def dtest(pos_nodes=0,
effect=0.0,
dist="euclidean",
n=100,
nodes=400,
nperms=4999,
iters=100):
import mkl
mkl.set_num_threads(2)
print "Start"
#print "Categorical Effect"
grp = np.repeat([0, 1], n / 2)
np.random.shuffle(grp)
#print "Design Matrix"
design = np.vstack((np.ones(n), grp)).T
cols = [1]
#print "Distance Matrices"
dmats = np.zeros((iters, n, n))
for i in xrange(iters):
#if (i % 10) == 0:
# print i,
# Data
## Fist, I created the matrix with the random data
points = np.random.standard_normal((n, nodes))
## Second, I select a random selection of nodes to add the effect
neg_nodes = nodes - pos_nodes
change_nodes = np.repeat([0, 1], [neg_nodes, pos_nodes])
np.random.shuffle(change_nodes)
## Finally, I add the effect to a select subjects and nodes
for i in (change_nodes == 1).nonzero()[0]:
points[grp == 1, i] += effect
# Compute Distances
if dist == "euclidean":
dmat = euclidean_distances(points)
elif dist == "pearson":
dmat = compute_distances(points)
else:
raise Exception("Unknown distance measure %s" % dist)
dmats[i] = dmat
#print ""
#print "MDMR"
pvals = []
Fvals = []
pvals, Fvals, _, _ = mdmr(dmats, design, cols, nperms)
#print "Done"
return pvals, Fvals
def dtest(n=50, d=0.0, r=0.0, model_covariate=True, niters=100, nperms=4999):
import mkl
mkl.set_num_threads(2)
d = float(d)
r = float(r)
# Data/Distances
pvals = np.zeros(niters)
Fvals = np.zeros(niters)
for i in xrange(niters):
# Design
## Categorical
gp = np.repeat([0, 1], n/2)
np.random.shuffle(gp)
x = gp*d + np.random.standard_normal(n)
## Continuous
# see http://stackoverflow.com/questions/16024677/generate-correlated-data-in-python-3-3
# and http://stats.stackexchange.com/questions/19367/creating-two-random-sequences-with-50-correlation?lq=1
uncorrelated = np.random.standard_normal((2,n))
motion = uncorrelated[0]
y = r*motion + np.sqrt(1-r**2)*uncorrelated[1]
## Design Matrix
if model_covariate:
design = np.vstack((np.ones(n), gp, motion)).T
else:
design = np.vstack((np.ones(n), gp)).T
# Date
points = np.vstack((x,y)).T
# Distances
dmat = euclidean_distances(points)
dmats = dmat[np.newaxis,:,:]
# Only the group effect is the variable of interest
cols = [1]
# Call MDMR
pval, Fval, _, _ = mdmr(dmats, design, cols, nperms)
pvals[i] = pval
Fvals[i] = Fval
return pvals, Fvals
def dtest(n=50, d=0.0, r=0.0, model_covariate=True, niters=100, nperms=4999):
import mkl
mkl.set_num_threads(2)
d = float(d)
r = float(r)
# Data/Distances
pvals = np.zeros(niters)
Fvals = np.zeros(niters)
for i in xrange(niters):
# Design
## Categorical
gp = np.repeat([0, 1], n / 2)
np.random.shuffle(gp)
x = gp * d + np.random.standard_normal(n)
## Continuous
# see http://stackoverflow.com/questions/16024677/generate-correlated-data-in-python-3-3
# and http://stats.stackexchange.com/questions/19367/creating-two-random-sequences-with-50-correlation?lq=1
uncorrelated = np.random.standard_normal((2, n))
motion = uncorrelated[0]
y = r * motion + np.sqrt(1 - r**2) * uncorrelated[1]
## Design Matrix
if model_covariate:
design = np.vstack((np.ones(n), gp, motion)).T
else:
design = np.vstack((np.ones(n), gp)).T
# Date
points = np.vstack((x, y)).T
# Distances
dmat = euclidean_distances(points)
dmats = dmat[np.newaxis, :, :]
# Only the group effect is the variable of interest
cols = [1]
# Call MDMR
pval, Fval, _, _ = mdmr(dmats, design, cols, nperms)
pvals[i] = pval
Fvals[i] = Fval
return pvals, Fvals
def dtest(pos_nodes=0, effect=0.0, dist="euclidean", n=100, nodes=400, nperms=4999, iters=100):
import mkl
mkl.set_num_threads(2)
print "Start"
#print "Categorical Effect"
grp = np.repeat([0, 1], n/2)
np.random.shuffle(grp)
#print "Design Matrix"
design = np.vstack((np.ones(n), grp)).T
cols = [1]
#print "Distance Matrices"
dmats = np.zeros((iters,n,n))
for i in xrange(iters):
#if (i % 10) == 0:
# print i,
# Data
## Fist, I created the matrix with the random data
points = np.random.standard_normal((n,nodes))
## Second, I select a random selection of nodes to add the effect
neg_nodes = nodes - pos_nodes
change_nodes = np.repeat([0,1], [neg_nodes, pos_nodes])
np.random.shuffle(change_nodes)
## Finally, I add the effect to a select subjects and nodes
for i in (change_nodes==1).nonzero()[0]:
points[grp==1,i] += effect
# Compute Distances
if dist == "euclidean":
dmat = euclidean_distances(points)
elif dist == "pearson":
dmat = compute_distances(points)
else:
raise Exception("Unknown distance measure %s" % dist)
dmats[i] = dmat
#print ""
#print "MDMR"
pvals = []; Fvals = [];
pvals, Fvals, _, _ = mdmr(dmats, design, cols, nperms)
#print "Done"
return pvals, Fvals
