Truncated normal distribution not giving positive values

Hi,

I have a code in which I am sampling from a normal distribution with a mean and standard deviation I give. There are two parameters following a normal distribution: ah and muv. In order to ensure that a negative value does not occur in any of the sampled values, three loops are introduced. The first loop finds out those row numbers of the randomly generated vector that have a negative value and assign those row numbers in a column vector called tag. The first loop is given below: l=0; % to the letter small "l" number zero is assigned for k1=1:dim % if ah(k1)<0 if ah(k1) < (0.0001) % k1 % mu(k1) l=l+1; tag(l)=k1; % assigns the row index of negative values to vector tag end end

The second loop again randomly samples from the normal distribution for those row indices that have a negative value. The second loop is given below: if l>0 for k2=1:l ah(tag(k2)) = random(name5,par_ah,std_ah,1,1); % VECTOR % will the above scaler necessarily be a poistive number ? % mu(tag(k2)) end end

The third loop checks if the numbers randomly sampled for the second time are positive or not. I do not get a problem for the parameter ah However for the parameter muv, I still end up getting negative values from the truncated normal distribution. This can also be seen from the graph generated.

The script file is copied and pasted below:

{ % All values are for DDT and p=0.04 clear all Nh=33898857; Nc=21571585; H=7933615; p=0.04; % p = percent increase in natural sand fly death rate t=90;% model is run with time after spray as 90 days % Type of Analysis analysis=1; % For Uncertainity analysis % analysis=2; % For Sensitivity analysis

if analysis==1 dim=1e5; % dim=30; reali=10; end

if analysis==2 dim=1e4; reali=2; end

model='Sand flies killed'; % examine the effect of Sand flies killed with different ...

    % response from my model is the optimal values of x and y 
    x_rlz=zeros(dim,reali); % a zero matrix called "R0 realizations" of dim rows and reali columns
    y_rlz=zeros(dim,reali);

    % 6 input parameters for getting the above response
    beta_all=zeros(dim,reali);
    ah_all=zeros(dim,reali);
    muv_all=zeros(dim,reali);
    Ct0_all=zeros(dim,reali); % DDT
%     Ct0_all=zeros(dim,reali); % Deltamethrin
    b1_all=zeros(dim,reali);
    b2_all=zeros(dim,reali);

    beta=zeros(dim,1);
    ah=zeros(dim,1);
    muv=zeros(dim,1);
    Ct0=zeros(dim,1);
    b1=zeros(dim,1);
    b2=zeros(dim,1);

    beta_vec=zeros(reali,6);
    ah_vec=zeros(reali,6);
    muv_vec=zeros(reali,6);
    Ct0_vec=zeros(reali,6);
    b1_vec=zeros(reali,6);
    b2_vec=zeros(reali,6);

    x_vec=zeros(reali,6);
    y_vec=zeros(reali,6);

    tag=zeros(dim,1);

   for j=1:reali % reali=10 because we are repeatedly sampling the I/P parameters using Monte carlo simluation 10 times for robustness

    j  % Iteration.............

    % names of distributions to be used
    % for each input parameter to the model

      name1='exponential';
      name2='beta';
      name3='gamma';
      name4='uniform';
      name5='normal';

% I/P parameter, ah has Normal Distribution, mean is 0.1792 and variance is 0.00396 par_ah=0.1792; % var_mu=0.00396344; std_ah=0.028; ah=random(name5,par_ah,std_ah,dim,1); % VECTOR

      ah_all(:,j)=ah;      

      % To have all non-negative values of ah we choose again values which
      % are negative
%      THIS BELOW CODE MAKES IT A TRUNCATED NORMAL distribution
      l=0;            % to the letter small "l" number zero is assigned
     for k1=1:dim
%          if ah(k1)<0 
       if ah(k1) < (0.0001)
    %         k1
    %         mu(k1)
             l=l+1;
             tag(l)=k1; % assigns the row index of negative values to vector tag
       end
     end

     if l>0
         for k2=1:l
             ah(tag(k2)) = random(name5,par_ah,std_ah,1,1);                 %  VECTOR
      % will the above scaler necessarily be a poistive number ?
             %       mu(tag(k2))
         end
     end

