mixpoissonreg ObjectsR/2_diagnostic_functions.R
predict.mixpoissonreg.RdFunction to obtain various predictions based on the fitted mixed Poisson regression models.
# S3 method for mixpoissonreg predict( object, newdata = NULL, type = c("response", "link", "precision", "variance"), se.fit = FALSE, interval = c("none", "confidence", "prediction"), level = 0.95, nsim_pred = 100, nsim_pred_y = 100, ... )
| object | object of class "mixpoissonreg" containing results from the fitted model. |
|---|---|
| newdata | optionally, a data frame in which to look for variables with which to predict. If omitted, the fitted response values will be provided. |
| type | the type of prediction. The default is the "response" type, which provided the estimated values for the means. The type "link" provides the estimates for the linear predictor. The type "precision" provides estimates for the precision parameters whereas the type "variance" provides estimates for the variances. |
| se.fit | logical switch indicating if standard errors on the scale of linear predictors should be returned. If |
| interval | Type of interval calculation for the response variables, 'none', 'confidence' or 'prediction'. If 'confidence', the confidence intervals for the means are returned.
If 'prediction', prediction intervals for future response variables are reported. For confidence intervals, the type of the prediction must be 'response' or 'link'.
For prediction intervals the type of prediction must be 'response'. For 'confidence' intervals, when using |
| level | Tolerance/confidence level. The default is set to 0.95. |
| nsim_pred | number of means and predictions to be generated in each step of the simulation. The default is set to 100. |
| nsim_pred_y | number of response variables generated for each pair of mean and precision to compute the prediction intervals. The default is set to 100. |
| ... | further arguments passed to or from other methods. |
A vector containing the predicted values if se.fit=FALSE, a list with
elements fit and se.fit if se.fit=TRUE, and a matrix if interval
is set to confidence or prediction.
The se.fit argument only returns a non-NA vector for type = 'link', that is, on the scale of the linear predictor for the mean parameter. For the response scale,
one can obtain confidence or prediction intervals. It is important to notice that confidence intervals must not be used for future observations as they will underestimate
the uncertainty. In this case prediction intervals should be used. Currently, we do not have closed-form expressions for the prediction interval and, therefore, they
are obtained by simulation and can be computationally-intensive.
fitted.mixpoissonreg, summary.mixpoissonreg, plot.mixpoissonreg, autoplot.mixpoissonreg,
coef.mixpoissonreg, vcov.mixpoissonreg,
plot.mixpoissonreg
# \donttest{ data("Attendance", package = "mixpoissonreg") daysabs_fit <- mixpoissonreg(daysabs ~ gender + math + prog | gender + math + prog, data = Attendance) predict(daysabs_fit, interval = "confidence")#> fit lwr upr #> 1 5.249554 4.139862 6.656699 #> 2 6.661619 5.400442 8.217322 #> 3 8.915540 7.212229 11.021121 #> 4 9.154674 7.343539 11.412490 #> 5 10.043298 7.759662 12.998998 #> 6 6.361844 5.017556 8.066289 #> 7 6.707698 5.394571 8.340463 #> 8 7.808208 6.044846 10.085968 #> 9 5.683400 4.582659 7.048535 #> 10 2.476910 1.856557 3.304547 #> 11 3.565250 2.608360 4.873180 #> 12 6.885712 