diff --git a/NAMESPACE b/NAMESPACE
index 92cc5bd2..11879f03 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -34,29 +34,14 @@ S3method(summary,estimate_slopes)
 S3method(summary,reshape_grouplevel)
 S3method(unstandardize,estimate_predicted)
 S3method(unstandardize,visualisation_matrix)
-S3method(visualisation_matrix,MixMod)
-S3method(visualisation_matrix,brmsfit)
 S3method(visualisation_matrix,character)
 S3method(visualisation_matrix,data.frame)
+S3method(visualisation_matrix,default)
 S3method(visualisation_matrix,double)
 S3method(visualisation_matrix,factor)
-S3method(visualisation_matrix,gamm)
-S3method(visualisation_matrix,glm)
-S3method(visualisation_matrix,glmerMod)
-S3method(visualisation_matrix,glmmTMB)
-S3method(visualisation_matrix,hurdle)
-S3method(visualisation_matrix,ivreg)
-S3method(visualisation_matrix,list)
-S3method(visualisation_matrix,lm)
-S3method(visualisation_matrix,lmerMod)
 S3method(visualisation_matrix,logical)
-S3method(visualisation_matrix,merMod)
 S3method(visualisation_matrix,numeric)
-S3method(visualisation_matrix,polr)
-S3method(visualisation_matrix,stanreg)
-S3method(visualisation_matrix,svyglm)
 S3method(visualisation_matrix,visualisation_matrix)
-S3method(visualisation_matrix,zeroinfl)
 S3method(visualisation_recipe,estimate_grouplevel)
 S3method(visualisation_recipe,estimate_means)
 S3method(visualisation_recipe,estimate_predicted)
diff --git a/R/visualisation_matrix.data.frame.R b/R/visualisation_matrix.data.frame.R
index e2d8de1f..5f3e7e3d 100644
--- a/R/visualisation_matrix.data.frame.R
+++ b/R/visualisation_matrix.data.frame.R
@@ -3,7 +3,7 @@
 #' Create a reference matrix, useful for visualisation, with evenly spread and combined values. `data_matrix()` is an alternative name for `visualisation_matrix()`.
 #'
 #' @param x An object from which to construct the reference grid.
-#' @param at,target Can be "all" or list of characters indicating columns of interest. Can also contain assignments (e.g., `at = "Sepal.Length = 2"` or `at = c("Sepal.Length = 2", "Species = 'setosa'")` - note the usage of single and double quotes to assign strings within strings). The remaining variables will be fixed. (`target` is the deprecated name of that argument).
+#' @param at,target Can be "all" or list of characters indicating columns of interest. Can also contain assignments (as named list, e.g. `at = list(c(Sepal.Length = c(2, 4), Species = "setosa"))`, or as string, e.g. `at = "Sepal.Length = 2"` or `at = c("Sepal.Length = 2", "Species = 'setosa'")` - note the usage of single and double quotes to assign strings within strings). The remaining variables will be fixed. (`target` is the deprecated name of that argument).
 #' @param length Length of numeric "at" variables.
 #' @param range Can be one of `c("range", "iqr", "ci", "hdi", "eti")`. If `"range"` (default), will use the min and max of the original vector as end-points. If any other interval, will spread within the range (the default CI width is `95%` but this can be changed by setting something else, e.g., `ci = 0.90`). See [IQR()] and [bayestestR::ci()].
 #' @param factors Type of summary for factors. Can be "reference" (set at the reference level), "mode" (set at the most common level) or "all" to keep all levels.
@@ -36,6 +36,9 @@
 #' visualisation_matrix(data, at = c("Sepal.Length = 3", "Species"))
 #' visualisation_matrix(data, at = c("Sepal.Length = c(3, 1)", "Species = 'setosa'"))
 #'
+#' # with list-style at-argument
+#' visualisation_matrix(data, at = list(Sepal.Length = c(1, 3), Species = "setosa"))
+#'
 #' # Standardize
 #' vizdata <- visualisation_matrix(data, at = "Sepal.Length")
 #' effectsize::standardize(vizdata)
@@ -59,8 +62,12 @@ visualisation_matrix.data.frame <- function(x, at = "all", target = NULL, factor
   if (!is.null(target)) at <- target
   target <- at
 
+  # find numerics that were coerced to factor in-formula
+  numeric_factors <- colnames(x)[sapply(x, function(i) isTRUE(attributes(i)$factor))]
+
+  specs <- NULL
+
   if (is.null(target)) {
-    specs <- NULL
     targets <- data.frame()
   } else {
     # Valid target argument
@@ -68,18 +75,12 @@ visualisation_matrix.data.frame <- function(x, at = "all", target = NULL, factor
       target <- names(x)
     }
 
-    if (is.numeric(target)) {
+    if (is.numeric(target) || is.logical(target)) {
       target <- names(x)[target]
     }
 
