linting

R/gamm4-functions.R

@@ -258,7 +258,7 @@ gamm4.wrapup <- function(gobj, ret, final_model) {
   sn <- names(gobj$G$random)
 
   if (length(object$family) == 1 &&
-      object$family[[1]]$family == "gaussian" && object$family[[1]]$link == "identity") {
+    object$family[[1]]$family == "gaussian" && object$family[[1]]$link == "identity") {
     linear <- TRUE
   } else {
     linear <- FALSE

R/summary.galamm.R

@@ -147,7 +147,7 @@ print.summary.galamm <- function(
   cat("\n")
   lme4::.prt.aictab(x$AICtab)
   cat("\n")
-  if(length(x$model$family) == 1) {
+  if (length(x$model$family) == 1) {
     lme4::.prt.resids(residuals(x) / sigma(x), digits = digits)
   }
 

data-raw/lifespan.R

@@ -5,21 +5,28 @@ set.seed(3)
 trajectories <- readRDS("data-raw/trajectories.rds")
 epmem_fun <- approxfun(
   trajectories$age,
-  trajectories$epmem_trajectory)
+  trajectories$epmem_trajectory
+)
 wmem_fun <- approxfun(
-  trajectories$age, trajectories$wmem_trajectory)
+  trajectories$age, trajectories$wmem_trajectory
+)
 execfun_fun <- approxfun(
-  trajectories$age, trajectories$execfun_trajectory)
+  trajectories$age, trajectories$execfun_trajectory
+)
 
 n_ids <- 1000
-Psi3 <- matrix(c(.08, .05, .05,
-                 .05, .12, .06,
-                 .05, .06, .24), ncol = 3)
+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")
+epmem_tests <- c(
+  "CVLTA1", "CVLTA2", "CVLTA3", "CVLTA4", "CVLTA5",
+  "CVLT5min", "CVLT30min"
+)
 wmem_tests <- c("DspanBwd", "DspanFwd")
 execfun_tests <- c("Stroop1", "Stroop2", "Stroop3", "Stroop4")
 
@@ -43,8 +50,10 @@ 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),
+  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)) %>%
@@ -112,7 +121,7 @@ lifespan <- tibble(
   mutate(
     retest = as.integer(timepoint > 1),
     y = case_when(
-      domain == "execfun" ~ (y - mean(y)) / sd(y),
+      domain == "execfun" ~ -(y - mean(y)) / sd(y),
       TRUE ~ y
     )
   ) %>%
@@ -124,4 +133,3 @@ mm <- model.matrix(~ 0 + domain, data = lifespan)
 lifespan <- cbind(lifespan, mm)
 
 usethis::use_data(lifespan, overwrite = TRUE)
-

dev-scripts/lifespan_demo.R

@@ -4,16 +4,6 @@ lmat <- matrix(c(
   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$domain == "execfun", 2, 1),
-  control = galamm_control(
-    optim_control = list(REPORT = 2, factr = 1e9, trace = 3, maxit = 2))
-)
-
 mod <- galamm(
   formula = cbind(y, 16 - y) ~ 0 + retest:domain + test +
     sl(age, by = domain,
@@ -30,8 +20,8 @@ mod <- galamm(
     theta = mod_init$parameters$parameter_estimates[mod_init$parameters$theta_inds]
   ),
   control = galamm_control(
-    optim_control = list(REPORT = 2, factr = 1e8, trace = 3, maxit = 10))
+    optim_control = list(REPORT = 1, trace = 3))
 )
-beepr::beep()
+
 summary(mod)
 plot_smooth(mod, scale = 0, pages = 1)