coda_glmnet_longitudinal_null: coda_glmnet_longitudinal_null

In coda4microbiome: Compositional Data Analysis for Microbiome Studies

coda_glmnet_longitudinal_null

Description

Performs a permutational test for the coda_glmnet_longitudinal() algorithm: It provides the distribution of results under the null hypothesis by implementing the coda_glmnet_longitudinal() on different rearrangements of the response variable.

Usage

coda_glmnet_longitudinal_null(
  x,
  y,
  x_time,
  subject_id,
  ini_time,
  end_time,
  niter = 100,
  covar = NULL,
  alpha = 0.9,
  lambda = "lambda.1se",
  nfolds = 10,
  sig = 0.05
)

Arguments

x	abundance matrix or data frame in long format (several rows per individual)
y	outcome (binary); data type: numeric, character or factor vector
x_time	observation times
subject_id	subject id
ini_time	initial time to be analyzed
end_time	end time to be analyzed
niter	number of sample iterations
covar	data frame with covariates (default = NULL)
alpha	elastic net parameter (default = 0.9)
lambda	penalization parameter (default = "lambda.1se")
nfolds	number of folds
sig	significance value (default = 0.05)

Value

list with "accuracy" and "confidence interval"

Author(s)

M. Calle - T. Susin

Examples

set.seed(123) # to reproduce the results

data(ecam_filtered, package = "coda4microbiome")   # load the data

x=x_ecam # microbiome abundance
x_time = metadata$day_of_life    # observation times
subject_id = metadata$studyid   # subject id
y= metadata$diet           # diet ("bd"= breast diet, "fd"=formula diet)
ini_time = 0
end_time = 90

 coda_glmnet_longitudinal_null (x,y, x_time, subject_id, ini_time, end_time,
                                      lambda="lambda.min",nfolds=4, niter=3)

coda4microbiome documentation built on Sept. 11, 2024, 8 p.m.