Comments (3)
Should be good to go now. Thank you for the suggestion and the great example!
library(aorsf)
library(survival)
lung_orsf <- na.omit(lung)
lung_orsf$surv <- Surv(lung_orsf$time, lung_orsf$status)
lung_orsf <- lung_orsf[, -c(2,3)]
fit <- orsf(lung_orsf, formula = surv ~ age + ph.ecog)
predict(fit, new_data = lung_orsf[1:5, ])
#> [,1]
#> [1,] 0.3283840
#> [2,] 0.3673719
#> [3,] 0.3346957
#> [4,] 0.4501146
#> [5,] 0.4982628
orsf_vi_negate(fit)
#> ph.ecog age
#> 0.2265581 -0.0412957
orsf_summarize_uni(object = fit, n_variables = 1)
#>
#> -- ph.ecog (VI Rank: 1) -----------------------
#>
#> |---------------- risk ----------------|
#> Value Mean Median 25th % 75th %
#> -1.3 0.2740017 0.2493573 0.2267411 0.3259105
#> 0.06 0.3642472 0.3664885 0.3522197 0.3812756
#>
#> Predicted risk at time t = 268 for top 1 predictors
Created on 2022-11-02 by the reprex package (v2.0.1)
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Absolutely - that makes sense to me. I will make an update soon.
from aorsf.
Awesome, thank you! 🙌
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Related Issues (20)
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