def test_1():
# adjusted R^2 for the trivial case (RSS=TSS)
y_1d = np.array([1, -1])
df = 1
MRSS = np.sum((y_1d - np.mean(y_1d))**2) / df
rank = 1
joy = adjR2(MRSS, y_1d, df, rank)
assert (joy == 0)
def test_1(): # adjusted R^2 for the trivial case (RSS=TSS) y_1d = np.array([1,-1]) df = 1 MRSS = np.sum((y_1d-np.mean(y_1d))**2)/df rank = 1 joy = adjR2(MRSS,y_1d,df,rank) assert(joy==0)
###################
# hrf (simple)
beta1, t, df1, p = t_stat_mult_regression(data_slice, X[:, 0:2])
MRSS1, fitted, residuals = glm_diagnostics(beta1, X[:, 0:2],
data_slice)
model1_slice = np.zeros(len(MRSS1))
rank1 = npl.matrix_rank(X[:, 0:2])
count = 0
for value in MRSS1:
model1_slice[count] = adjR2(value, np.array(data_slice[count, :]),
df1, rank1)
count += 1
adjr2_1 = adjr2_1 + model1_slice.tolist()
aic_1 = aic_1 + AIC_2(MRSS1, data_slice, df1, rank1).tolist()
bic_1 = bic_1 + BIC_2(MRSS1, data_slice, df1, rank1).tolist()
###################
# MODEL 2 #
###################
# hrf + drift
beta2, t, df2, p = t_stat_mult_regression(data_slice, X[:, 0:3])
# MODEL 1 #
###################
# hrf (simple)
beta1, t,df1, p = t_stat_mult_regression(data_slice, X[:, 0:2])
MRSS1, fitted, residuals = glm_diagnostics(beta1, X[:, 0:2], data_slice)
model1_slice = np.zeros(len(MRSS1))
rank1 = npl.matrix_rank(X[:, 0:2])
count = 0
for value in MRSS1:
model1_slice[count] = adjR2(value, np.array(data_slice[count, :]), df1, rank1)
count += 1
adjr2_1 = adjr2_1 + model1_slice.tolist()
aic_1 = aic_1 + AIC_2(MRSS1, data_slice, df1, rank1).tolist()
bic_1 = bic_1 + BIC_2(MRSS1, data_slice, df1, rank1).tolist()
###################
# MODEL 2 #
###################
# hrf + drift
beta2, t, df2, p = t_stat_mult_regression(data_slice, X[:, 0:3])
def model(MRSS, y_1d, df, rank):
return adjR2(MRSS, y_1d, df, rank)
def model(MRSS,y_1d,df, rank): return adjR2(MRSS,y_1d, df, rank)
