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Parallel TDMM fitting wrapper

tdmm.parallel.Rd

Fit TDMM with parallel chains through tdmm().

Usage

tdmm.parallel(data, family = c("gaussian", "bernoulli", "poisson"), nknots = NULL, subject.var = "subject.ID", 
    time.var = "time", y.var = "y", x.var = NULL, degree = 3, n.chains = 3, n.iter = 10000, n.burn = 3000, 
    n.thin = 5, n.adapt = 10000, n.cores = min(n.chains, parallel::detectCores()), jags.dir = NULL, quiet = FALSE, 
    seed = NULL, n_basis = NULL, control = NULL, priors = NULL, random_intercept = TRUE)

Arguments

data

Data frame with one row per observation and columns for subject ID, time, response, and covariates.

family

One of "gaussian", "bernoulli", or "poisson".

nknots

Deprecated nknots argument using the interval-count convention. Do not supply with n_basis.

subject.var

Name of the subject ID column.

time.var

Name of the numeric observation-time column.

y.var

Name of the response column.

x.var

Names of numeric covariates, in coefficient order; NULL selects all columns other than subject ID, time, and response.

degree

B-spline polynomial degree; default 3 (cubic).

n.chains

Number of chains; control is recommended.

n.iter

Total iterations; control is recommended.

n.burn

Burn-in iterations; control is recommended.

n.thin

Thinning interval; control is recommended.

n.adapt

Adaptation iterations; control is recommended.

n.cores

Number of parallel cores; control is recommended.

jags.dir

Optional model directory; ordinarily leave this NULL.

quiet

Suppress JAGS progress output when TRUE.

seed

Random seed; control is recommended.

n_basis

Actual number of B-spline basis functions. Omission resolves to 8.

control

Optional list from tdmm_control() controlling chains, iterations, burn-in, thinning, adaptation, seed, parallel execution, and cores.

priors

Optional family-specific list, normally obtained from tdmm.priors(family) and modified by the user.

random_intercept

TRUE (the default) includes a subject-specific random intercept for repeated observations. FALSE fits without one for independent observations.

Details

For parallel fitting, tdmm(control = tdmm_control(parallel = TRUE, cores = ...)) is recommended. This wrapper retains the existing tdmm.parallel() arguments and delegates fitting to tdmm().

Value

A fitted object of class "tdmm".

See also

tdmm

Examples

if (FALSE) { # \dontrun{
## my_long_data is a long-format data frame; see the tdmm() example.
fit <- tdmm.parallel(my_long_data, family = "gaussian",
                     subject.var = "id", time.var = "time", y.var = "y",
                     x.var = "baseline", random_intercept = TRUE,
                     n.cores = 2)
} # }