R/predict.slmfit.R

Defines functions predict.slmfit

Documented in predict.slmfit

#' Perform Finite Population Block Kriging
#'
#' Uses an object of class \code{slmfit} from the \code{\link{slmfit}()}
#'  function to predict the response on the unsampled sites.
#' The column of the data set that has the response should have numeric values for the observed response
#' on the sampled sites and `NA` for any site that was not sampled.
#' Note that there is no \code{newdata} argument to
#' \code{predict.slmfit()}: any point in space for which a prediction
#' is needed should be included in the original data set in \code{\link{slmfit}()}
#' with the response variable as \code{NA}.
#'
#' @param object is an object generated from \code{\link{slmfit}()}
#' @param wtscol is the name of the column that contains the weights
#' for prediction. The default setting predicts the population total
#' @param conf_level is the confidence level for a prediction
#' interval, 0.90 by default
#' @param ... further arguments passed to or from other methods.
#' @return a list with \itemize{
#'   \item the estimated population total
#'   \item the estimated prediction variance
#'   \item a data frame containing \enumerate{
#'        \item x-coordinates
#'        \item y-coordinates
#'        \item density predictions
#'        \item count predictions
#'        \item site-by-site density prediction variances
#'        \item site-by-site count prediction variances
#'        \item indicator variable for whether or not the each site was sampled
#'        \item estimated mean for each site
#'        \item area of each site
#'        }
#'    \item vector with estimated covariance parameters
#'    \item the formula used to fit the model in \code{slmfit()}
#'    \item the confidence level
#'    \item the confidence interval bounds
#' }
#' @examples
#' data(exampledataset) ## load a toy data set
#' slmobj <- slmfit(formula = counts ~ pred1 + pred2, data = exampledataset,
#' xcoordcol = 'xcoords', ycoordcol = 'ycoords', areacol = 'areavar')
#' predict(slmobj)
#' @import stats
#' @export


predict.slmfit <- function(object, wtscol = NULL,
                           conf_level = 0.90,...) {

  ## check to make sure object is of class `slmfit`

  if (class(object) != "slmfit") {
    stop("object must be of class 'slmfit' generated from the 'slmfit' function")
  }
  ## if wtscol is left out, we are predicting the population total.
  ## Otherwise, wtscol is the name of the column in the data set
  ## with the weights for the sites that we are predicting (eg. a vector
  ## of 1's and 0's for predicting the total of the sites marked with 1's)


  formula <- object$FPBKpredobj$formula
  data <- object$FPBKpredobj$data
  xcoordsTM <- object$FPBKpredobj$xcoordsTM
  ycoordsTM <- object$FPBKpredobj$ycoordsTM
  covparmests <- object$SpatialParmEsts
  areavar <- object$FPBKpredobj$areavar

  if (is.null(wtscol) == FALSE) {
    if (sum(names(data) == wtscol) == 0) {
    stop("wtscol must be the name of the column (in quotes) in the data used in 'slmfit' that specifies the column with the prediction weights. ")
    }
  }

  # if (is.null(wtscol) == FALSE) {
  #   if (wtscol %in% names(data) == FALSE) {
  #   stop("wtscol must be the name of the column (in quotes) in the data used in 'slmfit' that specifies the column with the prediction weights. ")
  #   }
  # }


   if (is.null(wtscol) == TRUE) {
    predwts <- rep(1, nrow(data))
  } else if (is.character(wtscol) == TRUE) {
    predwts <- data[ ,wtscol]
  } else{
    stop("wtscol must be a character specifying the name of the
      column of
      prediction weights in the data set")
  }






  fullmf <- stats::model.frame(formula, na.action =
      stats::na.pass, data = data)
  yvar <- stats::model.response(fullmf, "numeric")
  density <- yvar / areavar

  ind.sa <- !is.na(yvar)
  ind.un <- is.na(yvar)

  ## make sure that some of the response values are missing
  if (sum(ind.un) == 0) {
    stop("None of the values for the response variable are missing (NA). Therefore, prediction cannot be performed for any values of the response.")
  }

  data.sa <- data[ind.sa, , drop = FALSE]
  data.un <- data[ind.un, , drop = FALSE]

  B <- predwts
  Bs <- B[ind.sa]
  Bu <- B[ind.un]

  m.un <- stats::model.frame(formula, data.un, na.action =
      stats::na.pass)
  Xu <- stats::model.matrix(formula, m.un)
  X <- stats::model.matrix(formula, fullmf)

  ## sampled response values and design matrix
  m.sa <- stats::model.frame(formula, data.sa, na.action =
      stats::na.omit)
  z.sa <- stats::model.response(m.sa)
  Xs <- stats::model.matrix(formula, m.sa)
  z.density <- z.sa / areavar[ind.sa]


  Sigma <- object$FPBKpredobj$covmat

  ## used in the Kriging formulas
  Sigma.us <- Sigma[ind.un, ind.sa, drop = FALSE]
  Sigma.su <- t(Sigma.us)
  Sigma.ss <- Sigma[ind.sa, ind.sa, drop = FALSE]
  Sigma.uu <- Sigma[ind.un, ind.un, drop = FALSE]

