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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 survfit
glance(x, ...)

Arguments

x

An survfit object returned from survival::survfit().

...

Additional arguments passed to summary.survfit(). Important arguments include rmean.

Value

A tibble::tibble() with exactly one row and columns:

events

Number of events.

n.max

Maximum number of subjects at risk.

n.start

Initial number of subjects at risk.

nobs

Number of observations used.

records

Number of observations

rmean

Restricted mean (see [survival::print.survfit()]).

rmean.std.error

Restricted mean standard error.

conf.low

lower end of confidence interval on median

conf.high

upper end of confidence interval on median

median

median survival

Examples


# load libraries for models and data
library(survival)

# fit model
cfit <- coxph(Surv(time, status) ~ age + sex, lung)
sfit <- survfit(cfit)

# summarize model fit with tidiers + visualization
tidy(sfit)
#> # A tibble: 186 × 8
#>     time n.risk n.event n.censor estimate std.error conf.high conf.low
#>    <dbl>  <dbl>   <dbl>    <dbl>    <dbl>     <dbl>     <dbl>    <dbl>
#>  1     5    228       1        0    0.996   0.00419     1        0.988
#>  2    11    227       3        0    0.983   0.00845     1.00     0.967
#>  3    12    224       1        0    0.979   0.00947     0.997    0.961
#>  4    13    223       2        0    0.971   0.0113      0.992    0.949
#>  5    15    221       1        0    0.966   0.0121      0.990    0.944
#>  6    26    220       1        0    0.962   0.0129      0.987    0.938
#>  7    30    219       1        0    0.958   0.0136      0.984    0.933
#>  8    31    218       1        0    0.954   0.0143      0.981    0.927
#>  9    53    217       2        0    0.945   0.0157      0.975    0.917
#> 10    54    215       1        0    0.941   0.0163      0.972    0.911
#> # ℹ 176 more rows
glance(sfit)
#> # A tibble: 1 × 10
#>   records n.max n.start events rmean rmean.std.error median conf.low
#>     <dbl> <dbl>   <dbl>  <dbl> <dbl>           <dbl>  <dbl>    <dbl>
#> 1     228   228     228    165  381.            20.3    320      285
#> # ℹ 2 more variables: conf.high <dbl>, nobs <int>

library(ggplot2)

ggplot(tidy(sfit), aes(time, estimate)) +
  geom_line() +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .25)


# multi-state
fitCI <- survfit(Surv(stop, status * as.numeric(event), type = "mstate") ~ 1,
  data = mgus1, subset = (start == 0)
)

td_multi <- tidy(fitCI)

td_multi
#> # A tibble: 711 × 9
#>     time n.risk n.event n.censor estimate std.error conf.high conf.low
#>    <dbl>  <int>   <int>    <int>    <dbl>     <dbl>     <dbl>    <dbl>
#>  1     6    241       0        0    0.996   0.00414     1        0.988
#>  2     7    240       0        0    0.992   0.00584     1        0.980
#>  3    31    239       0        0    0.988   0.00714     1        0.974
#>  4    32    238       0        0    0.983   0.00823     1.00     0.967
#>  5    39    237       0        0    0.979   0.00918     0.997    0.961
#>  6    60    236       0        0    0.975   0.0100      0.995    0.956
#>  7    61    235       0        0    0.967   0.0115      0.990    0.944
#>  8   152    233       0        0    0.963   0.0122      0.987    0.939
#>  9   153    232       0        0    0.959   0.0128      0.984    0.934
#> 10   174    231       0        0    0.954   0.0134      0.981    0.928
#> # ℹ 701 more rows
#> # ℹ 1 more variable: state <chr>

ggplot(td_multi, aes(time, estimate, group = state)) +
  geom_line(aes(color = state)) +
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .25)