proc import datafile='/home/wngamou/multivariate_stats/surveydata2.txt'
out=mydataA1 dbms=tab replace;
run;
data mydataA2;
set mydataA1;
label X1='Feminists' X2='Conservatives'
    X3='Blacks' X4='The Women''s Movement' X5='Liberals' X6='Hispanic-Americans'
    X7='Lawyers' X8='Southerners' X9='Whites' X10='Jews' X11='Immigrants' X12='Asian-Americans';
    run;

/* Principal component analysis with covariance and varimax. */
Title 'Principal Component Factoring';
proc factor data=mydataA2
        msa
        method=principal
        cov
        scree
        score
        corr
        preplot
        plots(flip)=all
        rotate=v;
        var x:;
        run;

/* Principal Axis Factoring with covariance and varimax. */
Title 'Principal Axis Factoring';
proc factor data=mydataA2
        msa
        method=prinit
        priors=one
        cov
        maxiter=100
        scree
        score
        corr
        preplot
        plots(flip)=all
        rotate=v
        ;
        var x:;
        run;

/* Image factoring with covariance and varimax. */
Title 'Image Analysis';
proc factor data=mydataA2
        msa
        method=prinit
        priors=smc
        cov
        maxiter=100
        scree
        score
        corr
        preplot
        plots(flip)=all
        rotate=v
        ;
        var x:;

run;



title "Principal Component Factoring";
proc factor data=mydataA2 method=principal n=3 priors=one plot plots(flip)=all residuals;
run;


title "Principal Axis Factoring";
proc factor data= mydataA2 method=prinit n=3 priors=one plot plots(flip)=all residuals ;
run;


title "Image Analysis";
proc factor data=mydataA2 method=prinit priors=smc plot plots(flip)=all residuals ;
run;