From 87a86803a5bed331ee7f77866cbfab69acfd3850 Mon Sep 17 00:00:00 2001
From: Dominique Makowski <dom.mak19@gmail.com>
Date: Thu, 30 Nov 2023 15:48:32 +0000
Subject: [PATCH 01/13] Add Variance_Cumulative (explained variance) to summary
 of n_factors()

---
 R/n_factors.R | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/R/n_factors.R b/R/n_factors.R
index ca70ee042..5d14b3855 100644
--- a/R/n_factors.R
+++ b/R/n_factors.R
@@ -340,6 +340,14 @@ n_factors <- function(x,
     n_Methods = as.numeric(by(out, as.factor(out$n_Factors), function(out) n <- nrow(out)))
   )
 
+  # Add cumulative percentage of variance explained
+  fa <- factor_analysis(x, cor=cor, n = max(by_factors$n_Factors))  # Get it from our fa:: wrapper (TODO: that's probably not the most efficient)
+  varex <- attributes(fa)$summary
+  # Extract number of factors from EFA output (usually MR1, ML1, etc.)
+  varex$n_Factors <- as.numeric(gsub("[^\\d]+", "", varex$Component, perl=TRUE))
+  by_factors <- merge(by_factors, varex[, c("n_Factors", "Variance_Cumulative")], by = "n_Factors")
+
+
   attr(out, "summary") <- by_factors
   attr(out, "n") <- min(as.numeric(as.character(
     by_factors[by_factors$n_Methods == max(by_factors$n_Methods), "n_Factors"]

From 1c89a11dc9b057bbea644c82f792af792204692a Mon Sep 17 00:00:00 2001
From: Dominique Makowski <dom.mak19@gmail.com>
Date: Thu, 30 Nov 2023 16:05:25 +0000
Subject: [PATCH 02/13] please lintr

---
 R/n_factors.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/n_factors.R b/R/n_factors.R
index 5d14b3855..eaa210e39 100644
--- a/R/n_factors.R
+++ b/R/n_factors.R
@@ -341,10 +341,10 @@ n_factors <- function(x,
   )
 
   # Add cumulative percentage of variance explained
-  fa <- factor_analysis(x, cor=cor, n = max(by_factors$n_Factors))  # Get it from our fa:: wrapper (TODO: that's probably not the most efficient)
+  fa <- factor_analysis(x, cor = cor, n = max(by_factors$n_Factors))  # Get it from our fa:: wrapper (TODO: that's probably not the most efficient)
   varex <- attributes(fa)$summary
   # Extract number of factors from EFA output (usually MR1, ML1, etc.)
-  varex$n_Factors <- as.numeric(gsub("[^\\d]+", "", varex$Component, perl=TRUE))
+  varex$n_Factors <- as.numeric(gsub("[^\\d]+", "", varex$Component, perl = TRUE))
   by_factors <- merge(by_factors, varex[, c("n_Factors", "Variance_Cumulative")], by = "n_Factors")
 
 

From c815a159d6d1b8f9cb9676edaa87c57e2d9cbe54 Mon Sep 17 00:00:00 2001
From: Dominique Makowski <dom.mak19@gmail.com>
Date: Thu, 30 Nov 2023 16:17:05 +0000
Subject: [PATCH 03/13] add also as attribute

---
 R/n_factors.R | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/R/n_factors.R b/R/n_factors.R
index eaa210e39..6d6644258 100644
--- a/R/n_factors.R
+++ b/R/n_factors.R
@@ -345,9 +345,10 @@ n_factors <- function(x,
   varex <- attributes(fa)$summary
   # Extract number of factors from EFA output (usually MR1, ML1, etc.)
   varex$n_Factors <- as.numeric(gsub("[^\\d]+", "", varex$Component, perl = TRUE))
+  # Merge (and like that filter out empty methods)
   by_factors <- merge(by_factors, varex[, c("n_Factors", "Variance_Cumulative")], by = "n_Factors")
 
-
+  attr(out, "Variance_Explained") <- varex  # We add all the variance explained (for plotting)
   attr(out, "summary") <- by_factors
   attr(out, "n") <- min(as.numeric(as.character(
     by_factors[by_factors$n_Methods == max(by_factors$n_Methods), "n_Factors"]

From 9a0dcc649e8240897a2e1742b272ccacb595385a Mon Sep 17 00:00:00 2001
From: Daniel <mail@danielluedecke.de>
Date: Fri, 1 Dec 2023 09:01:55 +0100
Subject: [PATCH 04/13] add test