5.549246 8.544051 #> 13 6.278203 4.924513 8.004007 #> 14 9.361429 7.125088 12.299689 #> 15 3.206188 2.244399 4.580131 #> 16 4.419806 3.228922 6.049907 #> 17 8.126700 6.697776 9.860473 #> 18 8.073101 6.657572 9.789599 #> 19 7.326407 5.398103 9.943538 #> 20 7.756711 6.021142 9.992549 #> 21 8.856738 7.178386 10.927500 #> 22 5.319490 4.213676 6.715508 #> 23 6.072204 4.941004 7.462383 #> 24 5.249554 4.139862 6.656699 #> 25 8.974731 7.245656 11.116426 #> 26 8.474518 6.531633 10.995329 #> 27 7.654731 5.973045 9.809889 #> 28 8.856738 7.178386 10.927500 #> 29 5.011938 3.883977 6.467475 #> 30 7.230085 5.298626 9.865601 #> 31 7.912232 6.091596 10.277014 #> 32 8.400077 6.891833 10.238395 #> 33 6.112518 4.975708 7.509059 #> 34 6.153100 5.010156 7.556779 #> 35 5.078709 3.956568 6.519106 #> 36 8.915540 7.212229 11.021121 #> 37 7.454777 5.873904 9.461119 #> 38 4.568486 3.394132 6.149159 #> 39 12.114378 8.975794 16.350436 #> 40 8.073101 6.657572 9.789599 #> 41 8.234967 6.776799 10.006889 #> 42 8.126700 6.697776 9.860473 #> 43 7.810365 6.449843 9.457873 #> 44 6.574037 5.338584 8.095398 #> 45 6.977447 5.606453 8.683701 #> 46 8.511986 6.966191 10.400794 #> 47 4.881018 3.740461 6.369359 #> 48 6.193952 5.044338 7.605564 #> 49 6.931428 5.578012 8.613228 #> 50 5.954494 4.560270 7.774976 #> 51 7.164597 5.717033 8.978688 #> 52 5.390359 4.287630 6.776697 #> 53 5.797352 4.691941 7.163195 #> 54 7.072355 5.772832 8.664415 #> 55 9.911256 7.703727 12.751361 #> 56 5.462171 4.361616 6.840426 #> 57 11.797931 8.827134 15.768557 #> 58 11.955107 8.902039 16.055264 #> 59 8.740297 7.109424 10.745286 #> 60 8.073101 6.657572 9.789599 #> 61 6.402313 5.210926 7.866090 #> 62 8.511986 6.966191 10.400794 #> 63 7.308247 5.796759 9.213852 #> 64 5.874586 4.764066 7.243973 #> 65 8.344676 6.853955 10.159625 #> 66 6.444819 5.243325 7.921631 #> 67 13.119184 10.419092 16.518999 #> 68 9.276636 7.406888 11.618373 #> 69 3.565250 2.608360 4.873180 #> 70 7.407655 6.096842 9.000292 #> 71 4.946045 3.811918 6.417598 #> 72 4.722167 3.564836 6.255227 #> 73 3.101844 2.199943 4.373493 #> 74 6.795184 5.490723 8.409554 #> 75 3.041714 2.290183 4.039862 #> 76 14.297708 11.084370 18.442587 #> 77 6.360087 5.178212 7.811714 #> 78 5.112427 3.993040 6.545618 #> 79 7.912232 6.091596 10.277014 #> 80 8.856738 7.178386 10.927500 #> 81 8.180654 6.737520 9.932898 #> 82 4.753518 3.599590 6.277364 #> 83 8.937693 6.667941 11.980064 #> 84 5.390359 4.287630 6.776697 #> 85 4.848826 3.704977 6.345819 #> 86 10.542678 8.137934 13.658021 #> 87 7.358799 6.051247 8.948888 #> 88 9.338225 7.438020 11.723880 #> 89 1.964837 1.439096 2.682647 #> 90 6.661619 5.400442 8.217322 #> 91 8.511986 6.966191 10.400794 #> 92 6.404081 5.064310 8.098290 #> 93 4.219747 3.007566 5.920490 #> 94 9.805255 7.595295 12.658234 #> 95 9.525457 7.529365 12.050727 #> 96 7.260047 5.770479 9.134126 #> 97 6.977447 5.606453 8.683701 #> 98 7.405611 5.848466 9.377343 #> 99 6.032156 4.906055 7.416733 #> 100 7.860048 6.068328 10.180787 #> 101 10.404071 8.050150 13.446296 #> 102 6.235074 5.078244 7.655433 #> 103 7.308247 5.796759 9.213852 #> 104 7.808208 6.044846 10.085968 #> 105 7.673752 5.752735 10.236256 #> 106 8.511986 6.966191 10.400794 #> 107 15.479332 11.629127 20.604275 #> 108 6.797061 5.489392 8.416239 #> 109 5.952850 4.835474 7.328427 #> 110 6.318140 5.145189 7.758491 #> 111 4.753518 3.599590 6.277364 #> 112 9.977059 7.731820 12.874291 #> 113 6.931428 5.578012 8.613228 #> 114 6.705847 