CORRMV <Survo_data>,L computes means, standard deviations, and correlations from active variables and observations by accepting also cases containing missing values. The standard CORR module leaves out all incomplete cases. The default method (METHOD=1) is a simplified EM algorithm by S.Mustonen. In this method the data set is first standardized (means=0 stddevs=1) and the missing values are replaced by 0's. Thereafter estimates for missing values are improved iteratively by linear regressions where each variable is explained by all other variables. In each iteration, old estimates of missing values are replaced by the regression estimates. In one iteration, all regression parameters are obtained simply by updating the moment matrix of variables and by inverting it by the Cholesky method. Convergence of the process can be monitored by the mean squared difference of consecutive estimates of missing values. After ITER iterations (default ITER=20) the procedure is interrupted. To obtain unbiased estimates for variances, in sums of squares each term of a missing value is extended by the residual variance of the corres- ponding regression model. If the line for results (L) is given, the means, standard deviations, and correlations are printed in the edit field from line L onwards. If RESULTS=0 is given, only a summary of results is printed. In any case the results are saved in matrix files MSN.M and CORR.M as in CORR. By default, missing values are not replaced by any estimates. However, if a specification IMPUTE (or REPLACE) is given, missing values are filled in. By IMPUTE=REG they are replaced by their regression estimates. Please note that regression estimates of missing values are too well-adapted and the variability in the data is reduced. Thus, if means, standard deviations, and correlations were recomputed from the patched data, the variances would become smaller than those given by CORRMV from incomplete data. Also correlations would be more biased. By IMPUTE=REG+rand(123456789) missing values are replaced by reg.est+u*s where s is the square root of the residual variance of the regression model in question and u is a standard normal variate obtained by using the pseudo-random number generator rand with seed 123456789. In this case means, std.devs and correlations recomputed from the patched data are less biased. When METHOD=PAIRWISE is used, correlations are computed for non- missing pairs of observations. This may lead to more biased results than METHOD=1. Also the correlation matrix (CORR.M) may have negative eigenvalues (i.e. it is not positive definite or semidefinite). In METHOD=PAIRWISE the frequencies of observations for each pair of observations is saved as PAIRFREQ.M . 1 = More information on additional multivariate operations M = More information on multivariate analysis