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.

## Usage

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

## Arguments

- x
A

`crr`

object returned from`cmprsk::crr()`

.- 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`

.- 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.- ...
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`

. Two exceptions here are:

## See also

Other cmprsk tidiers:
`glance.crr()`

## Value

A `tibble::tibble()`

with columns:

- conf.high
Upper bound on the confidence interval for the estimate.

- conf.low
Lower bound on the confidence interval for the estimate.

- estimate
The estimated value of the regression term.

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

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

- std.error
The standard error of the regression term.

## Examples

```
library(cmprsk)
# time to loco-regional failure (lrf)
lrf_time <- rexp(100)
lrf_event <- sample(0:2, 100, replace = TRUE)
trt <- sample(0:1, 100, replace = TRUE)
strt <- sample(1:2, 100, replace = TRUE)
# fit model
x <- crr(lrf_time, lrf_event, cbind(trt, strt))
# summarize model fit with tidiers
tidy(x, conf.int = TRUE)
#> # A tibble: 2 × 7
#> term estimate std.error statistic p.value conf.low conf.high
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 trt -0.467 0.362 -1.29 0.2 -1.18 0.242
#> 2 strt 0.237 0.360 0.660 0.51 -0.468 0.943
glance(x)
#> # A tibble: 1 × 5
#> converged logLik nobs df statistic
#> <lgl> <dbl> <int> <dbl> <dbl>
#> 1 TRUE -125. 100 2 2.03
```