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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,127 @@ | ||
| # Simulate lifespan data from Section 4.1 in Sørensen et al. (2023) | ||
| library(tidyverse) | ||
| library(mvtnorm) | ||
| set.seed(3) | ||
| trajectories <- readRDS("data-raw/trajectories.rds") | ||
| epmem_fun <- approxfun( | ||
| trajectories$age, | ||
| trajectories$epmem_trajectory) | ||
| wmem_fun <- approxfun( | ||
| trajectories$age, trajectories$wmem_trajectory) | ||
| execfun_fun <- approxfun( | ||
| trajectories$age, trajectories$execfun_trajectory) | ||
|
|
||
| n_ids <- 1000 | ||
| Psi3 <- matrix(c(.08, .05, .05, | ||
| .05, .12, .06, | ||
| .05, .06, .24), ncol = 3) | ||
| Psi2 <- diag(c(.06, .8, .18)) | ||
|
|
||
| domains <- c("epmem", "wmem", "execfun") | ||
| epmem_tests <- c("CVLTA1", "CVLTA2", "CVLTA3", "CVLTA4", "CVLTA5", | ||
| "CVLT5min", "CVLT30min") | ||
| wmem_tests <- c("DspanBwd", "DspanFwd") | ||
| execfun_tests <- c("Stroop1", "Stroop2", "Stroop3", "Stroop4") | ||
|
|
||
| item_bias <- c( | ||
| CVLTA1 = -.3, | ||
| CVLTA2 = 0.7, | ||
| CVLTA3 = 1.5, | ||
| CVLTA4 = 1.8, | ||
| CVLTA5 = 2.2, | ||
| CVLT5min = 1.6, | ||
| CVLT30min = 1.8, | ||
| DspanBwd = -0.4, | ||
| DspanFwd = 0.3, | ||
| Stroop1 = 32, | ||
| Stroop2 = 23, | ||
| Stroop3 = 60, | ||
| Stroop4 = 65 | ||
| ) | ||
| loadings <- c(1, 1.8, 2.4, 2.8, 3, 3, 3.2, 1, 1, 7, 4, 20, 20) | ||
| names(loadings) <- names(item_bias) | ||
|
|
||
| lifespan <- tibble( | ||
| id = seq_len(n_ids), | ||
| age_at_baseline = runif(n_ids, min = min(trajectories$age), | ||
| max = max(trajectories$age) - 10), | ||
| timepoints = sample(6, n_ids, replace = TRUE, prob = c(.2, .4, .2, .1, .1, 1)) | ||
| ) %>% | ||
| mutate(domain = list(domains)) %>% | ||
| unnest(cols = "domain") %>% | ||
| mutate(domain = factor(domain, levels = domains)) %>% | ||
| nest_by(id, .keep = TRUE) %>% | ||
| pmap_dfr(function(id, data) { | ||
| data$zeta3 <- as.numeric(rmvnorm(1, sigma = Psi3)) | ||
| data | ||
| }) %>% | ||
| mutate( | ||
| timepoint = map(timepoints, ~ seq_len(.x)) | ||
| ) %>% | ||
| select(-timepoints) %>% | ||
| unnest(cols = timepoint) %>% | ||
| group_by(id, domain) %>% | ||
| mutate( | ||
| time_since_baseline = cumsum(c(0, runif(n(), min = .5, max = 2.5)[-1])), | ||
| age = age_at_baseline + time_since_baseline | ||
| ) %>% | ||
| ungroup() %>% | ||
| filter(between(age, min(trajectories$age), max(trajectories$age))) %>% | ||
| select(-age_at_baseline, -time_since_baseline) %>% | ||
| nest_by(id, timepoint, .keep = TRUE) %>% | ||
| pmap_dfr(function(id, timepoint, data) { | ||
| data$zeta2 <- as.numeric(rmvnorm(1, sigma = Psi2)) | ||
| data | ||
| }) %>% | ||
| mutate( | ||
| eta = case_when( | ||
| domain == "epmem" ~ epmem_fun(age), | ||
| domain == "wmem" ~ wmem_fun(age), | ||
| domain == "execfun" ~ -execfun_fun(age), | ||
