Predict probabilities
predict_probability.Rdpredict_probability() returns the provided dataset with predicted
probabilities for the response variable.
Arguments
- model
a model object, the output of
build_model().- new_dat
a cleaned metabolomics or compounds matrix. If no dataset is provided, predictions for the test dataset are returned.
Examples
path <- get_example_data("small_biocrates_example.xls")
dat <- read_data(path)
dat <- add_group(dat, "group")
dat <- complete_data(dat, "limit", "limit", "limit")
#> Completing 109 < LOD values...
#> Completing 6 < LLOQ values...
#> Completing 9 < ULOQ values...
model <- build_model(dat, "group", "2", "Lasso")
#> Warning: one multinomial or binomial class has fewer than 8 observations; dangerous ground
predict_probability(model)
#> probability_2 sample identification 2 C0 C2 C3 C3-DC (C4-OH) C3-OH
#> 1 0.1045564 K_Biocrates_4_1 1 45.1 5.28 0.352 0.17 0.019
#> 2 0.1167782 K_Biocrates_4_2 1 46.2 7.88 0.495 0.17 0.019
#> 3 0.8492329 K_Biocrates_4_18 1 30.1 10.50 0.191 0.17 0.019
#> 4 0.8253519 K_Biocrates_4_20 1 38.6 6.87 0.285 0.17 0.019
#> 5 0.1255293 K_Biocrates_4_24 1 52.0 9.55 0.544 0.17 0.019
#> 6 0.1046923 K_Biocrates_4_25 1 23.4 5.31 0.181 0.17 0.019
#> 7 0.1295654 K_Biocrates_4_51 0 44.6 10.30 0.200 0.17 0.019
#> 8 0.1209570 K_Biocrates_4_56 0 42.2 8.69 0.558 0.17 0.019
#> 9 0.1046470 K_Biocrates_4_69 0 34.4 5.30 0.328 0.17 0.019
#> 10 0.1136235 K_Biocrates_4_76 0 36.3 7.21 0.236 0.17 0.019
#> C3:1 C4 C5
#> 1 0.019 0.217 0.157
#> 2 0.015 0.248 0.231
#> 3 10.000 0.107 0.087
#> 4 10.000 0.107 0.105
#> 5 0.019 0.322 0.182
#> 6 0.019 0.246 0.109
#> 7 0.019 0.200 0.245
#> 8 0.017 0.287 0.238
#> 9 0.019 0.189 0.222
#> 10 0.019 0.153 0.135