Zelig (version 3.5.1)

model.matrix.multiple: 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 $n \times v$, where $v$ is the number of unique variables in all of the equations (including the int

Value

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

item

• eqn
• ...

code

model.matrix.default

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
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, '