Description Usage Arguments Value References Examples

View source: R/mvMISE_e_perm.r

This function calls mvMISE_e multiple times by permuting the row index (observations) of the covariate matrix X. It may take a long time to permute high-dimensional outcomes, but can be run in parallel using multiple nodes.

1 2 | ```
mvMISE_e_perm(nperm = 100, nnodes = 2, Y, X, id, Zidx = 1, maxIter = 100, tol = 0.001,
lambda = 0.05, cov_miss = NULL, miss_y = TRUE, sigma_diff = FALSE)
``` |

`nperm` |
the number of permutations. |

`nnodes` |
the number of nodes that will be used in parallel for permutations. |

`Y` |
an outcome matrix. Each row is a sample, and each column is an outcome variable, with potential missing values (NAs). |

`X` |
a covariate matrix. Each row is a sample, and each column is a covariate. The covariates can be common among all of the outcomes (e.g., age, gender) or outcome-specific. If a covariate is specific for the k-th outcome, one may set all the values corresponding to the other outcomes to be zero. If X is common across outcomes, the row number of X equals the row number of Y. Otherwise if X is outcome-specific, the row number of X equals the number of elements in Y, i.e., outcome-specific X is stacked across outcomes within each cluster. See the Examples for demonstration. |

`id` |
a vector for cluster/batch index, matching with the rows of Y, and X if it is not outcome specific. |

`Zidx` |
the column indices of matrix X used as the design matrix of random effects. The default is 1, i.e., a random intercept is included if the first column of X is a vector of 1s. If Zidx=c(1,2), then the model would estimate the random intercept and the random effects of the 2nd column in the covariate matrix X. The random-effects in this model are assumed to be independent. |

`maxIter` |
the maximum number of iterations for the EM algorithm. |

`tol` |
the tolerance level for the relative change in the observed-data log-likelihood function. |

`lambda` |
the tuning parameter for the graphical lasso penalty of the error precision matrix. It can be selected by AIC (an output). |

`cov_miss` |
the covariate that can be used in the missing-data model. If it is NULL, the missingness is assumed to be independent of the covariates. Check the Details for the missing-data model. If it is specified and the covariate is not outcome specific, its length equals the length of id. If it is outcome specific, the outcome-specific covariate is stacked across outcomes within each cluster. |

`miss_y` |
logical. If TRUE, the missingness depends on the outcome Y (see the Details). The default is TRUE. This outcome-dependent missing data pattern was motivated by and was observed in the mass-spectrometry-based quantitative proteomics data. |

`sigma_diff` |
logical. If TRUE, the sample error variance of the first sample is different from that for the rest of samples within each cluster. This option is designed and used when analyzing batch-processed proteomics data with the first sample in each cluster/batch being the common reference sample. The default is FALSE. |

The permutation based p-values for testing if fixed-effects (excluding the intercept) are zeros.

Jiebiao Wang, Pei Wang, Donald Hedeker, and Lin S. Chen. Using multivariate mixed-effects selection models for analyzing batch-processed proteomics data with non-ignorable missingness. Biostatistics. doi:10.1093/biostatistics/kxy022

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