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, conf.int = 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 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.level = 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.

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.


library(cmprsk) lrf_time <- rexp(100) #time to loco-regional failure (lrf) lrf_event <- sample(0:2, 100, replace = TRUE) trt <- sample(0:1, 100, replace = TRUE) strt <- sample(1:2, 100, replace = TRUE) x <- crr(lrf_time, lrf_event, cbind(trt, strt)) tidy(x, conf.int = TRUE)
#> # A tibble: 2 x 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 0.308 1.27 #> 2 strt 0.237 0.360 0.660 0.51 0.626 2.57
#> # A tibble: 1 x 5 #> converged logLik nobs df statistic #> <lgl> <dbl> <int> <dbl> <dbl> #> 1 TRUE -125. 100 2 2.03