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, ...)
x | A |
---|---|
conf.int | Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
conf.level | The confidence level to use for the confidence interval
if |
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
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.
if (requireNamespace("systemfit", quietly = TRUE)) { set.seed(27) library(systemfit) df <- data.frame( X = rnorm(100), Y = rnorm(100), Z = rnorm(100), W = rnorm(100) ) fit <- systemfit(formula = list(Y ~ Z, W ~ X), data = df, method = "SUR") 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