5.430870 8.280143 #> 115 5.952850 4.835474 7.328427 #> 116 7.456836 6.142152 9.052918 #> 117 8.073101 6.657572 9.789599 #> 118 14.778675 11.318380 19.296865 #> 119 5.722712 4.297481 7.620613 #> 120 7.808208 6.044846 10.085968 #> 121 7.654731 5.973045 9.809889 #> 122 6.767141 4.816608 9.507562 #> 123 7.308247 5.796759 9.213852 #> 124 4.568486 3.394132 6.149159 #> 125 4.390655 3.196577 6.030781 #> 126 6.361844 5.017556 8.066289 #> 127 14.109733 10.987608 18.119009 #> 128 7.758852 6.407017 9.395916 #> 129 6.707698 5.394571 8.340463 #> 130 8.126700 6.697776 9.860473 #> 131 7.556179 6.231843 9.161951 #> 132 8.400077 6.891833 10.238395 #> 133 9.977059 7.731820 12.874291 #> 134 6.318140 5.145189 7.758491 #> 135 9.867629 7.684599 12.670811 #> 136 6.885712 5.549246 8.544051 #> 137 5.954494 4.560270 7.774976 #> 138 6.278203 4.924513 8.004007 #> 139 5.045213 3.920210 6.493063 #> 140 13.032658 10.364154 16.388233 #> 141 9.338225 7.438020 11.723880 #> 142 6.154799 4.786300 7.914581 #> 143 14.876793 11.363913 19.475596 #> 144 5.390359 4.287630 6.776697 #> 145 8.400077 6.891833 10.238395 #> 146 6.979373 5.678685 8.577981 #> 147 7.606345 6.276187 9.218414 #> 148 7.166576 5.866420 8.754880 #> 149 8.740297 7.109424 10.745286 #> 150 12.861315 10.252585 16.133825 #> 151 6.112518 4.975708 7.509059 #> 152 5.759116 4.655645 7.124130 #> 153 12.117723 9.722787 15.102584 #> 154 9.933141 7.731524 12.761687 #> 155 13.560506 10.685459 17.209117 #> 156 11.566032 8.712517 15.354128 #> 157 6.235074 5.078244 7.655433 #> 158 5.608679 4.509247 6.976171 #> 159 3.565250 2.608360 4.873180 #> 160 2.349198 1.764785 3.127142 #> 161 2.349198 1.764785 3.127142 #> 162 2.318313 1.741307 3.086517 #> 163 3.612748 2.632626 4.957767 #> 164 7.758852 6.407017 9.395916 #> 165 2.141343 1.597293 2.870699 #> 166 2.962260 2.232504 3.930556 #> 167 2.141343 1.597293 2.870699 #> 168 2.754299 2.068834 3.666879 #> 169 2.057990 1.524209 2.778702 #> 170 6.404081 5.064310 8.098290 #> 171 2.461253 1.809409 3.347925 #> 172 2.318313 1.741307 3.086517 #> 173 2.228071 1.669856 2.972893 #> 174 8.455847 6.929244 10.318781 #> 175 2.057990 1.524209 2.778702 #> 176 2.228071 1.669856 2.972893 #> 177 2.700161 2.023284 3.603483 #> 178 4.136805 2.916383 5.867937 #> 179 3.001724 2.261488 3.984256 #> 180 2.629629 1.962229 3.524027 #> 181 6.979373 5.678685 8.577981 #> 182 11.417123 9.151571 14.243531 #> 183 3.734278 2.691878 5.180337 #> 184 1.964837 1.439096 2.682647 #> 185 2.700161 2.023284 3.603483 #> 186 2.004232 1.475497 2.722436 #> 187 11.045556 8.820322 13.832183 #> 188 2.664662 1.992791 3.563054 #> 189 5.180537 4.066286 6.600117 #> 190 11.646036 9.346009 14.512092 #> 191 2.560232 1.911928 3.428364 #> 192 2.595056 1.931628 3.486343 #> 193 3.834440 2.737903 5.370143 #> 194 9.154674 7.343539 11.412490 #> 195 5.285866 3.804014 7.344973 #> 196 9.740586 7.527522 12.604281 #> 197 2.526572 1.889976 3.377589 #> 198 6.979373 5.678685 8.577981 #> 199 6.532483 5.205337 8.197996 #> 200 2.461253 1.809409 3.347925 #> 201 2.754299 2.068834 3.666879 #> 202 3.809151 2.726506 5.321692 #> 203 3.565250 2.608360 4.873180 #> 204 2.257755 1.693824 3.009437 #> 205 7.606345 6.276187 9.218414 #> 206 2.735378 2.017584 3.708542 #> 207 9.361429 7.125088 12.299689 #> 208 2.349847 1.703730 3.240994 #> 209 11.879538 9.536561 14.798147 #> 210 2.809523 2.114071 3.733753 #> 211 2.923314 2.203249 3.878710 #> 212 1.991014 1.463346 2.708952 #> 213 10.270122 8.072834 