... | ... | @@ -7,4 +7,6 @@ The user can select different pre-specified model structures, including |
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* [Combined independent pathway / Cholesky model](/beate.stpourcain/grmsem/-/wikis/IPC%20model)
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* [Common pathway model](/beate.stpourcain/grmsem/-/wikis/Common%20pathway%20model)
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by setting the `model` parameter of `gsem.fit()` to `Cholesky`, `Independent`, `IPC` or `Common` respectively. `grmsem` fits, like GREML, all available data to the model. Each model can be adapted by the user by setting free parameters and starting values. Note that the likelihood for ill-specified models may not necessarily reach the global maximum and the model fit should be confirmed using different starting values. Although k, the number of different phenotypes is not restricted, in principle, computational demands will typically set a limit based on k x n \~ 30,000 for Cholesky decomposition models, where n is the number of observations per trait; models using less parameters can handle larger k x n. |
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by setting the `model` parameter of `gsem.fit()` to `Cholesky`, `Independent`, `IPC` or `Common` respectively. `grmsem` fits, like GREML, all available data to the model. Each model can be adapted by the user by setting free parameters and starting values. Note that the likelihood for ill-specified models may not necessarily reach the global maximum and the model fit should be confirmed using different starting values. Although k, the number of different phenotypes is not restricted, in principle, computational demands will typically set a limit based on k x n \~ 30,000 for Cholesky decomposition models, where n is the number of observations per trait; models using less parameters can handle larger k x n.
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**Installation instructions** can be found [here](Installation). |
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