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
> library("tram")
> library("lme4")
Loading required package: Matrix
> gamdat <- mgcv::gamSim(6, n = 500, scale = 2, verbose = FALSE)
Gu & Wahba 4 term additive model
> 
> ## -- Fixed effects only model
> m01 <- LmME(Reaction ~ Days, data = sleepstudy, fixed = c("Days" = 0.6))
> m02 <- Lm(Reaction ~ Days, data = sleepstudy, fixed = c("Days" = 0.6))
> stopifnot(m01$opt$convergence == 0)
> stopifnot(m02$convergence == 0)
> chkeq(logLik(m01), logLik(m02), check.attributes = FALSE)
> chkeq(coef(m01, with_baseline = TRUE), coef(m02, with_baseline = TRUE), tol = 1e-5)
> vc1 <- vcov(m01, pargroup = "all")
> vc2 <- vcov(m01, pargroup = "all", method = "analytical")
> chkeq(vc1, vcov(m02, with_baseline = TRUE), tol = 1e-3)
> chkeq(vc2, vcov(m02, with_baseline = TRUE), tol = 1e-5)
> 
> ## -- Compare with lmer
> m1 <- LmME(Reaction ~ Days + (Days | Subject), data = sleepstudy)
> m2 <- lmer(Reaction ~ Days + (Days | Subject), data = sleepstudy, REML = FALSE)
> stopifnot(m1$opt$convergence == 0)
> 
> ## logLik, fixed effects, sigma
> chkeq(logLik(m1), logLik(m2), check.attributes = FALSE)
> chkeq(fixef(m2), coef(m1, as.lm = TRUE), tol = 1e-6)
> chkeq(sigma(m1), sigma(m2), tol = 1e-5)
> 
> ## --- SEs of fixed effects
> vc1 <- vcov(m1, as.lm = TRUE, pargroup = "fixef")
> vc2 <- vcov(m2)
> chkeq(sqrt(diag(vc1)), sqrt(diag(vc2)), tol = 1e-4, check.attributes = FALSE)
> 
> ## --- Variances and correlations of the random effects
> vc1 <- VarCorr(m1, as.lm = TRUE)
> vc2 <- lme4::VarCorr(m2)
> chkeq(vc1$Subject$var, diag(vc2[[1]]), tol = 1e-4)
> chkeq(vc1$Subject$corr[1, 2], as.data.frame(vc2)[3, "sdcor"], tol = 1e-4)
> v <- diag(varcov(m1, as.lm = TRUE)[[1]])
> chkeq(v, vc1$Subject$var)
> ## check original parametrization of the covariance matrix and its as.lm version
> th1 <- varcov(m1, as.lm = TRUE, as.theta = TRUE)
> th2 <- varcov(m1, as.theta = TRUE)
> sig <- sigma(m1)
> chkeq(th1, th2 + c(log(sig), log(sig), 0))
> 
> ## --- Confidence intervals for the fixed effects
> ci1 <- confint(m1, as.lm = TRUE, pargroup = "fixef")
> ci2 <- confint(m2, 5:6, method = "Wald")
> chkeq(ci1, ci2, tol = 1e-5, check.attributes = FALSE)
> 
> ## --- Random effects
> re1 <- ranef(m1, as.lm = TRUE)
> re2 <- lme4::ranef(m2)
> chkid(colnames(re1$Subject), colnames(re2$Subject))
> chkeq(re1$Subject, re2$Subject, tol = 1e-4, check.attributes = FALSE)
> pr <- list(beta = coef(m1, with_baseline = TRUE), theta = varcov(m1, as.theta = TRUE))
> chkerr(ranef(m1, as.lm = TRUE, param = pr), "not supported")
> 
> ## --- Residuals
> res1 <- resid(m1, as.lm = TRUE)
> res2 <- resid(m2)
> chkeq(res1, res2, tol = 1e-5)
> 
> ## --- against gam
> mod_gm <- LmME(y ~ s(x0)+ x1 + s(x2) + (1|fac), data = gamdat)
> mod_gam <- mgcv::gam(y ~ s(x0)+ x1 + s(x2) + s(fac, bs = "re"), data = gamdat, method = "ML")
> vc <- varcov(mod_gm, as.lm = TRUE)
> chkid(names(vc), "fac")
> vc <- varcov(mod_gm, as.lm = TRUE, full = TRUE)
> chkid(names(vc), c("fac", "s(x0)", "s(x2)"))
> ## NOTE: different parameter structure only check intercept and coef of x1
> cf1 <- coef(mod_gm, as.lm = TRUE)[1:2]
> cf2 <- coef(mod_gam)[1:2]
> chkeq(cf1, cf2, tol = 1e-5)
> vc1 <- vcov(mod_gm, as.lm = TRUE, parm = c("(Intercept)", "x1"))
> vc2 <- vcov(mod_gam)[1:2, 1:2]
> chkeq(vc1, vc2, tol = 1e-2) ## NOTE: would it be more precise with numDeriv?
> ## logLik
> chkeq(as.numeric(logLik(mod_gm)), -mod_gam$gcv.ubre,
+       check.attributes = FALSE)
> 
> summarize_tests()
==========================
Number of failed tests: 0
==========================
> 
> options(oldopt)
> 
> proc.time()
   user  system elapsed 
   2.39    0.18    2.56