在下面的示例中,我设置了一个具有 3 个变量的 df,predict、var1 和 var2(一个因子)。
当我在 caret 或 glmnet 中运行模型时,因子被转换为虚拟变量,例如 var2b。
我想以编程方式提取变量名并匹配原始变量名,而不是虚拟变量名——有没有办法做到这一点?
这只是一个例子,我的现实世界问题有许多不同级别的变量,因此,我想避免手动执行此操作,例如尝试将“b”作为子串。
谢谢!
library(caret)
library(glmnet)
df <- data.frame(predict = c('Y','Y','N','Y','N','Y','Y','N','Y','N'), var1 = c(1,2,5,1,6,7,3,4,5,6),
var2 = c('a','a','b','b','a','a','a','b','b','a'))
str(df)
# 'data.frame': 10 obs. of 3 variables:
# $ predict: Factor w/ 2 levels "N","Y": 2 2 1 2 1 2 2 1 2 1
# $ var1 : num 1 2 5 1 6 7 3 4 5 6
# $ var2 : Factor w/ 2 levels "a","b": 1 1 2 2 1 1 1 2 2 1
test <- train(predict ~ .,
data = df,
method = 'glmnet',
trControl = trainControl(classProbs = TRUE,
summaryFunction = twoClassSummary,
allowParallel = FALSE),
metric = 'ROC',
tuneGrid = expand.grid(alpha = 1,
lambda = .005))
predictors(test)
# [1] "var1" "var2b"
varImp(test)
# glmnet variable importance
# Overall
# var2b 100
# var1 0
coef(test)
# NULL
#################
x <- model.matrix(as.formula(predict~.),data=df)
x <- x[,-1] ##remove intercept
df$predict <- ifelse(df$predict == 'Y', TRUE, FALSE)
glmnet1 <- glmnet::cv.glmnet(x = x,
y = df$predict,
type.measure='auc',
nfolds=3,
alpha=1,
parallel = FALSE)
rownames(coef(glmnet1))
# [1] "(Intercept)" "var1" "var2b