#
# fields  is a package for analysis of spatial data written for
# the R software environment.
# Copyright (C) 2022 Colorado School of Mines
# 1500 Illinois St., Golden, CO 80401
# Contact: Douglas Nychka,  douglasnychka@gmail.edu,
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with the R software environment if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA
# or see http://www.r-project.org/Licenses/GPL-2
##END HEADER
"Krig" <- function(x, Y, cov.function = "stationary.cov", 
                   lambda = NA, df = NA, GCV = FALSE, Z = NULL, cost = 1,
                   weights = NULL, m = 2, nstep.cv = 200, scale.type = "user", 
                   x.center = rep(0, ncol(x)), x.scale = rep(1, ncol(x)), sigma = NA, 
                   tau2 = NA, method = "REML", verbose = FALSE,
                   null.function = "Krig.null.function", wght.function = NULL, 
                   offset = 0,  na.rm = TRUE, cov.args = NULL, 
                   chol.args = NULL, null.args = NULL, wght.args = NULL, W = NULL, 
                   give.warnings = TRUE,
                   mean.obj = NA, 
                   sd.obj = NA,
                   ...)
  # the verbose switch prints many intermediate steps as an aid in debugging.
  #
{ 
  #
  # create output list
  out <- list()
  ###########################################################
  #  First series of steps simply store pieces of the passed
  #    information to the output list (i.e. the Krig object)
  ##########################################################
  out$call <- match.call()
  #   turn off warning based on options
  if( options()$warn < 0 ){
    give.warnings<- FALSE
  }
  if( !is.na(mean.obj)|!is.na(sd.obj)){
    stop( "correlation model no longer supported please use 
          fields version 11.6")
    
  } 
  #
  # save covariance function as its name
  #
  if( !is.character( cov.function)){
    out$cov.function.name <- as.character(substitute(cov.function))
  }
  else{ 
    out$cov.function.name<-cov.function
  } 
  #
  # save null space function as its name
  #
  out$null.function.name <- as.character(substitute(null.function))
  #
  # save weight  function as its name if it is not a NULL
  #
  if (is.null(wght.function)) {
    out$wght.function.name <- NULL
  }
  else {
    out$wght.function.name <- as.character(substitute(wght.function))
  }
  out$W <- W
  if (verbose) {
    print(out$cov.function.name)
    print(out$null.function.name)
    print(out$wght.function.name)
  }
  #
  # logical to indicate if the 'C' argument is present in cov.function
  # -- a bit of esoteric R code!
  C.arg.missing <- all(names(formals(get(out$cov.function.name))) != 
                         "C")
  if (C.arg.missing) 
    stop("Need to have C argument in covariance function\nsee Exp.cov.simple as an example")
  #
  # save parameters values possibly passed to the covariance function
  # also those added to call are assumed to be covariance arguments.
  if (!is.null(cov.args)) 
    out$args <- c(cov.args, list(...))
  else out$args <- list(...)
  #
  # default values for null space function
  out$null.args <- null.args
  #
  #       set degree of polynomial null space if this is default
  #       mkpoly is used so often is it helpful to include m argument
  #       by default in Krig call.
  if (out$null.function.name == "Krig.null.function") {
    out$null.args <- list(m = m)
    out$m <- m
  }
  #
  # default values for Cholesky decomposition, these are important
  # for sparse matrix decompositions used in Krig.engine.fixed.
  if (is.null(chol.args)) {
    out$chol.args <- list(pivot = FALSE)
  }
  else {
    out$chol.args <- chol.args
  }
  # additional arguments for weight matrix.
  out$wght.args <- wght.args
  #
  # the offset is the effective number of parameters used in the GCV
  # calculations -- unless this is part of an additive model this
  # is likely zero
  out$offset <- offset
  #
  # the cost is the multiplier applied to the GCV eff.df
  # 
  # lambda and df are two ways of parameterizing the smoothness
  # and are related by a monotonic function that unfortunately
  # depends on the locations of the data.
  # lambda can be used directly in the linear algebra, df
  # must be transformed to lambda numerically using the monotonic trransformation
  # tau2 is the error variance and sigma the multiplier for the covariance
  # method is how to determine lambda
  # the GCV logical forces the code to do the more elaborate decompositions
  # that faclitate estimating lambda -- even if a specific lambda value is
  # given.
  out$cost <- cost
  out$lambda <- lambda
  out$eff.df <- df
  out$tau2 <- tau2
  out$sigma <- sigma
  out$method <- method
  out$GCV <- GCV
  #
  # correlation model information
  # set this to FALSE -- has been depreciated.
  #
  out$correlation.model <- FALSE
  #
  # transformation info
  out$scale.type <- scale.type
  out$x.center <- x.center
  out$x.scale <- x.scale
  #
  if (verbose) {
    cat("  Cov function arguments in call  ", fill = TRUE)
    print(out$args)
    cat(" covariance function used is : ", fill = TRUE)
    print(out$cov.function.name)
  }
  ###############################################################
  # Begin modifications and transformations of input information
  # note that many of these manipulations follow a strategy
  # of passing the Krig object (out) to a function and
  # then appending the information from this function to
  # the Krig object (usually also called "out").
  #In this way the Krig object  is built up
  # in steps and the process is easier to follow.
  ###############################################################
  # various checks on x and  Y including removal of NAs in Y
  # Here is an instance of adding to the Krig object
  # in this case also some onerous bookkeeping making sure arguments are consistent
  out2 <- Krig.check.xY(x, Y, Z, weights, na.rm, verbose = verbose)
  out <- c(out, out2)
  # find replicates and collapse to means and pool variances.
  # Transform unique x locations 
  out2 <- Krig.transform.xY(out,  verbose = verbose)
  
