Data Science Pilot Action Set

Linear and Nonlinear Correlation Analysis Using the exploreCorrelation Action

This section contains PROC CAS code.

Note: Input data must be accessible in your CAS session, either as a CAS table or as a transient-scope table. A CAS table has a two-level name: the first level is your CAS engine libref, and the second level is the table name. You refer to this table in the CAS procedure by specifying only the second level. For more information about two-level names, see Chapter 2, Shared Concepts (SAS Viya: Machine Learning Procedures). A transient-scope table is called directly from the action and exists in memory for the duration of the action. For more information about accessing data, see SAS Viya: System Programming Guide. For more information about PROC CAS and programming in CASL, see SAS Cloud Analytic Services: CASL Programmer’s Guide and SAS Cloud Analytic Services: CASL Reference.

The following DATA step creates the reference data table mycas.dmagecr in your CAS session, keeping only the age, amount, coapp, duration, foreign, job, and good_bad variables. These statements assume that your CAS engine libref is named mycas, but you can substitute any appropriately defined CAS engine libref.

data mycas.dmagecr;
   set sampsio.dmagecr;
run;

The following statements run the exploreCorrelation action to analyze the correlation structure among the input variables. The action outputs a data table that contains the specified correlation statistics. Note that you can specify different sets of statistics for the nominal-nominal, nominal-interval (interval-nominal), and interval-interval pairs of variables. The action outputs the results of the analysis in an output data table named correlation_out.

proc cas;
   loadactionset "dataSciencePilot";
   dataSciencePilot.exploreCorrelation
                            / table     = "DMAGECR"
                              casOut    = {name    = "CORRELATION_OUT",
                                           replace = True}
                              stats = {nominalNominal={"mi", "su"},
                                       nominalInterval = {"mi", "su", "ftest"},
                                       intervalInterval = {"mi", "su", "pearson"}}
                              inputs    = {{name   = "age"},
                                           {name   = "amount"},
                                           {name   = "coapp"},
                                           {name   = "duration"},
                                           {name   = "foreign"},
                                           {name   = "job"}}
                              nominals  = {"coapp",  "foreign", "job"}
                              ;
   run;
   fetch / table = "CORRELATION_OUT";
   run;
quit;

The table parameter names the input reference data table. The casOut parameter names the output data table. The stats parameter specifies the set of correlation statistics to be computed for the three types of variable pairs: nominal-nominal, nominal-interval (interval-nominal), and interval-interval. The inputs parameter specifies that the age, amount, coapp, duration, foreign, and job variables be used as inputs. The nominals parameter specifies that the coapp, foreign, and job variables be used as nominal variables.

The CAS output table is shown in Output 11.4.1.

Output 11.4.1: Linear and Nonlinear Correlation

Results from table.fetch

Selected Rows from Table CORRELATION_OUT
_Index_	FirstVariable	SecondVariable	Type	FTest	ProbF	Pearson	MI	SU
1	age	coapp	_ni_1	0.4760598374	0.6213674487	.	0.0569117234	0.0203743996
2	age	foreign	_ni_2	0.0377654221	0.8459544813	.	0.0366299985	0.0135726197
3	age	job	_ni_3	9.2311591127	5.0223212E-6	.	0.1439877347	0.0449737593
4	amount	age	_ii_4	.	.	0.0327164167	0.157533392	0.0415035709
5	amount	coapp	_ni_1	5.0520611592	0.0065608658	.	0.1117193311	0.033948961
6	amount	foreign	_ni_2	2.5062714187	0.1137107593	.	0.070317726	0.0204682618
7	amount	job	_ni_3	41.857737606	1.771472E-25	.	0.1972747899	0.0559120431
8	duration	age	_ii_4	.	.	-0.036136374	0.0877997891	0.0254065779
9	duration	amount	_ii_5	.	.	0.6249841983	0.4977154476	0.1435409495
10	duration	coapp	_ni_1	1.1698717128	0.3108324871	.	0.022866107	0.0113106304
11	duration	foreign	_ni_2	19.431116448	0.000011559	.	0.0401058248	0.0210146317
12	duration	job	_ni_3	16.675218685	1.41484E-10	.	0.0704659213	0.0290523974
13	foreign	coapp	_nn_1	.	.	.	0.0073658606	0.01921124
14	job	coapp	_nn_1	.	.	.	0.0121146554	0.012413386
15	job	foreign	_nn_2	.	.	.	0.0072576768	0.0088412863

