logistic.model: Implementing binomial GLM (logistic regression) from scratch.
In ssarda/genomeutils: Utilities for Genome Analysis
Description
Usage
Arguments
Value
Description
This function accepts a covariate X and response Y and uses optimization to estimate the maximum likelihood parameters of the binomial GLM fit.
General-purpose optimization to estimate parameters is based on Nelder–Mead algorithm
The Akaike and Bayesian Information Criterion is also output to enable downstream model selection
Usage
1
logistic.model(X, Y)
Arguments
X
A Covariate vector of values.
Y
A Response vector of values.
Value
A vector containing the optimized MLE parameters of the fit as well as the AIC and BIC to enable downstream model selection
ssarda/genomeutils documentation built on May 30, 2019, 8:42 a.m.
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Description Usage Arguments Value
Description
This function accepts a covariate X and response Y and uses optimization to estimate the maximum likelihood parameters of the binomial GLM fit. General-purpose optimization to estimate parameters is based on Nelder–Mead algorithm The Akaike and Bayesian Information Criterion is also output to enable downstream model selection
Usage
1 | logistic.model(X, Y)
|
Arguments
X |
A Covariate vector of values. |
Y |
A Response vector of values. |
Value
A vector containing the optimized MLE parameters of the fit as well as the AIC and BIC to enable downstream model selection
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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