  out <- c(out, out2)
  
  # NOTE: knots have been transformed after this step
  #############################################################
  #  Figure out what to do
  #############################################################
  #
  # this functions works through the logic of
  # what has been supplied for lambda
  out2 <- Krig.which.lambda(out)
  out[names(out2)] <- out2  
  # Make weight matrix for observations
  #    ( this is proportional to the inverse square root of obs covariance)
  #     if a weight function or W has not been passed then this is
  #     diag( out$weightsM) for W
  #     The checks represent a limitation of this model to
  #     the  WBW type decoposition and no replicate observations.
  out$nondiag.W <- (!is.null(wght.function)) | (!is.null(W))
  # Do not continue if there there is a nondiagonal weight matrix
  # and replicate observations.
  if (out$nondiag.W) {
    if (out$knot.model | out$fixed.model) {
      stop("Non diagonal weight matrix for observations
                    not supported\nwith knots or fixed lambda.")
    }
    if (!is.na(out$tauHat.pure.error)) {
      stop("Non diagonal weight matrix not implemented
                    with replicate locations")
    }
  }
  #  make weight matrix and its square root having passed checks
  out <- c(out, Krig.make.W(out, verbose = verbose))
  #   Do the intensive linear algebra to find the solutions
  #   this is where all the heavy lifting happens.
  #
  #   Note that all the information is passed as a list
  #   including arguments to the cholesky decomposition
  #   used within Krig.engine.fixed
  #
  # The results are saved in the component matrices
  #
  # if method=='user' then just evaluate at single lambda
  #  fixed here means a fixed lambda
  #
  # For fixed lambda the decompositions with and without knots
  # are surprisingly similar and so are in one engine.
  ###########################################################
  if (out$fixed.model) {
    out$matrices <- Krig.engine.fixed(out, verbose = verbose)
    #  The trace of A matrix in fixed lambda case is not easily computed
    #  so set this to NA.
    out$eff.df <- NA
  }
  # alternative are
  # matrix decompositions suitable for
  # evaluation at many lambdas to facilitate GCV/REML estimates  etc.
  #
  else{
  # standard engine following the basic computations for thin plate splines
      out$matrices <- Krig.engine.default(out, verbose = verbose)
  # store basic information about decompositions
  }
  out$nt <- out$matrices$nt
  out$np <- out$matrices$np
  out$decomp <- out$matrices$decomp
  #
  # Now determine a logical vector to indicate coefficients tied to  the
  # the 'spatial drift' i.e. the fixed part of the model
  # that is not due to the Z covariates.
  # NOTE that the spatial drift coefficients must be the first columns of the
  # M matrix
  if (is.null(out$Z)) {
    out$ind.drift <- rep(TRUE, out$nt)
  }
  else {
    
