nowords

src/variousRegressionFunctions.jl

@@ -1,35 +1,46 @@
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 """
-    function PCR(X, y, kmax, centre=True, standardize=true)
+    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.
+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=true, standardize=true)
+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);
-stdX = std(X, dims=1);
+y    = y .- my;
 
 if centre
     X = X .- mX;
 end
 
-if standardize
+#if standardize
-    X = X ./ stdX;
+#    X = X ./ stdX;
-end
+#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);
-b0 = my .- mX * B
+    
-B  = [b0; B];
+if centre
-
+    b0 = my .- mX * B
+    B  = [b0; B];
+end
+        
 return B, U, s, V
 end