mmb (version 0.13.3)

neighborhood: Given Bayesian features, returns those samples from a dataset that exhibit a similarity (i.e., the neighborhood).

Description

The neighborhood \(N_i\) is defined as the set of samples that have a similarity greater than zero to the given sample \(s_i\). Segmentation is done using equality (==) for discrete features and less than or equal (<=) for continuous features. Note that feature values NA and NaN are also supported using is.na() and is.nan().

Usage

neighborhood(df, features, selectedFeatureNames = c(), retainMinValues = 0)

Arguments

df

data.frame to select the neighborhood from

features

data.frame of Bayes-features, used to segment/select the rows that should make up the neighborhood.

selectedFeatureNames

vector of names of features to use to demarcate the neighborhood. If empty, uses all features' names.

retainMinValues

DEFAULT 0 the amount of samples to retain during segmentation. For separating a neighborhood, this value typically should be 0, so that no samples are included that are not within it. However, for very sparse data or a great amount of variables, it might still make sense to retain samples.

Value

data.frame with rows that were selected as neighborhood. It is guaranteed that the rownames are maintained.

Examples

# NOT RUN {
nbh <- mmb::neighborhood(df = iris, features = mmb::createFeatureForBayes(
  name = "Sepal.Width", value = mean(iris$Sepal.Width)))

print(nrow(nbh))
# }