easystats
Marginal coefficients
Like here for glmmadaptive: https://github.com/drizopoulos/GLMMadaptive/blob/master/R/methods.R#L507
Nice to have for lme4 and glmmTMB as well. Might fit into this pkg?
What does this function do (~it seems like it's undocumented~ was looking in the wrong place)? Is it similar to estimate_slopes?
Thanks for the link. So it basically returns the parameters "averaged" over random levels? If so, that would be indeed very useful...
Let me explore this a bit :)
It seems that GLMMadaptive's method is not implemented for linear models (nlme, lme4 etc.). Moreover, the paper it is based on (Hedeker et al., 2018) describes it specifically for models with binary outcomes.
- Would that suggest that this only work for non-linear models?
- Does it seem feasible for you to reimplement this method and expand it to other packages (we could start by binomial models as well just to make sure our "extension" is correct)?
I've looked at the code, but it's quite obscure and complex... not sure how to re-implement it 😞
I've pasted the code formerly in `WIP_marginal_parameters.R` to here, in order to remove that file for now until/if we address this.
#' @title Marginal Parameters for Mixed Models
#'
#' Calculates marginal coefficients from fitted generalized linear mixed models.
#'
#' @param model A statistical model.
#' @param std_errors Logical indicating whether standard errors are to be computed.
#' @param link_fun A function transforming the mean of the repeated measurements outcome to the linear predictor scale. Typically, this derived from the family argument of mixed_model..
#' @param M Numeric scalar denoting the number of Monte Carlo samples.
#' @param K Numeric scalar denoting the number of samples from the sampling distribution of the maximum likelihood estimates.
#' @param seed Integer denoting the seed for the random number generation.
#' @param cores Integer giving the number of cores to use; applicable only when std_errors = TRUE.
#' @param sandwich Logical; if TRUE robust/sandwich standard errors are used in the calculations.
#' @param ... Arguments passed to or from other methods.
#'
#' @references Hedeker, D., du Toit, S. H., Demirtas, H., & Gibbons, R. D. (2018). A note on marginalization of regression parameters from mixed models of binary outcomes. Biometrics, 74(1), 354-361.
#'
#' @seealso \href{https://drizopoulos.github.io/GLMMadaptive/reference/marginal_coefs.html}{GLMMadaptive::marginal_coefs}.
#'
#'
#' @examples
#' \dontrun{
#' model <- GLMMadaptive::mixed_model(vs ~ mpg,
#' random = ~ 1 | cyl, data = mtcars,
#' family = binomial()
#' )
#' marginal_parameters(model)
#' }
#'
#' @importFrom stats model.offset offset plogis pnorm rnorm runif var vcov model.matrix
#' @importFrom utils as.relistable relist
#' @export
marginal_parameters <- function(model, ...) {
UseMethod("marginal_parameters")
}
#' @rdname marginal_parameters
#' @export
marginal_parameters.MixMod <- function(model, std_errors = FALSE, link_fun = NULL,
M = 3000, K = 100,
seed = 1, cores = max(parallel::detectCores() - 1, 1),
sandwich = FALSE, ...) {
if (!requireNamespace("MASS", quietly = TRUE)) {
stop("Package 'MASS' required for this function to work. Please install it by running `install.packages('MASS')`.")
}
if (!requireNamespace("parallel", quietly = TRUE)) {
stop("Package 'parallel' required for this function to work. Please install it by running `install.packages('parallel')`.")
}
object <- model
offset <- object$offset
X <- stats::model.matrix(object$Terms$termsX, object$model_frames$mfX)
id <- match(object$id[[1L]], unique(object$id[[1L]]))
Z <- mapply(.constructor_Z, object$Terms$termsZ, object$model_frames$mfZ,
MoreArgs = list(id = id), SIMPLIFY = FALSE
)
Z <- do.call("cbind", Z)
betas <- .fixef_MixMod(object)
D <- object$D
if (!is.null(object$gammas)) {
offset_zi <- stats::model.offset(object$model_frames$mfX_zi)
X_zi <- model.matrix_MixMod(object$Terms$termsX_zi, object$model_frames$mfX_zi)
if (!is.null(object$Terms$termsZ_zi)) {
Z_zi <- mapply(.constructor_Z, object$Terms$termsZ_zi, object$model_frames$mfZ_zi,
MoreArgs = list(id = id), SIMPLIFY = FALSE
)
Z_zi <- do.call("cbind", Z_zi)
} else {
Z_zi <- NULL
}
gammas <- .fixef_MixMod(object, "zero_part")
} else {
X_zi <- Z_zi <- gammas <- NULL
}
out <- list(betas = .marginal_parameters(object, X, betas, Z, X_zi, gammas, Z_zi, D, M, link_fun, seed, offset_zi))
if (std_errors) {
blocks <- split(seq_len(K), rep(seq_len(cores),
each = ceiling(K / cores),
length.out = K
))
D <- object$D
diag_D <- ncol(D) > 1 && all(abs(D[lower.tri(D)]) < sqrt(.Machine$double.eps))
list_thetas <- list(betas = betas, D = if (diag_D) log(diag(D)) else .chol_transf(D))
if (!is.null(object$phis)) {
list_thetas <- c(list_thetas, list(phis = object$phis))
}
if (!is.null(gammas)) {
list_thetas <- c(list_thetas, list(gammas = gammas))
}
tht <- unlist(as.relistable(list_thetas))
V <- vcov(object, sandwich = sandwich)
cluster_compute_marg_coefs <- function(block, tht, list_thetas, V, XX, Z, X_zi,
Z_zi, M, .marginal_parameters, .chol_transf,
object, link_fun, seed) {
if (!exists(".Random.seed", envir = .GlobalEnv)) {
runif(1)
}
RNGstate <- get(".Random.seed", envir = .GlobalEnv)
on.exit(assign(".Random.seed", RNGstate, envir = .GlobalEnv))
n_block <- length(block)
m_betas <- matrix(0.0, n_block, length(list_thetas[["betas"]]))
for (b in seq_along(block)) {
seed. <- seed + block[b]
set.seed(seed.)
