Update utility threshold increments

Usage

update_d(d, y, sys, ll, mu_d_0, Sigma_d_0, Tvec, step_scale = 0.1)

Arguments

d: [numeric(J - 2)]
Threshold increments.
y: [matrix(nrow = N, ncol = max(Tvec))]
Choices 1,...,J for each decider in each choice occasion.
sys: [matrix(nrow = N, ncol = max(Tvec))]
Systematic utilties for each decider in each choice occasion.
ll: [numeric(1)]
Current log-likelihood value.
mu_d_0: [numeric(J - 2)]
The mean vector of the normal prior for d .
Sigma_d_0: [matrix(J - 2, J - 2)]
The covariance matrix of the normal prior for d.
Tvec: [integer(N)]
Number of choice occasions per decider.
step_scale: [numeric(1)]
Scaling the variance for the Gaussian proposal.

Value

An update for d.

Examples

set.seed(1)
N <- 1000
d_true <- rnorm(2)
gamma <- c(-Inf, 0, cumsum(exp(d_true)), Inf)
X <- matrix(rnorm(N * 2L), ncol = 2L)
beta <- c(0.8, -0.5)
mu <- matrix(as.vector(X %*% beta), ncol = 1L)
U <- rnorm(N, mean = mu[, 1], sd = 1)
yvec <- as.integer(cut(U, breaks = gamma, labels = FALSE))
y <- matrix(yvec, ncol = 1L)
Tvec <- rep(1, N)
mu_d_0 <- c(0, 0)
Sigma_d_0 <- diag(2) * 5
d <- rnorm(2)
ll <- -Inf
R <- 1000
for (iter in seq_len(R)) {
  ans <- update_d(
    d = d, y = y, sys = mu, ll = ll, mu_d_0 = mu_d_0, Sigma_d_0 = Sigma_d_0,
    Tvec = Tvec
  )
  d  <- ans$d
  ll <- ans$ll
}
cbind("true" = d_true, "sample" = d) |> round(2)
#>       true      
#> [1,] -0.63 -0.83
#> [2,]  0.18  0.17