predict method class “smoothic”
Usage
# S3 method for class 'smoothic'
predict(object, newdata, ...)Arguments
- object
an object of class “
smoothic” which is the result of a call tosmoothic.- newdata
new data object
- ...
further arguments passed to or from other methods.
Examples
# Sniffer Data --------------------
# MPR Model ----
results <- smoothic(
formula = y ~ .,
data = sniffer,
family = "normal",
model = "mpr"
)
predict(results)
#> mu s
#> 1 23.85742 1.844882
#> 2 26.20599 2.820692
#> 3 27.57423 3.249511
#> 4 26.97700 3.158825
#> 5 26.36898 2.518755
#> 6 23.07260 2.008381
#> 7 22.01057 1.897847
#> 8 22.37036 1.952332
#> 9 25.87346 3.070670
#> 10 24.81508 2.984975
#> 11 25.21216 3.070670
#> 12 24.99629 3.070670
#> 13 27.58472 3.342801
#> 14 27.60730 3.342801
#> 15 27.48072 3.342801
#> 16 28.30795 3.639049
#> 17 21.45270 2.008381
#> 18 21.29305 1.952332
#> 19 20.59503 1.844882
#> 20 20.96633 1.952332
#> 21 32.01594 3.537492
#> 22 32.50231 3.639049
#> 23 33.28176 3.850994
#> 24 33.50812 3.961552
#> 25 33.50812 3.961552
#> 26 33.50812 3.961552
#> 27 31.98537 3.961552
#> 28 31.69593 3.961552
#> 29 31.46957 3.850994
#> 30 32.52009 3.961552
#> 31 32.43080 3.961552
#> 32 32.34151 3.961552
#> 33 31.98172 3.850994
#> 34 32.20808 3.961552
#> 35 31.62136 3.961552
#> 36 31.87772 3.850994
#> 37 31.52842 3.850994
#> 38 30.59661 3.743522
#> 39 31.40971 3.850994
#> 40 31.63607 3.961552
#> 41 32.17444 4.075283
#> 42 32.40080 4.192280
#> 43 31.60243 4.075283
#> 44 31.25735 3.961552
#> 45 24.01929 2.008381
#> 46 23.38056 2.008381
#> 47 25.59711 2.186369
#> 48 21.95337 2.125353
#> 49 20.59141 1.952332
#> 50 30.08767 3.639049
#> 51 30.73733 3.961552
#> 52 30.75205 3.961552
#> 53 31.30513 4.075283
#> 54 31.83986 4.075283
#> 55 31.39442 4.075283
#> 56 30.97841 4.075283
#> 57 42.03115 4.436446
#> 58 41.22855 4.436446
#> 59 42.82031 3.537492
#> 60 43.72577 3.961552
#> 61 50.99575 9.002164
#> 62 52.09085 9.799960
#> 63 52.09085 9.799960
#> 64 52.09085 9.799960
#> 65 50.65010 9.260605
#> 66 51.19212 9.799960
#> 67 32.54038 4.968269
#> 68 29.98616 4.192280
#> 69 30.54347 4.192280
#> 70 30.07545 4.192280
#> 71 23.83943 2.518755
#> 72 22.91869 2.380131
#> 73 41.00469 4.968269
#> 74 39.96523 4.694833
#> 75 37.75597 4.563811
#> 76 39.01757 5.110902
#> 77 45.91490 8.506717
#> 78 47.08093 9.002164
#> 79 47.39659 9.260605
#> 80 31.16748 4.192280
#> 81 31.50892 4.192280
#> 82 31.03041 4.075283
#> 83 31.07819 4.192280
#> 84 30.51040 4.075283
#> 85 30.18367 4.075283
#> 86 30.56240 4.075283
#> 87 30.56240 4.075283
#> 88 30.65169 4.075283
#> 89 30.78876 4.192280
#> 90 30.11274 4.192280
#> 91 30.92583 4.312635
#> 92 31.34606 4.192280
#> 93 32.09244 4.312635
#> 94 31.16748 4.192280
#> 95 30.69947 4.192280
#> 96 30.41004 4.192280
#> 97 30.20203 4.192280
#> 98 31.07819 4.192280
#> 99 31.16748 4.192280
#> 100 30.09438 4.075283
#> 101 30.65169 4.075283
#> 102 30.09438 4.075283
#> 103 30.56240 4.075283
#> 104 30.09438 4.075283
#> 105 30.56240 4.075283
#> 106 30.47311 4.075283
#> 107 29.88638 4.075283
#> 108 30.32075 4.192280
#> 109 33.02060 4.312635
#> 110 33.44083 4.192280
#> 111 33.06211 4.192280
#> 112 19.65056 1.952332
#> 113 19.29442 1.952332
#> 114 19.08641 1.952332
#> 115 18.73027 1.952332
#> 116 19.16099 1.952332
#> 117 19.00499 1.952332
#> 118 19.27913 2.066039
#> 119 19.38735 2.008381
#> 120 18.73027 1.952332
#> 121 21.44905 1.952332
#> 122 21.76106 1.952332
#> 123 22.45179 1.952332
#> 124 21.58248 1.952332
#> 125 21.80884 2.008381