-    # Deal with targets ==========================================================
-
-    # Find eventual user-defined specifications for each target
-    specs <- do.call(rbind, lapply(target, .visualisation_matrix_clean_target, x = x))
-    specs$varname <- as.character(specs$varname) # make sure it's a string not fac
-    specs <- specs[!duplicated(specs$varname), ] # Drop duplicates
+    # Deal with factor in-formula transformations ============================
 
-    # Deal with factor transformations
     x[] <- lapply(x, function(i) {
       if (isTRUE(attributes(i)$factor)) {
         as.factor(i)
@@ -88,23 +89,43 @@ visualisation_matrix.data.frame <- function(x, at = "all", target = NULL, factor
       }
     })
 
-    specs$is_factor <- sapply(x[specs$varname], function(x) is.factor(x) || is.character(x))
-    # Create target list of factors -----------------------------------------
-    facs <- list()
-    for (fac in specs[specs$is_factor == TRUE, "varname"]) {
-      facs[[fac]] <- visualisation_matrix(x[[fac]], target = specs[specs$varname == fac, "expression"])
-    }
+    # Deal with targets ==========================================================
 
-    # Create target list of numerics ----------------------------------------
-    nums <- list()
-    for (num in specs[specs$is_factor == FALSE, "varname"]) {
-      nums[[num]] <- visualisation_matrix(x[[num]],
-        target = specs[specs$varname == num, "expression"],
-        reference = reference[[num]],
-        ...
-      )
+    if (is.character(target)) {
+
+      # Find eventual user-defined specifications for each target
+      specs <- do.call(rbind, lapply(target, .visualisation_matrix_clean_target, x = x))
+      specs$varname <- as.character(specs$varname) # make sure it's a string not fac
+      specs <- specs[!duplicated(specs$varname), ] # Drop duplicates
+
+      specs$is_factor <- sapply(x[specs$varname], function(x) is.factor(x) || is.character(x))
+
+      # Create target list of factors -----------------------------------------
+      facs <- list()
+      for (fac in specs[specs$is_factor == TRUE, "varname"]) {
+        facs[[fac]] <- visualisation_matrix(x[[fac]],
+                                            target = specs[specs$varname == fac, "expression"])
+      }
+
+      # Create target list of numerics ----------------------------------------
+      nums <- list()
+      for (num in specs[specs$is_factor == FALSE, "varname"]) {
+        nums[[num]] <- visualisation_matrix(x[[num]],
+                                            target = specs[specs$varname == num, "expression"],
+                                            reference = reference[[num]],
+                                            ...
+        )
+      }
+    } else if (is.list(target)) {
+
+      # we have a list as at-values
+      facs <- target[sapply(x[names(target)], is.factor)]
+      nums <- target[sapply(x[names(target)], is.numeric)]
     }
-    # Assemble the two
+
+    # Assemble the two - the goal is to have two named lists, where variable
+    # names are the names of the list-elements: one list contains elements of
+    # numeric variables, the other one factors.
     targets <- expand.grid(c(nums, facs))
 
     # Preserve range ---------------------------------------------------------
@@ -160,6 +181,14 @@ visualisation_matrix.data.frame <- function(x, at = "all", target = NULL, factor
   # Reset row names
   row.names(targets) <- NULL
 
+  # convert factors back to numeric, if these variables were actually
+  # numeric in the original data
+  if (!is.null(numeric_factors) && length(numeric_factors)) {
+    for (i in numeric_factors) {
+      targets[[i]] <- .factor_to_numeric(targets[[i]])
+    }
+  }
+
   # Attributes
   attr(targets, "adjusted_for") <- ifelse(length(rest_vars) >= 1, rest_vars, NA)
   attr(targets, "at_specs") <- specs
diff --git a/R/visualisation_matrix.models.R b/R/visualisation_matrix.models.R
index 4fc50754..9f3a8861 100644
--- a/R/visualisation_matrix.models.R
+++ b/R/visualisation_matrix.models.R
@@ -3,29 +3,21 @@
 # -------------------------------------------------------------------------
 
 #' @export
-visualisation_matrix.glm <- function(x,
-                                     at = "all",
-                                     factors = "reference",
-                                     numerics = "mean",
-                                     preserve_range = TRUE,
-                                     reference = x,
-                                     include_smooth = TRUE,
-                                     include_random = FALSE,
-                                     include_response = FALSE,
-                                     ...) {
+visualisation_matrix.default <- function(x,
+                                         at = "all",
+                                         factors = "reference",
+                                         numerics = "mean",
+                                         preserve_range = TRUE,
+                                         reference = x,
+                                         include_smooth = TRUE,
+                                         include_random = FALSE,
+                                         include_response = FALSE,
+                                         data = NULL,
+                                         ...) {
   # Retrieve data from model
-  data <- insight::get_data(x)[insight::find_variables(x, "all", flatten = TRUE)]
-
-  # Deal with factor transformations
-  # f <- insight::find_terms(model)
-  data[] <- lapply(data, function(i) {
-    if (isTRUE(attributes(i)$factor)) {
-      as.factor(i)
-    } else {
-      i
-    }
-  })
-
+  if (is.null(data)) {
+    data <- insight::get_data(x)[insight::find_variables(x, "all", flatten = TRUE)]
+  }
 
