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 pyears
glance(x, ...)

Arguments

x

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

...

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 exactly one row and columns:

total

total number of person-years tabulated

offtable

total number of person-years off table

Examples

library(survival) temp.yr <- tcut(mgus$dxyr, 55:92, labels=as.character(55:91)) temp.age <- tcut(mgus$age, 34:101, labels=as.character(34:100)) ptime <- ifelse(is.na(mgus$pctime), mgus$futime, mgus$pctime) pstat <- ifelse(is.na(mgus$pctime), 0, 1) pfit <- pyears(Surv(ptime/365.25, pstat) ~ temp.yr + temp.age + sex, mgus, data.frame=TRUE) tidy(pfit)
#> # A tibble: 1,752 x 6 #> temp.yr temp.age sex pyears n event #> * <fct> <fct> <fct> <dbl> <dbl> <dbl> #> 1 71 34 female 0.00274 1 0 #> 2 68 35 female 0.00274 1 0 #> 3 72 35 female 0.00274 1 0 #> 4 69 36 female 0.00274 1 0 #> 5 73 36 female 0.00274 1 0 #> 6 69 37 female 0.00274 1 0 #> 7 70 37 female 0.00274 1 0 #> 8 74 37 female 0.00274 1 0 #> 9 70 38 female 0.00274 1 0 #> 10 71 38 female 0.00274 1 0 #> # ... with 1,742 more rows
glance(pfit)
#> # A tibble: 1 x 2 #> total offtable #> <dbl> <dbl> #> 1 8.32 0.727
# if data.frame argument is not given, different information is present in # output pfit2 <- pyears(Surv(ptime/365.25, pstat) ~ temp.yr + temp.age + sex, mgus) tidy(pfit2)
#> # A tibble: 37 x 402 #> pyears.34.female pyears.35.female pyears.36.female pyears.37.female #> * <dbl> <dbl> <dbl> <dbl> #> 1 0 0 0 0 #> 2 0 0 0 0 #> 3 0 0 0 0 #> 4 0 0 0 0 #> 5 0 0 0 0 #> 6 0 0 0 0 #> 7 0 0 0 0 #> 8 0 0 0 0 #> 9 0 0 0 0 #> 10 0 0 0 0 #> # ... with 27 more rows, and 398 more variables: pyears.38.female <dbl>, #> # pyears.39.female <dbl>, pyears.40.female <dbl>, pyears.41.female <dbl>, #> # pyears.42.female <dbl>, pyears.43.female <dbl>, pyears.44.female <dbl>, #> # pyears.45.female <dbl>, pyears.46.female <dbl>, pyears.47.female <dbl>, #> # pyears.48.female <dbl>, pyears.49.female <dbl>, pyears.50.female <dbl>, #> # pyears.51.female <dbl>, pyears.52.female <dbl>, pyears.53.female <dbl>, #> # pyears.54.female <dbl>, pyears.55.female <dbl>, pyears.56.female <dbl>, #> # pyears.57.female <dbl>, pyears.58.female <dbl>, pyears.59.female <dbl>, #> # pyears.60.female <dbl>, pyears.61.female <dbl>, pyears.62.female <dbl>, #> # pyears.63.female <dbl>, pyears.64.female <dbl>, pyears.65.female <dbl>, #> # pyears.66.female <dbl>, pyears.67.female <dbl>, pyears.68.female <dbl>, #> # pyears.69.female <dbl>, pyears.70.female <dbl>, pyears.71.female <dbl>, #> # pyears.72.female <dbl>, pyears.73.female <dbl>, pyears.74.female <dbl>, #> # pyears.75.female <dbl>, pyears.76.female <dbl>, pyears.77.female <dbl>, #> # pyears.78.female <dbl>, pyears.79.female <dbl>, pyears.80.female <dbl>, #> # pyears.81.female <dbl>, pyears.82.female <dbl>, pyears.83.female <dbl>, #> # pyears.84.female <dbl>, pyears.85.female <dbl>, pyears.86.female <dbl>, #> # pyears.87.female <dbl>, pyears.88.female <dbl>, pyears.89.female <dbl>, #> # pyears.90.female <dbl>, pyears.91.female <dbl>, pyears.92.female <dbl>, #> # pyears.93.female <dbl>, pyears.94.female <dbl>, pyears.95.female <dbl>, #> # pyears.96.female <dbl>, pyears.97.female <dbl>, pyears.98.female <dbl>, #> # pyears.99.female <dbl>, pyears.100.female <dbl>, pyears.34.male <dbl>, #> # pyears.35.male <dbl>, pyears.36.male <dbl>, pyears.37.male <dbl>, #> # pyears.38.male <dbl>, pyears.39.male <dbl>, pyears.40.male <dbl>, #> # pyears.41.male <dbl>, pyears.42.male <dbl>, pyears.43.male <dbl>, #> # pyears.44.male <dbl>, pyears.45.male <dbl>, pyears.46.male <dbl>, #> # pyears.47.male <dbl>, pyears.48.male <dbl>, pyears.49.male <dbl>, #> # pyears.50.male <dbl>, pyears.51.male <dbl>, pyears.52.male <dbl>, #> # pyears.53.male <dbl>, pyears.54.male <dbl>, pyears.55.male <dbl>, #> # pyears.56.male <dbl>, pyears.57.male <dbl>, pyears.58.male <dbl>, #> # pyears.59.male <dbl>, pyears.60.male <dbl>, pyears.61.male <dbl>, #> # pyears.62.male <dbl>, pyears.63.male <dbl>, pyears.64.male <dbl>, #> # pyears.65.male <dbl>, pyears.66.male <dbl>, pyears.67.male <dbl>, #> # pyears.68.male <dbl>, pyears.69.male <dbl>, pyears.70.male <dbl>, …
glance(pfit2)
#> # A tibble: 1 x 2 #> total offtable #> <dbl> <dbl> #> 1 8.32 0.727