R/lmerSLMADS.assign.R

In datashield/dsBase: DataSHIELD server site base functions

#' @title Fitting linear mixed effect models - serverside function
#' @description lmerSLMADS.assing is the same as lmerSLMADS2 which fits a linear
#' mixed effects model (lme) per study and saves the outcomes in each study
#' @details lmerSLMADS.assign is a serverside function called by ds.lmerSLMA on the clientside.
#' The analytic work engine is the lmer function in R which sits in the lme4 package.
#' lmerSLMADS.assign fits a linear mixed effects model (lme) including both fixed and random
#' effects - on data from each single data source and saves the regression outcomes on the serveside.
#' @param formula see help for ds.lmerSLMA
#' @param offset see help for ds.lmerSLMA
#' @param weights see help for ds.lmerSLMA
#' @param dataName see help for ds.lmerSLMA
#' @param REML see help for ds.lmerSLMA
#' @param control_type see help for ds.lmerSLMA
#' @param control_value.transmit see help for argument <control_value> for
#' function ds.lmerSLMA 
#' @param optimizer see help for ds.lmerSLMA
#' @param verbose see help for ds.lmerSLMA
#' @return writes lmerMod object summarising the fitted model to the serverside.
#' For more detailed information see help for ds.lmerSLMA.
#' @author TDemetris Avraam for DataSHIELD Development Team
#' @export
lmerSLMADS.assign <- function(formula, offset, weights, dataName, REML = TRUE,
			   control_type, control_value.transmit, optimizer, verbose=0){

  errorMessage <- "No errors"
  
  #############################################################
  #MODULE 1: CAPTURE THE nfilter SETTINGS
   thr <- dsBase::listDisclosureSettingsDS()
   nfilter.tab <- as.numeric(thr$nfilter.tab)
   nfilter.glm <- as.numeric(thr$nfilter.glm)
  #nfilter.subset <- as.numeric(thr$nfilter.subset)
  #nfilter.string <- as.numeric(thr$nfilter.string)
  #############################################################
  
  # Get the value of the 'data' parameter provided as character on the client side
  # Same is done for offset and weights lower down function
  
  if(!is.null(dataName)){
    dataDF <- eval(parse(text=dataName), envir = parent.frame())
  }else{
    dataDF <- NULL
  }
  
  # Put pipes back into formula
  #formula = as.formula(paste(formula,collapse="|"))
   formula <- Reduce(paste, deparse(formula))
   formula <- gsub("xxx", "|", formula, fixed = TRUE)
   formula <- gsub("yyy", "(", formula, fixed = TRUE)
   formula <- gsub("zzz", ")", formula, fixed = TRUE)
   formula <- gsub("ppp", "/", formula, fixed = TRUE)
   formula <- gsub("qqq", ":", formula, fixed = TRUE)

   formula2use <- stats::as.formula(formula, env = parent.frame())
   
  
  # # Rewrite formula extracting variables nested in strutures like data frame or list
  # # (e.g. D$A~D$B will be re-written A~B)
  # # Note final product is a list of the variables in the model (yvector and covariates)
  # # it is NOT a list of model terms - these are derived later
  # 
  # Convert formula into an editable character string
  formulatext <- Reduce(paste, deparse(stats::as.formula(formula)))

  # First save original model formala
  originalFormula <- formulatext

  # Convert formula string into separate variable names split by |
  formulatext <- gsub(" ", "", formulatext, fixed=TRUE)
  formulatext <- gsub("(", "", formulatext, fixed=TRUE)
  formulatext <- gsub("(1", "", formulatext, fixed=TRUE)
  formulatext <- gsub("(0", "", formulatext, fixed=TRUE)
  formulatext <- gsub(")", "", formulatext, fixed=TRUE)
  formulatext <- gsub("~", "|", formulatext, fixed=TRUE)
  formulatext <- gsub("+", "|", formulatext, fixed=TRUE)
  formulatext <- gsub("*", "|", formulatext, fixed=TRUE)
  formulatext <- gsub("/", "|", formulatext, fixed=TRUE)
  formulatext <- gsub(":", "|", formulatext, fixed=TRUE)
  formulatext <- gsub("||", "|", formulatext, fixed=TRUE)


