These two objects can be used to compute importance scores based on Analysis of Variance techniques.
Format
An object of class filtro::class_score_aov (inherits from filtro::class_score, S7_object) of length 1.
An object of class filtro::class_score_aov (inherits from filtro::class_score, S7_object) of length 1.
Value
An S7 object. The primary property of interest is in results. This
is a data frame of results that is populated by the fit() method and has
columns:
name: The name of the score (e.g.,aov_fstatoraov_pval).score: The estimates for each predictor.outcome: The name of the outcome column.predictor: The names of the predictor inputs.
These data are accessed using object@results (see examples below).
Details
These objects are used when either:
The predictors are numeric and the outcome is a factor/category, or
The predictors are factors and the outcome is numeric.
In either case, a linear model (via stats::lm()) is created with the proper
variable roles, and the overall p-value for the hypothesis that all means are
equal is computed via the standard F-statistic. The p-value that is returned
is transformed to be -log10(p_value) so that larger values are associated
with more important predictors.
Estimating the scores
In filtro, the score_* objects define a scoring method (e.g., data
input requirements, package dependencies, etc). To compute the scores for
a specific data set, the fit() method is used. The main arguments for
these functions are:
objectA score class object (e.g.,
score_aov_pval).formulaA standard R formula with a single outcome on the right-hand side and one or more predictors (or
.) on the left-hand side. The data are processed viastats::model.frame()dataA data frame containing the relevant columns defined by the formula.
...Further arguments passed to or from other methods.
case_weightsA quantitative vector of case weights that is the same length as the number of rows in
data. The default ofNULLindicates that there are no case weights.
Missing values are removed for each predictor/outcome combination being scored.
In cases where the underlying computations fail, the scoring proceeds silently, and a missing value is given for the score.
See also
Other class score metrics:
score_cor_pearson,
score_imp_rf,
score_info_gain,
score_roc_auc,
score_xtab_pval_chisq
Examples
# Analysis of variance where `class` is the class predictor and the numeric
# predictors are the outcomes/responses
cell_data <- modeldata::cells
cell_data$case <- NULL
# ANOVA p-value
cell_p_val_res <-
score_aov_pval |>
fit(class ~ ., data = cell_data)
cell_p_val_res@results
#> # A tibble: 56 × 4
#> name score outcome predictor
#> <chr> <dbl> <chr> <chr>
#> 1 aov_pval 0.0575 class angle_ch_1
#> 2 aov_pval 1.04 class area_ch_1
#> 3 aov_pval 73.2 class avg_inten_ch_1
#> 4 aov_pval 88.5 class avg_inten_ch_2
#> 5 aov_pval 0.0246 class avg_inten_ch_3
#> 6 aov_pval 27.8 class avg_inten_ch_4
#> 7 aov_pval 52.6 class convex_hull_area_ratio_ch_1
#> 8 aov_pval 60.0 class convex_hull_perim_ratio_ch_1
#> 9 aov_pval 50.7 class diff_inten_density_ch_1
#> 10 aov_pval 1.51 class diff_inten_density_ch_3
#> # ℹ 46 more rows
# ANOVA raw p-value
natrual_units <- score_aov_pval |> dont_log_pvalues()
