lme_cor: Linear Mixed Effect Models with random variable

In JacobAgerbo/QuickFixR: Helps scientist with data explorative tasks with user interface

Linear Mixed Effect Models with random variable

Description

Linear Mixed Effect Models with random variable

Usage

lme_cor(
  MAE,
  tax_level,
  feature,
  exp_var,
  random_var,
  datatype = c("logcpm", "relabu", "counts"),
  tolerance
)

Arguments

MAE	A multi-assay experiment object
tax_level	The classification level used for feature
feature	A given feature from count data, which should be correlated with variable
random_var	Random variable, which should be taken into account for correlation, eg. 'Location' or 'batch effect'.
datatype	Select datatype, like relative abundance (relabu), counts, or logcpm
tolerance	Tolerance of dispersion for correlation adjustment
variable	Numeric variable for correlation, eg. 'weight'

Value

A plotly object, a ggplot object for pdf saving, and a table with statistics

Examples

data_dir = system.file('inst/extdata/NWS_MAG_Profiling.rds', package = 'QuickFixR')
df <- readRDS(data_dir)
p <- lme_cor(df,
                    tax_level='Genus',
                    feature = 'Mycoplasma'
                    variable='Weight_g',
                    random_var="Location",
                    datatype = "relabu")
p

JacobAgerbo/QuickFixR documentation built on Sept. 20, 2023, 12:40 p.m.