| parse.par {Zelig} | R Documentation |
The parse.par function reshapes parameter vectors for
comfortability with the output matrix from model.matrix.multiple.
Use parse.par to identify sets of parameters; for example, within
optimization functions that require vector input, or within qi
functions that take matrix input of all parameters as a lump.
parse.par(par, terms, shape = "matrix", eqn = NULL)
par |
the vector (or matrix) of parameters |
terms |
the terms from either model.frame.multiple or
model.matrix.multiple |
shape |
a character string (either "matrix" or "vector")
that identifies the type of output structure |
eqn |
a character string (or strings) that identify the
parameters that you would like to subset from the larger par
structure |
A matrix or vector of the sub-setted (and reshaped) parameters for the specified
parameters given in "eqn". By default, eqn = NULL, such that all systematic
components are selected. (Systematic components have ExpVar = TRUE in the appropriate
describe.model function.)
If an ancillary parameter (for which ExpVar = FALSE in
describe.model) is specified in eqn, it is
always returned as a vector (ignoring shape). (Ancillary
parameters are all parameters that have intercept only formulas.)
Kosuke Imai <kimai@princeton.edu>; Gary King <king@harvard.edu>; Olivia Lau <olau@fas.harvard.edu>; Ferdinand Alimadhi <falimadhi@iq.harvard.edu>
model.matrix.multiple, parse.formula and the full Zelig manual at
http://gking.harvard.edu/zelig
# 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)