Ew1z: Ew1z
In bbreg: Bessel and Beta Regressions via Expectation-Maximization Algorithm for Continuous Bounded Data
Ew1z R Documentation
Ew1z
Description
Auxiliary function required in the Expectation-Maximization algorithm (E-step) and in the calculation of the
Fisher information matrix. It represents the conditional expected value E(W_i^s|Z_i), with s = -1; i.e.,
latent W_i^(-1) given the observed Z_i.
Usage
Ew1z(z, mu, phi)
Arguments
z
response vector with 0 < z_i < 1.
mu
mean parameter (vector having the same size of z).
phi
precision parameter (vector having the same size of z).
Value
Vector of expected values.
bbreg documentation built on March 18, 2022, 6:21 p.m.
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| Ew1z | R Documentation |
Ew1z
Description
Auxiliary function required in the Expectation-Maximization algorithm (E-step) and in the calculation of the Fisher information matrix. It represents the conditional expected value E(W_i^s|Z_i), with s = -1; i.e., latent W_i^(-1) given the observed Z_i.
Usage
Ew1z(z, mu, phi)
Arguments
z |
response vector with 0 < z_i < 1. |
mu |
mean parameter (vector having the same size of z). |
phi |
precision parameter (vector having the same size of z). |
Value
Vector of expected values.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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