iCoxph-class object, add (or update) standard error (SE)
estimates through bootstrap methods, or compute the coefficient estimates
from the given number of bootstrap samples.
1 2 3 4 5 6 7
A positive integer specifying number of bootstrap samples used for
SE estimates. A large number, such as 200, is often needed for a more
reliable estimation in practice. If
A character value specifying the way computing SE from bootstrap samples. The default method is based on median absolute deviation and the second method is based on inter-quartile, both of which are based on normality of the bootstrap estimates and provides robust estimates for SE. The third method estimates SE by the standard deviation of the bootstrap estimates.
A logical value. If
Other arguments for future usage. A warning will be thrown if any invalid argument is specified.
Three different methods are available for computing SE from bootstrap
samples through argument
se. Given the fact that the bootstrap
method is computationally intensive, the function returns the coefficient
estimates in a matrix from the given number of bootstrap samples when
return_beta = TRUE) is specified, which can be used in parallel
computing or high performance computing (HPC) cluster. The SE estimates can
be further computed based on estimates from bootstrap samples by users on
their own. The
return_beta = TRUE is implied, when
B = 1 is
iCoxph-class object or a numeric matrix that contains
the covariate coefficient estimates from the given number of bootstrap
samples in rows.
iCoxph for fitting integrative Cox model.
## See examples of function 'iCoxph'.
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