Linear and Nonlinear Correlation Analysis Using the exploreCorrelation Action

This section contains Lua code for the analysis in the CASL version of this example, which contains details about the results.

Note: In order to run this code, the data that are described in the CASL version need to be accessible to the CAS server. One way to do this is to convert the dmagecr data to the comma-separated-value (CSV) file dmagecr.csv and then use the following code to load the CSV file into CAS:

s:loadtable{casLib="casuser", path="dmagecr.csv"}

For more information about coding in Lua, see Getting Started with SAS Viya for Lua and SAS Viya: System Programming Guide.

s:loadactionset{actionset="dataSciencePilot"}

s:dataSciencePilot_exploreCorrelation {
                              table     = {name ="DMAGECR"},
                              casOut    = {name    = "CORRELATION_OUT",
                                           replace = True},
                              stats = {nominalNominal={"mi", "su"},
                                       nominalInterval = {"mi", "su", "ftest"},
                                       intervalInterval = {"mi", "su", "pearson"}},
                              inputs    = {{name   = "age"},
                                           {name   = "amount"},
                                           {name   = "coapp"},
                                           {name   = "duration"},
                                           {name   = "foreign"},
                                           {name   = "job"}},
                              nominals  = {"coapp",  "foreign", "job"}
}

s:fetech{table = {name = "CORRELATION_OUT"}}

For example output from the action, see the CASL code examples.

Linear and Nonlinear Correlation Analysis Using the exploreCorrelation Action

This section contains R code for the analysis in the CASL version of this example, which contains details about the results.

m <- cas.read.csv(s, "dmagecr.csv", casOut=list(name="dmagecr"))

For more information about coding in R, see Getting Started with SAS Viya for R and SAS Viya: System Programming Guide.

cas.loadActionSet(s,"dataSciencePilot")

cas.dataSciencePilot.exploreCorrelation(
   s,
   table     = list(name ="DMAGECR"),
   casOut    = list(name = "CORRELATION_OUT",
                    replace = True),
   stats = list(list(nominalNominal=list("mi", "su")),
                list(nominalNominal=list("mi", "su", "ftest")),
                list(nominalNominal=list("mi", "su", "pearson"))),
   inputs    = list("age",
                    "amount",
                    "coapp",
                    "duration",
                    "foreign",
                    "job"),
   nominals  = list("coapp",
                    "foreign",
                    "job")
)

cas.fetch(s, table = list(name = "CORRELATION_OUT"))

For example output from the action, see the CASL code examples.

Linear and Nonlinear Correlation Analysis Using the exploreCorrelation Action

This section contains Python code for the analysis in the CASL version of this example, which contains details about the results.

s.upload_file('dmagecr.csv')

For more information about coding in Python, see Getting Started with SAS Viya for Python and SAS Viya: System Programming Guide.

s.loadactionset(actionset="dataSciencePilot")
s.dataSciencePilot.exploreCorrelation(
     table     = {"name" : "DMAGECR"},
     casOut    = {"name" : "CORRELATION_OUT", "replace" : True},
     stats = {"nominalNominal":["mi", "su"],
              "nominalInterval":["mi", "su", "ftest"],
              "intervalInterval":["mi", "su", "pearson"]},
     inputs    = ["age", "amount", "coapp", "duration", "foreign", "job"],
     nominals  = ["coapp", "foreign", "job"]
 )
s.fetch(table = {"name":"CORRELATION_OUT"})

For example output from the action, see the CASL code examples.

Last updated: October 09, 2026