R:重新运行模型时 GLMNET 的奇怪行为 [英] R: GLMNET odd behavior when model is reran

问题描述

我正在尝试使用 LASSO 进行变量选择，并尝试使用 glmnet 包在 R 中实现.这是我目前写的代码:

I am trying to use LASSO for variable selection, and attempted the implementation in R using the glmnet package. This is the code I wrote so far:

 set.seed(1)
 library(glmnet)
 return =  matrix(ret.ff.zoo[which(index(ret.ff.zoo) == beta.df$date[1]),])
 data = matrix(unlist(beta.df[which(beta.df$date == beta.df$date[1]),][,-1]), ncol = num.factors)
 dimnames(data)[[2]] <- names(beta.df)[-1]
 model <- cv.glmnet(data, return, standardize = TRUE)
 coef(model)

这是我第一次运行时得到的:

This is what I obtain when I run it the first time:

 > coef(model)
 15 x 1 sparse Matrix of class "dgCMatrix"
                       1
 (Intercept) 0.009159452
 VAL         .          
 EQ          .          
 EFF         .          
 SIZE        0.018479078
 MOM         .          
 FSCR        .          
 MSCR        .          
 SY          .          
 URP         .          
 UMP         .          
 UNIF        .          
 OIL         .          
 DEI         .          
 PROD        .             

但是，这是我再次运行相同代码时获得的结果:

BUT, this is what I obtain when I run the SAME code once more:

 > coef(model)
 15 x 1 sparse Matrix of class "dgCMatrix"
                       1
 (Intercept) 0.008031915
 VAL         .          
 EQ          .          
 EFF         .          
 SIZE        0.021250778
 MOM         .          
 FSCR        .          
 MSCR        .          
 SY          .          
 URP         .          
 UMP         .          
 UNIF        .          
 OIL         .          
 DEI         .          
 PROD        .          

我不确定模型为什么会这样.如果系数在每次运行时都发生变化，我将如何选择最终模型?它是否在每次运行时使用不同的调整参数 $\lambda$?我以为 cv.glmnet 默认使用 model$lambda.1se ?！

I am not sure why the model behaves this way. How would I be able to choose a final model if the coefficients change at every run? Does it use a different tuning parameter $\lambda$ at every run? I thought that cv.glmnet uses model$lambda.1se by default?!

我刚刚开始学习这个包，如果我能得到任何帮助，我将不胜感激！

I have just started learning about this package, and would appreciate any help I can get!

谢谢！

推荐答案

该模型不是确定性的.在模型拟合之前运行 set.seed(1) 以产生确定性结果.

The model isn't deterministic. Run set.seed(1) before your model fit to produce deterministic results.

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