coxph_tidiers {broom} | R Documentation |
Tidy the coefficients of a Cox proportional hazards regression model, construct predictions, or summarize the entire model into a single row.
## S3 method for class 'coxph' tidy(x, exponentiate = FALSE, conf.int = 0.95, ...) ## S3 method for class 'coxph' augment(x, data = stats::model.frame(x), newdata, type.predict = "lp", type.residuals = "martingale", ...) ## S3 method for class 'coxph' glance(x, ...)
x |
"coxph" object |
exponentiate |
whether to report the estimate and confidence intervals on an exponential scale |
conf.int |
confidence level to be used for CI |
... |
Extra arguments, not used |
data |
original data for |
newdata |
new data on which to do predictions |
type.predict |
type of predicted value (see |
type.residuals |
type of residuals (see |
When the modeling was performed with na.action = "na.omit"
(as is the typical default), rows with NA in the initial data are omitted
entirely from the augmented data frame. When the modeling was performed
with na.action = "na.exclude"
, one should provide the original data
as a second argument, at which point the augmented data will contain those
rows (typically with NAs in place of the new columns). If the original data
is not provided to augment
and na.action = "na.exclude"
, a
warning is raised and the incomplete rows are dropped.
tidy
returns a data.frame with one row for each term,
with columns
estimate |
estimate of slope |
std.error |
standard error of estimate |
statistic |
test statistic |
p.value |
p-value |
augment
returns the original data.frame with additional
columns added:
.fitted |
predicted values |
.se.fit |
standard errors |
.resid |
residuals (not present if |
glance
returns a one-row data.frame with statistics
calculated on the cox regression.
if (require("survival", quietly = TRUE)) { 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() }