Glance accepts a model object and returns a tibble::tibble() with exactly one row of model summaries. The summaries are typically goodness of fit measures, p-values for hypothesis tests on residuals, or model convergence information.

Glance never returns information from the original call to the modelling function. This includes the name of the modelling function or any arguments passed to the modelling function.

Glance does not calculate summary measures. Rather, it farms out these computations to appropriate methods and gathers the results together. Sometimes a goodness of fit measure will be undefined. In these cases the measure will be reported as NA.

# S3 method for coxph
glance(x, ...)

Arguments

x

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

...

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.

Value

A one-row tibble::tibble with columns: TODO.

See also

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()