Description Usage Arguments Details Value Examples
Uses a built ikde.model to draw samples from posterior distribution using Stan
1 2 3 |
ikde.model |
An object of class ikde.model which has been built |
burn.iter |
Number of warmup iterations |
sample.iter |
Number of sampling iterations |
chains |
Number of independent chains to use |
control |
Control parameters used in the Markov chain. See ?rstan::stan for details. |
refresh |
How frequently should progress be reported, in numbers of iterations |
display.output |
Boolean indicating whether output from rstan::stan should be printed |
Takes a built ikde.model object, which contains model DSO, and fits the model using rstan::stan.
An object of S4 class stanfit. See rstan::stan for more details.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | data(lm.generated)
X <- lm.generated$X
y <- lm.generated$y
data <- list(N = list(type = "int<lower=1>", dim = 1, value = nrow(X)),
k = list(type = "int<lower=1>", dim = 1, value = ncol(X)),
X = list(type = "matrix", dim = "[N, k]", value = X),
y = list(type = "vector", dim = "[N]", value = y))
parameters <- list(beta = list(type = "vector", dim = "[k]"),
sigma_sq = list(type = "real<lower=0>", dim = 1))
model <- list(priors = c("beta ~ normal(0, 10);",
"sigma_sq ~ inv_gamma(1, 1);"),
likelihood = c("y ~ normal(X * beta, sqrt(sigma_sq));"))
ikde.model <- define.model(data, parameters, model)
ikde.model <- build.model(ikde.model)
stan.fit <- fit.model(ikde.model)
stan.extract <- extract(stan.fit)
# Only an estimation, may not exactly match presented result
print(apply(stan.extract$beta, 2, mean))
# [1] 3.199021 1.620546 4.489716 1.226508
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