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Spatial statistics in practice

Lab #3: - Jacobian estimation - estimating autoregressive models - estimating spatial filter models - estimating random effects models - mapping spatial filters. Spatial statistics in practice Center for Tropical Ecology and Biodiversity, Tunghai University & Fushan Botanical Garden.

imogene-murray
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Spatial statistics in practice

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  1. Lab #3:- Jacobian estimation- estimating autoregressive models- estimating spatial filter models- estimating random effects models - mapping spatial filters Spatial statistics in practice Center for Tropical Ecology and Biodiversity, Tunghai University & Fushan Botanical Garden

  2. SAS code for the Jacobian approximation FILENAME EIGEN 'D:\JYU-SUMMERSCHOOL2006\LAB#3\PR-EIG.TXT'; TITLE 'MATRIX C OR W JACOBIAN APPROXIMATION, IRREGULAR LATTICE'; *********************************************** * THE APPROPRIATE EIGENVALES MUST BE SELECTED * ***********************************************; DATA STEP0; INFILE EIGEN; INPUT ID LAMBDAC LAMBDAW; LAMBDA=LAMBDAW; X0=1; RUN; PROC UNIVARIATE NOPRINT; VAR LAMBDA; OUTPUT OUT=EXTREMEL MAX=LMAX MIN=LMIN; RUN; PROC PRINT DATA=EXTREMEL; VAR LMAX LMIN; RUN; PROC MEANS DATA=STEP0 NOPRINT; VAR X0; OUTPUT OUT=STEP0A SUM=N; RUN; DATA EXTREMEL(REPLACE=YES); SET EXTREMEL; SET STEP0A; FINISH = 0.999/LMAX; START = 0.999/LMIN; INC = (FINISH - START)/201; RUN; DATA STEP1; IF _N_=1 THEN SET EXTREMEL; SET STEP0; ARRAY JACOB{202} JAC1-JAC202; RHO = START; DO I = 1 TO 202; JACOB{I} = -LOG(1 - RHO*LAMBDA); RHO = RHO + INC; END; RUN; PROC MEANS NOPRINT; VAR JAC1-JAC202; OUTPUT OUT=JACOB1 MEAN=; RUN;

  3. PROC TRANSPOSE OUT=JACOB2; VAR JAC1-JAC202; RUN; DATA STEP2 (REPLACE=YES); SET EXTREMEL; DO RHO = START TO FINISH BY INC; OUTPUT; END; DROP START FINISH INC; RUN; DATA STEP2 (REPLACE=YES); SET JACOB2; SET STEP2; J = COL1; DROP COL1; RUN; PROC GPLOT; PLOT J*RHO; RUN; PROC NLIN DATA=STEP2 NOITPRINT MAXITER=15000 METHOD=MARQUARDT; PARMS A1=0.15 A2=0.15 D1=1.1 D2=1.1; BOUNDS D1<2 , D2<2 ; MODEL J = A1*(LOG(1 - RHO*LMIN)/(RHO*LMIN) + 1 - D1*LOG(1 - RHO*LMIN))+ A2*(LOG(1 - RHO*LMAX)/(RHO*LMAX) + 1 - D2*LOG(1 - RHO*LMAX)); OUTPUT OUT=TEMP3 ESS=ESS2 P=JHAT PARMS=A1 A2 D1 D2; RUN; PROC MEANS; VAR A1 A2 D1 D2; RUN; PROC GPLOT; PLOT J*RHO JHAT*RHO/OVERLAY; RUN; PROC UNIVARIATE DATA=STEP2 NOPRINT; VAR J; OUTPUT OUT=TOTALSS CSS=TSS; RUN; DATA TEMP3(REPLACE=YES); SET TOTALSS; SET TEMP3; RESS = ESS2/TSS; RUN; PROC PRINT; VAR ESS2 TSS RESS; RUN; one of three possible approximation equations