13.065475 #> 214 2.257755 1.693824 3.009437 #> 215 2.629629 1.962229 3.524027 #> 216 2.526572 1.889976 3.377589 #> 217 3.248902 2.261830 4.666738 #> 218 6.797061 5.489392 8.416239 #> 219 6.361844 5.017556 8.066289 #> 220 2.846952 2.144010 3.780364 #> 221 7.758852 6.407017 9.395916 #> 222 7.164597 5.717033 8.978688 #> 223 3.784028 2.715038 5.273911 #> 224 5.462171 4.361616 6.840426 #> 225 7.262051 5.959281 8.849623 #> 226 4.881018 3.740461 6.369359 #> 227 2.611564 1.944350 3.507737 #> 228 1.926217 1.402886 2.644770 #> 229 2.198778 1.645763 2.937619 #> 230 5.285866 3.804014 7.344973 #> 231 6.707698 5.394571 8.340463 #> 232 2.846952 2.144010 3.780364 #> 233 2.736134 2.053681 3.645370 #> 234 2.057990 1.524209 2.778702 #> 235 10.001850 7.799447 12.826165 #> 236 6.033822 4.649976 7.829504 #> 237 3.834440 2.737903 5.370143 #> 238 6.994784 5.054221 9.680425 #> 239 6.617683 5.369679 8.155744 #> 240 3.834440 2.737903 5.370143 #> 241 2.884084 2.097167 3.966276 #> 242 6.195662 4.832175 7.943882 #> 243 2.884881 2.173744 3.828665 #> 244 2.646356 1.965620 3.562846 #> 245 6.444819 5.243325 7.921631 #> 246 7.212164 5.743905 9.055740 #> 247 3.041714 2.290183 4.039862 #> 248 8.234967 6.776799 10.006889 #> 249 6.360087 5.178212 7.811714 #> 250 2.809523 2.114071 3.733753 #> 251 3.336966 2.481893 4.486633 #> 252 2.611564 1.944350 3.507737 #> 253 8.400077 6.891833 10.238395 #> 254 2.577229 1.922804 3.454389 #> 255 6.114206 4.740635 7.885762 #> 256 3.082237 2.318573 4.097427 #> 257 3.271375 2.442236 4.382007 #> 258 2.444345 1.833962 3.257877 #> 259 2.198778 1.645763 2.937619 #> 260 2.476910 1.856557 3.304547 #> 261 7.966962 6.575800 9.652436 #> 262 2.228071 1.669856 2.972893 #> 263 9.174889 7.147862 11.776749 #> 264 7.407655 6.096842 9.000292 #> 265 8.093161 5.749678 11.391813 #> 266 3.495172 2.571331 4.750935 #> 267 1.926217 1.402886 2.644770 #> 268 2.085408 1.548591 2.808311 #> 269 6.979373 5.678685 8.577981 #> 270 2.287833 1.717647 3.047297 #> 271 7.522918 5.599633 10.106785 #> 272 2.754299 2.068834 3.666879 #> 273 2.273372 1.629585 3.171494 #> 274 2.113190 1.572960 2.838961 #> 275 2.213376 1.657824 2.955100 #> 276 5.462171 4.361616 6.840426 #> 277 2.057990 1.524209 2.778702 #> 278 2.412209 1.811127 3.212779 #> 279 2.113190 1.572960 2.838961 #> 280 1.964837 1.439096 2.682647 #> 281 4.598816 3.427849 6.169791 #> 282 2.629629 1.962229 3.524027 #> 283 2.560232 1.911928 3.428364 #> 284 2.595056 1.931628 3.486343 #> 285 8.587419 6.635611 11.113335 #> 286 3.293094 2.455544 4.416321 #> 287 2.526572 1.889976 3.377589 #> 288 8.073101 6.657572 9.789599 #> 289 2.884881 2.173744 3.828665 #> 290 2.664662 1.992791 3.563054 #> 291 2.510601 1.855144 3.397643 #> 292 6.072204 4.941004 7.462383 #> 293 3.541736 2.596102 4.831819 #> 294 7.356768 5.822753 9.294922 #> 295 2.287833 1.717647 3.047297 #> 296 5.285866 3.804014 7.344973 #> 297 7.827611 5.907189 10.372359 #> 298 3.472120 2.558817 4.711403 #> 299 2.754299 2.068834 3.666879 #> 300 2.461253 1.809409 3.347925 #> 301 2.155560 1.609440 2.886990 #> 302 5.462171 4.361616 6.840426 #> 303 2.560232 1.911928 3.428364 #> 304 3.685183 2.668414 5.089381 #> 305 2.257755 1.693824 3.009437 #> 306 9.652358 7.588654 12.277279 #> 307 2.257755 1.693824 3.009437 #> 308 2.962260 2.232504 3.930556 #> 309 3.228366 2.415357 4.315033 #> 310 6.705847 5.430870 8.280143 #> 311 2.595056 1.931628 3.486343 #> 312 6.887614 5.584150 8.495334 #> 313 2.412875 1.763902 3.300617 #> 314 