     % Below Loop introduced by Kaushik on 9_14_2012 to check if negative
     % values do occur in the column vector ah, even after executing the
     % above two loops
      for k3=1:dim
         if ah(k3)<0
           fprintf(2,['In row number  ',num2str(k3),'   ah is negative   ,',num2str(ah(k3)),'\n']); 
         end 
      end     

      ah_vec(j,1)=mean(ah);
      ah_vec(j,2)=median(ah);
      ah_vec(j,3)=std(ah);
      ah_vec(j,4)=var(ah);
      ah_vec(j,5)=min(ah);
      ah_vec(j,6)=max(ah);

% % Steady state values of state ac=1-ah; % VECTOR Q=(Nh.*ah)./(ah.*Nh+ac*Nc); % VECTOR % a=h./m2; % VECTOR

      % I/P parameter muv also has a normal distribution just as ah above

      par_muv = 1/(0.47*30);
      %         var_mu=0.00396344;
      std_muv = 1/(0.42*30);
      muv=random(name5,par_muv,std_muv,dim,1);                 %  VECTOR

      muv_all(:,j)=muv;      % to the j th column of array muv, the column vector muv is assigneds

      % To have all non-negative values of muv we choose again values which
      % are negative
      l=0;            % to the letter small "l" number zero is assigned
     for k1=1:dim
         if muv(k1) < (0.0001)
    %         k1
    %         mu(k1)
             l=l+1;
             tag(l)=k1;
         end
     end

     if l>0
         for k2=1:l
             muv(tag(k2))=random(name5,par_muv,std_muv,1,1);                %  VECTOR
      % will the above scaler necessarily be a poistive number ?
             %       mu(tag(k2))
         end
     end

     for k3=1:dim
         if muv(k3)<0
           fprintf(2,['In row number  ',num2str(k3),'   muv is negative   ,',num2str(muv(k3)),'\n']); 
         end 
     end

      muv_vec(j,1)=mean(muv);
      muv_vec(j,2)=median(muv);
      muv_vec(j,3)=std(muv);
      muv_vec(j,4)=var(muv);
      muv_vec(j,5)=min(muv);
      muv_vec(j,6)=max(muv);

%%%%% % Uniform distribution % low_gam=0.2673; % for gamma=0.2838 % high_gam=0.3005; % for gamma=0.2838 low_Ct0= 0.4941; % for gamma=0.5 high_Ct0=0.5858; % for gamma=0.5 % low_gam=0.8053; % for gamma=0.8343 % high_gam=0.8630; % for gamma=0.8343 Ct0=random(name4,low_Ct0,high_Ct0,dim,1); % VECTOR % gamma=random(name5,0.8343,0.01,dim,1); % VECTOR par_Ct0=(low_Ct0+high_Ct0)/2; var_Ct0 = (high_Ct0-low_Ct0)^2/12; std_Ct0 = sqrt(var_Ct0);

    Ct0_all(:,j)=Ct0;

      Ct0_vec(j,1)=mean(Ct0);
      Ct0_vec(j,2)=median(Ct0);
      Ct0_vec(j,3)=std(Ct0);
      Ct0_vec(j,4)=var(Ct0);
      Ct0_vec(j,5)=min(Ct0);
      Ct0_vec(j,6)=max(Ct0);

      %%%%%%
      % Uniform distribution
       % low_gam=0.2673;              % for gamma=0.2838
       % high_gam=0.3005;             % for gamma=0.2838
        low_b1 = 0.0086794;               % for gamma=0.5
        high_b1= 0.04332;                % for gamma=0.5
       % low_gam=0.8053;             % for gamma=0.8343
       % high_gam=0.8630;              % for gamma=0.8343
      b1=random(name4,low_b1,high_b1,dim,1);           %  VECTOR
%            gamma=random(name5,0.8343,0.01,dim,1);           %  VECTOR
        par_b1=0.026; %par_b1=(low_b1+high_b1)/2;
%          var_b1 = (high_b1-low_b1)^2/12;
         std_b1 = 0.01; %sqrt(var_b1);

    b1_all(:,j)= b1;

      b1_vec(j,1)=mean(b1);
      b1_vec(j,2)=median(b1);
      b1_vec(j,3)=std(b1);
      b1_vec(j,4)=var(b1);
      b1_vec(j,5)=min(b1);
      b1_vec(j,6)=max(b1);
      %%%%%%

