Least Squares Regression for Continuous Dependent Variables

## Arguments

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). the name of a statistical model to estimate. For a list of other supported models and their documentation see: http://docs.zeligproject.org/articles/. 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 Amelia or to_zelig_mi). additional arguments passed to zelig, relevant for the model to be estimated. a factor variable contained in data. If supplied, zelig will subset the data frame based on the levels in the by variable, and estimate a model for each subset. This can save a considerable amount of effort. You may also use by to run models using MatchIt subclasses. If is set to 'TRUE' (default), the model citation will be printed to the console.

## Value

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.

## Details

Additional parameters avaialable to this model include:

## Methods

zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)

The zelig function estimates a variety of statistical models

## Examples

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  < 2e-16
#> gdp         -0.32360    0.06282   -5.15  4.4e-07
#> capmob       1.42194    0.16644    8.54  4.2e-16
#> trade        0.01985    0.00561    3.54  0.00045
#>
#> Residual standard error: 2.75 on 346 degrees of freedom
#> Multiple R-squared:  0.288,	Adjusted R-squared:  0.282
#> F-statistic: 46.6 on 3 and 346 DF,  p-value: <2e-16
#>
#> Next step: Use 'setx' method