R中的认知逻辑回归建模

Question

我正在尝试构建一个逻辑回归模型，只要响应变量保持不变，该模型就能够运行而不管预测变量的性质如何。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)

欢迎提出任何想法。

编辑： 当我使用代码时 glm(y ~., data = newdata) 正如@laterow 所建议的，它给出了一个错误声明：

 Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]:
 contrasts can be applied only to factors with 2 or more levels

Answer 1

如果您的预测变量是 data.frame 中的所有剩余变量，请使用以下命令选择它们：

x <- names(newdata)[-which(names(newdata) == "response.variable")]

names() 函数返回 data.frame 中所有变量的名称。超级有用的which() 函数返回向量中匹配某个条件表达式的元素的索引。