grad_log_likelihood: Gadient of the case-crossover log-likelihood.
In awstringer1/casecrossover: Approximate Bayesian Inference for Case Crossover Models
Description
Usage
Arguments
Value
View source: R/02-likelihood.R
Description
Compute the gradient of the log likelihood with respect to W = (delta,gamma,beta).
The gamma and beta parts are zero.
Usage
1
grad_log_likelihood(W, model_data)
Arguments
W
Parameter vector. First n elements are eta, then Gamma and beta.
model_data
A list of class "cc_modeldata" as returned by model_setup().
Value
A numeric vector containing the gradient. It's not stored as a sparseVector,
because it's (mathematically) not sparse. I guess the end is all zeroes though, so maybe
we should change this...?
awstringer1/casecrossover documentation built on March 11, 2021, 4:41 a.m.
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Description Usage Arguments Value
View source: R/02-likelihood.R
Description
Compute the gradient of the log likelihood with respect to W = (delta,gamma,beta). The gamma and beta parts are zero.
Usage
1 | grad_log_likelihood(W, model_data)
|
Arguments
W |
Parameter vector. First n elements are eta, then Gamma and beta. |
model_data |
A list of class "cc_modeldata" as returned by model_setup(). |
Value
A numeric vector containing the gradient. It's not stored as a sparseVector, because it's (mathematically) not sparse. I guess the end is all zeroes though, so maybe we should change this...?
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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