Description Usage Arguments Value See Also

View source: R/zeitzeiger_predict.R

`zeitzeigerBatch`

trains and tests a predictor on multiple datasets
independently, using `ComBat`

to correct for batch effects prior
to running `zeitzeiger`

. This function requires the `metapredict`

package.

1 2 3 4 5 6 | ```
zeitzeigerBatch(ematList, trainStudyNames, sampleMetadata, studyColname,
batchColname, timeColname, fitMeanArgs = list(rparm = NA, nknots = 3),
constVar = TRUE, fitVarArgs = list(rparm = NA), nTime = 10,
useSpc = TRUE, sumabsv = 2, orth = TRUE, nSpc = 2, betaSv = FALSE,
timeRange = seq(0, 1, 0.01), covariateName = NA, featuresExclude = NULL,
dopar = TRUE)
``` |

`ematList` |
Named list of matrices of measurements, one for each dataset, some of which will be for training, others for testing. Each matrix should have rownames corresponding to sample names and colnames corresponding to feature names. |

`trainStudyNames` |
Character vector of names in |

`sampleMetadata` |
data.frame containing relevant information for each sample across all datasets. |

`studyColname` |
Name of column in |

`batchColname` |
Name of column in |

`timeColname` |
Name of column in |

`fitMeanArgs` |
List of arguments to pass to |

`constVar` |
Logical indicating whether to assume constant variance as a function of the periodic variable. |

`fitVarArgs` |
List of arguments to pass to |

`nTime` |
Number of time-points by which to discretize the time-dependent behavior of each feature. Corresponds to the number of rows in the matrix for which the SPCs will be calculated. |

`useSpc` |
Logical indicating whether to use |

`sumabsv` |
L1-constraint on the SPCs, passed to |

`orth` |
Logical indicating whether to require left singular vectors
be orthogonal to each other, passed to |

`nSpc` |
Vector of the number of SPCs to use for prediction. If |

`betaSv` |
Logical indicating whether to use the singular values of the SPCs as weights in the likelihood calculation. |

`timeRange` |
Vector of values of the periodic variable at which to calculate likelihood. The time with the highest likelihood is used as the initial value for the MLE optimizer. |

`covariateName` |
Name of column(s) in |

`featuresExclude` |
Named list of character vectors corresponding to features to exclude from being used for prediction for the respective test datasets. |

`dopar` |
Logical indicating whether to process the folds in parallel.
Use |

`spcResultList` |
List of results from |

`timeDepLike` |
3-D array of likelihood, with dimensions for each test observation
(across all datasets), each element of |

`mleFit` |
List (for each element in |

`timePred` |
Matrix of predicted times for test observations by values of |

`zeitzeiger`

, `metapredict`

, `ComBat`

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