View source: R/survival-survreg-tidiers.R
| tidy.survreg | 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 'survreg' tidy(x, conf.level = 0.95, conf.int = FALSE, ...)
x |
An |
conf.level |
The confidence level to use for the confidence interval
if |
conf.int |
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
... |
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. |
term |
The name of the regression term. |
tidy(), survival::survreg()
Other survreg tidiers:
augment.survreg(),
glance.survreg()
Other survival tidiers:
augment.coxph(),
augment.survreg(),
glance.aareg(),
glance.cch(),
glance.coxph(),
glance.pyears(),
glance.survdiff(),
glance.survexp(),
glance.survfit(),
glance.survreg(),
tidy.aareg(),
tidy.cch(),
tidy.coxph(),
tidy.pyears(),
tidy.survdiff(),
tidy.survexp(),
tidy.survfit()
# load libraries for models and data library(survival) # fit model sr <- survreg( Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist = "exponential" ) # summarize model fit with tidiers + visualization tidy(sr) augment(sr, ovarian) glance(sr) # coefficient plot td <- tidy(sr, conf.int = TRUE) library(ggplot2) ggplot(td, aes(estimate, term)) + geom_point() + geom_errorbarh(aes(xmin = conf.low, xmax = conf.high), height = 0) + geom_vline(xintercept = 0)
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