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 = '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 t
1 61 80 41 568 10173 1
2 81 111 50 1110 10083 2
3 87 122 46 338 10258 3
4 87 131 36 555 10154 4
5 136 192 67 281 10207 5
6 147 188 104 571 10133 6
7 168 216 115 322 10197 7
8 185 238 125 503 10184 8
9 137 173 97 1078 10163 9
10 125 160 88 582 10104 10
11 64 93 27 926 10127 11
12 45 67 19 491 10164 12
13 35 60 9 504 10219 13
14 -4 32 -47 314 10177 14
15 88 126 46 269 10138 15
16 85 131 36 252 10164 16
17 95 134 51 342 10223 17
18 128 162 90 1464 10122 18
19 186 230 142 921 10161 19
20 182 232 119 115 10199 20
21 151 200 92 789 10160 21
22 106 143 70 495 10157 22
23 60 85 30 1279 10113 23
24 44 66 19 391 10276 24
25 30 54 2 352 10303 25
26 54 81 21 340 10232 26
27 72 100 41 715 10140 27
28 88 126 46 425 10121 28
29 153 204 94 413 10188 29
30 168 218 114 935 10164 30
31 181 227 130 680 10165 31
32 180 220 141 1472 10121 32
33 149 220 109 767 10166 33
34 84 120 46 1215 10091 34
35 85 110 58 1113 10121 35
36 42 67 17 711 10180 36
37 54 81 22 742 10197 37
38 30 52 5 225 10289 38
39 96 106 17 107 10220 39
40 110 156 57 457 10106 40
41 141 187 91 448 10141 41
42 159 204 106 385 10165 42
43 164 204 125 1518 10152 43
44 155 196 111 495 10188 44
45 135 204 99 1283 10110 45
46 93 124 63 751 10144 46
47 28 53 3 674 10206 47
48 56 77 30 1705 10045 48
49 56 77 30 894 10100 49
50 22 50 -9 309 10147 50
51 76 105 42 745 10149 51
52 83 125 38 806 10116 52
53 121 165 73 423 10138 53
54 151 194 102 506 10183 54
55 208 263 149 148 10174 55
56 179 225 132 494 10143 56
57 139 263 108 1794 10120 57
58 99 140 58 1329 10143 58
59 103 127 66 289 10181 59
60 57 86 24 1213 10161 60
61 44 71 15 1263 10121 61
62 70 95 43 910 10095 62
63 58 95 17 934 10114 63
64 91 133 47 228 10173 64
65 126 178 63 366 10164 65
66 146 160 101 45 10174 66
67 199 250 142 459 10155 67
68 194 251 131 253 10182 68
69 145 250 107 999 10109 69
70 131 173 85 182 10198 70
71 74 103 38 483 10167 71
72 -3 21 -36 401 10178 72
73 7 29 -15 47 10143 73
74 10 39 -21 665 10127 74
75 34 71 -3 102 10183 75
76 94 148 35 22 10178 76
77 105 144 62 445 10142 77
78 151 199 91 378 10207 78
79 162 206 110 419 10176 79
80 175 224 127 1046 10145 80
81 128 206 79 531 10172 81
82 115 152 76 809 10157 82
83 62 88 32 1416 10086 83
84 11 35 -18 369 10151 84
85 -7 23 -39 20 10236 85
86 64 92 30 882 10160 86
87 80 117 43 262 10233 87
88 77 120 26 186 10212 88
89 127 173 74 763 10150 89
90 158 202 111 1038 10100 90
91 173 217 123 558 10178 91
92 206 256 151 335 10161 92
93 147 217 95 242 10217 93
94 103 143 59 898 10173 94
95 73 95 47 498 10067 95
96 52 77 21 757 10117 96
97 52 76 23 843 10149 97
98 68 100 33 133 10238 98
99 77 108 39 1035 10211 99
100 94 132 61 1117 10030 100
101 147 195 91 341 10165 101
102 160 198 123 1304 10142 102
103 166 204 124 566 10126 103
104 167 212 112 756 10176 104
105 155 204 122 1761 10095 105
106 104 129 70 1469 10105 106
107 44 73 11 1370 10172 107
108 53 77 29 795 10180 108
109 56 80 26 920 10126 109
110 36 64 2 754 10154 110
111 76 109 38 1034 10107 111
112 99 138 58 617 10133 112
113 142 185 92 706 10158 113
114 150 198 97 832 10173 114
115 190 237 138 393 10171 115
116 176 223 127 1551 10130 116
117 175 237 136 675 10105 117
118 112 146 80 1225 10154 118
119 73 102 38 737 10206 119
120 52 77 23 1444 10078 120
121 48 70 22 452 10233 121
122 61 86 30 1157 10179 122
123 68 98 32 718 10197 123
124 97 141 55 419 10075 124
125 146 195 96 898 10147 125
126 160 205 110 417 10195 126
127 155 191 117 1207 10129 127
128 175 226 125 163 10175 128
129 163 191 127 643 10128 129
130 117 147 86 1333 10099 130
131 82 100 62 1625 10015 131
132 55 74 33 970 10079 132
133 32 56 6 787 10112 133
134 48 77 17 995 10170 134
135 53 80 24 669 10048 135
136 82 120 44 861 10119 136
137 139 186 85 247 10180 137
138 150 196 95 349 10168 138
139 184 229 140 994 10141 139
140 185 229 139 1213 10149 140
141 138 229 104 2540 10117 141
142 147 176 117 388 10140 142
143 77 104 42 907 10216 143
144 32 61 -4 778 10227 144
145 48 72 23 729 10209 145
146 72 99 42 1428 10097 146
147 76 113 34 462 10176 147
148 94 140 44 528 10158 148
149 133 174 89 325 10132 149
150 164 209 116 777 10154 150
151 174 205 133 686 10145 151
152 187 229 141 1464 10153 152
153 149 215 104 438 10199 153
154 102 136 63 792 10111 154
155 86 113 52 1089 10071 155
156 35 57 13 920 10151 156
157 31 55 2 680 10148 157
158 28 66 -10 206 10206 158
159 75 125 23 177 10235 159
160 102 149 45 438 10170 160
161 133 176 83 800 10164 161
162 178 230 114 278 10161 162
163 190 238 137 396 10155 163
164 190 245 132 101 10181 164
165 147 238 87 785 10200 165
166 83 124 39 724 10133 166
167 83 111 52 556 10139 167
168 46 72 18 905 10169 168
169 40 63 12 1199 10080 169
170 50 78 19 688 10191 170
171 61 100 18 443 10202 171
172 102 149 49 710 10128 172
173 117 166 61 273 10160 173
174 158 201 105 752 10170 174
175 170 214 123 852 10158 175
176 190 231 150 1838 10110 176
177 155 214 113 765 10181 177
178 117 151 84 453 10093 178
179 68 97 33 792 10206 179
180 40 68 7 490 10180 180
181 56 81 30 562 10202 181
182 28 55 -2 731 10193 182
183 66 99 28 315 10158 183
184 103 146 57 623 10139 184
185 122 170 68 423 10167 185
186 166 218 111 726 10188 186
187 176 218 132 1137 10147 187
188 164 207 115 773 10173 188
189 160 218 114 971 10180 189
190 139 178 102 547 10166 190
191 75 105 40 1004 10149 191
192 44 67 16 538 10167 192
193 22 47 -7 149 10243 193
194 32 55 11 504 10148 194
