model.matrix.multiple {Zelig} | R Documentation

## Design matrix for multivariate models

### Description

Use `model.matrix.multiple` after `parse.formula` to create a design matrix for multiple-equation models.

### Usage

    model.matrix.multiple(object, data, shape = "compact", eqn = NULL, ...)

### Arguments

`object` | the list of formulas output from `parse.formula`
`data` | a data frame created with `model.frame.multiple`
`shape` | a character string specifying the shape of the outputed matrix. Available options are "compact" (default) the output matrix will be an n x v, where v is the number of unique variables in all of the equations (including the intercept term) "array" the output is an n x K x J array where J is the total number of equations and K is the total number of parameters across all the equations. If a variable is not in a certain equation, it is observed as a vector of 0s. "stacked" the output will be a 2n x K matrix where K is the total number of parameters across all the equations.
`eqn` | a character string or a vector of character strings identifying the equations from which to construct the design matrix. The defaults to `NULL`, which only uses the systematic parameters (for which `DepVar = TRUE` in the appropriate `describe.model` function)
`...` | additional arguments passed to `model.matrix.default`

### Value

A design matrix or array, depending on the options chosen in `shape`, with appropriate terms attributes.

### Author(s)

Kosuke Imai <kimai@princeton.edu>; Gary King <king@harvard.edu>; Olivia Lau <olau@fas.harvard.edu>; Ferdinand Alimadhi <falimadhi@iq.harvard.edu>

### See Also

`parse.par`, `parse.formula` and the full Zelig manual at http://gking.harvard.edu/zelig

### Examples

    # Let's say that the name of the model is "bivariate.probit", and
    # the corresponding describe function is describe.bivariate.probit(),
    # which identifies mu1 and mu2 as systematic components, and an
    # ancillary parameter rho, which may be parameterized, but is estimated
    # as a scalar by default.  Let par be the parameter vector (including
    # parameters for rho), formulae a user-specified formula, and mydata
    # the user specified data frame.

    # Acceptable combinations of parse.par() and model.matrix() are as follows:
    ## Setting up
    ## Not run:
    data(sanction)
    formulae <- cbind(import, export) ~ coop + cost + target
    fml <- parse.formula(formulae, model = "bivariate.probit")
    D <- model.frame(fml, data = sanction)
    terms <- attr(D, "terms")

    ## Intuitive option
    Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
    X <- model.matrix(fml, data = D, shape = "stacked", eqn = c("mu1", "mu2")
    eta <- X

    ## Memory-efficient (compact) option (default)
    Beta <- parse.par(par, terms, eqn = c("mu1", "mu2"))
    X <- model.matrix(fml, data = D, eqn = c("mu1", "mu2"))
    eta <- X

    ## Computationally-efficient (array) option
    Beta <- parse.par(par, terms, shape = "vector", eqn = c("mu1", "mu2"))
    X <- model.matrix(fml, data = D, shape = "array", eqn = c("mu1", "mu2"))
    eta <- apply(X, 3, '
    ## End(Not run)

[Package Zelig version 3.4-3 Index]