calcVecLMs {CNVScope} | R Documentation |
Creates a matrix of linear regression p-values, log transformed from every combination of columns in the parent matrix.
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).
#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).