195 42 73 7 619 10105 195
196 86 124 47 176 10144 196
197 140 185 93 908 10136 197
198 163 213 104 290 10208 198
199 222 278 159 155 10192 199
200 166 205 129 2681 10111 200
201 183 278 140 179 10139 201
202 140 171 100 1243 10112 202
203 98 125 67 973 10147 203
204 69 92 44 860 10205 204
205 75 96 49 1029 10154 205
206 63 92 32 772 10087 206
207 81 118 40 805 10151 207
208 126 185 63 3 10217 208
209 139 183 92 1237 10106 209
210 171 215 133 939 10117 210
211 170 207 134 1799 10115 211
212 173 214 126 534 10148 212
213 144 207 102 1042 10191 213
214 105 142 64 270 10238 214
215 75 102 43 724 10183 215
216 41 66 16 783 10206 216
217 68 87 43 648 10138 217
218 53 90 11 465 10238 218
219 61 90 26 1292 10052 219
220 87 133 40 318 10110 220
221 155 205 94 747 10156 221
222 159 201 113 298 10160 222
223 180 220 137 1145 10141 223
224 175 210 140 1456 10116 224
225 138 220 93 612 10176 225
226 105 136 67 1136 10146 226
227 73 95 44 903 1125 227
228 26 52 -3 609 10180 228
229 12 40 -14 532 10133 229
230 35 60 4 672 10141 230
231 64 100 25 568 10141 231
232 115 169 57 234 10140 232
233 138 184 87 778 10187 233
234 138 202 107 436 10169 234
235 182 226 140 795 10128 235
236 191 239 136 298 10164 236
237 155 226 112 284 10208 237
238 113 149 70 852 10165 238
239 98 121 72 1307 10036 239
240 29 50 3 1166 10064 240
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Max Min Neerslag Luchtdruk t
1.695e+01 3.384e-01 6.917e-01 -5.929e-03 -4.219e-05 -5.557e-03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-30.276 -2.226 0.699 2.974 32.704
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.695e+01 6.465e+00 2.621 0.00934 **
Max 3.384e-01 2.112e-02 16.028 < 2e-16 ***
Min 6.917e-01 2.924e-02 23.652 < 2e-16 ***
Neerslag -5.929e-03 9.068e-04 -6.538 3.87e-10 ***
Luchtdruk -4.219e-05 6.174e-04 -0.068 0.94557
t -5.557e-03 5.201e-03 -1.068 0.28644
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.52 on 234 degrees of freedom
Multiple R-squared: 0.9894, Adjusted R-squared: 0.9892
F-statistic: 4378 on 5 and 234 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,] 2.536111e-03 5.072222e-03 9.974639e-01
[2,] 2.947809e-04 5.895617e-04 9.997052e-01
[3,] 4.616344e-05 9.232689e-05 9.999538e-01
[4,] 8.263582e-06 1.652716e-05 9.999917e-01
[5,] 5.126577e-05 1.025315e-04 9.999487e-01
[6,] 1.766829e-05 3.533659e-05 9.999823e-01
[7,] 3.101405e-06 6.202810e-06 9.999969e-01
[8,] 2.066237e-06 4.132474e-06 9.999979e-01
[9,] 3.764226e-07 7.528452e-07 9.999996e-01
[10,] 6.616903e-08 1.323381e-07 9.999999e-01
[11,] 2.133246e-08 4.266492e-08 1.000000e+00
[12,] 3.060220e-07 6.120439e-07 9.999997e-01
[13,] 9.866388e-08 1.973278e-07 9.999999e-01
[14,] 6.097743e-08 1.219549e-07 9.999999e-01
[15,] 2.150533e-08 4.301067e-08 1.000000e+00
[16,] 5.286823e-09 1.057365e-08 1.000000e+00
[17,] 1.244295e-09 2.488589e-09 1.000000e+00
[18,] 4.002442e-10 8.004885e-10 1.000000e+00
[19,] 9.117260e-11 1.823452e-10 1.000000e+00
[20,] 1.978769e-11 3.957537e-11 1.000000e+00
[21,] 4.389657e-12 8.779315e-12 1.000000e+00
[22,] 1.990060e-12 3.980120e-12 1.000000e+00
[23,] 4.269288e-13 8.538575e-13 1.000000e+00
[24,] 1.722246e-13 3.444492e-13 1.000000e+00
[25,] 2.748359e-03 5.496719e-03 9.972516e-01
[26,] 1.747654e-03 3.495308e-03 9.982523e-01
[27,] 1.140004e-03 2.280007e-03 9.988600e-01
[28,] 6.886579e-04 1.377316e-03 9.993113e-01
[29,] 4.463965e-04 8.927930e-04 9.995536e-01
[30,] 2.752375e-04 5.504749e-04 9.997248e-01
[31,] 8.995444e-01 2.009111e-01 1.004556e-01
[32,] 8.846737e-01 2.306526e-01 1.153263e-01
[33,] 8.621668e-01 2.756664e-01 1.378332e-01
[34,] 8.347425e-01 3.305150e-01 1.652575e-01
[35,] 8.028584e-01 3.942831e-01 1.971416e-01
[36,] 7.677580e-01 4.644841e-01 2.322420e-01
[37,] 9.415818e-01 1.168364e-01 5.841821e-02
[38,] 9.298163e-01 1.403675e-01 7.018374e-02
[39,] 9.182174e-01 1.635652e-01 8.178261e-02
[40,] 9.144377e-01 1.711247e-01 8.556234e-02
[41,] 8.959899e-01 2.080201e-01 1.040101e-01
[42,] 8.833536e-01 2.332927e-01 1.166464e-01
[43,] 8.594113e-01 2.811774e-01 1.405887e-01
[44,] 8.356939e-01 3.286122e-01 1.643061e-01
[45,] 8.071838e-01 3.856324e-01 1.928162e-01
[46,] 7.764795e-01 4.470410e-01 2.235205e-01
[47,] 7.421248e-01 5.157505e-01 2.578752e-01
[48,] 7.054353e-01 5.891294e-01 2.945647e-01
[49,] 9.998852e-01 2.295676e-04 1.147838e-04
[50,] 9.999020e-01 1.959474e-04 9.797371e-05
[51,] 9.998527e-01 2.946329e-04 1.473164e-04
[52,] 9.998420e-01 3.160750e-04 1.580375e-04
[53,] 9.997904e-01 4.192991e-04 2.096496e-04
[54,] 9.997134e-01 5.732828e-04 2.866414e-04
[55,] 9.996373e-01 7.253710e-04 3.626855e-04
[56,] 9.995117e-01 9.765829e-04 4.882914e-04
[57,] 9.996116e-01 7.767836e-04 3.883918e-04
[58,] 9.995966e-01 8.067572e-04 4.033786e-04
[59,] 9.994584e-01 1.083264e-03 5.416322e-04
[60,] 9.993183e-01 1.363406e-03 6.817028e-04
[61,] 9.999994e-01 1.195384e-06 5.976919e-07
[62,] 9.999991e-01 1.853200e-06 9.265999e-07
[63,] 9.999985e-01 3.005251e-06 1.502625e-06
[64,] 9.999976e-01 4.775106e-06 2.387553e-06
[65,] 9.999990e-01 1.959661e-06 9.798307e-07
[66,] 9.999984e-01 3.115298e-06 1.557649e-06
[67,] 9.999980e-01 4.092032e-06 2.046016e-06
[68,] 9.999972e-01 5.503670e-06 2.751835e-06
[69,] 9.999957e-01 8.698403e-06 4.349202e-06
[70,] 9.999970e-01 6.084202e-06 3.042101e-06
[71,] 9.999958e-01 8.388740e-06 4.194370e-06
[72,] 9.999943e-01 1.136727e-05 5.683635e-06
[73,] 9.999975e-01 4.980957e-06 2.490479e-06
[74,] 9.999962e-01 7.649338e-06 3.824669e-06
[75,] 9.999956e-01 8.835640e-06 4.417820e-06
