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 coxph
tidy(x, exponentiate = FALSE, conf.int = FALSE,
  conf.level = 0.95, ...)

Arguments

x

A coxph object returned from survival::coxph().

exponentiate

Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to FALSE.

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.level

The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.

...

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:

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.

Examples

library(survival) cfit <- coxph(Surv(time, status) ~ age + sex, lung) tidy(cfit)
#> # A tibble: 2 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 age 0.0170 0.00922 1.85 0.0646 #> 2 sex -0.513 0.167 -3.06 0.00218
tidy(cfit, exponentiate = TRUE)
#> # A tibble: 2 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 age 1.02 0.00922 1.85 0.0646 #> 2 sex 0.599 0.167 -3.06 0.00218
lp <- augment(cfit, lung) risks <- augment(cfit, lung, type.predict = "risk") expected <- augment(cfit, lung, type.predict = "expected") glance(cfit)
#> # A tibble: 1 x 15 #> n nevent statistic.log p.value.log statistic.sc p.value.sc statistic.wald #> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 228 165 14.1 0.000857 13.7 0.00105 13.5 #> # ... with 8 more variables: p.value.wald <dbl>, r.squared <dbl>, #> # r.squared.max <dbl>, concordance <dbl>, std.error.concordance <dbl>, #> # logLik <dbl>, AIC <dbl>, BIC <dbl>
# 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)
#> # A tibble: 9 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 toccfarm -1.90 1.38 -1.37 1.70e- 1 #> 2 toccoperatives 1.17 0.566 2.06 3.91e- 2 #> 3 toccprofessional -8.10 0.699 -11.6 4.45e-31 #> 4 toccsales -5.03 0.770 -6.53 6.54e-11 #> 5 tocccraftsmen:education -0.332 0.0569 -5.84 5.13e- 9 #> 6 toccfarm:education -0.370 0.116 -3.18 1.47e- 3 #> 7 toccoperatives:education -0.422 0.0584 -7.23 4.98e-13 #> 8 toccprofessional:education 0.278 0.0510 5.45 4.94e- 8 #> 9 toccsales:education NA 0 NA NA
glance(cl)
#> # A tibble: 1 x 13 #> n nevent statistic.log p.value.log statistic.sc p.value.sc statistic.wald #> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 4190 838 666. 1.90e-138 682. 5.01e-142 414. #> # ... with 6 more variables: p.value.wald <dbl>, r.squared <dbl>, #> # r.squared.max <dbl>, logLik <dbl>, AIC <dbl>, BIC <dbl>
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()