迴歸分析--多元迴歸
介紹一下多元迴歸分析中的統計量
- 總觀測值
- 總自變量
- 自由度:迴歸自由度 ,殘差自由度
- SST總平方和
- SSR迴歸平方和
- SSE殘差平方和
- MSR均方迴歸
- MSE均方殘差
- 判定係數R_square
- 調整的判定係數Adjusted_R_square
- 復相關係數Multiple_R
- 估計標準誤差
- F檢驗統計量
- 迴歸係數抽樣分佈的標準誤差
- 各回歸係數的t檢驗統計量
- 各回歸係數的置信區間
- 對數似然值(log likelihood)
- AIC準則
- BIC準則
案例分析及python實踐
# 導入相關包
import pandas as pd
import numpy as np
import math
import scipy
import matplotlib.pyplot as plt
from scipy.stats import t
# 構建數據
columns = {'A':"分行編號", 'B':"不良貸款(億元)", 'C':"貸款餘額(億元)", 'D':"累計應收貸款(億元)", 'E':"貸款項目個數", 'F':"固定資產投資額(億元)"}
data={"A":[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25],
"B":[0.9,1.1,4.8,3.2,7.8,2.7,1.6,12.5,1.0,2.6,0.3,4.0,0.8,3.5,10.2,3.0,0.2,0.4,1.0,6.8,11.6,1.6,1.2,7.2,3.2],
"C":[67.3,111.3,173.0,80.8,199.7,16.2,107.4,185.4,96.1,72.8,64.2,132.2,58.6,174.6,263.5,79.3,14.8,73.5,24.7,139.4,368.2,95.7,109.6,196.2,102.2],
"D":[6.8,19.8,7.7,7.2,16.5,2.2,10.7,27.1,1.7,9.1,2.1,11.2,6.0,12.7,15.6,8.9,0,5.9,5.0,7.2,16.8,3.8,10.3,15.8,12.0],
"E":[5,16,17,10,19,1,17,18,10,14,11,23,14,26,34,15,2,11,4,28,32,10,14,16,10],
"F":[51.9,90.9,73.7,14.5,63.2,2.2,20.2,43.8,55.9,64.3,42.7,76.7,22.8,117.1,146.7,29.9,42.1,25.3,13.4,64.3,163.9,44.5,67.9,39.7,97.1]
}
df = pd.DataFrame(data)
X = df[["C", "D", "E", "F"]]
Y = df[["B"]]
# 構建多元線性迴歸模型
from sklearn.linear_model import LinearRegression
lreg = LinearRegression()
lreg.fit(X, Y)
x = X
y_pred = lreg.predict(X)
y_true = np.array(Y).reshape(-1,1)
coef = lreg.coef_[0]
intercept = lreg.intercept_[0]
# 自定義函數
def log_like(y_true, y_pred):
"""
y_true: 真實值
y_pred:預測值
"""
sig = np.sqrt(sum((y_true - y_pred)**2)[0] / len(y_pred)) # 殘差標準差δ
y_sig = np.exp(-(y_true - y_pred) ** 2 / (2 * sig ** 2)) / (math.sqrt(2 * math.pi) * sig)
loglik = sum(np.log(y_sig))
return loglik
def param_var(x):
"""
x:只含自變量寬表
"""
n = len(x)
beta0 = np.ones((n,1))
df_to_matrix = x.as_matrix()
concat_matrix = np.hstack((beta0, df_to_matrix)) # 矩陣合併
transpose_matrix = np.transpose(concat_matrix) # 矩陣轉置
dot_matrix = np.dot(transpose_matrix, concat_matrix) # (X.T X)^(-1)
inv_matrix = np.linalg.inv(dot_matrix) # 求(X.T X)^(-1) 逆矩陣
diag = np.diag(inv_matrix) # 獲取矩陣對角線,即每個參數的方差
return diag
def param_test_stat(x, Se, intercept, coef, alpha=0.05):
n = len(x)
k = len(x.columns)
beta_array = param_var(x)
beta_k = beta_array.shape[0]
coef = [intercept] + list(coef)
std_err = []
t_Stat = []
P_value = []
t_intv = []
coefLower = []
coefupper = []
for i in range(beta_k):