[76,] 9.999936e-01 1.280268e-05 6.401342e-06
[77,] 9.999918e-01 1.648638e-05 8.243188e-06
[78,] 9.999885e-01 2.300207e-05 1.150104e-05
[79,] 9.999850e-01 2.996360e-05 1.498180e-05
[80,] 9.999810e-01 3.794208e-05 1.897104e-05
[81,] 9.999832e-01 3.368864e-05 1.684432e-05
[82,] 9.999783e-01 4.330390e-05 2.165195e-05
[83,] 9.999692e-01 6.153808e-05 3.076904e-05
[84,] 9.999555e-01 8.895232e-05 4.447616e-05
[85,] 9.999633e-01 7.339594e-05 3.669797e-05
[86,] 9.999559e-01 8.814470e-05 4.407235e-05
[87,] 9.999530e-01 9.401102e-05 4.700551e-05
[88,] 9.999312e-01 1.375236e-04 6.876182e-05
[89,] 9.999010e-01 1.979599e-04 9.897994e-05
[90,] 9.998776e-01 2.448009e-04 1.224005e-04
[91,] 9.998663e-01 2.674540e-04 1.337270e-04
[92,] 9.998234e-01 3.531969e-04 1.765985e-04
[93,] 9.997918e-01 4.164088e-04 2.082044e-04
[94,] 9.997119e-01 5.762662e-04 2.881331e-04
[95,] 9.996024e-01 7.952796e-04 3.976398e-04
[96,] 9.996543e-01 6.914744e-04 3.457372e-04
[97,] 9.996221e-01 7.557807e-04 3.778903e-04
[98,] 9.996081e-01 7.837055e-04 3.918527e-04
[99,] 9.995574e-01 8.851073e-04 4.425536e-04
[100,] 9.994995e-01 1.000908e-03 5.004538e-04
[101,] 9.993085e-01 1.382969e-03 6.914846e-04
[102,] 9.990679e-01 1.864145e-03 9.320725e-04
[103,] 9.988337e-01 2.332622e-03 1.166311e-03
[104,] 9.984128e-01 3.174500e-03 1.587250e-03
[105,] 9.981519e-01 3.696257e-03 1.848129e-03
[106,] 9.980048e-01 3.990430e-03 1.995215e-03
[107,] 9.973806e-01 5.238825e-03 2.619413e-03
[108,] 9.974770e-01 5.046018e-03 2.523009e-03
[109,] 9.990774e-01 1.845230e-03 9.226151e-04
[110,] 9.987800e-01 2.439961e-03 1.219981e-03
[111,] 9.983417e-01 3.316688e-03 1.658344e-03
[112,] 9.979143e-01 4.171328e-03 2.085664e-03
[113,] 9.976513e-01 4.697461e-03 2.348730e-03
[114,] 9.969627e-01 6.074505e-03 3.037252e-03
[115,] 9.959887e-01 8.022699e-03 4.011349e-03
[116,] 9.949532e-01 1.009365e-02 5.046826e-03
[117,] 9.938115e-01 1.237690e-02 6.188450e-03
[118,] 9.920542e-01 1.589165e-02 7.945825e-03
[119,] 9.897640e-01 2.047194e-02 1.023597e-02
[120,] 9.877486e-01 2.450271e-02 1.225135e-02
[121,] 9.846406e-01 3.071888e-02 1.535944e-02
[122,] 9.806983e-01 3.860346e-02 1.930173e-02
[123,] 9.763874e-01 4.722522e-02 2.361261e-02
[124,] 9.727572e-01 5.448554e-02 2.724277e-02
[125,] 9.679142e-01 6.417167e-02 3.208584e-02
[126,] 9.606428e-01 7.871433e-02 3.935717e-02
[127,] 9.543493e-01 9.130138e-02 4.565069e-02
[128,] 9.446832e-01 1.106335e-01 5.531676e-02
[129,] 9.362722e-01 1.274556e-01 6.372778e-02
[130,] 9.310426e-01 1.379149e-01 6.895744e-02
[131,] 9.175383e-01 1.649234e-01 8.246168e-02
[132,] 9.047447e-01 1.905107e-01 9.525533e-02
[133,] 9.852222e-01 2.955556e-02 1.477778e-02
[134,] 9.860065e-01 2.798691e-02 1.399345e-02
[135,] 9.824693e-01 3.506135e-02 1.753067e-02
[136,] 9.783476e-01 4.330483e-02 2.165242e-02
[137,] 9.766479e-01 4.670418e-02 2.335209e-02
[138,] 9.722531e-01 5.549376e-02 2.774688e-02
[139,] 9.654560e-01 6.908801e-02 3.454400e-02
[140,] 9.591625e-01 8.167494e-02 4.083747e-02
[141,] 9.502102e-01 9.957962e-02 4.978981e-02
[142,] 9.398118e-01 1.203763e-01 6.018817e-02
[143,] 9.280095e-01 1.439811e-01 7.199053e-02
[144,] 9.195675e-01 1.608650e-01 8.043248e-02
[145,] 9.471649e-01 1.056702e-01 5.283508e-02
[146,] 9.358344e-01 1.283311e-01 6.416556e-02
[147,] 9.238193e-01 1.523613e-01 7.618066e-02
[148,] 9.213196e-01 1.573609e-01 7.868043e-02
[149,] 9.076521e-01 1.846958e-01 9.234788e-02
[150,] 8.943620e-01 2.112761e-01 1.056380e-01
[151,] 8.756944e-01 2.486112e-01 1.243056e-01
[152,] 8.821484e-01 2.357031e-01 1.178516e-01
[153,] 8.719165e-01 2.561669e-01 1.280835e-01
[154,] 8.916319e-01 2.167362e-01 1.083681e-01
[155,] 8.760073e-01 2.479854e-01 1.239927e-01
[156,] 8.614122e-01 2.771755e-01 1.385878e-01
[157,] 8.886482e-01 2.227037e-01 1.113518e-01
[158,] 8.691137e-01 2.617726e-01 1.308863e-01
[159,] 8.504324e-01 2.991352e-01 1.495676e-01
[160,] 8.311531e-01 3.376939e-01 1.688469e-01
[161,] 8.077477e-01 3.845045e-01 1.922523e-01
[162,] 7.824459e-01 4.351081e-01 2.175541e-01
[163,] 7.503289e-01 4.993422e-01 2.496711e-01
[164,] 7.378443e-01 5.243114e-01 2.621557e-01
[165,] 7.285598e-01 5.428804e-01 2.714402e-01
[166,] 7.368263e-01 5.263474e-01 2.631737e-01
[167,] 7.064513e-01 5.870974e-01 2.935487e-01
[168,] 6.737496e-01 6.525009e-01 3.262504e-01
[169,] 6.990616e-01 6.018767e-01 3.009384e-01
[170,] 6.801164e-01 6.397672e-01 3.198836e-01
[171,] 6.400289e-01 7.199423e-01 3.599711e-01
[172,] 5.985592e-01 8.028816e-01 4.014408e-01
[173,] 5.803328e-01 8.393344e-01 4.196672e-01
[174,] 5.419076e-01 9.161849e-01 4.580924e-01
[175,] 4.976834e-01 9.953668e-01 5.023166e-01
[176,] 4.560579e-01 9.121159e-01 5.439421e-01
[177,] 4.407711e-01 8.815422e-01 5.592289e-01
[178,] 4.210512e-01 8.421024e-01 5.789488e-01
[179,] 3.801873e-01 7.603747e-01 6.198127e-01
[180,] 3.607835e-01 7.215670e-01 6.392165e-01
[181,] 3.292078e-01 6.584155e-01 6.707922e-01
[182,] 2.981429e-01 5.962858e-01 7.018571e-01
[183,] 2.601882e-01 5.203765e-01 7.398118e-01
[184,] 2.261873e-01 4.523746e-01 7.738127e-01
[185,] 1.991837e-01 3.983673e-01 8.008163e-01
[186,] 2.121878e-01 4.243757e-01 7.878122e-01
[187,] 1.791412e-01 3.582824e-01 8.208588e-01
[188,] 1.529640e-01 3.059281e-01 8.470360e-01
[189,] 1.294337e-01 2.588674e-01 8.705663e-01
[190,] 1.462027e-01 2.924055e-01 8.537973e-01
[191,] 1.935684e-01 3.871369e-01 8.064316e-01
[192,] 1.751837e-01 3.503674e-01 8.248163e-01
[193,] 6.936561e-01 6.126877e-01 3.063439e-01
