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tests/testthat/_snaps/plot_fit_on_data.md
In lnmixsurv: Bayesian Mixture Log-Normal Survival Model
plot_fit_on_data works for Bayesian model with covariates
Code
plot_fit_on_data(mod, data_model, interval = "credible", level = 0.95, type = "survival")$
preds
Output
# A tibble: 10,000 x 12
time n.risk n.event n.censor estimate std.error conf.high conf.low strata
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 3.09 6034 0 1 1 0 1 1 x=0
2 3.17 6033 0 1 1 0 1 1 x=0
3 3.19 6032 0 1 1 0 1 1 x=0
4 3.65 6031 0 1 1 0 1 1 x=0
5 4.01 6030 0 1 1 0 1 1 x=0
6 4.10 6029 0 1 1 0 1 1 x=0
7 4.27 6028 0 1 1 0 1 1 x=0
8 4.27 6027 1 0 1.00 0.000166 1 1.00 x=0
9 4.57 6026 0 1 1.00 0.000166 1 1.00 x=0
10 4.86 6025 0 1 1.00 0.000166 1 1.00 x=0
# i 9,990 more rows
# i 3 more variables: .pred_survival <dbl>, .pred_lower <dbl>,
# .pred_upper <dbl>
Code
plot_fit_on_data(mod, data_model, interval = "credible", level = 0.95, type = "hazard")$
preds
Output
# A tibble: 10,000 x 13
time n.risk n.event n.censor estimate std.error conf.high conf.low strata
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 3.09 6034 0 1 1 0 1 1 x=0
2 3.17 6033 0 1 1 0 1 1 x=0
3 3.19 6032 0 1 1 0 1 1 x=0
4 3.65 6031 0 1 1 0 1 1 x=0
5 4.01 6030 0 1 1 0 1 1 x=0
6 4.10 6029 0 1 1 0 1 1 x=0
7 4.27 6028 0 1 1 0 1 1 x=0
8 4.27 6027 1 0 1.00 0.000166 1 1.00 x=0
9 4.57 6026 0 1 1.00 0.000166 1 1.00 x=0
10 4.86 6025 0 1 1.00 0.000166 1 1.00 x=0
# i 9,990 more rows
# i 4 more variables: hazard_estimate <dbl>, .pred_hazard <dbl>,
# .pred_lower <dbl>, .pred_upper <dbl>
Code
plot_fit_on_data(mod, data_model, type = "survival")$preds
Output
# A tibble: 10,000 x 10
time n.risk n.event n.censor estimate std.error conf.high conf.low strata
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 3.09 6034 0 1 1 0 1 1 x=0
2 3.17 6033 0 1 1 0 1 1 x=0
3 3.19 6032 0 1 1 0 1 1 x=0
4 3.65 6031 0 1 1 0 1 1 x=0
5 4.01 6030 0 1 1 0 1 1 x=0
6 4.10 6029 0 1 1 0 1 1 x=0
7 4.27 6028 0 1 1 0 1 1 x=0
8 4.27 6027 1 0 1.00 0.000166 1 1.00 x=0
9 4.57 6026 0 1 1.00 0.000166 1 1.00 x=0
10 4.86 6025 0 1 1.00 0.000166 1 1.00 x=0
# i 9,990 more rows
# i 1 more variable: .pred_survival <dbl>
Code
plot_fit_on_data(mod, data_model, type = "hazard")$preds
Output
# A tibble: 10,000 x 11
time n.risk n.event n.censor estimate std.error conf.high conf.low strata
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 3.09 6034 0 1 1 0 1 1 x=0
2 3.17 6033 0 1 1 0 1 1 x=0
3 3.19 6032 0 1 1 0 1 1 x=0
4 3.65 6031 0 1 1 0 1 1 x=0
5 4.01 6030 0 1 1 0 1 1 x=0
6 4.10 6029 0 1 1 0 1 1 x=0
7 4.27 6028 0 1 1 0 1 1 x=0
