speed_theta_ml: Maximum likelihood estimation for negative binomial models
In linogaliana/gravity: Gravity model with large scale data
Description
Usage
Arguments
Value
References
See Also
Description
Simplified C++ function for theta.ml
Usage
1
speed_theta_ml(y, mu, limit = 10L, eps = 1e-06, trace = TRUE)
Arguments
y
Vector of observed values from the Negative Binomial.
mu
Estimated mean vector.
limit
Limit on the number of iterations.
eps
Tolerance to determine convergence.
trace
logical: should iteration progress be printed?
Value
A maximum-likelihood estimator for θ
References
<Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer>
See Also
fastglm.nb ; theta.ml ; glm.nb
linogaliana/gravity documentation built on April 24, 2020, 2:06 a.m.
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Description Usage Arguments Value References See Also
Description
Simplified C++ function for theta.ml
Usage
1 | speed_theta_ml(y, mu, limit = 10L, eps = 1e-06, trace = TRUE)
|
Arguments
y |
Vector of observed values from the Negative Binomial. |
mu |
Estimated mean vector. |
limit |
Limit on the number of iterations. |
eps |
Tolerance to determine convergence. |
trace |
logical: should iteration progress be printed? |
Value
A maximum-likelihood estimator for θ
References
<Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer>
See Also
fastglm.nb ; theta.ml ; glm.nb
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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