diff --git a/pyblm/crossvalidation.py b/pyblm/crossvalidation.py
index 9d41250..16e4989 100644
--- a/pyblm/crossvalidation.py
+++ b/pyblm/crossvalidation.py
@@ -101,6 +101,7 @@ def pca_val(a, a_max, nsets=None, center_axis=[0], method='cv'):
                 
     rmsep = sqrt(err).mean(1) # take mean over samples
     aopt = rmsep.mean(-1).argmin()
+    
     return rmsep, xhat, aopt, err
 
 def pls_val(X, Y, a_max=2, nsets=None, center_axis=[0,0], verbose=False):
@@ -145,7 +146,7 @@ def pls_val(X, Y, a_max=2, nsets=None, center_axis=[0,0], verbose=False):
     if nsets > X.shape[0]:
         print "nsets (%d) is larger than number of variables (%d).\nnsets:  %d -> %d" %(nsets, m, nsets, m)
         nsets = m
-    if n > 5*m:
+    if n > 15*m:
         # boosting (wide x)
         Yhat = _w_pls_predict(X, Y, a_max)
         
@@ -160,7 +161,7 @@ def pls_val(X, Y, a_max=2, nsets=None, center_axis=[0,0], verbose=False):
         
         # predictions
         for a in range(a_max):
-            Yhat[a,val,:] = ym + dot(xi, dat['B'][a])
+            Yhat[a,val] = ym + dot(xi, dat['B'][a])
     
     sep = (Y - Yhat)**2
     rmsep = sqrt(sep.mean(1)).T
@@ -236,9 +237,14 @@ def lpls_val(X, Y, Z, a_max=2, nsets=None,alpha=.5, center_axis=[2,0,2], zorth=F
                    center_axis=center_axis, zorth=zorth, verbose=verbose)
 
         # center test data
-        xi = X[val,:] - dat['mnx']
-        
-        ym = dat['mny'][val,:]
+        if center_axis[0] == 2:
+            xi = X[val,:] - dat['mnx'] - X[val,:].mean(1)[:,newaxis] + dat['mnx'].mean()
+        else:
+            xi = X[val,:] - dat['mnx']
+        if center_axis[1] == 2:
+            ym = dat['mny'] + Y[val,:].mean(1)[:,newaxis] - dat['mny'].mean()
+        else:
+            ym = dat['mny']
         # predictions
         for a in range(a_max):
             Yhat[a,val,:] = atleast_2d(ym + dot(xi, dat['B'][a]))