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 lm
tidy(
x,
conf.int = FALSE,
conf.level = 0.95,
exponentiate = FALSE,
quick = FALSE,
...
)
```# S3 method for summary.lm
tidy(x, ...)

x

An `lm`

object created by `stats::lm()`

.

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.

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 `FALSE`

.

quick

Logical indiciating if the only the `term`

and `estimate`

columns should be returned. Often useful to avoid time consuming
covariance and standard error calculations. Defaults to `FALSE`

.

...

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.

A `tibble::tibble()`

with one row for each term in the
regression. The tibble has columns:

The name of the regression term.

The estimated value of the regression term.

The standard error of the regression term.

The value of a statistic, almost always a T-statistic, to use in a hypothesis that the regression term is non-zero.

The two-sided p-value associated with the observed statistic.

The low end of a confidence interval for the regression
term. Included only if `conf.int = TRUE`

.

The high end of a confidence interval for the regression
term. Included only if `conf.int = TRUE`

.

If the linear model is an mlm object (multiple linear model), there is an additional column:

Which response column the coefficients correspond to (typically Y1, Y2, etc)

If you have missing values in your model data, you may need to refit
the model with `na.action = na.exclude`

.

Other lm tidiers:
`augment.glm()`

,
`augment.lm()`

,
`glance.glm()`

,
`glance.lm()`

,
`tidy.glm()`

# NOT RUN { library(ggplot2) library(dplyr) mod <- lm(mpg ~ wt + qsec, data = mtcars) tidy(mod) glance(mod) # coefficient plot d <- tidy(mod) %>% mutate( low = estimate - std.error, high = estimate + std.error ) ggplot(d, aes(estimate, term, xmin = low, xmax = high, height = 0)) + geom_point() + geom_vline(xintercept = 0) + geom_errorbarh() augment(mod) augment(mod, mtcars) # predict on new data newdata <- mtcars %>% head(6) %>% mutate(wt = wt + 1) augment(mod, newdata = newdata) au <- augment(mod, data = mtcars) ggplot(au, aes(.hat, .std.resid)) + geom_vline(size = 2, colour = "white", xintercept = 0) + geom_hline(size = 2, colour = "white", yintercept = 0) + geom_point() + geom_smooth(se = FALSE) plot(mod, which = 6) ggplot(au, aes(.hat, .cooksd)) + geom_vline(xintercept = 0, colour = NA) + geom_abline(slope = seq(0, 3, by = 0.5), colour = "white") + geom_smooth(se = FALSE) + geom_point() # column-wise models a <- matrix(rnorm(20), nrow = 10) b <- a + rnorm(length(a)) result <- lm(b ~ a) tidy(result) # }