tidy.gmm: Tidy a(n) gmm object

In tidyverse/broom: Convert Statistical Objects into Tidy Tibbles

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.

See Also

tidy(), gmm::gmm()

Other gmm tidiers: glance.gmm()

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)

tidyverse/broom documentation built on Nov. 9, 2024, 4:24 a.m.