---
 tests/testthat/test-n_factors.R | 13 +++++++++++++
 tests/testthat/test-pca.R       |  7 +++----
 2 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/tests/testthat/test-n_factors.R b/tests/testthat/test-n_factors.R
index 5480fdfaa..41198e9f1 100644
--- a/tests/testthat/test-n_factors.R
+++ b/tests/testthat/test-n_factors.R
@@ -71,3 +71,16 @@ test_that("n_factors, no rotation, psych only", {
     )
   )
 })
+
+test_that("n_factors, variance explained", {
+  skip_on_cran()
+  skip_if_not_installed("nFactors")
+  skip_if_not_installed("psych")
+  set.seed(333)
+  x <- n_factors(mtcars[, 1:4], type = "PCA")
+  expect_equal(
+    attributes(x)$Variance_Explained$Variance_Cumulative,
+    c(0.84126, 0.85088, 0.85859, 0.85859),
+    tolerance = 1e-4
+  )
+})
diff --git a/tests/testthat/test-pca.R b/tests/testthat/test-pca.R
index b6dba5bd1..85dbcd8f2 100644
--- a/tests/testthat/test-pca.R
+++ b/tests/testthat/test-pca.R
@@ -57,11 +57,10 @@ test_that("principal_components", {
 # predict ----------------------
 # N.B tests will fail if `GPArotation` package is not installed
 
-d <- na.omit(psych::bfi[, 1:25])
-model <- psych::fa(d, nfactors = 5)
-mp <- model_parameters(model, sort = TRUE, threshold = "max")
-
 test_that("predict model_parameters fa", {
+  d <- na.omit(psych::bfi[, 1:25])
+  model <- psych::fa(d, nfactors = 5)
+  mp <- model_parameters(model, sort = TRUE, threshold = "max")
   pr <- suppressMessages(
     predict(mp, names = c("Neuroticism", "Conscientiousness", "Extraversion", "Agreeableness", "Opennness"))
   )

From af9871ff930ba60fca10d4b9c70eb4bd99b212ef Mon Sep 17 00:00:00 2001
From: Daniel <mail@danielluedecke.de>
Date: Fri, 1 Dec 2023 09:02:05 +0100
Subject: [PATCH 05/13] update description

---
 DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index de8902ec4..731de5c86 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: parameters
 Title: Processing of Model Parameters
-Version: 0.21.3.4
+Version: 0.21.3.5
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",

From bdcd05ef42964a1e260f27905af394e057fff9aa Mon Sep 17 00:00:00 2001
From: Daniel <mail@danielluedecke.de>
Date: Fri, 1 Dec 2023 09:02:33 +0100
Subject: [PATCH 06/13] news

---
 NEWS.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/NEWS.md b/NEWS.md
index 778eb912a..b8cb37e53 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -11,6 +11,9 @@
 * `model_parameters()` for models of package *survey* now gives informative
   messages when `bootstrap = TRUE` (which is currently not supported).
 
+* `n_factors()` now also returns the explained variance for the number of
+  factors as attributes.
+
 # parameters 0.21.3
 
 ## Changes

From efa201b6e495ad60fbf0c4a4ed1fc6cb7871d196 Mon Sep 17 00:00:00 2001
From: Daniel <mail@danielluedecke.de>
Date: Fri, 1 Dec 2023 14:24:52 +0100
Subject: [PATCH 07/13] fix test

---
 tests/testthat/test-pca.R | 9 ++-------
 1 file changed, 2 insertions(+), 7 deletions(-)

diff --git a/tests/testthat/test-pca.R b/tests/testthat/test-pca.R
index 006f5c18f..0e67750ac 100644
--- a/tests/testthat/test-pca.R
+++ b/tests/testthat/test-pca.R
@@ -102,17 +102,12 @@ test_that("predict model_parameters fa", {
   )
   expect_identical(nrow(predict(mp, keep_na = FALSE)), 2436L)
   expect_identical(nrow(predict(mp, newdata = d[1:10, ], keep_na = FALSE)), 10L)
-})
-
-
-test_that("predict factor_analysis", {
-  model <- factor_analysis(d, n = 5)
-  expect_identical(nrow(predict(model, keep_na = FALSE)), 2436L)
-  expect_identical(nrow(predict(mp, newdata = d[1:10, ], keep_na = FALSE)), 10L)
   expect_named(
     predict(mp, names = c("A", "B", "C", "D", "E"), keep_na = FALSE),
     c("A", "B", "C", "D", "E")
   )
+  model <- factor_analysis(d, n = 5)
+  expect_identical(nrow(predict(model, keep_na = FALSE)), 2436L)
 })
 
 unloadNamespace("GPArotation")

