The “lm” Function in R

• Package: Base R (no specific package required)

• Purpose: Fits 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.

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

• Notable Optional Arguments:

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

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

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

• method: The fitting method to be used. The default is “qr” for the QR decomposition method.

• ...: Additional arguments to be passed to or from methods.

• Example:

• # Example data
  set.seed(123)
  x <- rnorm(100)
  y <- 2*x + rnorm(100)
  
  # Fit a linear model
  model <- lm(y ~ x)
  
  # Display the summary of the model
  summary(model)

• In this example, the lm function is used to fit a linear model with response variable y and predictor variable x. The summary of the model is then displayed, showing coefficients, standard errors, t-values, and other relevant information.