  4. SAS code for simultaneous autoregressive (SAR) modeling FILENAME ATTRIBUT 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-DEM&QUAD-DATA.TXT'; FILENAME CONN 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-CON.TXT'; FILENAME EIGEN 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-EIG.TXT'; TITLE 'SAR FOR PUERTO RICO DEM'; DATA STEP1; INFILE ATTRIBUT; INPUT IDDEM MELEV SELEV U V QUAD NAME$; U=U/1000; V=V/1000; Y=LOG(MELEV+17.5); * Y=(SELEV-25)**0.5; RUN; PROC SORT OUT=STEP1(REPLACE=YES); BY IDDEM; RUN; PROC STANDARD MEAN=0 STD=1 OUT=STEP1(REPLACE=YES); VAR Y; RUN; DATA STEP1 (REPLACE=YES); SET STEP1; INFILE CONN; INPUT MUNUM2 C1-C73; ARRAY CONY{73} CY1-CY73; ARRAY CON{73} C1-C73; CSUM = 0; DO I=1 TO 73; CSUM = CSUM + CON{I}; CONY{I} = Y*CON{I}; END; RUN; PROC MEANS SUM; VAR CSUM; RUN; PROC PRINT; VAR NAME IDDEM MUNUM2; RUN; PROC MEANS DATA=STEP1 NOPRINT; VAR CY1-CY73; OUTPUT OUT=CYOUT1 SUM=CY1-CY73; RUN; PROC TRANSPOSE DATA=CYOUT1 PREFIX=WY OUT=CYOUT2; VAR CY1-CY73; RUN; DATA STEP1 (REPLACE=YES); SET STEP1; SET CYOUT2; WY = WY1/CSUM; RUN; PROC REG; MODEL Y=WY; RUN;

  5. eigenvalues for the exact Jacobian DATA STEP2; INFILE EIGEN; INPUT IDE LAMBDAC LAMBDAW; LAMBDA=LAMBDAW; RUN; PROC TRANSPOSE DATA=STEP2 PREFIX=TLAM OUT=EOUT1; VAR LAMBDAW; RUN; DATA STEP1; SET STEP1; IF _N_ = 1 THEN SET EOUT1; RUN; PROC NLIN DATA=STEP1 NOITPRINT METHOD=MARQUARDT; PARMS RHO=0.5 B0=0; BOUNDS -1.5<RHO<1; ARRAY LAMBDAJ{73} TLAM1-TLAM73; JACOB = 0; DERJ = 0; DO I=1 TO 73; JACOB = JACOB + LOG(1 - RHO*LAMBDAJ{I}); DERJ = DERJ + -LAMBDAJ{I}/(1 - RHO*LAMBDAJ{I}); END; J=EXP(JACOB/73); DERJ = -DERJ/73; ZY = Y/J; MODEL ZY = (RHO*WY + B0*(1 - RHO))/J; OUTPUT OUT=TEMP1 PRED=YHAT R=YRESID PARMS=RHO B0; DER.RHO = ((RHO*WY + B0*(1 - RHO) - Y)*DERJ + WY - B0)/J; RUN; PROC REG; MODEL Y=YHAT; RUN; PROC UNIVARIATE NORMAL; VAR YRESID; RUN; SAS calculus not completely correct

  6. from the Jacobian approximation ************************** * * * JACOBIAN APPROXIMATION * * * **************************; PROC MEANS NOPRINT DATA=STEP2; VAR LAMBDA; OUTPUT OUT=EXTREMES MIN=LMIN MAX=LMAX; RUN; DATA STEP1(REPLACE=YES); SET STEP1; IF _N_=1 THEN SET EXTREMES(KEEP=LMIN LMAX); RUN; PROC NLIN DATA=STEP1 NOITPRINT METHOD=MARQUARDT MAXITER=500; PARMS RHO=0.5 B0=0; BOUNDS -1<RHO<1; A1 = 0.4276827; A2 = 0.2998183; D1 = 0.9853629; D2 = 1.0664149; IF RHO=0 THEN J=1; ELSE J = EXP(A1*(LOG(1 - RHO*LMIN)/(RHO*LMIN) + 1 - D1*LOG(1 - RHO*LMIN))+ A2*(LOG(1 - RHO*LMAX)/(RHO*LMAX) + 1 - D2*LOG(1 - RHO*LMAX)) ); IF RHO=0 THEN DERJ=0; ELSE DERJ = A1*(-LOG(1 - RHO*LMIN)/(LMIN*RHO**2) + (RHO*D1*LMIN - 1)/(RHO*(1 - RHO*LMIN)) ) + A2*(-LOG(1 - RHO*LMAX)/(LMAX*RHO**2) + (RHO*D2*LMAX - 1)/(RHO*(1 - RHO*LMAX)) ) ; ZY = Y*J; MODEL ZY = (RHO*WY + B0*(1 - RHO))*J; OUTPUT OUT=TEMP1 PRED=YHAT R=YRESID; DER.RHO = ( ( (RHO*WY + B0*(1 - RHO) ) - Y)*DERJ + WY - B0)*J; RUN;

