... | @@ -2,10 +2,10 @@ The grmsem package is a quantitative genetics tool supporting the modelling of m |
... | @@ -2,10 +2,10 @@ The grmsem package is a quantitative genetics tool supporting the modelling of m |
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The user can select different pre-specified model structures, including
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The user can select different pre-specified model structures, including
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* [Cholesky decomposition](/beate.stpourcain/grmsem/-/wikis/Cholesky%20model)
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* [Cholesky decomposition](Cholesk model)
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* [Independent pathway model](/beate.stpourcain/grmsem/-/wikis/Independent%20pathway%20model)
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* [Independent pathway model](Independent pathway model)
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* [Combined independent pathway / Cholesky model](/beate.stpourcain/grmsem/-/wikis/IPC%20model)
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* [Combined independent pathway / Cholesky model](IPC model)
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* [Common pathway model](/beate.stpourcain/grmsem/-/wikis/Common%20pathway%20model)
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* [Common pathway model](Common pathway model)
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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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... | @@ -15,8 +15,8 @@ Multivariate models with `grmsem` are time-consuming, especially with large numb |
... | @@ -15,8 +15,8 @@ Multivariate models with `grmsem` are time-consuming, especially with large numb |
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**Example: Quad-variate Cholesky decomposition model**
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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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* [Data simulation](Data simulation)
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* [Model fit and output](/beate.stpourcain/grmsem/-/wikis/Model%20fit%20and%20output)
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* [Model fit and output](Model fit and output)
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* [Standardisation of parameters](/beate.stpourcain/grmsem/-/wikis/Standardisation%20of%20parameters)
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* [Standardisation of parameters](Standardisation of parameters)
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* [Factorial co-heritabilities and environmentalities](/beate.stpourcain/grmsem/-/wikis/Factorial%20coheritabilities%20and%20environmentalities)
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* [Factorial co-heritabilities and environmentalities](Factorial coheritabilities and environmentalities)
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* [Bivariate heritabilities](/beate.stpourcain/grmsem/-/wikis/Bivariate%20heritabilities) |
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* [Bivariate heritabilities](Bivariate heritabilities) |
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