tidy.crr | R Documentation |
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 class '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
|
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. |
tidy()
, cmprsk::crr()
Other cmprsk tidiers:
glance.crr()
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)
glance(x)
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