| TRUE ~ NA_real_ | ||
| ) + zeta2 + zeta3, | ||
| test = case_when( | ||
| domain == "epmem" ~ list(epmem_tests), | ||
| domain == "wmem" ~ list(wmem_tests), | ||
| domain == "execfun" ~ list(execfun_tests), | ||
| TRUE ~ list(NA) | ||
| ) | ||
| ) %>% | ||
| unnest(cols = test) %>% | ||
| mutate( | ||
| retest_effect = case_when( | ||
| domain == "epmem" ~ .05 * timepoint > 1, | ||
| domain == "wmem" ~ .05 * timepoint > 1, | ||
| domain == "execfun" ~ -2 | ||
| ), | ||
| item_bias = item_bias[test], | ||
| lambda = loadings[test], | ||
| nu = retest_effect + item_bias + eta * lambda | ||
| ) %>% | ||
| select(-zeta2, -zeta3, -eta, -retest_effect, -item_bias, -lambda) %>% | ||
| rowwise() %>% | ||
| mutate( | ||
| y = case_when( | ||
| domain %in% c("epmem", "wmem") ~ rbinom(1, 16, prob = plogis(nu)), | ||
| domain == "execfun" ~ rnorm(1, mean = nu, sd = 8), | ||
| TRUE ~ NA_real_ | ||
| ), | ||
| test = factor(test, levels = c(epmem_tests, wmem_tests, execfun_tests)) | ||
| ) %>% | ||
| group_by(test) %>% | ||
| mutate( | ||
| retest = as.integer(timepoint > 1), | ||
| y = case_when( | ||
| domain == "execfun" ~ (y - mean(y)) / sd(y), | ||
| TRUE ~ y | ||
| ) | ||
| ) %>% | ||
| ungroup() %>% | ||
| select(-nu, -timepoint) %>% | ||
| as.data.frame() | ||
|
|
||
| mm <- model.matrix(~ 0 + domain, data = lifespan) | ||
| lifespan <- cbind(lifespan, mm) | ||
|
|
||
| usethis::use_data(lifespan, overwrite = TRUE) | ||
|
|
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,34 @@ | ||
| library(galamm) | ||
| lmat <- matrix(c( | ||
| 1, rep(NA, 6), rep(0, 6), | ||
| rep(0, 7), 1, NA, rep(0, 4), | ||
| rep(0, 9), 1, NA, NA, NA), ncol = 3) | ||
|
|
||
| mod_init <- galamm( | ||
| formula = cbind(y, 16 - y) ~ 0 + retest:domain + test + | ||
| sl(age, by = domain) + (0 + domain | id), | ||
| data = lifespan, | ||
| family = c(binomial, gaussian), | ||
| family_mapping = ifelse(lifespan$domain == "execfun", 2, 1), | ||
| control = galamm_control( | ||
| optim_control = list(REPORT = 2, factr = 1e9, trace = 3, maxit = 2)) | ||
| ) | ||
|
|
||
| mod <- galamm( | ||
| formula = cbind(y, 16 - y) ~ 0 + retest:domain + test + | ||
| sl(age, by = domain, | ||
| factor = c("epmem_ability", "wmem_ability", "execfun_ability")) + | ||
| (0 + domainepmem:epmem_ability + domainwmem:wmem_ability + | ||
| domainexecfun:execfun_ability | id), | ||
| data = lifespan, | ||
| family = c(binomial, gaussian), | ||
| family_mapping = ifelse(lifespan$domain == "execfun", 2, 1), | ||
| load.var = "test", | ||
| lambda = lmat, | ||
| factor = c("epmem_ability", "wmem_ability", "execfun_ability"), | ||
| control = galamm_control( | ||
| optim_control = list(REPORT = 2, factr = 1e9, trace = 3, maxit = 10)) | ||
| ) | ||
| beepr::beep() | ||
| summary(mod) | ||
| plot_smooth(mod, scale = 0, pages = 1) |
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