.999993e-01 1.429110e-06 7.145552e-07
[63,] 9.999990e-01 2.080076e-06 1.040038e-06
[64,] 9.999984e-01 3.259914e-06 1.629957e-06
[65,] 9.999975e-01 5.056364e-06 2.528182e-06
[66,] 9.999991e-01 1.772259e-06 8.861296e-07
[67,] 9.999986e-01 2.837903e-06 1.418951e-06
[68,] 9.999983e-01 3.458838e-06 1.729419e-06
[69,] 9.999977e-01 4.634166e-06 2.317083e-06
[70,] 9.999964e-01 7.229251e-06 3.614625e-06
[71,] 9.999972e-01 5.530308e-06 2.765154e-06
[72,] 9.999962e-01 7.675125e-06 3.837562e-06
[73,] 9.999945e-01 1.107301e-05 5.536504e-06
[74,] 9.999976e-01 4.825682e-06 2.412841e-06
[75,] 9.999962e-01 7.593244e-06 3.796622e-06
[76,] 9.999952e-01 9.589060e-06 4.794530e-06
[77,] 9.999932e-01 1.355239e-05 6.776197e-06
[78,] 9.999916e-01 1.685860e-05 8.429300e-06
[79,] 9.999877e-01 2.453670e-05 1.226835e-05
[80,] 9.999845e-01 3.094368e-05 1.547184e-05
[81,] 9.999804e-01 3.913221e-05 1.956611e-05
[82,] 9.999820e-01 3.591536e-05 1.795768e-05
[83,] 9.999765e-01 4.706922e-05 2.353461e-05
[84,] 9.999670e-01 6.597644e-05 3.298822e-05
[85,] 9.999540e-01 9.204388e-05 4.602194e-05
[86,] 9.999617e-01 7.667198e-05 3.833599e-05
[87,] 9.999525e-01 9.491304e-05 4.745652e-05
[88,] 9.999507e-01 9.853084e-05 4.926542e-05
[89,] 9.999276e-01 1.447392e-04 7.236962e-05
[90,] 9.998947e-01 2.106603e-04 1.053301e-04
[91,] 9.998728e-01 2.544995e-04 1.272497e-04
[92,] 9.998544e-01 2.912521e-04 1.456260e-04
[93,] 9.998031e-01 3.938267e-04 1.969134e-04
[94,] 9.997743e-01 4.513161e-04 2.256581e-04
[95,] 9.996820e-01 6.360239e-04 3.180120e-04
[96,] 9.995684e-01 8.632868e-04 4.316434e-04
[97,] 9.996301e-01 7.398483e-04 3.699241e-04
[98,] 9.995778e-01 8.444653e-04 4.222326e-04
[99,] 9.995616e-01 8.767746e-04 4.383873e-04
[100,] 9.995046e-01 9.908325e-04 4.954162e-04
[101,] 9.994266e-01 1.146788e-03 5.733942e-04
[102,] 9.992096e-01 1.580840e-03 7.904202e-04
[103,] 9.989440e-01 2.111915e-03 1.055958e-03
[104,] 9.986998e-01 2.600496e-03 1.300248e-03
[105,] 9.982393e-01 3.521364e-03 1.760682e-03
[106,] 9.980121e-01 3.975884e-03 1.987942e-03
[107,] 9.979236e-01 4.152871e-03 2.076436e-03
[108,] 9.973134e-01 5.373232e-03 2.686616e-03
[109,] 9.974879e-01 5.024255e-03 2.512128e-03
[110,] 9.990128e-01 1.974407e-03 9.872035e-04
[111,] 9.986784e-01 2.643161e-03 1.321580e-03
[112,] 9.982198e-01 3.560377e-03 1.780189e-03
[113,] 9.977887e-01 4.422586e-03 2.211293e-03
[114,] 9.974561e-01 5.087799e-03 2.543899e-03
[115,] 9.967516e-01 6.496709e-03 3.248354e-03
[116,] 9.957569e-01 8.486223e-03 4.243112e-03
[117,] 9.946055e-01 1.078908e-02 5.394541e-03
[118,] 9.935253e-01 1.294939e-02 6.474697e-03
[119,] 9.918143e-01 1.637133e-02 8.185667e-03
[120,] 9.895046e-01 2.099078e-02 1.049539e-02
[121,] 9.873491e-01 2.530185e-02 1.265092e-02
[122,] 9.841381e-01 3.172371e-02 1.586186e-02
[123,] 9.800024e-01 3.999530e-02 1.999765e-02
[124,] 9.752683e-01 4.946333e-02 2.473167e-02
[125,] 9.709092e-01 5.818165e-02 2.909082e-02
[126,] 9.652082e-01 6.958363e-02 3.479181e-02
[127,] 9.572638e-01 8.547241e-02 4.273621e-02
[128,] 9.495244e-01 1.009511e-01 5.047556e-02
[129,] 9.388633e-01 1.222735e-01 6.113674e-02
[130,] 9.315020e-01 1.369960e-01 6.849798e-02
[131,] 9.282296e-01 1.435408e-01 7.177038e-02
[132,] 9.142255e-01 1.715490e-01 8.577449e-02
[133,] 9.023544e-01 1.952912e-01 9.764559e-02
[134,] 9.810295e-01 3.794099e-02 1.897050e-02
[135,] 9.810621e-01 3.787576e-02 1.893788e-02
[136,] 9.769001e-01 4.619976e-02 2.309988e-02
[137,] 9.721570e-01 5.568604e-02 2.784302e-02
[138,] 9.684270e-01 6.314609e-02 3.157304e-02
[139,] 9.619368e-01 7.612642e-02 3.806321e-02
[140,] 9.539361e-01 9.212773e-02 4.606387e-02
[141,] 9.477343e-01 1.045314e-01 5.226569e-02
[142,] 9.372408e-01 1.255185e-01 6.275924e-02
[143,] 9.262633e-01 1.474735e-01 7.373674e-02
[144,] 9.154737e-01 1.690525e-01 8.452626e-02
[145,] 9.085718e-01 1.828564e-01 9.142820e-02
[146,] 9.321397e-01 1.357206e-01 6.786030e-02
[147,] 9.192237e-01 1.615527e-01 8.077635e-02
[148,] 9.060921e-01 1.878157e-01 9.390786e-02
[149,] 8.967958e-01 2.064084e-01 1.032042e-01
[150,] 8.779908e-01 2.440183e-01 1.220092e-01
[151,] 8.591195e-01 2.817610e-01 1.408805e-01
[152,] 8.392051e-01 3.215898e-01 1.607949e-01
[153,] 8.562962e-01 2.874076e-01 1.437038e-01
