joint_ms_va_par | R Documentation |
Computes the estimated variational parameters for each individual.
joint_ms_va_par(object, par = object$start_val)
object |
a joint_ms object from |
par |
parameter vector to be formatted. |
A list with one list for each individual with the estimated mean and covariance matrix.
# load in the data library(survival) data(pbc, package = "survival") # re-scale by year pbcseq <- transform(pbcseq, day_use = day / 365.25) pbc <- transform(pbc, time_use = time / 365.25) # create the marker terms m1 <- marker_term( log(bili) ~ 1, id = id, data = pbcseq, time_fixef = bs_term(day_use, df = 5L), time_rng = poly_term(day_use, degree = 1L, raw = TRUE, intercept = TRUE)) m2 <- marker_term( albumin ~ 1, id = id, data = pbcseq, time_fixef = bs_term(day_use, df = 5L), time_rng = poly_term(day_use, degree = 1L, raw = TRUE, intercept = TRUE)) # base knots on observed event times bs_term_knots <- with(pbc, quantile(time_use[status == 2], probs = seq(0, 1, by = .2))) boundary <- c(bs_term_knots[ c(1, length(bs_term_knots))]) interior <- c(bs_term_knots[-c(1, length(bs_term_knots))]) # create the survival term s_term <- surv_term( Surv(time_use, status == 2) ~ 1, id = id, data = pbc, time_fixef = bs_term(time_use, Boundary.knots = boundary, knots = interior)) # create the C++ object to do the fitting model_ptr <- joint_ms_ptr( markers = list(m1, m2), survival_terms = s_term, max_threads = 2L, ders = list(0L, c(0L, -1L))) # find the starting values start_vals <- joint_ms_start_val(model_ptr) # extract variational parameters for each individual VA_pars <- joint_ms_va_par(object = model_ptr,par = start_vals) # number of sets of variational parameters is equal to the number of subjects length(VA_pars) length(unique(pbc$id)) # mean and var-covar matrix for 1st individual VA_pars[[1]]
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