8 4.27 6027 1 0 1.00 0.000166 1 1.00 x=0
9 4.57 6026 0 1 1.00 0.000166 1 1.00 x=0
10 4.86 6025 0 1 1.00 0.000166 1 1.00 x=0
# i 9,990 more rows
# i 2 more variables: hazard_estimate <dbl>, .pred_hazard <dbl>
plot_fit_on_data works for Bayesian model with intercept only
Code
plot_fit_on_data(mod, data_model, interval = "credible", level = 0.95, type = "survival")$
preds
Output
# A tibble: 9,999 x 11
time n.risk n.event n.censor estimate std.error conf.high conf.low
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 3.09 10000 0 1 1 0 1 1
2 3.17 9999 0 1 1 0 1 1
3 3.19 9998 0 1 1 0 1 1
4 3.65 9997 0 1 1 0 1 1
5 4.01 9996 0 1 1 0 1 1
6 4.10 9995 0 1 1 0 1 1
7 4.27 9994 0 1 1 0 1 1
8 4.27 9993 1 0 1.00 0.000100 1 1.00
9 4.57 9992 0 1 1.00 0.000100 1 1.00
10 4.86 9991 0 1 1.00 0.000100 1 1.00
# i 9,989 more rows
# i 3 more variables: .pred_survival <dbl>, .pred_lower <dbl>,
# .pred_upper <dbl>
Code
plot_fit_on_data(mod, data_model, interval = "credible", level = 0.95, type = "hazard")$
preds
Output
# A tibble: 9,999 x 12
time n.risk n.event n.censor estimate std.error conf.high conf.low
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 3.09 10000 0 1 1 0 1 1
2 3.17 9999 0 1 1 0 1 1
3 3.19 9998 0 1 1 0 1 1
4 3.65 9997 0 1 1 0 1 1
5 4.01 9996 0 1 1 0 1 1
6 4.10 9995 0 1 1 0 1 1
7 4.27 9994 0 1 1 0 1 1
8 4.27 9993 1 0 1.00 0.000100 1 1.00
9 4.57 9992 0 1 1.00 0.000100 1 1.00
10 4.86 9991 0 1 1.00 0.000100 1 1.00
# i 9,989 more rows
# i 4 more variables: hazard_estimate <dbl>, .pred_hazard <dbl>,
# .pred_lower <dbl>, .pred_upper <dbl>
Code
plot_fit_on_data(mod, data_model, type = "survival")$preds
Output
# A tibble: 9,999 x 9
time n.risk n.event n.censor estimate std.error conf.high conf.low
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 3.09 10000 0 1 1 0 1 1
2 3.17 9999 0 1 1 0 1 1
3 3.19 9998 0 1 1 0 1 1
4 3.65 9997 0 1 1 0 1 1
5 4.01 9996 0 1 1 0 1 1
6 4.10 9995 0 1 1 0 1 1
7 4.27 9994 0 1 1 0 1 1
8 4.27 9993 1 0 1.00 0.000100 1 1.00
9 4.57 9992 0 1 1.00 0.000100 1 1.00
10 4.86 9991 0 1 1.00 0.000100 1 1.00
# i 9,989 more rows
# i 1 more variable: .pred_survival <dbl>
Code
plot_fit_on_data(mod, data_model, type = "hazard")$preds
Output
# A tibble: 9,999 x 10
time n.risk n.event n.censor estimate std.error conf.high conf.low
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 3.09 10000 0 1 1 0 1 1
2 3.17 9999 0 1 1 0 1 1
3 3.19 9998 0 1 1 0 1 1
4 3.65 9997 0 1 1 0 1 1
5 4.01 9996 0 1 1 0 1 1
6 4.10 9995 0 1 1 0 1 1
7 4.27 9994 0 1 1 0 1 1
8 4.27 9993 1 0 1.00 0.000100 1 1.00
9 4.57 9992 0 1 1.00 0.000100 1 1.00
10 4.86 9991 0 1 1.00 0.000100 1 1.00
