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 pyears tidy(x, ...)
| x | A |
|---|---|
| ... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble with one row for each time point and columns:
person-years of exposure
number of subjects contributing time
observed number of events
expected number of events (present only if a
ratetable term is present)
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
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 rowsglance(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