The SURVEYIMPUTE Procedure

specifies the absolute weighted convergence criterion. The expectation maximization (EM) algorithm stops when the maximum absolute difference between the fractional weights from the previous iteration and the fractional weights from the current iteration is less than r. The default value of r is 0.00001. For more information, see the section FEFI Algorithm.

specifies the maximum number (i) of donor cells allowed for a recipient unit. If the maximum number of donor cells exceeds MAXDONORCELLS=, then no imputation is performed. By default, MAXDONORCELLS=5000.

specifies the maximum number (i) of iterations for the expectation maximization (EM) algorithm. By default, MAXEMITER=100.

specifies a relative weighted convergence criterion. The expectation maximization (EM) algorithm stops when the maximum absolute relative difference between the weights from the previous iteration and the weights from the current iteration is less than r. For more information, see the section FEFI Algorithm. The default value of r is 0.001.

specifies the absolute weighted convergence criterion. The expectation maximization (EM) algorithm stops when the maximum absolute difference between the first-stage fractional weights from the previous iteration and the first-stage fractional weights from the current iteration is less than r. For more information, see the section FEFI Algorithm. The default value of r is 0.00001.

displays the weighted mean or the crossproduct of the weighted mean sum of squares for variables that are specified in the VAR statement but not in the CLASS statement.

You can specify one of the following stats:

Displayed statistics are from both two-stage FEFI and FHDI because PROC SURVEYIMPUTE must perform two-stage FEFI in order to determine the donor sets for FHDI. The closer the means and crossproducts for FHDI are to the ones for two-stage FEFI, the more confidence you can have that FHDI is as efficient as FEFI.

specifies the maximum number (i) of second-stage donor cells allowed for a recipient unit. If the maximum number of second-stage donor cells exceeds i, then no imputation is performed. By default, MAXDONORCELLS=5000.

specifies the maximum number (i) of iterations for the expectation maximization (EM) algorithm for the first-stage FEFI. By default, MAXEMITER=100.

specifies the relative weighted convergence criterion. The expectation maximization (EM) algorithm for the first-stage FEFI stops when the maximum absolute relative difference between the weights from the previous iteration and the weights from the current iteration is less than r. For more information, see the section FEFI Algorithm. The default value of r is 0.001.

adjusts the replicate weights for FHDI. For more information, see the section Replicate Weight Adjustments for FHDI. You can specify one of the following values for replicate-adjustment-option:

By default, REPWTADJ=NEIGHBOR.

specifies how to perform the probability proportional to size (PPS) with replacement selection. For more information, see "Second-Stage Selection" in section Fractional Hot-Deck Imputation Algorithm. You can specify one of the following selection-options:

By default, SELECTION=PPSPERPATN for METHOD=FHDI.

requests donor selection by using the approximate Bayesian bootstrap method. For more information, see the section Approximate Bayesian Bootstrap.

requests donor selection by using simple random samples without replacement. For more information, see the section Simple Random Samples without Replacement.

requests donor selection by using simple random samples with replacement. For more information, see the section Simple Random Samples with Replacement.

requests donor selection by using probability proportional to respondent weights with replacement. For more information, see the section Weighted Selection.

specifies the number of PSUs to select for the bootstrap replicate samples. You can provide bootstrap stratum sample sizes by specifying a list of values. Alternatively, you can provide a single bootstrap sample size value to use for all strata or for a nonstratified design. For more information, see the section Bootstrap Method.

Each bootstrap sample size, , must be a positive integer and must be less than , which is the total number of PSUs in stratum h. By default, = for a stratified design. For a nonstratified design, the bootstrap sample size value must be less than n (the total number of PSUs in the sample). By default, m = n – 1 for a nonstratified design. You can provide the bootstrap sample size by specifying one of the following forms:

specifies the number of replicates for bootstrap variance estimation, where number must be an integer greater than 1. Increasing the number of replicates improves the estimation precision but also increases the computation time. By default, REPS=250.

requests Fay’s method, which is a modification of the BRR method. For more information, see the section Unadjusted Fay’s BRR Replicate Weights.

You can specify the value of the Fay coefficient, which is used in converting the original sampling weights to replicate weights. The Fay coefficient must be a nonnegative number less than 1. By default, the value of the Fay coefficient is 0.5.

names a SAS data set that contains the Hadamard matrix for BRR replicate construction. If you do not provide a Hadamard matrix by using this method-option, PROC SURVEYIMPUTE generates an appropriate Hadamard matrix for replicate construction. For more information, see the sections Balanced Repeated Replication (BRR) Method and Hadamard Matrix.

If a Hadamard matrix of a particular dimension exists, it is not necessarily unique. Therefore, if you want to use a specific Hadamard matrix, you must provide the matrix as a SAS data set in the HADAMARD= method-option.

In the HADAMARD= input data set, each variable corresponds to a column of the Hadamard matrix, and each observation corresponds to a row of the matrix. You can use any variable names in the HADAMARD= data set. All values in the data set must equal either 1 or –1. You must ensure that the matrix you provide is indeed a Hadamard matrix—that is, , where is the Hadamard matrix of dimension R and is an identity matrix. PROC SURVEYIMPUTE does not check the validity of the Hadamard matrix that you provide.

The HADAMARD= input data set must contain at least H variables, where H denotes the number of first-stage strata in your design. If the data set contains more than H variables, PROC SURVEYIMPUTE uses only the first H variables. Similarly, the HADAMARD= input data set must contain at least H observations.

If you do not specify the REPS= method-option, then the number of replicates is equal to the number of observations in the HADAMARD= input data set. If you specify the number of replicates—for example, REPS=nreps—then the procedure uses the first nreps observations in the HADAMARD= data set to construct the replicates.

You can specify the PRINTH method-option to display the Hadamard matrix that the procedure uses to construct replicates for BRR.

displays the Hadamard matrix that is used to construct replicates for BRR. When you provide the Hadamard matrix in the HADAMARD= method-option, PROC SURVEYIMPUTE displays only the rows and columns that are actually used to construct replicates. For more information, see the sections Balanced Repeated Replication (BRR) Method and Hadamard Matrix.

The PRINTH method-option is not available when you use a REPWEIGHTS statement to provide replicate weights, because the procedure does not use a Hadamard matrix in this case.

specifies the number of replicates for BRR variance estimation. The value of number must be an integer greater than 1.

If you do not provide a Hadamard matrix by using the HADAMARD= method-option, the number of replicates should be greater than the number of strata and should be a multiple of 4. For more information, see the section Balanced Repeated Replication (BRR) Method. If a Hadamard matrix cannot be constructed for the REPS= value that you specify, the value is increased until a Hadamard matrix of that dimension can be constructed. Therefore, it is possible for the actual number of replicates used to be larger than the REPS= value that you specify.

If you provide a Hadamard matrix by using the HADAMARD= method-option, the value of REPS= must not be greater than the number of rows in the Hadamard matrix. If you provide a Hadamard matrix and do not specify the REPS= method-option, the number of replicates equals the number of rows in the Hadamard matrix.

If you do not specify the REPS= or HADAMARD= method-option and do not include a REPWEIGHTS statement, the number of replicates equals the smallest multiple of 4 that is greater than the number of strata.

If you provide replicate weights by using a REPWEIGHTS statement, PROC SURVEYIMPUTE does not use the REPS= method-option. When you use a REPWEIGHTS statement, the number of replicates equals the number of REPWEIGHTS variables.