Description Usage Arguments Details Value Author(s) References Examples

View source: R/cv_method_corr.R

Determine the probability of correct classification (PCC) for a high dimensional classification study employing Cross validation classifier. This is similar to cv_method, but features generated are correlated.

1 2 | ```
cv_method_corr(mu0, p, m, n, alpha_list, nrep, p1 = 0.5, ss = F, pcorr,
chol.rho,sampling.p=0.5)
``` |

`mu0` |
The effect size of the important features. |

`p` |
The number of the features in total. |

`m` |
The number of the important features. |

`n` |
The total sample size for the two groups. |

`alpha_list` |
The search grid for the p-value threshold. |

`nrep` |
The number of simulation replicates employed to compute the expected PCC and/or sensitivity and specificity. |

`p1` |
The prevalence of the group 1 in the population, default to 0.5. |

`ss` |
Boolean variable, default to FALSE. The TRUE value instruct the program to compute the sensitivity and the specificity of the classifier. |

`pcorr` |
Number of correlated features. |

`chol.rho` |
Cholesky decomposition of the covariance of the pcorr features that are correlated. It is assumed that the m important features are part of the pcorr correlated features. |

`sampling.p` |
The assumed proportion of group 1 samples in the training data; default of 0.5 assumes groups are equally represented regardless of p1. |

Refer to Sanchez, Wu, Song, Wang 2015, Section 3 and Supplementary materials.

If ss=FALSE, the function returns the expected PCC. If ss=TRUE, the function returns a vector containing the expected PCC, sensitivity and specificity.

Meihua Wu <[email protected]> Brisa N. Sanchez <[email protected]> Peter X.K. Song <[email protected]> Raymond Luu <[email protected]> Wen Wang <[email protected]>

Sanchez, B.N., Wu, M., Song, P.X.K., and Wang W. (2016). "Study design in high-dimensional classification analysis." Biostatistics, in press.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
## Sigma_1 in the paper
#first block is pcorr x pcorr of compound symmetry
#other diagonal block is Identity; off diagonal blocks are 0
pcorr=10
p=500
rho.cs=.8
#create first block
rho=diag(c((1-rho.cs)*rep(1,pcorr),rep(1,p-pcorr)))+ matrix(c(rho.cs*
rep(1,pcorr),rep(0,p-pcorr)),ncol=1) %*% c(rep(1,pcorr),rep(0,p-pcorr))
chol.rho1.500=chol(rho[1:pcorr,1:pcorr])
lmax= max(eigen(rho)$values)
print(lmax)
set.seed(1)
cv_method_corr(mu0=0.4,p=500,m=10,n=80,alpha_list=c(0.0000001,0.0001,0.01),
nrep=10,p1=0.6,ss=TRUE,pcorr=pcorr,chol.rho=chol.rho1.500,sampling.p=0.5)
#return 0.6689385 0.6806896 0.6513119
#alpha_list should be a dense grid of pvalue cut-offs;
#three values are used here for simplicity of the example
``` |

HDDesign documentation built on May 29, 2017, 8:13 p.m.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.