# i 9,989 more rows
# i 2 more variables: hazard_estimate <dbl>, .pred_hazard <dbl>
plot_fit_on_data works for EM model with covariates
Code
plot_fit_on_data(mod, data_model, type = "survival")$preds
Output
# A tibble: 10,000 x 10
time n.risk n.event n.censor estimate std.error conf.high conf.low strata
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 3.09 6034 0 1 1 0 1 1 x=0
2 3.17 6033 0 1 1 0 1 1 x=0
3 3.19 6032 0 1 1 0 1 1 x=0
4 3.65 6031 0 1 1 0 1 1 x=0
5 4.01 6030 0 1 1 0 1 1 x=0
6 4.10 6029 0 1 1 0 1 1 x=0
7 4.27 6028 0 1 1 0 1 1 x=0
8 4.27 6027 1 0 1.00 0.000166 1 1.00 x=0
9 4.57 6026 0 1 1.00 0.000166 1 1.00 x=0
10 4.86 6025 0 1 1.00 0.000166 1 1.00 x=0
# i 9,990 more rows
# i 1 more variable: .pred_survival <dbl>
Code
plot_fit_on_data(mod, data_model, type = "hazard")$preds
Output
# A tibble: 10,000 x 11
time n.risk n.event n.censor estimate std.error conf.high conf.low strata
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 3.09 6034 0 1 1 0 1 1 x=0
2 3.17 6033 0 1 1 0 1 1 x=0
3 3.19 6032 0 1 1 0 1 1 x=0
4 3.65 6031 0 1 1 0 1 1 x=0
5 4.01 6030 0 1 1 0 1 1 x=0
6 4.10 6029 0 1 1 0 1 1 x=0
7 4.27 6028 0 1 1 0 1 1 x=0
8 4.27 6027 1 0 1.00 0.000166 1 1.00 x=0
9 4.57 6026 0 1 1.00 0.000166 1 1.00 x=0
10 4.86 6025 0 1 1.00 0.000166 1 1.00 x=0
# i 9,990 more rows
# i 2 more variables: hazard_estimate <dbl>, .pred_hazard <dbl>
plot_fit_on_data works for EM model with intercept only
Code
plot_fit_on_data(mod, data_model, type = "survival")$preds
Output
# A tibble: 9,999 x 9
time n.risk n.event n.censor estimate std.error conf.high conf.low
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 3.09 10000 0 1 1 0 1 1
2 3.17 9999 0 1 1 0 1 1
3 3.19 9998 0 1 1 0 1 1
4 3.65 9997 0 1 1 0 1 1
5 4.01 9996 0 1 1 0 1 1
6 4.10 9995 0 1 1 0 1 1
7 4.27 9994 0 1 1 0 1 1
8 4.27 9993 1 0 1.00 0.000100 1 1.00
9 4.57 9992 0 1 1.00 0.000100 1 1.00
10 4.86 9991 0 1 1.00 0.000100 1 1.00
# i 9,989 more rows
# i 1 more variable: .pred_survival <dbl>
Code
plot_fit_on_data(mod, data_model, type = "hazard")$preds
Output
# A tibble: 9,999 x 10
time n.risk n.event n.censor estimate std.error conf.high conf.low
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 3.09 10000 0 1 1 0 1 1
2 3.17 9999 0 1 1 0 1 1
3 3.19 9998 0 1 1 0 1 1
4 3.65 9997 0 1 1 0 1 1
5 4.01 9996 0 1 1 0 1 1
6 4.10 9995 0 1 1 0 1 1
7 4.27 9994 0 1 1 0 1 1
8 4.27 9993 1 0 1.00 0.000100 1 1.00
9 4.57 9992 0 1 1.00 0.000100 1 1.00
10 4.86 9991 0 1 1.00 0.000100 1 1.00
# i 9,989 more rows
# i 2 more variables: hazard_estimate <dbl>, .pred_hazard <dbl>
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lnmixsurv documentation built on Sept. 11, 2024, 7:18 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