[154,] 8.512999e-01 2.974003e-01 1.487001e-01
[155,] 8.834700e-01 2.330601e-01 1.165300e-01
[156,] 8.715214e-01 2.569571e-01 1.284786e-01
[157,] 8.613647e-01 2.772707e-01 1.386353e-01
[158,] 8.813129e-01 2.373741e-01 1.186871e-01
[159,] 8.624053e-01 2.751893e-01 1.375947e-01
[160,] 8.415529e-01 3.168942e-01 1.584471e-01
[161,] 8.178302e-01 3.643396e-01 1.821698e-01
[162,] 7.909103e-01 4.181795e-01 2.090897e-01
[163,] 7.609714e-01 4.780571e-01 2.390286e-01
[164,] 7.298362e-01 5.403277e-01 2.701638e-01
[165,] 7.267061e-01 5.465878e-01 2.732939e-01
[166,] 7.284889e-01 5.430222e-01 2.715111e-01
[167,] 7.467213e-01 5.065575e-01 2.532787e-01
[168,] 7.202462e-01 5.595077e-01 2.797538e-01
[169,] 6.889485e-01 6.221031e-01 3.110515e-01
[170,] 7.039489e-01 5.921022e-01 2.960511e-01
[171,] 6.807618e-01 6.384765e-01 3.192382e-01
[172,] 6.427616e-01 7.144768e-01 3.572384e-01
[173,] 6.016132e-01 7.967736e-01 3.983868e-01
[174,] 5.763639e-01 8.472723e-01 4.236361e-01
[175,] 5.338470e-01 9.323059e-01 4.661530e-01
[176,] 4.921030e-01 9.842060e-01 5.078970e-01
[177,] 4.561351e-01 9.122702e-01 5.438649e-01
[178,] 4.527720e-01 9.055440e-01 5.472280e-01
[179,] 4.407522e-01 8.815045e-01 5.592478e-01
[180,] 4.017571e-01 8.035141e-01 5.982429e-01
[181,] 3.860370e-01 7.720740e-01 6.139630e-01
[182,] 3.521240e-01 7.042480e-01 6.478760e-01
[183,] 3.191397e-01 6.382794e-01 6.808603e-01
[184,] 2.810773e-01 5.621546e-01 7.189227e-01
[185,] 2.451433e-01 4.902866e-01 7.548567e-01
[186,] 2.160314e-01 4.320628e-01 7.839686e-01
[187,] 2.214216e-01 4.428432e-01 7.785784e-01
[188,] 1.873106e-01 3.746213e-01 8.126894e-01
[189,] 1.594134e-01 3.188269e-01 8.405866e-01
[190,] 1.385586e-01 2.771172e-01 8.614414e-01
[191,] 1.645997e-01 3.291993e-01 8.354003e-01
[192,] 2.158551e-01 4.317102e-01 7.841449e-01
[193,] 1.966338e-01 3.932675e-01 8.033662e-01
[194,] 7.022917e-01 5.954165e-01 2.977083e-01
[195,] 6.845056e-01 6.309887e-01 3.154944e-01
[196,] 6.344663e-01 7.310675e-01 3.655337e-01
[197,] 5.928163e-01 8.143675e-01 4.071837e-01
[198,] 5.392657e-01 9.214686e-01 4.607343e-01
[199,] 4.857342e-01 9.714683e-01 5.142658e-01
[200,] 4.361558e-01 8.723116e-01 5.638442e-01
[201,] 4.609081e-01 9.218161e-01 5.390919e-01
[202,] 4.349633e-01 8.699266e-01 5.650367e-01
[203,] 3.898670e-01 7.797340e-01 6.101330e-01
[204,] 3.342510e-01 6.685019e-01 6.657490e-01
[205,] 3.053545e-01 6.107090e-01 6.946455e-01
[206,] 3.273751e-01 6.547501e-01 6.726249e-01
[207,] 2.830014e-01 5.660027e-01 7.169986e-01
[208,] 2.315119e-01 4.630239e-01 7.684881e-01
[209,] 1.995496e-01 3.990992e-01 8.004504e-01
[210,] 1.584183e-01 3.168367e-01 8.415817e-01
[211,] 1.279954e-01 2.559908e-01 8.720046e-01
[212,] 9.781758e-02 1.956352e-01 9.021824e-01
[213,] 7.664442e-02 1.532888e-01 9.233556e-01
[214,] 1.639030e-01 3.278061e-01 8.360970e-01
[215,] 1.329662e-01 2.659323e-01 8.670338e-01
[216,] 1.027833e-01 2.055666e-01 8.972167e-01
[217,] 7.113517e-02 1.422703e-01 9.288648e-01
[218,] 2.223285e-01 4.446570e-01 7.776715e-01
[219,] 1.621102e-01 3.242203e-01 8.378898e-01
[220,] 1.162324e-01 2.324649e-01 8.837676e-01
[221,] 7.911061e-02 1.582212e-01 9.208894e-01
[222,] 4.863002e-02 9.726003e-02 9.513700e-01
[223,] 3.265490e-02 6.530980e-02 9.673451e-01
[224,] 2.347675e-02 4.695350e-02 9.765233e-01
[225,] 1.591209e-02 3.182417e-02 9.840879e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1mv4z1322142800.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2kyvd1322142800.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3vcnc1322142800.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4hl6f1322142800.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5qyve1322142800.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 = 240
Frequency = 1
1 2 3 4 5 6
-6.92385117 -0.39610337 0.01028704 5.16822448 10.44580241 -1.01108028
7 8 9 10 11 12
1.41078265 5.13390000 1.94224469 -2.40642809 3.48394972 -3.78666903
13 14 15 16 17 18
-4.43217138 3.59179379 -0.75567229 1.35315762 0.51554507 3.81698000
19 20 21 22 23 24
-0.39053856 5.98637641 8.51418128 -3.73735836 2.23798980 -5.04941748
25 26 27 28 29 30
-3.47724824 -1.81831978 -1.82039460 0.17910174 5.52474338 5.09594765
31 32 33 34 35 36
