# unload.Hst.ls: Convert a Space-Time Covariate into Data In widals: Weighting by Inverse Distance with Adaptive Least Squares for Massive Space-Time Data

## Description

Convert a spacio-temporal covariate into contemporaneous data

## Usage

 `1` ```unload.Hst.ls(Hst.ls, which.col, rgr.lags) ```

## Arguments

 `Hst.ls` Space-time covariates. A list of length τ, each element should be a n x p_st numeric matrix. `which.col` Which column of `Hst.ls[[i]]` to insert into the ith row of `Z`. A scalar positive integer. `rgr.lags` Temporal lagging of `Z`. A scalar integer.

## Value

A numeric τ x n matrix.

`load.Hst.ls.Z`, `load.Hst.ls.2Zs`.
 ``` 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``` ``` ###### here's an itty-bitty example tau <- 7 n <- 5 Hst.ls <- list() for(i in 1:tau) { Hst.ls[[i]] <- matrix(rnorm(n*4), nrow=n) } Zh <- unload.Hst.ls(Hst.ls, 1, 0) ## The function is currently defined as function (Hst.ls, which.col, rgr.lags) { n <- nrow(Hst.ls[[1]]) tau <- length(Hst.ls) Z.out <- matrix(NA, tau, n) min.ndx <- max(1, -min(rgr.lags) + 1) max.ndx <- min(tau, tau - max(rgr.lags)) for (i in min.ndx:max.ndx) { Z.out[i - rgr.lags, ] <- Hst.ls[[i]][, which.col] } return(Z.out) } ```