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 cross 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, ...)

Arguments

x

An survreg object returned from survival::survreg().

conf.level

confidence level for CI

...

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.lvel = 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

Value

A tibble::tibble() with columns:

conf.high

The upper end of a confidence interval for the term under consideration. Included only if `conf.int = TRUE`.

conf.low

The lower end of a confidence interval for the term under consideration. Included only if `conf.int = TRUE`.

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.

Examples

library(survival) sr <- survreg( Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist = "exponential" ) td <- tidy(sr) augment(sr, ovarian)
#> # 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
glance(sr)
#> # A tibble: 1 x 9 #> iter df statistic p.value logLik AIC BIC deviance df.residual #> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> #> 1 4 3 1.67 0.434 -97.2 200. 204. 34.0 23
# coefficient plot library(ggplot2) ggplot(td, aes(estimate, term)) + geom_point() + geom_errorbarh(aes(xmin = conf.low, xmax = conf.high), height = 0) + geom_vline(xintercept = 0)