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)