R lm() - lm function in R for Linear Regression

The R lm() function is used to fit linear models for performing linear regression, single stratum analysis of variance, and analysis of covariance.

The usage of the lm() function in R is as follows:

lm(formula, data, subset, weights, na.action,
   method = "qr", model = TRUE, x = FALSE, y = FALSE, qr = TRUE,
   singular.ok = TRUE, contrasts = NULL, offset, …)

Here, the parameters/arguments are defined as:

• formula: an object of class "formula" (or one that can be coerced into that class): a symbolic description of the model to be fitted.
• data: an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called.
• 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. Should be NULL or a numeric vector. If non-NULL, weighted least squares is used with weights weights (that is, minimizing sum(w*e^2)); otherwise ordinary least squares is used.
• na.action: a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The ‘factory-fresh’ default is na.omit. Another possible value is NULL, no action. Value na.exclude can be useful.
• method: the method to be used; for fitting, currently only method = "qr" is supported; method = "model.frame" returns the model frame (the same as with model = TRUE).
• model, x, y, q: If TRUE the corresponding components of the fit (the model frame, the model matrix, the response, the QR decomposition) are returned.
• singular.ok: If FALSE (the default in S but not in R) a singular fit is an error.
• contrasts: an optional list.
• offset: this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector or matrix of extents matching those of the response. One or more offset terms can be included in the formula instead or as well, and if more than one are specified their sum is used.
• …: additional arguments to be passed to the low level regression fitting functions.

Example Implementation of R lm() function:

The lm() function can be implemented in R according to the following example:

library(readxl) # Library for reading excel files
ageandheight <- read_excel("ageandheight.xls", sheet = "Untitled1") # Upload the data
lmHeight = lm(height~age, data = ageandheight) # Create linear regression model using lm
summary(lmHeight) # Review the results 

In the example above, you can substitute ageandheight.xls to be any dataset that you want.

Do you want to learn Python, Data Science, and Machine Learning while getting certified? Here are some best selling Datacamp courses that we recommend you enroll in:

1. Introduction to Python (Free Course) - 1,000,000+ students already enrolled!
2. Introduction to Data Science  in Python- 400,000+ students already enrolled!
3. Introduction to TensorFlow for Deep Learning with Python - 90,000+ students already enrolled!
4. Data Science and Machine Learning Bootcamp with R - 70,000+ students already enrolled!