Hi   I want to know if SAS can do a goodness of test for other less famous distributions? Such as Frechet, exponentiated frechet, Kumaraswamy..etc.   Thank you ... View more

Hello   I am using my previous code which is working fine, just changed the density. I keep getting warning messages "WARNING: Invalid argument resulted in missing value result." I check the line that caused this issue and printed it to see all values, but I found no problem with it.     Can you please help me figure out it and solve this issue.   Thank you proc iml; *seed=0; NN=10; theta1=2; theta2=10; del=3; case=2; n1=30; m1=15; k=J(m1,1,0); K[m1]=n1-m1; L=K; n2=n1; m2=m1; **********************************************************************************************************************; *********************** Exact OVL *******************************; R=(theta1/theta2); R_star=((m2-1)/m2)*R; R1=R_star; rho_Exact=2*sqrt(R1)/(1+R1); lambda_Exact=(2*sqrt(R1)/(1+R1))**2; delta_Exact=1-R1**(1/(1-R1))*abs(1-1/R1); ******************************************************************************; start Uniform_p(m,R) global(seed); V=J(m,1,0); U=J(m,1,0);W=J(m,1); W=randfun(m,"Uniform"); Do i=1 to m; Sum=0; Do j=m-i+1 to m; Sum=Sum+R[j]; End; Sum=Sum+i; V[i]=W[i]**(1/Sum); End; Do i=1 to m; U[i]=1-prod ( V[m: (m-i+1)] ); End; Call sort (U); return U; Finish; ********************************************************************; Start Adaptive(n,m,R,theta) Global(del) ; U=uniform_p(m,R); Y=( log ( 1 - (1- U)##(1/theta) ) ) ; X = - y ##(-1/del); T=X[m]+2; Do idx=1 to m-1; If (( X[idx] < T) & (T <= X[idx+1] )) then j=idx; End; If X[m]>T then Do; W=Uniform_p(m-j,R); WE= (1 - Exp(-1/T##del)); X_m_j =( -1/ ( log ( 1 - ( (1- W)##(1/theta) ) # WE ) ) )##del; X_Adptive= X[1:j]//X_m_j; Rm=n-m-sum(R[1:J]); R2=J(m-j,1,0);R2[m-J]=Rm; newR=R[1:j]//R2; End; Else Do; j= m; newR=R; X_Adptive=X; End; P1=[X_Adptive,j,newR,y]; return P1; Finish; ********** Subroutine to compute the MLEs **************; *********************************************************; start MLE(X,n,m,R,J); Part=1-Exp(- 1/X##(-del)); deno = R[m]*log(part[m])+ sum (log(part)) + sum( (R#log(part))[1:J] ) ; Theta_MLE = (- m/deno); return(Theta_MLE); finish; ******************************************************************************************; theta1_Adp=J(NN,1,0); theta2_Adp=J(NN,1,0);H_Adp=J(NN,1,0); R_star_Adp=J(NN,1,0); rho_star_Adp=J(NN,1,0);Var_rho_Adp=J(NN,1,0);Bias_rho_Adp=J(NN,1,0); lambda_star_Adp=J(NN,1,0);Var_lambda_Adp=J(NN,1,0);Bias_lambda_Adp=J(NN,1,0); delta_star_Adp=J(NN,1,0);Var_delta_Adp=J(NN,1,0);Bias_delta_Adp=J(NN,1,0); MSE_rho_Adp=J(NN,1,0);MSE_lambda_Adp=J(NN,1,0);MSE_delta_Adp=J(NN,1,0); Lower_rho_Adp=J(NN,1);Upper_rho_Adp=J(NN,1);Length_rho_Adp=J(NN,1); Lower_lambda_Adp=J(NN,1);Upper_lambda_Adp=J(NN,1);Length_lambda_Adp=J(NN,1); Lower_delta_Adp=J(NN,1);Upper_delta_Adp=J(NN,1);Length_delta_Adp=J(NN,1); COV1_Adp=J(NN,1,0);COV2_Adp=J(NN,1,0);COV3_Adp=J(NN,1,0);***COVi= stands