Ew1z: Ew1z
In vpnsctl/bbreg: Bessel and Beta Regressions via Expectation-Maximization Algorithm for Continuous Bounded Data
Description
Usage
Arguments
Value
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
1
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.
vpnsctl/bbreg documentation built on March 14, 2021, 12:11 a.m.
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Description Usage Arguments Value
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
1 | 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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