lm function in R does not give coefficients for all factor levels in categorical data

by

GE is dropped, alphabetically, as the intercept term. As eipi10 stated, you can interpret the coefficients for the other levels in states with GE as the baseline (statesLA = 0.1 meaning LA is, on average, 0.1x more than GE).

EDIT:

To respond to your updated question:

If you include all of the levels in a linear regression, you’re going to have a situation called perfect collinearity, which is responsible for the strange results you’re seeing when you force each category into its own variable. I won’t get into the explanation of that, just find a wiki, and know that linear regression doesn’t work if the variable coefficients are completely represented (and you’re also expecting an intercept term). If you want to see all of the levels in a regression, you can perform a regression without an intercept term, as suggested in the comments, but again, this is ill-advised unless you have a specific reason to.

As for the interpretation of GE in your y=mx+c equation, you can calculate the expected y by knowing that the levels of the other states are binary (zero or one), and if the state is GE, they will all be zero.

e.g.

y = x1b1 + x2b2 + x3b3 + c
y = b1(0) + b2(0) + b3(0) + c
y = c

If you don’t have any other variables, like in your first example, the effect of GE will be equal to the intercept term (0.6).

More Related Contents:

  1. Linear Regression and group by in R
  2. Fitting a linear model with multiple LHS
  3. `lm` summary not display all factor levels
  4. How does predict.lm() compute confidence interval and prediction interval?
  5. Fast pairwise simple linear regression between variables in a data frame
  6. Linear Regression with a known fixed intercept in R
  7. predict.lm() in a loop. warning: prediction from a rank-deficient fit may be misleading
  8. R: lm() result differs when using `weights` argument and when using manually reweighted data
  9. How `poly()` generates orthogonal polynomials? How to understand the “coefs” returned?
  10. predict.lm() with an unknown factor level in test data
  11. Messy plot when plotting predictions of a polynomial regression using lm() in R
  12. how to debug “factor has new levels” error for linear model and prediction [duplicate]
  13. How to create a loop that will make regression models in R? [closed]
  14. Add regression line equation and R^2 on graph
  15. Getting Warning: ” ‘newdata’ had 1 row but variables found have 32 rows” on predict.lm
  16. How to force R to use a specified factor level as reference in a regression?
  17. Adding a regression line on a ggplot
  18. Is there a better alternative than string manipulation to programmatically build formulas?
  19. Why is using update on a lm inside a grouped data.table losing its model data?
  20. How to Loop/Repeat a Linear Regression in R
  21. Plot polynomial regression curve in R
  22. How do I extract just the number from a named number (without the name)?
  23. lme4::lmer reports “fixed-effect model matrix is rank deficient”, do I need a fix and how to?
  24. Is there a faster lm function
  25. Showing string in formula and not as variable in lm fit
  26. Set one or more of coefficients to a specific integer
  27. Showing equation of nls model with ggpmisc
  28. Regression (logistic) in R: Finding x value (predictor) for a particular y value (outcome)
  29. Finding where two linear fits intersect in R
  30. Error in Confusion Matrix : the data and reference factors must have the same number of levels

Leave a Comment