The MIANALYZE Procedure
- Overview
- Getting Started
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Syntax
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Details
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ExamplesReading Means and Standard Errors from a DATA= Data SetReading Means and Covariance Matrices from a DATA= COV Data SetReading Regression Results from a DATA= EST Data SetReading Mixed Model Results from PARMS= and COVB= Data SetsReading Generalized Linear Model ResultsReading GLM Results from PARMS= and XPXI= Data SetsReading Logistic Model Results from a PARMS= Data SetReading Mixed Model Results with Classification CovariatesReading Nominal Logistic Model ResultsUsing a TEST statementCombining Correlation CoefficientsSensitivity Analysis with Control-Based Pattern ImputationSensitivity Analysis with Tipping-Point Approach
- References
- 64.1 Reading Means and Standard Errors from a DATA= Data Set
- 64.2 Reading Means and Covariance Matrices from a DATA= COV Data Set
- 64.3 Reading Regression Results from a DATA= EST Data Set
- 64.4 Reading Mixed Model Results from PARMS= and COVB= Data Sets
- 64.5 Reading Generalized Linear Model Results
- 64.6 Reading GLM Results from PARMS= and XPXI= Data Sets
- 64.7 Reading Logistic Model Results from a PARMS= Data Set
- 64.8 Reading Mixed Model Results with Classification Covariates
- 64.9 Reading Nominal Logistic Model Results
- 64.10 Using a TEST statement
- 64.11 Combining Correlation Coefficients
- 64.12 Sensitivity Analysis with Control-Based Pattern Imputation
- 64.13 Sensitivity Analysis with Tipping-Point Approach
The following statements generate five imputed data sets to be used in this section. The data set Fitness1 was created in the section Getting Started: MIANALYZE Procedure. See "The MI Procedure" chapter for details concerning the MI procedure.
proc mi data=Fitness1 seed=3237851 noprint out=outmi; var Oxygen RunTime RunPulse; run;
The Fish data described in the STEPDISC procedure are measurements of 159 fish of seven species caught in Finland’s Lake Laengelmaevesi.
For each fish, the length, height, and width are measured. See Chapter 96: The STEPDISC Procedure, for more information.
The Fish2 data set is constructed from the Fish data set and contains two species of fish. Some values have been set to missing, and the resulting data set has a monotone
missing pattern in the variables Length, Width, and Species.
The following statements create the Fish2 data set. It contains two species of fish in the Fish data set.
*-----------------------------Fish2 Data-----------------------------* | The data set contains two species of the fish (Parkki and Perch) | | and two measurements: Length and Width. | | Some values have been set to missing, and the resulting data set | | has a monotone missing pattern in the variables | | Length, Width, and Species. | *--------------------------------------------------------------------*; data Fish2; title 'Fish Measurement Data'; input Species $ Length Width @@; datalines; Parkki 16.5 2.3265 Parkki 17.4 2.3142 . 19.8 . Parkki 21.3 2.9181 Parkki 22.4 3.2928 . 23.2 3.2944 Parkki 23.2 3.4104 Parkki 24.1 3.1571 . 25.8 3.6636 Parkki 28.0 4.1440 Parkki 29.0 4.2340 Perch 8.8 1.4080 . 14.7 1.9992 Perch 16.0 2.4320 Perch 17.2 2.6316 Perch 18.5 2.9415 Perch 19.2 3.3216 . 19.4 . Perch 20.2 3.0502 Perch 20.8 3.0368 Perch 21.0 2.7720 Perch 22.5 3.5550 Perch 22.5 3.3075 . 22.5 . Perch 22.8 3.5340 . 23.5 . Perch 23.5 3.5250 Perch 23.5 3.5250 Perch 23.5 3.5250 Perch 23.5 3.9950 . 24.0 . Perch 24.0 3.6240 Perch 24.2 3.6300 Perch 24.5 3.6260 Perch 25.0 3.7250 . 25.5 3.7230 Perch 25.5 3.8250 Perch 26.2 4.1658 Perch 26.5 3.6835 . 27.0 4.2390 Perch 28.0 4.1440 Perch 28.7 5.1373 . 28.9 4.3350 . 28.9 . . 28.9 4.5662 Perch 29.4 4.2042 Perch 30.1 4.6354 Perch 31.6 4.7716 Perch 34.0 6.0180 . 36.5 6.3875 . 37.3 7.7957 . 39.0 . . 38.3 . Perch 39.4 6.2646 Perch 39.3 6.3666 Perch 41.4 7.4934 Perch 41.4 6.0030 Perch 41.3 7.3514 . 42.3 . Perch 42.5 7.2250 Perch 42.4 7.4624 Perch 42.5 6.6300 Perch 44.6 6.8684 Perch 45.2 7.2772 Perch 45.5 7.4165 Perch 46.0 8.1420 Perch 46.6 7.5958 ;
The following statements generate five imputed data sets to be used in this section. The default regression method is used
to impute missing values in continuous variable Width, and the discriminant function method is used to impute the variable Species.
proc mi data=Fish2 seed=1305417 out=outfish2; class Species; monotone logistic( Species= Length Width); var Length Width Species; run;
The Fish3 data set is constructed from the Fish data set and contains three species of fish. Some values have been set to missing, and the resulting data set has an arbitrary
missing pattern in the variables Length, Width, and Species.
