Least Squares Regression for Continuous 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. 
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.
zelig(formula, data, model = NULL, ..., weights = NULL, by,
bootstrap = FALSE)
The zelig function estimates a variety of statistical models
Vignette: http://docs.zeligproject.org/articles/zelig_ls.html
library(Zelig) data(macro) z.out1 < zelig(unem ~ gdp + capmob + trade, model = "ls", data = macro, cite = FALSE) summary(z.out1)#> Model: #> #> Call: #> z5$zelig(formula = unem ~ gdp + capmob + trade, data = macro) #> #> Residuals: #> Min 1Q Median 3Q Max #> 5.301 2.077 0.319 1.979 7.772 #> #> Coefficients: #> Estimate Std. Error t value Pr(>t) #> (Intercept) 6.18129 0.45057 13.72 < 2e16 #> gdp 0.32360 0.06282 5.15 4.4e07 #> capmob 1.42194 0.16644 8.54 4.2e16 #> trade 0.01985 0.00561 3.54 0.00045 #> #> Residual standard error: 2.75 on 346 degrees of freedom #> Multiple Rsquared: 0.288, Adjusted Rsquared: 0.282 #> Fstatistic: 46.6 on 3 and 346 DF, pvalue: <2e16 #> #> Next step: Use 'setx' method