# The MIANALYZE Procedure

### Example 76.11 Combining Correlation Coefficients

This example combines sample correlation coefficients that are computed from a set of imputed data sets by using Fisher’s z transformation.

Fisher’s z transformation of the sample correlation r is

The statistic z is approximately normally distributed, with mean

and variance , where is the population correlation coefficient and n is the number of observations.

The following statements use the CORR procedure to compute the correlation r and its associated Fisher’s z statistic between the variables Oxygen and RunTime for each imputed data set.

ods select none;
var Oxygen RunTime;
by _Imputation_;
ods output FisherPearsonCorr=outz;
run;
ods select all;


Because of the ODS SELECT statements, no output is displayed. The ODS OUTPUT statement is used to save Fisher’s z statistic in an output data set. The following statements display the number of observations and Fisher’s z statistic for each imputed data set in Output 76.11.1:

proc print data=outz (obs=10);
title 'Fisher''s Correlation Statistics (First 10 Imputations)';
var _Imputation_ NObs ZVal;
run;


Output 76.11.1: Output z Statistics

 Fisher's Correlation Statistics (First 10 Imputations)

Obs _Imputation_ NObs ZVal
1 1 31 -1.27869
2 2 31 -1.30715
3 3 31 -1.27922
4 4 31 -1.39243
5 5 31 -1.40146
6 6 31 -1.22323
7 7 31 -1.27163
8 8 31 -1.17783
9 9 31 -1.36075
10 10 31 -1.23652

The following statements generate the standard error associated with the z statistic, :

data outz;
set outz;
StdZ= 1. / sqrt(NObs-3);
run;


The following statements use the MIANALYZE procedure to generate a combined parameter estimate and its variance, as shown in Output 76.11.2. The ODS OUTPUT statement is used to save the parameter estimates in an output data set.

proc mianalyze data=outz;
ods output ParameterEstimates=parms;
modeleffects ZVal;
stderr StdZ;
run;


Output 76.11.2: Combining Fisher’s z Statistics

The MIANALYZE Procedure

Parameter Estimates (25 Imputations)
Parameter Estimate Std Error 95% Confidence Limits DF Minimum Maximum Theta0 t for H0:
Parameter=Theta0
Pr > |t|
ZVal -1.292006 0.202699 -1.68963 -0.89438 1403.6 -1.401459 -1.098356 0 -6.37 <.0001

In addition to the estimate for z, PROC MIANALYZE also generates 95% confidence limits for z, and . The following statements print the estimate and 95% confidence limits for z in Output 76.11.3:

proc print data=parms;
title 'Parameter Estimates with 95% Confidence Limits';
var Estimate LCLMean UCLMean;
run;


Output 76.11.3: Parameter Estimates with 95% Confidence Limits

 Parameter Estimates with 95% Confidence Limits

Obs Estimate LCLMean UCLMean
1 -1.292006 -1.68963 -0.89438

An estimate of the correlation coefficient with its corresponding 95% confidence limits is then generated from the following inverse transformation as described in the section Correlation Coefficients:

for , , and .

The following statements generate and display an estimate of the correlation coefficient and its 95% confidence limits, as shown in Output 76.11.4:

data corr_ci;
set parms;
r=       tanh( Estimate);
r_lower= tanh( LCLMean);
r_upper= tanh( UCLMean);
run;
proc print data=corr_ci;
title 'Estimated Correlation Coefficient'
' with 95% Confidence Limits';
var r r_lower r_upper;
run;


Output 76.11.4: Estimated Correlation Coefficient

 Estimated Correlation Coefficient with 95% Confidence Limits

Obs r r_lower r_upper
1 -0.85965 -0.93410 -0.71355