*Multilevel Modeling for the Uninitiated: SAS Code;

*Get Data (from R-code);  *NEED TO HAVE OWN DATAFILE PATH;  
PROC IMPORT datafile='/home/.../dragon1.csv'
	dbms=csv out=Dragon1 replace; getnames=yes; guessingrows=100; 
PROC MEANS data=Dragon1 mean;
	var Knights Archers; *Km=10.825, Am=7.868;
DATA Dragon1; set Dragon1;
	Knights_C = (Knights-10.825);
	Archers_C = (Archers-7.868);
PROC PRINT data=Dragon1;

PROC IMPORT datafile='/home/.../dragon2.csv'
	dbms=csv out=Dragon2 replace; getnames=yes; guessingrows=100; 
PROC PRINT data=Dragon2(obs=100);

PROC IMPORT datafile='/home/.../dragon3.csv'
	dbms=csv out=Dragon3 replace; getnames=yes; guessingrows=100; 
PROC PRINT data=Dragon3(obs=100);
*---------------------------------------------------------------------------------------------------------------------------;

*EXAMPLE 1: Fire Damage and Knights;

	*Model 1: Knight Standard Regression;
PROC GLIMMIX data=Dragon1 method=MMPL;
	model Fire_Damage=Knights_C/s;
	output out=D1M1_pred pred lcl ucl;
PROC SORT data=D1M1_pred;
	by Knights_C;	
PROC SGPLOT data=D1M1_pred noautolegend;
	band x=Knights_C lower=lcl upper=ucl;
	scatter x=Knights_C y=Fire_Damage;
	series x=Knights_C y=pred;
	
	*Model 2: Knight Level-2 Random Intercepts;
PROC GLIMMIX data=Dragon1 method=MMPL;
	class Region;
	model Fire_Damage=Knights_C/s;
	random Region;
	output out=D1M2_pred pred lcl ucl;
PROC SORT data=D1M2_pred;
	by Knights_C;		
PROC SGPLOT data=D1M2_pred noautolegend;
	band x=Knights_C lower=lcl upper=ucl/group=Region transparency=.90;
	scatter x=Knights_C y=Fire_Damage/group=Region;
	series x=Knights_C y=pred/group=Region;	
	
	*Model 3: Knight Level-2 Random Intercepts and Slopes;
PROC GLIMMIX data=Dragon1 method=MMPL;
	class Region;
	model Fire_Damage=Knights_C/s;
	random intercept Knights_C/subject = Region;
	output out=D1M3_pred pred lcl ucl;
PROC SORT data=D1M3_pred;
	by Region Knights_C;
PROC PRINT data=D1M3_pred;
PROC SGPLOT data=D1M3_pred noautolegend;
	band x=Knights_C lower=lcl upper=ucl/group=Region transparency=.90;
	scatter x=Knights_C y=Fire_Damage/group=Region;
	series x=Knights_C y=pred/group=Region;		
*---------------------------------------------------------------------------------------------------------------------------;

*EXAMPLE 2: Fire Damage and Archers;

	*Model 1: Archer Standard Regression;
PROC GLIMMIX data=Dragon1 method=MMPL;
	model Fire_Damage=Archers_C/s;
	output out=D2M1_pred pred lcl ucl;
PROC SORT data=D2M1_pred;
	by Archers_C;	
PROC SGPLOT data=D2M1_pred noautolegend;
	band x=Archers_C lower=lcl upper=ucl;
	scatter x=Archers_C y=Fire_Damage;
	series x=Archers_C y=pred;
	
	*Model 2: Archer Level-2 Random Intercepts;
PROC GLIMMIX data=Dragon1 method=MMPL;
	class Region;
	model Fire_Damage=Archers_C/s;
	random Region;
	output out=D2M2_pred pred lcl ucl;
PROC SORT data=D2M2_pred;
	by Archers_C;		
PROC SGPLOT data=D2M2_pred noautolegend;
	band x=Archers_C lower=lcl upper=ucl/group=Region transparency=.90;
	scatter x=Archers_C y=Fire_Damage/group=Region;
	series x=Archers_C y=pred/group=Region;	
	
	*Model 3: Archer Level-2 Random Intercepts and Slopes;
PROC GLIMMIX data=Dragon1 method=MMPL;
	class Region;
	model Fire_Damage=Archers_C/s;
	random intercept Archers/subject = Region;
	output out=D2M3_pred pred lcl ucl;
PROC SORT data=D2M3_pred;
	by Archers_C;		
PROC SGPLOT data=D2M3_pred noautolegend;
	band x=Archers_C lower=lcl upper=ucl/group=Region transparency=.90;
	scatter x=Archers_C y=Fire_Damage/group=Region;
	series x=Archers_C y=pred/group=Region;	
*---------------------------------------------------------------------------------------------------------------------------;

*EXAMPLE 3: Dragon Hits and Arrows;

	*Model 1: Arrow Standard Regression;
PROC GLIMMIX data=Dragon2 method=MMPL;
	model DragonHit=Arrows_C/s;
	output out=D3M1_pred pred lcl ucl;
PROC SORT data=D3M1_pred;
	by Arrows_C;	
PROC SGPLOT data=D3M1_pred noautolegend;
	band x=Arrows_C lower=lcl upper=ucl;
	scatter x=Arrows_C y=DragonHit;
	series x=Arrows_C y=pred;
	
