zelig                 package:Zelig                 R Documentation

_E_s_t_i_m_a_t_i_n_g _a _S_t_a_t_i_s_t_i_c_a_l _M_o_d_e_l

_D_e_s_c_r_i_p_t_i_o_n:

     The 'zelig' command estimates a variety of statistical models. 
     Use 'zelig' output with 'setx' and 'sim' to compute quantities of
     interest, such as predicted probabilities, expected values, and
     first differences, along with the associated measures of
     uncertainty (standard errors and confidence intervals).

_U_s_a_g_e:

     z.out <- zelig(formula, model, data, by, save.data, cite, ...) 

_A_r_g_u_m_e_n_t_s:

 formula: a symbolic representation of the model to be estimated, in
          the form 'y \~\, x1 + x2', where 'y' is the dependent
          variable and 'x1' and 'x2' are the explanatory variables, and
          'y', 'x1', and 'x2' are contained in the same dataset.  (You
          may include more than two explanatory variables, of course.) 
          The '+' symbol means ``inclusion'' not ``addition.''  You may
          also include interaction terms and main effects in the form
          'x1*x2' without computing them in prior steps; 'I(x1*x2)' to
          include only the interaction term and exclude the main
          effects; and quadratic terms in the form 'I(x1^2)'.  

   model: the name of a statistical model, enclosed in '""'. Type
          'help.zelig("models")' to see a list of currently supported
          models.  

    data: the name of a data frame containing the variables referenced
          in the formula, or a list of multiply imputed data frames
          each having the same variable names and row numbers (created
          by 'mi'). 

save.data: If is set to "TRUE", the input dataframe will be saved as an
          attribute ("zelig.data") of the zelig output object. 

    cite: If is set to "TRUE" (default), the model citation will be
          will be printed out when this function is called. 

      by: a factor variable contained in 'data'.  Zelig will subset the
          data frame based on the levels in the 'by' variable, and
          estimate a model for each subset.  This a particularly
          powerful option which will allow you to save a considerable
          amount of effort.  For example, to run the same model on all
          fifty states, you could type: 'z.out <- zelig(y ~ x1 + x2,
          data = mydata, model = "ls", by = "state")' You may also use
          'by' to run models using MatchIt subclass.  

     ...: additional arguments passed to 'zelig', depending on the
          model to be estimated. 

_V_a_l_u_e:

     Depending on the class of model selected, 'zelig' will return an
     object with elements including 'coefficients', 'residuals', and
     'formula' which may be summarized using 'summary(z.out)' or
     individually extracted using, for example, 'z.out\$coefficients'. 
     See the specific models listed above for additional output values,
     or simply type 'names(z.out)'.

_A_u_t_h_o_r(_s):

     Kosuke Imai <kimai@princeton.edu>; Gary King <king@harvard.edu>;
     Olivia Lau <olau@fas.harvard.edu>

_S_e_e _A_l_s_o:

     The full Zelig manual is available at <URL:
     http://gking.harvard.edu/zelig>.

