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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 crr
tidy(x, exponentiate = FALSE, = FALSE, conf.level = 0.95, ...)



A crr object returned from cmprsk::crr().


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.

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.


The confidence level to use for the confidence interval if = 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:

  • tidy() methods will warn when supplied an exponentiate argument if it will be ignored.

  • augment() methods will warn when supplied a newdata argument if it will be ignored.

See also

tidy(), cmprsk::crr()

Other cmprsk tidiers: glance.crr()


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.



# 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, = 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
#> # A tibble: 1 × 5
#>   converged logLik  nobs    df statistic
#>   <lgl>      <dbl> <int> <dbl>     <dbl>
#> 1 TRUE       -125.   100     2      2.03