Hi All
I found survfit function was very slow for a large
dataset and I am looking for an alternative way to quickly get the predicted
survival probabilities.
My
historical data set is a pool of loans with monthly observed default status for
24 months. I would like to fit the proportional hazard model with time varying
covariate such as unemployment rates and time constant variables at loan
application in a counting process format, and then use the model to predict the
probability of default in each month during next 2 years for a pool of new
loans.
I
have read some posts from other R users. It sounds like using (average survival
probability)^exp((X-means(X)*Beta) can quickly get the predicted survival
probabilities. My predictors for the model include both continuous variables
and categorical variables and my dataset is in counting process format with
both time varying and time constant predictors. So how should I take the mean?
I guess itÂ’s the mean of training data? And the denominator for the mean is the
number of observations (i.e, the number of rows of training data in the counting
process format)? What if the predictor is a categorical variable?
Any
comments and suggestions are greatly appreciated.
Thanks!
Ying
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
