Maximum likelihood for log-linear coefficients
A simplified version of
glm that does only parameter estimation.
This attempts to mimic the IRLS routine invoked by
returning extra “baggage" such as standard errors.
The columns of the standard design matrix to include in the model. For example, "c1", "c2" for main effects, and "c12" for interactions.
A design matrix with cell counts included in the column named "c". Must be a matrix (not a data frame)!
Convergence tolerance, intended to play the same role as
The maximum number of IRLS iterations. A warning appears if this maximum is ever reached.
Logical: If TRUE, include a normalization step after coefficient estimation, which resets the value of the intercept so that the sum of predicted values is exactly 1
The main purpose of
pirls is to obtain speed, for the special
circumstance in which one must fit gazillions of Poisson regression models,
where the only quantity of interested is the point estimates of the
regression coefficients. Matrix inversion is one of the most time consuming
steps in the function, and the overall speed can be improved by about 20
percent by modifying the source code to replace the
The vector of estimated log-linear coefficients. The first
coefficient is the intercept, and the remaining ones correspond to the
predictors argument, in that order
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