2.629629 1.962229 3.524027#> $fit #> 1 2 3 4 5 6 7 8 #> 1.6581431 1.8963626 2.1877958 2.2142646 2.3069055 1.8503182 1.9032559 2.0551755 #> 9 10 11 12 13 14 15 16 #> 1.7375496 0.9070117 1.2712342 1.9294486 1.8370838 2.2365980 1.1650828 1.4860957 #> 17 18 19 20 21 22 23 24 #> 2.0951549 2.0885377 1.9914852 2.0485583 2.1811786 1.6713775 1.8037217 1.6581431 #> 25 26 27 28 29 30 31 32 #> 2.1944130 2.1370638 2.0353239 2.1811786 1.6118227 1.9782508 2.0684100 2.1282409 #> 33 34 35 36 37 38 39 40 #> 1.8103389 1.8169561 1.6250571 2.1877958 2.0088551 1.5191818 2.4943930 2.0885377 #> 41 42 43 44 45 46 47 48 #> 2.1083893 2.0951549 2.0554516 1.8831282 1.9426830 2.1414753 1.5853538 1.8235733 #> 49 50 51 52 53 54 55 56 #> 1.9360658 1.7841461 1.9691519 1.6846119 1.7574012 1.9561935 2.2936711 1.6978464 #> 57 58 59 60 61 62 63 64 #> 2.4679242 2.4811586 2.1679442 2.0885377 1.8566593 2.1414753 1.9890035 1.7706356 #> 65 66 67 68 69 70 71 72 #> 2.1216237 1.8632765 2.5740756 2.2274990 1.2712342 2.0025140 1.5985882 1.5522678 #> 73 74 75 76 77 78 79 80 #> 1.1319967 1.9162142 1.1124212 2.6600993 1.8500421 1.6316743 2.0684100 2.1811786 #> 81 82 83 84 85 86 87 88 #> 2.1017721 1.5588850 2.1902775 1.6846119 1.5787366 2.3554316 1.9958968 2.2341162 #> 89 90 91 92 93 94 95 96 #> 0.6754094 1.8963626 2.1414753 1.8569354 1.4397753 2.2829184 2.2539679 1.9823863 #> 97 98 99 100 101 102 103 104 #> 1.9426830 2.0022379 1.7971045 2.0617928 2.3421972 1.8301905 1.9890035 2.0551755 #> 105 106 107 108 109 110 111 112 #> 2.0378057 2.1414753 2.7395057 1.9164903 1.7838701 1.8434249 1.5588850 2.3002883 #> 113 114 115 116 117 118 119 120 #> 1.9360658 1.9029798 1.7838701 2.0091312 2.0885377 2.6931853 1.7444429 2.0551755 #> 121 122 123 124 125 126 127 128 #> 2.0353239 1.9120787 1.9890035 1.5191818 1.4794785 1.8503182 2.6468648 2.0488344 #> 129 130 131 132 133 134 135 136 #> 1.9032559 2.0951549 2.0223656 2.1282409 2.3002883 1.8434249 2.2892596 1.9294486 #> 137 138 139 140 141 142 143 144 #> 1.7841461 1.8370838 1.6184399 2.5674584 2.2341162 1.8172322 2.6998025 1.6846119 #> 145 146 147 148 149 150 151 152 #> 2.1282409 1.9429591 2.0289828 1.9694279 2.1679442 2.5542239 1.8103389 1.7507840 #> 153 154 155 156 157 158 159 160 #> 2.4946691 2.2958768 2.6071616 2.4480725 1.8301905 1.7243152 1.2712342 0.8540740 #> 161 162 163 164 165 166 167 168 #> 0.8540740 0.8408396 1.2844686 2.0488344 0.7614331 1.0859524 0.7614331 1.0131631 #> 169 170 171 172 173 174 175 176 #> 0.7217299 1.8569354 0.9006706 0.8408396 0.8011364 2.1348581 0.7217299 0.8011364 #> 177 178 179 180 181 182 183 184 #> 0.9933115 1.4199237 1.0991868 0.9668426 1.9429591 2.4351142 1.3175546 0.6754094 #> 185 186 187 188 189 190 191 192 #> 0.9933115 0.6952610 2.4020282 0.9800770 1.6449087 2.4549658 0.9400977 0.9536082 #> 193 194 195 196 197 198 199 200 #> 1.3440234 2.2142646 1.6650364 2.2763012 0.9268633 1.9429591 1.8767870 0.9006706 #> 201 202 203 204 205 206 207 208 #> 1.0131631 1.3374062 1.2712342 0.8143708 2.0289828 1.0062698 2.2365980 0.8543501 #> 209 210 211 212 213 214 215 216 #> 2.4748175 1.0330147 1.0727179 0.6886438 2.3292389 0.8143708 0.9668426 0.9268633 #> 217 218 219 220 221 222 223 224 #> 1.1783172 1.9164903 1.8503182 1.0462491 2.0488344 1.9691519 1.3307890 1.6978464 #> 225 226 227 228 229 230 231 232 #> 1.9826624 1.5853538 0.9599493 0.6555578 0.7879019 1.6650364 1.9032559 1.0462491 #> 233 234 235 236 237 238 239 240 #> 1.0065459 0.7217299 2.3027701 1.7973806 