Description Usage Arguments Value Mathematical formula See Also Examples
bpr_likelihood
evaluates the Binomial distributed Probit regression
log likelihood function for a given set of coefficients, observations and
a design matrix.
1 | bpr_likelihood(w, H, data, is_NLL = FALSE)
|
w |
A vector of parameters (i.e. coefficients of the basis functions) |
H |
The |
data |
An |
is_NLL |
Logical, indicating if the Negative Log Likelihood should be returned. |
the log likelihood
The Binomial distributed Probit regression log likelihood function is computed by the following formula:
log p(y | f, w) = ∑_{l=1}^{L} log Binom(m_{l} | t_{l}, Φ(w^{t}h(x_{l})))
where h(x_l) are the basis functions.
1 2 3 4 5 6 7 | obj <- polynomial.object(M=2)
obs <- c(0,.2,.5, 0.6)
des_mat <- design_matrix(obj, obs)
H <- des_mat$H
w <- c(.1,.1,.1)
data <- matrix(c(10,12,15,7,9,8), ncol=2)
lik <- bpr_likelihood(w, H, data)
|
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