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 biglm tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)
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 |
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 |
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
tidy()
, biglm::biglm()
, biglm::bigglm()
Other biglm tidiers:
glance.biglm()
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 value of a T-statistic to use in a hypothesis that the regression term is non-zero.
The standard error of the regression term.
The name of the regression term.
if (FALSE) { library(biglm) bfit <- biglm(mpg ~ wt + disp, mtcars) tidy(bfit) tidy(bfit, conf.int = TRUE) tidy(bfit, conf.int = TRUE, conf.level = .9) glance(bfit) # bigglm: logistic regression bgfit <- bigglm(am ~ mpg, mtcars, family = binomial()) tidy(bgfit) tidy(bgfit, exponentiate = TRUE) tidy(bgfit, conf.int = TRUE) tidy(bgfit, conf.int = TRUE, conf.level = .9) tidy(bgfit, conf.int = TRUE, conf.level = .9, exponentiate = TRUE) glance(bgfit) }