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

Description Usage Arguments Value Note Author(s) References See Also

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

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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

Note

The "direction" of the concordance index (< 0.5 or > 0.5) is the opposite than the rcorr.cens function in the Hmisc package. So you can easily get the same results than rcorr.cens by changing the sign of x.

Author(s)

Benjamin Haibe-Kains, Markus Schroeder

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


survcomp documentation built on May 6, 2019, 2:28 a.m.