#'
#' @title glmDS2 called by ds.glm
#' @description This is the second serverside aggregate function called by ds.glm.
#' @details It is an aggregate function that uses the model structure and starting
#' beta.vector constructed by glmDS1 to iteratively fit the generalized linear model
#' that has been specified. The function glmDS2 also carries out a series of disclosure
#' checks and if the arguments or data fail any of those tests,
#' model construction is blocked and an appropriate serverside error message is
#' created and returned to ds.glm on the clientside.
#' For more details please see the extensive header for ds.glm.
#' @param formula a glm() formula consistent with R syntax eg U~x+y+Z to regress
#' variables U on x, y and Z
#' @param family a glm() family consistent with R syntax eg "gaussian", "poisson",
#' "binomial"
#' @param beta.vect a numeric vector created by the clientside function specifying the
#' vector of regression coefficients at the current iteration
#' @param offset an optional variable providing a regression offset
#' @param weights an optional variable providing regression weights
#' @param dataName an optional character string specifying a data.frame object holding
#' the data to be analysed under the specified model same
#' @author Paul Burton, for DataSHIELD Development Team
#' @export
#'
glmDS2 <- function (formula, family, beta.vect, offset, weights, dataName) {
#############################################################
#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
}
# 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="$", fixed=TRUE))
if(length(elt) > 1){
assign(elt[length(elt)], eval(parse(text=model.variables[i]), envir = parent.frame()), envir = parent.frame())
originalFormula.modified <- gsub(model.variables[i], elt[length(elt)], originalFormula, fixed=TRUE)
varnames <- append(varnames, elt[length(elt)])
}else{
varnames <- append(varnames, elt)
}
}
varnames <- unique(varnames)
#varnames.with.df<-varnames
if(!is.null(dataName)){
for(v in 1:length(varnames)){
varnames[v] <- paste0(dataName,"$",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
# cbindraw.text <- paste0("cbind(", paste(varnames, collapse=","), ")")
all.data <- eval(parse(text=cbindraw.text), envir = parent.frame())
#WORKS TO HERE
############################
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
#######################################
# 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
originalFormulaFormula <- stats::as.formula(originalFormula)
formula2use <- stats::as.formula(paste0(Reduce(paste, deparse(originalFormulaFormula))), env = parent.frame()) # here we need the formula as a 'call' object
mod.glm.ds <- stats::glm(formula2use, family=family, x=TRUE, control=stats::glm.control(maxit=1), contrasts=NULL, data=dataDF)
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), envir = parent.frame())
# now get the above table with no missing values (i.e. complete) and grab the offset variable (the last column)
row.noNA.YX <- stats::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), envir = parent.frame())
# 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), envir = parent.frame())
# 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), envir = parent.frame())
# 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[stats::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[stats::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[stats::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
##########################
#BACKUP DISCLOSURE TRAP
#If y, X or w data are invalid but user has modified clientside
#function (ds.glm) to circumvent trap, model will get to this point without
#giving a controlled shut down with a warning about invalid data.
#So as a safety measure, we will now use the same test that is used to
#trigger a controlled trap in the clientside function to destroy the
#score.vector and information.matrix in the study with the problem.
#So this will make model fail without explanation
#Disclosure code from glmDS1
errorMessage.combined <- NULL
dimX <- dim((X.mat))
##############################################################
#FIRST TYPE OF DISCLOSURE TRAP - TEST FOR OVERSATURATED MODEL#
##############################################################
glm.saturation.invalid <- 0
num.p <- dimX[2]
num.N <- dimX[1]
if(num.p>nfilter.glm*num.N){
glm.saturation.invalid <- 1
errorMessage.combined <- c(errorMessage.combined,"ERROR: Model has too many parameters, there is a possible risk of disclosure - please simplify model")
}
################################
#SECOND TYPE OF DISCLOSURE TRAP#
################################
#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.combined <- c(errorMessage.combined,"ERROR: y 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
dimX <- dim((X.mat))
num.Xpar <- dimX[2]
Xpar.invalid <- rep(0,num.Xpar)
x.invalid <- 0
for(pj in 1:num.Xpar){
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
}
}
}
if(x.invalid==1){
errorMessage.combined <- c(errorMessage.combined, "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
#Keep same object name as in glmDS1
w.vect <- weightsvar
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.combined <- c(errorMessage.combined,"ERROR: w vector is binary with one category less than filter threshold for table cell size")
}
}
#Check o vector validity
o.invalid <- 0
#if(!is.null(offsetvar))
#{
#Keep vector name consistent
o.vect <- offsetvar
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.combined<-c(errorMessage.combined,"ERROR: offset vector is binary with one category less than filter threshold for table cell size")
}
}
#}
disclosure.risk<-0
########################################################################
#If there is a disclosure risk DESTROY the info.matrix and score.vector#
########################################################################
if(y.invalid>0||w.invalid>0||o.invalid>0||sum(Xpar.invalid)>0||glm.saturation.invalid>0){
info.matrix <- NA
score.vector <- NA
disclosure.risk <- 1
errorMessage.combined <- c(errorMessage.combined,"MODEL FAILED: model or data invalid, info.matrix and score.vector destroyed")
}else{
errorMessage.combined <- "No errors"
}
return(list(family=f, info.matrix=info.matrix, score.vect=score.vect, numsubs=numsubs, dev=dev,
Nvalid=Nvalid,Nmissing=Nmissing,Ntotal=Ntotal,disclosure.risk=disclosure.risk,
errorMessage2=errorMessage.combined))
}
# AGGREGATE FUNCTION
# glmDS2
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.