	*Model 2: Arrow Level-2 Random Intercepts;
PROC GLIMMIX data=Dragon2 method=MMPL;
	class Cathedral;
	model DragonHit=Arrows_C/s;
	random Cathedral;
	output out=D3M2_pred pred lcl ucl;
PROC SORT data=D3M2_pred;
	by Arrows_C;	
PROC SGPLOT data=D3M2_pred noautolegend;
	*band x=Arrows_C lower=lcl upper=ucl/group=Cathedral transparency=.90;
	scatter x=Arrows_C y=DragonHit;
	series x=Arrows_C y=pred/group=Cathedral;
	
	*Model 3: Arrow Level-2 Random Intercepts and Slopes;
PROC GLIMMIX data=Dragon2 method=MMPL;
	class Cathedral;
	model DragonHit=Arrows_C/s;
	random intercept Arrows_C/subject=Cathedral;
	output out=D3M3_pred pred lcl ucl;
PROC SORT data=D3M3_pred;
	by Arrows_C;	
PROC SGPLOT data=D3M3_pred noautolegend;
	*band x=Arrows_C lower=lcl upper=ucl/group=Cathedral transparency=.90;
	scatter x=Arrows_C y=DragonHit;
	series x=Arrows_C y=pred/group=Cathedral;
*---------------------------------------------------------------------------------------------------------------------------;


*EXAMPLE 4: Dragon Gold and Training;

	*Model 1: Training Standard Regression;
PROC GLIMMIX data=Dragon3 method=MMPL;
	model DragonGold=TrainingTime_C/s;
	output out=D4M1_pred pred lcl ucl;
PROC SORT data=D4M1_pred;
	by TrainingTime_C;	
PROC SGPLOT data=D4M1_pred noautolegend;
	band x=TrainingTime_C lower=lcl upper=ucl;
	scatter x=TrainingTime_C y=DragonGold;
	series x=TrainingTime_C y=pred;	

	*Model 2: Training Level-2 Random Intercepts;
PROC GLIMMIX data=Dragon3 method=MMPL;
	class Commander_2;
	model DragonGold=TrainingTime_C/s;
	random Commander_2;
	output out=D4M2_pred pred lcl ucl;
PROC SORT data=D4M2_pred;
	by TrainingTime_C;	
PROC SGPLOT data=D4M2_pred noautolegend;
	band x=TrainingTime_C lower=lcl upper=ucl/group=Commander_2 transparency=.90;
	scatter x=TrainingTime_C y=DragonGold;
	series x=TrainingTime_C y=pred/group=Commander_2;	
	
	*Model 3: Training Level-3 Random Intercepts;
PROC GLIMMIX data=Dragon3 method=MMPL;
	class Commander_2 Cathedral;
	model DragonGold=TrainingTime_C/s;
	random Commander_2(Cathedral);
	output out=D4M3_pred pred lcl ucl;
PROC SORT data=D4M3_pred;
	by TrainingTime_C;	
PROC SGPLOT data=D4M3_pred noautolegend;
	where Cathedral='C1';
	band x=TrainingTime_C lower=lcl upper=ucl/group=Commander_2 transparency=.90;
	scatter x=TrainingTime_C y=DragonGold;
	series x=TrainingTime_C y=pred/group=Commander_2;
	yaxis ranges=(0-80); title "C1";
PROC SGPLOT data=D4M3_pred noautolegend;
	where Cathedral='C2';
	band x=TrainingTime_C lower=lcl upper=ucl/group=Commander_2 transparency=.90;
	scatter x=TrainingTime_C y=DragonGold;
	series x=TrainingTime_C y=pred/group=Commander_2;
	yaxis ranges=(0-80); title "C2";
PROC SGPLOT data=D4M3_pred noautolegend;
	where Cathedral='C3'; title "C2";
	band x=TrainingTime_C lower=lcl upper=ucl/group=Commander_2 transparency=.90;
	scatter x=TrainingTime_C y=DragonGold;
	series x=TrainingTime_C y=pred/group=Commander_2;
	yaxis ranges=(0-80); title "C3";
	
	*Model 4: Training Level-3 Random Intercepts and Slopes;
PROC GLIMMIX data=Dragon3 method=MMPL;
	class Commander Cathedral;
	model DragonGold=TrainingTime_C/s;
	random intercept TrainingTime_C/subject=Commander(Cathedral);
	random intercept TrainingTime_C/subject=Cathedral;
	output out=D4M4_pred pred lcl ucl;
PROC SORT data=D4M4_pred;
	by TrainingTime_C;
PROC SGPLOT data=D4M4_pred noautolegend;
	where Cathedral='C1';
	band x=TrainingTime_C lower=lcl upper=ucl/group=Commander transparency=.90;
	scatter x=TrainingTime_C y=DragonGold;
	series x=TrainingTime_C y=pred/group=Commander;
	yaxis ranges=(0-80); title "C1";	
PROC SGPLOT data=D4M4_pred noautolegend;
	where Cathedral='C2';
	band x=TrainingTime_C lower=lcl upper=ucl/group=Commander transparency=.90;
	scatter x=TrainingTime_C y=DragonGold;
	series x=TrainingTime_C y=pred/group=Commander;
	yaxis ranges=(0-80); title "C2";
PROC SGPLOT data=D4M4_pred noautolegend;
	where Cathedral='C3';
	band x=TrainingTime_C lower=lcl upper=ucl/group=Commander transparency=.90;
	scatter x=TrainingTime_C y=DragonGold;
	series x=TrainingTime_C y=pred/group=Commander;
	yaxis ranges=(0-80); title "C3";