Python Statsmodels Mixedlm（随机斜率和截距）R2

我在Statsmodels中安装了一个混合模型，如下所示：

#具有随机截距和斜率的最终模型
最终产量模型=smf.mixedlm（“转化产量~分数”，数据=最终产量模型数据，
组=最终模型数据['Stock'，缺失='drop'，
re_公式=‘分数’）
final\u volume\u fit=final\u volume\u model.fit（）
打印（最终卷匹配摘要（））
##################输出##################
混合线性模型回归结果
================================================================
模型：MixedLM因变量：转换体积
观察次数：181方法：REML
组别数目：13个等级：8698.0110
最小群体规模：13对数可能性：-1098.8257
最大组大小：15聚合：是
平均群体规模：13.9
----------------------------------------------------------------
科夫。标准误差。ZP>| z |[0.025 0.975]
----------------------------------------------------------------
截距330.777 74.894 4.417 0.000 183.989 477.566
得分39.102 16.390 2.386 0.017 6.979 71.224
组变量71886.612 333.046
x组得分Cov 2161.829 70.984
得分变量65.015 18.319
================================================================

我已经检查了模型假设（都很好！），现在想计算R2。 我是根据中川和Shielzeth的公式来做的：

边际R2

条件R2

var（f）=固定效应方差

var（r）=随机效应的方差

var（ε）=模型残差方差

我担心的是，在获得方程式的不同部分时，我无法确定是否正确理解Statsmodels的输出：

n = 181

# Obtain values for fixed- & random effects and model-residuals
fixed_effects = final_volume_fit.bse_fe
random_effects = final_volume_fit.bse_re
residuals = final_volume_fit.resid

# The fixed-, random- and residual variance
fixed_effect_variance = ((fixed_effects[0]**2)+(fixed_effects[1]**2))*n
random_effect_variance =((random_effects[0]**2)+(random_effects[1]**2)+(random_effects[2]**2))*n
residual_variance = [x**2 for x in residuals]
residual_variance = sum(residual_variance)/(n-2)

# Final calculation
marginal_R2 = (fixed_effect_variance)/(fixed_effect_variance+random_effect_variance+residual_variance)
conditional_R2 = (fixed_effect_variance+random_effect_variance)/(fixed_effect_variance+random_effect_variance+residual_variance)

############### Output #################
# marginal_R2:  0.04809224194663527
# conditional_R2:  0.9996329364055776

使用的数据：

fixed_effects: 
 Intercept    74.893656
Score        16.389560
dtype: float64

random_effects: 
 Group Var            333.046398
Group x Score Cov     70.984066
Score Var             18.318524
dtype: float64

resiudals:  [-44.02577774345405, -55.380815476000116, 0.5551467914537653, 24.419222256545936, 42.52322225654595, 76.3520335938154, -31.849853208546236, 105.17222225654595, -27.640777743454052, -51.66681547600012, -21.619353208546244, -14.458027743454053, 11.573071326361514, -43.89796640618461, -69.08432706508967, -200.8790151463973, -182.85991117349982, 3.370054168867682, 67.1446234841327, 107.94783882650017, 153.20901951123517, 306.1360195112352, 55.49401951123525, -22.14232706508966, -39.88522309219218, -19.779896380354614, -79.91794583113233, -61.767980488764806, 5.717034491288729, -29.142006764681184, 34.48203449128873, 14.700034491288726, -5.011996450688734, -10.371746450688732, -6.738965508711274, -13.568965508711273, -23.234965508711273, -18.699455194718766, 14.789158259198526, 3.562117003228554, -5.04891393874885, 3.3811170032285567, -27.641953808982805, -66.36607124278095, -94.03709472954057, -65.42876591490577, -84.23611821630024, -64.98961821630024, -38.10307124278091, 128.52799921749795, 168.35397573073828, -39.511000782502094, -42.02502426926168, 5.048405270459455, 57.2352961910172, 128.52230454537312, 38.089999217497905, -106.04053228102975, -73.08653228102969, -24.877435800848502, 159.11346771897024, 88.34987123878909, 17.58627475860783, -6.228339320667374, -35.52553228102977, -14.282532281029717, -19.540207642298128, -11.820532281029728, 17.10387123878911, 34.88846771897033, 40.36946771897033, -25.464942333496936, -5.803702846965834, -59.52094233349693, -39.34190241907507, 36.23101775208124, 121.74305766650309, 54.648057666503064, -5.887902419075061, 37.327858094393804, -53.3540221623406, -51.998062076762466, -46.35052216234061, -30.763862504653233, 46.17545681072153, 91.05194042624845, -57.54153103307135, -43.76575935851281, -120.26098768395423, 15.359183560126837, 46.86751231604575, 118.71112647876646, 30.370012316045745, 101.54201231604577, 41.868012316045736, -5.847987683954216, -3.5777022771524116, -62.94548768395424, -61.81034498055334, -87.59611665511193, 360.93789319100745, 98.00389319100736, 28.1565186696331, 68.31170593032027, 117.10220593032034, -175.8513886572747, -137.75841954830537, -139.35910680899258, -37.792886742772, -52.96679406967968, -2.265386742772023, -37.91710680899257, -69.1851068089926, -6.690960150112332, 72.00073098054312, 18.72625452828096, -22.20826901945688, 2.0006367895917094, -27.18300724558796, -20.10026901945689, -11.944269019456883, -7.8190072455879545, 14.98137501572279, -19.284269019456886, -16.00229256719473, -28.42400724558796, 21.61203984988768, -152.35548955431472, -31.63262921834348, -5.634039434325018, -93.13854941032702, 57.70544061367093, 187.80198051767917, -38.91505938632906, -82.24903943432503, 260.39147054167705, 43.76947054167704, 2.9439705416770607, -9.169799410327016, -37.88152945832297, -91.70699953031675, -45.5148254647398, -49.71843056261332, -79.5655897796251, -21.791615268992757, 156.2490694373867, 236.39789904589253, 348.48417453526025, -18.588825464739784, -95.36961526899275, -85.24098468175158, -78.78222036686628, -105.44203566048682, -125.00464075836035, -59.44795919238399, -91.19556701864133, 19.101933610284846, -62.31407142112448, -62.807817962030526, -111.32131984880891, -180.30782047773528, -140.59606764756745, 29.005182037969462, 300.02167889333873, 89.2391820379695, 132.92393172350648, 166.37218203796954, -38.33056827649352, -12.545107025784759, -30.392742746214935, -12.845198095677233, -52.76883381610742, -9.628804049082262, 1.3081959509177352, -16.484107025784752, 49.67116618389258, 103.54219595091774, -39.68083381610742, -27.600015955892317, -28.400410002487284, 48.99186320719008]

我在Jupyter笔记本中使用Python