Description Usage Arguments Value Examples
Numeric approximation routine
1 2 |
x |
An n-by-p design matrix. |
y |
A vector of observation of length n. |
b.list |
List of K-classes each entry being a k length parameter vector, |
class_probs |
Matrix (n x K) of normalized class probabilities. |
weight |
A n length vector of observation weight terms. This is currently designed to be either the exposure for a Poisson model or the number of trials for a Logistic model. |
K |
Number of EM classes to be fit. |
tol.1 |
Tolerance of the NR minimization. |
debug |
Boolen flag. Turn on to check optimization steps via messages. |
family |
GLM family to fit with. |
maxiter |
Maximum iterations of the NR methods for exiting before convergence. |
A list of parameter values on convergence for each of k-classes.
1 2 3 4 5 6 7 | x <- model.matrix(~ 1 , data = warpbreaks)
y <- warpbreaks$breaks
b.list <- list(1, 1)
class_probs = matrix(rep(0.5, 54*2), ncol = 2)
em.glm_numeric_fit(x = x, y = y, b.list = b.list, class_probs = class_probs)
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