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Using PROC LOGISTIC to Estimate the Rasch Model by Pan and Chen

http://support.sas.com/resources/papers/proceedings11/342-2011.pdf

Can this be right?  If right, it helps reduce the computational demand off the procedure.   Page 4:

"When thousands of persons take a test, the procedure takes a long time to estimate the parameters. It is well known that the Rasch model gives the same parameter estimates for each person who receives the same total score. So, variable ‘person’ is able to be replaced with variable ‘total’ when all examinees answer all items as shown by Nord (2008). After the model is fit, the estimate of the parameter for each person is equal to the estimate of the parameter of the total score corresponding to the person’s total score. The third code example and its output are shown as follows:"

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SAS Read from the Web

filename foo url "http://www.cnn.com";
data jaz;
infile foo length=len /*lrecl=32767*/;
input record $varying1000. len ;
put record $varying1000. len ;
if _n_=10 then stop;
run;

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MySQL Basic codes

DROP TABLE data._01_abc;

CREATE TABLE data._01_abc (

var1(22) NULL,

var2 int NULL,
var3 char(2) NULL);

LOAD DATA INFILE "sql.csv"
INTO TABLE data._01_abc
COLUMNS TERMINATED BY ','
LINES TERMINATED BY '\n'
IGNORE 1 LINES;

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SAS PROC IMPORT

PROC IMPORT OUT= WORK.list
DATAFILE= ".....xlsx"
DBMS=EXCEL REPLACE;
RANGE="Sheet1$";
GETNAMES=YES;
MIXED=NO;
SCANTEXT=YES;
USEDATE=YES;
SCANTIME=YES;
RUN;

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SQL: Create Table and Bulk Insert

 

CREATE TABLE DFC3
(
xxx VARCHAR(30),
xxx INT ,
xxx VARCHAR(30),
xxx VARCHAR(19),
xxx VARCHAR(6),
xxx INT,
xxx INT ,
xxx VARCHAR(19),
xxx INT ,
x  INT ,
xxx VARCHAR(33),
xx INT ,
x INT ,

)

GO
BULK
INSERT DFC3
FROM 'xxxx.csv'
WITH
(
FIELDTERMINATOR = ',',
ROWTERMINATOR = '\n'
)
GO
SELECT TOP 10 *
FROM DFC3
GO
SELECT *
FROM DFC3
GO

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Logistic regression, comparing group means

proc glimmix data=asdf namelen=32;
where disaster_type /*age_desc*/ ne "";
class GROUPING_D_RISK_PT;

model &out
=
GROUPING_D_RISK_PT

/solution ddfm=kr dist=binomial link=logit s STDCOEF ;

lsmeans GROUPING_D_RISK_PT / ilink diff;

output out=gmxout residual=resid;
ods output
ParameterEstimates=kaz1
CovParms=uekawa1
nobs=jeana
ModelInfo=estes
dimensions=diminfo
ConvergenceStatus=concon
FitStatistics=FITSTAT
Diffs=DIF_RESULT
;
run;