  # Remember model.variables and then varnames INCLUDE BOTH yvect AND linear predictor components
  model.variables <- unlist(strsplit(formulatext, split="|", fixed=TRUE))

  varnames <- c()
  for(i in 1:length(model.variables)){
    elt <- unlist(strsplit(model.variables[i], split="$", fixed=TRUE))
    if(length(elt) > 1){
      assign(elt[length(elt)], eval(parse(text=model.variables[i]), envir = parent.frame()), envir = parent.frame())
      originalFormula.modified <- gsub(model.variables[i], elt[length(elt)], originalFormula, fixed=TRUE)
      varnames <- append(varnames, elt[length(elt)])
    }else{
      varnames <- append(varnames, elt)
    }
  }
  varnames <- unique(varnames)

  if(!is.null(dataName)){
    for(v in 1:length(varnames)){
      varnames[v]<-paste0(dataName,"$",varnames[v])
      test.string.0 <- paste0(dataName,"$","0")
      test.string.1 <- paste0(dataName,"$","1")
      if(varnames[v]==test.string.0) varnames[v] <- "0"
      if(varnames[v]==test.string.1) varnames[v] <- "1"
    }
    cbindraw.text <- paste0("cbind(", paste(varnames, collapse=","), ")")
  }else{
    cbindraw.text <- paste0("cbind(", paste(varnames, collapse=","), ")")
  }

  # Identify and use variable names to count missings
  all.data <- eval(parse(text=cbindraw.text), envir = parent.frame())

  Ntotal <- dim(all.data)[1]

  nomiss.any <- stats::complete.cases(all.data)
  nomiss.any.data <- all.data[nomiss.any,]
  N.nomiss.any <- dim(nomiss.any.data)[1]

  Nvalid <- N.nomiss.any
  Nmissing <- Ntotal-Nvalid

  # formula2use <- stats::as.formula(paste0(Reduce(paste, deparse(originalFormula))), env = parent.frame()) # here we need the formula as a 'call' object

   ################################################################## 
   #sort out offset and weights
   if(is.null(offset))
   {
     varname.offset<-NULL
     #offset.to.use <- NULL
     cbindtext.offset <- paste0("offset.to.use <- NULL")
     eval(parse(text=cbindtext.offset), envir = parent.frame())
   }else{
     varname.offset <- paste0(offset)
   }
   
   if(!(is.null(offset)))
   {
     cbindtext.offset <- paste0("offset.to.use <- cbind(", offset,")")
     eval(parse(text=cbindtext.offset), envir = parent.frame())
   }
   
   if(is.null(weights))
   {
     varname.weights<-NULL
     cbindtext.weights <- paste0("weights.to.use <- NULL")
     eval(parse(text=cbindtext.weights), envir = parent.frame())
     #weights.to.use <- NULL
   }else{
     varname.weights <- paste0(weights)
   }
   
   
   if(!(is.null(weights)))
   {
     cbindtext.weights <- paste0("weights.to.use <- cbind(", weights,")")
     eval(parse(text=cbindtext.weights), envir = parent.frame())
     #cbindtext.weights <- paste0("cbind(", weights,")")
     #weights.to.use <- eval(parse(text=cbindtext.weights), envir = parent.frame())
   }
  
  #### BEFORE going further we use the glm1 checks

  # This command creates a formula object without the grouping factors for running the standard glm checks
  
  formula2use.glm <- lme4::nobars(formula2use)
  
  mod.glm.ds <- stats::glm(formula2use.glm, family="gaussian", x=TRUE, offset=offset.to.use, weights=weights.to.use, data=dataDF)

  y.vect<-mod.glm.ds$y
  X.mat<-mod.glm.ds$x
  pw.vect<-mod.glm.ds$prior.weights
  offset.vect<-mod.glm.ds$offset
  
  
  ##############################
  #TEST FOR OVERSATURATED MODEL#
  ##############################
  dimX<-dim((X.mat))
  
  
  glm.saturation.invalid<-0
  num.p<-as.numeric(dimX[2])
  num.N<-as.numeric(dimX[1])
  
  if(num.p>(nfilter.glm*num.N)){
    glm.saturation.invalid<-1
    errorMessage.gos<-paste0("ERROR: Model is oversaturated (too many model parameters relative to sample size)",
                             "leading to a possible risk of disclosure - please simplify model. With ",
                             num.p," parameters and nfilter.glm = ",round(nfilter.glm,4)," you need ",
                             round((num.p/nfilter.glm),0)," observations")
  }
  
  
  #########################
  #CHECK Y VECTOR VALIDITY#
  #########################
  
  y.invalid<-0
  
  #Count number of unique non-missing values - disclosure risk only arises with two levels
  unique.values.noNA.y<-unique(y.vect[stats::complete.cases(y.vect)])
  
  #If two levels, check whether either level 0 < n < nfilter.tab
  if(length(unique.values.noNA.y)==2){
    tabvar<-table(y.vect)[table(y.vect)>=1]   #tabvar counts n in all categories with at least one observation
    min.category<-min(tabvar)
    if(min.category<nfilter.tab){
      y.invalid<-1
      errorMessage.y<-"ERROR: y (response) vector is binary with one category less than filter threshold for table cell size"
    }
  }
  
