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)` . |

model |
the name of a statistical model to estimate.
For a list of 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
`Amelia` or `to_zelig_mi` ). |

... |
additional arguments passed to `zelig` ,
relevant for the model to be estimated. |

by |
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. For example, to run the same model on all fifty states, you could
use: ```
z.out <- zelig(y ~ x1 + x2, data = mydata, model = 'ls',
by = 'state')
``` You may also use `by` to run models using MatchIt
subclasses. |

cite |
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 many models include:

## Methods

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

The zelig function estimates a variety of statistical models

## See also

Vignette: http://docs.zeligproject.org/articles/zelig_ls.html

## Examples

#> How to cite this model in Zelig:
#> R Core Team. 2007.
#> ls: Least Squares Regression for Continuous Dependent Variables
#> in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau,
#> "Zelig: Everyone's Statistical Software," http://zeligproject.org/