R: Missing levels of categorical variable in summary of linear model In my formula I have several terms with the categorical variable color and color's interaction with other variables. Color can be either white or red. When looking at the summary of my model, color is replaced by colorwhite with no mention of colorred. Can someone explain the mechanics behind R only showing one level of my categorical variable in the summary? 
example:
>summary(lm(foo~bar+color+color:bar))
coefficients
bar
colorwhite
colorwhite:bar
 A: Update: I just see Charles's reply above. I think the documents he pointed to are very helpful. Thanks  Patrick Coulombe for discussion.
If your color variable has only two possible values 'white' and 'red', you will have only coefficients for colorwhite and colorwhite:bar. 
The point is that you have intercept. And the base case is color = red. 
Thecoefficient for colorwhite actually reflect the effect size of white, being compared to that of red, given the bar as 0. 
Thecoefficient for colorwhite:bar actually reflect the effect size of the combination of (interaction between) white and a unit of bar value, being compared to that of the combination of (interaction between) red and a unit of bar value, given the same bar value.
Please check this wikipedia page: 
"Always present effect sizes for primary outcomes...If the units of measurement are meaningful on a practical level (e.g., number of cigarettes smoked per day), then we usually prefer an unstandardized measure (regression coefficient or mean difference) to a standardized measure (r or d).
— L. Wilkinson and APA Task Force on Statistical Inference (1999, p. 599)
...
The term effect size can refer to a standardized measures of effect (such as r, Cohen's d, and odds ratio), or to an unstandardized measure (e.g., the raw difference between group means and unstandardized regression coefficients). " 
And also this page:
"Remember that regression analysis is used to produce an equation that will predict a dependent variable using one or more independent variables. This equation has the form
Y = b1X1 + b2X2 + ... + A
where Y is the dependent variable you are trying to predict, X1, X2 and so on are the independent variables you are using to predict it, b1, b2 and so on are the coefficients or multipliers that describe the size of the effect the independent variables are having on your dependent variable Y, and A is the value Y is predicted to have when all the independent variables are equal to zero.
...
In simple or multiple linear regression, the size of the coefficient for each independent variable gives you the size of the effect that variable is having on your dependent variable, and the sign on the coefficient (positive or negative) gives you the direction of the effect. In regression with a single independent variable, the coefficient tells you how much the dependent variable is expected to increase (if the coefficient is positive) or decrease (if the coefficient is negative) when that independent variable increases by one. In regression with multiple independent variables, the coefficient tells you how much the dependent variable is expected to increase when that independent variable increases by one, holding all the other independent variables constant. Remember to keep in mind the units which your variables are measured in.
"
