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The survivalmodels package implements neural networks
from the Python packages pycox. Importantly, this a
lighter but CRAN-compatible version of the ‘survivalmodels’ package
proposed by Raphael Sonabend based on the version 0.1.19. The complete
and updated version is available at this link.
# load dependencies
library(survival)
train <- simsurvdata(200)
# Fit the survival neural network
fit <- deepsurv(Surv(time, status) ~ ., data = train, frac = 0.3, activation = "relu",
num_nodes = c(4L, 8L, 4L, 2L), dropout = 0.1, early_stopping = TRUE, epochs = 100L,
batch_size = 32L)
# Return survivals for two independent individuals
test <- simsurvdata(1)
predict(fit, newdata = test)
#> 3.33999991416931 3.34299993515015 3.38000011444092 3.38899993896484
#> 0 0.9929 0.9858 0.9786 0.9715
#> 3.43600010871887 3.45600008964539 3.47300004959106 3.48600006103516
#> 0 0.9644 0.9573 0.9502 0.9431
#> 3.49499988555908 3.49900007247925 3.50300002098083 3.50799989700317
#> 0 0.9359 0.9218 0.9146 0.9075
#> 3.52600002288818 3.53500008583069 3.53699994087219 3.54699993133545
#> 0 0.9004 0.8933 0.8862 0.8791
#> 3.58899998664856 4.65999984741211 4.68200016021729 4.79400014877319
#> 0 0.8719 0.8648 0.8577 0.8506
#> 4.84000015258789 4.89699983596802 4.93200016021729 4.93699979782104
#> 0 0.8435 0.8363 0.8292 0.8221
#> 4.94500017166138 4.95900011062622 4.96199989318848 4.98600006103516
#> 0 0.815 0.8079 0.8008 0.7936
#> 4.98899984359741 4.99499988555908 4.99700021743774 5.00400018692017
#> 0 0.7865 0.7794 0.7723 0.7652
#> 5.00799989700317 5.01000022888184 5.02299976348877 5.02600002288818
#> 0 0.7581 0.7439 0.7368 0.7296
#> 5.02799987792969 5.07200002670288 5.18400001525879 5.30700016021729
#> 0 0.7225 0.7154 0.7083 0.7012
#> 5.34200000762939 5.35099983215332 5.35500001907349 5.3600001335144
#> 0 0.6941 0.6869 0.6798 0.6727
#> 5.36100006103516 5.38600015640259 5.39599990844727 5.40999984741211
#> 0 0.6656 0.6585 0.6513 0.6442
#> 5.41300010681152 5.42700004577637 5.42899990081787 5.43400001525879
#> 0 0.6371 0.63 0.6229 0.6158
#> 5.43699979782104 5.44700002670288 5.46700000762939 5.46799993515015
#> 0 0.6086 0.6015 0.5944 0.5733
#> 5.47100019454956 5.47499990463257 5.47700023651123 5.48699998855591
#> 0 0.5662 0.5591 0.5519 0.5378
#> 5.49300003051758 5.49399995803833 5.49499988555908 5.4980001449585
#> 0 0.5307 0.5235 0.5164 0.5093
#> 5.51300001144409 5.53599977493286 5.53800010681152 5.54099988937378
#> 0 0.5022 0.495 0.4809 0.4738
#> 5.54699993133545 5.55000019073486 5.55900001525879 5.56099987030029
#> 0 0.4667 0.4595 0.4524 0.4453
#> 5.56199979782104 5.56400012969971 5.56699991226196 5.57800006866455
#> 0 0.4382 0.431 0.4239 0.4168
#> 5.58500003814697 5.58799982070923 5.59600019454956 5.59700012207031
#> 0 0.4097 0.4025 0.3954 0.3883
#> 6.66099977493286 6.67500019073486 6.69000005722046 6.69099998474121
#> 0 0.3812 0.367 0.3599 0.3528
#> 6.74100017547607 6.77400016784668 6.77600002288818 6.78299999237061
#> 0 0.3457 0.3385 0.3314 0.3243
#> 6.80200004577637 6.80800008773804 6.80999994277954 6.81899976730347
#> 0 0.3172 0.31 0.3029 0.2958
#> 6.86899995803833 6.8769998550415
#> 0 0.2886 0.2886The survivalmodels package implements models from Python
using reticulate. In
order to use these models, the required Python packages must be
installed following with reticulate::py_install.
survivalmodels includes a helper function to install the
required pycox function (with pytorch if also required).
Before running any models in this package, if you have not already
installed pycox please run.
install_pycox(pip = TRUE, install_torch = FALSE)Install the latest release from CRAN:
install.packages("survivalmodels")Install the development version from GitHub:
remotes::install_github("RaphaelS1/survivalmodels")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.