Description Usage Arguments Value References Examples

Constructs a generalized linear model (glm) with a weighted latent environmental score and weighted latent genetic score using alternating optimization.

1 2 3 4 |

`data` |
data.frame of the dataset to be used. |

`genes` |
data.frame of the variables inside the genetic score |

`env` |
data.frame of the variables inside the environmental score |

`formula` |
Model formula. Use |

`start_genes` |
Optional starting points for genetic score (must be the same length as the number of columns of |

`start_env` |
Optional starting points for environmental score (must be the same length as the number of columns of |

`eps` |
Threshold for convergence (.01 for quick batch simulations, .0001 for accurate results). |

`maxiter` |
Maximum number of iterations. |

`family` |
Outcome distribution and link function (Default = gaussian). |

`ylim` |
Optional vector containing the known min and max of the outcome variable. Even if your outcome is known to be in [a,b], if you assume a Gaussian distribution, predict() could return values outside this range. This parameter ensures that this never happens. This is not necessary with a distribution that already assumes the proper range (ex: [0,1] with binomial distribution). |

`print` |
If FALSE, nothing except warnings will be printed (Default = TRUE). |

`print_steps` |
If TRUE, print the parameters at all iterations, good for debugging (Default = FALSE). |

`crossover` |
If not NULL, estimates the crossover point of |

`crossover_fixed` |
If TRUE, instead of estimating the crossover point of E, we force/fix it to the value of "crossover". (Used when creating a diathes-stress model) (Default = FALSE). |

`reverse_code` |
If TRUE, after fitting the model, the genes with negative weights are reverse coded (ex: |

`rescale` |
If TRUE, the environmental variables are automatically rescaled to the range [-1,1]. This improves interpretability (Default=FALSE). |

Returns an object of the class "LEGIT" which is list containing, in the following order: a glm fit of the main model, a glm fit of the genetic score, a glm fit of the environmental score, a list of the true model parameters (AIC, BIC, rank, df.residual, null.deviance) for which the individual model parts (main, genetic, environmental) don't estimate properly and the formula.

Alexia Jolicoeur-Martineau, Ashley Wazana, Eszter Szekely, Meir Steiner, Alison S. Fleming, James L. Kennedy, Michael J. Meaney, Celia M.T. Greenwood and the MAVAN team. *Alternating optimization for GxE modelling with weighted genetic and environmental scores: examples from the MAVAN study* (2017). arXiv:1703.08111.

1 2 3 4 5 6 7 8 9 10 | ```
train = example_2way(500, 1, seed=777)
fit_best = LEGIT(train$data, train$G, train$E, y ~ G*E, train$coef_G, train$coef_E)
fit_default = LEGIT(train$data, train$G, train$E, y ~ G*E)
summary(fit_default)
summary(fit_best)
train = example_3way(500, 2.5, seed=777)
fit_best = LEGIT(train$data, train$G, train$E, y ~ G*E*z, train$coef_G, train$coef_E)
fit_default = LEGIT(train$data, train$G, train$E, y ~ G*E*z)
summary(fit_default)
summary(fit_best)
``` |

LEGIT documentation built on June 24, 2018, 5:01 p.m.

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