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, ...)
x | A |
---|---|
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 |
conf.int | Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
conf.level | The confidence level to use for the confidence interval
if |
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
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.
if (requireNamespace("cmprsk", quietly = TRUE)) { 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) glance(x) } #> # A tibble: 1 × 5 #> converged logLik nobs df statistic #> <lgl> <dbl> <int> <dbl> <dbl> #> 1 TRUE -125. 100 2 2.03