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 class 'pyears'
tidy(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. Two exceptions here are:
expected is only present in the output when if a ratetable
term is present.
If the data.frame = TRUE argument is supplied to pyears,
this is simply the contents of x$data.
Other pyears tidiers:
glance.pyears()
Other survival tidiers:
augment.coxph(),
augment.survreg(),
glance.aareg(),
glance.cch(),
glance.coxph(),
glance.pyears(),
glance.survdiff(),
glance.survexp(),
glance.survfit(),
glance.survreg(),
tidy.aareg(),
tidy.cch(),
tidy.coxph(),
tidy.survdiff(),
tidy.survexp(),
tidy.survfit(),
tidy.survreg()
A tibble::tibble() with columns:
Expected number of events.
Person-years of exposure.
number of subjects contributing time
observed number of events
# load libraries for models and data
library(survival)
# generate and format data
temp.yr <- tcut(mgus$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
)
# summarize model fit with tidiers
tidy(pfit)
#> # A tibble: 1,752 × 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
#> # ℹ 1,742 more rows
glance(pfit)
#> # A tibble: 1 × 3
#> total offtable nobs
#> <dbl> <dbl> <int>
#> 1 8.32 0.727 241
# 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 × 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
#> # ℹ 27 more rows
#> # ℹ 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>, …
glance(pfit2)
#> # A tibble: 1 × 3
#> total offtable nobs
#> <dbl> <dbl> <int>
#> 1 8.32 0.727 241