Why is the canary starving?
What is the "canary thread" and why is it starving?
Evaluating logistic regression and interpretation of Hosmer-Lemeshow Goodness of Fit As we all know, there are 2 methods to evaluate the logistic regression model and they are testing very different things  Predictive power: Get a statistic that measures how well you can predict the dependent variable based on the independent variables. The well-known Pseudo R^2 are McFadden (1974) and Cox and Snell (1989).  Goodness-of-fit statistics The test is telling whether you could do even better by making the model more complicated, which is actually testing whether there are any non-linearities or interactions that you have missed.   I implemented both tests on my model, which added quadratic and interaction already:     &gt;summary(spec_q2)      Call:     glm(formula = result ~ Top + Right + Left + Bottom + I(Top^2) +       I(Left^2) + I(Bottom^2) + Top:Right + Top:Bottom + Right:Left,       family = binomial())       Coefficients:                  Estimate Std. Error z value Pr(&gt;|z|)         (Intercept)  0.955431   8.838584   0.108   0.9139         Top          0.311891   0.189793   1.643   0.1003         Right       -1.015460   0.502736  -2.020   0.0434 *       Left        -0.962143   0.431534  -2.230   0.0258 *       Bottom       0.198631   0.157242   1.263   0.2065         I(Top^2)    -0.003213   0.002114  -1.520   0.1285         I(Left^2)   -0.054258   0.008768  -6.188 6.09e-10 ***     I(Bottom^2)  0.003725   0.001782   2.091   0.0366 *       Top:Right    0.012290   0.007540   1.630   0.1031         Top:Bottom   0.004536   0.002880   1.575   0.1153         Right:Left  -0.044283   0.015983  -2.771   0.0056 **      ---     Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1     (Dispersion parameter for binomial family taken to be 1)     Null deviance: 3350.3  on 2799  degrees of freedom     Residual deviance: 1984.6  on 2789  degrees of freedom     AIC: 2006.6  and the predicted power is as below, the MaFadden is 0.4004, and the value between 0.2~0.4 should be taken to present very good fit of the model(Louviere et al (2000), Domenich and McFadden (1975))                                                :  &gt; PseudoR2(spec_q2)     McFadden     Adj.McFadden        Cox.Snell       Nagelkerke McKelvey.Zavoina           Effron            Count        Adj.Count     0.4076315        0.4004680        0.3859918        0.5531859        0.6144487        0.4616466        0.8489286        0.4712500           AIC    Corrected.AIC  2006.6179010     2006.7125925   and the goodness-of-fit statistics:  &gt; hoslem.test(result,phat,g=8)       Hosmer and Lemeshow goodness of fit (GOF) test    data:  result, phat   X-squared = 2800, df = 6, p-value &lt; 2.2e-16  As my understanding, GOF is actually testing the following null and alternative hypothesis:   H0: The models does not need interaction and non-linearity   H1: The models needs interaction and non-linearity  Since my models added interaction, non-linearity already and the p-value shows H0 should be rejected, so I came to the conclusion that my model needs interaction, non-linearity indeed. 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