logistic.loglik: Log likelihood function for logistic regression with a prior...
In jonlachmann/GMJMCMC: Genetically Modified Mode Jumping MCMC
logistic.loglik R Documentation
Log likelihood function for logistic regression with a prior p(m)=sum(total_width)
This function is created as an example of how to create an estimator that is used
to calculate the marginal likelihood of a model.
Description
Log likelihood function for logistic regression with a prior p(m)=sum(total_width)
This function is created as an example of how to create an estimator that is used
to calculate the marginal likelihood of a model.
Usage
logistic.loglik(y, x, model, complex, params = list(r = 1))
Arguments
y
A vector containing the dependent variable
x
The matrix containing the precalculated features
model
The model to estimate as a logical vector
complex
A list of complexity measures for the features
params
A list of parameters for the log likelihood, supplied by the user
jonlachmann/GMJMCMC documentation built on April 22, 2024, 4:21 a.m.
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| logistic.loglik | R Documentation |
Log likelihood function for logistic regression with a prior p(m)=sum(total_width) This function is created as an example of how to create an estimator that is used to calculate the marginal likelihood of a model.
Description
Log likelihood function for logistic regression with a prior p(m)=sum(total_width) This function is created as an example of how to create an estimator that is used to calculate the marginal likelihood of a model.
Usage
logistic.loglik(y, x, model, complex, params = list(r = 1))
Arguments
y |
A vector containing the dependent variable |
x |
The matrix containing the precalculated features |
model |
The model to estimate as a logical vector |
complex |
A list of complexity measures for the features |
params |
A list of parameters for the log likelihood, supplied by the user |
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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