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 across 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, ...)
x | An |
---|---|
conf.level | confidence level for CI |
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
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()
A tibble::tibble()
with columns:
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
The estimated value of the regression term.
The two-sided p-value associated with the observed statistic.
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
The standard error of the regression term.
The name of the regression term.
if (requireNamespace("survival", quietly = TRUE)) { 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) }