LEGIT (version 1.4.1)

LEGIT_to_IMLEGIT: LEGIT to IMLEGIT

Description

Transforms a LEGIT model into a IMLEGIT model (Useful if you want to do plot() or GxE_interaction_test() with a model resulting from a variable selection method which gave a IMLEGIT model)

Usage

LEGIT_to_IMLEGIT(
  fit,
  data,
  genes,
  env,
  formula,
  eps = 0.001,
  maxiter = 100,
  family = gaussian,
  ylim = NULL,
  print = TRUE
)

Value

Returns an object of the class "IMLEGIT" which is list containing, in the following order: a glm fit of the main model, a list of the glm fits of the latent variables and 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.

Arguments

fit

LEGIT 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).

References

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.

Examples

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)