Description Usage Arguments Format Value References Examples
Transforms a IMLEGIT model into a LEGIT model
1 |
fit |
IMLEGIT model |
data |
data.frame of the dataset to be used. |
genes |
data.frame of the variables inside the genetic score G (can be any sort of variable, doesn't even have to be genetic). |
env |
data.frame of the variables inside the environmental score E (can be any sort of variable, doesn't even have to be environmental). |
formula |
Model formula. Use E for the environmental score and G for the genetic score. Do not manually code interactions, write them in the formula instead (ex: G*E*z or G:E:z). |
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). |
An object of class function
of length 1.
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 | train = example_2way(500, 1, seed=777)
fit = LEGIT(train$data, train$G, train$E, y ~ G*E, train$coef_G, train$coef_E)
fit_IMLEGIT = LEGIT_to_IMLEGIT(fit,train$data, train$G, train$E, y ~ G*E)
fit_LEGIT = IMLEGIT_to_LEGIT(fit_IMLEGIT,train$data, train$G, train$E, y ~ G*E)
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