[194,] 6.647399e-01 6.705202e-01 3.352601e-01
[195,] 6.139063e-01 7.721873e-01 3.860937e-01
[196,] 5.801614e-01 8.396772e-01 4.198386e-01
[197,] 5.317642e-01 9.364716e-01 4.682358e-01
[198,] 4.877110e-01 9.754219e-01 5.122890e-01
[199,] 4.320439e-01 8.640878e-01 5.679561e-01
[200,] 4.289178e-01 8.578357e-01 5.710822e-01
[201,] 3.949240e-01 7.898481e-01 6.050760e-01
[202,] 3.554644e-01 7.109289e-01 6.445356e-01
[203,] 3.027897e-01 6.055794e-01 6.972103e-01
[204,] 2.662953e-01 5.325906e-01 7.337047e-01
[205,] 3.104027e-01 6.208054e-01 6.895973e-01
[206,] 2.585531e-01 5.171062e-01 7.414469e-01
[207,] 2.088241e-01 4.176482e-01 7.911759e-01
[208,] 1.916239e-01 3.832477e-01 8.083761e-01
[209,] 1.630014e-01 3.260028e-01 8.369986e-01
[210,] 1.243143e-01 2.486286e-01 8.756857e-01
[211,] 9.443541e-02 1.888708e-01 9.055646e-01
[212,] 6.801917e-02 1.360383e-01 9.319808e-01
[213,] 1.267166e-01 2.534332e-01 8.732834e-01
[214,] 9.972019e-02 1.994404e-01 9.002798e-01
[215,] 7.762470e-02 1.552494e-01 9.223753e-01
[216,] 5.185963e-02 1.037193e-01 9.481404e-01
[217,] 1.792449e-01 3.584898e-01 8.207551e-01
[218,] 1.342667e-01 2.685335e-01 8.657333e-01
[219,] 1.028287e-01 2.056574e-01 8.971713e-01
[220,] 6.448747e-02 1.289749e-01 9.355125e-01
[221,] 3.815259e-02 7.630518e-02 9.618474e-01
[222,] 2.015132e-02 4.030265e-02 9.798487e-01
[223,] 1.053409e-02 2.106818e-02 9.894659e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1qxqd1322155823.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/274x11322155823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38x1u1322155823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/468il1322155823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/51dmu1322155823.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
-7.57695738 -1.07835134 -0.59850146 4.56025514 9.85676391 -1.66025267
7 8 9 10 11 12
0.78701568 4.50274007 1.28194729 -3.03087937 2.88326981 -4.35591799
13 14 15 16 17 18
-4.98503265 3.10304341 -1.29969856 0.83091165 -0.01809913 3.18337165
19 20 21 22 23 24
-1.01011107 5.45038900 7.95584738 -4.27399028 1.67464835 -5.53896159
25 26 27 28 29 30
-3.94356425 -2.29193935 -2.33095513 -0.30326455 5.03511561 4.56267576
31 32 33 34 35 36
1.94346842 0.40327460 -12.63503154 2.44322707 -2.07066450 -4.53413111
37 38 39 40 41 42
-0.54056379 -6.02308691 32.70432942 4.19095409 1.13574866 2.64009229
43 44 45 46 47 48
1.22040666 -1.44664215 -11.17958020 -4.35132390 -4.26955331 3.04387432
49 50 51 52 53 54
-1.75657562 -3.10382965 -0.40318484 2.96076369 0.95002639 1.57601839
55 56 57 58 59 60
0.59737625 -1.72781788 -30.27551832 3.18575893 -0.10703246 2.30269923
61 62 63 64 65 66
0.90471100 -2.67343653 3.45917493 -1.32971409 8.19715473 6.10749033
67 68 69 70 71 72
2.74859831 3.80411001 -23.83145281 -1.38957545 -0.40102093 1.05537658
73 74 75 76 77 78
-8.27233118 -0.83757277 -3.44787613 3.73964129 -0.07032254 6.86794620
79 80 81 82 83 84
2.60413135 1.47527326 -9.27847452 -0.27491506 2.42052668 -2.25728185
85 86 87 88 89 90
-3.73062954 1.30394801 -3.81610644 3.48141569 5.76726971 2.99407393
91 92 93 94 95 96
1.78028620 0.89679265 -6.71314150 3.12474978 -4.70069185 -0.08170648
97 98 99 100 101 102
-0.60986410 -3.84933906 3.64533458 -2.21014224 4.12835134 -0.30694293
103 104 105 106 107 108
-1.39986293 6.32712488 -3.92165419 4.70341396 3.88678810 -4.32058902
109 110 111 112 113 114
0.48357682 1.52161111 3.05496387 -0.05907292 4.05141750 4.94657684
115 116 117 118 119 120
0.79109686 6.00722326 -11.14524548 -1.34473233 0.71174420 2.73978735
121 122 123 124 125 126
-4.06885673 2.16584878 1.12481751 -2.10897630 3.10490361 1.19270012
127 128 129 130 131 132
0.77550778 -2.78540125 -1.47421980 -0.12862204 -0.88848844 -2.90538680
133 134 135 136 137 138
-2.21599627 0.30954508 -2.47999385 0.29580772 2.96776449 4.27633386
139 140 141 142 143 144
-0.18945730 2.80655465 -12.11242856 -6.91994647 2.40982495 3.02137311
145 146 147 148 149 150
-3.66274733 2.20258674 1.27964869 3.62117967 -1.21069803 1.95487208
151 152 153 154 155 156
1.01550661 4.97818025 -8.76675386 1.42937098 2.58665605 -3.47825821
157 158 159 160 161 162
-0.61029823 -1.83505045 2.20650583 7.41719421 5.14684153 7.33964794
163 164 165 166 167 168
1.42821721 0.77527884 -4.66789534 2.75558502 -2.82702026 -1.03467936
169 170 171 172 173 174
1.90624887 -1.03148834 1.76814069 6.32800412 4.69035355 6.25675475
175 176 177 178 179 180
2.00464421 3.42506418 -6.58217661 -5.04989297 1.52188472 -0.46568963
181 182 183 184 185 186
-4.34082817 -0.40033100 -0.50440117 2.36111286 4.45110608 4.26656094
187 188 189 190 191 192
2.18165563 3.51169024 -2.33959948 -4.01090931 2.29379967 -2.00174857
193 194 195 196 197 198
-3.62179341 -6.67337922 0.68717147 -2.85978834 3.02324651 5.28309289
199 200 201 202 203 204
3.44649973 7.88127313 -22.26020454 4.93241158 -0.26768417 -2.85249852
205 206 207 208 209 210
-0.65929303 -1.06781587 2.80333514 4.47287324 5.40782716 -3.54210664
211 212 213 214 215 216
2.57797545 1.24938370 -5.76176759 -1.04899546 -0.29125525 -3.07571849
217 218 219 220 221 222
-2.65615363 2.38744389 4.91298233 0.91000069 9.74258226 -0.70215793
223 224 225 226 227 228
2.29357831 0.45121337 -12.41953494 4.10374724 0.13185816 -1.16158130
229 230 231 232 233 234
-3.94474836 0.67211592 0.99828037 4.53763015 4.94328203 -17.00519183
235 236 237 238 239 240
-1.82105799 2.60650708 -12.46888887 4.01274315 -0.19678189 1.72932200
> postscript(file="/var/wessaorg/rcomp/tmp/6gd9i1322155823.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 -7.57695738 NA