    mZ <- ncol(out$ZM)
    out$ind.drift <- c(rep(TRUE, out$nt - mZ), rep(FALSE, 
                                                   mZ))
  }
  if (verbose) {
    cat("null df: ", out$nt, "drift df: ", sum(out$ind.drift), 
        fill = TRUE)
  }
  #################################################
  # Do GCV and REML search over lambda if not fixed or if GCV variable is TRUE
  #  also does a search over likelihood for lambda.
  #################################################
  if (!out$fixed.model | out$GCV) {
    if (verbose) {
      cat("call to KrigFindLambda", fill = TRUE)
    }
    gcv.out <- KrigFindLambda(out, nstep.cv = nstep.cv, verbose = verbose, 
                        cost = out$cost, offset = out$offset, give.warnings=FALSE)
    out$gcv.grid <- gcv.out$gcv.grid
    #   save a handy summary table of the search results
    out$lambda.est <- gcv.out$lambda.est
    out$warningTable<- gcv.out$warningTable
    if( verbose){
      cat("summaries from grid search/optimization", fill=TRUE)
      print(out$lambda.est)
      print(out$warningTable)
    }
    if( give.warnings){
      #NOTE: only print out grid search warning forthe method of interest.
      printGCVWarnings( gcv.out$warningTable, method=method)
    }
    # assign the preferred lambda either from GCV/REML/MSE or the user value
    # NOTE: gcv/reml can be done but the estimate is
    # still evaluted at the passed user values of lambda (or df)
    # If df is passed need to calculate the implied lambda value
    if (out$method != "user") {
      out$lambda <- gcv.out$lambda.est[out$method, 1]
      out$eff.df <- out$lambda.est[out$method, 2]
    }
    else {
      if (!is.na(out$eff.df)) {
        out$lambda <- Krig.df.to.lambda(out$eff.df, out$matrices$D)
      }
      else {
        out$eff.df <- Krig.ftrace(out$lambda, out$matrices$D)
      }
    }
  }
  ##########################
  # end GCV/REML block
  ##########################
  #
  # Now we clean up what has happened and stuff 
  # information into output object.
  #
  ##########################################
  # find coeficients at prefered lambda
  # and evaluate the solution at observations
  ##########################################
  #   pass replicate group means -- no need to recalculate these.
  out2 <- Krig.coef(out, yM = out$yM, verbose = verbose)
  out <- c(out, out2)
  #######################################################################
  # fitted values and residuals and predicted values for full model and
  # also on the null space (fixed
  # effects). But be sure to do this at the nonmissing x's.
  ##################################################################
  out$fitted.values <- predict.Krig(out, x = out$x, Z = out$Z, 
                                    eval.correlation.model = FALSE)
  out$residuals <- out$y - out$fitted.values
  #
  # this is just M%*%d  note use of do.call using function name
  Tmatrix <- do.call(out$null.function.name, c(out$null.args, 
                                               list(x = out$x, Z = out$Z)))
  out$fitted.values.null <- as.matrix(Tmatrix) %*% out$d
  #
  # verbose block
  if (verbose) {
    cat("residuals", out$residuals, fill = TRUE)
  }
  #
  # find various estimates of tau and sigma
  out2 <- Krig.parameters(out)
  out <- c(out, out2)
  ################################################
  # assign the 'best' model as a default choice
  # either use the user supplied values or the results from
  # optimization
  ################################################
  passed.tau2 <- (!is.na(out$tau2))
  if (out$method == "user" & passed.tau2) {
    out$best.model <- c(out$lambda, out$tau2, out$sigma)
  }
  else {
    # in this case lambda is from opt. or supplied by user
    out$best.model <- c(out$lambda, out$tauHat.MLE^2, out$sigmahat)
  }
  # Note: values in best.model are used in subsquent functions as the choice
  # for these parameters!
  # set class
  
##########################
  out$rhohat<- out$sigmahat
  class(out) <- c("Krig")
  return(out)
}
