Slice-aware model fitting generics

These functions are S3 generics exported by JMbayes2 to enable method dispatch on the class of the data argument. When data is a "sliced_data" object (as produced by slicer), the corresponding slice-wise fitting methods are used.

Usage

lme(fixed, data, ...)
coxph(formula, data, ...)
mixed_model(fixed, data, ...)

Arguments

fixed: For lme() and mixed_model(), a model formula for the fixed effects.
formula: For coxph(), a survival model formula.
data: A data frame (default methods) or a "sliced_data" object (slice-wise methods).
...: Further arguments passed to the underlying model-fitting functions. For slice-wise methods, additional arguments include parallel_out and cores to control parallel execution across slices.

Details

When data is a regular data frame, the default methods call nlme::lme(), survival::coxph(), and GLMMadaptive::mixed_model(), respectively.

When data is a "sliced_data" object, the corresponding *.sliced_data methods fit the requested model independently within each slice and return a list of fits (one per slice).

Note

JMbayes2 exports S3 generics lme(), coxph(), and mixed_model() to enable dispatch on "sliced_data". When JMbayes2 is attached, these names mask nlme::lme, survival::coxph, and GLMMadaptive::mixed_model. Use the pkg::fun form to call the original functions.

Examples

if (FALSE) { # \dontrun{

slc <- slicer(n_slices = 2, id_var = "id", data_long = pbc2, data_surv = pbc2.id)
n_cores <- max(parallel::detectCores() - 1L, 1L)

lme_fit <- lme(fixed  = log(serBilir) ~ year * sex,
               data   = slc$long,
               random = ~ year | id,
               cores  = n_cores)

cox_fit <- coxph(formula = Surv(years, status2) ~ sex,
                 data    = slc$surv,
                 cores   = n_cores)

mxm_fit <- mixed_model(fixed  = ascites ~ year + sex,
                       data   = slc$long,
                       random = ~ year | id,
                       family = binomial(),
                       cores  = n_cores)
} # }