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latentFieldModel.R
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latentFieldModel.R
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#' latentFieldModel: function to define latent field models from dbViewR object
#'
#' @param db dbViewR object with valid column names for INLA model.
#' Smoothing only makes sense by age, location, and time. Factor variables cannot be smoothed!
#' @param shp sf object with residence_census_tract shapes (all higher levels assume iid and not local smoothing)
#' @param family non-standard family override (default = NULL).
#' @param neighborGraph non-standard neighbor graph (default = NULL)
#'
#' @return modelDefinition object for modelTrainR, list with fields
#' type = latentField
#' family : as input
#' formula : model definition for INLA
#' inputData : inla-prepared db$observed data
#' neighborGraph : as input or derived from shp during formula construction
#'
#' @import INLA
#'
#' @export
#' @examples
#' return h1n1pdm incidence model by time and location
#' modelDefinition <- smoothModel(db = dbViewR::selectFromDB(), shp = dbViewR::masterSpatialDB())
#'
latentFieldModel <- function(db , shp, family = NULL, neighborGraph = NULL){
#INLA data frame that may get augmented columns we don't need to see when we're done
inputData <- db$observedData
# identify intended family
if(is.null(family)){
if (all(inputData$n == inputData$positive)){
family = 'poisson'
} else if (any(inputData$n > inputData$positive)){
family = 'binomial'
} else if (any(inputData$n < inputData$positive)){
return('n < positive !!! invald db$observedData.')
}
}
# construct priors
hyper=list()
hyper$global <- list(prec = list( prior = "pc.prec", param = 1/10, alpha = 0.01))
hyper$local <- list(prec = list( prior = "pc.prec", param = 1/200, alpha = 0.01))
hyper$age <- list(prec = list( prior = "pc.prec", param = 1, alpha = 0.01))
hyper$time <- list(prec = list( prior = "pc.prec", param = 1/50, alpha = 0.01))
# unlike smoothing model, we only replicate latent fields across pathogens, but treat all other factors as fixed effects
# find pathogen types
if('pathogen' %in% names(db$observedData)){
levelSet <- levels(as.factor(inputData$pathogen))
numLevels <- length(levelSet)
validLatentFieldColumns <- c('pathogen')
} else {
return('error! must provide "pathogen" column.')
}
# set family across all levels
family <- rep(family,numLevels)
# build outcome matrix and replicate list for multiple likelihoods
outcome <- matrix(NA,nrow(inputData),numLevels)
replicateIdx <- matrix(NA,nrow(inputData),1)
for( k in levelSet){
idx <- inputData$pathogen %in% k
count <- which(levelSet %in% k)
outcome[idx, count] <- inputData$positive[idx]
replicateIdx[idx]<-count
}
# initialize formula for each level
if(numLevels>1){
outcomeStr <- paste('cbind(',paste(paste('outcome',1:numLevels,sep='.'),sep='',collapse=', '),')',sep='',collapse = '')
formula <- as.formula(paste(outcomeStr,'~','pathogen - 1 + catchment',sep=' '))
} else { # why does R do inconsistent stuff with column names!?!!
formula <- as.formula('outcome ~ 1 + catchment')
}
# factors as fixed effects, assuming no interaction terms
validFactorNames <- names(db$observedData)[ !( (names(db$observedData) %in% c('pathogen','n','positive')) |
grepl('row',names(db$observedData)) |
grepl('age',names(db$observedData)) |
grepl('residence_',names(db$observedData)) |
grepl('work_',names(db$observedData)) |
grepl('encounter',names(db$observedData)) )]
factorIdx <- names(db$observedData) %in% validFactorNames
for(COLUMN in names(db$observedData)[factorIdx]){
