In addition, any user can perform further selections based on the results printed in the console. Moreover, this multi-processing (MP) version overcomes the parallelization limitations of the existing parallel module, and this is accomplished by using vertical instead of horizontal partitions of large flat datasets, dynamic generation of the task pattern, tasks, and logs, all within a single execution of this second command, and the existing qsub module to automatically and continuously allocate the tasks to logical processors and thereby emulating with fewer resources a cluster environment.
In addition, PCDM4MP can trigger different instances of Stata, each using a distinct class of variables belonging to the same dataset and resulting after simple name filtering (first letter). The syntax is simple, and the tools show the exploration progress in real-time. Optionally, using threshold values for these three as parameters of PCDM, any user can select the most correlated variables based on high magnitude, significance, and support criteria. In addition, for each pair, they also report the corresponding significance and the number of non-null intersecting observations, and all this reporting is performed in a record-oriented manner (both source and output). They select variables based on identifying the absolute values of Pearson’s pairwise correlation coefficients between a chosen response variable and any other existing in the dataset. The paper describes PCDM and PCDM4MP as new tools and commands capable of exploring large datasets.
0 kommentar(er)
