Compute Linear Discriminant Function (LDF) Coefficients — compute_ldf

Computes the coefficients for Fisher's Linear Discriminant Function for each group (population/stock). This implementation assumes a common (pooled) covariance matrix across all groups, a standard assumption for LDA. The LDF is of the form: LDF_j(x) = C_j0 + C_j1*x1 + ... + C_jp*xp.

Usage

compute_ldf_coefficients(baseline)

Arguments

baseline: A list of numeric matrices, where each matrix represents the baseline (training) data for one population/stock. Each matrix should have observations as rows and variables as columns.

Value

A numeric matrix of LDF coefficients. Rows correspond to populations, and columns correspond to variables followed by the constant term. The dimensions are (np x (nv + 1)).

Examples

# Create dummy baseline data for 2 stocks with 2 variables (isotopes)
# Stock A
s1 <- matrix(c(-10, 2, -11, 2.1, -10.5, 2.2), ncol = 2)
colnames(s1) <- c("d13c", "d18o")
# Stock B
s2 <- matrix(c(-15, 5, -16, 5.1, -15.5, 5.2), ncol = 2)
colnames(s2) <- c("d13c", "d18o")

baseline_list <- list(StockA = s1, StockB = s2)

# Compute coefficients
ldf_coeffs <- compute_ldf_coefficients(baseline_list)
print(ldf_coeffs)
#>             d13c      d18o  Constant
#> StockA -41.23654 -40.91595 -172.8622
#> StockB -51.08841 -50.68277 -265.3082