Poisson Regression in R Programming (original) (raw)

Last Updated : 10 May, 2020

A Poisson Regression model is used to model count data and model response variables (Y-values) that are counts. It shows which X-values work on the Y-value and more categorically, it counts data: discrete data with non-negative integer values that count something. In other words, it shows which explanatory variables have a notable effect on the response variable. Poisson Regression involves regression models in which the response variable is in the form of counts and not fractional numbers.

Mathematical Equation:

log(y) = a + b1x1 + b2x2 + bnxn.....

Parameters:

• y: This parameter sets as a response variable.
• a and b: The parameter a and b are the numeric coefficients.
• x: This parameter is the predictor variable.

Creating Poisson Regression Model

The function used to create the Poisson regression model is the glm() function.

Syntax: glm(formula, data, family)Parameters:

• formula: This parameter is the symbol presenting the relationship between the variables.
• data: The parameter is the data set giving the values of these variables.
• family: This parameter R object to specify the details of the model. It's value is 'Poisson' for Logistic Regression.

Example: