17 Perform a multiple imputation using m 25 and 42 for the seed Note that it might take this longer to run because there are many variables and observations in this dataset Use all the variables in the dataset in the imputation model 18 Check diagnostics for a) convergence and b) plausibility of the imputed values What is your assessment of the diagnostics Do you see any problems The SAS System The MI Procedure Model Information Data Set FINAL NLSYV Method MCMC Multiple Imputation Chain Single Chain Initial Estimates for MCMC EM Posterior Mode Start Starting Value Prior Jeffreys Number of Imputations 25 Number of Burn in Iterations 200 Number of Iterations 100 Seed for random number generator 42 Missing Data Patterns Group ppvtr36 momage momed income momrace Freq Percent Group Means ppvtr36 momage momed income momrace 1 X X X X X 172 43 00 87 546512 23 645349 2 034884 35225 2 389535 2 X X X X 61 15 25 83 918033 23 655738 2 065574 20455 3 X X X X 40 10 00 81 975000 23 025000 2 000000 2 100000 4 X X X 23 5 75 85 043478 23 130435 2 217391 5 X X X X 18 4 50 87 555556 24 277778 77609 2 055556 6 X X X 7 1 75 85 285714 22 571429 10562 7 X X X 2 0 50 84 500000 26 500000 2 500000 8 X X 2 0 50 87 500000 23 000000 9 X X X X 35 8 75 24 457143 2 057143 22963 2 457143 10 X X X 18 4 50 24 388889 1 888889 19713 11 X X X 8 2 00 24 250000 1 875000 2 250000 12 X X 3 0 75 25 333333 2 333333 13 X X X 6 1 50 26 833333 25521 2 166667 14 X X 1 0 25 23 000000 100001 15 X X 2 0 50 24 500000 2 500000 16 X 2 0 50 23 500000 EM (Posterior Mode) Estimates TYPE NAME ppvtr36 momage momed income momrace MEAN 85 958631 23 755000 2 054701 31356 2 314806 COV ppvtr36 340 753486 8 998073 5 857543 301304 5 846261 COV momage 8 998073 10 029532 1 168166 86953 0 479500 COV momed 5 857543 1 168166 0 845286 29140 0 069645 COV income 301304 86953 29140 10126930881 9148 763305 COV momrace 5 846261 0 479500 0 069645 9148 763305 0 585287 Variance Information (25 Imputations) Variable Variance DF Relative Increase in Variance Fraction Missing Information Relative Efficiency Between Within Total ppvtr36 0 156165 0 868860 1 031272 248 57 0 186925 0 159223 0 993671 momed 0 000159 0 002159 0 002325 341 99 0 076784 0 071702 0 997140 income 3161077 26001711 29289232 297 42 0 126435 0 113174 0 995493 momrace 0 000592 0 001483 0 002099 139 98 0 414717 0 298153 0 988214 Parameter Estimates (25 Imputations) Variable Mean Std Error 95 ConfidenceLimits DF Minimum Maximum Mu0 t for H0 Mean Mu0 Pr t ppvtr36 85 984401 1 015516 83 98 87 98 248 57 85 437621 87 058063 0 84 67

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Question: 17. Perform a multiple imputation using m=25 and 42 for the seed. Note that it might take this longer to run because there are many

17. Perform a multiple imputation using m=25 and 42 for the seed. Note that it might take this longer to run because there are many variables and observations in this dataset. Use all the variables in the dataset in the imputation model.

18. Check diagnostics for a) convergence and b) plausibility of the imputed values. What is your assessment of the diagnostics? Do you see any problems?

The SAS System

The MI Procedure

Model Information
Data Set	FINAL.NLSYV
Method	MCMC
Multiple Imputation Chain	Single Chain
Initial Estimates for MCMC	EM Posterior Mode
Start	Starting Value
Prior	Jeffreys
Number of Imputations	25
Number of Burn-in Iterations	200
Number of Iterations	100
Seed for random number generator	42

Missing Data Patterns
Group	ppvtr36	momage	momed	income	momrace	Freq	Percent	Group Means
ppvtr36	momage	momed	income	momrace
1	X	X	X	X	X	172	43.00	87.546512	23.645349	2.034884	35225	2.389535
2	X	X	X	X	.	61	15.25	83.918033	23.655738	2.065574	20455	.
3	X	X	X	.	X	40	10.00	81.975000	23.025000	2.000000	.	2.100000
4	X	X	X	.	.	23	5.75	85.043478	23.130435	2.217391	.	.
5	X	X	.	X	X	18	4.50	87.555556	24.277778	.	77609	2.055556
6	X	X	.	X	.	7	1.75	85.285714	22.571429	.	10562	.
7	X	X	.	.	X	2	0.50	84.500000	26.500000	.	.	2.500000
8	X	X	.	.	.	2	0.50	87.500000	23.000000	.	.	.
9	.	X	X	X	X	35	8.75	.	24.457143	2.057143	22963	2.457143
10	.	X	X	X	.	18	4.50	.	24.388889	1.888889	19713	.
11	.	X	X	.	X	8	2.00	.	24.250000	1.875000	.	2.250000
12	.	X	X	.	.	3	0.75	.	25.333333	2.333333	.	.
13	.	X	.	X	X	6	1.50	.	26.833333	.	25521	2.166667
14	.	X	.	X	.	1	0.25	.	23.000000	.	100001	.
15	.	X	.	.	X	2	0.50	.	24.500000	.	.	2.500000
16	.	X	.	.	.	2	0.50	.	23.500000	.	.	.

EM (Posterior Mode) Estimates
_TYPE_	_NAME_	ppvtr36	momage	momed	income	momrace
MEAN		85.958631	23.755000	2.054701	31356	2.314806
COV	ppvtr36	340.753486	8.998073	5.857543	301304	5.846261
COV	momage	8.998073	10.029532	1.168166	86953	0.479500
COV	momed	5.857543	1.168166	0.845286	29140	0.069645
COV	income	301304	86953	29140	10126930881	9148.763305
COV	momrace	5.846261	0.479500	0.069645	9148.763305	0.585287

Variance Information (25 Imputations)
Variable	Variance	DF	Relative Increase in Variance	Fraction Missing Information	Relative Efficiency
Between	Within	Total
ppvtr36	0.156165	0.868860	1.031272	248.57	0.186925	0.159223	0.993671
momed	0.000159	0.002159	0.002325	341.99	0.076784	0.071702	0.997140
income	3161077	26001711	29289232	297.42	0.126435	0.113174	0.995493
momrace	0.000592	0.001483	0.002099	139.98	0.414717	0.298153	0.988214

Parameter Estimates (25 Imputations)
Variable	Mean	Std Error	95%ConfidenceLimits	DF	Minimum	Maximum	Mu0	t for H0: Mean=Mu0	Pr>\|t\|
ppvtr36	85.984401	1.015516	83.98	87.98	248.57	85.437621	87.058063	0	84.67	<.0001
momed	2.054406	0.048219	1.96	2.15	341.99	2.033018	2.078318	0	42.61	<.0001
income	31231	5411.952694	20580.25	41881.40	297.42	28107	34485	0	5.77	<.0001
momrace	2.321739	0.045811	2.23	2.41	139.98	2.264033	2.378004	0	50.68	<.0001

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