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 systemfit
tidy(x, conf.int = TRUE, conf.level = 0.95, ...)

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. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

Details

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

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.

Examples


# feel free to ignore the following line—it allows {broom} to supply 
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("systemfit", quietly = TRUE)) {

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)

}
#> 
#> Please cite the 'systemfit' package as:
#> Arne Henningsen and Jeff D. Hamann (2007). systemfit: A Package for Estimating Systems of Simultaneous Equations in R. Journal of Statistical Software 23(4), 1-40. http://www.jstatsoft.org/v23/i04/.
#> 
#> If you have questions, suggestions, or comments regarding the 'systemfit' package, please use a forum or 'tracker' at systemfit's R-Forge site:
#> https://r-forge.r-project.org/projects/systemfit/
#> # A tibble: 4 × 7
#>   term            estimate std.error statistic p.value conf.low conf.high
#>   <chr>              <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
#> 1 eq1_(Intercept)   0.109     0.0981     1.11    0.269  -0.0857    0.304 
#> 2 eq1_Z            -0.0808    0.0934    -0.865   0.389  -0.266     0.105 
#> 3 eq2_(Intercept)  -0.0495    0.110     -0.449   0.655  -0.269     0.170 
#> 4 eq2_X            -0.133     0.103     -1.30    0.198  -0.337     0.0707

Arguments

Details

See also

Value

Examples