% % Steady state values of state % m1=(1-Ct0)*m; % VECTOR

       %%%%%%
      % Uniform distribution
       % low_gam=0.2673;              % for gamma=0.2838
       % high_gam=0.3005;             % for gamma=0.2838
        low_b2 = 0.026358;               % for gamma=0.5
        high_b2= 0.095641;                % for gamma=0.5
       % low_gam=0.8053;             % for gamma=0.8343
       % high_gam=0.8630;              % for gamma=0.8343
      b2=random(name4,low_b2,high_b2,dim,1);           %  VECTOR
%            gamma=random(name5,0.8343,0.01,dim,1);           %  VECTOR
         par_b2=0.061; %par_b2=(low_b2+high_b2)/2;
         %var_b2 = (high_b2-low_b2)^2/12;
         std_b2 =0.02; %sqrt(var_b2);

    b2_all(:,j)= b2;

      b2_vec(j,1)=mean(b2);
      b2_vec(j,2)=median(b2);
      b2_vec(j,3)=std(b2);
      b2_vec(j,4)=var(b2);
      b2_vec(j,5)=min(b2);
      b2_vec(j,6)=max(b2);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % I/P parameter, beta has exponential Distribution, mean is 4.6 and s.d. is 2.6 par_beta=4.6; % var_mu=0.00396344; std_beta=2.6; % beta=random(name1,par_beta,std_beta,dim,1); % VECTOR beta=0.0001+random(name1,par_beta,[dim,1]); %Anuj said add 0.0001 % beta=random(name1,1/par_beta,dim,1); % beta=generate_exponential(1/par_beta);

% x = 0:0.1:10; y = exppdf(x,par_beta); plot(x,y) % beta=exprnd(par_beta,dim,1);

    beta_all(:,j)=beta;      

    %       MAKE SURE THAT NO VALUES SAMPLED FROM THE EXPONENTIAL DISTRIBUTION ARE CLOSE
%       TO ZERO, JUST AS IT WAS DONE IN THE TRUNCATED NORMAL ABOVE

      % To have all non-negative values of ah we choose again values which
      % are negative
      l=0;            % to the letter small "l" number zero is assigned
     for k1=1:dim
         if beta(k1)<0.0001 
    %         k1
    %         mu(k1)
             l=l+1;
             tag(l)=k1;
         end
     end

     if l>0
         for k2=1:l
             beta(tag(k2))=random(name1,par_beta,1);                 %  VECTOR
      % will the above scaler necessarily be a poistive number ?
             %       mu(tag(k2))
         end
     end

      beta_vec(j,1)=mean(beta);
      beta_vec(j,2)=median(beta);
      beta_vec(j,3)=std(beta);
      beta_vec(j,4)=var(beta);
      beta_vec(j,5)=min(beta);
      beta_vec(j,6)=max(beta);

       Z=Nc./beta;       %  VECTOR
L1=(Q.*Ct0).*(exp(-b1.*t));
L2=((1-Q).*Ct0).*(exp(-b2.*t));
K1=0.533*7933615; %K1=Ih*H; Iz for DDT is 0.533
K2=(0.533/2).*Z; %K2=Iz*Z;

kk1=L2./K2; kk2=L1/K1; kk3=p.*muv;

 for i=1:dim
   if      (kk1(i) < kk2(i)) && (kk3(i) <= L1(i))
           x_rlz(i)= K1*p.*muv(i)/(Nh*L1(i));
           y_rlz(i)=0;
   elseif  ( kk1(i) < kk2(i) ) && ( L1(i) < p*muv(i) <= (L1(i)+L2(i)) )
           x_rlz(i)= K1/Nh;
           y_rlz(i)= K2(i)*(p.*muv(i)-L1(i))/(Nc*L2(i));
   elseif  ( kk2(i) < kk1(i) ) &&  ( kk3(i) <= L2(i) )
           x_rlz(i)= 0;
           y_rlz(i)= K2(i)*p*muv(i)/(Nc*L2(i));
   elseif  (kk2(i) < kk1(i)) && ( L2(i) < p*muv(i) <= L1(i)+L2(i) )
           x_rlz(i)= K1(i)*(p*muv(i)-L2(i))/(Nh*L1(i));
           y_rlz(i)= K2*(p*muv(i)-L1(i))/(Nc*L2(i));
   end
 end

   end

   % #################################################################
% Sensitivity Analysis starts here........

