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«University of Nebraska - Lincoln DigitalCommons of Nebraska - Lincoln Dissertations and Theses in Statistics Statistics, Department of 8-2010 ...»

-- [ Page 14 ] --

Filename tmp3 "C:\Users\Jenny\Desktop\Flash Drive - June 2, 2008\PhD\Dissertation\Paper 1 - Intro and Model Lit Review\Curve of Factors Methodology\Analyze\tch_student_scores_missing.pvc";

Data Variance_Temp;

Infile tmp3 firstobs = 16 obs = 27 lrecl=55;

Input Error_Var 31-37 Error_SE 45-55 / Teacher_Var 31-37 Teacher_SE 45-55 / D1_Var 31-37 D1_SE 45-55 / D2D1_Cov 31D2D1_SE 45-55 / D2_Var 31-37 D2_SE 45-55 / D3D1_Cov 31- 37 D3D1_SE 45-55 / D3D2_Cov 31-37 D3D2_SE 45-55 / D3_Var 31-37 D3_SE 45-55 / D4D1_Cov 31-37 D4D1_SE 45-55 / D4D2_Cov 31-37 D4D2_SE 45-55 / D4D3_Cov 31-37 D4D3_SE 45-55 / D4_Var 31-37 D4_SE 45-55 ;

%End;

%Else %do;

*Create ASReml.pin file for ZCrt, ZMat and MatCrt Variance Components;

Filename tmp "C:\Users\Jenny\Desktop\Flash Drive - June 2, 2008\PhD\Dissertation\Paper 1 - Intro and Model Lit Review\Curve of Factors Methodology\Analyze\tch_student_scores_missing.pin";

Data _null_;

File tmp;

put 'F E1_var 1 * 1';

put 'F E2E1_cov 2 * 1';

put 'F E2_Var 3 * 1';

put 'F E3E1_Cov 4 * 1';

put 'F E3E2_Cov 5 * 1';

put 'F E3_Var 6 * 1';

put 'F E4E1_Cov 7 * 1';

put 'F E4E2_Cov 8 * 1';

put 'F E4E3_Cov 9 * 1';

put 'F E4_Var 10 * 1';

put 'F Teacher_Var 11 * 1';

Run;

%Let pinfile=tch_student_scores_missing.pin;

Data _null_;

Call System('cd C:\Users\Jenny\Desktop\Flash Drive - June 2, 2008\PhD\Dissertation\Paper 1 - Intro and Model Lit Review\Curve of Factors Methodology\Analyze');

X "'C:\Program Files\ASREML3\BIN\asreml.exe' -NP &pinfile";

Run;

Filename tmp3 "C:\Users\Jenny\Desktop\Flash Drive - June 2, 2008\PhD\Dissertation\Paper 1 - Intro and Model Lit Review\Curve of Factors Methodology\Analyze\tch_student_scores_missing.pvc";

Data Variance_Temp;

Infile tmp3 firstobs = 15 obs = 25 lrecl=55;;

Input E1_Var 31-37 E1_SE 45-55 / E2E1_Cov 31-37 E2E1_SE 45E2_Var 31-37 E2_SE 45-55 / E3E1_Cov 31-37 E3E1_SE 45- 55 / E3E2_Cov 31-37 E3E2_SE 45-55 / E3_Var 31-37 E3_SE 45- 55 / E4E1_Cov 31-37 E4E1_SE 45-55 / E4E2_Cov 31-37 E4E2_SE 45-55 / E4E3_Cov 31-37 E4E3_SE 45-55 / E4_Var 31-37 E4_SE 45-55 / Teacher_Var 31-37 Teacher_SE 45-55 ;

%End;

Exp = ≁

Model = "&model";

Run;

%If %eval(&nsim) = 1 %then %do;

Data MResults.Pred_Variance_&model;

Set Variance_Temp;

Run;

%End;

%Else %do;

Data MResults.Pred_Variance_&model;

Set MResults.Pred_Variance_&model Variance_Temp;

Run;

%End;

%mend;

*********************************************************;

* Create Macro to Simulate & Analyze Multiple Data Sets *;

*********************************************************;

%Macro sim(sims,oseed);

%Do nsim = 1 %to &sims;

