mvLMEM: Linear Mixed Effects Model Discriminant Analysis

In microresearcher/MicroVis: Microbiome Analysis and Visualization

Linear Mixed Effects Model Discriminant Analysis

Description

Linear Mixed Effects Model Discriminant Analysis

Usage

mvLMEM(
  dataset = NULL,
  formula = NULL,
  exclude = NULL,
  rank = NULL,
  zero.handling = "pseudo-count",
  alpha = 0.05,
  is.winsor = T
)

Arguments

dataset	MicroVis dataset. Defaults to active dataset.
formula	Formula for linear model. Defaults to simple linear model of all covariates.
exclude	Factors/covariates to exclude in linear model.
rank	Rank at which to conduct analysis.
zero.handling	(From linda function) A character string of 'pseudo-count' or 'imputation' indicating the zero handling method used when feature.dat is 'count'. If 'pseudo-count', apseudo.cnt will be added to each value in feature.dat. If 'imputation', then we use the imputation approach using the formula in the referenced paper. Basically, zeros are imputed with values proportional to the sequencing depth. When feature.dat is 'proportion', this parameter will be ignored and zeros will be imputed by half of the minimum for each feature.
alpha	Significance threshold. Defaults to 0.05
is.winsor	Whether to replace outliers (using winsorization)

Value

Results of linear modeling with log2fc, p-values, adjusted p-values for each covariate.

microresearcher/MicroVis documentation built on Feb. 8, 2024, 10:59 a.m.