R/STdata_processing.R

Defines functions detrendSTdata removeSTcovarMean estimateBetaFields createDataMatrix

Documented in createDataMatrix detrendSTdata estimateBetaFields removeSTcovarMean

##############################################
## FUNCTIONS THAT MANIPULATE STdata OBJECTS ##
##############################################
##Functions in this file:
## createDataMatrix    EX:ok
## estimateBetaFields  EX:ok
## removeSTcovarMean   EX:ok
## detrendSTdata       EX:ok

##' Creates a data matrix from a \code{STdata}/\code{STmodel} object.
##' Missing observations are marked as \code{NA}.
##' 
##' @title Create a Data Matrix
##' @param STdata A \code{STdata}/\code{STmodel} object containing
##'   observations. Use either this or the \code{obs}, \code{date}, and
##'   \code{ID} inputs. 
##' @param obs A vector of observations.
##' @param date A vector of observation times.
##' @param ID A vector of observation locations.
##' @param subset A subset of locations to extract the data matrix for. A warning
##'   is given for each name not found in \code{ID}.
##' @return Returns a matrix with dimensions (number of timepoints)-by-(number
##'   of locations). Row and column names of the matrix are taken as \code{ID}
##'   and \code{sort(unique(date))} respectively.
##' @author Johan Lindstrom
##' 
##' @example Rd_examples/Ex_createDataMatrix.R
##' 
##' @family data matrix
##' @family STdata functions
##' @family STmodel functions
##' @export
createDataMatrix <- function(STdata=NULL, obs=STdata$obs$obs,
                             date=STdata$obs$date, ID=STdata$obs$ID,
                             subset=NULL){
  if( !is.null(STdata) ){
    ##check class belonging
    stCheckClass(STdata, c("STdata","STmodel"), name="STdata")
  }
  if( any(length(obs)!=c(length(date),length(ID))) ){
    stop("'obs', 'date' and 'ID' are of unequal length")
  }
  ##cast ID to character (just to be safe)
  ID <- as.character(ID)
  ##find unique indecies
  date.ind <- sort(unique(date))
  ID.ind <- sort(unique(ID))
  ##construct the data matrix
  data <- matrix(NA, length(date.ind), length(ID.ind))
  ##drop NA values from obs, and the other vectors
  ID <- ID[!is.na(obs)]
  date <- date[!is.na(obs)]
  obs <- obs[!is.na(obs)]
  ##find indecies in the matrix
  I <- (match(ID,ID.ind)-1)*length(date.ind) + match(date,date.ind)
  data[I] <- obs
  ##add col- and rownames
  colnames(data) <- ID.ind
  rownames(data) <- as.character(date.ind)
  
  if( !is.null(subset) ){
    ##subset the data - first cast to character
    subset <- as.character(subset)
    ##check that all requested names exist in the colnames
    if( any(!(subset %in% colnames(data))) )
      warning( paste("Subset names not found in ID:",
                     paste(subset[!(subset %in% colnames(data))],collapse=", ")) )
    ##convert from a character vector to a index vector
    subset <- which(colnames(data) %in% subset)
    data <- data[,subset,drop=FALSE]
  }
  return(data)
}## createDataMatrix


##' Estimates the latent-beta fields for a \code{STdata}/\code{STmodel} object
##' by regressing the observations for each site on the temporal trends.
##' 
##' @title Regression Estimates of beta-Fields
##' @param STdata A \code{STdata}/\code{STmodel} object containing
##'   observations. Use either this or the \code{obs}, \code{date}, and
##'   \code{ID} inputs. 
##' @param subset A subset of locations for which to estimate the beta-fields. A
##'   warning is given for each name not found in \code{ID}.
##' @return A list with two matrices; the estimated beta-coefficients and
##'   standard deviations of the estimates.
##' @author Johan Lindstrom
##' 
##' @example Rd_examples/Ex_estimateBetaFields.R
##' 
##' @family data matrix
##' @family STdata functions
##' @family STmodel functions
##' @importFrom stats coefficients
##' @importFrom stats lm
##' @importFrom stats vcov
##' @export
estimateBetaFields <- function(STdata=NULL, subset=NULL){
  ##create the data-matrix
  D <- createDataMatrix(STdata, subset=subset)

  ##check and extract trend
  if( is.null(STdata$trend) ){
    message("No trend in STdata, assuming a constant")
    STdata <- updateTrend(STdata, n.basis=0)
  }

