# stnd.Hs: Standardize Spacial Covariates In widals: Weighting by Inverse Distance with Adaptive Least Squares for Massive Space-Time Data

## Description

Standardize spacial covariates with respect to both the space and time dimensions

## Usage

 `1` ```stnd.Hs(Hs, Hs0 = NULL, intercept = TRUE) ```

## Arguments

 `Hs` Spacial covariates (of supporting sites). An n x p_s numeric matrix. `Hs0` Spacial covariates (of interpolation sites). An n* x p_s numeric matrix. `intercept` Include intercept term? Boolean.

## Value

A named list.

 `sHs` An n x p_s numeric matrix. `sHs0` An n* x p_s numeric matrix. `h.mean` The covariates' mean over space. `h.sd` The covariates' standard deviation over space. `n` Number of support sites. `intercept` The supplied intercept argument.

`stnd.Ht`, `stnd.Hst.ls`, `applystnd.Hs`.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28``` ```##### Please see the examples in Hst.sumup ## The function is currently defined as function (Hs, Hs0 = NULL, intercept = TRUE) { n <- nrow(Hs) h.mean <- apply(Hs, 2, mean) h.sd <- apply(t(t(Hs) - h.mean), 2, function(x) { sqrt(sum(x^2)) }) h.sd[h.sd == 0] <- 1 sHs <- t((t(Hs) - h.mean)/h.sd) if (intercept) { sHs[, 1] <- 1/sqrt(n) } sHs0 <- NULL if (!is.null(Hs0)) { sHs0 <- t((t(Hs0) - h.mean)/h.sd) if (intercept) { sHs0[, 1] <- 1/sqrt(n) } } ls.out <- list(sHs = sHs, sHs0 = sHs0, h.mean = h.mean, h.sd = h.sd, n = n, intercept = intercept) return(ls.out) } ```