1.3440234 1.9451648 1.8897454 1.3440234 #> 241 242 243 244 245 246 247 248 #> 1.0592074 1.8238494 1.0594835 0.9731837 1.8632765 1.9757691 1.1124212 2.1083893 #> 249 250 251 252 253 254 255 256 #> 1.8500421 1.0330147 1.2050621 0.9599493 2.1282409 0.9467149 1.8106150 1.1256556 #> 257 258 259 260 261 262 263 264 #> 1.1852105 0.8937773 0.7879019 0.9070117 2.0753033 0.8011364 2.2164703 2.0025140 #> 265 266 267 268 269 270 271 272 #> 2.0910194 1.2513825 0.6555578 0.7349643 1.9429591 0.8276052 2.0179541 1.0131631 #> 273 274 275 276 277 278 279 280 #> 0.8212641 0.7481987 0.7945191 1.6978464 0.7217299 0.8805428 0.7481987 0.6754094 #> 281 282 283 284 285 286 287 288 #> 1.5257990 0.9668426 0.9400977 0.9536082 2.1502982 1.1918277 0.9268633 2.0885377 #> 289 290 291 292 293 294 295 296 #> 1.0594835 0.9800770 0.9205222 1.8037217 1.2646170 1.9956207 0.8276052 1.6650364 #> 297 298 299 300 301 302 303 304 #> 2.0576573 1.2447653 1.0131631 0.9006706 0.7680503 1.6978464 0.9400977 1.3043202 #> 305 306 307 308 309 310 311 312 #> 0.8143708 2.2672023 0.8143708 1.0859524 1.1719761 1.9029798 0.9536082 1.9297247 #> 313 314 #> 0.8808189 0.9668426 #> #> $se.fit #> 1 2 3 4 5 6 7 #> 0.12116582 0.10708454 0.10817432 0.11247337 0.13161810 0.12111206 0.11115656 #> 8 9 10 11 12 13 14 #> 0.13059912 0.10983377 0.14708830 0.15944813 0.11009723 0.12390966 0.13927602 #> 15 16 17 18 19 20 21 #> 0.18196498 0.16018011 0.09866471 0.09836039 0.15583842 0.12922756 0.10719788 #> 22 23 24 25 26 27 28 #> 0.11890116 0.10518212 0.12116582 0.10919121 0.13286305 0.12656713 0.10719788 #> 29 30 31 32 33 34 35 #> 0.13008558 0.15857597 0.13342071 0.10097323 0.10498724 0.10484324 0.12739015 #> 36 37 38 39 40 41 42 #> 0.10817432 0.12160203 0.15160154 0.15299341 0.09836039 0.09943268 0.09866471 #> 43 44 45 46 47 48 49 #> 0.09765272 0.10620992 0.11161672 0.10225024 0.13579078 0.10475030 0.11083536 #> 50 51 52 53 54 55 56 #> 0.13610671 0.11515613 0.11677659 0.10793817 0.10358907 0.12855692 0.11479988 #> 57 58 59 60 61 62 63 #> 0.14800973 0.15045084 0.10537081 0.09836039 0.10505383 0.10225024 0.11821874 #> 64 65 66 67 68 69 70 #> 0.10690713 0.10040888 0.10526757 0.11757137 0.11484327 0.15944813 0.09936054 #> 71 72 73 74 75 76 77 #> 0.13288800 0.14344633 0.17529164 0.10875415 0.14479319 0.12988159 0.10489087 #> 78 79 80 81 82 83 84 #> 0.12608471 0.13342071 0.10719788 0.09902233 0.14187258 0.14947540 0.11677659 #> 85 86 87 88 89 90 91 #> 0.13727783 0.13209188 0.09981432 0.11607947 0.15887771 0.10708454 0.10225024 #> 92 93 94 95 96 97 98 #> 0.11975601 0.17278073 0.13030315 0.11998032 0.11716094 0.11161672 0.12044025 #> 99 100 101 102 103 104 105 #> 0.10542757 0.13199711 0.13087305 0.10470857 0.11821874 0.13059912 0.14700794 #> 106 107 108 109 110 111 112 #> 0.10225024 0.14591739 0.10901871 0.10606874 0.10477891 0.14187258 0.13007587 #> 113 114 115 116 117 118 119 #> 0.11083536 0.10759406 0.10606874 0.09895896 0.09836039 0.13610319 0.14613223 #> 120 121 122 123 124 125 126 #> 0.13059912 0.12656713 0.17347704 0.11821874 0.15160154 0.16194076 0.12111206 #> 127 128 129 130 131 132 133 #> 0.12760273 0.09767562 0.11115656 0.09866471 0.09831483 0.10097323 0.13007587 #> 134 135 136 137 138 139 140 #> 0.10477891 0.12757450 0.11009723 0.13610671 0.12390966 0.12872407 0.11689257 #> 141 142 143 144 145 146 147 #> 0.11607947 0.12830570 0.13743094 0.11677659 0.10097323 0.10522612 0.09807333 #> 148 149 150 151 152 153 154 #> 0.10213623 0.10537081 