  7. The AR-SAR model specification 4th order effect

  8. SAS code for spatial filter: Gaussian RV FILENAME ATTRIBUT 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-DEM&QUAD-DATA.TXT'; FILENAME MC 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-MC-EIG.TXT'; FILENAME CONN 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-CON.TXT'; FILENAME EVEC 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-EIGENVECTORS.TXT'; FILENAME OUTFILE 'D:\JYU-SUMMERSCHOOL2006\LAB#3\GAUSSIAN-SF.TXT'; OPTIONS LINESIZE=72; TITLE 'SPATIAL FILTER MODEL FOR PUERTO RICO: GAUSSIAN RV'; DATA STEP1; INFILE ATTRIBUT; INPUT IDDEM MELEV SELEV U V QUAD NAME$; * Y=LOG(MELEV+17.5); Y=(SELEV-25)**0.5; Y0=Y; RUN; PROC SORT DATA=STEP1 OUT=STEP1(REPLACE=YES); BY NAME; RUN; PROC UNIVARIATE NORMAL; VAR Y; RUN; PROC STANDARD MEAN=0 STD=1 OUT=STEP1(REPLACE=YES); VAR Y0; RUN; DATA STEP1 (REPLACE=YES); SET STEP1; INFILE EVEC LRECL=2048; INPUT IDE E1-E73; RUN; ***************************************************************** * * * THE SET OF CANDIDATE EIGENVECTORS IS DETERMINED BY MCADJ >= 0 * * * *****************************************************************; PROC REG OUTEST=COEF; MODEL Y = E1-E18/SELECTION=STEPWISE SLE=0.10; OUTPUT OUT=TEMP P=YHAT R=YRESID; RUN; PROC UNIVARIATE NORMAL; VAR YRESID; RUN; may wish to make a Bonferroni adjustment here stepwise regression

  9. PROC TRANSPOSE DATA=COEF PREFIX=B OUT=COEF2; VAR E1-E18; RUN; DATA COEF(REPLACE=YES); INFILE MC; INPUT ID LAM_MCM MC MCADJ; IF MCADJ<0.25 THEN DELETE; RUN; DATA COEF(REPLACE=YES); SET COEF; SET COEF2; IF B1='.' THEN DELETE; IF B1=0 THEN DELETE; BSQ=B1**2; EMC=BSQ*MC; P=1; RUN; PROC PRINT; RUN; PROC MEANS SUM NOPRINT; VAR LAM_MCM P EMC BSQ; OUTPUT OUT=EMC SUM=SUML P SPANUM SPADEN; RUN; PROC STANDARD DATA=TEMP MEAN=0 STD=1 OUT=TEMP(REPLACE=YES); VAR YRESID; RUN; DATA STEP2(REPLACE=YES); INFILE CONN LRECL=1024; INPUT ID C1-C73; RUN; DATA STEP2(REPLACE=YES); SET STEP2; SET TEMP(KEEP=YRESID); SET STEP1(KEEP=Y0); ARRAY CONN{73} C1-C73; ARRAY ZC{73} ZC1-ZC73; ARRAY Y0C{73} Y0C1-Y0C73; CSUM=0; DO I=1 TO 73; CSUM = CSUM + CONN{I}; ZC{I} = YRESID*CONN{I}; Y0C{I} = Y0*CONN{I}; END; Z=YRESID; X0=1; RUN; PROC MEANS SUM NOPRINT; VAR CSUM X0; OUTPUT OUT=CSUM SUM=CSUM N; RUN; PROC PRINT; VAR CSUM; RUN;