  
  #Check x matrix validity 
  #Check no dichotomous x vectors with between 1 and filter.threshold 
  #observations at either level 
  
  Xpar.invalid<-rep(0,num.p)
  x.invalid<-0 #Any x parameter invalud
  
  for(pj in 1:num.p){
    unique.values.noNA<-unique((X.mat[,pj])[stats::complete.cases(X.mat[,pj])]) 
    
    if(length(unique.values.noNA)==2){
      tabvar<-table(X.mat[,pj])[table(X.mat[,pj])>=1] #tabvar counts n in all categories with at least one observation
      min.category<-min(tabvar)
      if(min.category<nfilter.tab){
        Xpar.invalid[pj]<-1
        x.invalid<-1
        errorMessage.x<-"ERROR: at least one column in X matrix is binary with one category less than filter threshold for table cell size"
      }
    }
  }
  
  
  
  
  #Check w vector validity
  w.invalid<-0
  
  if(!is.null(pw.vect))
  {
    w.vect<-pw.vect
    
    unique.values.noNA.w<-unique(w.vect[stats::complete.cases(w.vect)])
    
    if(length(unique.values.noNA.w)==2){
      tabvar<-table(w.vect)[table(w.vect)>=1]   #tabvar counts n in all categories with at least one observation
      min.category<-min(tabvar)
      if(min.category<nfilter.tab){
        w.invalid<-1
        errorMessage.w<-"ERROR: weights vector is binary with one category less than filter threshold for table cell size"
      }
    }
  }
  
  #Check o vector validity
  o.invalid<-0
  
  if(!is.null(offset.vect))
  {	
    #Keep vector name consistent
    o.vect<-offset.vect
    
    unique.values.noNA.o<-unique(o.vect[stats::complete.cases(o.vect)])
    
    if(length(unique.values.noNA.o)==2){
      tabvar<-table(o.vect)[table(o.vect)>=1]   #tabvar counts n in all categories with at least one observation
      min.category<-min(tabvar)
      if(min.category<nfilter.tab){
        o.invalid<-1
        errorMessage.o<-"ERROR: offset vector is binary with one category less than filter threshold for table cell size"
      }
    }
  }

  ###############################################
  #FIT MODEL AFTER CONFIRMING NO DISCLOSURE RISK#
  ###############################################
  
  disclosure.risk<-0
  
  if(y.invalid>0||w.invalid>0||o.invalid>0||sum(Xpar.invalid)>0||glm.saturation.invalid>0){
    disclosure.risk<-1
  }
  
  if(disclosure.risk==0)
  {

########################
# set up control object#
########################
   
#Temporary code
    control.obj<-lme4::lmerControl()


#Construct control object
control.text<-"lme4::lmerControl("


#Sort control_value 
if(!is.null(control_type)){
	
	if(!is.null(control_value.transmit))
	{
		if(!is.numeric(control_value.transmit))
		  {
		  control_value<-as.numeric(control_value.transmit)
		  }

		if(is.numeric(control_value.transmit))
		  {
		  control_value<-control_value.transmit
		  }
	}

if(is.null(control_type)){
	  control_value<-NULL
	 }
}	

#Sort control_type
if(!is.null(control_type)){

#Code for check.conv.grad
if(control_type=="check.conv.grad")
		{
			default.value<-2.0e-3
			
			if(is.null(control_value)){
			control_value<-default.value
			}
      full.control.text<-paste0(control.text,"check.conv.grad = lme4::.makeCC('warning', tol=",control_value,", relTol=NULL))")
			control.obj<-eval(parse(text=full.control.text))
		}
		
#Code for xtol_rel	
		if(control_type=="xtol_rel")
		{
			default.value<-1.0e-7
			
			if(is.null(control_value)){
			control_value<-default.value
			}
			
            full.control.text<-paste0(control.text,"optCtrl=list(",control_type,"=",control_value,"))")
			control.obj<-eval(parse(text=full.control.text))
		}
		
    }

if(!is.null(optimizer)&&optimizer!="nloptwrap")
	{
	control.obj$optimizer<-"nloptwrap"
	}


    mg <- lme4::lmer(formula2use, offset=offset, weights=weights, data=dataDF, REML = REML, verbose = verbose, control = control.obj)
    #iterations <- utils::capture.output(try(mg <- lme4::lmer(formula2use, offset=offset.to.use, weights=weights.to.use, data=dataDF, REML = REML, verbose = verbose, control = control.obj)))

    outlist <- mg
    
  }
  #tidy up in parent.frame()
  eval(quote(rm(offset.to.use)), envir = parent.frame())
  eval(quote(rm(weights.to.use)), envir = parent.frame())
  return(outlist)
  
}
# ASSIGN FUNCTION
# lmerSLMADS.assign