for coverage of OVLi, i=1,2,3**; var_rho_Adp=J(NN,1,0);var_lambda_Adp=J(NN,1,0);var_delta_Adp=J(NN,1,0); MSE1=J(NN,1,0);MSE2=J(NN,1,0);MSE3=J(NN,1,0); Do JJ=1 to NN; X=J(m1,1,0);Y=J(m2,1,0); X_ADP=J(m1,1,0);Y_ADP=J(m2,1,0); *********************************************************************; AdaptiveResX=Adaptive(n1,m1,K,theta1); X_ADP=AdaptiveResX$1; J1=AdaptiveResX$2; newK=AdaptiveResX$3; y=AdaptiveResX$4;print y; K=newK; AdaptiveResY=Adaptive(n2,m2,L,theta2); Y_ADP=AdaptiveResY$1; J2=AdaptiveResY$2; newL=AdaptiveResY$3; L=newL; Theta1_Adp[JJ] = MLE(X_ADP,n1,m1,K,J1); Theta2_Adp[JJ] = MLE(Y_ADP,n2,m2,L,J2); R_star_Adp[JJ]=(m2-1)/m2*(theta1_Adp[JJ]/theta2_Adp[JJ]); End; ** The estimated values of the OVL **; rho_star_Adp=2#sqrt(R_star_Adp)#(1+R_star_Adp)##(-1); lambda_star_Adp=rho_star_Adp##2; delta_star_Adp=1-R_star_Adp##(1/(1-R_star_Adp))#abs(1-1/R_star_Adp); ****** Asymptotic Variance of the Overlap ********; var_rho_Adp=R_star_Adp#(1-R_star_Adp)##2/(1+R_star_Adp)##4#(m1+m2-1)/(m1*(m2-2)); var_lambda_Adp=16#R_star_Adp##2#(1-R_star_Adp)##2/(1+R_star_Adp)##6#(m1+m2-1)/(m1*(m2-2)); var_delta_Adp=(m1+m2-1)/(m1*(m2-2))#R_star_Adp##(2/(1-R_star_Adp))#(log(R_star_Adp))##2/(1-R_star_Adp)##2; ****** Asymptotic Bias of the Overlap ********; Bias_rho_Adp=(m1+m2-1)/( m1*(m2-2))# sqrt(R_star_Adp)# ( 3#R_star_Adp##2-6#R_star_Adp-1)/( 4#(1+R_star_Adp)##3 ); Bias_lambda_Adp=(m1+m2-1)/(m1*(m2-2))#4#R_star_Adp##2#(R_star_Adp-2)/(1+R_star_Adp)##4; H_Adp= R_star_Adp##(1/(1-R_star_Adp)) # ( R_star_Adp#(2#R_star_Adp-log(R_star_Adp)-2)#log(R_star_Adp) - (R_star_Adp-1)##2 )/(R_star_Adp-1)##3; H_Adp=R_star_Adp##2 # R_star_Adp##((2#R_star_Adp-1)/(1-R_star_Adp)) # ( R_star_adp # (2#R_star_Adp-log(R_star_Adp)-2)#log(R_star_Adp) - (R_star_Adp-1)##2 )/(R_star_Adp-1)##3; Do JJ=1 to NN; if (R_star_Adp[JJ] > 1) then Bias_delta_Adp[JJ]=(m1+m2-1)/(2*m1*(m2-2))* H_Adp[JJ]/2; if (R_star_Adp[JJ] < 1) then Bias_delta_Adp[JJ]=-(m1+m2-1)/(2*m1*(m2-2))* H_Adp[JJ]/2; End; Theta1_h=theta1_Adp[:]; Theta2_h=theta2_Adp[:]; Bias_rho=Bias_rho_Adp[:]; Bias_lambda=Bias_lambda_Adp[:]; Bias_delta=Bias_delta_Adp[:]; ** I used MSE below b/c it provides smaller MSE values; MSE1 = ( rho_star_Adp - rho_Exact )##2; MSE2 = ( lambda_star_Adp - lambda_exact)##2; MSE3 = ( delta_star_Adp - delta_exact)##2; Var1= Var_rho_Adp[:]; Var2= Var_lambda_Adp[:]; Var3= Var_delta_Adp[:]; **** Interval estimation using Asymptotic technique ****; Do JJ=1 to NN; *************************** OVL1 *****************************; Lower_rho_Adp[JJ] = rho_star_Adp[JJ] - Bias_rho_Adp[JJ] - 