@@ -28,3 +28,5 @@ Likelihood ratio tests are often used to compare two models that one of them is
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@@ -28,3 +28,5 @@ Likelihood ratio tests are often used to compare two models that one of them is
where <imgsrc="https://latex.codecogs.com/svg.latex?L(M_0)"title="L(M_0)"/> and <imgsrc="https://latex.codecogs.com/svg.latex?L(M_1)"title="L(M_1)"/> are maximum values of the likelihood function for the simple and full models respectively.
where <imgsrc="https://latex.codecogs.com/svg.latex?L(M_0)"title="L(M_0)"/> and <imgsrc="https://latex.codecogs.com/svg.latex?L(M_1)"title="L(M_1)"/> are maximum values of the likelihood function for the simple and full models respectively.
It can be shown that the distribution for the statistic is chi-square with degree of freedom equal to the difference between number of parameters of the two models. Having both *LR* statistic and degree of freedom we can calculate the p-value of the test. If p-value is less than a predefined threshold (e.g. 0.05), two models are significantly different and the full model will be considered as the better fit to the data.