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 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 |
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()
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.
The name of the regression term.
library(survival) sr <- survreg( Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist = "exponential" ) tidy(sr)#> # A tibble: 3 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 6.96 1.32 5.27 0.000000139 #> 2 ecog.ps -0.433 0.587 -0.738 0.461 #> 3 rx 0.582 0.587 0.991 0.322#> # A tibble: 26 x 9 #> futime fustat age resid.ds rx ecog.ps .fitted .se.fit .resid #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 59 1 72.3 2 1 1 1224. 639. -1165. #> 2 115 1 74.5 2 1 1 1224. 639. -1109. #> 3 156 1 66.5 2 1 2 794. 350. -638. #> 4 421 0 53.4 2 2 1 2190. 1202. -1769. #> 5 431 1 50.3 2 1 1 1224. 639. -793. #> 6 448 0 56.4 1 1 2 794. 350. -346. #> 7 464 1 56.9 2 2 2 1420. 741. -956. #> 8 475 1 59.9 2 2 2 1420. 741. -945. #> 9 477 0 64.2 2 1 1 1224. 639. -747. #> 10 563 1 55.2 1 2 2 1420. 741. -857. #> # … with 16 more rows#> # A tibble: 1 x 9 #> iter df statistic logLik AIC BIC df.residual nobs p.value #> <int> <int> <dbl> <dbl> <dbl> <dbl> <int> <int> <dbl> #> 1 4 3 1.67 -97.2 200. 204. 23 26 0.434# 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)