2.46942744 0.99012071 -12.13525854 2.94488704 -1.56473543 -4.08821159
37 38 39 40 41 42
-0.10026260 -5.63152038 33.07645332 4.60813676 1.56834704 3.07071910
43 44 45 46 47 48
1.73629060 -1.01272817 -10.71535883 -3.93081495 -3.89797702 3.50227743
49 50 51 52 53 54
-1.35654879 -2.77920201 -0.02594578 3.32470099 1.30455905 1.94620777
55 56 57 58 59 60
0.96187513 -1.34746264 -29.86459754 3.56178221 0.21274927 2.64607476
61 62 63 64 65 66
1.24500845 -2.33805361 3.76012223 -1.06488291 8.46268970 6.39807546
67 68 69 70 71 72
3.06464786 4.08756442 -23.52986947 -1.13834296 -0.15556461 1.24434968
73 74 75 76 77 78
-8.08824297 -0.63179400 -3.28001711 3.90467681 0.14921440 7.08082040
79 80 81 82 83 84
2.83403014 1.75046089 -9.08730200 -0.05008883 2.66138843 -2.12142801
85 86 87 88 89 90
-3.64511356 1.48679177 -3.67815163 3.59020943 5.93902043 3.20835708
91 92 93 94 95 96
1.96129784 1.07331218 -6.59630067 3.27233448 -4.56735760 0.03762993
97 98 99 100 101 102
-0.49029294 -3.78407052 3.76457883 -2.06588755 4.21577394 -0.13074528
103 104 105 106 107 108
-1.27454460 6.43801778 -3.73517010 4.84214578 3.96917529 -4.26085782
109 110 111 112 113 114
0.54417590 1.54521701 3.11276551 -0.02474268 4.10044488 4.99633678
115 116 117 118 119 120
0.83681198 6.11442214 -11.09141374 -1.28732415 0.70373027 2.77222618
121 122 123 124 125 126
-4.11030155 2.16679512 1.08913856 -2.15423072 3.09951075 1.16048530
127 128 129 130 131 132
0.80424501 -2.83564666 -1.47954914 -0.12146061 -0.86894737 -2.95588906
133 134 135 136 137 138
-2.30729306 0.22415657 -2.57618072 0.20506748 2.84584099 4.16243882
139 140 141 142 143 144
-0.23278887 2.76929845 -12.11179573 -7.01809597 2.28164115 2.84731917
145 146 147 148 149 150
-3.81897870 2.09948747 1.09183148 3.43182411 -1.38143737 1.81870501
151 152 153 154 155 156
0.88970178 4.89142934 -8.95929371 1.24886974 2.41947989 -3.68451752
157 158 159 160 161 162
-0.84785633 -2.12920191 1.91302061 7.15189853 4.92847630 7.09422224
163 164 165 166 167 168
1.20684684 0.51994627 -4.93568497 2.48146942 -3.09678960 -1.31100059
169 170 171 172 173 174
1.64549704 -1.33516674 1.43213261 6.01976761 4.35358149 5.97599784
175 176 177 178 179 180
1.73936801 3.23961270 -6.87654830 -5.36651595 1.18046523 -0.84506300
181 182 183 184 185 186
-4.70307689 -0.78117968 -0.90030640 1.99050491 4.06227695 3.91527802
187 188 189 190 191 192
1.87593656 3.16264329 -2.68881296 -4.38557770 1.90648689 -2.43351589
193 194 195 196 197 198
-4.10430383 -7.11716503 0.23493557 -3.32563517 2.62028468 4.83181984
199 200 201 202 203 204
3.01429963 7.60191572 -22.71980510 4.53780197 -0.70239100 -3.31407057
205 206 207 208 209 210
-1.10903860 -1.55145088 2.30895582 3.91499177 4.95991076 -3.98317387
211 212 213 214 215 216
2.18841420 0.76322052 -6.24916380 -1.60462011 -0.82644420 -3.62812676
217 218 219 220 221 222
-3.19722291 1.78594363 4.38515706 0.30969205 9.18712334 -1.26758757
223 224 225 226 227 228
1.79335505 -0.02612178 -13.01471661 3.54613867 0.14451738 -1.80434874
229 230 231 232 233 234
-4.60144179 0.02880162 0.34827935 3.86608976 4.32274743 -17.63670374
235 236 237 238 239 240
-2.40772401 1.97133991 -13.13426360 3.36737285 -0.79653012 1.06894491
> postscript(file="/var/wessaorg/rcomp/tmp/6eul91322142800.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 = 240
Frequency = 1
lag(myerror, k = 1) myerror
0 -6.92385117 NA
1 -0.39610337 -6.92385117
2 0.01028704 -0.39610337
3 5.16822448 0.01028704
4 10.44580241 5.16822448
5 -1.01108028 10.44580241
6 1.41078265 -1.01108028
7 5.13390000 1.41078265
8 1.94224469 5.13390000
9 -2.40642809 1.94224469
10 3.48394972 -2.40642809
11 -3.78666903 3.48394972
12 -4.43217138 -3.78666903
13 3.59179379 -4.43217138
14 -0.75567229 3.59179379
15 1.35315762 -0.75567229
16 0.51554507 1.35315762
17 3.81698000 0.51554507
18 -0.39053856 3.81698000
19 5.98637641 -0.39053856
20 8.51418128 5.98637641
21 -3.73735836 8.51418128
22 2.23798980 -3.73735836
23 -5.04941748 2.23798980
24 -3.47724824 -5.04941748
25 -1.81831978 -3.47724824
26 -1.82039460 -1.81831978
27 0.17910174 -1.82039460
28 5.52474338 0.17910174
29 5.09594765 5.52474338
30 2.46942744 5.09594765
31 0.99012071 2.46942744
32 -12.13525854 0.99012071
