tidy.systemfit: Tidy a(n) systemfit object

View source: R/systemfit-tidiers.R

tidy.systemfitR Documentation

Tidy a(n) systemfit object


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.


## S3 method for class 'systemfit'
tidy(x, conf.int = TRUE, conf.level = 0.95, ...)



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


Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.


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.


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


A tibble::tibble() with columns:


Upper bound on the confidence interval for the estimate.


Lower bound on the confidence interval for the estimate.


The estimated value of the regression term.


The two-sided p-value associated with the observed statistic.


The standard error of the regression term.


The name of the regression term.

See Also

tidy(), systemfit::systemfit()



# load libraries for models and data

# 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, conf.int = TRUE)

broom documentation built on Aug. 30, 2022, 1:07 a.m.