R: Tidy a(n) gmm object

tidy.gmm {broom}

R Documentation

Tidy a(n) gmm object

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 across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

Usage

## S3 method for class 'gmm'
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)

Arguments

`x`	A `gmm` object returned from `gmm::gmm()`.
`conf.int`	Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to `FALSE`.
`conf.level`	The confidence level to use for the confidence interval if `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.
`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 `FALSE`.
`...`	Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in `...`, where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass `conf.lvel = 0.9`, all computation will proceed using `conf.level = 0.95`. Two exceptions here are: `tidy()` methods will warn when supplied an `exponentiate` argument if it will be ignored. `augment()` methods will warn when supplied a `newdata` argument if it will be ignored.

Value

A tibble::tibble() with columns:

`conf.high`	Upper bound on the confidence interval for the estimate.
`conf.low`	Lower bound on the confidence interval for the estimate.
`estimate`	The estimated value of the regression term.
`p.value`	The two-sided p-value associated with the observed statistic.
`statistic`	The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
`std.error`	The standard error of the regression term.
`term`	The name of the regression term.

Examples



# load libraries for models and data
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(term, estimate)) %>%
  ggplot(aes(estimate, variable)) +
  geom_point() +
  geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) +
  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(term, estimate)) %>%
  ggplot(aes(estimate, variable)) +
  geom_point() +
  geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) +
  geom_vline(xintercept = 0, color = "red", lty = 2)