plot_fit_on_data works for Bayesian model with covariates
Code
plot_fit_on_data(mod, data_model, interval = "credible", level = 0.95, type = "survival")$
preds
Output
# A tibble: 10,000 x 12
time n.risk n.event n.censor estimate std.error conf.high conf.low strata
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 3.09 6034 0 1 1 0 1 1 x=0
2 3.17 6033 0 1 1 0 1 1 x=0
3 3.19 6032 0 1 1 0 1 1 x=0
4 3.65 6031 0 1 1 0 1 1 x=0
5 4.01 6030 0 1 1 0 1 1 x=0
6 4.10 6029 0 1 1 0 1 1 x=0
7 4.27 6028 0 1 1 0 1 1 x=0
8 4.27 6027 1 0 1.00 0.000166 1 1.00 x=0
9 4.57 6026 0 1 1.00 0.000166 1 1.00 x=0
10 4.86 6025 0 1 1.00 0.000166 1 1.00 x=0
# i 9,990 more rows
# i 3 more variables: .pred_survival <dbl>, .pred_lower <dbl>,
# .pred_upper <dbl>
Code
plot_fit_on_data(mod, data_model, interval = "credible", level = 0.95, type = "hazard")$
preds
Output
# A tibble: 10,000 x 13
time n.risk n.event n.censor estimate std.error conf.high conf.low strata
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 3.09 6034 0 1 1 0 1 1 x=0
2 3.17 6033 0 1 1 0 1 1 x=0
3 3.19 6032 0 1 1 0 1 1 x=0
4 3.65 6031 0 1 1 0 1 1 x=0
5 4.01 6030 0 1 1 0 1 1 x=0
6 4.10 6029 0 1 1 0 1 1 x=0
7 4.27 6028 0 1 1 0 1 1 x=0
8 4.27 6027 1 0 1.00 0.000166 1 1.00 x=0
9 4.57 6026 0 1 1.00 0.000166 1 1.00 x=0
10 4.86 6025 0 1 1.00 0.000166 1 1.00 x=0
# i 9,990 more rows
# i 4 more variables: hazard_estimate <dbl>, .pred_hazard <dbl>,
# .pred_lower <dbl>, .pred_upper <dbl>
Code
plot_fit_on_data(mod, data_model, type = "survival")$preds
Output
# A tibble: 10,000 x 10
time n.risk n.event n.censor estimate std.error conf.high conf.low strata
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 3.09 6034 0 1 1 0 1 1 x=0
2 3.17 6033 0 1 1 0 1 1 x=0
3 3.19 6032 0 1 1 0 1 1 x=0
4 3.65 6031 0 1 1 0 1 1 x=0
5 4.01 6030 0 1 1 0 1 1 x=0
6 4.10 6029 0 1 1 0 1 1 x=0
7 4.27 6028 0 1 1 0 1 1 x=0
8 4.27 6027 1 0 1.00 0.000166 1 1.00 x=0
9 4.57 6026 0 1 1.00 0.000166 1 1.00 x=0
10 4.86 6025 0 1 1.00 0.000166 1 1.00 x=0
# i 9,990 more rows
# i 1 more variable: .pred_survival <dbl>
Code
plot_fit_on_data(mod, data_model, type = "hazard")$preds
Output
# A tibble: 10,000 x 11
time n.risk n.event n.censor estimate std.error conf.high conf.low strata
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 3.09 6034 0 1 1 0 1 1 x=0
2 3.17 6033 0 1 1 0 1 1 x=0
3 3.19 6032 0 1 1 0 1 1 x=0
4 3.65 6031 0 1 1 0 1 1 x=0
5 4.01 6030 0 1 1 0 1 1 x=0
6 4.10 6029 0 1 1 0 1 1 x=0
7 4.27 6028 0 1 1 0 1 1 x=0
8 4.27 6027 1 0 1.00 0.000166 1 1.00 x=0
9 4.57 6026 0 1 1.00 0.000166 1 1.00 x=0
10 4.86 6025 0 1 1.00 0.000166 1 1.00 x=0
