When left=-Inf and right=0 indicates that there is no left-censoring but there is a right censoring at 0. The same model as above but with the negative number of affairs as teh dependent variable can be estimated by

load affairs.mat
X=affairs(:,["age" "yearsmarried" "religiousness" "occupation" "rating"]);
y=affairs(:,"affairs").*(-1);
out=regressCens(y,X,"dispresults",true,"left",-Inf,"right",0);
% This estimation returns beta parameters that have the opposite sign of
% the beta parameters in the original model, but the estimate of sigma does
% not change.

Observations:
    Total    LeftCensored    Uncensored    RightCensored
    _____    ____________    __________    _____________

     601          0             150             451     

Coefficients
                     betaout     sebetaout     tout         pval   
                     ________    _________    _______    __________

    (Intercept)        -8.176      2.7425     -2.9812     0.0029883
    age               0.17937    0.079165      2.2657      0.023828
    yearsmarried     -0.55418     0.13463     -4.1162    4.3959e-05
    religiousness      1.6864     0.40385      4.1757    3.4159e-05
    occupation       -0.32603     0.25444     -1.2814       0.20056
    rating             2.2851     0.40801      5.6006    3.2682e-08
    sigma              8.2473     0.55409      14.884    8.1345e-43

Number of observations: 601, Error degrees of freedom:594
Log-likelihood: -705.5762

This example is taken from https://stats.oarc.ucla.edu/r/dae/tobit-models/ Consider the situation in which we have a measure of academic aptitude (scaled 200-800) which we want to model using reading and math test scores, as well as, the type of program the student is enrolled in (academic, general, or vocational). The problem here is that students who answer all questions on the academic aptitude test correctly receive a score of 800, even though it is likely that these students are not “truly” equal in aptitude. The same is true of students who answer all of the questions incorrectly. All such students would have a score of 200, although they may not all be of equal aptitude.

XX=readtable("https://stats.idre.ucla.edu/stat/data/tobit.csv","ReadRowNames",true);
XX.prog=categorical(XX.prog);
% The dataset contains 200 observations. The academic aptitude variable is
% "apt", the reading and math test scores are read and math respectively. The
% variable prog is the type of program the student is in, it is a
% categorical (nominal) variable that takes on three values, academic
% general, and vocational (prog = 3). 
% The scatterplot matrix is shown below
spmplot(XX(:,[1 2 4]));
% Now let’s look at the data descriptively. Note that in this dataset, the
% lowest value of apt is 352. That is, no students received a score of 200
% (the lowest score possible), meaning that even though censoring from
% below was possible, it does not occur in the dataset.
summary(XX)
% Define y and X
y=XX(:,"apt");
X=XX(:,["read", "math" "prog"]);
% Call regressCens
out=regressCens(y,X,'right',800,'left',-Inf,'dispresults',true);
% Show the plot of fitted vs residuals
fitted=out.X*out.Beta(1:end-1,1);
residuals=(y{:,1}-fitted)/out.Beta(end,1);
figure
scatter(fitted,residuals)
xlabel('Fitted values (yhat)')
ylabel('Residuals')

Variables:

    read: 200×1 double

        Values:

            Min          28   
            Median       50   
            Max          76   

    math: 200×1 double

        Values:

            Min          33   
            Median       52   
            Max          75   

    prog: 200×1 categorical

        Values:

            academic         45   
            general         105   
            vocational       50   

    apt: 200×1 double

        Values:

            Min         352   
            Median      633   
            Max         800   

Observations:
    Total    LeftCensored    Uncensored    RightCensored
    _____    ____________    __________    _____________

     200          0             183             17      

Coefficients
                       betaout    sebetaout     tout         pval   
                       _______    _________    _______    __________

    (Intercept)         209.57      32.774      6.3943    1.1763e-09
    read                2.6979     0.61879        4.36    2.1086e-05
    math                5.9145      0.7099      8.3314    1.4325e-14
    prog_general       -12.715      12.406     -1.0249        0.3067
    prog_vocational    -46.144      13.724     -3.3623    0.00093125
    sigma               65.677      3.4827      18.858    9.5605e-46

Number of observations: 200, Error degrees of freedom:194
Log-likelihood: -1041.0629