formula <- as.formula(paste(as.character(formula)[2],'~',paste(as.character(formula)[3],COLUMN,sep='+')))
}
# latent fields
for(COLUMN in names(inputData)[!(names(inputData) %in% c('positive','n'))]){
if(COLUMN == 'time_row'){
#INLA needs one column per random effect
inputData$time_row_rw2 <- inputData$time_row
inputData$time_row_IID <- inputData$time_row
formula <- update(formula, ~ . + f(time_row_rw2, model='rw2', hyper=modelDefinition$hyper$time, replicate=replicateIdx) +
f(time_row_IID, model='iid', hyper=modelDefinition$hyper$local, replicate=replicateIdx, constr = TRUE) )
validLatentFieldColumns <- c(validLatentFieldColumns,'time_row_rw2','time_row_IID')
}
if(COLUMN == 'age_row'){
inputData$age_row_rw2 <- inputData$age_row
inputData$age_row_IID <- inputData$age_row
formula <- update(formula, ~ . + f(age_row_rw2, model='rw2', hyper=modelDefinition$hyper$age, replicate=replicateIdx) +
f(age_row_IID, model='iid', hyper=modelDefinition$hyper$local, replicate=replicateIdx, constr = TRUE) )
validLatentFieldColumns <- c(validLatentFieldColumns,'age_row_rw2','age_row_IID')
}
if(COLUMN %in% c('residence_puma')){
inputData$residence_pumaRow <- match(inputData$residence_puma,unique(inputData$residence_puma))
if('time_row' %in% names(inputData)){
inputData$time_row_residence_puma <- inputData$time_row
formula <- update(formula, ~ . + f(residence_pumaRow, model='iid', hyper=modelDefinition$local, constr = TRUE, replicate=replicateIdx,
group = time_row_residence_puma, control.group=list(model="rw2")))
validLatentFieldColumns <- c(validLatentFieldColumns,'residence_pumaRow','time_row_residence_puma')
} else {
formula <- update(formula, ~ . + f(residence_pumaRow, model='iid', hyper=modelDefinition$hyper$global, replicate=replicateIdx))
validLatentFieldColumns <- c(validLatentFieldColumns,'residence_pumaRow')
}
}
if(COLUMN %in% c('residence_cra_name')){
inputData$residence_cra_nameRow <- match(inputData$residence_cra_name,unique(inputData$residence_cra_name))
if('time_row' %in% names(inputData)){
inputData$time_row_residence_cra_name <- inputData$time_row
formula <- update(formula, ~ . + f(residence_cra_nameRow, model='iid', hyper=modelDefinition$local, constr = TRUE, replicate=replicateIdx,
group = time_row_residence_cra_name, control.group=list(model="rw2")))
validLatentFieldColumns <- c(validLatentFieldColumns,'residence_cra_nameRow','time_row_residence_cra_name')
} else {
formula <- update(formula, ~ . + f(residence_cra_nameRow, model='iid', hyper=modelDefinition$hyper$global, replicate=replicateIdx))
validLatentFieldColumns <- c(validLatentFieldColumns,'residence_cra_nameRow')
}
}
if(COLUMN %in% c('residence_neighborhood_district_name')){
inputData$residence_neighborhood_district_nameRow <- match(inputData$residence_neighborhood_district_name,unique(inputData$residence_neighborhood_district_name))
if('time_row' %in% names(inputData)){
inputData$time_row_residence_neighborhood_district_name <- inputData$time_row
formula <- update(formula, ~ . + f(residence_neighborhood_district_nameRow, model='iid', hyper=modelDefinition$local, constr = TRUE, replicate=replicateIdx,
group = time_row_residence_neighborhood_district_name, control.group=list(model="rw2")))
validLatentFieldColumns <- c(validLatentFieldColumns,'residence_neighborhood_district_nameRow','time_row_residence_neighborhood_district_name')
} else {
formula <- update(formula, ~ . + f(residence_neighborhood_district_nameRow, model='iid', hyper=modelDefinition$hyper$global, replicate=replicateIdx))
validLatentFieldColumns <- c(validLatentFieldColumns,'residence_neighborhood_district_nameRow')
