% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gmm-tidiers.R
\name{tidy.gmm}
\alias{tidy.gmm}
\alias{gmm_tidiers}
\title{Tidy a(n) gmm object}
\usage{
\method{tidy}{gmm}(
  x,
  conf.int = FALSE,
  conf.level = 0.95,
  exponentiate = FALSE,
  quick = FALSE,
  ...
)
}
\arguments{
\item{x}{A \code{gmm} object returned from \code{\link[gmm:gmm]{gmm::gmm()}}.}

\item{conf.int}{Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to \code{FALSE}.}

\item{conf.level}{The confidence level to use for the confidence interval
if \code{conf.int = TRUE}. Must be strictly greater than 0 and less than 1.
Defaults to 0.95, which corresponds to a 95 percent confidence interval.}

\item{exponentiate}{Logical indicating whether or not to exponentiate the
the coefficient estimates. This is typical for logistic and multinomial
regressions, but a bad idea if there is no log or logit link. Defaults
to \code{FALSE}.}

\item{quick}{Logical indiciating if the only the \code{term} and \code{estimate}
columns should be returned. Often useful to avoid time consuming
covariance and standard error calculations. Defaults to \code{FALSE}.}

\item{...}{Additional arguments. Not used. Needed to match generic
signature only. \strong{Cautionary note:} Misspelled arguments will be
absorbed in \code{...}, where they will be ignored. If the misspelled
argument has a default value, the default value will be used.
For example, if you pass \code{conf.lvel = 0.9}, all computation will
proceed using \code{conf.level = 0.95}. Additionally, if you pass
\code{newdata = my_tibble} to an \code{\link[=augment]{augment()}} method that does not
accept a \code{newdata} argument, it will use the default value for
the \code{data} argument.}
}
\value{
A \code{\link[tibble:tibble]{tibble::tibble()}} with one row for each term in the
regression. The tibble has columns:

\item{term}{The name of the regression term.}
\item{estimate}{The estimated value of the regression term.}
\item{std.error}{The standard error of the regression term.}
\item{statistic}{The value of a statistic, almost always a T-statistic,
to use in a hypothesis that the regression term is non-zero.}
\item{p.value}{The two-sided p-value associated with the observed
statistic.}
\item{conf.low}{The low end of a confidence interval for the regression
term. Included only if \code{conf.int = TRUE}.}
\item{conf.high}{The high end of a confidence interval for the regression
term. Included only if \code{conf.int = TRUE}.}
}
\description{
Tidy summarizes information about the components of a model.
A model component might be a single term in a regression, a single
hypothesis, a cluster, or a class. Exactly what tidy considers to be a
model component varies cross models but is usually self-evident.
If a model has several distinct types of components, you will need to
specify which components to return.
}
\examples{

if (requireNamespace("gmm", quietly = TRUE)) {

  library(gmm)
  
  # examples come from the "gmm" package
  ## CAPM test with GMM
  data(Finance)
  r <- Finance[1:300, 1:10]
  rm <- Finance[1:300, "rm"]
  rf <- Finance[1:300, "rf"]

  z <- as.matrix(r-rf)
  t <- nrow(z)
  zm <- rm-rf
  h <- matrix(zm, t, 1)
  res <- gmm(z ~ zm, x = h)

  # tidy result
  tidy(res)
  tidy(res, conf.int = TRUE)
  tidy(res, conf.int = TRUE, conf.level = .99)

  # coefficient plot
  library(ggplot2)
  library(dplyr)
  tidy(res, conf.int = TRUE) \%>\%
    mutate(variable = reorder(variable, estimate)) \%>\%
    ggplot(aes(estimate, variable)) +
    geom_point() +
    geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) +
    facet_wrap(~ term) +
    geom_vline(xintercept = 0, color = "red", lty = 2)

  # from a function instead of a matrix
  g <- function(theta, x) {
  	e <- x[,2:11] - theta[1] - (x[,1] - theta[1]) \%*\% matrix(theta[2:11], 1, 10)
  	gmat <- cbind(e, e*c(x[,1]))
  	return(gmat) }

  x <- as.matrix(cbind(rm, r))
  res_black <- gmm(g, x = x, t0 = rep(0, 11))

  tidy(res_black)
  tidy(res_black, conf.int = TRUE)

  ## APT test with Fama-French factors and GMM

  f1 <- zm
  f2 <- Finance[1:300, "hml"] - rf
  f3 <- Finance[1:300, "smb"] - rf
  h <- cbind(f1, f2, f3)
  res2 <- gmm(z ~ f1 + f2 + f3, x = h)

  td2 <- tidy(res2, conf.int = TRUE)
  td2

  # coefficient plot
  td2 \%>\%
    mutate(variable = reorder(variable, estimate)) \%>\%
    ggplot(aes(estimate, variable)) +
    geom_point() +
    geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) +
    facet_wrap(~ term) +
    geom_vline(xintercept = 0, color = "red", lty = 2)
}

}
\seealso{
\code{\link[=tidy]{tidy()}}, \code{\link[gmm:gmm]{gmm::gmm()}}

Other gmm tidiers: 
\code{\link{glance.gmm}()}
}
\concept{gmm tidiers}
