R/glmDS2.R

In datashield/dsModelling: DataSHIELD server site functions for statistical modelling

#'
#' @title Fits a generalized linear model
#' @description Fits a GLM for one iteration only
#' @details This function cannot be called through 'datashield.aggregate' like other aggregate functions
#' It is only called from within the client function \code{ds.glm} in the package \code{dsModellingClient}
#' @param formula an object of class \code{formula}.
#' @param family a character, description of the error distribution and link function to used in the model.
#' @param beta.vect a string character: the starting values for the parameters in the linear predictor.
#' @param offset  a character, null or a numeric vector that can be used to specify an a priori known component 
#' to be included in the linear predictor during fitting.
#' @param weights  a character, the name of an optional vector of 'prior weights' to be used in the fitting 
#' process. Should be NULL or a numeric vector.
#' @param data a character, the name of an optional data frame containing the variables in 
#' in the \code{formula}. 
#' @return a list which contains: the fitted \code{family}, a score vector and an information matrix
#' @author Gaye, A.; Burton, P.; Laflamme, P.
#' @export
#'
glmDS2 <- function (formula, family, beta.vect, offset, weights, data) {
  
# 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(data)){
    dataTable <- NULL 
  }else{
    dataTable <- eval(parse(text=data))
  }

 
# 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(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("+", "|", 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])))
      originalFormula <- gsub(model.variables[i], elt[length(elt)], originalFormula, fixed=TRUE)
      varnames <- append(varnames, elt[length(elt)])
    }else{
      varnames <- append(varnames, elt)
    }
  }
varnames <- unique(varnames)
  
# Now fit model specified in formula: by using x=TRUE this is how we generate all of the model terms
# and the data that underlie them. This will include a vector of 1s for the intercept and
# any dummy variables required for factors
 
    formula2use <- as.formula(paste0(Reduce(paste, deparse(originalFormula)))) # here we need the formula as a 'call' object
    mod.glm.ds <- glm(formula2use, family=family, x=TRUE, control=glm.control(maxit=1), contrasts=NULL, data=dataTable)

 X.mat.orig <- as.matrix(mod.glm.ds$x)
 y.vect.orig <-as.vector(mod.glm.ds$y)
 f<-mod.glm.ds$family

# Remove rows of offset or weights which contain NA in any Y or X variable
# Rows where offset or weights are missing but Y and X are non-NA, remain at this stage
    cbindtext <- paste0("cbind(", paste(varnames, collapse=","), ")")
    dtemp <- eval(parse(text=cbindtext))
    # now get the above table with no missing values (i.e. complete) and grab the offset variable (the last column)
    row.noNA.YX<-complete.cases(dtemp)

#Both weights and offset
if(!(is.null(weights))&&!(is.null(offset))){
    cbindtext <- paste0("cbind(", paste(varnames, collapse=","), ",", weights, ",", offset,")")
    dtemp <- eval(parse(text=cbindtext))
    # now get the above table with no missing values (i.e. complete) and grab the offset variable (the last column)
    cmplt <- dtemp[row.noNA.YX,]
    offsetvar.orig <- cmplt[, dim(cmplt)[2]] 
    weightsvar.orig <- cmplt[, (dim(cmplt)[2]-1)]    
  }
 
#Offset no weights 
if(is.null(weights)&&!(is.null(offset))){
    cbindtext <- paste0("cbind(", paste(varnames, collapse=","), ",", offset, ")")
    dtemp <- eval(parse(text=cbindtext))
    # now get the above table with no missing values (i.e. complete) and grab the offset variable (the last column)
    cmplt <- dtemp[row.noNA.YX,]
    offsetvar.orig <- cmplt[, dim(cmplt)[2]]
  }
  
#Weights no offset
if(!(is.null(weights))&&(is.null(offset))){
    cbindtext <- paste0("cbind(", paste(varnames, collapse=","), ",", weights, ")")
    dtemp <- eval(parse(text=cbindtext))
    # now get the above table with no missing values (i.e. complete) and grab the offset variable (the last column)
    cmplt <- dtemp[row.noNA.YX,]
    weightsvar.orig <- cmplt[, dim(cmplt)[2]]    
  } 
 
#Now work with y vector and X matrix from actual model (with all terms explicit)

#Strip rows of y, X matrix, offset and weights if missing values in offset or weights
#If an offset is not specified then NAs in it are meaningless and so have no impact
#If weights are not specified then NAs in it are meaningless and so have no impact
 
