Fixed intercepts Poisson regression
Fixed intercepts Poisson regression.
fipois.reg(y, x, id, tol = 1e-07, maxiters = 100)
y |
The dependent variable, a numerical vector with integer, non negative valued data. |
x |
A matrix with the indendent variables. |
id |
A numerical variable with 1, 2, ... indicating the subject. Unbalanced design is of course welcome. |
tol |
The tolerance value to terminate the Newton-Raphson algorithm. This is set to 10^{-7} by default. |
maxiters |
The maximum number of iterations that can take place during the fitting. |
Fixed intercepts Poisson regression for clustered count data is fitted. According to Demidenko (2013), when the number of clusters (N) is small and the number of observations per cluster (n_i) is relatively large, say min(n_i) > N, one may assume that the intercept α_i = β + u_i is fixed and unknown (i=1,...,N).
A list including:
be |
The regression coefficients. |
seb |
The standard errors of the regression coefficients. |
ai |
The estimated fixed intercepts fore ach cluster of observations. |
covbeta |
The covariance matrix of the regression coefficients. |
loglik |
The maximised log-likelihood value. |
iters |
The number of iteration the Newton-Raphson required. |
Michail Tsagris
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
Eugene Demidenko (2013). Mixed Models: Theory and Applications with R, pages 388-389, 2nd Edition. New Jersey: Wiley \& Sons (excellent book).
y <- rpois(200, 10) id <- sample(1:10, 200, replace = TRUE) x <- rpois(200, 10) fipois.reg(y, x, id)
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