1.96*sqrt(Var_rho_Adp[JJ]); Upper_rho_Adp[JJ] = rho_star_Adp[JJ] - Bias_rho_Adp[JJ] + 1.96*sqrt(Var_rho_Adp[JJ]); Length_rho_Adp[JJ]=Upper_rho_Adp[JJ]-Lower_rho_Adp[JJ]; If ( (rho_exact >= Lower_rho_Adp[JJ])& (rho_exact <= upper_rho_Adp[JJ]) ) then COV1_Adp[JJ]=1;* COV is the coverage; *************************************************************************************; *************************** OVL2 *****************************; Lower_lambda_Adp[JJ] = lambda_star_Adp[JJ] - Bias_lambda_Adp[JJ] - 1.96*sqrt(Var_lambda_Adp[JJ]); Upper_lambda_Adp[JJ] = lambda_star_Adp[JJ] - Bias_lambda_Adp[JJ] + 1.96*sqrt(Var_lambda_Adp[JJ]); Length_lambda_Adp[JJ]= Upper_lambda_Adp[JJ] - Lower_lambda_Adp[JJ]; If ( (lambda_exact >= Lower_lambda_Adp[JJ])& (lambda_exact <= upper_lambda_Adp[JJ]) ) then COV2_Adp[JJ]=1; **************************************************************************************; *************************** OVL3 ******************************; Lower_delta_Adp[JJ] = delta_star_Adp[JJ] - Bias_delta_Adp[JJ] - 1.96*sqrt(Var_delta_Adp[JJ]); Upper_delta_Adp[JJ] = delta_star_Adp[JJ] - Bias_delta_Adp[JJ] + 1.96*sqrt(Var_delta_Adp[JJ]); Length_delta_Adp[JJ]= Upper_delta_Adp[JJ] -Lower_delta_Adp[JJ]; If ( (delta_exact >= Lower_delta_Adp[JJ])& (delta_exact <= upper_delta_Adp[JJ]) ) then COV3_Adp[JJ]=1; end; ***************** END Of Simulations ************************; ********************************************************************************************************; BiasOVL1_Adp=ABS(Bias_rho_Adp[:]); ** why I added 0.001 is explained above ; BiasOVL2_Adp=ABS(Bias_lambda_Adp[:]); BiasOVL3_Adp=ABS(Bias_delta_Adp[:]); MSE11=MSE1[:]; MSE22=MSE2[:]; MSE33=MSE3[:]; length_OVL1_Adp=Length_rho_Adp[:]; length_OVL2_Adp=Length_lambda_Adp[:]; length_OVL3_Adp=Length_delta_Adp[:]; coverage1_Adp=COV1_Adp[:]; coverage2_Adp=COV2_Adp[:]; coverage3_Adp=COV3_Adp[:]; ****formatting the output *******; *print Lower_rho_RSS exact_rho upper_rho_RSS; Bias=J(3,1);Length=J(3,1);coverage=J(3,1); MSE=J(3,1,0); Bias[1,1]=(BiasOVL1_Adp ); Bias[2,1]=(BiasOVL2_Adp ); Bias[3,1]=(BiasOVL3_Adp ); MSE[1,1]= MSE11; MSE[2,1]= (MSE22 ); MSE[3,1]= (MSE33 ); Length[1,1]=(length_OVL1_Adp ); Length[2,1]=(length_OVL2_Adp ); Length[3,1]=(length_OVL3_Adp ); coverage[1,1]=(COV1_Adp[:]); coverage[2,1]=(COV2_Adp[:] ); coverage[3,1]=(COV3_Adp[:] ); names={rho,lambda,delta}; Print "Results from T3"; print case n1 m1; print names bias " " MSE " " Length " " coverage ; quit;   ... 