#Both weights and offset
 if(!(is.null(weights))&&!(is.null(offset))){
 YXWO.orig<-cbind(y.vect.orig,X.mat.orig,weightsvar.orig,offsetvar.orig)
 YXWO.complete<-YXWO.orig[complete.cases(YXWO.orig),]
 numcol.YXWO<-dim(YXWO.orig)[2]
 y.vect<-YXWO.complete[,1]
#NB - must specify X.mat as.matrix because otherwise with a one parameter linear predictor
#ie just the column of 1s for the intercept, X.mat is n x 1 and defaults to vector which does
#not then work in the matrix multiplication code below 
 X.mat<-as.matrix(YXWO.complete[,(2:(numcol.YXWO-2))])
 weightsvar<-YXWO.complete[,numcol.YXWO-1]
 offsetvar<-YXWO.complete[,numcol.YXWO]
 }

#Offset no weights
 if(is.null(weights)&&!(is.null(offset))){
 YXO.orig<-cbind(y.vect.orig,X.mat.orig,offsetvar.orig)
 YXO.complete<-YXO.orig[complete.cases(YXO.orig),]
 numcol.YXO<-dim(YXO.orig)[2]
 y.vect<-YXO.complete[,1]
#NB - must specify X.mat as.matrix because otherwise with a one parameter linear predictor
#ie just the column of 1s for the intercept, X.mat is n x 1 and defaults to vector which does
#not then work in the matrix multiplication code below 
 X.mat<-as.matrix(YXO.complete[,(2:(numcol.YXO-1))])
 weightsvar<-rep(1,length(y.vect))
 offsetvar<-YXO.complete[,numcol.YXO]
 }
 
#Weights no offset
 if(!(is.null(weights))&&(is.null(offset))){
 YXW.orig<-cbind(y.vect.orig,X.mat.orig,weightsvar.orig)
 YXW.complete<-YXW.orig[complete.cases(YXW.orig),]
 numcol.YXW<-dim(YXW.orig)[2]
 y.vect<-YXW.complete[,1]
 X.mat<-as.matrix(YXW.complete[,(2:(numcol.YXW-1))])
 weightsvar<-YXW.complete[,numcol.YXW]
 offsetvar<-rep(0,length(y.vect))
 }
 
 #No weights or offset
 if(is.null(weights)&&(is.null(offset))){
 y.vect<-y.vect.orig
 X.mat<-X.mat.orig
 weightsvar<-rep(1,length(y.vect))
 offsetvar<-rep(0,length(y.vect))
 }
 
 
numsubs<-length(y.vect)

#Convert beta.vect from transmittable (character) format to numeric 

  beta.vect.n <- as.numeric(unlist(strsplit(beta.vect, split=",")))
 
  
#If an offset is specified, add it directly to the values in the linear predictor
  if(!is.null(offset)){
    lp.vect <- (X.mat%*%beta.vect.n)+offsetvar
  }else{
    lp.vect <- (X.mat%*%beta.vect.n)   
  }
   
#Use the available functions for family f to generate the components giving the deviance and
#the working weights for the IRLS algorithm
 
  mu.vect<-f$linkinv(lp.vect)
  mu.eta.val<-f$mu.eta(lp.vect)
  var.vect<-f$variance(mu.vect)
  
#If a prior weights vector is specified multiply the working weights by the prior weights 
  if(!is.null(weights)){
  W.vect<-as.vector(mu.eta.val^2/var.vect)
  W.vect<-W.vect*weightsvar
  dev<-sum(f$dev.resids(y.vect, mu.vect, rep(1, length(y.vect)))*weightsvar)
  }else{
  W.vect<-as.vector(mu.eta.val^2/var.vect)
  dev<-sum(f$dev.resids(y.vect, mu.vect, rep(1, length(y.vect))))
  }

#Generate information matrix as XWX  
  WX.mat<-W.vect*X.mat
  info.matrix<-t(X.mat)%*%WX.mat
  
#Generate score vector as XWz (where z is working response vector on scale of linear predictor)
#See theoretical basis in the .pdf in RELEVANT.GLM.THEORY directory.
#Note mu.et.val is first differential of inverse link function (d.mu by d.eta)
#which is inverse of first diff of link function (g') in thoretical explanation

  u.vect<-(y.vect-mu.vect)*1/mu.eta.val
  W.u.mat<-matrix(W.vect*u.vect)
  score.vect<-t(X.mat)%*%W.u.mat
  
  return(list(family=f, info.matrix=info.matrix, score.vect=score.vect, numsubs=numsubs, dev=dev))
  
}
#glmDS2