R: Multivariate version of 'coda"s 'spectrum0.ar()'.

spectrum0.mvar {ergm} | R Documentation

## Multivariate version of `coda`'s `spectrum0.ar()`.

### Description

Its return value, divided by `nrow(cbind(x))`, is the estimated variance-covariance matrix of the sampling distribution of the mean of `x` if `x` is a multivatriate time series with AR(p) structure, with p determined by AIC.

### Usage

    spectrum0.mvar(
      x,
      order.max = NULL,
      aic = is.null(order.max),
      tol = .Machine$double.eps^0.5,
      ...
    )

### Arguments

`x` | a matrix with observations in rows and variables in columns.
`order.max` | maximum (or fixed) order for the AR model.
`aic` | use AIC to select the order (up to `order.max`).
`tol` | drop components until the reciprocal condition number of the transformed variance-covariance matrix is greater than this.
`...` | additional arguments to `ar()`.

### Value

A square matrix with dimension equalling to the number of columns of `x`, with an additional attribute `"infl"` giving the factor by which the effective sample size is reduced due to autocorrelation, according to the Vats, Flegal, and Jones (2015) estimate for ESS.

### Note

`ar()` fails if `crossprod(x)` is singular, which is remedied by mapping the variables onto the principal components of `x`, dropping redundant dimentions.

[Package ergm version 4.1.2 Index]