new_tht <- relist(MASS::mvrnorm(1, tht, V), skeleton = list_thetas)
new_betas <- new_tht$betas
new_D <- if (diag_D) diag(exp(new_tht$D), length(new_tht$D)) else .chol_transf(new_tht$D)
new_gammas <- new_tht$gammas
m_betas[b, ] <- .marginal_parameters(object, XX, new_betas, Z, X_zi,
new_gammas, Z_zi, new_D, M, link_fun,
seed = seed., offset_zi
)
}
m_betas
}
cl <- parallel::makeCluster(cores)
res <- parallel::parLapply(cl, blocks, cluster_compute_marg_coefs,
tht = tht,
list_thetas = list_thetas, V = V, XX = X, Z = Z,
X_zi = X_zi, Z_zi = Z_zi, M = M,
object = object, .marginal_parameters = .marginal_parameters,
.chol_transf = .chol_transf, link_fun = link_fun, seed = seed
)
parallel::stopCluster(cl)
out$var_betas <- stats::var(do.call("rbind", res))
dimnames(out$var_betas) <- list(names(out$betas), names(out$betas))
ses <- sqrt(diag(out$var_betas))
coef_table <- cbind(
"Estimate" = out$betas, "Std.Err" = ses,
"z-value" = out$betas / ses,
"p-value" = 2 * stats::pnorm(abs(out$betas / ses), lower.tail = FALSE)
)
out$coef_table <- coef_table
}
class(out) <- "m_coefs"
out
}
#' @keywords internal
.marginal_parameters <- function(object, X, betas, Z, X_zi, gammas, Z_zi, D, M,
link_fun, seed, offset_zi) {
if (!exists(".Random.seed", envir = .GlobalEnv)) {
runif(1)
}
RNGstate <- get(".Random.seed", envir = .GlobalEnv)
on.exit(assign(".Random.seed", RNGstate, envir = .GlobalEnv))
mu_fun <- object$Funs$mu_fun
if (is.null(link_fun)) {
link_fun <- object$family$linkfun
}
if (is.null(link_fun)) {
stop("you must specify the 'link_fun' argument.\n")
}
Xbetas <- c(X %*% betas)
if (!is.null(offset)) {
Xbetas <- Xbetas + offset
}
if (!is.null(gammas)) {
eta_zi <- c(X_zi %*% gammas)
if (!is.null(offset_zi)) {
eta_zi <- eta_zi + offset_zi
}
}
id <- match(object$id[[1]], unique(object$id[[1]]))
nRE <- ncol(D)
N <- nrow(X)
n <- length(unique(id))
eS <- eigen(D, symmetric = TRUE)
ev <- eS$values
V <- eS$vectors %*% diag(sqrt(pmax(ev, 0)), nRE)
marg_inv_mu <- numeric(N)
for (i in seq_len(n)) {
set.seed(seed + i)
id_i <- id == i
b <- V %*% matrix(stats::rnorm(M * nRE), nRE, M)
Zb <- Z[id_i, , drop = FALSE] %*% b[seq_len(ncol(Z)), , drop = FALSE]
mu <- mu_fun(Xbetas[id_i] + Zb)
if (!is.null(gammas)) {
eta_zi_id_i <- eta_zi[id_i]
if (!is.null(object$Terms$termsZ_zi)) {
eta_zi_id_i <- eta_zi_id_i + Z_zi[id_i, , drop = FALSE] %*%
b[-seq_len(ncol(Z)), , drop = FALSE]
}
mu <- plogis(eta_zi_id_i, lower.tail = FALSE) * mu
}
marg_inv_mu[id_i] <- link_fun(rowMeans(mu))
}
res <- c(solve(crossprod(X), crossprod(X, marg_inv_mu)))
names(res) <- names(betas)
res
}
#' @keywords internal
.constructor_Z <- function(termsZ_i, mfZ_i, id) {
n <- length(unique(id))
Zmats <- vector("list", n)
for (i in seq_len(n)) {
mf <- stats::model.frame(termsZ_i, mfZ_i[id == i, , drop = FALSE],
drop.unused.levels = TRUE
)
mm <- stats::model.matrix(termsZ_i, mf)
assign <- attr(mm, "assign")
Zmats[[i]] <- mm[, c(t(sapply(unique(assign), function(x) which(assign == x)))),
drop = FALSE
]
}
do.call("rbind", Zmats)
}
#' @keywords internal
.chol_transf <- function(x) {
if (any(is.na(x) | !is.finite(x))) {
stop("NA or infinite values in 'x'.\n")
}
if (is.matrix(x)) {
k <- nrow(x)
U <- chol(x)
U[cbind(1:k, 1:k)] <- log(U[cbind(1:k, 1:k)])
U[upper.tri(U, TRUE)]
} else {
nx <- length(x)
k <- round((-1 + sqrt(1 + 8 * nx)) / 2)
mat <- matrix(0, k, k)
mat[upper.tri(mat, TRUE)] <- x
mat[cbind(1:k, 1:k)] <- exp(mat[cbind(1:k, 1:k)])
res <- crossprod(mat)
attr(res, "L") <- t(mat)[lower.tri(mat, TRUE)]
res
}
