Fitting Generalized Linear Models

Coursera Developing Data Products Course Project

Great application which make incredible fitting of GLM model

• Easy to interact
• Ability to select predictors list
• Ability to change predictors interaction

summary(iris)

  Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
 Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
 Median :5.800   Median :3.000   Median :4.350   Median :1.300  
 Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
 Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
       Species  
 setosa    :50  
 versicolor:50  
 virginica :50  

model <- glm(Species ~ ., iris, family="binomial")

Call:  glm(formula = Species ~ ., family = "binomial", data = iris)

Coefficients:
 (Intercept)  Sepal.Length   Sepal.Width  Petal.Length   Petal.Width  
      16.946       -11.759        -7.842        20.088        21.608  

Degrees of Freedom: 149 Total (i.e. Null);  145 Residual
Null Deviance:      191 
Residual Deviance: 3.294e-09    AIC: 10