This is a function for obtaining predictions and optionally estimates standard errors of those prediction from a fitted COM-Poisson regression object.
an object class 'cmp', obtained from a call to glm.cmp.
optionally, a data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used.
logical; indicating if standard errors are required.
the type of prediction required. The default is 'link' which is the scale of the linear predictor i.e., a log scale; the alternative 'response' is on the scale of the response variable. The value of this argument can be abbreviated.
other arguments passed to or from other methods (currently unused).
If se.fit = FALSE, a vector of predictions.
If se.fit = TRUE, a list with components
Predictions, as for se.fit = FALSE.
Estimated standard errors.
If newdata is omitted the predictions are based on the data used for the fit.
data(takeoverbids)
M.bids <- glm.cmp(numbids ~ leglrest + rearest + finrest + whtknght
+ bidprem + insthold + size + sizesq + regulatn, data = takeoverbids)
predict(M.bids)
#> 1 2 3 4 5 6
#> 1.006520439 0.259798415 0.760894043 0.173563231 0.186989281 0.738303942
#> 7 8 9 10 11 12
#> 0.587684738 0.050151487 -0.239498379 0.149493408 -0.008940169 0.648098267
#> 13 14 15 16 17 18
#> 0.793359563 0.356154989 0.538734250 -0.109723268 0.824314336 0.620810142
#> 19 20 21 22 23 24
#> 0.429319781 -0.077869062 -0.087971578 0.410690247 0.761266536 1.158484166
#> 25 26 27 28 29 30
#> 0.633285642 0.457811006 0.410892149 -0.006126878 0.686482725 0.085854346
#> 31 32 33 34 35 36
#> 0.469727819 0.567591406 0.690071020 0.473377977 0.623235430 1.508710911
#> 37 38 39 40 41 42
#> 0.038686705 0.268718894 0.487154793 0.335030629 0.062407157 0.498533191
#> 43 44 45 46 47 48
#> 0.853990737 0.706558470 -0.135615317 1.081189874 0.841626627 0.823324139
#> 49 50 51 52 53 54
#> -0.068564608 0.782033305 0.371943887 0.111768673 1.262534427 -0.346123566
#> 55 56 57 58 59 60
#> 0.502852566 0.546200980 0.032174615 1.047181723 0.533173071 0.242221545
#> 61 62 63 64 65 66
#> 0.024244093 0.576721142 0.319115437 0.785721372 0.617575208 0.610406612
#> 67 68 69 70 71 72
#> 0.616707908 0.331403187 0.511811152 -0.153840055 0.779165199 1.163077471
#> 73 74 75 76 77 78
#> 0.471507471 0.953415150 1.005668703 0.612203958 -0.055584835 0.065573850
#> 79 80 81 82 83 84
#> 0.027090711 1.059891981 0.105072680 0.831850710 1.149229071 0.128355342
#> 85 86 87 88 89 90
#> 0.003942262 0.656098448 0.052975482 0.226948308 0.597855879 0.663110989
#> 91 92 93 94 95 96
#> 0.420506219 0.576372277 0.267177026 0.683802076 0.752178210 0.672292288
#> 97 98 99 100 101 102
#> 0.251141873 0.453608300 0.738483417 0.164948531 0.132788396 1.384596155
#> 103 104 105 106 107 108
#> 0.131476519 0.486750959 0.696339624 0.085272668 1.029993029 0.333375596
#> 109 110 111 112 113 114
#> 0.133424658 -0.048414784 0.972490527 0.797795515 0.065160477 0.158214903
#> 115 116 117 118 119 120
