CODEWITHSUNDEEP
rfunction
rg4s
rg4s

Reputation: 897

How to extract objects from a regression model list in R?

I want to write a function my_function which takes a dataframe as an input. In this function I run a logit regression to predict significant factors which influenced the dicision whether the observation was prohibited or not. Significance is defined as the p-value lesser than 0.05 (p-value < 0.05). I do not know how to extract these factors.

my_function(data){
   fit <- glm(is_prohibited ~ ., data, family = "binomial")
}

I do not know how to check factors for the significance and extract factors that I need because I can only extract coefficients of the model.

Data:

structure(list(is_prohibited = c("No", "No", "No", "No", "No", 
"No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "Yes", 
"Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", 
"Yes", "Yes", "Yes", "Yes", "Yes"), weight = c(69L, 79L, 82L, 
81L, 84L, 81L, 64L, 76L, 77L, 88L, 86L, 84L, 89L, 84L, 86L, 82L, 
84L, 88L, 88L, 89L, 86L, 76L, 87L, 67L, 70L, 76L, 71L, 91L, 72L, 
88L), length = c(53L, 52L, 54L, 50L, 48L, 51L, 53L, 52L, 53L, 
52L, 46L, 52L, 52L, 50L, 47L, 54L, 54L, 50L, 49L, 50L, 50L, 57L, 
47L, 50L, 50L, 51L, 52L, 52L, 48L, 54L), width = c(17L, 21L, 
20L, 23L, 19L, 20L, 16L, 20L, 23L, 23L, 19L, 17L, 22L, 23L, 23L, 
24L, 20L, 21L, 20L, 17L, 20L, 18L, 21L, 24L, 21L, 23L, 18L, 21L, 
17L, 20L), type = c("Suitcase", "Bag", "Suitcase", "Bag", "Suitcase", 
"Bag", "Suitcase", "Bag", "Suitcase", "Bag", "Suitcase", "Bag", 
"Suitcase", "Bag", "Suitcase", "Bag", "Suitcase", "Bag", "Suitcase", 
"Bag", "Suitcase", "Bag", "Suitcase", "Bag", "Suitcase", "Bag", 
"Suitcase", "Bag", "Suitcase", "Bag")), row.names = c(NA, 30L
), class = "data.frame")

Upvotes: 0

Views: 475

Answers (1)

Otto K&#228;ssi
Otto K&#228;ssi

Reputation: 3083

I believe you are looking for broom::tidy :

fit <- glm(as.factor(is_prohibited) ~ ., data, family = "binomial")

library(broom)
tidy(fit)

  term         estimate std.error statistic p.value
  <chr>           <dbl>     <dbl>     <dbl>   <dbl>
1 (Intercept)  -0.798     11.3      -0.0704   0.944
2 weight        0.00713    0.0534    0.133    0.894
3 length        0.0201     0.166     0.121    0.903
4 width        -0.0330     0.174    -0.189    0.850
5 typeSuitcase -0.265      0.822    -0.322    0.747

tidy(fit) returns a standard data frame where you can access coefficients, p-values etc for further calculations.

Upvotes: 1

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