%Let seed = &oseed + ≁

*Run COF Macro to create PP, Zcrt, Zmat and Zmatcrt Datasets;

%cof_missing(2000,&seed,Tch_Student_Scores_pp,Tch_Student_S cores_zcrt,Tch_Student_Scores_zmat,Tch_Student_Scores_zmatc rt);

*Run Analyze Macro to analyze each dataset;

%analyze(Tch_Student_Scores_pp,pp);

%analyze(Tch_Student_Scores_zcrt,zc);

%analyze(Tch_Student_Scores_zmat,zm);

%analyze(Tch_Student_Scores_zmatcrt,mc);

%End;

%mend;

%sim(1000,20060902);

********************************************************;

*** Create Datasets for Plots and Summary Statistics ***;

********************************************************;

*Merge True and Predicted Teacher Effects from the 1000 Simulations;

Proc Sort Data = MResults.True_Teacher_Effects;

By Exp Teacher;

Proc Sort Data = MResults.Pred_Teacher_Effects;

By Exp Teacher;

Run;

Data MResults.Teacher_Effects;

Merge MResults.Pred_Teacher_Effects MResults.True_Teacher_Effects;

By Exp Teacher;

Run;

*Create Dataset (MResults.Stats_Plot) for RMSE and SEPE Plots;

Data MResults.Teacher_Effects;

Set MResults.Teacher_Effects;

SPE = (Pred_Percentile - True_Percentile)**2;

PE = (Pred_Percentile - True_Percentile);

Run;

Proc Sort Data = MResults.Teacher_Effects;

By Year True_Percentile Model;

Run;

Proc Means Data = MResults.Teacher_Effects Mean noprint;

By Year True_Percentile Model;

Var SPE;

Output Out = Stats1 Mean = MSE;

Run;

Proc Means Data = MResults.Teacher_Effects VAR noprint;

By Year True_Percentile Model;

Var PE;

Output Out = Stats2 Var = VPE;

Run;

Data MResults.Stats_Plot;

Merge Stats1 Stats2;

By Year True_Percentile Model;

Run;

Data MResults.Stats_Plot;

Set MResults.Stats_Plot;

Drop _Freq_ _Type_;

RMSE = sqrt(MSE);

SEPE = sqrt(VPE);

Run;

Proc Sort Data = MResults.Stats_Plot;

By Year Model;

Run;

PROC EXPORT DATA= MResults.Stats_PLOT OUTFILE= "C:\Users\Jenny\Desktop\Flash Drive June 2, 2008\PhD\Dissertation\Paper 1 - Intro and Model Lit Review\Curve of Factors Methodology\MGraphs\MStats_Plot.csv" DBMS=CSV REPLACE;

PUTNAMES=YES;

RUN;

*Create Dataset for Bias Plot;

Proc Sort Data = MResults.True_Teacher_Effects;

By Exp Teacher;

Proc Sort Data = MResults.Pred_Teacher_Effects;

By Exp Teacher;

Run;

Data MResults.Teacher_Effects;

Merge MResults.Pred_Teacher_Effects MResults.True_Teacher_Effects;

By Exp Teacher;

Run;

Proc Sort Data=MResults.Teacher_Effects;

By Year True_Percentile Model;

Run;

Proc Means Data = MResults.Teacher_Effects Mean noprint;

By Year True_Percentile Model;

Var Pred_Percentile;

Output Out = Stats3 Mean = Mean_Pred;

Run;

Data MResults.Bias_Plot;

Set Stats3;

Bias = Mean_Pred - True_Percentile;

Drop _Freq_ _Type_;

Run;

PROC EXPORT DATA= MResults.Bias_Plot OUTFILE= "C:\Users\Jenny\Desktop\Flash Drive June 2, 2008\PhD\Dissertation\Paper 1 - Intro and Model Lit Review\Curve of Factors Methodology\MGraphs\MBias_Plot.csv" DBMS=CSV REPLACE;

PUTNAMES=YES;

RUN;

*Create Dataset (MResults.Percentile_Plot) for 25th, 50th and 75th Percentile Sampling Distribution Plots;

Data MResults.Percentile;

Set MResults.Teacher_Effects;

If Ceil(True_Rank) = 5 or Ceil(True_Rank) = 10 or Ceil(True_Rank) = 16;

