r 中的逻辑回归误差

Question

I'm creating a model with several thousand variables, all of which have a majority of values equal to NA. I am able to successfully run logistic regression on some variables but not others.

Here's my code to input the large amount of vars:

model_vars <- names(dataset[100:4000])
vars<- paste("DP ~ ", paste(model_vars, collapse= " + "))  

This formats it with the dependant variable and each Independant variable having a "+" between. I then run this through the glm function:

glm(vars, data = training, family = binomial)

Here is the error I get when certain variables are included:

Error in family$linkfun(mustart) : 
Argument mu must be a nonempty numeric vector

I cannot figure out why this is occuring and why the regression works for certain variables and not others. I can't see any trend in the variables that cause the error. Could someone clarify why this error shows up?

Answer 1

For others with that cryptic error message. Perhaps the data frame is empty?

This reproduces the message:

d=data.frame(x=c(NA),y=c(NA))
d=d[complete.cases(d),]
m=glm(y~.,d,family = 'binomial')

Error in family$linkfun(mustart) : Argument mu must be a nonempty numeric vector

Answer 2

I had the error:

Error in family$linkfun(mustart) : 
  Argument mu must be a nonempty numeric vector

when using logistic regression with glm() , like:

glm(y~x,data=df, family='binomial')

after subsetting and standardizing data frames in a loop.

It turned out, that (some of) the subsetted and standardized data frames contained NA , which caused the error.

Answer 3

我也有这个错误，原因是 if_else 函数返回一个逻辑向量，而不是预期的数字变量。