The script begins by creating a 'Fitness1' dataset from internal data (datalines), which simulates fitness measurements with intentional missing values. Then, the MI procedure is used for the first time to generate multiple imputations via an MCMC method and stores the result in the 'outex10' table. A second execution of PROC MI is performed on the imputed dataset with the nimpute=0 option, which is typically done to analyze the imputation results without generating new ones.
Data Analysis
Type : INTERNAL_CREATION
The data is created directly in the script via a DATA step with a DATALINES statement. The 'Fitness1' dataset contains fitness measurements (Oxygen, RunTime, RunPulse) for several individuals.
1 Code Block
DATA STEP Data
Explanation :
This DATA STEP block creates the 'Fitness1' table. The 'input' statement reads three numeric variables (Oxygen, RunTime, RunPulse). The ' @@' (double trailing at) specifier keeps the pointer on the input data line, allowing multiple observations to be read from a single line. The data is provided directly in the code via the 'datalines' statement.
This DATA STEP block creates the 'Fitness1' table. The 'input' statement reads three numeric variables (Oxygen, RunTime, RunPulse). The ' @@' (double trailing at) specifier keeps the pointer on the input data line, allowing multiple observations to be read from a single line. The data is provided directly in the code via the 'datalines' statement.
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| 1 | DATA Fitness1; |
| 2 | INPUT Oxygen RunTime RunPulse @code_sas_json/8_SAS_Intro_ReadFile_MultiCol_@@.json; |
| 3 | DATALINES; |
| 4 | 44.609 11.37 178 45.313 10.07 185 |
| 5 | 54.297 8.65 156 59.571 . . |
| 6 | 49.874 9.22 . 44.811 11.63 176 |
| 7 | . 11.95 176 . 10.85 . |
| 8 | 39.442 13.08 174 60.055 8.63 170 |
| 9 | 50.541 . . 37.388 14.03 186 |
| 10 | 44.754 11.12 176 47.273 . . |
| 11 | 51.855 10.33 166 49.156 8.95 180 |
| 12 | 40.836 10.95 168 46.672 10.00 . |
| 13 | 46.774 10.25 . 50.388 10.08 168 |
| 14 | 39.407 12.63 174 46.080 11.17 156 |
| 15 | 45.441 9.63 164 . 8.92 . |
| 16 | 45.118 11.08 . 39.203 12.88 168 |
| 17 | 45.790 10.47 186 50.545 9.93 148 |
| 18 | 48.673 9.40 186 47.920 11.50 170 |
| 19 | 47.467 10.50 170 |
| 20 | ; |
| 21 |
2 Code Block
PROC MI
Explanation :
This Multiple Imputation (MI) procedure processes the 'Fitness1' dataset. It uses an MCMC (Markov Chain Monte Carlo) method with monotone imputation to generate values for missing data in the specified variables. The 'seed' option ensures the reproducibility of the imputation. The results, including the multiple imputed datasets, are stored in the 'outex10' output table.
This Multiple Imputation (MI) procedure processes the 'Fitness1' dataset. It uses an MCMC (Markov Chain Monte Carlo) method with monotone imputation to generate values for missing data in the specified variables. The 'seed' option ensures the reproducibility of the imputation. The results, including the multiple imputed datasets, are stored in the 'outex10' output table.
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| 1 | PROC MI DATA=Fitness1 seed=17655417 out=outex10; |
| 2 | mcmc impute=monotone; |
| 3 | var Oxygen RunTime RunPulse; |
| 4 | RUN; |
3 Code Block
PROC MI
Explanation :
The MI procedure is called again, this time on the 'outex10' table which contains the imputed data. The 'nimpute=0' option indicates that no new imputation should be performed. This type of call is generally used to obtain descriptive statistics or analyses on all imputed data, by combining the results of different imputations.
The MI procedure is called again, this time on the 'outex10' table which contains the imputed data. The 'nimpute=0' option indicates that no new imputation should be performed. This type of call is generally used to obtain descriptive statistics or analyses on all imputed data, by combining the results of different imputations.
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| 1 | |
| 2 | PROC MI |
| 3 | DATA=outex10 seed=15541 nimpute=0; |
| 4 | var Oxygen RunTime RunPulse; |
| 5 | RUN; |
| 6 |
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