more ordinal augment cleanup/comments

NAMESPACE

@@ -2,6 +2,7 @@
 
 S3method(as.data.frame,ranef.lme)
 S3method(augment,brmsfit)
+S3method(augment,clmm)
 S3method(augment,gamm4)
 S3method(augment,glmmTMB)
 S3method(augment,glmmadmb)
@@ -22,6 +23,7 @@ S3method(glance,lme)
 S3method(glance,lqmm)
 S3method(glance,merMod)
 S3method(glance,stanreg)
+S3method(predict,clmm)
 S3method(ranef,MCMCglmm)
 S3method(sigma,brmsfit)
 S3method(tidy,MCMCglmm)
@@ -87,6 +89,7 @@ importFrom(stats,confint)
 importFrom(stats,cov2cor)
 importFrom(stats,logLik)
 importFrom(stats,median)
+importFrom(stats,model.frame)
 importFrom(stats,na.omit)
 importFrom(stats,plogis)
 importFrom(stats,pnorm)

R/ordinal_tidiers.R

@@ -1,3 +1,4 @@
+#' @export
 predict.clmm <- function(object, ...) {
     ## hack clmm object so it looks sufficiently like a clm[m]2 object
     ##  for the predict.clm2 method to work ...
@@ -10,7 +11,7 @@ predict.clmm <- function(object, ...) {
 
 ## predict values for every level in an ordinal response
 ## copied/modified from 
-predict.all.clmm <- function(object, newdata, ...) {
+predict_all_clmm <- function(object, newdata, ...) {
     respvar <- attr(object$terms, "response")
     mf <- model.frame(object)
     nlev <- length(levels(mf[[respvar]]))
@@ -36,16 +37,25 @@ augment.clmm <- function( x,
     if (!missing(newdata)) data <- newdata
 
     ## STUB
+    ## call predict_all_clmm
+    ## generate mean predictions via
+    ##  sweep([pred matrix], MARGIN = 2, FUN = "*", STATS = seq(ncol[pred matrix])) |> rowMeans
+    ## get std dev similarly (sqrt(rowMeans(sweep with (1:n)^2)))
+    ## residuals, pearson residuals?
 }
 
+## simulate method will look like this:
+## pred_matrix <- predict_all_clmm()
+## apply(pred_matrix, 1, \(p) rmultinom(1, size = 1, prob = p))
+
 if (FALSE) {
     library(ordinal)
 
     fmm1 <- clmm(rating ~ temp + contact + (1|judge), data = wine)
     fmm2 <- clmm2(rating ~ temp + contact, random = judge, data = wine)
 
 
-    mm <- predict.all.clmm(fmm1)
+    mm <- predict_all_clmm(fmm1)
     stopifnot(all.equal(predict(fmm1), predict(fmm2),
                         tolerance  = 1e-6))