kfLLH: Computes the data distribution

In ilyaZar/RcppSMCkalman: Kalman Filtering and Backward Simulation via RcppSMC

Computes the data distribution

Description

The data distribution, i.e. the (log-)likelihood, given all parameters \theta=A, B, C, D, P, Q, as described in Data distribution - observed likelihood computation of the Details section from kfLGSSM.

Usage

kfLLH(yObs, wReg, xtt1, Ptt1, C, D, R, dimX, dimY, TT, LOG = TRUE)

Arguments

yObs	A matrix or vector of measurements (observations): rows: multivariate dimension columns: time series dimension T If Y is a univariate process, yObs can be passed as a vector of length T. If nrow(yObs) = 1, then yObs becomes a vector of length T.
wReg	Matrix (vector) of regressors for the measurement process of dimension ncol(D) x T. For a single regressors wReg is a vector of length T.
xtt1	predictive means as produced by kfMFPD (if PDSTORE = TRUE)
Ptt1	predictive variances as produced by kfMFPD (if PDSTORE = TRUE)
C	Parameter (or system) matrix of dimension dimY x dimX.
D	Parameter (or system) matrix of dimension dimY x numW.
R	Error VCM of measurement process of dimension dimY x dimY
dimX	integer giving the dimension of the latent state process
dimY	integer giving the dimension of the measurement process
TT	integer giving the length of the time series
LOG	logical; if TRUE, then the logarithm of the likelihood is returned

Value

the (logarithmic, if LOG=TRUE) value of the data likelihood

ilyaZar/RcppSMCkalman documentation built on Oct. 19, 2023, 11 a.m.