predict.rq.counts.RdThis function computes predictions based on fitted linear quantile models.
# S3 method for class 'rq.counts'
predict(object, newdata, offset,
na.action = na.pass, type = "response",
namevec = NULL, ...)an rq.counts object.
an optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
an offset to be used with newdata.
function determining what should be done with missing values in newdata. The default is to predict NA.
the type of prediction required. The default "response" is on the scale of the response variable, i.e. the values are back-transformed using the inverse of the transformation \(h^{-1}(Xb)\); the alternative "link" is on the scale of the linear predictors \(h(y) = Xb\); finally, predictions for marginal effects are given with "maref".
character giving the name of the covariate with respect to which the marginal effect is to be computed. If type = "maref", this argument is required. See maref.rq.counts.
not used.
a vector or a matrix or an array of predictions.
# Esterase data
data(esterase)
# Fit quantiles 0.25 and 0.75
fit <- rq.counts(Count ~ Esterase, tau = 0.5, data = esterase, M = 50)
cbind(fit$fitted.values, predict(fit, type = "response"))
#> [,1] [,2]
#> 1 125.0889 125.0889
#> 2 141.4300 141.4300
#> 3 144.9469 144.9469
#> 4 147.0990 147.0990
#> 5 147.8235 147.8235
#> 6 149.2832 149.2832
#> 7 157.5741 157.5741
#> 8 159.1304 159.1304
#> 9 159.1304 159.1304
#> 10 159.9144 159.9144
#> 11 160.7022 160.7022
#> 12 162.2895 162.2895
#> 13 163.8925 163.8925
#> 14 163.8925 163.8925
#> 15 165.5115 165.5115
#> 16 167.1465 167.1465
#> 17 168.7976 168.7976
#> 18 171.3051 171.3051
#> 19 178.1760 178.1760
#> 20 179.9364 179.9364
#> 21 180.8232 180.8232
#> 22 182.6099 182.6099
#> 23 183.5098 183.5098
#> 24 184.4143 184.4143
#> 25 185.3232 185.3232
#> 26 186.2365 186.2365
#> 27 189.9354 189.9354
#> 28 189.9354 189.9354
#> 29 191.8124 191.8124
#> 30 194.6628 194.6628
#> 31 196.5866 196.5866
#> 32 199.5081 199.5081
#> 33 201.4800 201.4800
#> 34 201.4800 201.4800
#> 35 204.4744 204.4744
#> 36 204.4744 204.4744
#> 37 206.4954 206.4954
#> 38 206.4954 206.4954
#> 39 210.5978 210.5978
#> 40 211.6361 211.6361
#> 41 211.6361 211.6361
#> 42 212.6795 212.6795
#> 43 213.7281 213.7281
#> 44 214.7818 214.7818
#> 45 217.9744 217.9744
#> 46 220.1292 220.1292
#> 47 220.1292 220.1292
#> 48 220.1292 220.1292
#> 49 222.3054 222.3054
#> 50 224.5031 224.5031
#> 51 226.7226 226.7226
#> 52 226.7226 226.7226
#> 53 226.7226 226.7226
#> 54 233.5138 233.5138
#> 55 234.6654 234.6654
#> 56 235.8226 235.8226
#> 57 235.8226 235.8226
#> 58 236.9856 236.9856
#> 59 240.5090 240.5090
#> 60 246.4984 246.4984
#> 61 247.7141 247.7141
#> 62 248.9358 248.9358
#> 63 253.8834 253.8834
#> 64 256.3940 256.3940
#> 65 261.4900 261.4900
#> 66 270.6535 270.6535
#> 67 273.3303 273.3303
#> 68 276.0336 276.0336
#> 69 294.2697 294.2697
#> 70 294.2697 294.2697
#> 71 298.6467 298.6467
#> 72 298.6467 298.6467
#> 73 298.6467 298.6467
#> 74 300.1201 300.1201
#> 75 304.5842 304.5842
#> 76 307.5971 307.5971
#> 77 313.7129 313.7129
#> 78 313.7129 313.7129
#> 79 318.3795 318.3795
#> 80 332.8006 332.8006
#> 81 336.0932 336.0932
#> 82 341.0932 341.0932
#> 83 344.4679 344.4679
#> 84 346.1678 346.1678
#> 85 353.0517 353.0517
#> 86 356.5448 356.5448
#> 87 360.0726 360.0726
#> 88 370.8671 370.8671
#> 89 370.8671 370.8671
#> 90 376.3853 376.3853
#> 91 403.2438 403.2438
#> 92 409.2443 409.2443
#> 93 413.2942 413.2942
#> 94 419.4445 419.4445
#> 95 447.1724 447.1724
#> 96 447.1724 447.1724
#> 97 476.7353 476.7353
#> 98 488.6198 488.6198
#> 99 560.8682 560.8682
#> 100 606.8577 606.8577
#> 101 703.5041 703.5041
#> 102 717.5042 717.5042
#> 103 735.3971 735.3971
#> 104 787.9084 787.9084
#> 105 803.5894 803.5894
#> 106 815.5547 815.5547
#> 107 835.8945 835.8945
#> 108 959.5442 959.5442
#> 109 983.4772 983.4772
#> 110 1064.1503 1064.1503
#> 111 1388.5688 1388.5688
#> 112 1395.4285 1395.4285
#> 113 1416.2118 1416.2118