applystnd.Hst.ls: Standardize Space-Time Covariates with Existing Object In widals: Weighting by Inverse Distance with Adaptive Least Squares for Massive Space-Time Data

Description

Standardize spacio-temporal covariates with respect to both the space and time dimensions

Usage

 `1` ```applystnd.Hst.ls(Hst0.ls, x) ```

Arguments

 `Hst0.ls` Space-time covariates (of interpolation sites). A list of length τ, each element should be a n* x p_st numeric matrix. `x` Space-time standardization object, as created by `stnd.Hst.ls`.

Value

An unnamed list of length τ, each element a n* x p_st numeric matrix.

Examples

 ``` 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 29 30 31 32 33 34 35 36 37 38``` ``` tau <- 20 n.all <- 10 Hst.ls.all <- list() for(tt in 1:tau) { Hst.ls.all[[tt]] <- cbind(rnorm(n.all, 1, 0.1), rnorm(n.all, -200, 21)) } ndx.interp <- c(1,3,5) ndx.support <- I(1:n.all)[ -ndx.interp ] Hst.ls <- subsetsites.Hst.ls(Hst.ls.all, ndx.support) xsnst.obj <- stnd.Hst.ls(Hst.ls) Hst0.ls <- subsetsites.Hst.ls(Hst.ls.all, ndx.interp) sHst0.ls <- applystnd.Hst.ls(Hst0.ls, xsnst.obj) Hst.sumup(xsnst.obj\$sHst.ls) Hst.sumup(sHst0.ls) ## The function is currently defined as function (Hst0.ls, x) { tau <- length(Hst0.ls) sHst0.ls <- list() for (i in 1:tau) { sHst0.ls[[i]] <- t((t(Hst0.ls[[i]]) - x\$h.mean)/x\$h.sd) } return(sHst0.ls) } ```

widals documentation built on May 29, 2017, 10:11 p.m.