Least Squares Regression for Continuous Dependent Variables

Least Squares Regression for Continuous Dependent Variables

Arguments

formula	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
data	an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which `lm`` is called. Imputed data sets created with amelia or to_zelig_mi can also be supplied.
...	arguments to pass to lm.
model	a character string specifying the model type. NOTE: only required if using the Zelig 4 wrappers.
weights	an optional vector of weight values or character string identifying a weighting variable in data. Weights adjust the observed sample distribution in the data to an underlying population of interest. If the supplied weights are all integer values, then zelig rebuilds a new version of the dataset by duplicating observations according to their weight (and removing observations with zero weight). If the weights are continuously valued, `zelig`` bootstraps the supplied dataset, using the relative weights as bootstrap probabilities.
by	an optional character string identifying a variable in data to conduct the model analysis along. This can be used to run separate models for different levels of a categorical variable.
bootstrap	logical whether or not to bootstrap the data set.
cite	logical whether or not to return model citation information to the console when the model has been estimated.

Details

Use least squares regression analysis to estimate the best linear predictor for the specified dependent variables.

Vignette: http://docs.zeligproject.org/en/latest/zelig-ls.html

Methods

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

The zelig function estimates a variety of statistical models

Examples

not_run({
  # Workflow: Zelig 5 Reference Classes
  z5 <- zls$new()
  z5$zelig(Y ~ X1 + X ~ X, weights = w, data = mydata)
  z5$setx()
  z5$sim()
  z5$graph()

  # Workflow: Zelig 4 Wrappers
  z.out <- zelig(Y ~ X1 + X2, model = "ls", weights = w, data = mydata,
                 cite = FALSE)
  x.out <- setx(z.out)
  s.out <- sim(z.out, x = x.out)
  plot(s.out)
})


# Basic Example with First Differences (using Zelig 4 wrappers) #

data(macro)

# Estimate model
z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "ls", data = macro)

# Set explanatory variables to their default (mean/mode) values, with high
# (80th percentile) and low (20th percentile) values for the trade variable
x.high <- setx(z.out1, trade = quantile(macro$trade, 0.8))
x.low <- setx(z.out1, trade = quantile(macro$trade, 0.2))

# Simulate quantities of interest
s.out1 <- sim(z.out1, x = x.high, x1 = x.low)

not_run({
  # Plot first differences
  plot(s.out1)
})