You will need to add the ZEROS option to the WEIGHT statement. proc freq data=one; tables caseevent*controlevent / agree; weight number/zeros; exact mcnemar; run; ... View more

N is only defined locally, that is within the JackSampMat module.  To reference it outside the module you will need to define it globally by adding the GLOBAL clause to the module definition.   proc iml; /* If x is univariate, you can construct a matrix where each column contains a jackknife sample. Use for univariate column vector x when n < 20000 */ start JackSampMat(x) global(n); n = nrow(x); B = j(n-1, n,0); do i = 1 to n; B[,i] = remove(x, i)`; /* transpose to column vevtor */ end; return B; finish; /* Input: matrix where each column of X is a bootstrap sample. Return a row vector of statistics, one for each column. */ start EvalStatMat(x); return std(x); /* <== Example: return std dev of each sample */ finish; x = {58,67,74,74,80,89,95,97,98,107}; /* Weight gain (g) for 10 rats */ /* optional: visualize the matrix of jackknife samples */ *M = JackSampMat(x); *print M[c=("S1":"S10") r=("1":"9")]; /* Jackknife method for univariate data */ /* 1. compute observed statistic */ T = EvalStatMat(x); /* 2. compute same statistic on each jackknife sample */ T_LOO = EvalStatMat( JackSampMat(x) ); /* LOO = "Leave One Out" */ /* 3. compute mean of the LOO statistics */ T_Avg = mean( T_LOO` ); /* transpose T_LOO */ /* 4 & 5. compute jackknife estimates of bias and standard error */ biasJack = (n-1)*(T_Avg - T); stdErrJack = sqrt( (n-1)/n * ssq(T_LOO - T_Avg) ); result = T || T_Avg || biasJack || stdErrJack; print result[c={"Estimate" "Mean Jackknife Estimate" "Bias" "Std Error"}]; ... View more

Although it is easier to program with IML, you don't necessarily need to use it.  You could use Proc MEANS and a Data Step to do the same thing.  The code below will replicate the example he has in the header of the program.  You could probably turn this into a MACRO if you had a lot of these to do, but the gist of it would be:   data test; input g2 @@; g=sqrt(g2); cards; 5.8 7.2 6.1 8.5 ; proc means data=test; var g2 g; output out=out1 sum=sum_g2 sum_g n=m uss=ssq_g2 ssq_g; run; data out1; set out1; df=3; mg2=sum_g2/m; r=(1+1/m)*(ssq_g-(sum_g**2)/m)/(m-1); f=(mg2/df - r*(m-1)/(m+1))/(1+r); ddf=(m-1)*(1+1/r)**2/df**(3/m); p=1-probf(f,df,ddf); run; proc print; run; ... View more