0.11566236 0.10498724 0.10852425 0.11234734 0.12784456 #> 155 156 157 158 159 160 161 #> 0.12157249 0.14454949 0.10470857 0.11132095 0.15944813 0.14594417 0.14594417 #> 162 163 164 165 166 167 168 #> 0.14602490 0.16147588 0.09767562 0.14955506 0.14430287 0.14955506 0.14601177 #> 169 170 171 172 173 174 175 #> 0.15319373 0.11975601 0.15697749 0.14602490 0.14714517 0.10158732 0.15319373 #> 176 177 178 179 180 181 182 #> 0.14714517 0.14724215 0.17836013 0.14447393 0.14937093 0.10522612 0.11285340 #> 183 184 185 186 187 188 189 #> 0.16700081 0.15887771 0.14724215 0.15626098 0.11478263 0.14823793 0.12356285 #> 190 191 192 193 194 195 196 #> 0.11225532 0.14897488 0.15063803 0.17185579 0.11247337 0.16784986 0.13150001 #> 197 198 199 200 201 202 203 #> 0.14811442 0.10522612 0.11587082 0.15697749 0.14601177 0.17060780 0.15944813 #> 204 205 206 207 208 209 210 #> 0.14662629 0.09807333 0.15529324 0.13927602 0.16404894 0.11208598 0.14510434 #> 211 212 213 214 215 216 217 #> 0.14428054 0.15710389 0.12282589 0.14662629 0.14937093 0.14811442 0.18477029 #> 218 219 220 221 222 223 224 #> 0.10901871 0.12111206 0.14468189 0.09767562 0.11515613 0.16938230 0.11479988 #> 225 226 227 228 229 230 231 #> 0.10087555 0.13579078 0.15052409 0.16175106 0.14780759 0.16784986 0.11115656 #> 232 233 234 235 236 237 238 #> 0.14468189 0.14638637 0.15319373 0.12689885 0.13292008 0.17185579 0.16578926 #> 239 240 241 242 243 244 245 #> 0.10662295 0.17185579 0.16256428 0.12681495 0.14440701 0.15172515 0.10526757 #> 246 247 248 249 250 251 252 #> 0.11613975 0.14479319 0.09943268 0.10489087 0.14510434 0.15104384 0.15052409 #> 253 254 255 256 257 258 259 #> 0.10097323 0.14945714 0.12982069 0.14525967 0.14913351 0.14658350 0.14780759 #> 260 261 262 263 264 265 266 #> 0.14708830 0.09791357 0.14714517 0.12737832 0.09936054 0.17442950 0.15661453 #> 267 268 269 270 271 272 273 #> 0.16175106 0.15184915 0.10522612 0.14625250 0.15064203 0.14601177 0.16986973 #> 274 275 276 277 278 279 280 #> 0.15063520 0.14745855 0.11479988 0.15319373 0.14622390 0.15063520 0.15887771 #> 281 282 283 284 285 286 287 #> 0.14993437 0.14937093 0.14897488 0.15063803 0.13155726 0.14973714 0.14811442 #> 288 289 290 291 292 293 294 #> 0.09836039 0.14440701 0.14823793 0.15437001 0.10518212 0.15847535 0.11931217 #> 295 296 297 298 299 300 301 #> 0.14625250 0.16784986 0.14361856 0.15572751 0.14601177 0.15697749 0.14906610 #> 302 303 304 305 306 307 308 #> 0.11479988 0.14897488 0.16471530 0.14662629 0.12273083 0.14662629 0.14430287 #> 309 310 311 312 313 314 #> 0.14802772 0.10759406 0.15063803 0.10703892 0.15984496 0.14937093 #>#> 1 2 3 4 5 6 7 8 #> 6.934132 6.934132 6.934132 6.934132 6.934132 6.934132 6.934132 6.934132 #> 9 10 11 12 13 14 15 16 #> 6.934132 2.672897 2.672897 6.934132 6.934132 10.650000 2.672897 6.934132 #> 17 18 19 20 21 22 23 24 #> 6.934132 6.934132 10.650000 6.934132 6.934132 6.934132 6.934132 6.934132 #> 25 26 27 28 29 30 31 32 #> 6.934132 10.650000 6.934132 6.934132 6.934132 10.650000 6.934132 6.934132 #> 33 34 35 36 37 38 39 40 #> 6.934132 6.934132 6.934132 6.934132 6.934132 6.934132 10.650000 6.934132 #> 41 42 43 44 45 46 47 48 #> 6.934132 6.934132 6.934132 6.934132 6.934132 6.934132 6.934132 6.934132 #> 49 50 51 52 53 54 55 56 #> 6.934132 6.934132 6.934132 6.934132 6.934132 6.934132 6.934132 6.934132 #> 57 58 59 60 61 62 63 64 #> 10.650000 10.650000 6.934132 6.934132 6.934132 6.934132 6.934132 6.934132 #> 65 66 67 68 