The “glm” Function in R

• Package: Base R (no specific package required)

• Purpose: Fits generalized linear models to data.

• General Class: Statistical Modeling

• Required Argument(s):

• formula: A symbolic description of the model to be fitted. In the form response ~ terms.

• family: A description of the error distribution and link function to be used in the model.

• Notable Optional Arguments:

• data: An optional data frame in which to interpret the variables.

• weights: An optional vector of weights to be used in the fitting process.

• subset: An optional vector specifying a subset of observations to be used in the fitting process.

• na.action: A function that indicates what should happen when data contain missing values.

• start: A starting point for the algorithm to optimize the parameters.

• Example:

• # Example data
  set.seed(123)
  x <- rnorm(100)
  y_binary <- rbinom(100, 1, plogis(2*x))
  
  # Fit a logistic regression model
  model <- glm(y_binary ~ x, family = binomial)
  
  # Display the summary of the model
  summary(model)

• In this example, the glm function is used to fit a logistic regression model with a binary response variable y_binary and a predictor variable x. The summary of the model is then displayed, showing coefficients, standard errors, z-values, and other relevant information.