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Create composite index from multiple indicators

Source: R/normalization.R
create_composite_index.Rd

Aggregates normalized indicators into a single composite score.

Usage

create_composite_index(
  data,
  indicators,
  weights = NULL,
  name = "composite_index",
  aggregation = c("weighted_mean", "geometric_mean", "min", "max"),
  na.rm = TRUE,
  scale_to_100 = NULL
)

Arguments

data

An sf object or data.frame with normalized indicators

indicators

Character vector of indicator column names to include

weights

Numeric vector of weights for each indicator (same length as indicators). If NULL, equal weights are used. Weights are automatically normalized to sum to 1.

name

Character. Name for the composite index column. Default "composite_index".

aggregation

Character. Aggregation method. Options:

  • "weighted_mean" - Weighted arithmetic mean (default)

  • "geometric_mean" - Weighted geometric mean (good for multiplicative effects)

  • "min" - Minimum value (conservative, limiting factor approach)

  • "max" - Maximum value (optimistic)

na.rm

Logical. Remove NA values in aggregation? Default TRUE.

scale_to_100

Logical. Scale result to 0-100? Default TRUE for weighted_mean, FALSE otherwise.

Value

The input data with an added composite index column

Details

The composite index combines multiple normalized indicators into a single score.

Aggregation methods:

  • Weighted mean: Standard linear combination, assumes indicators contribute additively

  • Geometric mean: Better for indicators with multiplicative relationships

  • Min: Conservative approach, final score limited by weakest indicator

  • Max: Optimistic approach, final score driven by strongest indicator

Weights are normalized internally to sum to 1. For example: weights = c(2, 1, 1) becomes c(0.5, 0.25, 0.25)

See also

normalize_indicators

Examples

if (FALSE) { # \dontrun{
# Equal weights
results <- create_composite_index(
  normalized_data,
  indicators = c("carbon_norm", "biodiversity_norm", "water_norm")
)

# Custom weights (carbon 50\
results <- create_composite_index(
  normalized_data,
  indicators = c("carbon_norm", "biodiversity_norm", "water_norm"),
  weights = c(0.5, 0.3, 0.2),
  name = "ecosystem_health"
)

# Geometric mean for multiplicative effects
results <- create_composite_index(
  normalized_data,
  indicators = c("carbon_norm", "water_norm"),
  aggregation = "geometric_mean"
)

# Limiting factor approach
results <- create_composite_index(
  normalized_data,
  indicators = c("carbon_norm", "biodiversity_norm"),
  aggregation = "min",
  name = "conservation_potential"
)
} # }