#' Interface between normal model and Zelig
#' This function is exclusively for use by the `zelig' function
#' @param formula a formula
#' @param weights a numeric vector
#' @param ... ignored parameters
#' @param data a data.frame
#' @return a list to be coerced into a zelig.call object
#' @export
#' @author Matt Owen \email{mowen@@iq.harvard.edu}
zelig2normal <- function(formula, weights=NULL, ..., data)
z(
glm,
# .hook = "robust.glm.hook",
formula = formula,
weights = weights,
family = gaussian,
model = F,
data = data
)
#' Param Method for the 'normal' Zelig Model
#' @note This method is used by the 'normal' Zelig model
#' @usage \method{param}{normal}(obj, num=1000, ...)
#' @S3method param negbinom
#' @param obj a 'zelig' object
#' @param num an integer specifying the number of simulations to sample
#' @param ... ignored
#' @return a list to be cast as a 'parameters' object
#' @author Matt Owen \email{mowen@@iq.harvard.edu}
param.normal <- function(obj, num=1000, ...) {
degrees.freedom <- .fitted$df.residual
sig2 <- summary(.fitted)$dispersion
list(
simulations = mvrnorm(n=num, mu=coef(.fitted), Sigma=vcov(.fitted)),
alpha = sqrt(degrees.freedom * sig2 / rchisq(num, degrees.freedom)),
link = function (x) x,
linkinv = function (x) x
)
}
#' Compute quantities of interest for 'normal' Zelig models
#' @usage \method{qi}{normal}(obj, x, x1=NULL, y=NULL, num=1000, param=NULL)
#' @S3method qi normal
#' @param obj a 'zelig' object
#' @param x a 'setx' object or NULL
#' @param x1 an optional 'setx' object
#' @param y this parameter is reserved for simulating average treatment effects,
#' though this feature is currentlysupported by only a handful of models
#' @param num an integer specifying the number of simulations to compute
#' @param param a parameters object
#' @return a list of key-value pairs specifying pairing titles of quantities of
#' interest with their simulations
#' @author Matt Owen \email{mowen@@iq.harvard.edu}
qi.normal <- function(obj, x, x1=NULL, y=NULL, num=1000, param=NULL) {
# get `num` samples from the underlying distribution
coef <- coef(param)
alpha <- alpha(param)
# theta = eta, because inverse of
# normal models' link function is
# the identity
theta <- matrix(coef %*% t(x), nrow=nrow(coef))
#
pr <- matrix(NA, nrow=nrow(theta), ncol=ncol(theta))
#
ev <- theta
ev1 <- pr1 <- fd <- NA
for (i in 1:nrow(ev))
pr[i,] <- rnorm(ncol(ev), mean = ev[i,], sd = alpha[i])
# if x1 is not NULL, run more simultations
# ...
if (!is.null(x1)) {
# quantities of interest
lis1 <- qi(obj, x1, num=num, param=param)
# pass values over
ev1 <- lis1[[1]]
pr1 <- lis1[[3]]
# compute first differences
fd <- ev1 - ev
}
# return
list("Expected Values: E(Y|X)" = ev,
"Expected Values: E(Y|X1)" = ev1,
"Predicted Values: Y|X" = pr,
"Predicted Values: Y|X1" = pr1,
"First Differences: E(Y|X1) - E(Y|X)" = fd
)
}
#' Describe the \code{normal} model to Zelig
#' @usage \method{describe}{normal}(...)
#' @S3method describe normal
#' @param ... ignored parameters
#' @return a list to be processed by `as.description'
#' @author Matt Owen \email{mowen@@iq.harvard.edu}
#' @export
describe.normal <- function(...) {
# parameters object
parameters <- list(pi = list(
equations = c(1, 1),
tags.allowed = FALSE,
dep.var = TRUE,
exp.var = TRUE
)
)
# return list
list(authors = c("Kosuke Imai", "Gary King", "Olivia Lau"),
year = 2008,
category = "continuous",
parameters = parameters,
text = "Normal Regression for Continuous Dependent Variables"
)
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.