       ## give warning if covariance matrix cannot be inverted
      # if(abs(det(Sigma.ss)) <= 1e-21) {
      #   warning("Covariance matrix is compulationally singular and
      #     cannot be inverted")
      # }

  Sigma.ssi <- object$FPBKpredobj$covmatsampi

  ## the generalized least squares regression coefficient estimates
  betahat <- object$CoefficientEsts

  ## estimator for the mean vector
  muhats <- Xs %*% betahat
  muhatu <- Xu %*% betahat

  muhat <- rep(NA, nrow(data))
  muhat[ind.sa == TRUE] <- muhats
  muhat[ind.sa == FALSE] <- muhatu


  ## matrices used in the kriging equations
  ## notation follow Ver Hoef (2008)
  Cmat <- Sigma.ss %*% as.matrix(Bs * areavar[ind.sa]) +
    Sigma.su %*% as.matrix(Bu * areavar[ind.un])
  Dmat <- t(X) %*% matrix(B * areavar) - t(Xs) %*% Sigma.ssi %*% Cmat
  Vmat <- solve(t(Xs) %*% Sigma.ssi %*% Xs)

  ## the predicted values for the sites that were not sampled
  zhatu <- Sigma.us %*% Sigma.ssi %*% (z.density -
      muhats) + muhatu
  zhatucount <- zhatu * areavar[ind.un]


  ## creating a column in the outgoing data set for predicted densities as
  ## well as a column indicating whether or not the observation was sampled
  ## or predicted
  preddensity <- density
  preddensity[is.na(preddensity) == TRUE] <- zhatu

  pred.persite <- preddensity * areavar


  sampind <- rep(1, length(yvar))
  sampind[is.na(yvar) == TRUE] <- 0

  ## adding the site-by-site predictions

  W <- t(Xu) - t(Xs) %*% Sigma.ssi %*% Sigma.su
  sitecov <- Sigma.uu - Sigma.us %*% Sigma.ssi %*% Sigma.su +
    t(W) %*% Vmat %*% W
  sitevar <- diag(sitecov)

  densvar <- rep(NA, nrow(data))
  densvar[sampind == 1] <- 0
  densvar[sampind == 0] <- sitevar

  countcov <- areavar[ind.un] *
    sitevar * areavar[ind.un]
  countcovnodet <- countcov

  countvar <- rep(NA, nrow(data))
  countvar[sampind == 1] <- 0
  countvar[sampind == 0] <- countcov

  ## the FPBK predictor
  FPBKpredictor <- (t(B) %*% preddensity) ## density
  FPBKpredictorcount <- (t(B) %*% pred.persite) ## count

##  pred.var.obs <- (t(as.matrix(B)) %*% Sigma %*%
##    as.matrix(B) -
##    t(Cmat) %*% Sigma.ssi %*% Cmat +
##    t(Dmat) %*% Vmat %*% Dmat) ## density
  pred.var.count <- (t(as.matrix(B * areavar)) %*% Sigma %*%
      as.matrix(B * areavar) -
      t(Cmat) %*% Sigma.ssi %*% Cmat +
      t(Dmat) %*% Vmat %*% Dmat)

  ## returns a list with 3 components:
  ## 1.) the kriging predictor and prediction variance
  ## 2.) a matrix with x and y coordinates, kriged predctions, and
  ## indicators for whether sites were sampled or not
  ## 3.) a vector of the estimated spatial parameters

  df_out <- data.frame(cbind(data, xcoordsTM, ycoordsTM,
    preddensity, pred.persite, densvar, countvar, sampind, muhat, areavar))

  # data <- data.frame(y = 1:10, x = 2:11)
  #
  # fullmf <- stats::model.frame(formula, na.action =
  #   stats::na.pass, data = data)

  colnames(df_out) <- c(colnames(data), "xcoordsTM_", "ycoordsTM_",
    paste(base::all.vars(formula)[1], "_pred_density",
      sep = ""),
    paste(base::all.vars(formula)[1], "_pred_count",
      sep = ""),
    paste(base::all.vars(formula)[1], "_predvar_density",
      sep = ""),
    paste(base::all.vars(formula)[1], "_predvar_count",
      sep = ""),
    paste(base::all.vars(formula)[1], "_sampind",
      sep = ""),
    paste(base::all.vars(formula)[1], "_muhat",
      sep = ""),
    paste(base::all.vars(formula)[1], "_areas",
      sep = ""))

  conf_bounds <- as.numeric(FPBKpredictorcount) + c(1, -1) *
    stats::qnorm((1 - conf_level) / 2) *
    sqrt(as.numeric(pred.var.count))

  names(conf_bounds) <- c("lower", "upper")

  obj <- list(FPBKpredictorcount, pred.var.count,
    df_out,
    as.vector(covparmests),
    formula = formula,
    conf_level = conf_level,
    conf_bounds = conf_bounds)

  names(obj) <- c("FPBK_Prediction", "PredVar",
    "Pred_df", "SpatialParms", "formula", "conf_level",
    "conf_bounds")

  class(obj) <- "predict.slmfit"

  return(obj)
}

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sptotal documentation built on July 6, 2021, 5:07 p.m.