R/checking.R

In BayesFactor: Computation of Bayes factors for common designs

createDataTypes <- function(formula, whichRandom, data, analysis){
  factors <- rownames(attr(terms(formula, data = data),"factors"))[-1]
  cnames <- colnames(data)
  
  # check status of data columns
  types = sapply(cnames, function(name, data){
    ifelse(is.factor(data[,name]), "fixed", "continuous") 
  }, data = data)
  
  # restrict to only columns of interest
  types = types[ names(types) %in% factors ] 
  if(length(types) <1 ) return(c())
  
  if( any(types[ names(types) %in% whichRandom ] == "continuous") ) 
    stop("Nonfactors are specified as random.")
  if(length(whichRandom)>0)
    types[ names(types) %in% whichRandom ] = "random"  
  
  #### check various analysis types
  
  ## ANOVA can only accept factors
  if( any(types=="continuous") & analysis == "anova" )
    stop("anovaBF() cannot be used with nonfactor independent variables. Use lmBF() or regressionBF() instead.")
  
  ## regression can only accept nonfactors
  if( any(types %in% c("fixed", "random")) & analysis == "regression" )
    stop("regressionBF() cannot be used with factor independent variables. Use lmBF() or anovaBF() instead.")
  
  #### End checking analysis types
  return(types)
}



checkFormula <- function(formula, data, analysis){
  if(length(formula) < 3) stop("LHS of formula must be given.")
  cnames = colnames(data)
  
  dv = stringFromFormula(formula[[2]])
  
  if(!is.numeric(data[,dv])) stop("Dependent variable must be numeric.")
  factors = fmlaFactors(formula, data)
  terms = colnames(attr(terms(formula, data = data),"factors"))  
  
  if(is.null(factors)) return()
  if(factors[1] %in% terms) stop("Dependent variable cannot be a predictor.")
  if(!all(factors %in% cnames)) stop("Some variables missing in data frame.")
  
  if(analysis=="regression"){
    RHS = stringFromFormula(formula[[3]])
    if( grepl(":",RHS,fixed=TRUE) ) stop("Interactions not allowed in regression.")
  }
  
  if(analysis=="lm" | analysis=="anova" | analysis == "regression" | analysis == "indept")
    if(attr(terms(formula, data = data),"intercept") == 0) stop("Formula must include intercept.")            
  
  if(analysis=="indept")
    if( length(factors)>2 ) stop("Indep. groups t test can only support 1 factor as predictor.")  
  
  invisible()
}

checkEffects <- function(effects, data, dataTypes){
  if(!all(effects %in% colnames(data))) stop("Term in formula missing in data")
  if(!all(effects %in% names(dataTypes))) stop("Term in formula missing in dataTypes")
  # add more checking code here
  # most importantly, to check consistancy of data factors and dataTypes
  # no factors should be labeled as continuous, etc
}