Stata's xtlogit (fe, re) equivalent in R? -


Allows fixed effects and random effects specifications of the revised ration through static fixes and then commands accordingly. I was wondering what is the equivalent command for these specifications in R

Only this type of specificity I know that mixed effect is empty regression 

  mymixedlogit & lt; - Glam (Y ~ x1 + x2 + x3 + (1 | x4), data = d, family = binomial)   
 But I'm not sure whether this map should be used by any predefined command for.  
  
 
 
  using the command  command quickly logistics regression with different barriers The model is made to fit immediately with different slopes (or a mixed model with equal, fixed and random effects).  
 In order to fit different multi-level logistics regression models in R (that is, a random effect is the regressive regression model), you can run the following using the built-in "MTCARS" data set :  
  Data (MTCARS) head (MTCARS) M & lt; - Glam (MTCARAR ~ $ ~ 1 + MTCRA $ wt + (1 | mtcars $ gear), family = "binomial") Summary (m) # and you can check fixed and random effects fixtif (m); In order to fit a different blocking slope model in the state, you definitely use the xtlogit command (not in the same use, but evenly set in "Auto" data stat set):                Like to do (for example, what different verses Not different? What is the slope of the individual, if there is this model 2 levels or more? What level crossing is classified or not?). For a similar look, see Andrew Jailman on "Fixed" vs "Random" Effects  
 Update: The excellent comment from Ben Blaker given below has shown that this is more informative in R when  predict  instead of using  data = mtcars < To use the / code> option, say, dollar signaling:  
  Data (MTCAR) M1 and LT; - Glamer (MTCARS $ am ~ 1 + mtcars $ wt + (1 | mtcars $ gear), family = "binomial") M2 and LT; - Glam (am ~ 1 + wt + (1 | gear), family = "binomial", data = MTCAR) P1 and LT; - Prophecy (M1); P2 and LT; - Predictive (M2) name (P1) # Notitative ... Name (P2) # Very informative!

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