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 speedlm tidy(x, conf.int = FALSE, 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 |
speedglm::speedlm()
, tidy.lm()
Other speedlm tidiers:
augment.speedlm()
,
glance.speedglm()
,
glance.speedlm()
,
tidy.speedglm()
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 (requireNamespace("speedglm", quietly = TRUE)) { mod <- speedglm::speedlm(mpg ~ wt + qsec, data = mtcars, fitted = TRUE) tidy(mod) glance(mod) augment(mod) } #> Joining, by = c("term", "estimate") #> # A tibble: 32 × 6 #> .rownames mpg wt qsec .fitted .resid #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21 2.62 16.5 21.8 -0.815 #> 2 Mazda RX4 Wag 21 2.88 17.0 21.0 -0.0482 #> 3 Datsun 710 22.8 2.32 18.6 25.3 -2.53 #> 4 Hornet 4 Drive 21.4 3.22 19.4 21.6 -0.181 #> 5 Hornet Sportabout 18.7 3.44 17.0 18.2 0.504 #> 6 Valiant 18.1 3.46 20.2 21.1 -2.97 #> 7 Duster 360 14.3 3.57 15.8 16.4 -2.14 #> 8 Merc 240D 24.4 3.19 20 22.2 2.17 #> 9 Merc 230 22.8 3.15 22.9 25.1 -2.32 #> 10 Merc 280 19.2 3.44 18.3 19.4 -0.185 #> # … with 22 more rows