33 2.94488704 -12.13525854
34 -1.56473543 2.94488704
35 -4.08821159 -1.56473543
36 -0.10026260 -4.08821159
37 -5.63152038 -0.10026260
38 33.07645332 -5.63152038
39 4.60813676 33.07645332
40 1.56834704 4.60813676
41 3.07071910 1.56834704
42 1.73629060 3.07071910
43 -1.01272817 1.73629060
44 -10.71535883 -1.01272817
45 -3.93081495 -10.71535883
46 -3.89797702 -3.93081495
47 3.50227743 -3.89797702
48 -1.35654879 3.50227743
49 -2.77920201 -1.35654879
50 -0.02594578 -2.77920201
51 3.32470099 -0.02594578
52 1.30455905 3.32470099
53 1.94620777 1.30455905
54 0.96187513 1.94620777
55 -1.34746264 0.96187513
56 -29.86459754 -1.34746264
57 3.56178221 -29.86459754
58 0.21274927 3.56178221
59 2.64607476 0.21274927
60 1.24500845 2.64607476
61 -2.33805361 1.24500845
62 3.76012223 -2.33805361
63 -1.06488291 3.76012223
64 8.46268970 -1.06488291
65 6.39807546 8.46268970
66 3.06464786 6.39807546
67 4.08756442 3.06464786
68 -23.52986947 4.08756442
69 -1.13834296 -23.52986947
70 -0.15556461 -1.13834296
71 1.24434968 -0.15556461
72 -8.08824297 1.24434968
73 -0.63179400 -8.08824297
74 -3.28001711 -0.63179400
75 3.90467681 -3.28001711
76 0.14921440 3.90467681
77 7.08082040 0.14921440
78 2.83403014 7.08082040
79 1.75046089 2.83403014
80 -9.08730200 1.75046089
81 -0.05008883 -9.08730200
82 2.66138843 -0.05008883
83 -2.12142801 2.66138843
84 -3.64511356 -2.12142801
85 1.48679177 -3.64511356
86 -3.67815163 1.48679177
87 3.59020943 -3.67815163
88 5.93902043 3.59020943
89 3.20835708 5.93902043
90 1.96129784 3.20835708
91 1.07331218 1.96129784
92 -6.59630067 1.07331218
93 3.27233448 -6.59630067
94 -4.56735760 3.27233448
95 0.03762993 -4.56735760
96 -0.49029294 0.03762993
97 -3.78407052 -0.49029294
98 3.76457883 -3.78407052
99 -2.06588755 3.76457883
100 4.21577394 -2.06588755
101 -0.13074528 4.21577394
102 -1.27454460 -0.13074528
103 6.43801778 -1.27454460
104 -3.73517010 6.43801778
105 4.84214578 -3.73517010
106 3.96917529 4.84214578
107 -4.26085782 3.96917529
108 0.54417590 -4.26085782
109 1.54521701 0.54417590
110 3.11276551 1.54521701
111 -0.02474268 3.11276551
112 4.10044488 -0.02474268
113 4.99633678 4.10044488
114 0.83681198 4.99633678
115 6.11442214 0.83681198
116 -11.09141374 6.11442214
117 -1.28732415 -11.09141374
118 0.70373027 -1.28732415
119 2.77222618 0.70373027
120 -4.11030155 2.77222618
121 2.16679512 -4.11030155
122 1.08913856 2.16679512
123 -2.15423072 1.08913856
124 3.09951075 -2.15423072
125 1.16048530 3.09951075
126 0.80424501 1.16048530
127 -2.83564666 0.80424501
128 -1.47954914 -2.83564666
129 -0.12146061 -1.47954914
130 -0.86894737 -0.12146061
131 -2.95588906 -0.86894737
132 -2.30729306 -2.95588906
133 0.22415657 -2.30729306
134 -2.57618072 0.22415657
135 0.20506748 -2.57618072
136 2.84584099 0.20506748
137 4.16243882 2.84584099
138 -0.23278887 4.16243882
139 2.76929845 -0.23278887
140 -12.11179573 2.76929845
141 -7.01809597 -12.11179573
142 2.28164115 -7.01809597
143 2.84731917 2.28164115
144 -3.81897870 2.84731917
145 2.09948747 -3.81897870
146 1.09183148 2.09948747
147 3.43182411 1.09183148
148 -1.38143737 3.43182411
149 1.81870501 -1.38143737
150 0.88970178 1.81870501
151 4.89142934 0.88970178
152 -8.95929371 4.89142934
153 1.24886974 -8.95929371
154 2.41947989 1.24886974
155 -3.68451752 2.41947989
156 -0.84785633 -3.68451752
157 -2.12920191 -0.84785633
158 1.91302061 -2.12920191
159 7.15189853 1.91302061
160 4.92847630 7.15189853
161 7.09422224 4.92847630
162 1.20684684 7.09422224
163 0.51994627 1.20684684
164 -4.93568497 0.51994627
165 2.48146942 -4.93568497
166 -3.09678960 2.48146942
167 -1.31100059 -3.09678960
168 1.64549704 -1.31100059
169 -1.33516674 1.64549704
170 1.43213261 -1.33516674
171 6.01976761 1.43213261
172 4.35358149 6.01976761
173 5.97599784 4.35358149
174 1.73936801 5.97599784
175 3.23961270 1.73936801
176 -6.87654830 3.23961270
177 -5.36651595 -6.87654830
178 1.18046523 -5.36651595
179 -0.84506300 1.18046523
180 -4.70307689 -0.84506300
181 -0.78117968 -4.70307689
182 -0.90030640 -0.78117968
183 1.99050491 -0.90030640
184 4.06227695 1.99050491
185 3.91527802 4.06227695
186 1.87593656 3.91527802
187 3.16264329 1.87593656
188 -2.68881296 3.16264329
189 -4.38557770 -2.68881296
