R Under development (unstable) (2024-06-30 r86854 ucrt) -- "Unsuffered Consequences"
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> ## -- Test utils & settings
> source("test_util.R")
> .run_test <- identical(Sys.getenv("NOT_CRAN"), "true")
> oldopt <- options(digits = 4)
> set.seed(100)
> 
> library("tramME")
Loading required package: tram
Loading required package: mlt
Loading required package: basefun
Loading required package: variables
Loading required package: mvtnorm
> data("sleepstudy", package = "lme4")
> 
> # -- newdata w/ response
> fit <- LmME(Reaction ~ Days + (Days | Subject), data = sleepstudy)
> pr1 <- predict(fit, newdata = sleepstudy[1:20, ], type = "trafo")
> pr2 <- predict(fit, type = "trafo")[1:20]
> chkid(pr1, pr2)
> 
> ## -- newdata w/o response
> nd <- sleepstudy
> nd[["Reaction"]] <- NULL
> pr1 <- predict(fit, newdata = nd, ranef = ranef(fit, raw = TRUE), type = "trafo", K = 100)
> chkid(unname(dim(pr1)), c(100L, nrow(sleepstudy)))
> chkerr(pr2 <- predict(fit, newdata = nd, K = 100))
> 
> ## -- check "zero" option of ranef
> nd <- sleepstudy[c(4, 14, 24), ]
> pr1 <- predict(fit, newdata = nd, ranef = rep(0, 6), q = c(300, 320), type = "distribution")
> pr2 <- predict(fit, newdata = nd, ranef = "zero", q = c(300, 320), type = "distribution")
> chkid(pr1, pr2)
> 
> ## -- various formatting of ranef
> pr1 <- predict(fit, ranef = ranef(fit, raw = TRUE), type = "quantile", prob = 0.5)
> ## ... also with formatted ranefs
> pr2 <- predict(fit, ranef = ranef(fit), type = "quantile", prob = 0.5)
> chkid(pr1, pr2)
> 
> ## -- compare w/ lmer
> library("lme4")
Loading required package: Matrix
> lm1 <- LmME(Reaction ~ Days + (Days || Subject), data = sleepstudy)
> lm2 <- lmer(Reaction ~ Days + (Days || Subject), data = sleepstudy, REML = FALSE)
> chkeq(predict(lm1, type = "lp") * sigma(lm1) + coef(lm1, as.lm = TRUE)[1],
+       fitted(lm2), tol = 1e-6)
> 
> ## -- w/ Surv response & w/o shift terms
> library("survival")
> fit2 <- SurvregME(Surv(time, status) | rx ~ 1 + (1 | litter), data = rats,
+                   dist = "rayleigh")
> pdf(file = NULL)
> pl <- plot(fit2, newdata = rats[1:2, ], ranef = -1, type = "survivor", K = 100,
+            col = 1)
> dev.off()
null device 
          1 
> chkeq(dim(pl), c(100L, 2L), check.attributes = FALSE)
> 
> ## -- compare with tram
> m1 <- Lm(dist ~ speed, data = cars, fixed = c(speed = 0.3))
> m2 <- LmME(dist ~ speed, data = cars, fixed = c(speed = 0.3))
> stopifnot(m2$opt$convergence == 0)
> chkeq(logLik(m1), logLik(m2), check.attributes = FALSE)
> nd <- cars
> nd[["dist"]] <- NULL
> pr1 <- predict(as.mlt(m1), newdata = nd, type = "trafo", K = 200)
> pr2 <- predict(m2, newdata = nd, type = "trafo", K = 200)
> chkeq(pr1, pr2, tol = 1e-6)
> 
> data("neck_pain", package = "ordinalCont")
> m1 <- ColrME(vas ~ laser * time, data = neck_pain, bounds = c(0, 1), support = c(0, 1))
> m2 <- Colr(vas ~ laser * time, data = neck_pain, bounds = c(0, 1), support = c(0, 1))
> stopifnot(m2$opt$convergence == 0)
> chkeq(logLik(m1), logLik(m2), check.attributes = FALSE)
> nd <- neck_pain[1, ]
> nd$vas <- NULL
> pr1 <- predict(m1, newdata = nd, K = 5, type = "distribution")
> pr2 <- predict(as.mlt(m2), newdata = nd, K = 5, type = "distribution")
> chkeq(pr1, pr2, tol = 1e-5)
> 
> ## -- edge cases
> mod <- LmME(Reaction ~ Days + (Days | Subject), data = sleepstudy, nofit = TRUE)
> pr <- predict(mod, ranef = "zero", type = "trafo")
> stopifnot(all(is.na(pr)))
> coef(mod) <- c(-5, 2, 0.05)
> pr <- predict(mod, ranef = "zero", type = "trafo")
> stopifnot(all(!is.na(pr)))
> chkerr(pr <- predict(mod), "supply random effects")
> 
> ## -- check random effects
> m <- CoxphME(Surv(time, status) ~ rx + (1 | litter), data = rats, log_first = TRUE)
> nd <- rats[1, ]
> pr <- predict(m, newdata = nd, type = "trafo", ranef = "zero", K = 5)
> pr2 <- predict(m, newdata = nd, type = "trafo", ranef = 1, K = 5)
> chkeq(pr + 1, pr2)
> m <- LmME(Reaction ~ Days + (1 | Subject), data = sleepstudy)
> nd <- sleepstudy[10, ]
> nd$Reaction <- NULL
> pr <- predict(m, newdata = nd, type = "trafo", ranef = "zero", K = 5)
> pr2 <- predict(m, newdata = nd, type = "trafo", ranef = 2, K = 5)
> chkeq(pr - 2, pr2)
> 
> ## FIXME Check warning messages
> ## oc <- optim_control(ok_warnings = FALSE)
> ## chkwarn(m <- update(m, control = oc), wm = "NA/NaN")
> ## TODO: also check in the precious m the lack of warning messages
> 
> ## --- w/ smooth terms
> ## lp
> library("mgcv")
Loading required package: nlme

