predict.IMLEGIT

Constructs genotype x environment interaction (GxE) models where
G is a weighted sum of genetic variants (genetic score) and E is a weighted
sum of environments (environmental score) using the alternating optimization algorithm
by Jolicoeur-Martineau et al. (2017) <arXiv:1703.08111>. This approach has greatly
enhanced predictive power over traditional GxE models which include only a single
genetic variant and a single environmental exposure. Although this approach was
originally made for GxE modelling, it is flexible and does not require the use of
genetic and environmental variables. It can also handle more than 2 latent variables
(rather than just G and E) and 3-way interactions or more. The LEGIT model produces
highly interpretable results and is very parameter-efficient thus it can even be
used with small sample sizes (n < 250). Tools to determine the type of interaction
(vantage sensitivity, diathesis-stress or differential susceptibility), with any
number of genetic variants or environments, are available <arXiv:1712.04058>. The
software can now produce mixed-effects LEGIT models through the lme4 package.

Alexia Jolicoeur-Martineau

LEGIT

Latent Environmental & Genetic InTeraction (LEGIT) Model

predict.IMLEGIT function

<dl><dt>object</dt>
<dd>An object of class "IMLEGIT", usually, a result of a call to IMLEGIT.</dd>
<dt>data</dt>
<dd>data.frame of the dataset to be used.</dd>
<dt>latent_var</dt>
<dd>list of data.frame. The elements of the list are the datasets used to construct each latent variable. For interpretability and proper convergence, not using the same variable in more than one latent variable is highly recommended. It is recommended to set names to the list elements to prevent confusion because otherwise, the latent variables will be named L1, L2, ...</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd></dl>

Arguments

Predictions of IMLEGIT fits — predict.IMLEGIT

<dl>

<dt>object</dt>
<dd>An object of class "IMLEGIT", usually, a result of a call to IMLEGIT.</dd>


<dt>data</dt>
<dd>data.frame of the dataset to be used.</dd>


<dt>latent_var</dt>
<dd>list of data.frame. The elements of the list are the datasets used to construct each latent variable. For interpretability and proper convergence, not using the same variable in more than one latent variable is highly recommended. It is recommended to set names to the list elements to prevent confusion because otherwise, the latent variables will be named L1, L2, ...</dd>


<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd>

</dl>

predict.IMLEGIT: Predictions of IMLEGIT fits

Description

Usage

Value

Arguments

Examples