190 1.90648689 -4.38557770
191 -2.43351589 1.90648689
192 -4.10430383 -2.43351589
193 -7.11716503 -4.10430383
194 0.23493557 -7.11716503
195 -3.32563517 0.23493557
196 2.62028468 -3.32563517
197 4.83181984 2.62028468
198 3.01429963 4.83181984
199 7.60191572 3.01429963
200 -22.71980510 7.60191572
201 4.53780197 -22.71980510
202 -0.70239100 4.53780197
203 -3.31407057 -0.70239100
204 -1.10903860 -3.31407057
205 -1.55145088 -1.10903860
206 2.30895582 -1.55145088
207 3.91499177 2.30895582
208 4.95991076 3.91499177
209 -3.98317387 4.95991076
210 2.18841420 -3.98317387
211 0.76322052 2.18841420
212 -6.24916380 0.76322052
213 -1.60462011 -6.24916380
214 -0.82644420 -1.60462011
215 -3.62812676 -0.82644420
216 -3.19722291 -3.62812676
217 1.78594363 -3.19722291
218 4.38515706 1.78594363
219 0.30969205 4.38515706
220 9.18712334 0.30969205
221 -1.26758757 9.18712334
222 1.79335505 -1.26758757
223 -0.02612178 1.79335505
224 -13.01471661 -0.02612178
225 3.54613867 -13.01471661
226 0.14451738 3.54613867
227 -1.80434874 0.14451738
228 -4.60144179 -1.80434874
229 0.02880162 -4.60144179
230 0.34827935 0.02880162
231 3.86608976 0.34827935
232 4.32274743 3.86608976
233 -17.63670374 4.32274743
234 -2.40772401 -17.63670374
235 1.97133991 -2.40772401
236 -13.13426360 1.97133991
237 3.36737285 -13.13426360
238 -0.79653012 3.36737285
239 1.06894491 -0.79653012
240 NA 1.06894491
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.39610337 -6.92385117
[2,] 0.01028704 -0.39610337
[3,] 5.16822448 0.01028704
[4,] 10.44580241 5.16822448
[5,] -1.01108028 10.44580241
[6,] 1.41078265 -1.01108028
[7,] 5.13390000 1.41078265
[8,] 1.94224469 5.13390000
[9,] -2.40642809 1.94224469
[10,] 3.48394972 -2.40642809
[11,] -3.78666903 3.48394972
[12,] -4.43217138 -3.78666903
[13,] 3.59179379 -4.43217138
[14,] -0.75567229 3.59179379
[15,] 1.35315762 -0.75567229
[16,] 0.51554507 1.35315762
[17,] 3.81698000 0.51554507
[18,] -0.39053856 3.81698000
[19,] 5.98637641 -0.39053856
[20,] 8.51418128 5.98637641
[21,] -3.73735836 8.51418128
[22,] 2.23798980 -3.73735836
[23,] -5.04941748 2.23798980
[24,] -3.47724824 -5.04941748
[25,] -1.81831978 -3.47724824
[26,] -1.82039460 -1.81831978
[27,] 0.17910174 -1.82039460
[28,] 5.52474338 0.17910174
[29,] 5.09594765 5.52474338
[30,] 2.46942744 5.09594765
[31,] 0.99012071 2.46942744
[32,] -12.13525854 0.99012071
[33,] 2.94488704 -12.13525854
[34,] -1.56473543 2.94488704
[35,] -4.08821159 -1.56473543
[36,] -0.10026260 -4.08821159
[37,] -5.63152038 -0.10026260
[38,] 33.07645332 -5.63152038
[39,] 4.60813676 33.07645332
[40,] 1.56834704 4.60813676
[41,] 3.07071910 1.56834704
[42,] 1.73629060 3.07071910
[43,] -1.01272817 1.73629060
[44,] -10.71535883 -1.01272817
[45,] -3.93081495 -10.71535883
[46,] -3.89797702 -3.93081495
[47,] 3.50227743 -3.89797702
[48,] -1.35654879 3.50227743
[49,] -2.77920201 -1.35654879
[50,] -0.02594578 -2.77920201
[51,] 3.32470099 -0.02594578
[52,] 1.30455905 3.32470099
[53,] 1.94620777 1.30455905
[54,] 0.96187513 1.94620777
[55,] -1.34746264 0.96187513
[56,] -29.86459754 -1.34746264
[57,] 3.56178221 -29.86459754
[58,] 0.21274927 3.56178221
[59,] 2.64607476 0.21274927
[60,] 1.24500845 2.64607476
[61,] -2.33805361 1.24500845
[62,] 3.76012223 -2.33805361
[63,] -1.06488291 3.76012223
[64,] 8.46268970 -1.06488291
[65,] 6.39807546 8.46268970
[66,] 3.06464786 6.39807546
[67,] 4.08756442 3.06464786
[68,] -23.52986947 4.08756442
[69,] -1.13834296 -23.52986947
[70,] -0.15556461 -1.13834296
[71,] 1.24434968 -0.15556461
[72,] -8.08824297 1.24434968
[73,] -0.63179400 -8.08824297
[74,] -3.28001711 -0.63179400
[75,] 3.90467681 -3.28001711
[76,] 0.14921440 3.90467681
[77,] 7.08082040 0.14921440
[78,] 2.83403014 7.08082040
[79,] 1.75046089 2.83403014
[80,] -9.08730200 1.75046089
[81,] -0.05008883 -9.08730200
[82,] 2.66138843 -0.05008883
[83,] -2.12142801 2.66138843
[84,] -3.64511356 -2.12142801
[85,] 1.48679177 -3.64511356
[86,] -3.67815163 1.48679177
[87,] 3.59020943 -3.67815163
[88,] 5.93902043 3.59020943
[89,] 3.20835708 5.93902043
[90,] 1.96129784 3.20835708
[91,] 1.07331218 1.96129784
[92,] -6.59630067 1.07331218
[93,] 3.27233448 -6.59630067
[94,] -4.56735760 3.27233448
[95,] 0.03762993 -4.56735760
[96,] -0.49029294 0.03762993
