Error in Hosmer Lemeshow GOF test calculation

Dear all,

I am trying to calculate the Hosmer and Lemeshow Good-of-Fitness test, following the steps presented in the book "Logistic Regression. A Self- Learning Text" by David Kleinbaum and M. Klein.

The following functions calculates all the steps, gives as outputs the chi-squared, degrees of freedom and the p-value, as well as some interesting plots described in the manual, which look fine to me.

However, I get a completely different result when I run this test in R (holsem.test, from ResourceSelection package).

I was surprised I could not find any code online, so maybe this could be useful in the future. Neither could I see what I am doing wrong. Maybe R uses a very different method?

If you are also keen to find out and see this work, please feel free to join.

Thank you a lot,

    % This function performs the Hoslem-Lemeshow Goodness-of-Fit test
    % 06-January-2017 [e-mail]
    %
    % Inputs
    %       Q: The number of groups or quantiles (normally Q=10).
    %       y: 1D array of observations. Must be a series of {0,1}. 
    %       fit: 1D array of the model's fitted values.
    % Outputs
    %       chi2: The Hosmer-Lemeshow chi-square statistics.
    %       df: Degrees-of-freedom.
    %       p: p-value

    function [chi2,df,p]=hoslem(fit,y,Q)

    [ordems,I]=sort(fit,'descend'); % Sorts the fitted values
    % Calculates the length of each decile.
    % Note that the division of length/10 may not be exact. Therefore, the
    % considered series may differ from original inputs in up to 10
    % observations, at most. This error shold be to non-significant for very
    % long series.
    qlen=round(length(y)/Q)-1;      
    qlens=zeros(Q+1,1); 
    for i=1:Q+1
        qlens(i,1)=1+(i-1)*qlen;    % Determines the thresholds of deciles   
    end
    qlims=zeros(Q+1,1);             % Positions of thresholds
    for i=1:Q+1
        qlims(i,1)=ordems(qlens(i,1));
    end
    plot(1:Q+1,qlims,'o')
    % Couts Observed Cases por Decile (Ocq)
    ocq=zeros(Q,1); t=1; count=0;
    for i=1:max(qlens)-1
        if count==qlen
            t=t+1; count=0;
        end
        ocq(t,1)=ocq(t,1)+(y(I(i)));
        count=count+1;
    end
     % Couts Non-Observed Cases por Decile (Oncq)
    oncq=zeros(Q,1); t=1; count=0;
    for i=1:max(qlens)-1
        if count==qlen
            t=t+1; count=0;
        end
        if (y(I(i)))==0
        oncq(t,1)=oncq(t,1)+1;
        end
        count=count+1;
    end
    figure(1)
    plot(ocq); hold on; plot(oncq)
    legend('Observed cases','Non-observed cases'); hold off
    % Expected cases (Ecq) e noncases (Encq) per Decile
    ecq=zeros(Q,1);
    t=1; count=0;
    for i=1:max(qlens)-1
        if count==qlen
            t=t+1; count=0;
        end
        ecq(t,1)=ecq(t,1)+ordems(i);
        count=count+1;
    end
    figure(2)
    plot(ecq,ocq,'-o'); hold on;
    xlabel('Expected cases'); ylabel('Oobserved cases');
    encq=qlen-ecq;
    % Calculates HL statistics
    chi2= sum(sum(((ocq-ecq).^2)/ecq)) + sum(sum(((oncq-encq).^2)/encq));
    df=Q-2; 
    p=1-chi2cdf(chi2,Q-2);

    end