tidy.coxph {broom} | R Documentation

## Tidy a(n) coxph object

### Description

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.

### Usage

    ## S3 method for class 'coxph'
    tidy(x, exponentiate = FALSE, conf.int = TRUE,
      conf.level = 0.95, ...)

### Arguments

`x` | A `coxph` object returned from `survival::coxph()`.
`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 `FALSE`.
`conf.int` | Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to `FALSE`.
`conf.level` | The confidence level to use for the confidence interval if `conf.int = TRUE`. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.

`...`

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.

### Value

A tibble::tibble with one row for each term and columns:

`estimate` | estimate of slope
`std.error` | standard error of estimate
`statistic` | test statistic
`p.value` | p-value

### See Also

`tidy()`, `survival::coxph()`

Other coxph tidiers: `augment.coxph`, `glance.coxph`

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.pyears`, `tidy.survdiff`, `tidy.survexp`, `tidy.survfit`, `tidy.survreg`

### Examples

    library(survival)

    cfit <- coxph(Surv(time, status) ~ age + sex, lung)

    tidy(cfit)
    tidy(cfit, exponentiate = TRUE)

    lp <- augment(cfit, lung)
    risks <- augment(cfit, lung, type.predict = "risk")
    expected <- augment(cfit, lung, type.predict = "expected")

    glance(cfit)

    # also works on clogit models
    resp <- levels(logan$occupation)
    n <- nrow(logan)
    indx <- rep(1:n, length(resp))
    logan2 <- data.frame(
      logan[indx,],
      id = indx,
      tocc = factor(rep(resp, each=n))
    )

    logan2$case <- (logan2$occupation == logan2$tocc)

    cl <- clogit(case ~ tocc + tocc:education + strata(id), logan2)
    tidy(cl)
    glance(cl)

    library(ggplot2)

    ggplot(lp, aes(age, .fitted, color = sex)) +
      geom_point()

    ggplot(risks, aes(age, .fitted, color = sex)) +
      geom_point()

    ggplot(expected, aes(time, .fitted, color = sex)) +
      geom_point()

[Package broom version 0.5.2 Index]