TimeSurvProb | R Documentation |
Given a readily fitted regularized Cox regression model, this function predicts the cumulative survival probabilities for new data at time points determined by the user. The function uses c060-package's functionality for computing base hazard, and then performs linear predictions for new observations using the fitted regularized Cox regression model.
TimeSurvProb(
fit,
time,
event,
olddata,
newdata,
s,
times = c(1:36) * 30.5,
plot = FALSE
)
fit |
A single regularized Cox regression model fitted using glmnet |
time |
Time to events for the training data |
event |
Event indicators for the training data (0 censored, 1 event) |
olddata |
The old data matrix used to fit the original 'fit' glmnet-object |
newdata |
The new data matrix for which to predict time-to-event prediction (should comform to the old data matrix) |
s |
The optimal lambda parameter as used in the glmnet-package for its fit objects |
times |
The time points at which to estimate the cumulative survival probabilities (by default in days) |
plot |
Should the cumulative survival probabilities be plotted as a function of time |
Cumulative survival probabilities at the chosen time points
Teemu Daniel Laajala teelaa@utu.fi
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