From 06d990278de25dbe5b8d499dba6c5f5d3c1cdbd7 Mon Sep 17 00:00:00 2001
From: Joakim Skogholt <onlinereg@skogholt.xyz>
Date: Wed, 15 May 2024 09:48:57 +0200
Subject: [PATCH] Added pcr and pls (bidiag2)

---
 src/Ting.jl                       |   5 ++
 src/variousRegressionFunctions.jl | 123 ++++++++++++++++++++++++++++++
 2 files changed, 128 insertions(+)
 create mode 100644 src/variousRegressionFunctions.jl

diff --git a/src/Ting.jl b/src/Ting.jl
index 59d88d5..6d05dbe 100644
--- a/src/Ting.jl
+++ b/src/Ting.jl
@@ -30,7 +30,12 @@ export TRSegCVUpdateFair
 export TRSegCVNaive
 export TRSegCVFair
 
+# From "variousRegressionFunctions.jl"
+export PCR
+export bidiag2
+
 include("convenience.jl")
 include("TR.jl")
+include("variousRegressionFunctions")
 
 end
\ No newline at end of file
diff --git a/src/variousRegressionFunctions.jl b/src/variousRegressionFunctions.jl
new file mode 100644
index 0000000..29032a8
--- /dev/null
+++ b/src/variousRegressionFunctions.jl
@@ -0,0 +1,123 @@
+
+
+
+
+
+
+"""
+    function PCR(X, y, kmax, centre=true)#, standardize=true)
+
+Principal Component Regression (PCR).
+Inputs: Data matrix, response vector, maximum number of components.
+A constant term is included in the modeling if centre==true.
+Outputs: B (matrix of size (p+1) x kmax), U, s, V
+ADD STANDARDIZATION?? (NEED TO THINK THROUGH PREDICTION WITH NEW DATA)
+
+X, y = importData("Beer");
+B, \\_ = PCR(X, y, 10, true, false);
+"""
+function PCR(X, y, kmax, centre::Bool=true)#, standardize=true)
+
+stdX = std(X, dims=1);
+mX   = mean(X, dims=1);
+my   = mean(y, dims=1);
+y    = y .- my;
+
+if centre
+    X = X .- mX;
+end
+
+#if standardize
+#    X = X ./ stdX;
+#end
+
+U, s, V = svd(X, full=false);
+
+q  = s[1:kmax].^(-1) .*(U[:,1:kmax]'y);
+B  = cumsum(V[:,1:kmax] .* q', dims=2);
+    
+if centre
+    b0 = my .- mX * B
+    B  = [b0; B];
+end
+        
+return B, U, s, V
+end
+
+
+
+
+
+"""
+    bidiag2(X, y, A)
+    
+Julia version of the bidiag2 MATLAB function in Björck and Indahl (2017)
+    
+### Arguments
+    - 'X' - Data matrix of predictors
+    - 'y' - Vector of responses
+    - 'A' - Number of components
+
+### Returns:
+    - beta    - Matrix with regression coefficients (including constant term if centre==true)
+    - W, B, T - Matrices such that X \approx T*B*W'.
+"""
+function bidiag2(X, y, A, centre=true)
+
+n, p = size(X);
+
+w    = zeros(p, 1);
+W    = zeros(p, A);
+t    = zeros(n, 1);
+T    = zeros(n, A);
+P    = zeros(p, A);
+beta = zeros(p, A);
+B    = zeros(A,2);
+
+mX   = mean(X, dims=1);
+my   = mean(y, dims=1);
+y    = y .- my;
+
+if centre
+    X = X .- mX;
+end
+
+w      = X'y;
+w      = w / norm(w);
+W[:,1] = w;
+t      = X*w;
+rho    = norm(t);
+t      = t / rho;
+T[:,1] = t;
+
+B[1,1]    = rho;
+d         = w / rho;
+beta[:,1] = (t'y) .* d;
+
+for a in 2:A
+    w         = X'*t - rho * w;
+    w         = w - W*(W'*w);
+    theta     = norm(w);
+    w         = w / theta;
+    W[:,a]    = w;
+    t         = X * w - theta * t;
+    t         = t - T*(T'*t);
+    rho       = norm(t);
+    t         = t / rho;
+    T[:,a]    = t;
+    B[a-1, 2] = theta;
+    B[a, 1]   = rho;
+    d         = (w - theta*d) / rho;
+    beta[:,a] = beta[:,a-1] + (t'y) .* d;
+end
+    
+if centre
+    b0 = my .- mX * beta
+    beta  = [b0; beta];
+end
+
+B = Bidiagonal(B[:,1], B[1:end-1,2], :U);
+  
+returnvals = beta, W, B, T
+return returnvals
+end
\ No newline at end of file