# i 9,990 more rows
# i 2 more variables: hazard_estimate <dbl>, .pred_hazard <dbl>
plot_fit_on_data works for Bayesian model with intercept only
Code
plot_fit_on_data(mod, data_model, interval = "credible", level = 0.95, type = "survival")$
preds
Output
# A tibble: 9,999 x 11
time n.risk n.event n.censor estimate std.error conf.high conf.low
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 3.09 10000 0 1 1 0 1 1
2 3.17 9999 0 1 1 0 1 1
3 3.19 9998 0 1 1 0 1 1
4 3.65 9997 0 1 1 0 1 1
5 4.01 9996 0 1 1 0 1 1
6 4.10 9995 0 1 1 0 1 1
7 4.27 9994 0 1 1 0 1 1
8 4.27 9993 1 0 1.00 0.000100 1 1.00
9 4.57 9992 0 1 1.00 0.000100 1 1.00
10 4.86 9991 0 1 1.00 0.000100 1 1.00
# i 9,989 more rows
# i 3 more variables: .pred_survival <dbl>, .pred_lower <dbl>,
# .pred_upper <dbl>
Code
plot_fit_on_data(mod, data_model, interval = "credible", level = 0.95, type = "hazard")$
preds
Output
# A tibble: 9,999 x 12
time n.risk n.event n.censor estimate std.error conf.high conf.low
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 3.09 10000 0 1 1 0 1 1
2 3.17 9999 0 1 1 0 1 1
3 3.19 9998 0 1 1 0 1 1
4 3.65 9997 0 1 1 0 1 1
5 4.01 9996 0 1 1 0 1 1
6 4.10 9995 0 1 1 0 1 1
7 4.27 9994 0 1 1 0 1 1
8 4.27 9993 1 0 1.00 0.000100 1 1.00
9 4.57 9992 0 1 1.00 0.000100 1 1.00
10 4.86 9991 0 1 1.00 0.000100 1 1.00
# i 9,989 more rows
# i 4 more variables: hazard_estimate <dbl>, .pred_hazard <dbl>,
# .pred_lower <dbl>, .pred_upper <dbl>
Code
plot_fit_on_data(mod, data_model, type = "survival")$preds
Output
# A tibble: 9,999 x 9
time n.risk n.event n.censor estimate std.error conf.high conf.low
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 3.09 10000 0 1 1 0 1 1
2 3.17 9999 0 1 1 0 1 1
3 3.19 9998 0 1 1 0 1 1
4 3.65 9997 0 1 1 0 1 1
5 4.01 9996 0 1 1 0 1 1
6 4.10 9995 0 1 1 0 1 1
7 4.27 9994 0 1 1 0 1 1
8 4.27 9993 1 0 1.00 0.000100 1 1.00
9 4.57 9992 0 1 1.00 0.000100 1 1.00
10 4.86 9991 0 1 1.00 0.000100 1 1.00
# i 9,989 more rows
# i 1 more variable: .pred_survival <dbl>
Code
plot_fit_on_data(mod, data_model, type = "hazard")$preds
Output
# A tibble: 9,999 x 10
time n.risk n.event n.censor estimate std.error conf.high conf.low
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 3.09 10000 0 1 1 0 1 1
2 3.17 9999 0 1 1 0 1 1
3 3.19 9998 0 1 1 0 1 1
4 3.65 9997 0 1 1 0 1 1
5 4.01 9996 0 1 1 0 1 1
6 4.10 9995 0 1 1 0 1 1
7 4.27 9994 0 1 1 0 1 1
8 4.27 9993 1 0 1.00 0.000100 1 1.00
9 4.57 9992 0 1 1.00 0.000100 1 1.00
10 4.86 9991 0 1 1.00 0.000100 1 1.00
# i 9,989 more rows
# i 2 more variables: hazard_estimate <dbl>, .pred_hazard <dbl>
plot_fit_on_data works for EM model with covariates
