High Performance Statistical Modeling - SAS Support
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High Performance Statistical Modeling - SAS Support
execution modes of high-performance statistical modeling procedures to scale to a wide range of problem sizes. 1. SAS®
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SAS® and Big ; options set=GRIDINSTALLLOC="/opt/TKGrid"; libname gridLib greenplm server = "hpa.sas.com" user = XXXXX password = YYYYY database = myDB; proc hpgenselect data=gridLib.sgf2013; class c:; model yPoisson = x: c: / dist=poisson; selection method=stepwise(choose=sbc); performance details; run;
Figure 8 Distributed Mode Execution The HPGENSELECT Procedure Performance Information Host Node Execution Mode Grid Mode Number of Compute Nodes Number of Threads per Node
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Figure 8 continued Model Information Data Source Response Variable Class Parameterization Distribution Link Function Optimization Technique
GRIDLIB.SGF2013_SIM ypoisson GLM Poisson Log Newton-Raphson with Ridging
Selection Information Selection Method Select Criterion Stop Criterion Choose Criterion Effect Hierarchy Enforced Entry Significance Level (SLE) Stay Significance Level (SLS) Stop Horizon Number of Observations Read Number of Observations Used
Figure 9 shows the “Selection Summary” table and the “Selected Effects” table. You see that this model includes all the true effects (even xTiny). However, the final model that is chosen based on the SBC includes two nuisance effects, even though all the true effects are selected before any of the nuisance effects.
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Figure 9 Alongside Greenplum Execution The HPGENSELECT Procedure Selection Summary
