Solved – How to calculate Prob > chi2 in R to test model fit of conditional logistic regression

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")     ``

``                 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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