  10. PROC MEANS DATA=STEP2 NOPRINT; VAR Y0C1-Y0C73; OUTPUT OUT=Y0COUT1 SUM=Y0C1-Y0C73; RUN; PROC TRANSPOSE DATA=Y0COUT1 PREFIX=Y0C OUT=Y0COUT2; VAR Y0C1-Y0C73; RUN; PROC MEANS DATA=STEP2 NOPRINT; VAR ZC1-ZC73; OUTPUT OUT=ZCOUT1 SUM=ZC1-ZC73; RUN; PROC TRANSPOSE DATA=ZCOUT1 PREFIX=ZC OUT=ZCOUT2; VAR ZC1-ZC73; RUN; DATA STEP2(REPLACE=YES); SET STEP2(KEEP=X0 Z CSUM Y0); SET ZCOUT2(KEEP=ZC1); SET Y0COUT2(KEEP=Y0C1); Y0C=Y0C1; ZC=ZC1; DROP ZC1; RUN; PROC REG DATA=STEP2 OUTEST=DEN NOPRINT; MODEL CSUM=X0/NOINT; RUN; PROC REG DATA=STEP2 OUTEST=NUM0 NOPRINT; MODEL Y0C=Y0/NOINT; RUN; PROC REG DATA=STEP2 OUTEST=NUM NOPRINT; MODEL ZC=Z/NOINT; RUN; DATA STEP3; SET DEN; SET NUM0; SET NUM; SET CSUM(KEEP=CSUM N); SET EMC(KEEP=SUML SPANUM SPADEN P); MC0=Y0/X0; MC=Z/X0; EMC = -(1+(N/CSUM)*SUML)/(N-P-1); ZMC = (MC-EMC)/SQRT(2/CSUM); MCSPA=SPANUM/SPADEN; RUN; PROC PRINT; VAR MC0 MC EMC ZMC MCSPA; RUN; PROC STANDARD DATA=TEMP OUT=TEMP(REPLACE=YES) MEAN=0; VAR YHAT; RUN; DATA _NULL_; SET TEMP; FILE OUTFILE; PUT IDDEM Y YHAT; RUN; Residual diagnostics; MC distribution theory known for this case YHAT is the spatial filter; output for mapping purposes

  11. SAS code for spatial filter: binomial RV FILENAME INDATA 'D:\JYU-SUMMERSCHOOL2006\LAB#3\PR-POP-1899&2000.TXT'; FILENAME EVEC 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-EIGENVECTORS.TXT'; FILENAME OUTFILE 'D:\JYU-SUMMERSCHOOL2006\LAB#3\BINOMIAL-SF.TXT'; TITLE 'SPATIAL FILTER MODEL FOR PUERTO RICO: BINOMIAL RV'; DATA STEP1; INFILE INDATA; INPUT ID P1899 U1988 P2000 U2000 QUAD NAME$; Y=U2000/P2000; RUN; PROC SORT DATA=STEP1 OUT=STEP1(REPLACE=YES); BY NAME; RUN; DATA STEP1 (REPLACE=YES); SET STEP1; INFILE EVEC LRECL=2048; INPUT IDE E1-E73; RUN; ******************************************************************** * THE SET OF CANDIDATE EIGENVECTORS IS DETERMINED BY MCADJ >= 0.25 * ********************************************************************; PROC LOGISTIC; MODEL U2000/P2000 = E1-E18/SELECTION=STEPWISE SLE=0.10 SCALE=WILLIAMS; OUTPUT OUT=TEMP P=YHAT; RUN; PROC REG; MODEL Y=YHAT; RUN; PROC GENMOD; MODEL U2000/P2000 = E1 E3 E4 E10 E14 E18/DIST=BINOMIAL SCALE=DEVIANCE; OUTPUT OUT=SF XBETA=XBETA P=YHAT; RUN; DATA STEP1(REPLACE=YES); SET STEP1; Y=(U2000-1280)/(P2000-1057); TRY=LOG(Y/(1-Y)); W=1/((P2000-1057)*Y*(1-Y)); RUN; PROC REG; MODEL TRY=E1-E18/NOINT SELECTION=STEPWISE SLE=0.10; WEIGHT W; RUN; PROC STANDARD DATA=SF OUT=SF(REPLACE=YES) MEAN=0; VAR XBETA; RUN; DATA _NULL_; SET SF; FILE OUTFILE; YRESID=Y-YHAT; PUT ID Y XBETA YRESID; RUN; stepwise selection quasi- likelihood normal approximation XBETA is the spatial filter; output for mapping purposes

