Likelihood Ratio Test in R

Likelihood Ratio Test in R
The likelihood-ratio test in statistics compares the goodness of fit of two nested regression models based on the ratio of their likelihoods, specifically one obtained by maximization over the entire parameter space and another obtained after imposing some constraint.

A nested model is simply a subset of the predictor variables in the overall regression model.

For instance, consider the following regression model with four predictor variables.

Y = β0 + β1×1 + β2×2 + β3×3 + β4×4 + ε

The following model, with only two of the original predictor variables, is an example of a nested model.

Y = β0 + β1×1 + β2×2 + ε

To see if these two models differ significantly, we can use a likelihood ratio test with the following null and alternative hypotheses.
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#rstats #likelyhood #modelfit #datascience #regression #RStudio

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[krishnaiyer]

can you show manual computation of all result values in excel say under broom package, connect between aic and log likelihood of any one model, what is deviance etc. thanks