Logistic Regression for Dichotomous Dependent Variables
formula  a symbolic representation of the model to be
estimated, in the form 

model  the name of a statistical model to estimate. For a list of other supported models and their documentation see: http://docs.zeligproject.org/articles/. 
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

...  additional arguments passed to 
by  a factor variable contained in 
cite  If is set to 'TRUE' (default), the model citation will be printed to the console. 
below  (defaults to 0) The point at which the dependent variable is censored from below. If any values in the dependent variable are observed to be less than the censoring point, it is assumed that that particular observation is censored from below at the observed value. (See for a Bayesian implementation that supports both left and right censoring.) 
robust  defaults to FALSE. If TRUE, zelig() computes robust standard errors based on sandwich estimators (see and ) and the options selected in cluster. 
if  robust = TRUE, you may select a variable to define groups of correlated observations. Let x3 be a variable that consists of either discrete numeric values, character strings, or factors that define strata. Then z.out < zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3", model = "tobit", data = mydata) means that the observations can be correlated within the strata defined by the variable x3, and that robust standard errors should be calculated according to those clusters. If robust = TRUE but cluster is not specified, zelig() assumes that each observation falls into its own cluster. 
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,
coef(z.out)
. See
http://docs.zeligproject.org/articles/getters.html for a list of
functions to extract model components. You can also extract whole fitted
model objects using from_zelig_model
.
Additional parameters avaialable to this model include:
weights: vector of weight values or a name of a variable in the dataset by which to weight the model. For more information see: http://docs.zeligproject.org/articles/weights.html.
bootstrap: logical or numeric. If FALSE
don't use bootstraps to
robustly estimate uncertainty around model parameters due to sampling error.
If an integer is supplied, the number of boostraps to run.
For more information see:
http://docs.zeligproject.org/articles/bootstraps.html.
show(signif.stars = FALSE, subset = NULL, bagging = FALSE)
Display a Zelig object
Vignette: http://docs.zeligproject.org/articles/zelig_logit.html
library(Zelig) data(turnout) z.out1 < zelig(vote ~ age + race, model = "logit", data = turnout, cite = FALSE) summary(z.out1)#> Model: #> #> Call: #> z5$zelig(formula = vote ~ age + race, data = turnout) #> #> Deviance Residuals: #> Min 1Q Median 3Q Max #> 1.927 1.296 0.707 0.777 1.072 #> #> Coefficients: #> Estimate Std. Error z value Pr(>z) #> (Intercept) 0.03837 0.17692 0.22 0.82832 #> age 0.01126 0.00305 3.69 0.00023 #> racewhite 0.64555 0.13448 4.80 1.6e06 #> #> (Dispersion parameter for binomial family taken to be 1) #> #> Null deviance: 2266.7 on 1999 degrees of freedom #> Residual deviance: 2228.8 on 1997 degrees of freedom #> AIC: 2235 #> #> Number of Fisher Scoring iterations: 4 #> #> Next step: Use 'setx' methodsummary(z.out1, odds_ratios = TRUE)#> Model: #> #> Call: #> z5$zelig(formula = vote ~ age + race, data = turnout) #> #> Deviance Residuals: #> Min 1Q Median 3Q Max #> 1.927 1.296 0.707 0.777 1.072 #> #> Coefficients: #> Estimate (OR) Std. Error (OR) z value Pr(>z) #> (Intercept) 1.03911 0.18384 0.22 0.82832 #> age 1.01133 0.00309 3.69 0.00023 *** #> racewhite 1.90704 0.25646 4.80 1.6e06 *** #>  #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> (Dispersion parameter for binomial family taken to be 1) #> #> Null deviance: 2266.7 on 1999 degrees of freedom #> Residual deviance: 2228.8 on 1997 degrees of freedom #> AIC: 2235 #> #> Number of Fisher Scoring iterations: 4 #>#> #> sim x : #>  #> ev #> mean sd 50% 2.5% 97.5% #> [1,] 0.748 0.0115 0.748 0.727 0.771 #> pv #> 0 1 #> [1,] 0.25 0.75plot(s.out1)