predict method class “smoothic”
Usage
# S3 method for 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 23.85760 1.843143
#> 2 26.20577 2.819553
#> 3 27.57393 3.248783
#> 4 26.97674 3.158004
#> 5 26.36887 2.517375
#> 6 23.07293 2.006704
#> 7 22.01106 1.896125
#> 8 22.37082 1.950631
#> 9 25.87340 3.069761
#> 10 24.81516 2.983984
#> 11 25.21220 3.069761
#> 12 24.99640 3.069761
#> 13 27.58446 3.342172
#> 14 27.60701 3.342172
#> 15 27.48047 3.342172
#> 16 28.30763 3.638758
#> 17 21.45328 2.006704
#> 18 21.29366 1.950631
#> 19 20.59575 1.843143
#> 20 20.96697 1.950631
#> 21 32.01540 3.537081
#> 22 32.50170 3.638758
#> 23 33.28106 3.850963
#> 24 33.50739 3.961662
#> 25 33.50739 3.961662
#> 26 33.50739 3.961662
#> 27 31.98482 3.961662
#> 28 31.69540 3.961662
#> 29 31.46907 3.850963
#> 30 32.51949 3.961662
#> 31 32.43023 3.961662
#> 32 32.34096 3.961662
#> 33 31.98119 3.850963
#> 34 32.20752 3.961662
#> 35 31.62086 3.961662
#> 36 31.87720 3.850963
#> 37 31.52797 3.850963
#> 38 30.59626 3.743357
#> 39 31.40925 3.850963
#> 40 31.63558 3.961662
#> 41 32.17388 4.075543
#> 42 32.40021 4.192698
#> 43 31.60194 4.075543
#> 44 31.25690 3.961662
#> 45 24.01988 2.006704
#> 46 23.38122 2.006704
#> 47 25.59750 2.184779
#> 48 21.95426 2.123731
#> 49 20.59243 1.950631
#> 50 30.08741 3.638758
#> 51 30.73695 3.961662
#> 52 30.75168 3.961662
#> 53 31.30470 4.075543
#> 54 31.83937 4.075543
#> 55 31.39396 4.075543
#> 56 30.97801 4.075543
#> 57 42.02968 4.437208
#> 58 41.22722 4.437208
#> 59 42.81876 3.537081
#> 60 43.72409 3.961662
#> 61 50.99305 9.011810
#> 62 52.08800 9.811520
#> 63 52.08800 9.811520
#> 64 52.08800 9.811520
#> 65 50.64744 9.270862
#> 66 51.18937 9.811520
#> 67 32.53978 4.969837
#> 68 29.98592 4.192698
#> 69 30.54313 4.192698
#> 70 30.07518 4.192698
#> 71 23.84001 2.517375
#> 72 22.91940 2.378657
#> 73 41.00339 4.969837
#> 74 39.96406 4.695977
#> 75 37.75504 4.564759
#> 76 39.01650 5.112699
#> 77 45.91288 8.515219
#> 78 47.07874 9.011810
#> 79 47.39434 9.270862
#> 80 31.16707 4.192698
#> 81 31.50848 4.192698
#> 82 31.03000 4.075543
#> 83 31.07780 4.192698
#> 84 30.51006 4.075543
#> 85 30.18337 4.075543
#> 86 30.56205 4.075543
#> 87 30.56205 4.075543
#> 88 30.65132 4.075543
#> 89 30.78838 4.192698
#> 90 30.11245 4.192698
#> 91 30.92545 4.313221
#> 92 31.34560 4.192698
#> 93 32.09187 4.313221
#> 94 31.16707 4.192698
#> 95 30.69912 4.192698
#> 96 30.40970 4.192698
#> 97 30.20172 4.192698
#> 98 31.07780 4.192698
#> 99 31.16707 4.192698
#> 100 30.09410 4.075543
#> 101 30.65132 4.075543
#> 102 30.09410 4.075543
#> 103 30.56205 4.075543
#> 104 30.09410 4.075543
#> 105 30.56205 4.075543
#> 106 30.47279 4.075543
#> 107 29.88612 4.075543
#> 108 30.32043 4.192698
#> 109 33.01995 4.313221
#> 110 33.44010 4.192698
#> 111 33.06142 4.192698
#> 112 19.65147 1.950631
#> 113 19.29533 1.950631
#> 114 19.08735 1.950631
#> 115 18.73121 1.950631
#> 116 19.16189 1.950631
#> 117 19.00591 1.950631
#> 118 19.28004 2.064388
#> 119 19.38822 2.006704
#> 120 18.73121 1.950631
#> 121 21.44965 1.950631
#> 122 21.76161 1.950631
#> 123 22.45227 1.950631
#> 124 21.58309 1.950631
#> 125 21.80942 2.006704