tidy.cch {broom} | R Documentation

## Tidy a(n) cch 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 'cch'
    tidy(x, conf.level = 0.95, ...)

### Arguments

`x` | An `cch` object returned from `survival::cch()`.
`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.

### Value

A `tibble::tibble()` with one row for each term in the regression. The tibble has columns:

`term` | The name of the regression term.
`estimate` | The estimated value of the regression term.
`std.error` | The standard error of the regression term.
`statistic` | The value of a statistic, almost always a T-statistic, to use in a hypothesis that the regression term is non-zero.
`p.value` | The two-sided p-value associated with the observed statistic.
`conf.low` | The low end of a confidence interval for the regression term. Included only if `conf.int = TRUE`.
`conf.high` | The high end of a confidence interval for the regression term. Included only if `conf.int = TRUE`.

### See Also

`tidy()`, `survival::cch()`

Other cch tidiers: `glance.cch`, `glance.survfit`

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

### Examples

    library(survival)

    # examples come from cch documentation
    subcoh <- nwtco$in.subcohort
    selccoh <- with(nwtco, rel==1|subcoh==1)
    ccoh.data <- nwtco[selccoh,]
    ccoh.data$subcohort <- subcoh[selccoh]
    ## central-lab histology
    ccoh.data$histol <- factor(ccoh.data$histol,labels=c("FH","UH"))
    ## tumour stage
    ccoh.data$stage <- factor(ccoh.data$stage,labels=c("I","II","III" ,"IV"))
    ccoh.data$age <- ccoh.data$age/12 # Age in years

    fit.ccP <- cch(Surv(edrel, rel) ~ stage + histol + age, data = ccoh.data,
                   subcoh = ~subcohort, id= ~seqno, cohort.size = 4028)

    tidy(fit.ccP)

    # coefficient plot
    library(ggplot2)
    ggplot(tidy(fit.ccP), aes(x = estimate, y = term)) +
      geom_point() +
      geom_errorbarh(aes(xmin = conf.low, xmax = conf.high), height = 0) +
      geom_vline(xintercept = 0)

[Package broom version 0.5.2 Index]