1 -1.07835134 -7.57695738
2 -0.59850146 -1.07835134
3 4.56025514 -0.59850146
4 9.85676391 4.56025514
5 -1.66025267 9.85676391
6 0.78701568 -1.66025267
7 4.50274007 0.78701568
8 1.28194729 4.50274007
9 -3.03087937 1.28194729
10 2.88326981 -3.03087937
11 -4.35591799 2.88326981
12 -4.98503265 -4.35591799
13 3.10304341 -4.98503265
14 -1.29969856 3.10304341
15 0.83091165 -1.29969856
16 -0.01809913 0.83091165
17 3.18337165 -0.01809913
18 -1.01011107 3.18337165
19 5.45038900 -1.01011107
20 7.95584738 5.45038900
21 -4.27399028 7.95584738
22 1.67464835 -4.27399028
23 -5.53896159 1.67464835
24 -3.94356425 -5.53896159
25 -2.29193935 -3.94356425
26 -2.33095513 -2.29193935
27 -0.30326455 -2.33095513
28 5.03511561 -0.30326455
29 4.56267576 5.03511561
30 1.94346842 4.56267576
31 0.40327460 1.94346842
32 -12.63503154 0.40327460
33 2.44322707 -12.63503154
34 -2.07066450 2.44322707
35 -4.53413111 -2.07066450
36 -0.54056379 -4.53413111
37 -6.02308691 -0.54056379
38 32.70432942 -6.02308691
39 4.19095409 32.70432942
40 1.13574866 4.19095409
41 2.64009229 1.13574866
42 1.22040666 2.64009229
43 -1.44664215 1.22040666
44 -11.17958020 -1.44664215
45 -4.35132390 -11.17958020
46 -4.26955331 -4.35132390
47 3.04387432 -4.26955331
48 -1.75657562 3.04387432
49 -3.10382965 -1.75657562
50 -0.40318484 -3.10382965
51 2.96076369 -0.40318484
52 0.95002639 2.96076369
53 1.57601839 0.95002639
54 0.59737625 1.57601839
55 -1.72781788 0.59737625
56 -30.27551832 -1.72781788
57 3.18575893 -30.27551832
58 -0.10703246 3.18575893
59 2.30269923 -0.10703246
60 0.90471100 2.30269923
61 -2.67343653 0.90471100
62 3.45917493 -2.67343653
63 -1.32971409 3.45917493
64 8.19715473 -1.32971409
65 6.10749033 8.19715473
66 2.74859831 6.10749033
67 3.80411001 2.74859831
68 -23.83145281 3.80411001
69 -1.38957545 -23.83145281
70 -0.40102093 -1.38957545
71 1.05537658 -0.40102093
72 -8.27233118 1.05537658
73 -0.83757277 -8.27233118
74 -3.44787613 -0.83757277
75 3.73964129 -3.44787613
76 -0.07032254 3.73964129
77 6.86794620 -0.07032254
78 2.60413135 6.86794620
79 1.47527326 2.60413135
80 -9.27847452 1.47527326
81 -0.27491506 -9.27847452
82 2.42052668 -0.27491506
83 -2.25728185 2.42052668
84 -3.73062954 -2.25728185
85 1.30394801 -3.73062954
86 -3.81610644 1.30394801
87 3.48141569 -3.81610644
88 5.76726971 3.48141569
89 2.99407393 5.76726971
90 1.78028620 2.99407393
91 0.89679265 1.78028620
92 -6.71314150 0.89679265
93 3.12474978 -6.71314150
94 -4.70069185 3.12474978
95 -0.08170648 -4.70069185
96 -0.60986410 -0.08170648
97 -3.84933906 -0.60986410
98 3.64533458 -3.84933906
99 -2.21014224 3.64533458
100 4.12835134 -2.21014224
101 -0.30694293 4.12835134
102 -1.39986293 -0.30694293
103 6.32712488 -1.39986293
104 -3.92165419 6.32712488
105 4.70341396 -3.92165419
106 3.88678810 4.70341396
107 -4.32058902 3.88678810
108 0.48357682 -4.32058902
109 1.52161111 0.48357682
110 3.05496387 1.52161111
111 -0.05907292 3.05496387
112 4.05141750 -0.05907292
113 4.94657684 4.05141750
114 0.79109686 4.94657684
115 6.00722326 0.79109686
116 -11.14524548 6.00722326
117 -1.34473233 -11.14524548
118 0.71174420 -1.34473233
119 2.73978735 0.71174420
120 -4.06885673 2.73978735
121 2.16584878 -4.06885673
122 1.12481751 2.16584878
123 -2.10897630 1.12481751
124 3.10490361 -2.10897630
125 1.19270012 3.10490361
126 0.77550778 1.19270012
127 -2.78540125 0.77550778
128 -1.47421980 -2.78540125
129 -0.12862204 -1.47421980
130 -0.88848844 -0.12862204
131 -2.90538680 -0.88848844
132 -2.21599627 -2.90538680
133 0.30954508 -2.21599627
134 -2.47999385 0.30954508
135 0.29580772 -2.47999385
136 2.96776449 0.29580772
137 4.27633386 2.96776449
138 -0.18945730 4.27633386
139 2.80655465 -0.18945730
140 -12.11242856 2.80655465
141 -6.91994647 -12.11242856
142 2.40982495 -6.91994647
143 3.02137311 2.40982495
144 -3.66274733 3.02137311
145 2.20258674 -3.66274733
146 1.27964869 2.20258674
147 3.62117967 1.27964869
148 -1.21069803 3.62117967
149 1.95487208 -1.21069803
150 1.01550661 1.95487208
151 4.97818025 1.01550661
152 -8.76675386 4.97818025
153 1.42937098 -8.76675386
154 2.58665605 1.42937098
155 -3.47825821 2.58665605
156 -0.61029823 -3.47825821
157 -1.83505045 -0.61029823
158 2.20650583 -1.83505045
159 7.41719421 2.20650583
160 5.14684153 7.41719421
161 7.33964794 5.14684153
162 1.42821721 7.33964794
163 0.77527884 1.42821721
164 -4.66789534 0.77527884
165 2.75558502 -4.66789534
166 -2.82702026 2.75558502
167 -1.03467936 -2.82702026
168 1.90624887 -1.03467936
169 -1.03148834 1.90624887
170 1.76814069 -1.03148834
171 6.32800412 1.76814069
172 4.69035355 6.32800412
173 6.25675475 4.69035355
174 2.00464421 6.25675475
175 3.42506418 2.00464421
176 -6.58217661 3.42506418
177 -5.04989297 -6.58217661
178 1.52188472 -5.04989297
179 -0.46568963 1.52188472
180 -4.34082817 -0.46568963
181 -0.40033100 -4.34082817
182 -0.50440117 -0.40033100
183 2.36111286 -0.50440117
184 4.45110608 2.36111286
185 4.26656094 4.45110608
186 2.18165563 4.26656094
187 3.51169024 2.18165563
188 -2.33959948 3.51169024
189 -4.01090931 -2.33959948
190 2.29379967 -4.01090931
191 -2.00174857 2.29379967
192 -3.62179341 -2.00174857
193 -6.67337922 -3.62179341
194 0.68717147 -6.67337922
195 -2.85978834 0.68717147
196 3.02324651 -2.85978834
197 5.28309289 3.02324651
198 3.44649973 5.28309289
199 7.88127313 3.44649973
200 -22.26020454 7.88127313
201 4.93241158 -22.26020454
202 -0.26768417 4.93241158
203 -2.85249852 -0.26768417
204 -0.65929303 -2.85249852
