# Get started

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  0.859702

Tidymodels 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   0.613

The 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)