Description Usage Arguments Value Elements in mh list Examples
View source: R/mcmc_functions.R
This is the basic computing function for MH and should not be called directly except by experienced users.
1 2 3 4 5 6 7 8 9 10 11 |
N |
Number of MCMC samples |
theta.init |
Vector of initial values for the parameters |
qPROP |
Function to generate proposal |
qFUN |
Probability for proposal function. First argument is where to evaluate, and second argument is the conditional parameter |
logPOSTERIOR |
Function to calculate and return the log posterior given a vector of values of |
nu |
Single value or vector parameter passed to |
varnames |
Optional vector of theta parameter names |
param |
List of additional parameters for |
... |
Additional parameters for |
List for mh
mh
listN
Number of MCMC samples
theta
Nested list of length N
of the sampled values of theta
for each chain
thetaCombined
List of dataframes containing sampled values, one for each chain
r
NULL for Metropolis-Hastings
theta.all
Nested list of all parameter values of theta
sampled prior to accept/reject step for each
r.all
NULL for Metropolis-Hastings
accept
Number of accepted proposals. The ratio accept
/ N
is the acceptance rate
accept_v
Vector of length N
indicating which samples were accepted
M
NULL for Metropolis-Hastings
algorithm
MH
for Metropolis-Hastings
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | # Logistic regression example
X <- cbind(1, seq(-100, 100, by=0.25))
betavals <- c(-0.9, 0.2)
lodds <- X %*% betavals
prob1 <- as.numeric(1 / (1 + exp(-lodds)))
set.seed(9874)
y <- sapply(prob1, function(xx) {
sample(c(0, 1), 1, prob=c(1-xx, xx))
})
f1 <- mh.fit(N = 2000,
theta.init = rep(0, 2),
nu = c(0.03, 0.001),
qPROP = qprop,
qFUN = qfun,
logPOSTERIOR = logistic_posterior,
varnames = paste0("beta", 0:1),
y=y, X=X)
f1$accept / f1$N
|
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