FCS <options>;
The FCS statement specifies a multivariate imputation by fully conditional specification methods. If you specify an FCS statement, you must also specify a VAR statement.
Table 75.2 summarizes the options available for the FCS statement.
Table 75.2: Summary of Options in FCS
Option 
Description 

Imputation Details 

Specifies the number of burnin iterations 

Data Set 

Outputs parameter estimates used in iterations 

ODS Graphics Output 

Displays trace plots 

Imputation Methods 

Specifies the discriminant function method 

Specifies the logistic regression method 

Specifies the regression method 

Specifies the predictive mean matching method 
The following options are available for the FCS statement in addition to the imputation methods specified (in alphabetical order):
The discriminant function, logistic regression, regression, and predictive mean matching methods are available in the FCS statement. You specify each method with the syntax
method < (<imputed < = effects > > </ options>) >
That is, for each method, you can specify the imputed variables and, optionally, a set of effects to impute these variables. Each effect is a variable or a combination of variables in the VAR statement. The syntax for the specification of effects is the same as for the GLM procedure. See Chapter 46: The GLM Procedure, for more information.
One general form of an effect involving several variables is
X1 * X2 * A * B * C ( D E )
where A
, B
, C
, D
, and E
are classification variables and X1
and X2
are continuous variables.
When an FCS statement is used without specifying any methods, the regression method is used for all imputed continuous variables and the discriminant function method is used for all imputed classification variables. In this case, for each imputed continuous variable, all other variables in the VAR statement are used as the covariates, and for each imputed classification variable, all other continuous variables in the VAR statement are used as the covariates.
When a method for continuous variables is specified without imputed variables, the method is used for all continuous variables in the VAR statement that are not specified in other methods. Similarly, when a method for classification variables is specified without imputed variables, the method is used for all classification variables in the VAR statement that are not specified in other methods.
For each imputed variable that does not use the discriminant function method, if no covariates are specified, then all other variables in the VAR statement are used as the covariates. That is, each continuous variable is used as a regressor effect, and each classification variable is used as a main effect. For an imputed variable that uses the discriminant function method, if no covariates are specified, then all other variables in the VAR statement are used as the covariates with the CLASSEFFECTS=INCLUDE option, and all other continuous variables in the VAR statement are used as the covariates with the CLASSEFFECTS=EXCLUDE option (which is the default).
With an FCS statement, the variables are imputed sequentially in the order specified in the VAR statement. For a continuous variable, you can use a regression method or a regression predicted mean matching method to impute missing values. For a nominal classification variable, you can use either a discriminant function method or a logistic regression method (generalized logit model) to impute missing values without using the ordering of the class levels. For an ordinal classification variable, you can use a logistic regression method (cumulative logit model) to impute missing values by using the ordering of the class levels. For a binary classification variable, either a discriminant function method or a logistic regression method can be used. By default, a regression method is used for a continuous variable, and a discriminant function method is used for a classification variable.
Note that except for the regression method, all other methods impute values from the observed values. See the section FCS Methods for Data Sets with Arbitrary Missing Patterns for a detailed description of the FCS methods.
You can specify the following imputation methods in an FCS statement (in alphabetical order):
With an FCS statement, the missing values of variables in the VAR statement are imputed. After the initial filled in, these variables with missing values are imputed sequentially in the order specified in the VAR statement in each iteration. For example, the following MI procedure statements use the regression method to impute variable from effect , the regression method to impute variable from effects and , the logistic regression method to impute variable from effects , , and , and the default regression method for continuous variables to impute variable from effects , , and :
proc mi; class c1; fcs reg(y1= y2); fcs reg(y3= y1 y2); fcs logistic(c1= y1 y2 y1*y2); var y1 y2 y3 c1; run;