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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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**Installation instructions** can be found [here](Installation).
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Multivariate models with `grmsem` are time-consuming, especially with large numbers of observations per trait. To illustrate the functionality of `grmsem` follow-up analyses, we carried out several analyses using a range of different [data sets](https://gitlab.gwdg.de/beate.stpourcain/grmsem_external), as described in detail in the vignette. An example of a large data set, with a defined genetic architecture but high run-time, is shown here.
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**Example: Quad-variate Cholesky decomposition model**
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* [Data simulation](/beate.stpourcain/grmsem/-/wikis/Data%20simulation)
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* [Model fit and output](/beate.stpourcain/grmsem/-/wikis/Model%20fit%20and%20output)
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* [Standardisation of parameters](/beate.stpourcain/grmsem/-/wikis/Standardisation%20of%20parameters)
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* [Factorial co-heritabilities and environmentalities](/beate.stpourcain/grmsem/-/wikis/Factorial%20coheritabilities%20and%20environmentalities)
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* [Bivariate heritabilities](/beate.stpourcain/grmsem/-/wikis/Bivariate%20heritabilities) |
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