Assigns each sample (observation) to a population/stock based on the highest Linear Discriminant Function (LDF) score. It also computes posterior probabilities (or values proportional to them if priors are not explicitly used).
Arguments
- samples
A numeric matrix of samples to be classified. Rows are observations, columns are variables.
- coefs
A numeric matrix of LDF coefficients, as returned by `compute_ldf_coefficients`. Rows are populations, columns are coefficients for variables followed by the constant term.
- freq
Optional numeric vector of frequency weights for the samples. Defaults to 1 for each sample if NULL. (Used mainly for HISEA compatibility).
- type
Character, indicates the type of run (e.g., "S" for simulation). This parameter is from HISEA Fortran and affects a condition for processing, though its impact here is minor if `freq` are positive. Default "S".
Value
A list containing:
- class
An integer vector of predicted class labels (1 to NP) for each sample.
- likelihood
A numeric matrix where rows are samples and columns are populations. Contains posterior probabilities of class membership (assuming equal priors, rows sum to 1).
Examples
# 1. Prepare coefficients
s1 <- matrix(rnorm(20, mean = -10), ncol = 2)
s2 <- matrix(rnorm(20, mean = -15), ncol = 2)
coefs <- compute_ldf_coefficients(list(StockA = s1, StockB = s2))
# 2. Prepare unknown samples (mixture)
unknown_samples <- matrix(rnorm(10, mean = -12), ncol = 2)
colnames(unknown_samples) <- c("Var1", "Var2")
# 3. Classify
results <- classify_samples(samples = unknown_samples, coefs = coefs)
# Check predictions and probabilities
print(results$class)
#> [1] 1 2 1 1 1
print(results$likelihood)
#> StockA StockB
#> [1,] 9.999962e-01 3.846787e-06
#> [2,] 5.018485e-05 9.999498e-01
#> [3,] 1.000000e+00 3.250438e-11
#> [4,] 8.953844e-01 1.046156e-01
#> [5,] 8.083537e-01 1.916463e-01