Returns model predictions from a model fitted with rf(), rf_repeat(), or rf_spatial().
Arguments
- model
A model produced by
rf(),rf_repeat(), orrf_spatial().
Value
A vector with predictions, or median of the predictions across repetitions if the model was fitted with rf_repeat().
Examples
#loading example data
data(plant_richness_df)
#fitting a random forest model
rf.model <- rf(
data = plant_richness_df,
dependent.variable.name = "richness_species_vascular",
predictor.variable.names = colnames(plant_richness_df)[5:21],
n.cores = 1,
verbose = FALSE
)
#get vector of predictions
x <- get_predictions(rf.model)
x
#> [1] 5089.9686 4553.2001 1498.6098 6997.1780 10917.1038 2874.9074
#> [7] 4879.5548 6091.4630 2929.2654 4314.0733 2908.4935 2868.7256
#> [13] 719.7016 6064.0372 7862.8904 7124.7102 3743.8252 4495.1800
#> [19] 5421.9854 3174.6544 6122.7444 6087.2123 11985.6153 2638.0687
#> [25] 656.5402 6315.4036 2527.4894 2570.6949 1239.0518 1606.8977
#> [31] 3755.0483 7468.7728 4479.5603 3248.7257 7644.6147 6270.9530
#> [37] 2426.1410 2738.7782 850.1533 1234.8311 2617.0397 3664.5525
#> [43] 3228.7475 3386.2269 3249.1632 3554.6452 2486.2164 1784.2087
#> [49] 1830.1257 4164.4311 5287.7065 2983.7874 1577.6187 3138.6107
#> [55] 608.9623 10850.7427 4202.2291 2099.1342 1480.4952 898.6531
#> [61] 2391.1838 2341.7634 10007.5996 5485.5437 2915.7275 3239.8695
#> [67] 9772.2748 4726.4460 1299.4710 3844.4597 4606.0415 1152.8957
#> [73] 9136.2741 5494.1387 5610.2730 988.4926 2358.1904 1998.0878
#> [79] 1330.7636 3839.3374 2204.8646 1570.0205 2811.1915 2672.7551
#> [85] 3624.0725 756.0369 2674.0807 2596.1948 2734.3257 1961.9983
#> [91] 5926.9062 1267.6369 1775.5636 8993.5469 4065.1506 4379.9417
#> [97] 8145.7530 6970.5516 9551.2491 3101.2651 2931.6995 5297.6180
#> [103] 1694.3413 2543.9139 3606.4275 2420.4880 6755.3152 4282.0070
#> [109] 6552.2939 1112.3188 2591.1244 3779.8972 2689.5633 2098.8618
#> [115] 6621.7904 850.5737 2263.2761 1778.2165 3340.5704 911.3395
#> [121] 1652.2408 10666.7822 1308.8814 3415.6255 4019.1710 4771.1122
#> [127] 3212.5594 4067.9657 2670.4533 3837.7822 4648.7099 2616.5089
#> [133] 937.7306 4701.3611 6722.2070 8483.8930 1003.5022 2364.6951
#> [139] 3276.8705 1127.2561 6836.0442 2874.5358 1247.4081 5940.0026
#> [145] 5074.3997 1647.3183 3268.1437 4380.2851 1555.8793 635.3892
#> [151] 2881.3722 2869.9599 6154.1375 2403.1722 6432.8062 7045.6842
#> [157] 7674.0356 2743.9269 3380.3008 8534.0471 3489.7143 2363.4290
#> [163] 4920.7653 7381.5103 13682.3877 2919.3904 5012.3926 1996.3059
#> [169] 1906.3266 2104.9318 6543.4690 933.4083 1486.7635 4218.4585
#> [175] 2972.5470 3953.2742 3185.9614 14807.0903 3086.9862 501.3080
#> [181] 4866.7396 5612.4179 744.5291 3884.0051 6603.9572 3207.8454
#> [187] 5156.6593 8456.9709 4216.9714 4103.2941 4899.9128 9343.0380
#> [193] 4616.3915 5840.3574 9494.0949 894.0851 493.6557 14360.0564
#> [199] 2197.8118 1552.3253 3053.2866 3018.5477 5774.0916 6913.9601
#> [205] 3881.2811 4141.5380 6801.3874 3222.3693 2691.8048 1523.4945
#> [211] 2833.9950 3252.8523 5578.6175 9307.4694 5222.6798 7801.7061
#> [217] 1525.1829 4950.1168 1961.4530 2779.0307 2964.0033 2835.1727
#> [223] 6196.7575 4831.0779 3574.9780 4149.5948 2233.7732