205 -1.06781587 -0.65929303
206 2.80333514 -1.06781587
207 4.47287324 2.80333514
208 5.40782716 4.47287324
209 -3.54210664 5.40782716
210 2.57797545 -3.54210664
211 1.24938370 2.57797545
212 -5.76176759 1.24938370
213 -1.04899546 -5.76176759
214 -0.29125525 -1.04899546
215 -3.07571849 -0.29125525
216 -2.65615363 -3.07571849
217 2.38744389 -2.65615363
218 4.91298233 2.38744389
219 0.91000069 4.91298233
220 9.74258226 0.91000069
221 -0.70215793 9.74258226
222 2.29357831 -0.70215793
223 0.45121337 2.29357831
224 -12.41953494 0.45121337
225 4.10374724 -12.41953494
226 0.13185816 4.10374724
227 -1.16158130 0.13185816
228 -3.94474836 -1.16158130
229 0.67211592 -3.94474836
230 0.99828037 0.67211592
231 4.53763015 0.99828037
232 4.94328203 4.53763015
233 -17.00519183 4.94328203
234 -1.82105799 -17.00519183
235 2.60650708 -1.82105799
236 -12.46888887 2.60650708
237 4.01274315 -12.46888887
238 -0.19678189 4.01274315
239 1.72932200 -0.19678189
240 NA 1.72932200
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.07835134 -7.57695738
[2,] -0.59850146 -1.07835134
[3,] 4.56025514 -0.59850146
[4,] 9.85676391 4.56025514
[5,] -1.66025267 9.85676391
[6,] 0.78701568 -1.66025267
[7,] 4.50274007 0.78701568
[8,] 1.28194729 4.50274007
[9,] -3.03087937 1.28194729
[10,] 2.88326981 -3.03087937
[11,] -4.35591799 2.88326981
[12,] -4.98503265 -4.35591799
[13,] 3.10304341 -4.98503265
[14,] -1.29969856 3.10304341
[15,] 0.83091165 -1.29969856
[16,] -0.01809913 0.83091165
[17,] 3.18337165 -0.01809913
[18,] -1.01011107 3.18337165
[19,] 5.45038900 -1.01011107
[20,] 7.95584738 5.45038900
[21,] -4.27399028 7.95584738
[22,] 1.67464835 -4.27399028
[23,] -5.53896159 1.67464835
[24,] -3.94356425 -5.53896159
[25,] -2.29193935 -3.94356425
[26,] -2.33095513 -2.29193935
[27,] -0.30326455 -2.33095513
[28,] 5.03511561 -0.30326455
[29,] 4.56267576 5.03511561
[30,] 1.94346842 4.56267576
[31,] 0.40327460 1.94346842
[32,] -12.63503154 0.40327460
[33,] 2.44322707 -12.63503154
[34,] -2.07066450 2.44322707
[35,] -4.53413111 -2.07066450
[36,] -0.54056379 -4.53413111
[37,] -6.02308691 -0.54056379
[38,] 32.70432942 -6.02308691
[39,] 4.19095409 32.70432942
[40,] 1.13574866 4.19095409
[41,] 2.64009229 1.13574866
[42,] 1.22040666 2.64009229
[43,] -1.44664215 1.22040666
[44,] -11.17958020 -1.44664215
[45,] -4.35132390 -11.17958020
[46,] -4.26955331 -4.35132390
[47,] 3.04387432 -4.26955331
[48,] -1.75657562 3.04387432
[49,] -3.10382965 -1.75657562
[50,] -0.40318484 -3.10382965
[51,] 2.96076369 -0.40318484
[52,] 0.95002639 2.96076369
[53,] 1.57601839 0.95002639
[54,] 0.59737625 1.57601839
[55,] -1.72781788 0.59737625
[56,] -30.27551832 -1.72781788
[57,] 3.18575893 -30.27551832
[58,] -0.10703246 3.18575893
[59,] 2.30269923 -0.10703246
[60,] 0.90471100 2.30269923
[61,] -2.67343653 0.90471100
[62,] 3.45917493 -2.67343653
[63,] -1.32971409 3.45917493
[64,] 8.19715473 -1.32971409
[65,] 6.10749033 8.19715473
[66,] 2.74859831 6.10749033
[67,] 3.80411001 2.74859831
[68,] -23.83145281 3.80411001
[69,] -1.38957545 -23.83145281
[70,] -0.40102093 -1.38957545
[71,] 1.05537658 -0.40102093
[72,] -8.27233118 1.05537658
[73,] -0.83757277 -8.27233118
[74,] -3.44787613 -0.83757277
[75,] 3.73964129 -3.44787613
[76,] -0.07032254 3.73964129
[77,] 6.86794620 -0.07032254
[78,] 2.60413135 6.86794620
[79,] 1.47527326 2.60413135
[80,] -9.27847452 1.47527326
[81,] -0.27491506 -9.27847452
[82,] 2.42052668 -0.27491506
[83,] -2.25728185 2.42052668
[84,] -3.73062954 -2.25728185
[85,] 1.30394801 -3.73062954
[86,] -3.81610644 1.30394801
[87,] 3.48141569 -3.81610644
[88,] 5.76726971 3.48141569
[89,] 2.99407393 5.76726971
[90,] 1.78028620 2.99407393
[91,] 0.89679265 1.78028620
[92,] -6.71314150 0.89679265
[93,] 3.12474978 -6.71314150
[94,] -4.70069185 3.12474978
[95,] -0.08170648 -4.70069185
[96,] -0.60986410 -0.08170648
[97,] -3.84933906 -0.60986410
[98,] 3.64533458 -3.84933906
[99,] -2.21014224 3.64533458
[100,] 4.12835134 -2.21014224
[101,] -0.30694293 4.12835134
[102,] -1.39986293 -0.30694293
[103,] 6.32712488 -1.39986293
[104,] -3.92165419 6.32712488
[105,] 4.70341396 -3.92165419
[106,] 3.88678810 4.70341396
[107,] -4.32058902 3.88678810
[108,] 0.48357682 -4.32058902
[109,] 1.52161111 0.48357682
[110,] 3.05496387 1.52161111
[111,] -0.05907292 3.05496387
[112,] 4.05141750 -0.05907292
[113,] 4.94657684 4.05141750
[114,] 0.79109686 4.94657684
[115,] 6.00722326 0.79109686
[116,] -11.14524548 6.00722326
[117,] -1.34473233 -11.14524548
[118,] 0.71174420 -1.34473233
[119,] 2.73978735 0.71174420
[120,] -4.06885673 2.73978735
[121,] 2.16584878 -4.06885673
[122,] 1.12481751 2.16584878
[123,] -2.10897630 1.12481751
[124,] 3.10490361 -2.10897630
[125,] 1.19270012 3.10490361
[126,] 0.77550778 1.19270012
[127,] -2.78540125 0.77550778
[128,] -1.47421980 -2.78540125
[129,] -0.12862204 -1.47421980
[130,] -0.88848844 -0.12862204
[131,] -2.90538680 -0.88848844
[132,] -2.21599627 -2.90538680
[133,] 0.30954508 -2.21599627
[134,] -2.47999385 0.30954508
[135,] 0.29580772 -2.47999385
[136,] 2.96776449 0.29580772
[137,] 4.27633386 2.96776449
[138,] -0.18945730 4.27633386
[139,] 2.80655465 -0.18945730
[140,] -12.11242856 2.80655465
[141,] -6.91994647 -12.11242856
[142,] 2.40982495 -6.91994647
[143,] 3.02137311 2.40982495
[144,] -3.66274733 3.02137311
[145,] 2.20258674 -3.66274733
[146,] 1.27964869 2.20258674
[147,] 3.62117967 1.27964869
[148,] -1.21069803 3.62117967
[149,] 1.95487208 -1.21069803
[150,] 1.01550661 1.95487208
[151,] 4.97818025 1.01550661
