Logistic Regression for Dichotomous Dependent Variables
| 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 |
| ... | arguments to pass to |
| 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. |
Logistic regression specifies a dichotomous dependent variable as a function of a set of explanatory variables.
Vignette: http://docs.zeligproject.org/en/latest/zelig_logit.html
not_run({ # Workflow: Zelig 5 Reference Classes z5 <- zlogit$new() z5$zelig(Y ~ X1 + X ~ X, weights = w, data = mydata) z5$setx() z5$sim() # Workflow: Zelig 4 Wrappers z.out <- zelig(Y ~ X1 + X2, model = "logit", weights = w, data = mydata) x.out <- setx(z.out) s.out <- sim(z.out, x = x.out, x1 = NULL) }) # Basic example with Zelig 4 wrappers data(turnout) # Estimate model z.out1 <- zelig(vote ~ age + race, model = "logit", data = turnout, cite = FALSE) # Set fitted values for a 36 year old white subject x.out1 <- setx(z.out1, age = 36, race = "white") # Simulate s.out1 <- sim(z.out1, x = x.out1) not_run({ # Plot quantities of interest plot(s.out1) })