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Hello My code is supposed to simulate two samples from exponential distribution based on progressive type II. Then, I must modify the samples by truncating each one at a particular time and replacing the truncated values with new ones. The final samples  should be used to estimate the overlap function between the two samples. I have three truncation choices: after 30%, 80%, or 0% of the data. My problem is that all results are almost identical. No matter if I do the truncation or not!!! I also found that if I fix the percentage of the truncation and change the censoring scheme (i.e., to censor the data at the beginning, at the end, or in the middle(results remain unchanged) which also doesn't make any sense!!! I would greatly appreciate any help in resolving this matter. proc iml; *Results for T1, T2 and T3 have striking similarities, I checked everything and don't know what is the problem, Results for Ti, i=1,2,3 when changing the censoring scheme also produce stricking similarities!!!! **********************************************************************************************************************; seed=0; NN=10000;*NN is the number of simulations; theta1=1; theta2=5; case=17; n1=80; m1=50; k=J(m1,1,0); K[m1]=n1-m1; L=K; n2=n1; m2=m1; /**********************************************************************************************************************; case=16; n1=80; m1=50; k=J(m1,1,0); K[1]=n1-m1; L=K; n2=n1; m2=m1; case=17; n1=80; m1=50; k=J(m1,1,0); K[m1]=n1-m1; L=K; n2=n1; m2=m1; case=18; n1=80; m1=50; k=J(m1,1,0); k[41:50]=3; L=K; n2=n1; m2=m1; */ *********************** Exact OVL *******************************; R=(theta1/theta2); R_star=((m2-1)/m2)*R; Exact_rho=2*sqrt(R_star)/(1+R_star); Exact_lambda=(Exact_rho)**2; Exact_delta=1-R_star**(1/(1-R_star))*abs(1-1/R_star); ******************************************************************************; start Uniform_p(m,R) global(seed); ** The Required Progressively Type-II from U(0,1); V=J(m,1,0); U=J(m,1,0);W=J(m,1); Call randseed(seed); Call randgen(W, "Uniform"); Do i=1 to m; Sum=0; Do j=m-i+1 to m; Sum=Sum+R[j]; End; Sum=Sum+i; V[i]=W[i]**(1/Sum); End; Do i=1 to m; U[i]=1-prod ( V[m: (m-i+1)] ); End; Call sort (U); return U; Finish; ********************************************************************; Start Adaptive(X, T, n,m,R,theta) ; Do idx=1 to m-1; If (( X[idx] < T) & (T <= X[idx+1] )) then j=idx; End; If X[m]>T then Do; W=Uniform_p(m-j,R); X_m_j = T - theta*log(1-W); X_Adptive= X[1:j]//X_m_j; Rm=n-m-sum(R[1:J]); R2=J(m-j,1,0);R2[m-J]=Rm; newR=R[1:j]//R2; End; else Do; j= m; newR=R; X_Adptive=X; End; P1=[X_Adptive,j,newR]; return P1; Finish; ********** Subroutine to compute the MLEs **************; *********************************************************; start MLE(X,n,m,R,Rm,J); Theta_MLE=( Rm*X[m]+X[+]+ sum( (R#X)[1:J] ) )/m; return(Theta_MLE); finish; ******************************************************************************************; theta1_Adp=J(NN,1,0); theta2_Adp=J(NN,1,0);H_Adp=J(NN,1,0); R_star_Adp=J(NN,1,0); rho_star_Adp=J(NN,1,0);Var_rho_Adp=J(NN,1,0);Bias_rho_Adp=J(NN,1,0); lambda_star_Adp=J(NN,1,0);Var_lambda_Adp=J(NN,1,0);Bias_lambda_Adp=J(NN,1,0); delta_star_Adp=J(NN,1,0);Var_delta_Adp=J(NN,1,0);Bias_delta_Adp=J(NN,1,0); MSE_rho_Adp=J(NN,1,0);MSE_lambda_Adp=J(NN,1,0);MSE_delta_Adp=J(NN,1,0); Lower_rho_Adp=J(NN,1);Upper_rho_Adp=J(NN,1);Length_rho_Adp=J(NN,1); Lower_lambda_Adp=J(NN,1);Upper_lambda_Adp=J(NN,1);Length_lambda_Adp=J(NN,1); Lower_delta_Adp=J(NN,1);Upper_delta_Adp=J(NN,1);Length_delta_Adp=J(NN,1); COV1_Adp=J(NN,1,0);COV2_Adp=J(NN,1,0);COV3_Adp=J(NN,1,0);***COVi= stands for coverage of OVLi, i=1,2,3**; var_rho_Adp=J(NN,1,0);var_lambda_Adp=J(NN,1,0);var_delta_Adp=J(NN,1,0); Do JJ=1 to NN; X=J(m1,1,0);Y=J(m2,1,0); X_ADP=J(m1,1,0);Y_ADP=J(m2,1,0); **** Data set I; ***************; X = - theta1 * log (1- uniform_p(m1,K)); * Create Progressive type II data; *T1=X[ceil(0.3*m1)]; * Keep the top 80% and replace the remaining data using Adaptive module; *T1=X[ceil(0.8*m1)]; * Keep the top 30% and replace the remaining data using Adaptive module; T1=X[m1]+2; * Do not truncate the data; AdaptiveResX=Adaptive(X, T1,n1,m1,K,theta1);**Create Adaptive hybrid progressive type II; X_ADP=AdaptiveResX$1;* This is my Adaptive IIPH that I will use to find MLEs; J1=AdaptiveResX$2; newK=AdaptiveResX$3; K=newK; Km=K[m1]; **** Data set II; ****************; Y = - theta2 * log (1- uniform_p(m2,L)); *T2=Y[ceil(0.3*m2)]; *T2=Y[ceil(0.8*m2)]; T2=Y[m2]+2; AdaptiveResY=Adaptive(Y, T2,n2,m2,L,theta2); Y_ADP=AdaptiveResY$1;* This is my Adaptive IIPH that I will use to find MLEs; J2=AdaptiveResY$2; newL=AdaptiveResY$3; L=newL; Lm=L[m2]; *print J1 T1 J2 T2 k newk l newl; *print X T1 J1 X_adp " " Y T2 J2 Y_adp; *********************************************************************; *Theta1_Adp[JJ]=( Km* X_ADP[m1]+X_ADP[+] + sum( ( K#X_ADP )[1:J1] ) )/m1; *Theta2_Adp[JJ]=( Lm* Y_ADP[m2]+Y_ADP[+] + sum( ( L#Y_ADP )[1:J2] ) )/m2; Theta1_Adp[JJ]=MLE(X_ADP,n1,m1,K,Km,J1); Theta2_Adp[JJ] =MLE(Y_ADP,n2,m2,L,Lm,J2); R_star_Adp[JJ]=(m2-1)/m2*(theta1_Adp[JJ]/theta2_Adp[JJ]); End; ** The estimated values of the OVL **; rho_star_Adp=2#sqrt(R_star_Adp)#(1+R_star_Adp)##(-1); lambda_star_Adp=rho_star_Adp##2; delta_star_Adp=1-R_star_Adp##(1/(1-R_star_Adp))#abs(1-1/R_star_Adp); ****** Asymptotic Variance of the Overlape ********; var_rho_Adp=R_star_Adp#(1-R_star_Adp)##2/(1+R_star_Adp)##4#(m1+m2-1)/(m1*(m2-2)); var_lambda_Adp=16#R_star_Adp##2#(1-R_star_Adp)##2/(1+R_star_Adp)##6#(m1+m2-1)/(m1*(m2-2)); var_delta_Adp=R_star_Adp##(2/(1-R_star_Adp))#(log(R_star_Adp))##2/(1-R_star_Adp)##2#(m1+m2-1)/(m1*(m2-2)); ****** Asymptotic Bias of the Overlape ********; Bias_rho_Adp=(m1+m2-1)/( m1*(m2-2))# sqrt(R_star_Adp)# ( 3#R_star_Adp##2-6#R_star_Adp-1)/( 4#(1+R_star_Adp)##3 ); Bias_lambda_Adp=(m1+m2-1)/(m1*(m2-2))#4#R_star_Adp##2#(R_star_Adp-2)/(1+R_star_Adp)##4; H_Adp= R_star_Adp##(1/(1-R_star_Adp)) # ( R_star_Adp#(2#R_star_Adp-log(R_star_Adp)-2)#log(R_star_Adp) - (R_star_Adp-1)##2 )/(R_star_Adp-1)##3; Do JJ=1 to NN; if (R_star_Adp[JJ] > 1) then Bias_delta_Adp[JJ]=(m1+m2-1)/(2*m1*(m2-2))* H_Adp[JJ]; if (R_star_Adp[JJ] < 1) then Bias_delta_Adp[JJ]=-(m1+m2-1)/(2*m1*(m2-2))* H_Adp[JJ]; end; Theta1_h=theta1_Adp[:]; Theta2_h=theta2_Adp[:]; Bias_rho=Bias_rho_Adp[:]; Bias_lambda=Bias_lambda_Adp[:]; Bias_delta=Bias_delta_Adp[:]; **** Interval estimation using Asymptotic technique ****; Do JJ=1 to NN; *************************** OVL1 *****************************; Lower_rho_Adp[JJ] = rho_star_Adp[JJ] - Bias_rho_Adp[JJ] - 1.96*sqrt(Var_rho_Adp[JJ]); Upper_rho_Adp[JJ] = rho_star_Adp[JJ] - Bias_rho_Adp[JJ] + 1.96*sqrt(Var_rho_Adp[JJ]); Length_rho_Adp[JJ]=Upper_rho_Adp[JJ]-Lower_rho_Adp[JJ]; If ( (Exact_rho >= Lower_rho_Adp[JJ])& (Exact_rho <= upper_rho_Adp[JJ]) ) then COV1_Adp[JJ]=1;* COV is the coverage; *************************************************************************************; *************************** OVL2 *****************************; Lower_lambda_Adp[JJ] = lambda_star_Adp[JJ] - Bias_lambda_Adp[JJ] - 1.96*sqrt(Var_lambda_Adp[JJ]); Upper_lambda_Adp[JJ] = lambda_star_Adp[JJ] - Bias_lambda_Adp[JJ] + 1.96*sqrt(Var_lambda_Adp[JJ]); Length_lambda_Adp[JJ]= Upper_lambda_Adp[JJ] - Lower_lambda_Adp[JJ]; If ( (Exact_lambda >= Lower_lambda_Adp[JJ])& (Exact_lambda <= upper_lambda_Adp[JJ]) ) then COV2_Adp[JJ]=1; **************************************************************************************; *************************** OVL3 ******************************; Lower_delta_Adp[JJ] = delta_star_Adp[JJ] - Bias_delta_Adp[JJ] - 1.96*sqrt(Var_delta_Adp[JJ]); Upper_delta_Adp[JJ] = delta_star_Adp[JJ] - Bias_delta_Adp[JJ] + 1.96*sqrt(Var_delta_Adp[JJ]); Length_delta_Adp[JJ]= Upper_delta_Adp[JJ] -Lower_delta_Adp[JJ]; If ( (Exact_delta >= Lower_delta_Adp[JJ])& (Exact_delta <= upper_delta_Adp[JJ]) ) then COV3_Adp[JJ]=1; end; ***************** END Of Simulations ************************; ********************************************************************************************************; BiasOVL1_Adp=ABS(Bias_rho_Adp[:]); BiasOVL2_Adp=ABS(Bias_lambda_Adp[:]); BiasOVL3_Adp=ABS(Bias_delta_Adp[:]); varOVL1=VAR_rho_Adp[:]; VAROVL2=VAR_lambda_Adp[:]; VAROVL3=VAR_delta_Adp[:]; length_OVL1_Adp=Length_rho_Adp[:]; length_OVL2_Adp=Length_lambda_Adp[:]; length_OVL3_Adp=Length_delta_Adp[:]; coverage1_Adp=COV1_Adp[:]; coverage2_Adp=COV2_Adp[:]; coverage3_Adp=COV3_Adp[:]; ****formating