  F <- STdata$trend
  ##drop the date column
  F$date <- NULL

  #create matrix of outputs
  beta <- matrix(NA, dim(D)[2], dim(STdata$trend)[2])
  dimnames(beta) <- list(colnames(D), c("const", colnames(F)))
  beta.sd <- beta

  ##estimate the beta-coeficients at each location
  for(i in 1:dim(D)[2]){
    tmp <- lm(D[,i] ~ as.matrix(F))
    beta[i,] <- coefficients( tmp )
    if( any(!is.na(coefficients(tmp))) ){
      beta.sd[i,!is.na(coefficients(tmp))] <- sqrt(diag(vcov(tmp)))
    }
  }
  
  return( list(beta=beta, beta.sd=beta.sd) )
}##function estimateBetaFields


##' Removes the temporal mean at each location for the spatio-temporal
##' covariares. The means are added to the \code{covar} field in the returned
##' object and can be used as geographic covariates.
##' 
##' @title Mean-Centre the Spatio-Temporal Covariate
##' @param STdata A \code{STdata} object, see \code{\link{mesa.data.raw}}.
##' @return Returns a modfied version of the input, where the spatio-temporal
##'   covariates have been expanded to include covariates where the site by
##'   site temporal average has been removed. The averages are seen as geographic
##'   covariates and added to \code{STdata$covars}.
##' 
##' @example Rd_examples/Ex_removeSTmean.R
##' @author Johan Lindstrom
##' @family STdata functions
##' @export
removeSTcovarMean <- function(STdata){
  ##check class belonging
  stCheckClass(STdata, "STdata", name="STdata")
    
  if( is.null(STdata$SpatioTemporal) ){
    ##no ST-covariates
    return(STdata)
  }
    
  ##compute mean at each locaiont for spatio-temporal covariate
  ST.mean <- apply(STdata$SpatioTemporal, 2:3, mean)
  ##add names
  colnames(ST.mean) <- paste("mean", colnames(ST.mean), sep=".")
  ##expand  the average
  tmp <- array(rep(ST.mean, each=dim(STdata$SpatioTemporal)[1]),
               dim(STdata$SpatioTemporal))
  
  ##subtract the means from the spatio-temporal data array
  tmp <- STdata$SpatioTemporal - tmp
  ##create new array that combines mean 0 and original ST-covar
  new <- array(NA, c(dim(tmp)[1:2],2*dim(tmp)[3]))
  new[,,1:dim(tmp)[3]] <- STdata$SpatioTemporal
  new[,,(dim(tmp)[3]+1):(2*dim(tmp)[3])] <- tmp
  ##names for the new array
  rownames(new) <- rownames(STdata$SpatioTemporal)
  colnames(new) <- colnames(STdata$SpatioTemporal)
  dimnames(new)[[3]] <- c(dimnames(tmp)[[3]],
                          paste("mean.0", dimnames(tmp)[[3]], sep="."))
  ##use this as a new spatio-temporal covariate
  STdata$SpatioTemporal <- new
  
  ##add the mean to the LUR data, matching locations
  IND <- match(STdata$covars$ID, rownames(ST.mean))
  names <- c(colnames(STdata$covars), colnames(ST.mean))
  STdata$covars <- cbind(STdata$covars, ST.mean[IND,])
  colnames(STdata$covars) <- names

