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(). 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.

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

## 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 rowsglance(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)