使用因子和数字预测变量执行套索正则化？
Performing lasso regularization with factors and numeric predictors?
我有一个数据集，我想在其中执行套索以消除特征。由于我是 R 的新手，我目前正在关注 R 中的在线指南。数据存储在数据框中。目标已从数据框中删除并存储在其自己的单列数据框中。这是一个回归问题，目标是数字。这是我正在尝试的代码 运行:
library(glmnet)
lasso_model <- cv.glmnet(
x = as.matrix(train),
y = train_target,
alpha = 1)
以下是有关数据集的信息：
'data.frame': 9798 obs. of 55 variables:
$ acres: num 0.186 2.991 0.144 0.218 0.173 ...
$ above: int 1754 3030 1531 834 1022 1528 768 1184 2026 3176 ...
$ basement: int 0 1811 500 440 0 476 0 0 732 0 ...
$ baths: Factor w/ 7 levels "0","1","2","3",..: 3 4 3 3 2 3 2 2 3 3 ...
$ toilets: Factor w/ 5 levels "0","1","2","3",..: 1 3 2 1 1 2 1 1 2 2 ...
$ fireplaces: Factor w/ 6 levels "0","1","2","3",..: 2 2 2 2 1 1 1 2 2 2 ...
$ beds: Factor w/ 7 levels "1","2","3","4",..: 4 5 2 2 2 3 2 2 3 5 ...
$ rooms: Factor w/ 15 levels "0","1","2","3",..: 5 5 5 4 5 3 3 3 4 6 ...
$ age: int 103 17 13 46 116 12 93 93 42 100 ...
$ yearsfromsale: Factor w/ 3 levels "2","3","4": 2 2 2 1 2 2 3 3 1 1 ...
$ car: Factor w/ 4 levels "0","1","2","3": 1 4 3 1 1 3 1 1 4 1 ...
$ city_DES.MOINES: Factor w/ 2 levels "0","1": 2 1 1 2 2 1 2 2 2 2 ...
$ city_JOHNSTON: Factor w/ 2 levels "0","1": 1 2 2 1 1 1 1 1 1 1 ...
$ city_WEST.DES.MOINES: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ city_CLIVE: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ city_URBANDALE: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ city_ALTOONA: Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...
$ city_BONDURANT: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ city_CROCKER.TWNSHP: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ city_GRIMES: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ city_POLK.CITY: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ city_PLEASANT.HILL: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ city_WINDSOR.HEIGHTS: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50315: Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ...
$ zip_50321: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
$ zip_50320: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50312: Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 2 ...
$ zip_50314: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50311: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50309: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50316: Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 2 1 1 ...
$ zip_50317: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50313: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 1 1 ...
$ zip_50310: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50322: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50131: Factor w/ 2 levels "0","1": 1 1 2 1 1 1 1 1 1 1 ...
$ zip_50111: Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
$ zip_50265: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50266: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50325: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50323: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50009: Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...
$ zip_50035: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50023: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50226: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50021: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50327: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50324: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ walkout_0: Factor w/ 2 levels "0","1": 2 1 2 2 2 1 2 2 2 2 ...
$ walkout_1: Factor w/ 2 levels "0","1": 1 2 1 1 1 2 1 1 1 1 ...
$ condition_Normal: Factor w/ 2 levels "0","1": 1 2 2 1 1 2 1 1 1 1 ...
$ condition_Above.Normal: Factor w/ 2 levels "0","1": 2 1 1 2 2 1 2 1 1 2 ...
$ condition_Below.Normal: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 2 1 ...
$ AC_1: Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 1 ...
尝试 运行 lasso_model 行时，这是我遇到的错误：
Error in cbind2(1, newx) %*% nbeta :
invalid class 'NA' to dup_mMatrix_as_dgeMatrix
本质上，我希望能够确定要删除哪些变量。任何帮助都会很棒！
好吧，这是一个强烈的怀疑。
您的数据框中有因素。 as.matrix 将它们转换为字符串，而不是数字，glmnet 不知道如何处理它们：
> df <- data.frame(a=as.factor(c('0', '1', '2')), b=as.factor(c('0', '0', '1')))
> df
a b
1 0 0
2 1 0
3 2 1
> as.matrix(df)
a b
[1,] "0" "0"
[2,] "1" "0"
[3,] "2" "1"
尝试将它们显式转换回数字（有点迂回，但应该有效）：
> as.matrix(data.frame(lapply(df, function(x) as.numeric(as.character(x)))))
a b
[1,] 0 0
[2,] 1 0
[3,] 2 1
我有一个数据集，我想在其中执行套索以消除特征。由于我是 R 的新手，我目前正在关注 R 中的在线指南。数据存储在数据框中。目标已从数据框中删除并存储在其自己的单列数据框中。这是一个回归问题，目标是数字。这是我正在尝试的代码 运行:
library(glmnet)
lasso_model <- cv.glmnet(
x = as.matrix(train),
y = train_target,
alpha = 1)
以下是有关数据集的信息：
'data.frame': 9798 obs. of 55 variables:
$ acres: num 0.186 2.991 0.144 0.218 0.173 ...
