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Calculate feature NMIs for a data list and a solutions data frame

Source: R/nmi.R
calc_nmis.Rd

Normalized mutual information scores can be used to indirectly measure how important a feature may have been in producing a cluster solution. This function will calculate the normalized mutual information between cluster solutions in a solutions data frame as well as cluster solutions created by including only a single feature from a provided data list, but otherwise using all the same hyperparameters as specified in the original SNF config. Note that NMIs can be calculated between two cluster solutions regardless of what features were actually used to create those cluster solutions. For example, a feature that was not involved in producing a particular cluster solution may still have a high NMI with that cluster solution (typically because it was highly correlated with a different feature that was used).

Usage

calc_nmis(
  dl,
  sol_df,
  transpose = TRUE,
  ignore_inclusions = TRUE,
  verbose = FALSE
)

Arguments

dl

A nested list of input data from data_list().

sol_df

Result of batch_snf storing cluster solutions and the settings that were used to generate them. Use the same value as was used in the original call to batch_snf().

transpose

If TRUE, will transpose the output data frame.

ignore_inclusions

If TRUE, will ignore the inclusion columns in the solutions data frame and calculate NMIs for all features. If FALSE, will give NAs for features that were dropped on a given settings_df row.

verbose

If TRUE, output progress to console.

Value

A "data.frame" class object containing one row for every feature in the provided data list and one column for every solution in the provided solutions data frame. Populated values show the calculated NMI score for each feature-solution combination.

Examples

input_dl <- data_list(
    list(gender_df, "gender", "demographics", "categorical"),
    list(diagnosis_df, "diagnosis", "clinical", "categorical"),
    uid = "patient_id"
)

sc <- snf_config(input_dl, n_solutions = 2)
#> ℹ No distance functions specified. Using defaults.
#> ℹ No clustering functions specified. Using defaults.

sol_df <- batch_snf(input_dl, sc)

calc_nmis(input_dl, sol_df)
#>     feature        s1        s2
#> 1    gender 0.2120130 0.2684083
#> 2 diagnosis 0.9240358 0.9035042