}
}
# Do we want the option of neighbor smoothing at larger scales?
if(COLUMN == 'residence_census_tract'){
if(exists('shp')){
neighborGraph <- constructAdjacencyNetwork(shp)
inputData$residence_census_tractRow <- shp$rowID[match(inputData$residence_census_tract,shp$residence_census_tract)]
if('time_row' %in% names(inputData)){
inputData$time_row_residence_census_tract <- inputData$time_row
formula <- update(formula, ~ . + f(residence_census_tractRow, model='besag', graph=modelDefinition$neighborGraph, constr = TRUE, hyper=modelDefinition$hyper$local, replicate=replicateIdx,
group = time_row_residence_census_tract, control.group=list(model="rw2")))
validLatentFieldColumns <- c(validLatentFieldColumns,'residence_census_tractRow','time_row_residence_census_tract')
} else {
formula <- update(formula, ~ . + f(residence_census_tractRow, model='bym2', graph=modelDefinition$neighborGraph, constr = TRUE, hyper=modelDefinition$hyper$local, replicate=replicateIdx))
validLatentFieldColumns <- c(validLatentFieldColumns,'residence_census_tractRow')
}
} else {
inputData$residence_census_tractRow <- match(inputData$residence_census_tract,unique(inputData$residence_census_tract))
if('time_row' %in% names(inputData)){
inputData$time_row_residence_census_tract <- inputData$time_row
formula <- update(formula, ~ . + f(residence_census_tractRow, model='iid', graph=modelDefinition$neighborGraph, hyper=modelDefinition$hyper$local, replicate=replicateIdx,
group = time_row_residence_census_tract, control.group=list(model="rw2")))
validLatentFieldColumns <- c(validLatentFieldColumns,'residence_census_tractRow','time_row_residence_census_tract')
} else {
formula <- update(formula, ~ . + f(residence_census_tractRow, model='iid', graph=modelDefinition$neighborGraph, hyper=modelDefinition$hyper$local, replicate=replicateIdx))
validLatentFieldColumns <- c(validLatentFieldColumns,'residence_census_tractRow')
}
}
}
}
# linear combination of pathogen and latent fields
# find unique rows after discarding factors that are being averaged over
lc.data <- data.frame(inputData[,names(inputData) %in% validLatentFieldColumns], replicateIdx = replicateIdx)
lc.rowIdx <- !duplicated(lc.data)
lc.data <- lc.data[lc.rowIdx,]
# generate list of desired linear combinations # https://groups.google.com/forum/#!topic/r-inla-discussion-group/_e2C2L7Wc30
lcIdx=c()
spentColumn<-rep(FALSE,length(validLatentFieldColumns))
for(COLUMN in validLatentFieldColumns){
if(COLUMN %in% c('pathogen') ){
if(numLevels>1){
# need to promote pathogen levels to independent columns! https://groups.google.com/forum/#!topic/r-inla-discussion-group/IaTSakB7qy4
pathogenNames <- paste('pathogen',levelSet,sep='')
} else { # why does R do inconsistent thing with column names?????!
pathogenNames <- '(Intercept)'
}
} else if (!(COLUMN == 'time_row_residence_puma' )) {
groupIdx<-grepl( paste0('_',gsub('Row','',COLUMN)) ,validLatentFieldColumns) # this nasty thing will get refactored: https://github.com/seattleflu/incidence-mapper/issues/13
if(any(groupIdx & !spentColumn)){ # grouped?
lcIdx[[COLUMN]] <- inla.idx(lc.data[[COLUMN]], group = lc.data[[validLatentFieldColumns[groupIdx]]], replicate = lc.data$replicateIdx)
spentColumn[groupIdx]<-TRUE
} else if(!spentColumn[validLatentFieldColumns %in% COLUMN]) {
lcIdx[[COLUMN]] <- inla.idx(lc.data[[COLUMN]], replicate = lc.data$replicateIdx)
}
}
spentColumn[validLatentFieldColumns %in% COLUMN]<-TRUE
}
# generate list of desired linear combinations # https://groups.google.com/forum/#!topic/r-inla-discussion-group/_e2C2L7Wc30
lc.latentField <- vector("list", nrow(lc.data))
w<-vector("list", length(names(lcIdx))+1)
w[[length(names(lcIdx))+1]]<-1 #pathogen
for(k in 1:nrow(lc.data)){
for(n in 1:length(names(lcIdx))){
w[[n]]<-rep(0,nrow(lc.data))
w[[n]][lcIdx[[n]][k]]<-1
}
names(w) <- c(names(lcIdx),pathogenNames[lc.data$replicateIdx[k]])
lc <- inla.make.lincomb(w)
names(lc)<- paste0('latent_field',k)
lc.latentField[k]<-lc
lc.data$latentField[k]<-names(lc)
if( (k %% 100) == 0){
print(k/nrow(lc.data))
}
}
# get original values for linear combination categories
lc.colIdx <- (names(inputData) %in% c('pathogen',db$queryList$GROUP_BY$COLUMN)) & !(names(inputData) %in% validFactorNames)
lc.data <- inputData[lc.rowIdx,lc.colIdx]
df <- data.frame(outcome = outcome, inputData, replicateIdx)
if(any(grepl('residence', names(inputData)) | grepl('work', names(inputData)))){
spatial_domain<-shp$domain[1]
} else {
spatial_domain <- NULL
}
modelDefinition <- list(type='latent_field', family = family, formula = formula, lincomb = lc.latentField,
inputData = df, neighborGraph=neighborGraph, hyper=hyper,
latentFieldData = lc.data,
observedData = db$observedData,
queryList = db$queryList,
spatial_domain = spatial_domain)
return(modelDefinition)
}