  12. SAS code for spatial filter: Poisson RV FILENAME INDATA1 'D:\JYU-SUMMERSCHOOL2006\LAB#3\PR-POP-1899&2000.TXT'; FILENAME INDATA2 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-AREAS-COMPETITION.TXT'; FILENAME EVEC 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-EIGENVECTORS.TXT'; FILENAME OUTFILE 'D:\JYU-SUMMERSCHOOL2006\LAB#3\POISSON-SF.TXT'; OPTIONS LINESIZE=72; TITLE 'SPATIAL FILTER MODEL FOR PUERTO RICO: POISSON RV'; DATA STEP1A; INFILE INDATA1; INPUT ID1 P1899 U1988 P2000 U2000 QUAD NAME$; DENOM=100000000000; RUN; PROC SORT DATA=STEP1A OUT=STEP1A(REPLACE=YES); BY NAME; RUN; DATA STEP1B; INFILE INDATA2; INPUT ID2 AREAM AREACT MCT_RATIO TRMCT_RATIO AREAPT MPT_RATIO TRMPT_RATIO NAME$; LNAREAM=LOG(AREAM); RUN; PROC SORT DATA=STEP1B OUT=STEP1B(REPLACE=YES); BY NAME; RUN; DATA STEP1; MERGE STEP1A STEP1B; BY NAME; RUN; DATA STEP1(REPLACE=YES); SET STEP1; INFILE EVEC LRECL=2048; INPUT IDE E1-E73; TRY=LOG(P2000/AREAM - 441830); RUN; ******************************************************************** * * * THE SET OF CANDIDATE EIGENVECTORS IS DETERMINED BY MCADJ >= 0.25 * * * ********************************************************************; PROC GENMOD; MODEL P2000 = E1-E18/DIST=POISSON OFFSET=LNAREAM; RUN; denominator for binomial approximation offset variable must be converted to its log form no stepwise Poisson regression option in SAS

  13. quasi- likelihood ***************************************************************************** * * * ORDER OF REMOVAL (BACKWARD ELIMINATION): 13, 5, 17, 14, 11, 15, 16, 9, 18 * * * *****************************************************************************; PROC GENMOD; MODEL P2000 = E1-E4 E6-E8 E10 E12/DIST=POISSON OFFSET=LNAREAM SCALE=DEVIANCE; RUN; ********************************************************************* * * * ORDER OF REMOVAL (BACKWARD ELIMINATION): 17, 14, 11, 13, 15, 5, 9 * * * *********************************************************************; PROC GENMOD; MODEL P2000 = E1-E4 E6-E8 E10 E12 E16 E18/DIST=NB OFFSET=LNAREAM; OUTPUT OUT=TEMP XBETA=XBETA P=YHAT; RUN; DATA TEMP(REPLACE=YES); SET TEMP; Y=P2000/AREAM; YHAT=YHAT/AREAM; RUN; PROC REG; MODEL Y=YHAT; RUN; PROC LOGISTIC; MODEL P2000/DENOM = E1-E18/SELECTION=BACKWARD OFFSET=LNAREAM SCALE=WILLIAMS SLS=0.10; RUN; PROC REG; MODEL TRY=E1-E18/SELECTION=BACKWARD SLS=0.10; RUN; PROC STANDARD DATA=TEMP OUT=TEMP(REPLACE=YES) MEAN=0; VAR XBETA; RUN; DATA _NULL_; SET TEMP; FILE OUTFILE; XBETA=XBETA-LNAREAM; YRESID=Y-YHAT; PUT ID1 Y XBETA YRESID; RUN; negative binomial binomial approximation normal approximation XBETA is the spatial filter; output for mapping purposes

