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The model can be used with the usual formula interface or using the
tidymodels and censored structure.
Formula interface:
library(lnmixsurv)
library(readr)
mod1 <- survival_ln_mixture(Surv(y, delta) ~ x,
sim_data$data,
starting_seed = 20)
mod1
#> survival_ln_mixture
#> formula: Surv(y, delta) ~ x
#> observations: 10000
#> predictors: 2
#> mixture groups: 2
#> ------------------
#> estimate std.error cred.low cred.high
#> (Intercept)_1 3.7005261 0.008960928 3.6874161 3.7127982
#> x1_1 0.6860558 0.014259390 0.6664254 0.7050614
#> (Intercept)_2 9.4916865 0.162788376 9.2698034 10.0927974
#> x1_2 0.1611774 0.613334336 -0.4628000 1.3511508
#>
#> Auxiliary parameter(s):
#> estimate std.error cred.low cred.high
#> phi_1 2.6484958 0.048153081 2.5935736 2.712449
#> phi_2 3.9597761 2.912488989 1.7242030 9.644528
#> eta_1 0.8551168 0.003429121 0.8505096 0.859702Tidymodels approach:
library(censored)
library(ggplot2)
library(dplyr)
library(tidyr)
library(purrr)
mod_spec <- survival_reg() |>
set_engine("survival_ln_mixture", starting_seed = 20) |>
set_mode("censored regression")
mod2 <- mod_spec |>
fit(Surv(y, delta) ~ x, sim_data$data) The estimates are easily obtained using tidy method. See
?tidy.survival_ln_mixture for extra options.
tidy(mod1)
#> # A tibble: 4 × 3
#> term estimate std.error
#> <chr> <dbl> <dbl>
#> 1 (Intercept)_1 3.70 0.00896
#> 2 x1_1 0.686 0.0143
#> 3 (Intercept)_2 9.49 0.163
#> 4 x1_2 0.161 0.613
tidy(mod2)
#> # A tibble: 4 × 3
#> term estimate std.error
#> <chr> <dbl> <dbl>
#> 1 (Intercept)_1 3.70 0.00896
#> 2 x1_1 0.686 0.0143
#> 3 (Intercept)_2 9.49 0.163
#> 4 x1_2 0.161 0.613The predictions can be easily obtained from a fit.
library(ggplot2)
library(dplyr)
library(tidyr)
library(purrr)
models <- list(formula = mod1, tidymodels = mod2)
new_data <- sim_data$data |> distinct(x)
pred_sob <- map(models, ~ predict(.x, new_data,
type = "survival",
eval_time = seq(120)
))
bind_rows(pred_sob, .id = "modelo") |>
group_by(modelo) |>
mutate(id = new_data$x) |>
ungroup() |>
unnest(cols = .pred) |>
ggplot(aes(x = .eval_time, y = .pred_survival, col = id)) +
geom_line() +
theme_bw() +
facet_wrap(~modelo)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.