Description Usage Arguments Value Examples
sample_from_constraints
returns a sample from the conditional
multivariate normal, restricted by affine constraints.
The constraints are coded by a linear matrix and an offset vector:
linear_part %*% Z <= offset.
The sampling uses a Gibbs sampler, and requires an initial vector
that meets the restriction.
1 2 | sample_from_constraints(linear_part, offset, mean_param, covariance,
initial_point, ndraw = 8000, burnin = 2000)
|
linear_part |
r x d matrix for r restrictions and d dimension of Z |
offset |
r-dim vector of offsets |
mean_param |
d-dim mean vector of the unconditional normal |
covariance |
d x d covariance matrix of unconditional normal |
initial_point |
d-dim vector that initializes the sampler (must meet restrictions) |
ndraw |
size of sample |
burnin |
samples to throw away before storing |
Z ndraw x d matrix of samples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | # Compute conditional mean of correlated lower-truncated vector
constr = thresh2constraints(3, lower = c(1,1,1))
covariance = matrix(c(1,0.5,0,0.5,1,0.5,0,0.5,1),nc=3)
samp = sample_from_constraints(linear_part = constr$linear_part,
offset= constr$offset,
mean_param = c(0,0,0),
covariance = covariance,
initial_point = c(1.5,1.5,1.5),
ndraw=500,
burnin=2000)
# all points should be >= 1
any(samp<1)
colMeans(samp)
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