glmmTMB ordinal support #1221
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| Original file line number | Diff line number | Diff line change |
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| # glmmTMB ordinal family ------------------------------------------------ | ||
| # ======================================================================= | ||
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| # glmmTMB's `ordinal()` family stores the K-1 thresholds as family-specific | ||
| # parameters ("psi", softmax-parameterized so that they stay ordered) rather | ||
| # than as fixed effects, and fixes the intercept to zero via an internal map. | ||
| # To match `ordinal::clm()`/`clmm()`, we report the thresholds (on the | ||
| # threshold scale) as conditional parameters ahead of the slopes, drop the | ||
| # non-estimated intercept, and transform the covariance matrix with the | ||
| # delta method. `vcov(x, full = TRUE)` is on the internal psi scale. | ||
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| .is_glmmtmb_ordinal <- function(x) { | ||
| inherits(x, "glmmTMB") && | ||
| identical(.safe(x$modelInfo$family$family), "ordinal") | ||
| } | ||
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| # is the intercept fixed to zero by glmmTMB's internal map, i.e. not | ||
| # estimated? This is the default unless the user supplied a beta map | ||
| .glmmtmb_fixed_intercept <- function(x) { | ||
| cf <- .safe(lme4::fixef(x)$cond) | ||
| bmap <- .safe(x$obj$env$map$beta) | ||
| icpt <- which(names(cf) == "(Intercept)") | ||
| length(icpt) == 1L && !is.null(bmap) && is.na(bmap[icpt]) | ||
| } | ||
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| # named vector of thresholds followed by the estimated fixed effects | ||
| .glmmtmb_ordinal_conditional <- function(x) { | ||
| cf <- lme4::fixef(x)$cond | ||
| if (.glmmtmb_fixed_intercept(x)) { | ||
| cf <- cf[names(cf) != "(Intercept)"] | ||
| } | ||
| c(glmmTMB::family_params(x), cf) | ||
| } | ||
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| # covariance matrix of c(thresholds, fixed effects) on the threshold scale. | ||
| # theta_j = qlogis(cumsum(softmax(c(psi, 0)))_j) is a joint function of all | ||
| # psi elements, so the Jacobian is J[j, m] = s[m] * ((m <= j) - C_j) / | ||
| # (C_j * (1 - C_j)), with s = softmax(c(psi, 0)) and C_j = cumsum(s)[j] | ||
| .glmmtmb_ordinal_varcov <- function(x) { | ||
| check_if_installed(c("lme4", "glmmTMB")) | ||
| V <- .safe_vcov(x, full = TRUE) | ||
| thresholds <- glmmTMB::family_params(x) | ||
| cf <- lme4::fixef(x)$cond | ||
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| # internal parameter ids ("cond1", "psi1", ...) are carried as names of the | ||
| # row labels; fall back to the labels if these are missing | ||
| ids <- names(rownames(V)) | ||
| if (is.null(ids)) { | ||
| psi_i <- match(names(thresholds), rownames(V)) | ||
| cond_i <- match(names(cf), rownames(V)) | ||
| } else { | ||
| psi_i <- which(startsWith(ids, "psi")) | ||
| cond_i <- which(startsWith(ids, "cond")) | ||
| } | ||
| # non-estimated parameters (the mapped intercept) have NA variance | ||
| cond_i <- cond_i[!is.na(cond_i)] | ||
| cond_i <- cond_i[!is.na(diag(V)[cond_i])] | ||
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| psi <- unname(x$fit$par[names(x$fit$par) == "psi"]) | ||
| k <- length(psi) | ||
| w <- exp(c(psi, 0) - max(psi, 0)) | ||
| s <- w / sum(w) | ||
| Cj <- cumsum(s)[seq_len(k)] | ||
| J_psi <- outer( | ||
| seq_len(k), | ||
| seq_len(k), | ||
| function(j, m) s[m] * ((m <= j) - Cj[j]) / (Cj[j] * (1 - Cj[j])) | ||
| ) | ||
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| J <- diag(k + length(cond_i)) | ||
| J[seq_len(k), seq_len(k)] <- J_psi | ||
| idx <- c(psi_i, cond_i) | ||
| out <- J %*% V[idx, idx, drop = FALSE] %*% t(J) | ||
| nms <- c(names(thresholds), unname(rownames(V)[cond_i])) | ||
| dimnames(out) <- list(nms, nms) | ||
| out | ||
| } | ||