if analysis==2

 [sorted_beta, order_beta] = sort(beta_all(:,reali));
[sorted_ah, order_ah] = sort(ah_all(:,reali));
[sorted_muv, order_muv] = sort(muv_all(:,reali));
[sorted_Ct0, order_Ct0] = sort(Ct0_all(:,reali));
[sorted_b1, order_b1] = sort(b1_all(:,reali));
[sorted_b2, order_b2] = sort(b2_all(:,reali));
% y (R0) is rank transformed
[sorted_x, order_x]=sort(x_rlz(:,reali));
[sorted_y, order_y]=sort(y_rlz(:,reali));

for i=1:dim beta_rank(order_beta(i))=i; ah_rank(order_ah(i))=i; muv_rank(order_muv(i))=i; Ct0_rank(order_Ct0(i))=i; b1_rank(order_b1(i))=i; b2_rank(order_b2(i))=i; x_rank(order_x(i))=i; y_rank(order_y(i))=i; end

% Fixing first the realization we want rank DATA (done above) FROM say last one "realz" % Compute regression coefficients for a linear model with an interaction term:

X_x_beta = [ones(size(ah_rank')) ah_rank' muv_rank' Ct0_rank' b1_rank' b2_rank']; X_y_beta = [ones(size(ah_rank')) ah_rank' muv_rank' Ct0_rank' b1_rank' b2_rank']; % building linear regression model with y (R0) as response and remaining independent % variables as predictor, see equation (2)on page 181 of the Kirschner % reserach paper [b_x_beta, bint_x_beta, r_x_beta]= regress(x_rank', X_x_beta); [b_y_beta, bint_y_beta, r_y_beta]= regress(y_rank', X_y_beta); % Removes NaN data % building linear regression model with xj(gamma) as response and the % remaining remaining independent variables as the predictors [b_beta_x, bint_beta_x, r_beta_x]= regress(beta_rank', X_x_beta); [b_beta_y, bint_beta_y, r_beta_y]= regress(beta_rank', X_y_beta);

 [prcc_x_beta, p_val_x_beta]= corrcoef(r_beta_x, r_x_beta);
 [prcc_y_beta, p_val_y_beta]= corrcoef(r_beta_y, r_y_beta);

prcc(1)=prcc_x_beta(1,2); % computing prcc index p_val(1)=p_val_x_beat(1,2); % computing p vlaue

prcc(2)=prcc_y_beta(1,2); % computing prcc index p_val(2)=p_val_y_beat(1,2); % computing p vlaue

X_x_ah = [ones(size(beta_rank')) beta_rank' muv_rank' Ct0_rank' b1_rank' b2_rank']; X_y_ah = [ones(size(beta_rank')) beta_rank' muv_rank' Ct0_rank' b1_rank' b2_rank'];

[b_x_ah, bint_x_ah, r_x_ah]= regress(x_rank', X_x_ah); % Removes NaN data [b_ah_x, bint_ah_x, r_ah_x]= regress(ah_rank', X_x_ah); [b_y_ah, bint_y_ah, r_y_ah]= regress(y_rank', X_y_ah); % Removes NaN data [b_ah_y, bint_ah_y, r_ah_y]= regress(ah_rank', X_y_ah);

%R = corrcoef(X) returns a matrix R of correlation coefficients calculated %from an input matrix X whose rows are observations and whose columns are variables. [prcc_x_ah, p_val_x_ah]= corrcoef(r_ah_x, r_x_ah); [prcc_y_ah, p_val_y_ah]= corrcoef(r_ah_y, r_y_ah);

prcc(3)=prcc_x_ah(1,2); p_val(3)=p_val_x_ah(1,2); prcc(4)=prcc_y_ah(1,2); p_val(4)=p_val_y_ah(1,2);

X_x_muv = [ones(size(beta_rank')) beta_rank' ah_rank' Ct0_rank' b1_rank' b2_rank']; X_y_muv = [ones(size(beta_rank')) beta_rank' ah_rank' Ct0_rank' b1_rank' b2_rank']; % WHAT does R0 stand for in below line? [b_x_muv, bint_x_muv, r_x_muv]= regress(x_rank', X_x_muv); % Removes NaN data % what does Rd below line do ? % [b_muv_rd, bint_muv_rd, r_muv_rd]= regress(muv_rank', X_Rd_muv); [b_muv_x, bint_muv_x, r_muv_x]= regress(muv_rank', X_x_muv);