In order to combine point estimates for any statistic in a multiple imputation setting, you must have a standard error associated with that point estimate.  The issue with using Proc FREQ is that it does not give you standard errors for either the percentages or the frequencies.  My suggestion would be to use Proc SURVEYFREQ instead and combine the results similar to the approach in the attached example.  Note that you must use the WTFREQ option on the TABLES statement even if you do not have a WEIGHT statement in order to get a standard error.   /* Generate Data */ proc format; value ResponseCode 1 = 'Very Unsatisfied' 2 = 'Unsatisfied' 3 = 'Neutral' 4 = 'Satisfied' 5 = 'Very Satisfied'; run; proc format; value UserCode 1 = 'New Customer' 0 = 'Renewal Customer'; run; proc format; value SchoolCode 1 = 'Middle School' 2 = 'High School'; run; proc format; value DeptCode 0 = 'Faculty' 1 = 'Admin/Guidance'; run; data SIS_Survey; format Response ResponseCode.; format NewUser UserCode.; format SchoolType SchoolCode.; format Department DeptCode.; do _imputation_=1 to 2; drop j; retain seed1 111; retain seed2 222; retain seed3 333; State = 'GA'; NewUser = 1; do School=1 to 71; call rantbl( seed1, .45, .55, SchoolType ); Department = 0; call rannor( seed3, x ); SamplingWeight = 25 + x * 2; do j=1 to 2; if ( SchoolType = 1 ) then call rantbl( seed2, .16, .21, .30, .24, .09, Response); else call rantbl( seed2, .18, .23, .30, .22, .07, Response); output; end; output; Department = 1; call rannor( seed3, x ); SamplingWeight = 15 + x * 1.5; do j=1 to 2; if ( SchoolType = 1 ) then call rantbl( seed2, .10, .15, .33, .28, .14, Response ); else call rantbl( seed2, .13, .20, .30, .26, .11, Response); output; end; end; NewUser = 0; do School=72 to 134; call rantbl( seed1, .45, .55, SchoolType ); Department = 0; call rannor( seed3, x ); SamplingWeight = 25 + x * 2; do j=1 to 2; if ( SchoolType = 1 ) then call rantbl( seed2, .16, .21, .30, .24, .09, Response); else call rantbl( seed2, .18, .23, .30, .22, .07, Response); output; end; output; Department = 1; call rannor( seed3, x ); SamplingWeight = 15 + x * 1.5; do j=1 to 2; if ( SchoolType = 1 ) then call rantbl( seed2, .10, .15, .33, .28, .14, Response ); else call rantbl( seed2, .13, .20, .30, .26, .11, Response); output; end; end; State = 'NC'; NewUser = 1; do School = 135 to 218; call rantbl( seed1, .45, .55, SchoolType ); Department = 0; call rannor( seed3, x ); SamplingWeight = 25 + x * 2; if ( SchoolType = 1 ) then call rantbl( seed2, .16, .21, .30, .24, .09, Response); else call rantbl( seed2, .18, .23, .30, .22, .07, Response); output; output; output; Department = 1; call rannor( seed3, x ); SamplingWeight = 15 + x * 1.5; if ( SchoolType = 1 ) then call rantbl( seed2, .10, .15, .33, .28, .14, Response ); else call rantbl( seed2, .13, .20, .30, .26, .11, Response); output; output; end; NewUser = 0; do School = 219 to 274; call rantbl( seed1, .45, .55, SchoolType ); Department = 0; call rannor( seed3, x ); SamplingWeight = 25 + x * 2; do j=1 to 2; if ( SchoolType = 1 ) then call rantbl( seed2, .16, .21, .30, .24, .09, Response); else call rantbl( seed2, .18, .23, .30, .22, .07, Response); output; end; output; Department = 1; call rannor( seed3, x ); SamplingWeight = 15 + x * 1.5; if ( SchoolType = 1 ) then call rantbl( seed2, .10, .15, .33, .28, .14, Response ); else call rantbl( seed2, .13, .20, .30, .26, .11, Response); output; output; end; State = 'SC'; NewUser = 1; do School = 275 to 328; call rantbl( seed1, .45, .55, SchoolType ); Department = 0; call rannor( seed3, x ); SamplingWeight = 25 + x * 2; do j=1 to 2; if ( SchoolType = 1 ) then call rantbl( seed2, .16, .21, .30, .24, .09, Response); else call rantbl( seed2, .18, .23, .30, .22, .07, Response); output; end; output; Department = 1; call rannor( seed3, x ); SamplingWeight = 15 + x * 1.5; if ( SchoolType = 1 ) then call rantbl( seed2, .10, .15, .33, .28, .14, Response ); else call rantbl( seed2, .13, .20, .30, .26, .11, Response); output; output; end; NewUser = 0; do School = 329 to 370; call rantbl( seed1, .45, .55, SchoolType ); Department = 0; call rannor( seed3, x ); SamplingWeight = 25 + x * 2; do j=1 to 2; if ( SchoolType = 1 ) then call rantbl( seed2, .16, .21, .30, .24, .09, Response); else call rantbl( seed2, .18, .23, .30, .22, .07, Response); output; end; output; Department = 1; call rannor( seed3, x ); SamplingWeight = 15 + x * 1.5; if ( SchoolType = 1 ) then call rantbl( seed2, .10, .15, .33, .28, .14, Response ); else call rantbl( seed2, .13, .20, .30, .26, .11, Response); output; output; end; end; run; /*Run SURVEYFREQ by _IMPUTATION_ assuming the MI step is already done*/ proc surveyfreq data=SIS_Survey; by _imputation_; tables Response*schooltype/wtfreq; ods output CrossTabs=mi_ctab; run; /*Sort the data by the TABLES variables which is called RESPONSE here*/ proc sort data=mi_ctab; by response schooltype _imputation_; run; /*Run MIANALYZE with STDERR option*/ proc mianalyze data=mi_ctab; by response schooltype;*this would be the TABLES variable; modeleffects wgtfreq; stderr stdDev; title 'Results for Weighted Frequency'; run;       ... View more

You have to use both the CLASS statement and PARMS= input in Proc MIANALYZE to combine the Parameter Estimates from GLIMMIX.   Try  proc mianalyze  parms(classvar=full)=a_mvn; class biol; modeleffects intercept biol decile; run;   ... View more

when you have an interaction involving all the main effects in the model, the odds ratios are not reported by default.  Instead you need to use the ODDSRATIO statement to request the specific odds ratios you are interested in.   The link below has a good discussion of how to report and request odds ratios when you have an interaction. https://support.sas.com/kb/24/455.html  ... View more