$ above: int 1754 3030 1531 834 1022 1528 768 1184 2026 3176 ...
$ basement: int 0 1811 500 440 0 476 0 0 732 0 ...
$ baths: Factor w/ 7 levels "0","1","2","3",..: 3 4 3 3 2 3 2 2 3 3 ...
$ toilets: Factor w/ 5 levels "0","1","2","3",..: 1 3 2 1 1 2 1 1 2 2 ...
$ fireplaces: Factor w/ 6 levels "0","1","2","3",..: 2 2 2 2 1 1 1 2 2 2 ...
$ beds: Factor w/ 7 levels "1","2","3","4",..: 4 5 2 2 2 3 2 2 3 5 ...
$ rooms: Factor w/ 15 levels "0","1","2","3",..: 5 5 5 4 5 3 3 3 4 6 ...
$ age: int 103 17 13 46 116 12 93 93 42 100 ...
$ yearsfromsale: Factor w/ 3 levels "2","3","4": 2 2 2 1 2 2 3 3 1 1 ...
$ car: Factor w/ 4 levels "0","1","2","3": 1 4 3 1 1 3 1 1 4 1 ...
$ city_DES.MOINES: Factor w/ 2 levels "0","1": 2 1 1 2 2 1 2 2 2 2 ...
$ city_JOHNSTON: Factor w/ 2 levels "0","1": 1 2 2 1 1 1 1 1 1 1 ...
$ city_WEST.DES.MOINES: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ city_CLIVE: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ city_URBANDALE: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ city_ALTOONA: Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...
$ city_BONDURANT: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ city_CROCKER.TWNSHP: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ city_GRIMES: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ city_POLK.CITY: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ city_PLEASANT.HILL: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ city_WINDSOR.HEIGHTS: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50315: Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 1 1 ...
$ zip_50321: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
$ zip_50320: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50312: Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 2 ...
$ zip_50314: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50311: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50309: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50316: Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 2 1 1 ...
$ zip_50317: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50313: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 1 1 ...
$ zip_50310: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50322: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50131: Factor w/ 2 levels "0","1": 1 1 2 1 1 1 1 1 1 1 ...
$ zip_50111: Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
$ zip_50265: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50266: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50325: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50323: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50009: Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...
$ zip_50035: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50023: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50226: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50021: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50327: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ zip_50324: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ walkout_0: Factor w/ 2 levels "0","1": 2 1 2 2 2 1 2 2 2 2 ...
$ walkout_1: Factor w/ 2 levels "0","1": 1 2 1 1 1 2 1 1 1 1 ...
$ condition_Normal: Factor w/ 2 levels "0","1": 1 2 2 1 1 2 1 1 1 1 ...
$ condition_Above.Normal: Factor w/ 2 levels "0","1": 2 1 1 2 2 1 2 1 1 2 ...
$ condition_Below.Normal: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 2 1 ...
$ AC_1: Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 1 ...
尝试 运行 lasso_model 行时，这是我遇到的错误：
Error in cbind2(1, newx) %*% nbeta :
invalid class 'NA' to dup_mMatrix_as_dgeMatrix
本质上，我希望能够确定要删除哪些变量。任何帮助都会很棒！
好吧，这是一个强烈的怀疑。
您的数据框中有因素。 as.matrix 将它们转换为字符串，而不是数字，glmnet 不知道如何处理它们：
> df <- data.frame(a=as.factor(c('0', '1', '2')), b=as.factor(c('0', '0', '1')))
> df
a b
1 0 0
2 1 0
3 2 1
> as.matrix(df)
a b
[1,] "0" "0"
[2,] "1" "0"
[3,] "2" "1"
尝试将它们显式转换回数字（有点迂回，但应该有效）：
> as.matrix(data.frame(lapply(df, function(x) as.numeric(as.character(x)))))
a b
[1,] 0 0
[2,] 1 0
[3,] 2 1