tidy.systemfit: Tidy a(n) systemfit 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 systemfit
tidy(x, conf.int = TRUE, conf.level = 0.95, ...)

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.
std.error: The standard error of the regression term.
term: The name of the regression term.

Arguments

x

A systemfit object produced by a call to systemfit::systemfit().

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.

...

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.

Details

This tidy method works with any model objects of class systemfit. Default returns a tibble of six columns.

Examples

Run this code

if (FALSE) { # rlang::is_installed("systemfit")

set.seed(27)

# load libraries for models and data
library(systemfit)

# generate data
df <- data.frame(
  X = rnorm(100),
  Y = rnorm(100),
  Z = rnorm(100),
  W = rnorm(100)
)

# fit model
fit <- systemfit(formula = list(Y ~ Z, W ~ X), data = df, method = "SUR")

# summarize model fit with tidiers
tidy(fit)
tidy(fit, conf.int = TRUE)
}