#> 0.281981137 0.566765615 0.317799545 1.004602390 0.471858789 0.826524942
#> 121 122 123 124 125 126
#> 0.007167728 0.554654865 0.831965305 0.319462447 -0.075292452 1.430628677
predict(M.bids, type = "response")
#> 1 2 3 4 5 6 7 8
#> 2.7360641 1.2966687 2.1401888 1.1895359 1.2056144 2.0923837 1.7998165 1.0514304
#> 9 10 11 12 13 14 15 16
#> 0.7870225 1.1612458 0.9910997 1.9119014 2.2108113 1.4278288 1.7138362 0.8960821
#> 17 18 19 20 21 22 23 24
#> 2.2803167 1.8604346 1.5362122 0.9250855 0.9157869 1.5078582 2.1409861 3.1851015
#> 25 26 27 28 29 30 31 32
#> 1.8837899 1.5806102 1.5081627 0.9938919 1.9867154 1.0896476 1.5995588 1.7640131
#> 33 34 35 36 37 38 39 40
#> 1.9938571 1.6054081 1.8649522 4.5208992 1.0394448 1.3082873 1.6276785 1.3979832
#> 41 42 43 44 45 46 47 48
#> 1.0643956 1.6463047 2.3490024 2.0270032 0.8731785 2.9481854 2.3201379 2.2780599
#> 49 50 51 52 53 54 55 56
#> 0.9337331 2.1859124 1.4505516 1.1182541 3.5343677 0.7074251 1.6534311 1.7266808
#> 57 58 59 60 61 62 63 64
#> 1.0326978 2.8496088 1.7043317 1.2740764 1.0245404 1.7801919 1.3759101 2.1939891
#> 65 66 67 68 69 70 71 72
#> 1.8544260 1.8411799 1.8528183 1.3929213 1.6683100 0.8574091 2.1796519 3.1997653
#> 73 74 75 76 77 78 79 80
#> 1.6024080 2.5945553 2.7337347 1.8444921 0.9459318 1.0677716 1.0274610 2.8860592
#> 81 82 83 84 85 86 87 88
#> 1.1107913 2.2975669 3.1557591 1.1369569 1.0039500 1.9272583 1.0544038 1.2547650
#> 89 90 91 92 93 94 95 96
#> 1.8182161 1.9408208 1.5227322 1.7795709 1.3062717 1.9813968 2.1216163 1.9587221
#> 97 98 99 100 101 102 103 104
#> 1.2854924 1.5739813 2.0927593 1.1793324 1.1420083 3.9932129 1.1405111 1.6270214
#> 105 106 107 108 109 110 111 112
#> 2.0063951 1.0890140 2.8010463 1.3956714 1.1427352 0.9527385 2.6445225 2.2206402
#> 113 114 115 116 117 118 119 120
#> 1.0673303 1.1714179 1.3257537 1.7625570 1.3741008 2.7308213 1.6029710 2.2853632
#> 121 122 123 124 125 126
#> 1.0071935 1.7413399 2.2978302 1.3763877 0.9274722 4.1813271
predict(M.bids, se.fit = TRUE, type = "response")
#> $fit
#> 1 2 3 4 5 6 7 8
#> 2.7360641 1.2966687 2.1401888 1.1895359 1.2056144 2.0923837 1.7998165 1.0514304
#> 9 10 11 12 13 14 15 16
#> 0.7870225 1.1612458 0.9910997 1.9119014 2.2108113 1.4278288 1.7138362 0.8960821
#> 17 18 19 20 21 22 23 24
#> 2.2803167 1.8604346 1.5362122 0.9250855 0.9157869 1.5078582 2.1409861 3.1851015
#> 25 26 27 28 29 30 31 32
#> 1.8837899 1.5806102 1.5081627 0.9938919 1.9867154 1.0896476 1.5995588 1.7640131
#> 33 34 35 36 37 38 39 40
#> 1.9938571 1.6054081 1.8649522 4.5208992 1.0394448 1.3082873 1.6276785 1.3979832
#> 41 42 43 44 45 46 47 48
#> 1.0643956 1.6463047 2.3490024 2.0270032 0.8731785 2.9481854 2.3201379 2.2780599
#> 49 50 51 52 53 54 55 56
#> 0.9337331 2.1859124 1.4505516 1.1182541 3.5343677 0.7074251 1.6534311 1.7266808
#> 57 58 59 60 61 62 63 64
#> 1.0326978 2.8496088 1.7043317 1.2740764 1.0245404 1.7801919 1.3759101 2.1939891
#> 65 66 67 68 69 70 71 72
