Calculate Log Odds Ratio Between Categories

Compares word usage between categories using the log-odds ratio with a uniform Dirichlet prior, ranked by z-score. Identifies words distinctively used in one category. For the informative-prior weighted log-odds, see calculate_weighted_log_odds().

Usage

calculate_log_odds_ratio(
  dfm_object,
  group_var,
  comparison_mode = c("binary", "one_vs_rest", "pairwise"),
  reference_level = NULL,
  top_n = 10,
  min_count = 5
)

Arguments

dfm_object

A quanteda dfm object

group_var

Character, name of the grouping variable in docvars

comparison_mode

Character, one of "binary", "one_vs_rest", or "pairwise"

• binary: Compare two categories directly

• one_vs_rest: Compare each category against all others combined

• pairwise: Compare all pairs of categories

reference_level

Character, reference category for binary comparison (default: first level)

top_n

Number of top terms per comparison (default: 10)

min_count

Minimum word count to include (default: 5)

Value

Data frame with columns:

• term: The word/feature

• category1: First category in comparison

• category2: Second category in comparison

• count1: Count in category 1

• count2: Count in category 2

• odds1: Smoothed odds in category 1, (count + 1) / (total + V - count - 1)

• odds2: Smoothed odds in category 2

• odds_ratio: Ratio of odds

• log_odds_ratio: Log of odds ratio (positive = more in compared category)

• variance: Approximate variance of the log odds ratio

• z_score: Log odds ratio divided by its standard error

• significant: TRUE when |z| >= 1.96

Terms are ranked by absolute z-score.

Examples

# \donttest{
articles <- TextAnalysisR::SpecialEduTech[1:20, ]
corpus <- quanteda::corpus(
  articles$abstract,
  docvars = data.frame(reference_type = articles$reference_type)
)
dfm_object <- quanteda::dfm(quanteda::tokens(corpus))
log_odds <- calculate_log_odds_ratio(dfm_object, "reference_type")
# }