diff --git a/pyblm/crossvalidation.py b/pyblm/crossvalidation.py
index e78875d..fd96904 100644
--- a/pyblm/crossvalidation.py
+++ b/pyblm/crossvalidation.py
@@ -6,7 +6,7 @@ __all__ = ['lpls_val', 'pls_jk', 'lpls_jk']
 __docformat__ = "restructuredtext en"
 
 from numpy import dot,empty,zeros,sqrt,atleast_2d,argmax,asarray,median,\
-     array_split,arange
+     array_split,arange, isnan, any
 from numpy.random import shuffle
 
 from engines import pls
@@ -188,6 +188,8 @@ def pls_jk(X, Y, a_opt, nsets=None, center_axis=True, verbose=False):
         if verbose:
             print "Segment number: %d" %i
         dat = pls(X[cal,:], Y[cal,:], a_opt, scale='loads', mode='fast', center_axis=[0, 0])
+        if any(isnan(dat['W'])):
+            1/0
         Wcv[i,:,:] = dat['W']
         
     return Wcv
diff --git a/pyblm/engines.py b/pyblm/engines.py
index 821f3aa..2ee20f6 100644
--- a/pyblm/engines.py
+++ b/pyblm/engines.py
@@ -8,7 +8,7 @@ __docformat__ = "restructuredtext en"
 from math import sqrt as msqrt
 
 from numpy import dot,empty,zeros,apply_along_axis,newaxis,finfo,sqrt,r_,expand_dims,\
-minimum
+minimum, any, isnan
 from numpy.linalg import inv,svd
 from scipy.sandbox import arpack
 
@@ -335,16 +335,30 @@ def pls(X, Y, aopt=2, scale='scores', mode='normal', center_axis=[0, 0]):
             w = XY.reshape(n, l)
             w = w/vnorm(w)
         elif n < l: # more yvars than xvars
-            s, w = arpack.eigen_symmetric(dot(XY, XY.T),k=1, tol=1e-10, maxiter=100)
-            #w, s, vh = svd(dot(XY, XY.T))
-            #w = w[:,:1]
+
+            #!!! fixme
+            # Arpack has convergence problems on large equal eigenvalues
+            # which is typical for design/category in Y so we switch to regular svd.
+            # Need to decide wether to remove arpack here or check for system
+            # with many samples, many x-vars and many non-orth y-vars (where arpack speed
+            # shines)
+            #############
+            
+            #s, w = arpack.eigen_symmetric(dot(XY, XY.T),k=1, tol=1e-10, maxiter=1000)
+            #if s[0] == 0:
+            #    print "Arpack did not converge... using svd"
+            w, s, vh = svd(dot(XY, XY.T))
+            w = w[:,:1]
         else: # more xvars than yvars
-            s, q = arpack.eigen_symmetric(dot(XY.T, XY), k=1, tol=1e-10, maxiter=100)
-            #q, s, vh = svd(dot(XY.T, XY))
-            #q = q[:,:1]
+            #s, q = arpack.eigen_symmetric(dot(XY.T, XY), k=1, tol=1e-10, maxiter=1000)
+            #if s[0] == 0:
+            #    print "Arpack did not converge... using svd"
+            q, s, vh = svd(dot(XY.T, XY))
+            q = q[:,:1]
             
             w = dot(XY, q)
             w = w/vnorm(w)
+        
         r = w.copy()
         if i > 0:
             for j in range(0, i, 1):
diff --git a/pyblm/statistics.py b/pyblm/statistics.py
index a06ff02..dec2cc0 100644
--- a/pyblm/statistics.py
+++ b/pyblm/statistics.py
@@ -262,8 +262,7 @@ def lpls_qvals(X, Y, Z, aopt=None, alpha=.3, zx_alpha=.5, n_iter=20,
     return _fdr(cal_tsq_z, pert_tsq_z, median), _fdr(cal_tsq_x, pert_tsq_x, median)
 
 
-def pls_qvals(X, Y, aopt, alpha=.3, zx_alpha=.5, n_iter=20,
-              sim_method='shuffle',p_center='med', cov_center=median,
+def pls_qvals(X, Y, aopt, alpha=.3, n_iter=20,p_center='med', cov_center=median,
               crot=True,strict=False, center_axis=[0,0], nsets=None, zorth=False):
 
     """Returns qvals for pls model by permutation analysis.