Introduction to antedep

In antedep: Antedependence Models for Longitudinal Data

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(antedep)
set.seed(123)

Overview

antedep supports three model families:

• AD (Gaussian) for continuous longitudinal outcomes
• INAD for count longitudinal outcomes
• CAT (categorical AD) for discrete state longitudinal outcomes

Packaged datasets include bolus_inad, cattle_growth, cochlear_implant, labor_force_cat, and race_100km.

data("race_100km")
dim(race_100km$y)
colMeans(race_100km$y)

Production-Readiness Matrix

| Model | Data type | Complete-data fit/logLik | Missing-data fit/logLik | Notes | |---|---|---|---|---| | AD | Continuous | Ready | Ready (fit_gau, logL_gau) | Missing-data fit supports EM / observed-data likelihood modes | | INAD | Counts | Ready | Ready (fit_inad, logL_inad) | Missing-data fit supports na_action = "marginalize" | | CAT | Categorical states | Ready | Ready (fit_cat, logL_cat) | Missing-data fit supports orders 0, 1, 2 |

Visualizing Longitudinal Data

data("bolus_inad")
plot_profile(
  bolus_inad$y,
  blocks = bolus_inad$blocks,
  block_labels = c("Group 1", "Group 2"),
  title = "Bolus Longitudinal Profiles"
)

# More detailed dependence diagnostic
plot_prism(bolus_inad$y)

AD Workflow (Continuous Outcomes)

Complete-data fit and order comparison

y_gau <- simulate_gau(n_subjects = 20, n_time = 4, order = 1, phi = 0.5)
fit_gau1 <- fit_gau(y_gau, order = 1)
fit_gau2 <- fit_gau(y_gau, order = 2)

c(order1_logLik = fit_gau1$log_l, order2_logLik = fit_gau2$log_l)
bic_order_gau(y_gau, max_order = 2)$table

# Example AD order test
test_order_gau(y_gau, p = 0, q = 1)$p_value

Missing-data AD fit

y_gau_miss <- y_gau
y_gau_miss[sample(length(y_gau_miss), 8)] <- NA

fit_gau_em <- fit_gau(y_gau_miss, order = 1, na_action = "em", em_max_iter = 10)
fit_gau_cc <- fit_gau(y_gau_miss, order = 1, na_action = "complete")

c(em_logLik = fit_gau_em$log_l, complete_case_logLik = fit_gau_cc$log_l)
fit_gau_em$pct_missing

AD mean and covariance structure tests (complete data)

These AD inference tests currently require complete data.

# One-sample and two-sample mean-profile tests
mu0 <- colMeans(y_gau)
blocks_gau <- rep(1:2, each = nrow(y_gau) / 2)

test_one_sample_gau(y_gau, mu0 = mu0, p = 1)$p_value
test_two_sample_gau(y_gau, blocks = blocks_gau, p = 1)$p_value

# Wald contrast test: H0 that adjacent means are equal
C <- diff(diag(ncol(y_gau)))
test_contrast_gau(y_gau, C = C, p = 1)$p_value

# Covariance homogeneity across groups
test_homogeneity_gau(y_gau, blocks = blocks_gau, p = 1)$p_value

INAD Workflow (Count Outcomes)

Complete-data INAD fit

y_inad <- simulate_inad(
  n_subjects = 20,
  n_time = 4,
  order = 1,
  thinning = "binom",
  innovation = "pois",
  alpha = 0.4,
  theta = 3
)

fit_inad1 <- fit_inad(y_inad, order = 1, thinning = "binom", innovation = "pois")
fit_inad1$log_l
bic_order_inad(y_inad, max_order = 2, thinning = "binom", innovation = "pois")$table

Missing-data INAD fit

y_inad_miss <- y_inad
y_inad_miss[sample(length(y_inad_miss), 10)] <- NA

fit_inad_miss <- fit_inad(
  y_inad_miss,
  order = 1,
  thinning = "binom",
  innovation = "pois",
  na_action = "marginalize",
  max_iter = 10
)

fit_inad_miss$log_l
fit_inad_miss$pct_missing

# Missing-data LRTs (observed-data likelihood)
blocks_inad <- rep(1:2, each = nrow(y_inad_miss) / 2)
test_order_inad(y_inad_miss, order_null = 0, order_alt = 1, blocks = blocks_inad)$p_value
test_homogeneity_inad(y_inad_miss, blocks = blocks_inad, order = 1, test = "mean")$p_value

CAT Workflow (Categorical Outcomes)

Complete-data CAT fit and inference

y_cat <- simulate_cat(n_subjects = 20, n_time = 4, order = 1, n_categories = 3)
fit_cat1 <- fit_cat(y_cat, order = 1)
fit_cat1$log_l

# Wald CIs (complete data)
ci_cat1 <- ci_cat(fit_cat1, parameters = "marginal")
head(ci_cat1$marginal, 3)

# Order tests for CAT
run_order_tests_cat(y_cat, max_order = 2)$table

Missing-data CAT fit

y_cat_miss <- y_cat
y_cat_miss[sample(length(y_cat_miss), 12)] <- NA

fit_cat_miss <- fit_cat(y_cat_miss, order = 1, na_action = "marginalize")
fit_cat_miss$log_l
fit_cat_miss$settings$na_action_effective

# EM alternative for CAT missing data (orders 0/1)
fit_cat_miss_em <- fit_cat(y_cat_miss, order = 1, na_action = "em", em_max_iter = 10)
fit_cat_miss_em$log_l

# Missing-data order/homogeneity tests are supported
blocks_cat <- rep(1:2, each = nrow(y_cat_miss) / 2)
test_order_cat(y_cat_miss, order_null = 0, order_alt = 1)$p_value
test_homogeneity_cat(y_cat_miss, blocks = blocks_cat, order = 1)$p_value

Additional Inference Helpers (Complete Data)

# INAD homogeneity/stationarity tools
blocks <- rep(1:2, each = nrow(y_inad) / 2)
run_homogeneity_tests_inad(y_inad, blocks = blocks, order = 1)
run_stationarity_tests_inad(y_inad, order = 1, blocks = blocks)

# CAT stationarity/time-invariance tools
test_timeinvariance_cat(y_cat, order = 1)
test_stationarity_cat(y_cat, order = 1)

Known Limitations

• EM entry points: em_gau (Gaussian), em_inad (INAD), and em_cat (CAT, orders 0/1) are available; for CAT order 2 with missing data, use fit_cat(na_action = "marginalize").
• For CAT missingness, fit_cat(na_action = "marginalize") supports orders 0/1/2, while fit_cat(na_action = "em") (or em_cat) supports orders 0/1 with optional safeguard step-halving.
• Missing-data confidence intervals are not implemented yet (ci_gau, ci_inad, ci_cat require complete-data fits).
• AD missing-data LRT/mean/covariance tests are not implemented yet.
• CAT missing-data time-invariance/stationarity tests (test_timeinvariance_cat, test_stationarity_cat, run_stationarity_tests_cat) are not implemented yet.
• Current order support is up to order 2 for AD, INAD, and CAT.
• CAT missing-data marginalization currently supports orders 0, 1, and 2.

Recommended Check Command

Use the local one-command workflow before pushing:

Rscript scripts/check-package.R --as-cran --no-manual

antedep documentation built on April 25, 2026, 1:06 a.m.