I used the clogit function (from the survival package) to run a conditional logistic regression in R with a big dataset of 1:M matched pairs with n=300368964 and number of events= 39995.

`model <- clogit(Alliance ~ OVB + CVC + BVB + strata(Strata), method="exact") `

I received following results:

` coef exp(coef) se(coef) z Pr(>|z|) OVB -0.0498174 0.9514031 0.0166275 -2.996 0.00273 ** BVB 0.0277405 1.0281289 0.0304956 0.910 0.36300 CVC 1.1709851 3.2251683 0.1089709 10.746 < 2e-16 *** EarlyStage -1.3215824 0.2667129 0.0205851 -64.201 < 2e-16 *** AvgVCSize 0.0087976 1.0088364 0.0002035 43.224 < 2e-16 *** NumberVC 0.0643579 1.0664740 0.0034502 18.653 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Rsquare= 0 (max possible= 0.001 ) Likelihood ratio test= 6511 on 6 df, p=0 Wald test = 6471 on 6 df, p=0 Score (logrank) test = 6801 on 6 df, p=0 `

Since Rsquare equals 0 and the test ratios seems very high, I tried to plot the results to check whether the model fits. But I wasn't able to plot it properly.

I would online many papers which use the ratio Prob > chi2 = 0 from Stata as test ratio to proof the model fit.

How could I calculate this ratio in R? Are there any other ways I could check the model fit of my clogit results?

I would appreciate any help.

Thanks you very much in advance.

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#### Best Answer

In the diagram below, the parts on the right are from the document you linked, and the parts on the left are in your output in your question. I have marked corresponding parts with the same colors (the values won't be the same in this case because they're for different data sets):

Now, the thing in green is called the likelihood ratio test statistic. For sufficiently large sample size it has approximately a chi-square distribution.

The thing in red is called the p-value. It is not correctly defined in the Stata information you linked. It is the probability of getting a chi-square value *at least* as large as the one you observed if the null hypothesis were true. It is correctly defined in the first sentence here.

You decide significance by comparing the p-value with your significance level. (You haven't said what significance level you're using.)

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