Hi I use similar concept in glm function un logistic regression. here is the sample code defining, how the formula is constructed and how the dependent and independent variables are passed on to the formula.
#create data frame
mydata <- data.frame(pp=c(.1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1, 1.1, 1.3, 1.5, 1.7),
qq=c(1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2, 2.1, 2.3, 2.5, 2.7),
rr=c(2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3, 2.1, 2.3, 3.5, 3.7),
ss=c(3.1,3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4, 4, 2.1, 2.3, 4.5, 4.7),
Class=c("benign", "malignant", "benign", "malignant", "benign", "benign", "benign", "benign", "benign", "malignant", "malignant", "malignant", "malignant", "malignant", "malignant"))
#Identify and assign as dependent and which value is called binaryone
mdependvar <- 'Class'
mbinaryOne <- 'malignant'
#convert dependent variable "Class" as factor
mydata[mdependvar] <- factor(ifelse(mydata[mdependvar] == mbinaryOne, 1, 0), levels = c(0, 1))
#Declare the list of Independent variables
mindependvar <- c('pp','qq','rr','ss')
#Start constructing the formula
xxx <- paste0("mydata$",mdependvar)
f1 <- as.formula(paste(paste( text=xxx,"~"), paste("+",paste (mdependvar , sep = " ", collapse = "+"))))
#Apply formula
glm_model <- glm(f1, family = "binomial", data=mydata)
#Summary output
summary(glm_model)
if you need more information on this formula and how to select and pass a dependent and independent variables, you may refer to my Logistic Regression Multi-model in my channel "Happy Learning-GP" on YouTube