Code
plot_fit_on_data(mod, data_model, type = "survival")$preds
Output
# A tibble: 10,000 x 10
time n.risk n.event n.censor estimate std.error conf.high conf.low strata
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 3.09 6034 0 1 1 0 1 1 x=0
2 3.17 6033 0 1 1 0 1 1 x=0
3 3.19 6032 0 1 1 0 1 1 x=0
4 3.65 6031 0 1 1 0 1 1 x=0
5 4.01 6030 0 1 1 0 1 1 x=0
6 4.10 6029 0 1 1 0 1 1 x=0
7 4.27 6028 0 1 1 0 1 1 x=0
8 4.27 6027 1 0 1.00 0.000166 1 1.00 x=0
9 4.57 6026 0 1 1.00 0.000166 1 1.00 x=0
10 4.86 6025 0 1 1.00 0.000166 1 1.00 x=0
# i 9,990 more rows
# i 1 more variable: .pred_survival <dbl>
Code
plot_fit_on_data(mod, data_model, type = "hazard")$preds
Output
# A tibble: 10,000 x 11
time n.risk n.event n.censor estimate std.error conf.high conf.low strata
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 3.09 6034 0 1 1 0 1 1 x=0
2 3.17 6033 0 1 1 0 1 1 x=0
3 3.19 6032 0 1 1 0 1 1 x=0
4 3.65 6031 0 1 1 0 1 1 x=0
5 4.01 6030 0 1 1 0 1 1 x=0
6 4.10 6029 0 1 1 0 1 1 x=0
7 4.27 6028 0 1 1 0 1 1 x=0
8 4.27 6027 1 0 1.00 0.000166 1 1.00 x=0
9 4.57 6026 0 1 1.00 0.000166 1 1.00 x=0
10 4.86 6025 0 1 1.00 0.000166 1 1.00 x=0
# i 9,990 more rows
# i 2 more variables: hazard_estimate <dbl>, .pred_hazard <dbl>
plot_fit_on_data works for EM model with intercept only
Code
plot_fit_on_data(mod, data_model, type = "survival")$preds
Output
# A tibble: 9,999 x 9
time n.risk n.event n.censor estimate std.error conf.high conf.low
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 3.09 10000 0 1 1 0 1 1
2 3.17 9999 0 1 1 0 1 1
3 3.19 9998 0 1 1 0 1 1
4 3.65 9997 0 1 1 0 1 1
5 4.01 9996 0 1 1 0 1 1
6 4.10 9995 0 1 1 0 1 1
7 4.27 9994 0 1 1 0 1 1
8 4.27 9993 1 0 1.00 0.000100 1 1.00
9 4.57 9992 0 1 1.00 0.000100 1 1.00
10 4.86 9991 0 1 1.00 0.000100 1 1.00
# i 9,989 more rows
# i 1 more variable: .pred_survival <dbl>
Code
plot_fit_on_data(mod, data_model, type = "hazard")$preds
Output
# A tibble: 9,999 x 10
time n.risk n.event n.censor estimate std.error conf.high conf.low
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 3.09 10000 0 1 1 0 1 1
2 3.17 9999 0 1 1 0 1 1
3 3.19 9998 0 1 1 0 1 1
4 3.65 9997 0 1 1 0 1 1
5 4.01 9996 0 1 1 0 1 1
6 4.10 9995 0 1 1 0 1 1
7 4.27 9994 0 1 1 0 1 1
8 4.27 9993 1 0 1.00 0.000100 1 1.00
9 4.57 9992 0 1 1.00 0.000100 1 1.00
10 4.86 9991 0 1 1.00 0.000100 1 1.00
# i 9,989 more rows
# i 2 more variables: hazard_estimate <dbl>, .pred_hazard <dbl>
Try the lnmixsurv package in your browser
Any scripts or data that you put into this service are public.
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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