R/widals.predict.R

In widals: Weighting by Inverse Distance with Adaptive Least Squares

widals.predict <-
function(Z, Hs, Ht, Hst.ls, locs, lags, b.lag, Hs0, Hst0.ls, locs0, geodesic=FALSE, wrap.around=NULL, GP, stnd.d=FALSE, ltco=-16) {
    tau <- nrow(Z)
    n <- nrow(locs)
    k <- length(lags)
    n0 <- nrow(locs0)
    
    rho <- GP[ 1 ]
    reg <- GP[ 2 ]
    alpha <- GP[ 3 ]
    beta <- GP[ 4 ]
    flatten <- GP[ 5 ]
    
    locs0.3D <- cbind( locs0, rep(0, n0) )
    locs.long.3D <- cbind( rep(locs[ ,1], k), rep(locs[ ,2], k),   beta*rep( lags, each=n ) )
    
    z.lags.vec <- rep( lags, each=n )
    
    #	xalsp <- als.prepare(NULL, Z, lags, is.na(Z), tt.rng=1:nrow(Z)) #### tt.rng not used
    #	obsZatLags <- xalsp$X.sub
    #	rm(xalsp)
    
    
    ALS <- H.als.b(Z=Z, Hs=Hs, Ht=Ht, Hst.ls=Hst.ls, rho=rho, reg=reg, b.lag=b.lag, Hs0=Hs0, Ht0=Ht, Hst0.ls=Hst0.ls)
    
    Y.als <- ALS$Z.hat ; dim(Y.als)
    Y0.als <- ALS$Z0.hat ; dim(Y0.als)
    rm(ALS)
    
    #yalsp <- als.prepare(NULL, Y.als, lags, is.na(Y.als), tt.rng=1:nrow(Y.als)) #### tt.rng not used
    
    Z.delta <- Z - Y.als
    Z.delta <- Z.clean.up( Z.delta )
    
    #rm(yalsp)
    
    #	y0alsp <- als.prepare(NULL, Y0.als, lags, is.na(Y.als), tt.rng=1:nrow(Y.als)) #### tt.rng not used
    #	y0hat <- y0alsp$Y.sub
    #	rm(y0alsp)
    
    #	deltaZatLags[ is.na(deltaZatLags) ] <- mean(deltaZatLags, na.rm=TRUE)
    
    Z.adj <- crispify( locs1=locs0.3D, locs2=locs.long.3D, Z.delta=Z.delta, z.lags.vec=z.lags.vec, geodesic=geodesic,
    alpha=alpha, flatten=flatten, self.refs=c(-1), lags=lags, stnd.d=stnd.d, log10cutoff=ltco )
    
    ## sum( is.na(Z)
    
    #	Y0.idw <- y0hat  +  Z.adj
    #	Z0.idw <- y.unalign(Y0.idw, lags)
    
    Z0.wid <- Y0.als + Z.adj
    
    #	assign( "ALS.Y0", y0hat, pos=.GlobalEnv )
    #	assign( "ALS.actual", obsZatLags, pos=.GlobalEnv )
    #	assign( "ALS.pred", predZatLags, pos=.GlobalEnv )
    
    return(Z0.wid)
}