我必须对包进行 Lasso 回归，但glmnet我无法生成我的 x model.matrix 我的 data.frame：108 个观察值，Y 响应变量，24 个预测变量，这是一个概述：
CONVENTIONAL_HUmin CONVENTIONAL_HUmean CONVENTIONAL_HUstd CONVENTIONAL_HUmax
1 37.9400539686119 63.4903779286635 11.7592095845857 85.2375439991287
2 23.8400539686119 80.5903779286635 15.0592095845857 125.837543999129
3 19.3035945249441 73.2764716205565 12.8816244173147 130.24141901586
CONVENTIONAL_HUQ1 CONVENTIONAL_HUQ2 CONVENTIONAL_HUQ3 HISTO_Skewness HISTO_Kurtosis
1 54.9938390994964 65.4873070322704 72.8863025473031 -0.203420585259268 2.25208159159488
2 70.8938390994964 80.3873070322704 91.4863025473031 -0.117420585259268 2.91208159159488
3 64.4689755423307 73.8666609177099 81.7351818199415 -0.0908104900456161 2.8751327713366
HISTO_ExcessKurtosis HISTO_Entropy_log10 HISTO_Entropy_log2 HISTO_Energy...Uniformity.
1 -0.751917020142877 0.701345471328916 2.32782599847774 0.219781577333287
2 -0.0887170201428774 0.793345471328916 2.63782599847774 0.184781577333287
3 -0.127231561113029 0.738530858918985 2.45445652190669 0.206887426065656
GLZLM_SZE GLZLM_LZE GLZLM_LGZE GLZLM_HGZE GLZLM_SZLGE
1 0.366581916604228 35.7249100350856 8.7285612359045e-05 11497.6407737833 3.22615226279017e-05
2 0.693581916604228 984.424910035086 8.5685612359045e-05 11697.6407737833 5.98615226279017e-05
3 0.622711792823853 1103.10288991619 8.5573088970709e-05 11571.7421733917 5.33303855950858e-05
GLZLM_SZHGE GLZLM_LZLGE GLZLM_LZHGE GLZLM_GLNU GLZLM_ZLNU
1 4164.91570215061 0.00314512237564268 405585.990838764 2.66964898745512 2.47759091065361
2 8064.91570215061 0.0835651223756427 11581585.9908388 12.9796489874551 38.5375909106536
3 7295.45317481887 0.0949686480587339 12926109.9421091 15.0930512668698 37.6083347285291
GLZLM_ZP Y
1 0.219643444043173 1
2 0.112643444043173 0
3 0.104031438564764 0
我的 model.matrix 代码
x=model.matrix(Y~.,data=data.det)
它一般是一个非常大的 model.matrix，有 244728 个元素！似乎它已经复制了 24 的每个预测器一百次！以下是 data.matrix 的概述：
(Intercept) CONVENTIONAL_HUmin-10.5599460313881
CONVENTIONAL_HUmin-117.359946031388 CONVENTIONAL_HUmin-13.0599460313881
CONVENTIONAL_HUmin-154.359946031388 CONVENTIONAL_HUmin-17.6599460313881
CONVENTIONAL_HUmin-18.3599460313881 CONVENTIONAL_HUmin-2.87994603138811
CONVENTIONAL_HUmin-21.281710504529 CONVENTIONAL_HUmin-28.3599460313881
CONVENTIONAL_HUmin-3.44994603138811 CONVENTIONAL_HUmin-3.89640547505594
CONVENTIONAL_HUmin-67.0599460313881 CONVENTIONAL_HUmin-682.359946031388
CONVENTIONAL_HUmin-9.08171050452898 CONVENTIONAL_HUmin1.04428949547101
CONVENTIONAL_HUmin1.63928949547101 CONVENTIONAL_HUmin10.8400539686119
CONVENTIONAL_HUmin10.968289495471 CONVENTIONAL_HUmin11.5400539686119
CONVENTIONAL_HUmin11.618289495471 CONVENTIONAL_HUmin11.6400539686119
CONVENTIONAL_HUmin12.518289495471 CONVENTIONAL_HUmin12.5400539686119
CONVENTIONAL_HUmin13.4400539686119 CONVENTIONAL_HUmin13.6400539686119
CONVENTIONAL_HUmin13.7400539686119 CONVENTIONAL_HUmin13.818289495471
CONVENTIONAL_HUmin14.5400539686119 CONVENTIONAL_HUmin14.6693017607572
CONVENTIONAL_HUmin14.8400539686119 CONVENTIONAL_HUmin16.9400539686119
CONVENTIONAL_HUmin17.0400539686119 CONVENTIONAL_HUmin17.618289495471
CONVENTIONAL_HUmin18.2400539686119 CONVENTIONAL_HUmin18.8400539686119
CONVENTIONAL_HUmin19.3035945249441 CONVENTIONAL_HUmin20.0400539686119
CONVENTIONAL_HUmin20.818289495471 CONVENTIONAL_HUmin21.0400539686119
CONVENTIONAL_HUmin21.118289495471 CONVENTIONAL_HUmin21.3400539686119
CONVENTIONAL_HUmin21.5400539686119 CONVENTIONAL_HUmin21.9400539686119
...
attr(,"contrasts")$CONVENTIONAL_HUmin
[1] "contr.treatment"
一点也不方便，因为我最终在 Lasso Regression 的输入 x 中得到了更多的预测变量，这使得预测变量的危险选择更加存在
你知道功能障碍的根源吗？有什么建议可以解决这个问题吗？