  14. ArcView mapping of spatial filters Dark red: very high Light red: high Gray: medium Light green: low Dark green: very low • The SAS output files need to be converted to *.dbf files with column headers • MCs can be computed with MapStat3 for the binomial and Poisson regressions residuals Poisson binomial Gaussian

  15. SAS code for spatially structured random effects linear modeling FILENAME INDATA1 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-AREAS-COMPETITION.TXT'; FILENAME INDATA2 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-DEM&QUAD-DATA.TXT'; TITLE 'MIXED MODEL FOR THE PR DATA'; ************************************************************************** * READ IN GEOREFERENCED DATA;THEN CENTER THE SELEDTED ATTRIBUTE VARIABLE * **************************************************************************; DATA STEP1; INFILE INDATA1 LRECL=1024; INPUT ID AREAM AREACT MCT_RATIO TRMCT_RATIO AREAPT MPT_RATIO TRMPT_RATIO NAME$; * Y = LOG(AREAM + 0.001); * Y = TRMPT_RATIO; RUN; PROC SORT DATA=STEP1 OUT=STEP1(REPLACE=YES); BY NAME; RUN; DATA STEP2; INFILE INDATA2 LRECL=1024; INPUT IDDEM MELEV SELEV U V QUAD NAME$; U=U/1000; V=V/1000; * Y=LOG(MELEV+17.5); Y=(SELEV-25)**0.5; X0=1; RUN; PROC SORT DATA=STEP2 OUT=STEP2(REPLACE=YES); BY NAME; RUN; DATA STEP3; MERGE STEP1 STEP2; BY NAME; RUN; PROC REG; MODEL Y=/; RUN; PROC MIXED METHOD=ML MAXITER=500 COVTEST SCORING=10; MODEL Y=X0/NOINT S; REPEATED/SUB=INTERCEPT; RUN;

  16. maximum likelihood estimation PROC MIXED METHOD=ML MAXITER=500 COVTEST SCORING=10; MODEL Y=X0/NOINT S; PARMS (13, 10); REPEATED/SUB=INTERCEPT TYPE=SP(EXP) (U V); RUN; PROC MIXED METHOD=ML MAXITER=500 COVTEST SCORING=10; MODEL Y=X0/NOINT S; PARMS (13, 10); REPEATED/SUB=INTERCEPT TYPE=SP(GAU) (U V); RUN; PROC MIXED METHOD=ML MAXITER=500 COVTEST SCORING=10; MODEL Y=X0/NOINT S; PARMS (13, 10); REPEATED/SUB=INTERCEPT TYPE=SP(SPH) (U V); RUN; PROC MIXED METHOD=ML MAXITER=500 COVTEST SCORING=10; MODEL Y=X0/NOINT S; PARMS (0.1, 13); REPEATED/SUB=INTERCEPT TYPE=SP(POW) (U V); RUN; PROC MIXED METHOD=ML MAXITER=500 COVTEST SCORING=10; MODEL Y=X0/NOINT S; PARMS (13, 10, 1); REPEATED/SUB=INTERCEPT TYPE=SP(MATERN) (U V); RUN; initial parameter values semivariogram model geocodings random effect

