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Two stage covariate filtering

Source: R/covariance_filter.R
covariance_filter.Rd

The covariance_filter returns a set of covariates for land use land cover change (LULCC) models based on a two-stage variable selection: a first statistical fit estimates a covariate's quality for a given prediction task. A second step selects all variables below a given correlation threshold: We iterate over a correlation matrix ordered in the first step. Starting within the leftmost column, all rows (i.e. candidates) greater than the given threshold are dropped from the full set of candidates. This candidate selection is retained and used to select the next column, until no further columns are left to investigate. The columns that were iterated over are those returned as a character vector of selected variable names.

Usage

covariance_filter(
  data,
  result_col = "result",
  rank_fun = rank_poly_glm,
  weights = compute_balanced_weights(data[[result_col]]),
  corcut = 0.7,
  ...
)

rank_poly_glm(x, y, weights = NULL, ...)

compute_balanced_weights(trans_result, legacy = FALSE)

select_by_correlation(cor_mat, corcut)

Arguments

data

A data.table of target variable and candidate covariates to be filtered; wide format with one predictor per column.

result_col

Name of the column representing the transition results (0: no trans, 1: trans)

rank_fun

Optional function to compute ranking scores for each covariate. Should take arguments (x, y, weights, ...) and return a single numeric value (lower = better). Defaults to polynomial GLM p-value ranking.

weights

Optional weights vector

corcut

Correlation cutoff threshold

...

Additional arguments passed to rank_fun.

x

A numeric vector representing a single covariate

y

A binary outcome vector (0/1)

trans_result

Binary outcome vector (0/1)

legacy

Bool, use legacy weighting?

cor_mat

Absolute correlation matrix

Value

A set of column names (covariates) to retain

Details

The function first ranks covariates using the provided ranking function (default: quasibinomial polynomial GLM). Then, it iteratively removes highly (Pearson) correlated variables based on the correlation cutoff threshold, preserving variables in order of their ranking. See https://github.com/ethzplus/evoland-plus-legacy/blob/main/R/lulcc.covfilter.r for where the concept came from. The original author was Antoine Adde, with edits by Benjamin Black. A similar mechanism is found in https://github.com/antadde/covsel/.

Functions

  • rank_poly_glm(): Default ranking function using polynomial GLM. Returns the lower p value for each of the polynomial terms

  • compute_balanced_weights(): Compute class-balanced weights for imbalanced binary outcomes; returns a numeric vector

  • select_by_correlation(): Implements the iterative selection procedure.