se_belta = np.sqrt(Se**2 * beta_array[i]) # 迴歸係數的抽樣標準誤差
t = coef[i] / se_belta # 用於檢驗迴歸係數的t統計量, 即檢驗統計量t
p_value = scipy.stats.t.sf(np.abs(t), n-k-1)*2 # 用於檢驗迴歸係數的P值(P_value)
t_score = scipy.stats.t.isf(alpha/2, df = n-k-1) # t臨界值
coef_lower = coef[i] - t_score * se_belta # 迴歸係數(斜率)的置信區間下限
coef_upper = coef[i] + t_score * se_belta # 迴歸係數(斜率)的置信區間上限
std_err.append(round(se_belta, 3))
t_Stat.append(round(t,3))
P_value.append(round(p_value,3))
t_intv.append(round(t_score,3))
coefLower.append(round(coef_lower,3))
coefupper.append(round(coef_upper,3))
dict_ = {"coefficients":list(map(lambda x:round(x, 4), coef)),
'std_err':std_err,
't_Stat':t_Stat,
'P_value':P_value,
't臨界值':t_intv,
'Lower_95%':coefLower,
'Upper_95%':coefupper}
index = ["intercept"] + list(x.columns)
stat = pd.DataFrame(dict_, index=index)
return stat
# 自定義函數(計算輸出各回歸分析統計量)
def get_lr_stats(x, y_true, y_pred, coef, intercept, alpha=0.05):
n = len(x)
k = len(x.columns)
ssr = sum((y_pred - np.mean(y_true))**2)[0] # 迴歸平方和 SSR
sse = sum((y_true - y_pred)**2)[0] # 殘差平方和 SSE
sst = ssr + sse # 總平方和 SST
msr = ssr / k # 均方迴歸 MSR
mse = sse / (n-k-1) # 均方殘差 MSE
R_square = ssr / sst # 判定係數R^2
Adjusted_R_square = 1-(1-R_square)*((n-1) / (n-k-1)) # 調整的判定係數
Multiple_R = np.sqrt(R_square) # 復相關係數
Se = np.sqrt(sse/(n - k - 1)) # 估計標準誤差
loglike = log_like(y_true, y_pred)[0]
AIC = 2*(k+1) - 2 * loglike # (k+1) 代表k個迴歸參數或係數和1個截距參數
BIC = -2*loglike + (k+1)*np.log(n)
# 線性關係的顯著性檢驗
F = (ssr / k) / (sse / ( n - k - 1 )) # 檢驗統計量F (線性關係的檢驗)
pf = scipy.stats.f.sf(F, k, n-k-1) # 用於檢驗的顯著性F,即Significance F
Fa = scipy.stats.f.isf(alpha, dfn=k, dfd=n-k-1) # F臨界值
# 迴歸係數的顯著性檢驗
stat = param_test_stat(x, Se, intercept, coef, alpha=alpha)
# 輸出各回歸分析統計量
print('='*80)
print('df_Model:{} df_Residuals:{}'.format(k, n-k-1), '\n')
print('loglike:{} AIC:{} BIC:{}'.format(round(loglike,3), round(AIC,1), round(BIC,1)), '\n')
print('SST:{} SSR:{} SSE:{} MSR:{} MSE:{} Se:{}'.format(round(sst,4),
round(ssr,4),
round(sse,4),
round(msr,4),
round(mse,4),
round(Se,4)), '\n')
print('Multiple_R:{} R_square:{} Adjusted_R_square:{}'.format(round(Multiple_R,4),
round(R_square,4),
round(Adjusted_R_square,4)), '\n')
print('F:{} pf:{} Fa:{}'.format(round(F,4), pf, round(Fa,4)))
print('='*80)
print(stat)
print('='*80)
return 0
輸出結果如下:
對比statsmodels下ols結果:
參考資料:
【1】https://www.zhihu.com/question/328568463
【2】https://blog.csdn.net/qq_38998213/article/details/83480147