Library

BBml(e, W::T Xa::T, ΣA::AbstractMatrix, BI::T) where T<:AbstractMatrix

Compute the covariance matrix of the weighted TLS solution ($\Sigma_B$), using a Sandwich estimator. Requires the computed error vector e and it's weight matrix W, the predictor matrix Xa (with intercept term) and its covariance matrix ΣA, and $BI = B ⊗ I_n$.

Bmem(Y::AbstractVector, X::AbstractMatrix, Ωv::AbstractMatrix, ΣA::AbstractMatrix; Bprior=MvNormal(I(size(X,2)+1)))

Compute a quasi-Bayesian posterior for the Measurement Error Model (one iteration). Requires the response vector Y and the predictor matrix X (without intercept term), and their respective covariance matrices Ωv and ΣA.

Bml(Y::AbstractVector, X::AbstractMatrix, Ωv::AbstractMatrix, ΣA::AbstractMatrix; Bprior=MvNormal(I(size(X,2)+1)))

Compute the weighted TLS solution {$B_\text{ml}$}, using a fixed point iteration. Requires the response vector Y and the predictor matrix X (without intercept term), and their respective covariance matrices Ωv and ΣA.

Bout(D::DataFrame; Bprior=MvNormal(size(D,2)-1), lags=0:25, ycol=:ya)

Compute a quasi-Bayesian posterior for the Measurement Error Model (two-step iteration). Requires a DataFrame D containing all variables, the name of the response variable can be specified by ycol. lags are the lags retained for modelling the lagged covariance matrix of the predictor noise, $\Sigma_A$.

makeprior(Y::AbstractVector, X::AbstractMatrix, Bprior=MvNormal(I(size(D,2)-1)); ip=1.0)

Construct a prior with an informed intercept from Y, X and Bprior, where Bprior is the distribution of the predictor coefficients (not including the intercept). A prior for the intercept is calculated using Y, X and Bprior. ip scales the estimated variance of the intercept.

makeprior(D::DataFrame, Bprior=MvNormal(I(size(D,2)-1)); ycol=:ya, ip=1.0)

Construct a prior with an informed intercept from a dataframe D. ycol is the column name of the response variable. Bprior is the distribution of the predictor coefficients (not including the intercept). ip scales the estimated variance of the intercept.

OmegaV(V::AbstractVector, εV::AbstractVector; lags=0:25)

Estimate covariance matrix $\Omega_V$ from the error vector V, subject to known εV. $n$-length vector εV is the heteroscedastic standard deviation of $V$. The underlying matrix Ω is assumed to be toeplitz for lags lags.

SigmaA(A::AbstractMatrix, εA::AbstractMatrix; lags=0:25)

Estimate $\Sigma_A$ from the error field A, subject to known εA. The underlying Ω matrices are assumed to be toeplitz for lags lags.