Confusion about the values returned by vcov(MixMod) about glmmadaptive HOT 4 CLOSED

________________________________ Από: Shafayet Khan Shafee ***@***.***> Στάλθηκε: Saturday, August 12, 2023 9:29:50 AM Προς: drizopoulos/GLMMadaptive ***@***.***> Κοιν.: Subscribed ***@***.***> Θέμα: [drizopoulos/GLMMadaptive] Confusion about the values returned by vcov(MixMod) (Issue #49) Waarschuwing: Deze e-mail is afkomstig van buiten de organisatie. Klik niet op links en open geen bijlagen, tenzij u de afzender herkent en weet dat de inhoud veilig is. Caution: This email originated from outside of the organization. Do not click links or open attachments unless you recognize the sender and know the content is safe. Consider the following reprex from the package article Methods for MixMod Objects <https://drizopoulos.github.io/GLMMadaptive/articles/Methods.html>, library(GLMMadaptive) set.seed(1234) n <- 100 # number of subjects K <- 8 # number of measurements per subject t_max <- 15 # maximum follow-up time # we construct a data frame with the design: # everyone has a baseline measurement, and then measurements at random follow-up times DF <- data.frame(id = rep(seq_len(n), each = K), time = c(replicate(n, c(0, sort(runif(K - 1, 0, t_max))))), sex = rep(gl(2, n/2, labels = c("male", "female")), each = K)) # design matrices for the fixed and random effects X <- model.matrix(~ sex * time, data = DF) Z <- model.matrix(~ time, data = DF) betas <- c(-2.13, -0.25, 0.24, -0.05) # fixed effects coefficients D11 <- 0.48 # variance of random intercepts D22 <- 0.1 # variance of random slopes # we simulate random effects b <- cbind(rnorm(n, sd = sqrt(D11)), rnorm(n, sd = sqrt(D22))) # linear predictor eta_y <- as.vector(X %*% betas + rowSums(Z * b[DF$id, ])) # we simulate binary longitudinal data DF$y <- rbinom(n * K, 1, plogis(eta_y)) fm <- mixed_model(fixed = y ~ sex * time, random = ~ time | id, data = DF, family = binomial()) Now the estimated variance-covariance matrix of the maximum likelihood estimates of random effects is returned using the vcov()<https://rdrr.io/r/stats/vcov.html> method, vcov(fm, parm = "var-cov") #> D_11 D_12 D_22 #> D_11 0.42942062 -0.09963969 0.5065884 #> D_12 -0.09963969 0.03701847 -0.2117451 #> D_22 0.50658839 -0.21174511 1.3651870 Then that package article says about the returned value of vcov, The elements of this covariance matrix that correspond to the elements of the covariance matrix of the random effects (i.e., the elements D_xx) are on the log-Cholesky scale. I am really confused by the above line. What does it mean by The elements of this covariance matrix are on the log-Cholesky scale.? Please Note that my end goal is to get the standard errors of the estimated random effects that is, $Var(D_{11})$, $Var(D_{12})$, $Var(D_{22})$. So do I need to apply any transformation on the resulting matrix to get these? Would you please help me to clear up the confusion and help me to reach my goal? Thanks. — Reply to this email directly, view it on GitHub<#49>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/ADE7TTZNVXZGCG4VAPLQSPTXU4WG5ANCNFSM6AAAAAA3NYRYJY>. You are receiving this because you are subscribed to this thread.Message ID: ***@***.***>