[英]Function for Logistic Regression Training Set
I am trying to create a function to test a logistic regression model developed on a training set. 我正在尝试创建一个功能来测试在训练集上开发的逻辑回归模型。
For example 例如
train <- filter(y, folds != i)
test <- filter(y, folds == i)
I want to be able to use the formula for different data sets. 我希望能够将公式用于不同的数据集。 For example, if I were to take y
to be a response variable such as “ low
” in the birthwt data set and x
to be the explanatory variables eg “age", “race”
how would I implement these arguments into glm.train formula without having to type the function separately for different data sets ? 例如,如果我将y
用作反应变量,例如birthwt数据集中的“ low
”,而将x
用作解释变量,例如“age", “race”
我将如何将这些参数实现为glm.train公式不必为不同的数据集分别键入函数?
glm.train <- glm(y ~x, family = binomial, data = train)
You can use reformulate
to create a formula based on strings: 您可以使用reformulate
公式以基于字符串创建公式:
x <- c("age", "race")
y <- "low"
form <- reformulate(x, response = y)
# low ~ age + race
Use this formula for glm
: 对glm
使用以下公式:
glm.train <- glm(form, family = binomial, data = train)
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