Ceil_True_Rank = Ceil(True_Rank); *CEIL function adjusts for mean ranks from ties;

Ceil_Pred_Rank = Ceil(Pred_Rank); *CEIL function adjusts for mean ranks from ties;

Ceil_True_Percentile = (Ceil_True_Rank*100)/21;

Ceil_Pred_Percentile = (Ceil_Pred_Rank*100)/21;

Dummy = 1;

Run;

Proc Sort Data = MResults.Percentile;

By Ceil_True_Percentile Ceil_Pred_Percentile Year Model;

Run;

Proc Means Data = MResults.Percentile Sum noprint;

By Ceil_True_Percentile Ceil_Pred_Percentile Year Model;

Var Dummy;

Output out = Stats Sum = sum;

Run;

Data Zero;

Do Ceil_True_Rank = 1 to 20;

Ceil_True_Percentile = 100*(Ceil_True_Rank/21);

Do Ceil_Pred_Rank = 1 to 20;

Ceil_Pred_Percentile = 100*(Ceil_Pred_Rank/21);

Do Year = 1 to 4;

Do Model2 = 1 to 4;

Output;

End;

End;

End;

End;

Run;

Data Zero;

Set Zero;

If Ceil_True_Rank = 5 or Ceil_True_Rank = 10 or Ceil_True_Rank = 16;

If Model2 = 1 then Model = 'mc';

If Model2 = 2 then Model = 'pp';

If Model2 = 3 then Model = 'zc';

If Model2 = 4 then Model = 'zm';

Run;

Data Test;

Merge Stats Zero;

By Ceil_True_Percentile Ceil_Pred_Percentile Year Model;

Run;

Data MResults.Percentile_Plot;

Set Test;

If Sum =. then Sum = 0;

Relative_Freq = Sum/1000;

Drop _Freq_ _Type_ Model2;

Run;

PROC EXPORT DATA= MResults.Percentile_PLOT OUTFILE= "C:\Users\Jenny\Desktop\Flash Drive June 2, 2008\PhD\Dissertation\Paper 1 - Intro and Model Lit Review\Curve of Factors Methodology\MGraphs\MPercentile_Plot.csv" DBMS=CSV REPLACE;

PUTNAMES=YES;

RUN;

*Create Dataset (MResults.Prob75_Plot) for 75th Percentile Classification Probability Plots;

Data Percentile75;

Set MResults.Teacher_Effects;

If Pred_Percentile =75 then Dummy75 = 1;

Else Dummy75 = 0;

Run;

Proc Sort Data = Percentile75;

By True_Percentile Year Model;

Run;

Proc Sort Data = MResults.Teacher_Effects;

By True_Percentile Year Model;

Run;

Proc Means Data = Percentile75 Sum noprint;

By True_Percentile Year Model;

Var Dummy75;

Output out = Stats75 Sum = sum;

Run;

Data MResults.Prob75_Plot;

Set Stats75;

Relative_Freq = Sum/1000;

Drop _Freq_ _Type_;

Run;

PROC EXPORT DATA= MResults.PROB75_PLOT OUTFILE= "C:\Users\Jenny\Desktop\Flash Drive June 2, 2008\PhD\Dissertation\Paper 1 - Intro and Model Lit Review\Curve of Factors Methodology\MGraphs\MProb75_Plot.csv" DBMS=CSV REPLACE;

PUTNAMES=YES;

RUN;

*Obtain Mean and SD of Teacher Var and Avg Teacher Var SE for each Model;

Proc Means Data = MResults.Pred_Variance_pp Mean Std;

Var Teacher_Var Teacher_SE;

Output Out = VarStats;

Run;

Proc Means Data = MResults.Pred_Variance_zc Mean Std;

Var Teacher_Var Teacher_SE;

Output Out = VarStats;

Run;

Proc Means Data = MResults.Pred_Variance_zm Mean Std;

Var Teacher_Var Teacher_SE;

Output Out = VarStats;

Run;

Proc Means Data = MResults.Pred_Variance_mc Mean Std;

Var Teacher_Var Teacher_SE;

Output Out = VarStats;

Pages:     | 1 |   ...   | 12 | 13 ||


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