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survcomp (version 1.22.0)

mrmr.cindex.ensemble: Function to compute the concordance index for survival or binary class prediction

Description

Function to compute the minimum redundancy - maximum relevance (mRMR) ranking for a risk prediction or a binary classification task based on the concordance index. The mRMR feature selection has been adapted to use the concordance index to estimate the correlation between a variable and the output (binary or survival) data.

Usage

mrmr.cindex.ensemble(x, surv.time, surv.event, cl, weights, comppairs=10, strat, alpha = 0.05, outx = TRUE, method = c("conservative", "noether", "nam"), alternative = c("two.sided", "less", "greater"), maxparents, maxnsol, nboot = 200, na.rm = FALSE)

Arguments

x
a vector of risk predictions.
surv.time
a vector of event times.
surv.event
a vector of event occurence indicators.
cl
a vector of binary class indicators.
weights
weight of each sample.
comppairs
threshold for compairable patients.
strat
stratification indicator.
alpha
apha level to compute confidence interval.
outx
set to TRUE to not count pairs of observations tied on x as a relevant pair. This results in a Goodman-Kruskal gamma type rank correlation.
method
can take the value conservative, noether or name (see paper Pencina et al. for details).
alternative
a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" (concordance index is greater than 0.5) or "less" (concordance index is less than 0.5). You can specify just the initial letter.
maxparents
maximum number of candidate variables to be added in the ranking solutions tree.
maxnsol
maximum number of ranking solutions to be considered.
nboot
number of bootstraps to compute standard error of a ranking solution.
na.rm
TRUE if missing values should be removed.

Value

A mRMR ranking

References

Harrel Jr, F. E. and Lee, K. L. and Mark, D. B. (1996) "Tutorial in biostatistics: multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing error", Statistics in Medicine, 15, pages 361--387.

Pencina, M. J. and D'Agostino, R. B. (2004) "Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation", Statistics in Medicine, 23, pages 2109--2123, 2004.

See Also

rcorr.cens, phcpe, coxphCPE