[b_y_muv, bint_y_muv, r_y_muv]= regress(y_rank', X_y_muv); % Removes NaN data [b_muv_y, bint_muv_y, r_muv_y]= regress(muv_rank', X_y_muv);

 [prcc_x_muv, p_val_x_muv]= corrcoef(r_muv_x, r_x_muv);
 [prcc_y_muv, p_val_y_muv]= corrcoef(r_muv_y, r_y_muv);

prcc(5)=prcc_x_muv(1,2); p_val(5)=p_val_x_muv(1,2);

prcc(6)=prcc_y_muv(1,2); p_val(6)=p_val_y_muv(1,2);

X_x_Ct0 = [ones(size(mu_rank')) beta_rank' ah_rank' muv_rank' b1_rank' b2_rank']; X_y_Ct0 = [ones(size(mu_rank')) beta_rank' ah_rank' muv_rank' b1_rank' b2_rank'];

[b_x_Ct0, bint_x_Ct0, r_x_Ct0]= regress(x_rank', X_x_Ct0); % Removes NaN data [b_Ct0_x, bint_Ct0_x, r_Ct0_x]= regress(Ct0_rank', X_x_Ct0);

[b_y_Ct0, bint_y_Ct0, r_y_Ct0]= regress(Ct0_rank', X_y_Ct0); [b_Ct0_y, bint_Ct0_y, r_Ct0_y]= regress(Ct0_rank', X_y_Ct0);

 [prcc_x_Ct0, p_val_x_Ct0]= corrcoef(r_Ct0_x, r_x_Ct0);
[prcc_y_Ct0, p_val_y_Ct0]= corrcoef(r_Ct0_y, r_y_Ct0);

prcc(7)=prcc_x_Ct0(1,2); p_val(7)=p_val_x_Ct0(1,2); prcc(8)=prcc_y_Ct0(1,2); p_val(8)=p_val_y_Ct0(1,2);

X_x_b1 = [ones(size(beta_rank')) beta_rank' muv_rank' Ct0_rank' ah_rank' b2_rank']; X_y_b1 = [ones(size(beta_rank')) beta_rank' muv_rank' Ct0_rank' ah_rank' b2_rank'];

[b_x_b1, bint_x_b1, r_x_b1]= regress(x_rank', X_x_b1); % Removes NaN data [b_b1_x, bint_b1_x, r_b1_x]= regress(b1_rank', X_x_b1); [b_y_b1, bint_y_b1, r_y_b1]= regress(y_rank', X_y_b1); % Removes NaN data [b_b1_y, bint_b1_y, r_b1_y]= regress(b1_rank', X_y_b1);

%R = corrcoef(X) returns a matrix R of correlation coefficients calculated %from an input matrix X whose rows are observations and whose columns are variables. [prcc_x_b1, p_val_x_b1]= corrcoef(r_b1_x, r_x_b1); [prcc_y_b1, p_val_y_b1]= corrcoef(r_b1_y, r_y_b1);

prcc(9)=prcc_x_b1(1,2); p_val(9)=p_val_x_b1(1,2); prcc(10)=prcc_y_b1(1,2); p_val(10)=p_val_y_b1(1,2);

X_x_b2 = [ones(size(beta_rank')) beta_rank' muv_rank' Ct0_rank' ah_rank' b1_rank']; X_y_b2 = [ones(size(beta_rank')) beta_rank' muv_rank' Ct0_rank' ah_rank' b1_rank'];

[b_x_b2, bint_x_b2, r_x_b2]= regress(x_rank', X_x_b2); % Removes NaN data [b_b2_x, bint_b2_x, r_b2_x]= regress(b2_rank', X_x_b2); [b_y_b2, bint_y_b2, r_y_b2]= regress(y_rank', X_y_b2); % Removes NaN data [b_b2_y, bint_b2_y, r_b2_y]= regress(b2_rank', X_y_b2);