[152,] -8.76675386 4.97818025
[153,] 1.42937098 -8.76675386
[154,] 2.58665605 1.42937098
[155,] -3.47825821 2.58665605
[156,] -0.61029823 -3.47825821
[157,] -1.83505045 -0.61029823
[158,] 2.20650583 -1.83505045
[159,] 7.41719421 2.20650583
[160,] 5.14684153 7.41719421
[161,] 7.33964794 5.14684153
[162,] 1.42821721 7.33964794
[163,] 0.77527884 1.42821721
[164,] -4.66789534 0.77527884
[165,] 2.75558502 -4.66789534
[166,] -2.82702026 2.75558502
[167,] -1.03467936 -2.82702026
[168,] 1.90624887 -1.03467936
[169,] -1.03148834 1.90624887
[170,] 1.76814069 -1.03148834
[171,] 6.32800412 1.76814069
[172,] 4.69035355 6.32800412
[173,] 6.25675475 4.69035355
[174,] 2.00464421 6.25675475
[175,] 3.42506418 2.00464421
[176,] -6.58217661 3.42506418
[177,] -5.04989297 -6.58217661
[178,] 1.52188472 -5.04989297
[179,] -0.46568963 1.52188472
[180,] -4.34082817 -0.46568963
[181,] -0.40033100 -4.34082817
[182,] -0.50440117 -0.40033100
[183,] 2.36111286 -0.50440117
[184,] 4.45110608 2.36111286
[185,] 4.26656094 4.45110608
[186,] 2.18165563 4.26656094
[187,] 3.51169024 2.18165563
[188,] -2.33959948 3.51169024
[189,] -4.01090931 -2.33959948
[190,] 2.29379967 -4.01090931
[191,] -2.00174857 2.29379967
[192,] -3.62179341 -2.00174857
[193,] -6.67337922 -3.62179341
[194,] 0.68717147 -6.67337922
[195,] -2.85978834 0.68717147
[196,] 3.02324651 -2.85978834
[197,] 5.28309289 3.02324651
[198,] 3.44649973 5.28309289
[199,] 7.88127313 3.44649973
[200,] -22.26020454 7.88127313
[201,] 4.93241158 -22.26020454
[202,] -0.26768417 4.93241158
[203,] -2.85249852 -0.26768417
[204,] -0.65929303 -2.85249852
[205,] -1.06781587 -0.65929303
[206,] 2.80333514 -1.06781587
[207,] 4.47287324 2.80333514
[208,] 5.40782716 4.47287324
[209,] -3.54210664 5.40782716
[210,] 2.57797545 -3.54210664
[211,] 1.24938370 2.57797545
[212,] -5.76176759 1.24938370
[213,] -1.04899546 -5.76176759
[214,] -0.29125525 -1.04899546
[215,] -3.07571849 -0.29125525
[216,] -2.65615363 -3.07571849
[217,] 2.38744389 -2.65615363
[218,] 4.91298233 2.38744389
[219,] 0.91000069 4.91298233
[220,] 9.74258226 0.91000069
[221,] -0.70215793 9.74258226
[222,] 2.29357831 -0.70215793
[223,] 0.45121337 2.29357831
[224,] -12.41953494 0.45121337
[225,] 4.10374724 -12.41953494
[226,] 0.13185816 4.10374724
[227,] -1.16158130 0.13185816
[228,] -3.94474836 -1.16158130
[229,] 0.67211592 -3.94474836
[230,] 0.99828037 0.67211592
[231,] 4.53763015 0.99828037
[232,] 4.94328203 4.53763015
[233,] -17.00519183 4.94328203
[234,] -1.82105799 -17.00519183
[235,] 2.60650708 -1.82105799
[236,] -12.46888887 2.60650708
[237,] 4.01274315 -12.46888887
[238,] -0.19678189 4.01274315
[239,] 1.72932200 -0.19678189
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.07835134 -7.57695738
2 -0.59850146 -1.07835134
3 4.56025514 -0.59850146
4 9.85676391 4.56025514
5 -1.66025267 9.85676391
6 0.78701568 -1.66025267
7 4.50274007 0.78701568
8 1.28194729 4.50274007
9 -3.03087937 1.28194729
10 2.88326981 -3.03087937
11 -4.35591799 2.88326981
12 -4.98503265 -4.35591799
13 3.10304341 -4.98503265
14 -1.29969856 3.10304341
15 0.83091165 -1.29969856
16 -0.01809913 0.83091165
17 3.18337165 -0.01809913
18 -1.01011107 3.18337165
19 5.45038900 -1.01011107
20 7.95584738 5.45038900
21 -4.27399028 7.95584738
22 1.67464835 -4.27399028
23 -5.53896159 1.67464835
24 -3.94356425 -5.53896159
25 -2.29193935 -3.94356425
26 -2.33095513 -2.29193935
27 -0.30326455 -2.33095513
28 5.03511561 -0.30326455
29 4.56267576 5.03511561
30 1.94346842 4.56267576
31 0.40327460 1.94346842
32 -12.63503154 0.40327460
33 2.44322707 -12.63503154
34 -2.07066450 2.44322707
35 -4.53413111 -2.07066450
36 -0.54056379 -4.53413111
37 -6.02308691 -0.54056379
38 32.70432942 -6.02308691
39 4.19095409 32.70432942
40 1.13574866 4.19095409
41 2.64009229 1.13574866
42 1.22040666 2.64009229
43 -1.44664215 1.22040666
44 -11.17958020 -1.44664215
45 -4.35132390 -11.17958020
46 -4.26955331 -4.35132390
47 3.04387432 -4.26955331
48 -1.75657562 3.04387432
49 -3.10382965 -1.75657562
50 -0.40318484 -3.10382965
51 2.96076369 -0.40318484
52 0.95002639 2.96076369
53 1.57601839 0.95002639
54 0.59737625 1.57601839
55 -1.72781788 0.59737625
56 -30.27551832 -1.72781788
57 3.18575893 -30.27551832
58 -0.10703246 3.18575893
59 2.30269923 -0.10703246
60 0.90471100 2.30269923
61 -2.67343653 0.90471100
62 3.45917493 -2.67343653
63 -1.32971409 3.45917493
64 8.19715473 -1.32971409
65 6.10749033 8.19715473
66 2.74859831 6.10749033
67 3.80411001 2.74859831
68 -23.83145281 3.80411001
69 -1.38957545 -23.83145281
70 -0.40102093 -1.38957545
71 1.05537658 -0.40102093
72 -8.27233118 1.05537658
73 -0.83757277 -8.27233118
74 -3.44787613 -0.83757277
75 3.73964129 -3.44787613
76 -0.07032254 3.73964129
77 6.86794620 -0.07032254
78 2.60413135 6.86794620
79 1.47527326 2.60413135
80 -9.27847452 1.47527326
81 -0.27491506 -9.27847452
82 2.42052668 -0.27491506
83 -2.25728185 2.42052668
84 -3.73062954 -2.25728185
85 1.30394801 -3.73062954
86 -3.81610644 1.30394801
87 3.48141569 -3.81610644
88 5.76726971 3.48141569
89 2.99407393 5.76726971
90 1.78028620 2.99407393
91 0.89679265 1.78028620
92 -6.71314150 0.89679265
93 3.12474978 -6.71314150
94 -4.70069185 3.12474978
95 -0.08170648 -4.70069185
96 -0.60986410 -0.08170648
97 -3.84933906 -0.60986410
98 3.64533458 -3.84933906
99 -2.21014224 3.64533458
100 4.12835134 -2.21014224
101 -0.30694293 4.12835134
102 -1.39986293 -0.30694293
103 6.32712488 -1.39986293
104 -3.92165419 6.32712488
105 4.70341396 -3.92165419