69 70 71 72 #> 6.934132 6.934132 10.650000 6.934132 2.672897 6.934132 6.934132 6.934132 #> 73 74 75 76 77 78 79 80 #> 2.672897 6.934132 2.672897 10.650000 6.934132 6.934132 6.934132 6.934132 #> 81 82 83 84 85 86 87 88 #> 6.934132 6.934132 10.650000 6.934132 6.934132 10.650000 6.934132 6.934132 #> 89 90 91 92 93 94 95 96 #> 2.672897 6.934132 6.934132 6.934132 6.934132 10.650000 6.934132 6.934132 #> 97 98 99 100 101 102 103 104 #> 6.934132 6.934132 6.934132 6.934132 10.650000 6.934132 6.934132 6.934132 #> 105 106 107 108 109 110 111 112 #> 10.650000 6.934132 10.650000 6.934132 6.934132 6.934132 6.934132 6.934132 #> 113 114 115 116 117 118 119 120 #> 6.934132 6.934132 6.934132 6.934132 6.934132 10.650000 6.934132 6.934132 #> 121 122 123 124 125 126 127 128 #> 6.934132 10.650000 6.934132 6.934132 6.934132 6.934132 10.650000 6.934132 #> 129 130 131 132 133 134 135 136 #> 6.934132 6.934132 6.934132 6.934132 6.934132 6.934132 10.650000 6.934132 #> 137 138 139 140 141 142 143 144 #> 6.934132 6.934132 6.934132 10.650000 6.934132 6.934132 10.650000 6.934132 #> 145 146 147 148 149 150 151 152 #> 6.934132 6.934132 6.934132 6.934132 6.934132 10.650000 6.934132 6.934132 #> 153 154 155 156 157 158 159 160 #> 10.650000 10.650000 10.650000 10.650000 6.934132 6.934132 2.672897 2.672897 #> 161 162 163 164 165 166 167 168 #> 2.672897 2.672897 2.672897 6.934132 2.672897 2.672897 2.672897 2.672897 #> 169 170 171 172 173 174 175 176 #> 2.672897 6.934132 2.672897 2.672897 2.672897 6.934132 2.672897 2.672897 #> 177 178 179 180 181 182 183 184 #> 2.672897 6.934132 2.672897 2.672897 6.934132 10.650000 2.672897 2.672897 #> 185 186 187 188 189 190 191 192 #> 2.672897 2.672897 10.650000 2.672897 6.934132 10.650000 2.672897 2.672897 #> 193 194 195 196 197 198 199 200 #> 2.672897 6.934132 6.934132 10.650000 2.672897 6.934132 6.934132 2.672897 #> 201 202 203 204 205 206 207 208 #> 2.672897 2.672897 2.672897 2.672897 6.934132 2.672897 10.650000 2.672897 #> 209 210 211 212 213 214 215 216 #> 10.650000 2.672897 2.672897 2.672897 10.650000 2.672897 2.672897 2.672897 #> 217 218 219 220 221 222 223 224 #> 2.672897 6.934132 6.934132 2.672897 6.934132 6.934132 2.672897 6.934132 #> 225 226 227 228 229 230 231 232 #> 6.934132 6.934132 2.672897 2.672897 2.672897 6.934132 6.934132 2.672897 #> 233 234 235 236 237 238 239 240 #> 2.672897 2.672897 10.650000 6.934132 2.672897 10.650000 6.934132 2.672897 #> 241 242 243 244 245 246 247 248 #> 2.672897 6.934132 2.672897 2.672897 6.934132 6.934132 2.672897 6.934132 #> 249 250 251 252 253 254 255 256 #> 6.934132 2.672897 2.672897 2.672897 6.934132 2.672897 6.934132 2.672897 #> 257 258 259 260 261 262 263 264 #> 2.672897 2.672897 2.672897 2.672897 6.934132 2.672897 10.650000 6.934132 #> 265 266 267 268 269 270 271 272 #> 10.650000 2.672897 2.672897 2.672897 6.934132 2.672897 10.650000 2.672897 #> 273 274 275 276 277 278 279 280 #> 2.672897 2.672897 2.672897 6.934132 2.672897 2.672897 2.672897 2.672897 #> 281 282 283 284 285 286 287 288 #> 6.934132 2.672897 2.672897 2.672897 10.650000 2.672897 2.672897 6.934132 #> 289 290 291 292 293 294 295 296 #> 2.672897 2.672897 2.672897 6.934132 2.672897 6.934132 2.672897 6.934132 #> 297 298 299 300 301 302 303 304 #> 10.650000 2.672897 2.672897 2.672897 2.672897 6.934132 2.672897 2.672897 #> 305 306 307 308 309 310 311 312 #> 2.672897 6.934132 2.672897 2.672897 2.672897 6.934132 2.672897 6.934132 #> 313 314 #> 2.672897 2.672897