| estimate_parameters {EMLI} | R Documentation |
estimate_parameters
Description
Calculates maximum likelihood estimates of the statistical measures of the output-differenced version of the one-dimensional cumulative structural equation model with shock-error output measurement equation and assumptions of normality and independence.
Usage
estimate_parameters(dat, tol)
Arguments
dat |
An (n + 1) x (m + 1) data frame of finite numeric elements (possibly except for row 1 columns 1 to m) containing observed input (columns 1 to m) and output (column m + 1) data of the original model. |
tol |
A tolerance parameter of the golden section search algorithm used for minimizing the one-dimensional likelihood function (vector of length 1, finite positive numeric element). |
Value
A list consisting of 3 elements: 1) estimate of the covariance at lag 0 of the data that result from the output-differenced model (Sigma; (m + 1) x (m + 1) matrix of numeric elements); 2) estimate of the only non-zero element of the negative covariance at lag 1 of the data that result from the output-differenced model (sigma_y^2; vector of length 1, numeric element); 3) estimate of the mean of the data that result from the output-differenced model (mu; (m + 1) x 1 matrix of numeric elements).
Examples
set.seed(1)
m <- 4
k <- 2
L <- matrix(runif((m + 1) * k, min = -10, max = 10), nrow = m + 1)
sigma <- matrix(runif(m + 2, min = 0, max = 10), nrow = m + 2)
mu <- matrix(runif(m + 1, min = -10, max = 10), nrow = m + 1)
data <- generate_data(100, L, sigma, mu)
estimate_parameters(data, 0.00001)