get_elts_gauss: The R implementation to get the elements necessary for...
In sqyu/genscore: Generalized Score Matching Estimators

Description Usage Arguments Details Value Examples

The R implementation to get the elements necessary for calculations for the gaussian setting on R^p.

get_elts_gauss(
  x,
  centered = TRUE,
  profiled_if_noncenter = TRUE,
  scale = "",
  diagonal_multiplier = 1
)

`x`	An `n` by `p` matrix, the data matrix, where `n` is the sample size and `p` the dimension.
`centered`	A boolean, whether in the centered setting (assume μ=η=0) or not. Default to `TRUE`.
`profiled_if_noncenter`	A boolean, whether in the profiled setting (λ_η=0) if non-centered. Parameter ignored if `centered==TRUE`. Default to `TRUE`.
`scale`	A string indicating the scaling method. Returned without being checked or used in the function body. Default to `"norm"`.
`diagonal_multiplier`	A number >= 1, the diagonal multiplier.

For details on the returned values, please refer to get_elts_ab or get_elts.

A list that contains the elements necessary for estimation.

`n`	The sample size.
`p`	The dimension.
`centered`	The centered setting or not. Same as input.
`scale`	The scaling method. Same as input.
`diagonal_multiplier`	The diagonal multiplier. Same as input.
`diagonals_with_multiplier`	A vector that contains the diagonal entries of Γ after applying the multiplier.
`setting`	The setting `"gaussian"`.
`Gamma_K`	The Γ matrix with no diagonal multiplier. In the non-profiled non-centered setting, this is the Γ sub-matrix corresponding to K. Except for the profiled setting, this is xx'/n.
`Gamma_K_eta`	Returned in the non-profiled non-centered setting. The Γ sub-matrix corresponding to interaction between K and η. The minus column means of `x`.
`t1,t2`	Returned in the profiled non-centered setting, where theη estimate can be retrieved from t1-t2\hat{K}* after appropriate resizing.

n <- 50
p <- 30
mu <- rep(0, p)
K <- diag(p)
x <- mvtnorm::rmvnorm(n, mean=mu, sigma=solve(K))
# Equivalently:

x2 <- gen(n, setting="gaussian", abs=FALSE, eta=c(K%*%mu), K=K, domain=make_domain("R",p),
       finite_infinity=100, xinit=NULL, burn_in=1000, thinning=100, verbose=FALSE)

elts <- get_elts_gauss(x, centered=TRUE, scale="norm", diag=1.5)
elts <- get_elts_gauss(x, centered=FALSE, profiled=FALSE, scale="sd", diag=1.9)