Hals.snow {widals}

Fit ALS

Description

Fit Adaptive Least Squares

Usage

Hals.snow(j, Z, Hs, Ht, Hst.ls, b.lag, GP.mx)

Arguments

j

Index used by snowfall. A scalar integer. Which row of GP.mx to use for the ALS hyperparameters, GP.

Z

Data. A \tau x n numeric matrix.

Hs

Spacial covariates. An n x p_s numeric matrix.

Ht

Temporal covariates. An \tau x p_t numeric matrix.

Hst.ls

Space-time covariates. A list of length \tau, each element containing a n x p_st numeric matrix.

b.lag

ALS lag. A scalar integer, typically -1 (a-prior), or 0 (a-posteriori).

GP.mx

Hyperparameters. A k.glob x 2 non-negative matrix. See MSS.snow.

Value

A \tau x n numeric matrix. The ALS predictions of Z.

See Also

Hals.fastcv.snow, MSS.snow.

Examples

set.seed(9999)


library(SSsimple)

tau <- 280
n.all <- 35

Hs.all <- matrix(rnorm(n.all), nrow=n.all)
Ht <- matrix(rnorm(tau*2), nrow=tau)
Hst.ls.all <- list()
for(i in 1:tau) { Hst.ls.all[[i]] <- matrix(rnorm(n.all*3), nrow=n.all) }

Hst.combined <- list()
for(i in 1:tau) { 
    Hst.combined[[i]] <- cbind( Hs.all, matrix(Ht[i, ], nrow=n.all, 
    ncol=ncol(Ht), byrow=TRUE), Hst.ls.all[[i]] ) 
}

######## use SSsimple to simulate
sssim.obj <- SS.sim.tv( 0.999, Hst.combined, 0.1, diag(1, n.all), tau )



Z.all <- sssim.obj$Z
Z <- Z.all
n <- n.all

Hst.ls <- Hst.ls.all

Hs <- Hs.all

xrho <- 1/10
xreg <- 1/10
b.lag <- -1

GP.mx <- matrix(c(xrho, xreg), nrow=1)

Zcv <- Hals.snow(j=1, Z, Hs, Ht, Hst.ls, b.lag, GP.mx) 


test.rng <- 20:tau

errs.sq <- (Z - Zcv)^2
sqrt( mean(errs.sq[test.rng, ]) )