  return(STdata)
}## removeSTcovarMean


##' Removes an estimated time-trend from the observations in a
##' \code{STdata} object. Returns a modifed \code{STdata} object with no trend;
##' the new object can be used to fit a simpler model.
##' 
##' Sometimes there is no apparent spatial structure to the time-trend
##' amplitude, or there is not enough identifiability in the data to properly
##' model the structure. In that case, it is possible, at least as a sensitivity
##' analysis, to de-trend the observations and run a model with a spatial field
##' for the intercept only (apart from the spatio-temporal residual field).
##' 
##' \code{detrendSTdata} will remove the trends from the observations, using
##' \code{STdata$trend}. 'method' is applied as \cr
##' \code{metod(STdata$obs$obs ~ F,...)} \cr
##' where \code{F} is the temporal trend from \code{STdata$trend} for each
##' observation; or as \cr
##' \code{metod(STdata$obs$obs ~ F*obs.region,...)} \cr
##' where \cr
##' \code{obs.region = factor(region[ match(STdata$obs$ID, STdata$covars$ID)])}.
##' allowing for different trends in different region (i.e. interaction between
##' the time trend(s) and region identifiers).
##' \cr
##' \code{ predict( method(...) )} is then subtracted from
##' \code{STdata$obs$obs}, detrending the data.
##' 
##' @title Removes Temporal Trend from Observations in a \code{STdata} Object
##' 
##' @param STdata A \code{STdata} object, see \code{\link{mesa.data.raw}}.
##' @param region Vector of the same length and order as \code{STdata$covars$ID}.
##'   Indicates region(s) in which different trends are to be fitted and removed.
##' @param method Method for fitting the trend (set to \code{method=lm} if
##'   \code{is.null(method)}); should produce output that allows
##'   the use of \code{\link[stats:predict]{predict}}. Possible options include
##'   \code{\link[stats:lm]{lm}}, \code{\link[MASS:rlm]{rlm}}, or
##'   \code{\link[MASS:lqs]{lqs}}.
##' @param ... Additional parameters passed to \code{method}.
##' 
##' @return Returns a modfied version of the input, with detrended observations
##'   and some changes:
##'     \item{STdata$obs}{Has an additional column, \code{removed.trend}, with
##'                       the amount subtracted per observation.}
##'     \item{STdata$trend}{Is reduced to only the \code{date} column, indicating
##'                         a constant trend.}
##'     \item{STdata$old.trend}{The previous \code{STdata$trend}, which was used
##'                             for detrending.}
##'     \item{STdata$fit.trend}{The result of \code{method}; the trend component
##'                             removed for each observations can be obtained as
##'                             \code{predict(STdata$fit.trend)}. NOTE: Aditional
##'                             functions, such as \code{\link{createSTmodel}}, might
##'                             reorder \code{STdata$obs} implying that\cr
##'            \code{STdata$obs$removed.trend != predict(STdata$fit.trend)}.}
##' 
##' @example Rd_examples/Ex_detrendSTdata.R
##' 
##' @author Assaf P. Oron and Johan Lindstrom
##' @family STdata functions
##' @export
detrendSTdata <- function(STdata, region=NULL, method=lm, ...){
  ##check class belonging
  stCheckClass(STdata, "STdata", name="STdata")
  
  ##default method
  if( is.null(method) ){
    method <- lm
  }
  ##method must be a function
  if( !is.function(method) ){
    stop("'method' must be a function, e.g. method=lm")
  }
  ##if no trend, we can't detrend
  if( is.null(STdata$trend) || dim(STdata$trend)[2]==1 ){
    warning("No trend present in 'STdata', unable to detrend!.")
    return(STdata)
  }
  ##check if data already detrended
  if( !is.null(STdata$old.trend) ){
    warning("Data already detrended once. Second detrending might be a bad idea.")
  }

  ##Should the removed trend depend on the location
  if( !is.null(region) ){
    if( length(region) != length(STdata$covars$ID) ){
      stop("length(region) not equal to number of locations in STdata$covars$ID")
    }
    obs.region <- factor(region[ match(STdata$obs$ID, STdata$covars$ID)])
  }else{
    obs.region <- factor(rep(1, length(STdata$obs$obs)))
  }

  ##locate the date column in the trend object
  I.date <- which( names(STdata$trend)=="date" )
  ##create a matrix with the trend for the observation locations
  trend.obs <- as.matrix(STdata$trend[ match(STdata$obs$date, STdata$trend$date), -I.date])

  ##if only one level, then don't do factor regression
  if( nlevels(obs.region)==1 ){
    fit.trend <- method( STdata$obs$obs ~ trend.obs, ...)
  }else{
    fit.trend <- method( STdata$obs$obs ~ trend.obs*obs.region, ...)
  }
  ##save old trend, and information about the fit.
  STdata$old.trend <- STdata$trend
  STdata$fit.trend <- fit.trend
  ##remove fitted trends
  STdata$obs$removed.trend <- predict(STdata$fit.trend)
  STdata$obs$obs <- STdata$obs$obs - STdata$obs$removed.trend
  ##replace trend with a constant (just the dates)
  STdata$trend <- data.frame(date=STdata$trend$date)
  
  ##return modified object
  return(STdata)
}## detrendSTdata

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SpatioTemporal documentation built on June 25, 2018, 9:03 a.m.