Hals.fastcv.snow: ALS Spacial Cross-Validation

In widals: Weighting by Inverse Distance with Adaptive Least Squares

Description

Fit Adaptive Least Squares with k-fold cross-validation

Usage

Hals.fastcv.snow(j, rm.ndx, Z, Hs, Ht, Hst.ls, GP.mx)

Arguments

j	Index used by snowfall. A scalar integer. Which row of GP.mx to use for the ALS hyperparameters, GP.
rm.ndx	A list of vectors of indices to remove for k-fold cross-validation.
Z	Data. A τ x n numeric matrix.
Hs	Spacial covariates. An n x p_s numeric matrix.
Ht	Temporal covariates. An τ x p_t numeric matrix.
Hst.ls	Space-time covariates. A list of length τ, each element containing a n x p_st numeric matrix.
GP.mx	Hyperparameters. A k.glob x 2 non-negative matrix. See MSS.snow.

Value

A τ x n numeric matrix. The ALS cross-validated predictions of Z.

See Also

Hals.snow, MSS.snow.

Examples

set.seed(99999)



library(SSsimple)

tau <- 70
n.all <- 14

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*2), 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, 0.01, 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

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

rm.ndx <- create.rm.ndx.ls(n, 10)

Zcv <- Hals.fastcv.snow(j=1, rm.ndx, Z, Hs, Ht, Hst.ls, GP.mx) 



test.rng <- 20:tau

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

widals documentation built on Dec. 8, 2019, 1:07 a.m.