fdp_staplmer.fit | R Documentation |
Functional Dirichlet Process Spatial Temporal Aggregated Predictor Linear Mixed Effects Regression Model Fit
fdp_staplmer.fit(
y,
Z,
X,
W,
subj_mat,
subj_n,
weights = rep(1, length(y)),
alpha_a = 1,
alpha_b = 1,
sigma_a = 1,
sigma_b = 1,
tau_a = 1,
tau_b = 1,
K = 5L,
rank_one,
rank_two,
threshold = 0L,
iter_max,
burn_in,
thin = 1L,
fix_alpha = FALSE,
bw = FALSE,
seed = NULL,
chain = 1L,
logging = FALSE
)
y |
vector of outcomes |
Z |
design matrix |
X |
stap design matrix |
W |
group terms design matrix from |
subj_mat |
matrix indexing subject-measurement locations in (Z,X,W) |
subj_n |
vector of number of subject measurements |
weights |
weights for weighted regression - default is vector of ones |
alpha_a |
alpha gamma prior hyperparameter |
alpha_b |
alpha gamma prior hyperparameter |
sigma_a |
precision gamma prior hyperparameter |
sigma_b |
precision gamma prior hyperparameter |
tau_a |
penalty parameters gamma prior hyperparameter |
tau_b |
penalty parameters gamma prior hyperparameter |
K |
truncation number for DP mixture components |
rank_one |
rank of first smoothing matrix |
rank_two |
rank of second smoothing matrix |
threshold |
number of members per cluster at which cluster is included in regression |
iter_max |
maximum number of iterations |
burn_in |
number of iterations to burn-in |
thin |
number by which to thin samples |
fix_alpha |
boolean value |
bw |
boolean value indicating whether or not subject decomposition is used |
seed |
random number generator seed will be set to default value if not by user |
chain |
chain label |
logging |
boolean parameter indicating whether or not a single iteration should be run with print messages indicating successful completion of the Sampler's sub modules |
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