The following statements create the Fish3 data set. It contains two species of fish in the Fish data set.
*-----------------------------Fish3 Data-----------------------------* | The data set contains three species of the fish | | (Parkki, Perch, and Roach) and two measurements: Length and Width. | | Some values have been set to missing, and the resulting data set | | has an arbitrary missing pattern in the variables | | Length, Width, and Species. | *--------------------------------------------------------------------*; data Fish3; title 'Fish Measurement Data'; input Species $ Length Width @@; datalines; Roach 16.2 2.2680 Roach 20.3 2.8217 Roach 21.2 . Roach . 3.1746 Roach 22.2 3.5742 Roach 22.8 3.3516 Roach 23.1 3.3957 . 23.7 . Roach 24.7 3.7544 Roach 24.3 3.5478 Roach 25.3 . Roach 25.0 3.3250 Roach 25.0 3.8000 Roach 27.2 3.8352 Roach 26.7 3.6312 Roach 26.8 4.1272 Roach 27.9 3.9060 Roach 29.2 4.4968 Roach 30.6 4.7736 Roach 35.0 5.3550 Parkki 16.5 2.3265 Parkki 17.4 . Parkki 19.8 2.6730 Parkki 21.3 2.9181 Parkki 22.4 3.2928 Parkki 23.2 3.2944 Parkki 23.2 3.4104 Parkki 24.1 3.1571 . . 3.6636 Parkki 28.0 4.1440 Parkki 29.0 4.2340 Perch 8.8 1.4080 . 14.7 1.9992 Perch 16.0 2.4320 Perch 17.2 2.6316 Perch 18.5 2.9415 Perch 19.2 3.3216 . 19.4 3.1234 Perch 20.2 . Perch 20.8 3.0368 Perch 21.0 2.7720 Perch 22.5 3.5550 Perch 22.5 3.3075 Perch 22.5 3.6675 Perch . 3.5340 Perch 23.5 3.4075 Perch 23.5 3.5250 Perch 23.5 3.5250 . 23.5 3.5250 Perch 23.5 3.9950 Perch 24.0 3.6240 Perch 24.0 3.6240 Perch 24.2 3.6300 Perch 24.5 3.6260 Perch 25.0 3.7250 Perch . 3.7230 Perch 25.5 3.8250 Perch . 4.1658 Perch 26.5 3.6835 . 27.0 4.2390 Perch . 4.1440 Perch 28.7 5.1373 . 28.9 4.3350 Perch 28.9 4.3350 Perch 28.9 4.5662 Perch 29.4 4.2042 Perch 30.1 4.6354 Perch 31.6 4.7716 Perch 34.0 6.0180 Perch 36.5 6.3875 Perch 37.3 7.7957 Perch 39.0 . Perch 38.3 6.7408 Perch . 6.2646 . 39.3 . Perch 41.4 7.4934 Perch 41.4 6.0030 Perch 41.3 7.3514 Perch 42.3 7.1064 Perch 42.5 7.2250 Perch 42.4 7.4624 Perch 42.5 6.6300 Perch 44.6 6.8684 Perch 45.2 7.2772 Perch 45.5 7.4165 Perch 46.0 8.1420 . 46.6 7.5958 ;
The following statements generate five imputed data sets to be used in this section. The default regression method is used
to impute missing values in continuous variable Width, and the nominal logistic regression method is used to impute the variable Species.
proc mi data=Fish3 seed=30535 out=outfish3; class Species; fcs logistic ( Species= Length Width / link=glogit); var Length Width Species; run;
Example 64.1 through Example 64.7 use different input option combinations to combine parameter estimates computed from different procedures. Example 64.8 combines parameter estimates with classification variables, and Example 64.9 combines nominal logistic regression parameter estimates Example 64.10 shows the use of a TEST statement, and Example 64.11 combines statistics that are not directly derived from procedures.
The MI procedure provides sensitivity analysis for the MAR assumption. Example 64.12 illustrate sensitivity analysis by using the pattern-mixture model approach, and Example 64.13 performs sensitivity analysis by searching and examining the tipping point that reverses the study conclusion.