Description Usage Arguments Value Examples

Creates a matrix of linear regression p-values, log transformed from every combination of columns in the parent matrix.

1 2 3 | ```
calcVecLMs(bin_data, use_slurm = F, job_finished = F,
slurmjob = NULL, n_nodes = NULL, cpus_on_each_node = 2,
memory_per_node = "2g", walltime = "4:00:00")
``` |

`bin_data` |
The parent matrix, with columns to have linear regression performed on them. |

`use_slurm` |
Paralleize over a number of slurm HPC jobs? If false, the program will simply run locally. |

`job_finished` |
Are all the slurm jobs finished and the results need retrieving? |

`slurmjob` |
the slurm job object produced by rslurm::slurm_apply(), after running the function initially. |

`n_nodes` |
the number of nodes used in your slurm job. |

`cpus_on_each_node` |
The number of cpus used on each node |

`memory_per_node` |
the amount of ram per node (e.g. "32g" or "2g") |

`walltime` |
Time for job to be completed for SLURM scheduler in hh:mm:ss format. Defaults to 4h. |

The output matrix, or if using slurm, the slurm job object (which should be saved as an rds file and reloaded when creating the output matrix).

1 2 3 4 5 6 7 8 | ```
#small example
#bin_data<-matrix(runif(5*5),ncol=5)
foreach::registerDoSEQ()
#full_matrix<-suppressWarnings(calcVecLMs(bin_data))
#Please note that lm() will make a warning when there are two vectors that are too close
#numerically (this will always happen along the diagonal).
#This is normal behavior and is controlled & accounted for using this function as well as
#the postProcessLinRegMatrix function (which converts the infinite values to a maximum).
``` |

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