proc cas; factorAnalysis.faExtract / table={name="cars", caslib="casuser"} inputs={"MSRP", "EngineSize", "Cylinders", "Horsepower", "MPG_City", "MPG_Highway", "Weight", "Wheelbase"} nFactors={3} method={name="ML"} priors={type="SMC"} rotate={type="VARIMAX"} display={names={"Eigenvalues", "FactorP...
proc cas; factorAnalysis.faNFactors / table={name="factor_data", caslib="casuser"} inputs={"x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8"}; run;
proc cas; factorAnalysis.faNFactors / table={name="factor_data", caslib="casuser"} inputs={"x1", "x2", "x3", "x4", "x5", "x6", "x7", "x8"} criteria={{type="EIGENVALUE", threshold=1.0}, {type="PARALLEL", nSimulations=500, seed=12345, alpha=0.05}} nFactors="MAX" priors={type="SMC"}; run;
proc cas; ica.fastIca table={name="mixed_signals", caslib="casuser"} inputs={"x1", "x2", "x3"} n=3; run;
proc cas; ica.fastIca table={name="mixed_signals", caslib="casuser"} inputs={"x1", "x2", "x3"} n=3 method={name="SYMMETRIC", maxIter=1000, tolerance=1e-6} gFunction="LOGCOSH" seed=9876 output={casOut={name="ica_scores", replace=true}, component="IndComp", white="WhitenedVar"} display={names={"Eig...
proc cas; fpca.fPcaScore table={name="score_data", caslib="casuser"} model={name="fpca_model", caslib="casuser"} output={casout={name="score_results", caslib="casuser", replace=true}}; run;
proc cas; fpca.fPcaScore table={name="score_data", caslib="casuser"} id={"id"} inputs={"value"} model={name="fpca_model", caslib="casuser"} output={casout={name="advanced_scores", replace=true}, npc=2} outputTables={names={"ScoreInfo"}, replace=true}; run;
proc cas; table.fileInfo / caslib="casuser"; run;
proc cas; table.fileInfo / caslib="casuser" path="%.csv" kbytes=true; run;
proc cas; ruleMining.fism / table={name="market_basket", caslib="casuser"} idVariable="id_transaccion" tgtVariable="producto" suppct=40 out={name="sets_frecuentes", caslib="casuser"}; run;