106 3.88678810 4.70341396
107 -4.32058902 3.88678810
108 0.48357682 -4.32058902
109 1.52161111 0.48357682
110 3.05496387 1.52161111
111 -0.05907292 3.05496387
112 4.05141750 -0.05907292
113 4.94657684 4.05141750
114 0.79109686 4.94657684
115 6.00722326 0.79109686
116 -11.14524548 6.00722326
117 -1.34473233 -11.14524548
118 0.71174420 -1.34473233
119 2.73978735 0.71174420
120 -4.06885673 2.73978735
121 2.16584878 -4.06885673
122 1.12481751 2.16584878
123 -2.10897630 1.12481751
124 3.10490361 -2.10897630
125 1.19270012 3.10490361
126 0.77550778 1.19270012
127 -2.78540125 0.77550778
128 -1.47421980 -2.78540125
129 -0.12862204 -1.47421980
130 -0.88848844 -0.12862204
131 -2.90538680 -0.88848844
132 -2.21599627 -2.90538680
133 0.30954508 -2.21599627
134 -2.47999385 0.30954508
135 0.29580772 -2.47999385
136 2.96776449 0.29580772
137 4.27633386 2.96776449
138 -0.18945730 4.27633386
139 2.80655465 -0.18945730
140 -12.11242856 2.80655465
141 -6.91994647 -12.11242856
142 2.40982495 -6.91994647
143 3.02137311 2.40982495
144 -3.66274733 3.02137311
145 2.20258674 -3.66274733
146 1.27964869 2.20258674
147 3.62117967 1.27964869
148 -1.21069803 3.62117967
149 1.95487208 -1.21069803
150 1.01550661 1.95487208
151 4.97818025 1.01550661
152 -8.76675386 4.97818025
153 1.42937098 -8.76675386
154 2.58665605 1.42937098
155 -3.47825821 2.58665605
156 -0.61029823 -3.47825821
157 -1.83505045 -0.61029823
158 2.20650583 -1.83505045
159 7.41719421 2.20650583
160 5.14684153 7.41719421
161 7.33964794 5.14684153
162 1.42821721 7.33964794
163 0.77527884 1.42821721
164 -4.66789534 0.77527884
165 2.75558502 -4.66789534
166 -2.82702026 2.75558502
167 -1.03467936 -2.82702026
168 1.90624887 -1.03467936
169 -1.03148834 1.90624887
170 1.76814069 -1.03148834
171 6.32800412 1.76814069
172 4.69035355 6.32800412
173 6.25675475 4.69035355
174 2.00464421 6.25675475
175 3.42506418 2.00464421
176 -6.58217661 3.42506418
177 -5.04989297 -6.58217661
178 1.52188472 -5.04989297
179 -0.46568963 1.52188472
180 -4.34082817 -0.46568963
181 -0.40033100 -4.34082817
182 -0.50440117 -0.40033100
183 2.36111286 -0.50440117
184 4.45110608 2.36111286
185 4.26656094 4.45110608
186 2.18165563 4.26656094
187 3.51169024 2.18165563
188 -2.33959948 3.51169024
189 -4.01090931 -2.33959948
190 2.29379967 -4.01090931
191 -2.00174857 2.29379967
192 -3.62179341 -2.00174857
193 -6.67337922 -3.62179341
194 0.68717147 -6.67337922
195 -2.85978834 0.68717147
196 3.02324651 -2.85978834
197 5.28309289 3.02324651
198 3.44649973 5.28309289
199 7.88127313 3.44649973
200 -22.26020454 7.88127313
201 4.93241158 -22.26020454
202 -0.26768417 4.93241158
203 -2.85249852 -0.26768417
204 -0.65929303 -2.85249852
205 -1.06781587 -0.65929303
206 2.80333514 -1.06781587
207 4.47287324 2.80333514
208 5.40782716 4.47287324
209 -3.54210664 5.40782716
210 2.57797545 -3.54210664
211 1.24938370 2.57797545
212 -5.76176759 1.24938370
213 -1.04899546 -5.76176759
214 -0.29125525 -1.04899546
215 -3.07571849 -0.29125525
216 -2.65615363 -3.07571849
217 2.38744389 -2.65615363
218 4.91298233 2.38744389
219 0.91000069 4.91298233
220 9.74258226 0.91000069
221 -0.70215793 9.74258226
222 2.29357831 -0.70215793
223 0.45121337 2.29357831
224 -12.41953494 0.45121337
225 4.10374724 -12.41953494
226 0.13185816 4.10374724
227 -1.16158130 0.13185816
228 -3.94474836 -1.16158130
229 0.67211592 -3.94474836
230 0.99828037 0.67211592
231 4.53763015 0.99828037
232 4.94328203 4.53763015
233 -17.00519183 4.94328203
234 -1.82105799 -17.00519183
235 2.60650708 -1.82105799
236 -12.46888887 2.60650708
237 4.01274315 -12.46888887
238 -0.19678189 4.01274315
239 1.72932200 -0.19678189
> 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/7ufl31322155823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8x5gn1322155823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9271a1322155823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
Warning messages:
1: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
2: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10czbl1322155823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wdg31322155823.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/12lzon1322155823.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/13dait1322155823.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/14nsci1322155823.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/157xl41322155823.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/1632r61322155823.tab")
+ }
>
> try(system("convert tmp/1qxqd1322155823.ps tmp/1qxqd1322155823.png",intern=TRUE))
character(0)
> try(system("convert tmp/274x11322155823.ps tmp/274x11322155823.png",intern=TRUE))
character(0)
> try(system("convert tmp/38x1u1322155823.ps tmp/38x1u1322155823.png",intern=TRUE))
character(0)
> try(system("convert tmp/468il1322155823.ps tmp/468il1322155823.png",intern=TRUE))
character(0)
> try(system("convert tmp/51dmu1322155823.ps tmp/51dmu1322155823.png",intern=TRUE))
character(0)
> try(system("convert tmp/6gd9i1322155823.ps tmp/6gd9i1322155823.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ufl31322155823.ps tmp/7ufl31322155823.png",intern=TRUE))
character(0)
> try(system("convert tmp/8x5gn1322155823.ps tmp/8x5gn1322155823.png",intern=TRUE))
character(0)
> try(system("convert tmp/9271a1322155823.ps tmp/9271a1322155823.png",intern=TRUE))
character(0)
> try(system("convert tmp/10czbl1322155823.ps tmp/10czbl1322155823.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.657 0.554 7.284