|The MIANALYZE Procedure|
This example combines sample correlation coefficients computed from a set of imputed data sets by using Fisher’s transformation.
Fisher’s transformation of the sample correlation is
The statistic is approximately normally distributed with mean
and variance , where is the population correlation coefficient and is the number of observations.
The following statements use the CORR procedure to compute the correlation and its associated Fisher’s statistic between variables Oxygen and RunTime for each imputed data set. The ODS statement is used to save Fisher’s statistic in an output data set.
proc corr data=outmi fisher(biasadj=no); var Oxygen RunTime; by _Imputation_; ods output FisherPearsonCorr= outz; run;
The following statements display the number of observations and Fisher’s statistic for each imputed data set in Output 55.10.1:
proc print data=outz; title 'Fisher''s Correlation Statistics'; var _Imputation_ NObs ZVal; run;
The following statements generate the standard error associated with the 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 55.10.2. The ODS 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;
In addition to the estimate for , PROC MIANALYZE also generates confidence limits for , and . The following statements print the estimate and confidence limits for in Output 55.10.3:
proc print data=parms; title 'Parameter Estimates with 95% Confidence Limits'; var Estimate LCLMean UCLMean; run;
An estimate of the correlation coefficient with its corresponding 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 confidence limits, as shown in Output 55.10.4:
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;
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