SVD Algorithm

SVD.pls <- function(x, y, n.components = NULL) {
  # Step 1: Center and scale X and Y
  X <- scale(x, center = TRUE, scale = TRUE)
  Y <- scale(y, center = TRUE, scale = TRUE)

  x.mean <- attr(X, "scaled:center")
  x.sd <- attr(X, "scaled:scale")
  y.mean <- attr(Y, "scaled:center")
  y.sd <- attr(Y, "scaled:scale")

  n <- nrow(X)
  p <- ncol(X)
  q <- ncol(Y)

  # Determine number of components
  rank_X <- qr(X)$rank
  if (is.null(n.components)) {
    n.components <- rank_X
  } else {
    n.components <- min(n.components, rank_X)
  }

  # Preallocate matrices
  T <- matrix(0, n, n.components)  # X scores
  U <- matrix(0, n, n.components)  # Y scores
  P_loadings <- matrix(0, p, n.components)  # X loadings
  W <- matrix(0, p, n.components)  # X weights
  Q_loadings <- matrix(0, q, n.components)  # Y loadings (reference)
  B_vector <- numeric(n.components)

  # Initial total sum of squares
  SSX_total <- sum(X^2)
  SSY_total <- sum(Y^2)

  X_explained <- numeric(n.components)
  Y_explained <- numeric(n.components)

  # Store initial X and Y
  E <- X
  F <- Y

  for (h in seq_len(n.components)) {
    # Step 1: Cross-covariance matrix
    R <- t(E) %*% F

    # Step 2: SVD of R
    svd_R <- svd(R)
    w <- svd_R$u[, 1, drop = FALSE]
    c <- svd_R$v[, 1, drop = FALSE]

    # Step 3: Scores
    t <- E %*% w
    t <- t / sqrt(sum(t^2))  # normalize t
    u <- F %*% c

    # Step 4: Loadings
    p <- t(E) %*% t

    # Step 5: Regression scalar b
    b <- drop(t(t) %*% u)

    # Step 6: Deflation
    E <- E - t %*% t(p)
    F <- F - b * t %*% t(c)

    # Store results
    T[, h] <- t
    U[, h] <- u
    P_loadings[, h] <- p
    W[, h] <- w
    Q_loadings[, h] <- c
    B_vector[h] <- b

    # Explained variance
    X_explained[h] <- sum(p^2) / SSX_total * 100
    Y_explained[h] <- (b^2) / SSY_total * 100
  }

  # Cumulative variance explained
  X_cum_explained <- cumsum(X_explained)
  Y_cum_explained <- cumsum(Y_explained)

  # Clean up effective components
  effective_components <- sum(B_vector != 0)

  P_loadings <- P_loadings[, seq_len(effective_components), drop = FALSE]
  Q_loadings <- Q_loadings[, seq_len(effective_components), drop = FALSE]
  W <- W[, seq_len(effective_components), drop = FALSE]
  T <- T[, seq_len(effective_components), drop = FALSE]
  U <- U[, seq_len(effective_components), drop = FALSE]
  B_vector <- B_vector[seq_len(effective_components)]
  X_explained <- X_explained[seq_len(effective_components)]
  Y_explained <- Y_explained[seq_len(effective_components)]
  X_cum_explained <- X_cum_explained[seq_len(effective_components)]
  Y_cum_explained <- Y_cum_explained[seq_len(effective_components)]

  # Normalize C (Y weights)
  C <- apply(Q_loadings, 2, function(c) c / sqrt(sum(c^2)))

  # Pseudo-inverse of P_loadings
  svd_P <- svd(P_loadings)
  d_inv <- ifelse(svd_P$d > .Machine$double.eps, 1 / svd_P$d, 0)
  P_pinv <- t(svd_P$v %*% diag(d_inv) %*% t(svd_P$u))
  P_pinv <- P_pinv[, seq_len(effective_components), drop = FALSE]

  # Final scaled coefficients
  B_scaled <- P_pinv %*% diag(B_vector) %*% t(Q_loadings)

  # Rescale to original units
  B_original <- sweep(B_scaled, 2, y.sd, "*")
  B_original <- sweep(B_original, 1, x.sd, "/")

  rownames(B_original) <- colnames(x)
  colnames(B_original) <- colnames(y)

  intercept <- rep(0, length(y.mean))
  names(intercept) <- colnames(y)

  list(
    T = T,  # X scores
    U = U,  # Y scores
    W = W,  # X weights
    C = C,  # Y weights (normalized)
    P_loadings = P_loadings,  # X loadings (reference)
    Q_loadings = Q_loadings,  # Y loadings (reference)
    B_vector = B_vector,
    coefficients = B_original,
    intercept = intercept,
    X_explained = X_explained,
    Y_explained = Y_explained,
    X_cum_explained = X_cum_explained,
    Y_cum_explained = Y_cum_explained
  )
}