%R = corrcoef(X) returns a matrix R of correlation coefficients calculated %from an input matrix X whose rows are observations and whose columns are variables. [prcc_x_b2, p_val_x_b2]= corrcoef(r_b2_x, r_x_b2); [prcc_y_b2, p_val_y_b2]= corrcoef(r_b2_y, r_y_b2);

prcc(11)=prcc_x_b2(1,2); p_val(11)=p_val_x_b2(1,2); prcc(12)=prcc_y_b2(1,2); p_val(12)=p_val_y_b2(1,2);

end

% Sensitivity Analysis ends here........ % #################################################################

% ################################################################# % Uncertainity Analysis starts here........

if analysis==1 % % mean,median,std,iqr %

     mean_r0i_x= mean(x_rlz)';
     median_r0i_x=median(x_rlz)';
     mean_r0i_y= mean(y_rlz)';
     median_r0i_y=median(y_rlz)';

     std_r0i_x=std(x_rlz)';
     iqr_r0i_x=iqr(x_rlz)';
     std_r0i_y=std(y_rlz)';
     iqr_r0i_y=iqr(y_rlz)';

for k=1:length(x_rlz(1,:)) % can write as k=1:reali ??? t1=x_rlz(:,k); % pg1r0i(k)=length(t1(find(t1<=2)))/length(t1); % INTRODUCED UNDERSCORE X AS I HAVE ONE LOOP BELOW FOR Y AS WELL pg1r0i_x(k)=length(t1(find(t1<=2.5)))/length(t1); % Use this when mean(gamma)=0.23 end % You can use a logical expression to define X. For example, find(X > 2) % returns linear indices corresponding to the entries of X that are greater than 2. pg1r0i_x=pg1r0i_x'; pg1minusr0_x=1-pg1r0i_x;

 %%mean over all realizations

for k=1:length(y_rlz(1,:)) % can write as k=1:reali ??? t1=y_rlz(:,k); % pg1r0i(k)=length(t1(find(t1<=2)))/length(t1); % INTRODUCED UNDERSCORE Y BELOW pg1r0i_y(k)=length(t1(find(t1<=2.5)))/length(t1); % Use this when mean(gamma)=0.23 end % You can use a logical expression to define X. For example, find(X > 2) % returns linear indices corresponding to the entries of X that are greater than 2. pg1r0i_y=pg1r0i_y'; pg1minusr0_y=1-pg1r0i_y;

 %%mean over all realizations

   mean_alli_x=mean([mean_r0i_x median_r0i_x iqr_r0i_x std_r0i_x  pg1r0i_x pg1minusr0_x]);
   mean_alli_y=mean([mean_r0i_y median_r0i_y iqr_r0i_y std_r0i_y  pg1r0i_y pg1minusr0_y]);
%
%standard error
%
     se1_x=std([mean_r0i_x median_r0i_x iqr_r0i_x std_r0i_x pg1r0i_x pg1minusr0_x]);     
     ser1_x=se1_x/sqrt(length(mean_r0i_x));

     se1_y=std([mean_r0i_y median_r0i_y iqr_r0i_y std_r0i_y pg1r0i_y pg1minusr0_y]);     
     ser1_y=se1_y/sqrt(length(mean_r0i_y));

     %
%variation coefficient
%
     cv1_x=se1_x./mean_alli_x;
     cv1_y=se1_y./mean_alli_y;

end

if analysis==1

figure(1)

subplot(3,3,1); hist(beta,100) title('Distribution of \beta','fontsize',22);

subplot(3,3,2);
 hist(ah,100)
 title('Distribution of a_h','fontsize',22);

subplot(3,3,3);
  hist(muv,100)
  title('Distribution of \mu_v','fontsize',22);

subplot(3,3,4);
  hist(Ct0,100)
  title('Distribution of C_{t0}','fontsize',22);

subplot(3,3,5);
  hist(b1,100)
  title('Distribution of b_1','fontsize',22);

subplot(3,3,6); hist(b2,100) title('Distribution of b_2','fontsize',22);

subplot(3,3,7);
  hist(x_rlz(:,reali),100)
  title('Distribution of x','fontsize',22)

    subplot(3,3,8);
    hist(y_rlz(:,reali),100)
    title('Distribution of y','fontsize',22)

end

 }

Can someone kindly suggest why I get negative values (type:min(muv) ) for muv’s distribution whereas for ah, all randomly sampled values are positive, as seen in the graph.

Thanks.