用R进行多层回归分析
来自OBHRM百科
Lichaoping(讨论 | 贡献)2017年3月10日 (五) 08:23的版本
可以用R自带的函数lm()来做,或者用AutoModel包来完成
用AutoModel包进行多层回归分析
脚本与注释
library(AutoModel) # 启用AutoModel包。如果没有安装,请先安装。安装方法,请在R控制台输入:install.packages("AutoModel")。 # 多层回归分析。因变量为y,第一层的自变量为:lag.quarterly.revenue,第二层新增的自变量为:price.index,income.level。数据来源AutoModel包自带的freeny数据 run_model("y", c("lag.quarterly.revenue"), c("price.index", "income.level"),dataset=freeny)
结果
> run_model("y", c("lag.quarterly.revenue"), c("price.index", "income.level"),dataset=freeny) REGRESSION OUTPUT Durbin-Watson = 2.11 p value = 0.4729 Partial Regression plots (all relationships should be linear): Plot of studentized residuals: uniform distibution across predicted values requiredCorrelation Matrix for model (correlation >.70 indicates severe multicollinearity) y lag.quarterly.revenue price.index income.level y 1.0000 0.9978 -0.9895 0.9839 lag.quarterly.revenue 0.9978 1.0000 -0.9894 0.9817 price.index -0.9895 -0.9894 1.0000 -0.9539 income.level 0.9839 0.9817 -0.9539 1.0000 Variance inflation factor (<10 desired): lag.quarterly.revenue price.index income.level 194.85 78.58 45.52 Standardized Residuals (observations > 3.00 problematic): No significant outliers Cook's distance (values >.2 problematic): 1963.25 0.8918 Normality of standardized model residuals: Shapiro-Wilk (p-value): 0.5586 Model change statistics R R^2 Adj R^2 SE Est. Delta R^2 F Change df1 df2 p Fch Sig Model 1 0.9978 0.9956 0.9955 0.0212 0.9956 8360.3793 1 37 0 *** Model 2 0.9988 0.9977 0.9975 0.0159 0.0021 15.4599 2 35 0 *** Model 1 : y ~ lag.quarterly.revenue Model 2 : y ~ lag.quarterly.revenue + price.index + income.level Model Coefficients Model term estimate std.error statistic p.value sig Model 1 (Intercept) 0.04169 0.10138 0.4112 0.6833 Model 1 lag.quarterly.revenue 0.99827 0.01092 91.4351 0.0000 *** Model 2 (Intercept) 4.97077 1.24046 4.0072 0.0003 *** Model 2 lag.quarterly.revenue 0.37305 0.11418 3.2673 0.0024 ** Model 2 price.index -0.81887 0.17152 -4.7742 0.0000 *** Model 2 income.level 0.75435 0.14454 5.2189 0.0000 ***