tidy.biglm {broom} | R Documentation |
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 cross 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 'biglm' tidy( x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, quick = 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 |
quick |
Logical indiciating if the only the |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble()
with one row for each term in the
regression. The tibble has columns:
term |
The name of the regression term. |
estimate |
The estimated value of the regression term. |
std.error |
The standard error of the regression term. |
statistic |
The value of a statistic, almost always a T-statistic, to use in a hypothesis that the regression term is non-zero. |
p.value |
The two-sided p-value associated with the observed statistic. |
conf.low |
The low end of a confidence interval for the regression
term. Included only if |
conf.high |
The high end of a confidence interval for the regression
term. Included only if |
tidy()
, biglm::biglm()
, biglm::bigglm()
Other biglm tidiers:
glance.biglm()
if (require("biglm", quietly = TRUE)) { 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) }