   # Deal with intercept-only models
   if (include_response == FALSE) {
@@ -76,38 +68,6 @@ visualisation_matrix.glm <- function(x,
 
 
 
-#' @export
-visualisation_matrix.lm <- visualisation_matrix.glm
-#' @export
-visualisation_matrix.brmsfit <- visualisation_matrix.glm
-#' @export
-visualisation_matrix.stanreg <- visualisation_matrix.glm
-#' @export
-visualisation_matrix.polr <- visualisation_matrix.glm
-#' @export
-visualisation_matrix.merMod <- visualisation_matrix.glm
-#' @export
-visualisation_matrix.lmerMod <- visualisation_matrix.glm
-#' @export
-visualisation_matrix.glmerMod <- visualisation_matrix.glm
-#' @export
-visualisation_matrix.glmmTMB <- visualisation_matrix.glm
-#' @export
-visualisation_matrix.MixMod <- visualisation_matrix.glm
-#' @export
-visualisation_matrix.svyglm <- visualisation_matrix.glm
-#' @export
-visualisation_matrix.hurdle <- visualisation_matrix.glm
-#' @export
-visualisation_matrix.zeroinfl <- visualisation_matrix.glm
-#' @export
-visualisation_matrix.ivreg <- visualisation_matrix.glm
-#' @export
-visualisation_matrix.gamm <- visualisation_matrix.glm
-#' @export
-visualisation_matrix.list <- visualisation_matrix.glm # list is gamm4
-
-
 
 # -------------------------------------------------------------------------
 # Below are visualisation_matrix functions that work on visualisation_matrix
@@ -123,3 +83,29 @@ visualisation_matrix.visualisation_matrix <- function(x, reference = attributes(
 
   grid
 }
+
+
+
+# helpers -------------
+.factor_to_numeric <- function(x, lowest = NULL)
+{
+  if (is.numeric(x)) {
+    return(x)
+  }
+  if (is.logical(x)) {
+    return(as.numeric(x))
+  }
+  if (anyNA(suppressWarnings(as.numeric(as.character(stats::na.omit(x)))))) {
+    if (is.character(x)) {
+      x <- as.factor(x)
+    }
+    x <- droplevels(x)
+    levels(x) <- 1:nlevels(x)
+  }
+  out <- as.numeric(as.character(x))
+  if (!is.null(lowest)) {
+    difference <- min(out) - lowest
+    out <- out - difference
+  }
+  out
+}
diff --git a/man/visualisation_matrix.Rd b/man/visualisation_matrix.Rd
index 72682cb0..808c8705 100644
--- a/man/visualisation_matrix.Rd
+++ b/man/visualisation_matrix.Rd
@@ -32,7 +32,7 @@ data_matrix(x, ...)
 
 \item{...}{Arguments passed to or from other methods (for instance, \code{length} or \code{range} to control the spread of numeric variables.).}
 
-\item{at, target}{Can be "all" or list of characters indicating columns of interest. Can also contain assignments (e.g., \code{at = "Sepal.Length = 2"} or \code{at = c("Sepal.Length = 2", "Species = 'setosa'")} - note the usage of single and double quotes to assign strings within strings). The remaining variables will be fixed. (\code{target} is the deprecated name of that argument).}
+\item{at, target}{Can be "all" or list of characters indicating columns of interest. Can also contain assignments (as named list, e.g. \code{at = list(c(Sepal.Length = c(2, 4), Species = "setosa"))}, or as string, e.g. \code{at = "Sepal.Length = 2"} or \code{at = c("Sepal.Length = 2", "Species = 'setosa'")} - note the usage of single and double quotes to assign strings within strings). The remaining variables will be fixed. (\code{target} is the deprecated name of that argument).}
 
 \item{factors}{Type of summary for factors. Can be "reference" (set at the reference level), "mode" (set at the most common level) or "all" to keep all levels.}
 
@@ -72,6 +72,9 @@ visualisation_matrix(data, at = c("Sepal.Length", "Species"), numerics = 0)
 visualisation_matrix(data, at = c("Sepal.Length = 3", "Species"))
 visualisation_matrix(data, at = c("Sepal.Length = c(3, 1)", "Species = 'setosa'"))
 
+# with list-style at-argument
+visualisation_matrix(data, at = list(Sepal.Length = c(1, 3), Species = "setosa"))
+
 # Standardize
 vizdata <- visualisation_matrix(data, at = "Sepal.Length")
 effectsize::standardize(vizdata)