[97,] -3.78407052 -0.49029294
[98,] 3.76457883 -3.78407052
[99,] -2.06588755 3.76457883
[100,] 4.21577394 -2.06588755
[101,] -0.13074528 4.21577394
[102,] -1.27454460 -0.13074528
[103,] 6.43801778 -1.27454460
[104,] -3.73517010 6.43801778
[105,] 4.84214578 -3.73517010
[106,] 3.96917529 4.84214578
[107,] -4.26085782 3.96917529
[108,] 0.54417590 -4.26085782
[109,] 1.54521701 0.54417590
[110,] 3.11276551 1.54521701
[111,] -0.02474268 3.11276551
[112,] 4.10044488 -0.02474268
[113,] 4.99633678 4.10044488
[114,] 0.83681198 4.99633678
[115,] 6.11442214 0.83681198
[116,] -11.09141374 6.11442214
[117,] -1.28732415 -11.09141374
[118,] 0.70373027 -1.28732415
[119,] 2.77222618 0.70373027
[120,] -4.11030155 2.77222618
[121,] 2.16679512 -4.11030155
[122,] 1.08913856 2.16679512
[123,] -2.15423072 1.08913856
[124,] 3.09951075 -2.15423072
[125,] 1.16048530 3.09951075
[126,] 0.80424501 1.16048530
[127,] -2.83564666 0.80424501
[128,] -1.47954914 -2.83564666
[129,] -0.12146061 -1.47954914
[130,] -0.86894737 -0.12146061
[131,] -2.95588906 -0.86894737
[132,] -2.30729306 -2.95588906
[133,] 0.22415657 -2.30729306
[134,] -2.57618072 0.22415657
[135,] 0.20506748 -2.57618072
[136,] 2.84584099 0.20506748
[137,] 4.16243882 2.84584099
[138,] -0.23278887 4.16243882
[139,] 2.76929845 -0.23278887
[140,] -12.11179573 2.76929845
[141,] -7.01809597 -12.11179573
[142,] 2.28164115 -7.01809597
[143,] 2.84731917 2.28164115
[144,] -3.81897870 2.84731917
[145,] 2.09948747 -3.81897870
[146,] 1.09183148 2.09948747
[147,] 3.43182411 1.09183148
[148,] -1.38143737 3.43182411
[149,] 1.81870501 -1.38143737
[150,] 0.88970178 1.81870501
[151,] 4.89142934 0.88970178
[152,] -8.95929371 4.89142934
[153,] 1.24886974 -8.95929371
[154,] 2.41947989 1.24886974
[155,] -3.68451752 2.41947989
[156,] -0.84785633 -3.68451752
[157,] -2.12920191 -0.84785633
[158,] 1.91302061 -2.12920191
[159,] 7.15189853 1.91302061
[160,] 4.92847630 7.15189853
[161,] 7.09422224 4.92847630
[162,] 1.20684684 7.09422224
[163,] 0.51994627 1.20684684
[164,] -4.93568497 0.51994627
[165,] 2.48146942 -4.93568497
[166,] -3.09678960 2.48146942
[167,] -1.31100059 -3.09678960
[168,] 1.64549704 -1.31100059
[169,] -1.33516674 1.64549704
[170,] 1.43213261 -1.33516674
[171,] 6.01976761 1.43213261
[172,] 4.35358149 6.01976761
[173,] 5.97599784 4.35358149
[174,] 1.73936801 5.97599784
[175,] 3.23961270 1.73936801
[176,] -6.87654830 3.23961270
[177,] -5.36651595 -6.87654830
[178,] 1.18046523 -5.36651595
[179,] -0.84506300 1.18046523
[180,] -4.70307689 -0.84506300
[181,] -0.78117968 -4.70307689
[182,] -0.90030640 -0.78117968
[183,] 1.99050491 -0.90030640
[184,] 4.06227695 1.99050491
[185,] 3.91527802 4.06227695
[186,] 1.87593656 3.91527802
[187,] 3.16264329 1.87593656
[188,] -2.68881296 3.16264329
[189,] -4.38557770 -2.68881296
[190,] 1.90648689 -4.38557770
[191,] -2.43351589 1.90648689
[192,] -4.10430383 -2.43351589
[193,] -7.11716503 -4.10430383
[194,] 0.23493557 -7.11716503
[195,] -3.32563517 0.23493557
[196,] 2.62028468 -3.32563517
[197,] 4.83181984 2.62028468
[198,] 3.01429963 4.83181984
[199,] 7.60191572 3.01429963
[200,] -22.71980510 7.60191572
[201,] 4.53780197 -22.71980510
[202,] -0.70239100 4.53780197
[203,] -3.31407057 -0.70239100
[204,] -1.10903860 -3.31407057
[205,] -1.55145088 -1.10903860
[206,] 2.30895582 -1.55145088
[207,] 3.91499177 2.30895582
[208,] 4.95991076 3.91499177
[209,] -3.98317387 4.95991076
[210,] 2.18841420 -3.98317387
[211,] 0.76322052 2.18841420
[212,] -6.24916380 0.76322052
[213,] -1.60462011 -6.24916380
[214,] -0.82644420 -1.60462011
[215,] -3.62812676 -0.82644420
[216,] -3.19722291 -3.62812676
[217,] 1.78594363 -3.19722291
[218,] 4.38515706 1.78594363
[219,] 0.30969205 4.38515706
[220,] 9.18712334 0.30969205
[221,] -1.26758757 9.18712334
[222,] 1.79335505 -1.26758757
[223,] -0.02612178 1.79335505
[224,] -13.01471661 -0.02612178
[225,] 3.54613867 -13.01471661
[226,] 0.14451738 3.54613867
[227,] -1.80434874 0.14451738
[228,] -4.60144179 -1.80434874
[229,] 0.02880162 -4.60144179
[230,] 0.34827935 0.02880162
[231,] 3.86608976 0.34827935
[232,] 4.32274743 3.86608976
[233,] -17.63670374 4.32274743
[234,] -2.40772401 -17.63670374
[235,] 1.97133991 -2.40772401