Attaching package: 'nlme'

The following object is masked from 'package:lme4':

    lmList

The following object is masked from 'package:mlt':

    coef<-

This is mgcv 1.9-1. For overview type 'help("mgcv-package")'.
> fit_sm1 <- LmME(Reaction ~ s(Days) + (1 | Subject), data = sleepstudy)
> fit_sm2 <- gam(Reaction ~ s(Days) + s(Subject, bs = "re"), data = sleepstudy, method = "ML")
> ## fitted REs
> pr1a <- (predict(fit_sm1) - coef(fit_sm1, with_baseline = TRUE)[1]) * sigma(fit_sm1)
> pr2 <- predict(fit_sm2)
> chkeq(pr1a, c(pr2), tol = 1e-5)
> ## zero REs
> pr1b <- (predict(fit_sm1, ranef = "zero") - coef(fit_sm1, with_baseline = TRUE)[1]) * sigma(fit_sm1)
> pr2 <- predict(fit_sm2, exclude = "s(Subject)")
> chkeq(pr1b, c(pr2), tol = 1e-5)
> ## new data
> nd <- sleepstudy[1:10,  ]
> pr1c <- (predict(fit_sm1, newdata = nd) - coef(fit_sm1, with_baseline = TRUE)[1]) * sigma(fit_sm1)
> chkeq(pr1a[1:10], pr1c)
> pr2 <- predict(fit_sm2, newdata = nd)
> chkeq(pr1c, c(pr2), tol = 1e-5)
> 
> ## density
> data("mcycle", package = "MASS")
> m <- LmME(accel ~ s(times, bs = "cr"), data = mcycle)
> nd <- data.frame(times = seq(5, 30, by = 5))
> qs <- seq(-134, 75, length.out = 50)
> lp <- predict(m, nd, type = "lp")
> lp2 <- (lp - coef(m, with_baseline = TRUE)[1]) * sigma(m)
> pr1 <- predict(m, nd, type = "density", q = qs)
> pr2 <- sapply(lp2, function(x) dnorm(qs, mean = x, sd = sigma(m)))
> chkeq(pr1, pr2, check.attributes = FALSE)
> 
> ## a complicated plot
> mod_su <- SurvregME(Surv(time, status) | celltype ~ age + s(karno, by = trt),
+                     data = veteran, dist = "loglogistic")
> pdf(file = NULL)
> plot(mod_su, type = "density")
> dev.off()
null device 
          1 
> 
> summarize_tests()
==========================
Number of failed tests: 0
==========================
> 
> options(oldopt)
> 
> proc.time()
   user  system elapsed 
   4.26    0.32    4.59