widals (version 0.6.2)

Hst.sumup: Create Covariance Matrix

Description

Calculate the covariance matrix of all model covariates

Usage

Hst.sumup(Hst.ls, Hs = NULL, Ht = NULL)

Value

A \((p_s+p_t+p_st)\) x \((p_s+p_t+p_st)\) numeric, symmetrix, non-negative definite matrix.

Arguments

Hst.ls

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

Hs

Spacial covariates. An \(n\) x \(p_s\) numeric matrix.

Ht

Temporal covariates. An \(\tau\) x \(p_t\) numeric matrix.

Details

Important: The order of the arguments in this function is NOT the same as in the returned covariance matrix. The order in the covariance matrix is the same as in other functions in this package: Hs, Ht, Hst.ls.

Examples

	
tau <- 20
n <- 10
Ht <- cbind(sin(1:tau), cos(1:tau))

Hs <- cbind(rnorm(10), rnorm(n, 5, 49))

Hst.ls <- list()
for(tt in 1:tau) {
Hst.ls[[tt]] <- cbind(rnorm(n, 1, 0.1), rnorm(n, -200, 21))
}


Hst.sumup(Hst.ls, Hs, Ht)



########### standardize all covariates

x1 <- stnd.Hst.ls(Hst.ls, NULL)$sHst.ls
x2 <- stnd.Hs(Hs, NULL, FALSE)$sHs
x3 <- stnd.Ht(Ht, n)


Hst.sumup(x1, x2, x3)



## The function is currently defined as
function (Hst.ls, Hs = NULL, Ht = NULL) 
{
    tau <- length(Hst.ls)
    if(tau < 1) { tau <- nrow(Ht) }
    if(is.null(tau)) { tau <- 10 ; cat("tau assumed to be 10.", "\n") }
    n <- nrow(Hst.ls[[1]])
    if(is.null(n)) { n <- nrow(Hs) }
    big.sum <- 0
    for (i in 1:tau) {
        if (!is.null(Ht)) {
            Ht.mx <- matrix(Ht[i, ], n, ncol(Ht), byrow = TRUE)
        }
        else {
            Ht.mx <- NULL
        }
        big.sum <- big.sum + crossprod(cbind(Hs, Ht.mx, Hst.ls[[i]]))
    }
    return(big.sum)
  }