#> 1.8544260 1.8411799 1.8528183 1.3929213 1.6683100 0.8574091 2.1796519 3.1997653
#> 73 74 75 76 77 78 79 80
#> 1.6024080 2.5945553 2.7337347 1.8444921 0.9459318 1.0677716 1.0274610 2.8860592
#> 81 82 83 84 85 86 87 88
#> 1.1107913 2.2975669 3.1557591 1.1369569 1.0039500 1.9272583 1.0544038 1.2547650
#> 89 90 91 92 93 94 95 96
#> 1.8182161 1.9408208 1.5227322 1.7795709 1.3062717 1.9813968 2.1216163 1.9587221
#> 97 98 99 100 101 102 103 104
#> 1.2854924 1.5739813 2.0927593 1.1793324 1.1420083 3.9932129 1.1405111 1.6270214
#> 105 106 107 108 109 110 111 112
#> 2.0063951 1.0890140 2.8010463 1.3956714 1.1427352 0.9527385 2.6445225 2.2206402
#> 113 114 115 116 117 118 119 120
#> 1.0673303 1.1714179 1.3257537 1.7625570 1.3741008 2.7308213 1.6029710 2.2853632
#> 121 122 123 124 125 126
#> 1.0071935 1.7413399 2.2978302 1.3763877 0.9274722 4.1813271
#>
#> $se.fit
#> 1 2 3 4 5 6 7 8
#> 0.3402751 0.1951586 0.3192383 0.1856791 0.2017284 0.2285924 0.2843976 0.1673450
#> 9 10 11 12 13 14 15 16
#> 0.1354558 0.2377528 0.1382763 0.2598963 0.2881049 0.3530388 0.2519876 0.1372570
#> 17 18 19 20 21 22 23 24
#> 0.2760218 0.1931022 0.1882145 0.1272999 0.1577324 0.1883346 0.3538923 0.5316561
#> 25 26 27 28 29 30 31 32
#> 0.3065825 0.1798093 0.1810061 0.1266048 0.2766896 0.1766235 0.3111384 0.2605271
#> 33 34 35 36 37 38 39 40
#> 0.3972297 0.2600573 0.4139108 0.8832404 0.1394984 0.1833743 0.1737456 0.2559799
#> 41 42 43 44 45 46 47 48
#> 0.2021823 0.3062401 0.2662871 0.2856640 0.2036064 0.4909806 0.2941149 0.3278111
#> 49 50 51 52 53 54 55 56
#> 0.1830901 0.2786935 0.2187783 0.2681164 0.6083031 0.1479473 0.1719353 0.2971255
#> 57 58 59 60 61 62 63 64
#> 0.1241198 0.4962078 0.3335392 0.1966683 0.1726263 0.1897436 0.3767169 0.4024306
#> 65 66 67 68 69 70 71 72
#> 0.1925067 0.3610495 0.1968374 0.2420857 0.1944374 0.1977156 0.2454950 0.4401718
#> 73 74 75 76 77 78 79 80
#> 0.3021758 0.3658728 0.4415121 0.1983794 0.1192305 0.1283505 0.1257534 0.7621930
#> 81 82 83 84 85 86 87 88
#> 0.1848032 0.4474163 1.1878133 0.1438040 0.4573916 0.2226022 0.1775844 0.1750979
#> 89 90 91 92 93 94 95 96
#> 0.4894346 0.3991260 0.1843452 0.2429471 0.2404118 0.2520445 0.3595403 0.3233065
#> 97 98 99 100 101 102 103 104
#> 0.2420078 0.1694099 0.4229082 0.1632455 0.1986237 0.8447635 0.2447838 0.2018725
#> 105 106 107 108 109 110 111 112
#> 0.2482435 0.2107349 0.4198116 0.2171761 0.1852620 0.1790163 0.3660629 0.3193619
#> 113 114 115 116 117 118 119 120
#> 0.1420823 0.1764619 0.1972491 0.2720481 0.2396269 0.5496270 0.2992039 0.3995579
#> 121 122 123 124 125 126
#> 0.1758039 0.3321550 0.4027205 0.3047164 0.1720074 0.8861603
#>
newdataframe <- data.frame(
bidprem = 1, finrest = 0, insthold = 0.05,
leglrest = 0, rearest = 1, regulatn = 0, size = 0.1, whtknght = 1,
sizesq = .1^2
)
predict(M.bids, se.fit = TRUE, newdata = newdataframe, type = "response")
#> $fit
#> 1
#> 1.844806
#>
#> $se.fit
#> [,1]
#> 1 0.3875488
#>