  17. OLS: 12.5690 Linear mixed model (Gaussian assumption) without nugget

  18. OLS: 12.5690 Linear mixed model (Gaussian assumption) with nugget

  19. SAS code for spatially structured random effects generalized linear modeling FILENAME INDATA 'D:\JYU-SUMMERSCHOOL2006\LAB#3\PR-URBAN-DATA.TXT'; FILENAME EVECS 'D:\JYU-SUMMERSCHOOL2006\LAB#3\PR-EXPANDED-EVECS.TXT'; OPTIONS LINESIZE=72; TITLE 'GENERALIZED LINEAR MIXED MODEL FOR THE PR DATA'; ******************************************************************* * READ IN GEOREFERENCED DATA; THEN CENTER THE ELEVATION VARIABLE * *******************************************************************; DATA STEP1; INFILE INDATA; INPUT ID Y1899 Y1910 Y1920 Y1930 Y1935 Y1940 Y1950 Y1960 Y1970 Y1980 Y1990 Y2000 MELEV SELEV U V AAR NAME$; NUM=Y1899; U=U/1000; V=V/1000; ELEV=LOG(MELEV/SELEV-0.6); UTELEV=MELEV/SELEV-0.6; IF AAR=1 THEN ISJ=1; ELSE ISJ=0; IF AAR=2 THEN IA =1; ELSE IA =0; IF AAR=3 THEN IM =1; ELSE IM =0; IF AAR=4 THEN IP =1; ELSE IP =0; IF AAR=5 THEN IC =1; ELSE IC =0; IA=IA-ISJ; IM=IM-ISJ; IP=IP-ISJ; IC=IC-ISJ; DENOM=100; Y=NUM/DENOM; P=Y; RUN;

  20. PROC STANDARD MEAN=0 STD=1 OUT=STEP1(REPLACE=YES); VAR ELEV; RUN; DATA STEP2; INFILE EVECS LRECL=1024; INPUT IDE E1-E45; RUN; DATA STEP2(REPLACE=YES); SET STEP2; SET STEP1; RUN; DATA STEP4; SET STEP2(KEEP=ID DENOM ELEV UTELEV IA IP IM IC Y1899 E1-E45); NUM=Y1899; TIME=1899; DROP Y1899; RUN; DATA STEP3; SET STEP2(KEEP=ID DENOM ELEV UTELEV IA IP IM IC Y1910 E1-E45); NUM=Y1910; TIME=1910; DROP Y1910; RUN; DATA STEP4(REPLACE=YES); SET STEP4 STEP3; RUN; DATA STEP3(REPLACE=YES); SET STEP2(KEEP=ID DENOM ELEV UTELEV IP IM IC Y1920 E1-E45); NUM=Y1920; TIME=1920; DROP Y1920; RUN; DATA STEP4(REPLACE=YES); SET STEP4 STEP3; RUN; DATA STEP3(REPLACE=YES); SET STEP2(KEEP=ID DENOM ELEV UTELEV IP IM IC Y1930 E1-E45); NUM=Y1930; TIME=1930; DROP Y1930; RUN; DATA STEP4(REPLACE=YES); SET STEP4 STEP3; RUN; DATA STEP3(REPLACE=YES); SET STEP2(KEEP=ID DENOM ELEV UTELEV IP IM IC Y1935 E1-E45); NUM=Y1935; TIME=1935; DROP Y1935; RUN; DATA STEP4(REPLACE=YES); SET STEP4 STEP3; RUN; DATA STEP3(REPLACE=YES); SET STEP2(KEEP=ID DENOM ELEV UTELEV IP IM IC Y1940 E1-E45); NUM=Y1940; TIME=1940; DROP Y1940; RUN; DATA STEP4(REPLACE=YES); SET STEP4 STEP3; RUN; DATA STEP3(REPLACE=YES); SET STEP2(KEEP=ID DENOM ELEV UTELEV IP IM IC Y1950 E1-E45); NUM=Y1950; TIME=1950; DROP Y1950; RUN; DATA STEP4(REPLACE=YES); SET STEP4 STEP3; RUN; DATA STEP3(REPLACE=YES); SET STEP2(KEEP=ID DENOM ELEV UTELEV IP IM IC Y1960 E1-E45); NUM=Y1960; TIME=1960; DROP Y1960; RUN; DATA STEP4(REPLACE=YES); SET STEP4 STEP3; RUN; DATA STEP3(REPLACE=YES); SET STEP2(KEEP=ID DENOM ELEV UTELEV IP IM IC Y1970 E1-E45); NUM=Y1970; TIME=1970; DROP Y1970; RUN; DATA STEP4(REPLACE=YES); SET STEP4 STEP3; RUN; DATA STEP3(REPLACE=YES); SET STEP2(KEEP=ID DENOM ELEV UTELEV IP IM IC Y1980 E1-E45); NUM=Y1980; TIME=1980; DROP Y1980; RUN; DATA STEP4(REPLACE=YES); SET STEP4 STEP3; RUN; DATA STEP3(REPLACE=YES); SET STEP2(KEEP=ID DENOM ELEV UTELEV IP IM IC Y1990 E1-E45); NUM=Y1990; TIME=1990; DROP Y1990; RUN; DATA STEP4(REPLACE=YES); SET STEP4 STEP3; RUN; DATA STEP3(REPLACE=YES); SET STEP2(KEEP=ID DENOM ELEV UTELEV IP IM IC Y2000 E1-E45); NUM=Y2000; TIME=2000; DROP Y2000; RUN; DATA STEP4(REPLACE=YES); SET STEP4 STEP3; RUN; PROC SORT OUT=STEP5; BY ID DESCENDING TIME; RUN;