}
#' @keywords internal
.fixef_MixMod <- function(object, sub_model = c("main", "zero_part"), ...) {
sub_model <- match.arg(sub_model)
if (sub_model == "main") {
object$coefficients
} else {
if (!is.null(object$gammas)) {
object$gammas
} else {
stop("the fitted model does not have an extra zero-part.")
}
}
}
#' @importFrom stats model.matrix
#' @keywords internal
model.matrix_MixMod <- function(object, type = c("fixed", "random", "zi_fixed", "zi_random"), ...) {
type <- match.arg(type)
switch(type,
"fixed" = stats::model.matrix(object$Terms$termsX, object$model_frames$mfX),
"random" = {
id <- object$id[[1]]
id <- match(id, unique(id))
Z <- mapply(.constructor_Z, object$Terms$termsZ, object$model_frames$mfZ,
MoreArgs = list(id = id), SIMPLIFY = FALSE
)
do.call("cbind", Z)
},
"zi_fixed" = stats::model.matrix(object$Terms$termsX_zi, object$model_frames$mfX_zi),
"zi_random" = {
id <- object$id[[1]]
id <- match(id, unique(id))
Z <- mapply(.constructor_Z, object$Terms$termsZ_zi, object$model_frames$mfZ_zi,
MoreArgs = list(id = id), SIMPLIFY = FALSE
)
do.call("cbind", Z)
}
)
}
#' @keywords internal
model.frame_MixMod <- function(formula, type = c(
"fixed", "random", "zi_fixed",
"zi_random"
), ...) {
type <- match.arg(type)
switch(type, "fixed" = formula$model_frames$mfX, "random" = formula$model_frames$mfZ,
"zi_fixed" = formula$model_frames$mfX_zi,
"zi_random" = formula$model_frames$mfZ_zi
)
}
# model <- GLMMadaptive::mixed_model(Sepal.Length ~ Sepal.Width, random=~1|Species, data=iris, family="gaussian")
# model <- lme4::lmer(Sepal.Length ~ Sepal.Width + (1|Species), data=iris)
# model <- nlme::lme(Sepal.Length ~ Sepal.Width, random=~1|Species, data=iris)
# GLMMadaptive::marginal_coefs(model)
Maybe there's also some essential overlap with the https://github.com/leeper/margins package. I still think it's useful to have such a method in this package, but I think I still did not fully understand all details in computation, in particular the jacobian matrix and related SE/CI.
Would that suggest that this only work for non-linear models?
For linear models, the average random effect is the fixed effect on the DV scale.
But for non-linear models, the mean(inverse_link(rand_theta)) is unequal to inverse_link(mean(rand_theta)).
But for non-linear models, the mean(inverse_link(rand_theta)) is unequal to inverse_link(mean(rand_theta)).
Jensen's inequality, if I recall right...
Maybe this would rather fit in estimate, which goal is to estimate new parameters / predictions in models?
Marginal coefficients are no newly estimated parameters or even predictions. So I would rather put it into this package. You were probably talking about average marginal effects? These are better suited in estimate.
right (still have trouble conceptualising marginal coefs I think :)
Although, I must take a closer look at estimate_slope(). Maybe these functions are indeed more similar than I initially thought.
Maybe we can also look at the code for this package: https://cran.r-project.org/web/packages/mfx/index.html
The complication that kept me from implementing marginal coefficients into jtools (where I intended to use margins as a backend) is that you have to deal with interaction terms, which have no marginal coefficient as I understand it. So you may surprise users who fit interactions by not returning an interaction term. The package may also surprise itself by having a number of marginal coefficients that is not equal to the number of regression coefficients.
