mrmr.cindex.ensemble {survcomp} | R Documentation

## 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

### 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`

[Package survcomp version 1.40.0 Index]