title: How can I estimate variable influence of the factors in my model rather than just factor levels?
body: <p>I created a regression model And i want to estimate an influence analysis for each factor.</p>
  
  <p>Meaning take the square Wald-estimation (z-value) for a specific factor and divide it by sum of squares of their Wald-estimation. and that how I estimate the influence of specific factor.</p>
  
  <p>My problem is that the factors are divided by their levels.</p>
  
  <p>I will give an example:</p>
  
  <pre><code>model&lt;-glm(formula = form,
              family = binomial("logit"),
              data   = Train)
  View(summary(model)$coefficients)
  </code></pre>
  
  <p><a href="https://i.stack.imgur.com/vGSxa.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/vGSxa.png" alt="enter image description here"></a></p>
  
  <p>In the table we can see that the factor <code>dom_time_Colnames</code> is divided into 4 levels. Same thing happened with <code>first_byte_downdload_Colnames</code>.</p>
  
  <p>I want to take the factors z-values and not their levels z-values. </p>
  
  <p>How I do it? <code>anova()</code> is a good idea but it doesn't stop running for me. I search for creative solution that give me output like the the z-value in glm summary or deviance in anova for the all factor and not for theirs levels.</p>
  
  <p><strong>Here is a reproducible example:</strong></p>
  
  <pre><code>Data&lt;-data.frame(Species=iris$Species)
  for(i in 1:ncol(iris)){
    if(is.numeric(iris[,i])){
      result=quantile(x = iris[,i],probs = seq(0,1,0.1)) 
      out&lt;-cut(iris[,i], breaks = unique(result),include.lowest = TRUE)
      Data&lt;-data.frame(Data,out)
      colnames(Data)[length(Data)]&lt;-colnames(iris)[i]
    } else {
      next()
    }
  }
  Data$y&lt;-rbinom(n = nrow(Data),size = 1,prob = 0.1)
  form&lt;-formula(y~.)
  model&lt;-glm(formula = form,
              family = binomial("logit"),
              data = Data)
  View(summary(model)$coefficients)
  </code></pre>
  
  <p>we can see that the factor sepal or Petal is divided to its levels.<br></p>