R poisson regression
0 likes452 views
This document discusses Poisson regression in R. Poisson regression is a type of regression where the response variable is counts, like number of births or wins. The document shows how to create a Poisson regression model in R using the glm() function, specifying the Poisson family. It uses the built-in warpbreaks data to predict the number of warp breaks based on wool type and tension level, and the summary of the model shows that wool type B and higher tension levels have a significant impact on the number of breaks.
R poisson regression
- 1. Swipe R - Poisson Regression
- 2. Poisson Regression involves regression models in which the response variable is in the form of counts and not fractional numbers. For example, the count of number of births or number of wins in a football match series. Also the values of the response variables follow a Poisson distribution. The general mathematical equation for Poisson regression is:- log(y) = a + b1x1 + b2x2 + bnxn R - Poisson Regression
- 3. Following is the description of the parameters used − y is the response variable. a and b are the numeric coefficients. x is the predictor variable. The function used to create the Poisson regression model is the glm() function. The basic syntax for glm() function in Poisson regression is:- glm(formula,data,family)
- 4. Following is the description of the parameters used in above functions − formula is the symbol presenting the relationship between the variables. data is the data set giving the values of these variables. family is R object to specify the details of the model. It's value is 'Poisson' for Logistic Regression.
- 5. Example We have the in-built data set "warpbreaks" which describes the effect of wool type (A or B) and tension (low, medium or high) on the number of warp breaks per loom. Let's consider "breaks" as the response variable which is a count of number of breaks. The wool "type" and "tension" are taken as predictor variables. Input Data input <- warpbreaks print(head(input))
- 6. When we execute the above code, it produces the following result:- breaks wool tension 1 26 A L 2 30 A L 3 54 A L 4 25 A L 5 70 A L 6 52 A L
- 7. Create Regression Model output <-glm(formula = breaks ~ wool+tension, data = warpbreaks, family = poisson) print(summary(output)) When we execute the above code, it produces the following result:- Call: glm(formula = breaks ~ wool + tension, family = poisson, data = warpbreaks) Deviance Residuals: Min 1Q Median 3Q Max -3.6871 -1.6503 -0.4269 1.1902 4.2616
- 8. Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 3.69196 0.04541 81.302 < 2e-16 *** woolB -0.20599 0.05157 -3.994 6.49e-05 *** tensionM -0.32132 0.06027 -5.332 9.73e-08 *** tensionH -0.51849 0.06396 -8.107 5.21e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 297.37 on 53 degrees of freedom Residual deviance: 210.39 on 50 degrees of freedom AIC: 493.06 Number of Fisher Scoring iterations: 4
- 9. In the summary we look for the p-value in the last column to be less than 0.05 to consider an impact of the predictor variable on the response variable. As seen the wooltype B having tension type M and H have impact on the count of breaks.
- 10. R - Time Series Analysis R - Nonlinear Least Square R - Decision Tree Stay Tuned with Topics for next Post