cell_pval_natrual_res <-
natrual_units |>
fit(class ~ ., data = cell_data)
cell_pval_natrual_res@results
#> # A tibble: 56 × 4
#> name score outcome predictor
#> <chr> <dbl> <chr> <chr>
#> 1 aov_pval 8.76e- 1 class angle_ch_1
#> 2 aov_pval 9.05e- 2 class area_ch_1
#> 3 aov_pval 6.02e-74 class avg_inten_ch_1
#> 4 aov_pval 3.02e-89 class avg_inten_ch_2
#> 5 aov_pval 9.45e- 1 class avg_inten_ch_3
#> 6 aov_pval 1.47e-28 class avg_inten_ch_4
#> 7 aov_pval 2.63e-53 class convex_hull_area_ratio_ch_1
#> 8 aov_pval 1.08e-60 class convex_hull_perim_ratio_ch_1
#> 9 aov_pval 1.90e-51 class diff_inten_density_ch_1
#> 10 aov_pval 3.07e- 2 class diff_inten_density_ch_3
#> # ℹ 46 more rows
# ANOVA t/F-statistic
cell_t_stat_res <-
score_aov_fstat |>
fit(class ~ ., data = cell_data)
cell_t_stat_res@results
#> # A tibble: 56 × 4
#> name score outcome predictor
#> <chr> <dbl> <chr> <chr>
#> 1 aov_fstat 0.0244 class angle_ch_1
#> 2 aov_fstat 2.87 class area_ch_1
#> 3 aov_fstat 360. class avg_inten_ch_1
#> 4 aov_fstat 444. class avg_inten_ch_2
#> 5 aov_fstat 0.00477 class avg_inten_ch_3
#> 6 aov_fstat 127. class avg_inten_ch_4
#> 7 aov_fstat 251. class convex_hull_area_ratio_ch_1
#> 8 aov_fstat 289. class convex_hull_perim_ratio_ch_1
#> 9 aov_fstat 241. class diff_inten_density_ch_1
#> 10 aov_fstat 4.68 class diff_inten_density_ch_3
#> # ℹ 46 more rows
# ---------------------------------------------------------------------------
library(dplyr)
# Analysis of variance where `chem_fp_*` are the class predictors and
# `permeability` is the numeric outcome/response
permeability <-
modeldata::permeability_qsar |>
# Make the problem a little smaller for time; use 50 predictors
select(1:51) |>
# Make the binary predictor columns into factors
mutate(across(starts_with("chem_fp"), as.factor))
perm_p_val_res <-
score_aov_pval |>
fit(permeability ~ ., data = permeability)
perm_p_val_res@results
#> # A tibble: 50 × 4
#> name score outcome predictor
#> <chr> <dbl> <chr> <chr>
#> 1 aov_pval 1.88 permeability chem_fp_0001
#> 2 aov_pval 1.63 permeability chem_fp_0002
#> 3 aov_pval 1.36 permeability chem_fp_0003
#> 4 aov_pval 1.36 permeability chem_fp_0004
#> 5 aov_pval 1.36 permeability chem_fp_0005
#> 6 aov_pval 10.6 permeability chem_fp_0006
#> 7 aov_pval NA permeability chem_fp_0007
#> 8 aov_pval NA permeability chem_fp_0008
#> 9 aov_pval 0.265 permeability chem_fp_0009
#> 10 aov_pval 0.341 permeability chem_fp_0010
#> # ℹ 40 more rows
# Note that some `lm()` calls failed and are given NA score values. For
# example:
table(permeability$chem_fp_0007)
#>
#> 1
#> 165
perm_t_stat_res <-
score_aov_fstat |>
fit(permeability ~ ., data = permeability)
perm_t_stat_res@results
#> # A tibble: 50 × 4
#> name score outcome predictor
#> <chr> <dbl> <chr> <chr>
#> 1 aov_fstat 6.28 permeability chem_fp_0001
#> 2 aov_fstat 5.22 permeability chem_fp_0002
#> 3 aov_fstat 4.13 permeability chem_fp_0003
#> 4 aov_fstat 4.13 permeability chem_fp_0004
#> 5 aov_fstat 4.13 permeability chem_fp_0005
#> 6 aov_fstat 51.3 permeability chem_fp_0006
#> 7 aov_fstat NA permeability chem_fp_0007
#> 8 aov_fstat NA permeability chem_fp_0008
#> 9 aov_fstat 0.371 permeability chem_fp_0009
#> 10 aov_fstat 0.559 permeability chem_fp_0010
#> # ℹ 40 more rows