Predictions from rq.counts Objects
predict.rq.counts.RdThis function computes predictions based on fitted linear quantile models.
Usage
# S3 method for class 'rq.counts'
predict(object, newdata, offset,
na.action = na.pass, type = "response",
namevec = NULL, ...)Arguments
- object
an
rq.countsobject.- newdata
an optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
- offset
an offset to be used with
newdata.- na.action
function determining what should be done with missing values in
newdata. The default is to predictNA.- type
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".- namevec
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. Seemaref.rq.counts.- ...
not used.
Examples
# 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]
#> 1 125.0841 125.0841
#> 2 141.4248 141.4248
#> 3 144.9417 144.9417
#> 4 147.0938 147.0938
#> 5 147.8182 147.8182
#> 6 149.2779 149.2779
#> 7 157.5686 157.5686
#> 8 159.1250 159.1250
#> 9 159.1250 159.1250
#> 10 159.9089 159.9089
#> 11 160.6967 160.6967
#> 12 162.2840 162.2840
#> 13 163.8870 163.8870
#> 14 163.8870 163.8870
#> 15 165.5059 165.5059
#> 16 167.1409 167.1409
#> 17 168.7920 168.7920
#> 18 171.2995 171.2995
#> 19 178.1702 178.1702
#> 20 179.9306 179.9306
#> 21 180.8174 180.8174
#> 22 182.6040 182.6040
#> 23 183.5040 183.5040
#> 24 184.4084 184.4084
#> 25 185.3173 185.3173
#> 26 186.2306 186.2306
#> 27 189.9295 189.9295
#> 28 189.9295 189.9295
#> 29 191.8064 191.8064
#> 30 194.6568 194.6568
#> 31 196.5806 196.5806
#> 32 199.5020 199.5020
#> 33 201.4738 201.4738
#> 34 201.4738 201.4738
#> 35 204.4682 204.4682
#> 36 204.4682 204.4682
#> 37 206.4892 206.4892
#> 38 206.4892 206.4892
#> 39 210.5915 210.5915
#> 40 211.6298 211.6298
#> 41 211.6298 211.6298
#> 42 212.6732 212.6732
#> 43 213.7218 213.7218
#> 44 214.7755 214.7755
#> 45 217.9681 217.9681
#> 46 220.1229 220.1229
#> 47 220.1229 220.1229
#> 48 220.1229 220.1229
#> 49 222.2990 222.2990
#> 50 224.4967 224.4967
#> 51 226.7161 226.7161
#> 52 226.7161 226.7161
#> 53 226.7161 226.7161
#> 54 233.5073 233.5073
#> 55 234.6588 234.6588
#> 56 235.8161 235.8161
#> 57 235.8161 235.8161
#> 58 236.9790 236.9790
#> 59 240.5024 240.5024
#> 60 246.4918 246.4918
#> 61 247.7075 247.7075
#> 62 248.9292 248.9292
#> 63 253.8766 253.8766
#> 64 256.3872 256.3872
#> 65 261.4832 261.4832
#> 66 270.6466 270.6466
#> 67 273.3234 273.3234
#> 68 276.0266 276.0266
#> 69 294.2626 294.2626
#> 70 294.2626 294.2626
#> 71 298.6395 298.6395
#> 72 298.6395 298.6395
#> 73 298.6395 298.6395
#> 74 300.1129 300.1129
#> 75 304.5770 304.5770
#> 76 307.5899 307.5899
#> 77 313.7056 313.7056
#> 78 313.7056 313.7056
#> 79 318.3722 318.3722
#> 80 332.7933 332.7933
#> 81 336.0858 336.0858
#> 82 341.0858 341.0858
#> 83 344.4605 344.4605
#> 84 346.1604 346.1604
#> 85 353.0442 353.0442
#> 86 356.5374 356.5374
#> 87 360.0652 360.0652
#> 88 370.8596 370.8596
#> 89 370.8596 370.8596
#> 90 376.3777 376.3777
#> 91 403.2361 403.2361
#> 92 409.2367 409.2367
#> 93 413.2866 413.2866
#> 94 419.4369 419.4369
#> 95 447.1647 447.1647
#> 96 447.1647 447.1647
#> 97 476.7277 476.7277
#> 98 488.6122 488.6122
#> 99 560.8607 560.8607
#> 100 606.8504 606.8504
#> 101 703.4973 703.4973
#> 102 717.4976 717.4976
#> 103 735.3906 735.3906
#> 104 787.9023 787.9023
#> 105 803.5835 803.5835
#> 106 815.5489 815.5489
#> 107 835.8889 835.8889
#> 108 959.5400 959.5400
#> 109 983.4733 983.4733
#> 110 1064.1475 1064.1475
#> 111 1388.5712 1388.5712
#> 112 1395.4311 1395.4311
#> 113 1416.2147 1416.2147