Logistic regression for medical statistics -- Conflict between results by Mathematica and other statistical softwares

Actually, I am trying to recover an example in a textbook via Mathematica. The situation is to study the relationship between esophagus cancer and smoking as well as excessive drinking. And the original data is quite simple:

data = {{0, 0, 199, 63, 136},
{0, 1, 170, 63, 107},
{1, 0, 101, 44, 57},
{1, 1, 416, 265, 151}};

where in each row (sub-list), the entries are for x1 (smoking: no-0, yes-1), x2 (excessive drinking: no-0, yes-1), total number, number of positive and number of negative (total number = number of positive + number of negative).

The textbook gives, e.g., the estimate and standard error of the linear coefficients b0 (Intercept), b1 and b2 as (picture coming from http://statpages.info/logistic.html) However, when I use LogitModelFit (which accepts data with structure {{x1, x2, y},...}, and y is the ratio of positive samples: number of positive / total number) with the below new data

dataNew = {{0., 0., 0.316583},
{0., 1., 0.370588},
{1., 0., 0.435644},
{1., 1., 0.637019}};
logitM = LogitModelFit[dataNew, {x1, x2}, {x1, x2}];

Then logitM["ParameterTable"] gives The problem is that the results obtained by Mathematica do not accord with those by the textbook.

So my first questions are:

1. Have I used LogitModelFit correctly?

2. How could one get the consistent results as shown in the first picture?

P.S. I am using Windows 10 and Mathematica 11.2.

------------------------------

Conclusion to Question 1: I indeed did not use LogitModelFit correctly.

Conclusion to Question 2: See the selected answer.

And thanks a lot to @JimB and @J. M..

• "an example in a textbook" - can you mention which textbook, please? Nov 13 '17 at 4:56
• @J.M. The textbook is in Chinese. If necessary, I could translate it. Nov 13 '17 at 4:58
• Hmm... did the textbook at least mention what software they were using that generated those results? Nov 13 '17 at 4:59
• @J.M. The software used in the textbook is SPSS. Nov 13 '17 at 5:03
• I don't have access to SPSS at the moment, but indeed I am getting conflicting results from Mathematica and the webpage you linked to. Let me check further... Nov 13 '17 at 5:16

You probably should use GeneralizedLinearModelFit given the structure of your data. One needs to turn the data into an array of the following form: {smoking, drinking,status}:

newdata = {{0, 0, 1}, {0, 1, 1}, {1, 0, 1}, {1, 1, 1},
{0, 0, 0}, {0, 1, 0}, {1, 0, 0}, {1, 1, 0}};

Then construct the Weights from the associated frequency counts:

weights = Flatten[{data[[All, 4]], data[[All, 5]]}];

The analysis is performed by

glm = GeneralizedLinearModelFit[
newdata, {smoking, drinking}, {smoking, drinking},
ExponentialFamily -> "Binomial", Weights -> weights];
glm["ParameterTable"] Update

You actually can fit the model with the same structure using LogitModelFit:

lmf = LogitModelFit[newdata, {smoking, drinking}, {smoking, drinking}, Weights -> weights];
lmf["ParameterTable"]
• Ah, that's more like it! So, LogitModelFit[] is broken here? Nov 13 '17 at 5:55
• Sorry @J.M. I was too slow. It's not broken. I just have a tendency/bias to use GeneralizedLinearModelFit rather than the more specialized functions (LogitModelFit, ProbitModelFit, etc.). But I added the use of LogitModelFit in the answer.
– JimB
Nov 13 '17 at 6:03
• @JimB Thank you very much! Nov 13 '17 at 6:11
• @J.M. Also thank you for your timely response! Nov 13 '17 at 6:13