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| # per-category probabilities (or the most likely category) from | ||
| # `predict(type = "probs")`, reshaped to long format like `get_predicted.clm()` | ||
| .get_predicted_glmmtmb_ordinal <- function( | ||
| x, | ||
| data = NULL, | ||
| predict = "expectation", | ||
| ci = NULL, | ||
| include_random = "default", | ||
| verbose = TRUE, | ||
| dots = list() | ||
| ) { | ||
| # `type` takes precedence over `predict`, as in `.get_predicted_args()` | ||
| if (is.null(dots$type)) { | ||
| requested <- predict[1] | ||
| } else { | ||
| requested <- dots$type[1] | ||
| } | ||
| classification <- identical(requested, "classification") | ||
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| if (requested %in% c("prediction", "predicted") && verbose) { | ||
| format_warning( | ||
| "\"prediction\" is currently not supported by the `predict` argument for `glmmTMB` models.", | ||
| "Changing to `predict=\"expectation\"`." | ||
| ) | ||
| } | ||
| if (classification && !is.null(ci)) { | ||
| if (verbose) { | ||
| format_warning("Confidence intervals are not available for classification.") | ||
| } | ||
| ci <- NULL | ||
| } | ||
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| # sanitize input. This also handles the `predict`/`type` alert and the | ||
| # aliases; the type passed to `predict()` is always "probs" | ||
| my_args <- do.call( | ||
| .get_predicted_args, | ||
| c( | ||
| list( | ||
| x, | ||
| data = data, | ||
| predict = predict, | ||
| ci = ci, | ||
| include_random = include_random, | ||
| verbose = verbose | ||
| ), | ||
| dots | ||
| ) | ||
| ) | ||
| my_args$predict <- ifelse(classification, "classification", "expectation") | ||
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| # remaining dot-arguments are forwarded to `predict()`, as for other | ||
| # families; those managed here take precedence | ||
| predict_args <- list( | ||
| x, | ||
| newdata = my_args$data, | ||
| type = "probs", | ||
| re.form = my_args$re.form, | ||
| allow.new.levels = my_args$allow_new_levels, | ||
| se.fit = !classification | ||
| ) | ||
| dots[c("type", "newdata", "re.form", "allow.new.levels", "se.fit")] <- NULL | ||
| rez <- do.call(stats::predict, c(predict_args, dots)) | ||
| if (classification) { | ||
| probs <- rez | ||
| } else { | ||
| probs <- rez$fit | ||
| } | ||
| resp_levels <- colnames(probs) | ||
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| # the response is not a focal predictor of the long-format output | ||
| resp <- find_response(x) | ||
| if (!is.null(my_args$data) && !is.null(resp)) { | ||
| my_args$data <- my_args$data[, setdiff(colnames(my_args$data), resp), drop = FALSE] | ||
| } | ||
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| if (classification) { | ||
| out <- factor( | ||
| resp_levels[max.col(probs, ties.method = "first")], | ||
| levels = resp_levels | ||
| ) | ||
| return(.get_predicted_out(out, my_args = my_args)) | ||
| } | ||
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| # predictions matrix to long format (column-major, i.e. by response level) | ||
| out <- .get_predicted_out(probs, my_args = my_args) | ||
| ci_data <- data.frame( | ||
| Row = out$Row, | ||
| Response = out$Response, | ||
| SE = as.vector(rez$se.fit), | ||
| stringsAsFactors = FALSE | ||
| ) | ||
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| # intervals on the logit scale via the delta method, as in | ||
| # `ordinal:::predict.clm()`, so that they stay within [0, 1] | ||
| if (!is.null(ci) && !is.na(ci)) { | ||
| crit_val <- stats::qnorm((1 + ci) / 2) | ||
| p <- out$Predicted | ||
| se_logit <- ci_data$SE / (p * (1 - p)) | ||
| ci_data$CI_low <- stats::plogis(stats::qlogis(p) - crit_val * se_logit) | ||
| ci_data$CI_high <- stats::plogis(stats::qlogis(p) + crit_val * se_logit) | ||
| } | ||
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| attr(out, "ci_data") <- ci_data | ||
| out | ||
| } |
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