title: Binary Logistic Regression using R
body: <p>So I'm trying to fit a binary logistic regression model for a question to estimate the odds of the disease and here is the original disease outbreak data (there are 196 observations and I deleted some data entries):</p>
  
  <p>Column 1: ID (person)</p>
  
  <p>Column 2: Age of the person</p>
  
  <p>Column 3: SES (Socio-economic status of the person) (1=upper class, 2=middle class, 3=lower class)</p>
  
  <p>Column 4: Sect (categorical: two different regions)</p>
  
  <p>Column 5: Y (1=disease, 0=no disease)</p>
  
  <p>Column 6: Savings (1=person has savings, 0=no savings)</p>
  
  <pre><code>1     33      1      1      0      1
  2     35      1      1      0      1
  3      6      1      1      0      0
  ...
  194     31      3      1      0      0
  195     85      3      1      0      1
  196     24      2      1      0      0
  </code></pre>
  
  <p>I tried the following command to fit the binary regression model:</p>
  
  <pre><code>lm1=glm(Y~factor(Age)+factor(SES)+factor(Sect)+factor(Savings),family=binomial("logit"))
  summary(lm1)
  </code></pre>
  
  <p>and not surprisingly, it is a mess because there are too many age terms (the age terms are from 2 to 85)... So my question is, would someone be able to help me to modify my command so I'm able to have an age estimate, for example, 5 or 10 year intervals increment?</p>
  
  <p>Also, the above model doesn't include any interaction terms. So if I was about to consider, say SES*Age interaction and I would like to see the age estimate for each every 5 or 10 years, how should I write the input command?</p>