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 modeling function. This includes the name of the modeling function or any arguments passed to the modeling 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.
Glance returns the same number of columns regardless of whether the
model matrix is rank-deficient or not. If so, entries in columns
that no longer have a well-defined value are filled in with an NA
of the appropriate type.
Usage
# S3 method for class 'pyears'
glance(x, ...)Arguments
- x
A
pyearsobject returned fromsurvival::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 passconf.lvel = 0.9, all computation will proceed usingconf.level = 0.95. Two exceptions here are:
See also
Other pyears tidiers:
tidy.pyears()
Other survival tidiers:
augment.coxph(),
augment.survreg(),
glance.aareg(),
glance.cch(),
glance.coxph(),
glance.survdiff(),
glance.survexp(),
glance.survfit(),
glance.survreg(),
tidy.aareg(),
tidy.cch(),
tidy.coxph(),
tidy.pyears(),
tidy.survdiff(),
tidy.survexp(),
tidy.survfit(),
tidy.survreg()
Value
A tibble::tibble() with exactly one row and columns:
- nobs
Number of observations used.
- total
total number of person-years tabulated
- offtable
total number of person-years off table
Examples
# 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>, …
glance(pfit2)
#> # A tibble: 1 × 3
#> total offtable nobs
#> <dbl> <dbl> <int>
#> 1 8.32 0.727 241