mbecModelVarianceLMM: Estimate Explained Variance with Linear Mixed Models

In buschlab/MBECS: Evaluation and correction of batch effects in microbiome data-sets

Description

The function offers a selection of methods/algorithms to estimate the proportion of variance that can be attributed to covariates of interest. This shows, how much variation is explained by the treatment effect, which proportion is introduced by processing in batches and the leftover variance, i.e., residuals that are not currently explained. Covariates of interest (CoI) are selected by the user and the function will incorporate them into the model.

Usage

mbecModelVarianceLMM(model.form, model.vars, tmp.cnts, tmp.meta, type)

Arguments

model.form	Formula for linear mixed model, function will create simple additive linear mixed model if this argument is not supplied.
model.vars	Covariates to use for model building if argument 'model.form' is not given.
tmp.cnts	Abundance matrix in 'sample x feature' orientation.
tmp.meta	Covariate table that contains at least the used variables.
type	String the denotes data source, i.e., one of "otu","clr" or "tss" for the transformed counts or the label of the batch corrected count-matrix.

Details

Linear Mixed Model (lmm): Only the first covariate is considered a mixed effect. A model is fitted to each OTU respectively and the proportion of variance extracted for each covariate. (OTU_x ~ covariate_2.. + covariate_n + (1|covariate_1)

Value

Data.frame that contains proportions of variance for given covariates in a linear mixed modelling approach.

buschlab/MBECS documentation built on Jan. 21, 2022, 1:27 a.m.