  21. PROC NLMIXED DATA=STEP5; PARMS B0=0 BE=0 BIM=0 BIP=0 BIC=0 S2U=1 BE1 =0 BE2 =0 BE3 =0 BE4 =0 BE5 =0 BE6 =0 BE7 =0 BE9 =0 BE10=0 BE11=0 BE16=0 BE17=0 BE18=0 BE19=0 BE20=0 BE21=0 BE23=0 BE26=0 BE27=0 BE33=0 BE35=0; ETA = B0 + ALPHA + BE*ELEV + BIM*IM + BIP*IP + BIC*IC + BE1*E1+BE2*E2+BE3*E3+BE4*E4+BE5*E5+BE6*E6+BE7*E7+BE9*E9+BE10*E10+BE11*E11+BE16*E16+ BE17*E17+BE18*E18+BE19*E19+BE20*E20+BE21*E21+BE23*E23+BE26*E26+BE27*E27+BE33*E33+BE35*E35; P = EXP(ETA)/(1+EXP(ETA)); MODEL NUM ~ BINOMIAL(100,P); RANDOM ALPHA ~ NORMAL(0,S2U) SUBJECT=ID; PREDICT P OUT=POUT; RUN; PROC NLMIXED DATA=STEP5; PARMS B0=0 BE=0 BIM=0 BIC=0 S2U=1 BE2 =0 BE6 =0 BE10=0 BE11=0 BE19=0 BE35=0; ETA = B0 + ALPHA + BE*ELEV + BIM*IM + BIC*IC + BE2*E2+ BE6*E6+ BE10*E10+BE11*E11+ BE19*E19+ BE35*E35; P = EXP(ETA)/(1+EXP(ETA)); MODEL NUM ~ BINOMIAL(100,P); RANDOM ALPHA ~ NORMAL(0,S2U) SUBJECT=ID; PREDICT P OUT=POUT; RUN; PROC SORT OUT=POUT(REPLACE=YES); BY TIME ID; RUN; DATA POUT(REPLACE=YES); SET POUT; SET STEP4(KEEP=NUM); P=NUM/100; RUN; PROC REG; MODEL P=PRED; BY TIME; RUN; PROC GPLOT; PLOT P*PRED; BY TIME; RUN; PROC GENMOD DATA=STEP2; MODEL NUM/DENOM= ELEV E1 E4 E6 E7 E11 E17 E23 E26 E33/DIST=B SCALE=P; OUTPUT OUT=POUT P=PRED; RUN;

  22. Spatial filters structuring the random effects MC = 0.274 GR = 0.795 MC = 0.877 GR = 0.227 MC = -0.459 GR = 1.582

  23. The random effect MC = -0.074, GR = 0.973

  24. What you should have learned in today’s lab: • Calculate the J approximation for a given connectivity matrix (both C and W) • Normal approximation: estimate AR, SAR, AR-SAR and SF models • Estimate a SF GLM (Poisson or binomial) • Estimate a univariate LMM • Estimate a bivariate (time) SF-GLMM • Construct maps of the SFs • Construct maps of the random effects

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