Contents
How are weights determined in a binomial GLM?
For true likelihood based binomial GLMs, the weight argument is determined by the number of trials and cannot be varied. Similar considerations apply to other count-based GLM families such as Poisson and negative binomial .
How is P I related to log binomial regression?
Log-binomial regression In the GLM framework, the conditional distribution of Y i given the predictor variables is binomial, with the mean response related to the predictors by the link function log (μ i). In log-binomial regression, μ i is often denoted as p i, because E (Y i) is a probability with a value between zero and one.
Can a GLM be set to value other than 1?
Similar considerations apply to other count-based GLM families such as Poisson and negative binomial . You can only set the GLM prior weights for those families to a value other than 1 if you are willing to embrace a quasi-likelihood model. weights are not calculated endogenously.
How is the conditional distribution of Y i Independent in GLM?
The observations from the n individuals are independent. In the GLM framework, the conditional distribution of Y i given the predictor variables is binomial, with the mean response related to the predictors by the link function log ( μ i ).
How to calculate the coefficients of the GLM function?
glm(Y~1,weights=w*1000,family=binomial) Call: glm(formula = Y ~ 1, family = binomial, weights = w * 1000) Coefficients: (Intercept) -3.153e+15 I saw many other examples like this even with some moderate scaling in weights.
How to avoid divergence in logistic-r GLM function?
} This does not happen if you start with weights <- 1:nrow (y) or say weights <- 1:nrow (y) * 100. Notice that you can avoid divergence by setting the mustart argument.
What kind of weights are used in GLM?
glm implement precision weights: inverse-variance weights that can be used to model differential precision with which the outcome variable was estimated. Functions in the “survey” package implement sampling weights: inverse of the probability of particular observation to be selected from the population to the sample.
Can a GLM be set to anything other than 1?
For binary regression, the GLM weights should never be set to any value other than 1 (which is the default value). To see this, recall what the definition of a weight is for a binary GLM. The variance of the i th binary variable y i is assumed to be v a r (y i) = 1 w i μ i (1 − μ i)
How to fit a negative binomial regression in R?
It would appear that the negative binomial distribution would better approximate the distribution of the counts. To fit a negative binomial model in R we turn to the glm.nb () function in the MASS package (a package that comes installed with R). Again we only show part of the summary output:
Which is a function of the negative binomial distribution?
This suggests it might serve as a useful approximation for modeling counts with variability different from its mean. The variance of a negative binomial distribution is a function of its mean and has an additional parameter, k, called the dispersion parameter.