[236,] -13.13426360 1.97133991
[237,] 3.36737285 -13.13426360
[238,] -0.79653012 3.36737285
[239,] 1.06894491 -0.79653012
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.39610337 -6.92385117
2 0.01028704 -0.39610337
3 5.16822448 0.01028704
4 10.44580241 5.16822448
5 -1.01108028 10.44580241
6 1.41078265 -1.01108028
7 5.13390000 1.41078265
8 1.94224469 5.13390000
9 -2.40642809 1.94224469
10 3.48394972 -2.40642809
11 -3.78666903 3.48394972
12 -4.43217138 -3.78666903
13 3.59179379 -4.43217138
14 -0.75567229 3.59179379
15 1.35315762 -0.75567229
16 0.51554507 1.35315762
17 3.81698000 0.51554507
18 -0.39053856 3.81698000
19 5.98637641 -0.39053856
20 8.51418128 5.98637641
21 -3.73735836 8.51418128
22 2.23798980 -3.73735836
23 -5.04941748 2.23798980
24 -3.47724824 -5.04941748
25 -1.81831978 -3.47724824
26 -1.82039460 -1.81831978
27 0.17910174 -1.82039460
28 5.52474338 0.17910174
29 5.09594765 5.52474338
30 2.46942744 5.09594765
31 0.99012071 2.46942744
32 -12.13525854 0.99012071
33 2.94488704 -12.13525854
34 -1.56473543 2.94488704
35 -4.08821159 -1.56473543
36 -0.10026260 -4.08821159
37 -5.63152038 -0.10026260
38 33.07645332 -5.63152038
39 4.60813676 33.07645332
40 1.56834704 4.60813676
41 3.07071910 1.56834704
42 1.73629060 3.07071910
43 -1.01272817 1.73629060
44 -10.71535883 -1.01272817
45 -3.93081495 -10.71535883
46 -3.89797702 -3.93081495
47 3.50227743 -3.89797702
48 -1.35654879 3.50227743
49 -2.77920201 -1.35654879
50 -0.02594578 -2.77920201
51 3.32470099 -0.02594578
52 1.30455905 3.32470099
53 1.94620777 1.30455905
54 0.96187513 1.94620777
55 -1.34746264 0.96187513
56 -29.86459754 -1.34746264
57 3.56178221 -29.86459754
58 0.21274927 3.56178221
59 2.64607476 0.21274927
60 1.24500845 2.64607476
61 -2.33805361 1.24500845
62 3.76012223 -2.33805361
63 -1.06488291 3.76012223
64 8.46268970 -1.06488291
65 6.39807546 8.46268970
66 3.06464786 6.39807546
67 4.08756442 3.06464786
68 -23.52986947 4.08756442
69 -1.13834296 -23.52986947
70 -0.15556461 -1.13834296
71 1.24434968 -0.15556461
72 -8.08824297 1.24434968
73 -0.63179400 -8.08824297
74 -3.28001711 -0.63179400
75 3.90467681 -3.28001711
76 0.14921440 3.90467681
77 7.08082040 0.14921440
78 2.83403014 7.08082040
79 1.75046089 2.83403014
80 -9.08730200 1.75046089
81 -0.05008883 -9.08730200
82 2.66138843 -0.05008883
83 -2.12142801 2.66138843
84 -3.64511356 -2.12142801
85 1.48679177 -3.64511356
86 -3.67815163 1.48679177
87 3.59020943 -3.67815163
88 5.93902043 3.59020943
89 3.20835708 5.93902043
90 1.96129784 3.20835708
91 1.07331218 1.96129784
92 -6.59630067 1.07331218
93 3.27233448 -6.59630067
94 -4.56735760 3.27233448
95 0.03762993 -4.56735760
96 -0.49029294 0.03762993
97 -3.78407052 -0.49029294
98 3.76457883 -3.78407052
99 -2.06588755 3.76457883
100 4.21577394 -2.06588755
101 -0.13074528 4.21577394
102 -1.27454460 -0.13074528
103 6.43801778 -1.27454460
104 -3.73517010 6.43801778
105 4.84214578 -3.73517010
106 3.96917529 4.84214578
107 -4.26085782 3.96917529
108 0.54417590 -4.26085782
109 1.54521701 0.54417590
110 3.11276551 1.54521701
111 -0.02474268 3.11276551
112 4.10044488 -0.02474268
113 4.99633678 4.10044488
114 0.83681198 4.99633678
115 6.11442214 0.83681198
116 -11.09141374 6.11442214
117 -1.28732415 -11.09141374
118 0.70373027 -1.28732415
119 2.77222618 0.70373027
120 -4.11030155 2.77222618
121 2.16679512 -4.11030155
122 1.08913856 2.16679512
123 -2.15423072 1.08913856
124 3.09951075 -2.15423072
125 1.16048530 3.09951075
126 0.80424501 1.16048530
127 -2.83564666 0.80424501
128 -1.47954914 -2.83564666
129 -0.12146061 -1.47954914
130 -0.86894737 -0.12146061
131 -2.95588906 -0.86894737
132 -2.30729306 -2.95588906
133 0.22415657 -2.30729306
134 -2.57618072 0.22415657
135 0.20506748 -2.57618072
136 2.84584099 0.20506748
137 4.16243882 2.84584099
138 -0.23278887 4.16243882
139 2.76929845 -0.23278887
140 -12.11179573 2.76929845
141 -7.01809597 -12.11179573
142 2.28164115 -7.01809597
143 2.84731917 2.28164115
144 -3.81897870 2.84731917
145 2.09948747 -3.81897870
146 1.09183148 2.09948747
147 3.43182411 1.09183148
148 -1.38143737 3.43182411
149 1.81870501 -1.38143737
150 0.88970178 1.81870501
151 4.89142934 0.88970178
152 -8.95929371 4.89142934
153 1.24886974 -8.95929371
