init1.k {poisson.glm.mix} | R Documentation |
Initialization 1 for the \beta_{k}
parameterization (m=3
).
Description
This function is the small initialization procedure (Initialization 1) for parameterization m=3
. The selected values are the ones that initialize the EM algorithm bkmodel
.
Usage
init1.k(reference, response, L, K, t2, m2,mnr)
Arguments
reference |
a numeric array of dimension |
response |
a numeric array of count data with dimension |
L |
numeric vector of positive integers containing the partition of the |
K |
positive integer denoting the number of mixture components. |
t2 |
positive integer denoting the number of different runs. |
m2 |
positive integer denoting the number of iterations for each run. |
mnr |
positive integer denoting the maximum number of Newton-Raphson iterations. |
Value
alpha |
numeric array of dimension |
beta |
numeric array of dimension |
psim |
numeric vector of length |
ll |
numeric, the value of the loglikelihood, computed according to the |
Author(s)
Panagiotis Papastamoulis
See Also
Examples
## load a simulated dataset according to the b_jk model
## number of observations: 500
## design: L=(3,2,1)
data("simulated_data_15_components_bjk")
x <- sim.data[,1]
x <- array(x,dim=c(length(x),1))
y <- sim.data[,-1]
## initialize the parameters for a 2 component mixture
## the number of the small runs are t2 = 3
## each one consisting of m2 = 5 iterations of the EM.
start1 <- init1.k(reference=x, response=y, L=c(3,2,1),
K=2, m2=5, t2=3,mnr = 5)
summary(start1)