bi_mr_aic: comparing bi-direction causal models using AIC
In funfunchen/simpleMR: Mendelian Randomization implementation
Description
Usage
Arguments
Value
Description
calculate the Akaike information criterion (AIC) for four causal inference models;
implementation of the Pickrell's method
Usage
1
bi_mr_aic(X.set, Y.set)
Arguments
X.set
Trait X ascertainment data set
Y.set
Trait Y ascertainment data set
Value
relative likelihood of the best non-causal model compared to the best causal model.
funfunchen/simpleMR documentation built on May 15, 2019, 4:11 a.m.
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Description Usage Arguments Value
Description
calculate the Akaike information criterion (AIC) for four causal inference models; implementation of the Pickrell's method
Usage
1 | bi_mr_aic(X.set, Y.set)
|
Arguments
X.set |
Trait X ascertainment data set |
Y.set |
Trait Y ascertainment data set |
Value
relative likelihood of the best non-causal model compared to the best causal model.
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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