154 2.41947989 1.24886974
155 -3.68451752 2.41947989
156 -0.84785633 -3.68451752
157 -2.12920191 -0.84785633
158 1.91302061 -2.12920191
159 7.15189853 1.91302061
160 4.92847630 7.15189853
161 7.09422224 4.92847630
162 1.20684684 7.09422224
163 0.51994627 1.20684684
164 -4.93568497 0.51994627
165 2.48146942 -4.93568497
166 -3.09678960 2.48146942
167 -1.31100059 -3.09678960
168 1.64549704 -1.31100059
169 -1.33516674 1.64549704
170 1.43213261 -1.33516674
171 6.01976761 1.43213261
172 4.35358149 6.01976761
173 5.97599784 4.35358149
174 1.73936801 5.97599784
175 3.23961270 1.73936801
176 -6.87654830 3.23961270
177 -5.36651595 -6.87654830
178 1.18046523 -5.36651595
179 -0.84506300 1.18046523
180 -4.70307689 -0.84506300
181 -0.78117968 -4.70307689
182 -0.90030640 -0.78117968
183 1.99050491 -0.90030640
184 4.06227695 1.99050491
185 3.91527802 4.06227695
186 1.87593656 3.91527802
187 3.16264329 1.87593656
188 -2.68881296 3.16264329
189 -4.38557770 -2.68881296
190 1.90648689 -4.38557770
191 -2.43351589 1.90648689
192 -4.10430383 -2.43351589
193 -7.11716503 -4.10430383
194 0.23493557 -7.11716503
195 -3.32563517 0.23493557
196 2.62028468 -3.32563517
197 4.83181984 2.62028468
198 3.01429963 4.83181984
199 7.60191572 3.01429963
200 -22.71980510 7.60191572
201 4.53780197 -22.71980510
202 -0.70239100 4.53780197
203 -3.31407057 -0.70239100
204 -1.10903860 -3.31407057
205 -1.55145088 -1.10903860
206 2.30895582 -1.55145088
207 3.91499177 2.30895582
208 4.95991076 3.91499177
209 -3.98317387 4.95991076
210 2.18841420 -3.98317387
211 0.76322052 2.18841420
212 -6.24916380 0.76322052
213 -1.60462011 -6.24916380
214 -0.82644420 -1.60462011
215 -3.62812676 -0.82644420
216 -3.19722291 -3.62812676
217 1.78594363 -3.19722291
218 4.38515706 1.78594363
219 0.30969205 4.38515706
220 9.18712334 0.30969205
221 -1.26758757 9.18712334
222 1.79335505 -1.26758757
223 -0.02612178 1.79335505
224 -13.01471661 -0.02612178
225 3.54613867 -13.01471661
226 0.14451738 3.54613867
227 -1.80434874 0.14451738
228 -4.60144179 -1.80434874
229 0.02880162 -4.60144179
230 0.34827935 0.02880162
231 3.86608976 0.34827935
232 4.32274743 3.86608976
233 -17.63670374 4.32274743
234 -2.40772401 -17.63670374
235 1.97133991 -2.40772401
236 -13.13426360 1.97133991
237 3.36737285 -13.13426360
238 -0.79653012 3.36737285
239 1.06894491 -0.79653012
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7o49z1322142800.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/86wet1322142800.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9sjsd1322142800.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
Warning messages:
1: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
2: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10hord1322142800.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/110fpi1322142800.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/120wxr1322142800.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13us591322142800.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14nrib1322142800.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15wra31322142800.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16oowh1322142800.tab")
+ }
>
> try(system("convert tmp/1mv4z1322142800.ps tmp/1mv4z1322142800.png",intern=TRUE))
character(0)
> try(system("convert tmp/2kyvd1322142800.ps tmp/2kyvd1322142800.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vcnc1322142800.ps tmp/3vcnc1322142800.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hl6f1322142800.ps tmp/4hl6f1322142800.png",intern=TRUE))
character(0)
> try(system("convert tmp/5qyve1322142800.ps tmp/5qyve1322142800.png",intern=TRUE))
character(0)
> try(system("convert tmp/6eul91322142800.ps tmp/6eul91322142800.png",intern=TRUE))
character(0)
> try(system("convert tmp/7o49z1322142800.ps tmp/7o49z1322142800.png",intern=TRUE))
character(0)
> try(system("convert tmp/86wet1322142800.ps tmp/86wet1322142800.png",intern=TRUE))
character(0)
> try(system("convert tmp/9sjsd1322142800.ps tmp/9sjsd1322142800.png",intern=TRUE))
character(0)
> try(system("convert tmp/10hord1322142800.ps tmp/10hord1322142800.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.581 0.745 7.504