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

Upper bound on the confidence interval for the estimate.

conf.low

Lower bound on the confidence interval for the estimate.

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)