sqi_entropy: Soil Quality Index: Entropy Weighting Method
In SQIpro: Comprehensive Soil Quality Index Computation and Visualization

sqi_entropy

R Documentation

Soil Quality Index: Entropy Weighting Method

Description

Computes SQI using Shannon entropy to derive objective weights for each variable. Variables with higher information entropy (greater discriminating power among groups) receive higher weights. This removes subjectivity from weight assignment.

The entropy weight for variable j is:

e_j = -\frac{1}{\ln n} \sum_{i=1}^{n} p_{ij} \ln(p_{ij})

w_j = \frac{1 - e_j}{\sum_k (1 - e_k)}

where p_{ij} = \bar{S}_{ij} / \sum_i \bar{S}_{ij}.

Usage

sqi_entropy(scored, config, group_cols = "LandUse", mds_vars = NULL)

Arguments

`scored`	A scored data frame from `score_all`.
`config`	A `sqi_config` object.
`group_cols`	Character vector of grouping column names.
`mds_vars`	Character vector of MDS variable names.

Value

A data frame with group columns, SQI_entropy, and attribute entropy_weights (named numeric vector).

References

Shannon, C.E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/j.1538-7305.1948.tb01338.x")}

Li, P., Qian, H., & Wu, J. (2010). Groundwater quality assessment based on improved water quality index in Pengyang County, Ningxia, Northwest China. E-Journal of Chemistry, 7, 209–216. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1155/2010/451304")}

Examples

data(soil_data)
cfg <- make_config(
  variable = c("pH","EC","BD","OC","MBC","Clay"),
  type     = c("opt","less","less","more","more","opt"),
  opt_low  = c(6.0, NA, NA, NA, NA, 20),
  opt_high = c(7.0, NA, NA, NA, NA, 35)
)
scored <- score_all(soil_data, cfg, group_cols = c("LandUse","Depth"))
result <- sqi_entropy(scored, cfg, group_cols = c("LandUse","Depth"))
attr(result, "entropy_weights")

SQIpro documentation built on April 20, 2026, 5:06 p.m.