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 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.

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) # 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)
#> # A tibble: 5 x 7 #> term estimate std.error statistic p.value conf.low conf.high #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 stageII 0.735 0.168 4.36 1.30e- 5 0.404 1.06 #> 2 stageIII 0.597 0.173 3.44 5.77e- 4 0.257 0.937 #> 3 stageIV 1.38 0.205 6.76 1.40e-11 0.983 1.79 #> 4 histolUH 1.50 0.160 9.38 0. 1.19 1.81 #> 5 age 0.0433 0.0237 1.82 6.83e- 2 -0.00324 0.0898
# 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)