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R - 从插入符号和glmnet套索模型对象中提取因子预测符名称

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在下面的例子中，我设置了一个带有3个变量的df，预测，var1和var2（一个因子） .

当我在插入符或glmnet中运行模型时，该因子将转换为虚拟变量，例如var2b .

我想以编程方式提取变量名称并匹配原始变量名称，而不是虚拟变量名称 - 有没有办法做到这一点？

这只是一个例子，我的真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，例如尝试子串出“b” .

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
r r-caret glmnet lasso

2 回答

  • 1

    'train'对象的 formula 方法返回一个'formula'对象，其中包含您要查找的属性 .

    f1 <- formula(test)
f1
# predict ~ var1 + var2
# attr(,"variables")
# list(predict, var1, var2)
# attr(,"factors")
#         var1 var2
# predict    0    0
# var1       1    0
# var2       0    1
# attr(,"term.labels")
# [1] "var1" "var2"
# attr(,"order")
# [1] 1 1
# attr(,"intercept")
# [1] 1
# attr(,"response")
# [1] 1
# attr(,"predvars")
# list(predict, var1, var2)
# attr(,"dataClasses")
#   predict      var1      var2 
#  "factor" "numeric"  "factor" 
attr(f1, "term.labels")
# [1] "var1" "var2"

    似乎'cv.glmnet'对象中的变量名称不可用 . 我不知道收集这些的优雅方式 . glmnetUtils 包可能具有一些生活质量功能 .

    这是你可以尝试的一些代码;请注意，这将返回误报，因为它是按输入数据中的模式搜索列名（例如“var11”将匹配“var1”） .

    # a generic method
termLabels <- function(object, ...) {
    UseMethod("termLabels")
}
# add for the train object too to save typing
termLabels.train <- function(object, ...) {
    attr(formula(object), "term.labels")
}
# try to find term labels for cv.glmnet object
# lambda must be provided and snaps to search grid
# allowed column names must be provided from corresponding data object
termLabels.cv.glmnet <- function(object, lambda, names, ...) {
    if (missing(lambda)) { stop("lambda is missing") }
    if (missing(names)) { stop("names is missing") }
    # match lambda
    lambdaArray <- object$glmnet.fit$a0
    if (lambda > max(lambdaArray) || lambda < min(lambdaArray)) {
        stop(paste("lambda must be in range", 
            paste(range(lambdaArray), collapse = ":")))
    }
    # find closest lambda
    whichLambda <- which.min(abs(lambdaArray - lambda))
    message(paste("using lambda", lambdaArray[whichLambda]))
    # matrix of parameter estimates
    betaLambda <- object$glmnet.fit$beta[, whichLambda, drop = FALSE]
    # non-zero estimates
    betaLambda <- betaLambda[betaLambda[, 1] != 0, , drop = FALSE]
    vars <- rownames(betaLambda)
    # search with names as pattern
    # note, does not account for nested names, e.g. var1 and var11
    matchNames <- apply(matrix(names), MARGIN = 1, FUN = grepl, x = vars)
    names[apply(matchNames, MARGIN = 2, FUN = any)]
}
termLabels(glmnet1, lambda = 1, names = colnames(df))
# using lambda 0.998561314952713
# [1] "var1" "var2"
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  • 1

    Per @ CSJCampbell的回答：glmnetUtils包允许你使用glmnet和cv.glmnet对象执行此操作 .

    library(glmnetUtils)
m <- glmnet(mpg ~ ., data=mtcars)
all.vars(m$terms)

m2 <- cv.glmnet(mpg ~ ., data=mtcars)
all.vars(m2$terms)

    请注意， all.vars 也适用于大多数其他R模型对象：

    m3 <- lm(mpg ~ ., data=mtcars)
all.vars(delete.response(m3$terms))

    glmnetUtils可用on CRAN，或者你可以从Github获得dev version . 我目前正在完成一项重大更新，该更新应尽快发布到CRAN .

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