我正在尝试构建一个Logistic回归模型，无论响应变量的性质如何，该模型都可以运行，而与预测变量的性质无关。这是我的想法的一个示例：
#Take in the data
newdata = read.csv("book1.csv", head = TRUE)
#Store the response variable whose column heading you know beforehand
y = "response.variable"
#Identify the predictor variables (this is where I am stuck)
#Below is the algorithm of what I have in mind though
1) Take remaining column headings except "response.variable"
2) Store them as x = c(other headings)
#Form of model
model.form = reformulate(x, response = y)
#Build model
logit = glm(model.form, family = "binomial", data = newdata)
任何想法都欢迎。
编辑：当我使用@laterow建议的代码glm（y〜。，data = newdata）时，它给出了一条错误声明：
Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]:
contrasts can be applied only to factors with 2 or more levels
如果您的预测变量是data.frame中所有其余变量，请使用以下内容进行选择：
x <- names(newdata)[-which(names(newdata) == "response.variable")]
该names()函数返回data.frame中所有变量的名称。超级有用which()函数返回向量中与某些条件表达式匹配的元素的索引。
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