Glance accepts a model object and returns a `tibble::tibble()`

with exactly one row of model summaries. The summaries are typically
goodness of fit measures, p-values for hypothesis tests on residuals,
or model convergence information.

Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.

Glance does not calculate summary measures. Rather, it farms out these
computations to appropriate methods and gathers the results together.
Sometimes a goodness of fit measure will be undefined. In these cases
the measure will be reported as `NA`

.

Glance returns the same number of columns regardless of whether the
model matrix is rank-deficient or not. If so, entries in columns
that no longer have a well-defined value are filled in with an `NA`

of the appropriate type.

## Usage

```
# S3 method for crr
glance(x, ...)
```

## Arguments

- x
A

`crr`

object returned from`cmprsk::crr()`

.- ...
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:
`tidy.crr()`

## Value

A `tibble::tibble()`

with exactly one row and columns:

- converged
Logical indicating if the model fitting procedure was succesful and converged.

- df
Degrees of freedom used by the model.

- logLik
The log-likelihood of the model. [stats::logLik()] may be a useful reference.

- nobs
Number of observations used.

- statistic
Test statistic.

## 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 -0.338 0.344 -0.983 0.33 -1.01 0.336
#> 2 strt 0.374 0.354 1.06 0.29 -0.320 1.07
glance(x)
#> # A tibble: 1 × 5
#> converged logLik nobs df statistic
#> <lgl> <dbl> <int> <dbl> <dbl>
#> 1 TRUE -132. 100 2 2.12
```