the output *******; *print Lower_rho_RSS exact_rho upper_rho_RSS; Bias=J(3,1);Length=J(3,1);coverage=J(3,1); Var=J(3,1,0); bias[1,1]=Round(BiasOVL1_Adp,0.0001 ); bias[2,1]=Round(BiasOVL2_Adp,0.0001 ); bias[3,1]=Round(BiasOVL3_Adp,0.0001 ); Var[1,1]=Round(VarOVL1,0.0001 ); Var[2,1]=Round(VarOVL2,0.0001 ); Var[3,1]=Round(VarOVL3,0.0001 ); Length[1,1]=Round(length_OVL1_Adp,0.0001 ); Length[2,1]=Round(length_OVL2_Adp,0.0001 ); Length[3,1]=Round(length_OVL3_Adp,0.0001 ); coverage[1,1]=Round(COV1_Adp[:],0.001 ); coverage[2,1]=Round(COV2_Adp[:],0.001 ); coverage[3,1]=Round(COV3_Adp[:],0.001 ); names={rho,lambda,delta}; print case R n1 m1; print names bias " " Var " " Length " " coverage ; quit;   ... 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Hi   I am having a nightmare!!! I am having an error with part of my code. I believe that I know what is the error message means but I can't figure out how to solve it. I checked everything, I printed every value to make sure that it is well defined and yet once SAS reached that statement, it doesn't execute it!!! Please help me. Thank you   proc iml;   seed=22;  theta1=0.1; theta2=0.5;   NN=5;   case=1; n1=20;  m1=5; k=J(m1,1,0); K[1]=n1-m1;    ******************************************************************************; start Uniform_p(m,R)  global(seed);   ** The Required Progressively Type-II from U(0,1);   V=J(m,1,0); U=J(m,1,0);W=J(m,1);   Call randseed(seed); Call randgen(W, "Uniform");   Do i=1 to m; Sum=0; Do j=m-i+1 to m; Sum=Sum+R[j]; End; Sum=Sum+i; V[i]=W[i]**(1/Sum);   End;   Do i=1 to m; U[i]=1-prod ( V[m: (m-i+1)]   ); End;   Call sort (U);  return U; Finish;   ********************************************************************;   Start Adaptive(X, T, n, m, R,theta) ;   Do idx=1 to m-1; If (( X[idx] < T) & (T <= X[idx+1] )) then j=idx;            End;   If X[m]>T then      Do; W=Uniform_p(m-j,R); X_m_j = T - theta*log(1-W);     X_Adptive= X[1:j]//X_m_j; RJ=R((m-j),1,0); RJ[(m-j)]=n-m-sum(R[1:J]); newR=R[1:J]//RJ;   End;   else    Do; j= m; newR=R; X_Adptive=X; End;   P1=[X_Adptive,j,newR];   return P1; Finish;   Do JJ=1 to NN; X=J(m1,1,0); X_ADP=J(m1,1,0);   X = - theta1 * log (1- uniform_p(m1,K)); T1=X[Floor(4*m1/5)];   AdaptiveResX=Adaptive(X, T1,n1,m1,K,theta1); X_ADP=AdaptiveResX$1;* This is my Adaptive IIPH that I will use to find MLEs; J1=AdaptiveResX$2; newK=AdaptiveResX$3;   print J1 K newK ; End; Quit;     ... View more

Hi   I keep getting errors regarding variables in my module although these values are well defined. Sometimes SAS gives no error at all and some other times SAS doesn't let it go. It feels like the program is been hunted😊 Can any one help me please figure out where is the problem.    Thank you all ... View more