Skip to content
Related Articles

Related Articles

Poisson Regression in R Programming
  • 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:

    Approach: To understand how we can create:

    • We use the data set “warpbreaks”.
    • Considering “breaks” as the response variable.
    • The wool “type” and “tension” are taken as predictor variables.

    Code:

    filter_none

    edit
    close

    play_arrow

    link
    brightness_4
    code

    input <- warpbreaks
    print(head(input))

    chevron_right

    
    

    Output:

    Create Regression Model

    Approach: Creating the poisson regression model:

    • Take the parameters which are required to make model.
    • let’s use summary() function to find the summary of the model for data analysis.

    Example:

    filter_none

    edit
    close

    play_arrow

    link
    brightness_4
    code

    output <-glm(formula = breaks ~ wool + tension,
                 data = warpbreaks, family = poisson)
    print(summary(output))     

    chevron_right

    
    

    Output:

    Creating Poisson Regression Model using glm() function

    Approach: Creating the regression model with the help of the glm() function as:

    • With the help of this function, easy to make model.
    • Now we draw a graph for the relation between “formula”, “data” and “family”.

    Example:

    filter_none

    edit
    close

    play_arrow

    link
    brightness_4
    code

    output_result <-glm(formula = breaks ~ wool + tension,
                        data = warpbreaks, family = poisson)  
    output_result 

    chevron_right

    
    

    Output:

    My Personal Notes arrow_drop_up
Recommended Articles
Page :