请给出5种多元分析统计的代码例子

多元分析是一种统计方法，用于研究多个变量之间的相互影响以及它们与响应变量的关系。以下是五个常见的多元分析相关的Python代码示例，使用pandas、NumPy和sklearn库：

1. **线性回归（Linear Regression）**:

import pandas as pd
from sklearn.linear_model import LinearRegression

# 加载数据
data = pd.read_csv('data.csv')

# 定义特征和目标变量
X = data[['feature1', 'feature2']]
y = data['target']

model = LinearRegression()
model.fit(X, y)

2. **主成分分析（PCA）**:

from sklearn.decomposition import PCA

data = pd.read_csv('data.csv')
X = data.drop('target', axis=1)

pca = PCA(n_components=2)  # 保留两个主成分
principal_components = pca.fit_transform(X)

3. **方差膨胀因子（VIF）检查**:

from statsmodels.stats.outliers_influence import variance_inflation_factor

data = pd.read_csv('data.csv')
vif = pd.DataFrame()
vif["Features"] = data.columns[:-1]
vif["VIF Factor"] = [variance_inflation_factor(data.values, i) for i in range(vif.shape[0])]

4. **多元线性判别分析（MLDA）**:

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

lda = LinearDiscriminantAnalysis()
lda.fit(X_train, y_train)
y_pred = lda.predict(X_test)

5. **因子分析（Factor Analysis）**:

from factor_analyzer import FactorAnalyzer

fa = FactorAnalyzer(rotation='varimax')  # 可选旋转方法
fa.fit(X)
factor_scores = fa.transform(X)