Description Usage Arguments Details Value Author(s) References Examples

View source: R/cv_method_MC_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_MC, but instead features generated are correlated.

1 2 | ```
cv_method_MC_corr(mu0, p, m, n, alpha_list, nrep, p1 = 0.5, ss = F, ntest,
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. |

`ntest` |
Sample size for the test dataset. |

`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 2016, supplementary materials. This function is used to verify if a study using the sample sizes in Table 1 of the manuscript attains the PCC target via MC simulations.

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 19 20 21 | ```
## 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=matrix(rep(0,p^2),nrow=p)
rho[1:pcorr,1:pcorr]=rho.cs
diag(rho)=rep(1,p)
chol.rho1.500=chol(rho[1:pcorr,1:pcorr])
set.seed(1)
cv_method_MC_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,ntest=100,pcorr=10,chol.rho=chol.rho1.500)
#return: 0.623 0.670 0.576
#alpha_list should be a dense list of p-value cutoffs;
#here we only use a few values to ease computation of the example.
``` |

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