Prepare longitudinal phenotype-plus-beta data for mixed-effects analyses — prepareMethylationGLMM

Load the merged longitudinal phenotype-plus-beta object, ensure that a subject identifier column is available, validate the requested modeling variables, convert selected variables to factors, and return a single in-memory object for downstream mixed-effects modeling helpers.

prepareMethylationGLMM_T1T2Data(
  inputPheno,
  personVar = "person",
  timeVar = "Timepoint",
  phenotypes,
  covariates,
  factorVars,
  prsMap = NULL,
  cpgPrefix = "cg",
  cpgLimit = NA,
  interactionTerm = NULL,
  verbose = FALSE,
  logs = FALSE,
  log_dir = NULL,
  log_file = "log_methylationGLMM_T1T2.txt"
)

Arguments

inputPheno: Character. Path to the merged longitudinal phenotype-plus- beta object created by preprocessingPheno().
personVar: Character. Name of the subject identifier column.
timeVar: Character. Name of the time variable.
phenotypes: Character vector or comma-separated string of phenotype variables to model.
covariates: Character vector or comma-separated string of covariate variables to adjust for.
factorVars: Character vector or comma-separated string of variables that should be converted to factors before modeling.
prsMap: Character vector or comma-separated string of phenotype-to-PRS mappings in the form "Phenotype:PRS".
cpgPrefix: Character. Prefix used to identify methylation columns.
cpgLimit: Integer or NA. Maximum number of CpGs to retain. NA keeps all matching CpGs.
interactionTerm: Character or NULL. Optional interaction term.
verbose: Logical. If TRUE, emit progress messages with message().
logs: Logical. If TRUE, write the same messages to a log file.
log_dir: Character or NULL. Directory used for the log file when logs = TRUE.
log_file: Character. File name used when logs = TRUE.

Value

A list with class "dnaEPICO_methylationGLMM_T1T2_data" containing the prepared analysis data, parsed variable selections, CpG columns, timepoint summaries, and subject-ID diagnostics.

Examples

ex <- dnaEPICO:::exampleMethylationGLMMStateDnaEpico()
prepared_data <- prepareMethylationGLMM_T1T2Data(
  inputPheno = ex$inputPath,
  personVar = "person",
  timeVar = "Timepoint",
  phenotypes = "score",
  covariates = "sex",
  factorVars = "sex,Timepoint",
  cpgLimit = 2,
  verbose = FALSE,
  logs = FALSE
)
names(prepared_data)
#>  [1] "data"                     "personVar"               
#>  [3] "timeVar"                  "phenotypes"              
#>  [5] "covariates"               "factorVars"              
#>  [7] "prsMap"                   "cpgColumns"              
#>  [9] "cpgPrefix"                "cpgLimit"                
#> [11] "interactionTerm"          "requestedInteractionTerm"
#> [13] "personCreated"            "personCounts"            
#> [15] "personMappingPreview"     "timepointSummary"