From 47f0597bdbb2fa9da21124857ffecc8022a5a81d Mon Sep 17 00:00:00 2001
From: Daniel <mail@danielluedecke.de>
Date: Fri, 1 Dec 2023 16:23:38 +0100
Subject: [PATCH 08/13] implement #755

---
 R/standardize_info.R | 16 ++++++------
 R/utils_pca_efa.R    | 59 ++++++++++++++++++++++++--------------------
 2 files changed, 40 insertions(+), 35 deletions(-)

diff --git a/R/standardize_info.R b/R/standardize_info.R
index 9993c60b2..0d6fe5b9f 100644
--- a/R/standardize_info.R
+++ b/R/standardize_info.R
@@ -382,12 +382,12 @@ standardize_info.default <- function(model,
   }
 
   if (info$is_linear) {
-    if (!robust) {
-      sd_y <- datawizard::weighted_sd(response, w)
-      mean_y <- datawizard::weighted_mean(response, w)
-    } else {
+    if (robust) {
       sd_y <- datawizard::weighted_mad(response, w)
       mean_y <- datawizard::weighted_median(response, w)
+    } else {
+      sd_y <- datawizard::weighted_sd(response, w)
+      mean_y <- datawizard::weighted_mean(response, w)
     }
   } else {
     sd_y <- 1
@@ -567,12 +567,12 @@ standardize_info.default <- function(model,
     response <- as.numeric(data[, variable])
   }
 
-  if (!robust) {
-    sd_x <- datawizard::weighted_sd(response, weights)
-    mean_x <- datawizard::weighted_mean(response, weights)
-  } else {
+  if (robust) {
     sd_x <- datawizard::weighted_mad(response, weights)
     mean_x <- datawizard::weighted_median(response, weights)
+  } else {
+    sd_x <- datawizard::weighted_sd(response, weights)
+    mean_x <- datawizard::weighted_mean(response, weights)
   }
 
   list(sd = f * sd_x, mean = mean_x)
diff --git a/R/utils_pca_efa.R b/R/utils_pca_efa.R
index 56bfaf932..1f22586a8 100644
--- a/R/utils_pca_efa.R
+++ b/R/utils_pca_efa.R
@@ -107,7 +107,7 @@ summary.parameters_efa <- function(object, ...) {
 
 
   x <- as.data.frame(t(x[, cols]))
-  x <- cbind(data.frame("Parameter" = row.names(x), stringsAsFactors = FALSE), x)
+  x <- cbind(data.frame(Parameter = row.names(x), stringsAsFactors = FALSE), x)
   names(x) <- c("Parameter", attributes(object)$summary$Component)
   row.names(x) <- NULL
 
@@ -149,40 +149,45 @@ predict.parameters_efa <- function(object,
                                    verbose = TRUE,
                                    ...) {
   attri <- attributes(object)
-  if (is.null(newdata)) {
-    if ("scores" %in% names(attri)) {
-      out <- as.data.frame(attri$scores)
-      if (isTRUE(keep_na)) {
-        out <- .merge_na(object, out, verbose)
+
+  if (inherits(attri$model, c("psych", "principal"))) {
+    if (is.null(newdata)) {
+      if ("scores" %in% names(attri)) {
+        out <- as.data.frame(attri$scores)
+        if (isTRUE(keep_na)) {
+          # Because pre-made scores don't preserve NA
+          out <- .merge_na(object, out)
+        }
+      } else {
+        d <- attri$data_set
+        d <- d[vapply(d, is.numeric, logical(1))]
+        out <- as.data.frame(stats::predict(attri$model, data = d))
       }
     } else {
-      if ("dataset" %in% names(attri)) {
-        out <- as.data.frame(stats::predict(attri$model, data = attri$dataset))
+      # psych:::predict.principal(object, data)
+      out <- predict(attri$model, data = newdata)
+    }
+  } else if (inherits(attri$model, c("psych", "fa"))) {
+    if (is.null(newdata)) {
+      if ("scores" %in% names(attri)) {
+        out <- as.data.frame(attri$scores)
+        if (isTRUE(keep_na)) {
+          # Because pre-made scores don't preserve NA
+          out <- .merge_na(object, out)
+        }
       } else {
-        insight::format_error(
-          "Could not retrieve data nor model. Please report an issue on {.url https://github.com/easystats/parameters/issues}."
-        )
+        d <- attri$data_set
+        d <- d[vapply(d, is.numeric, logical(1))]
+        out <- as.data.frame(stats::predict(attri$model, data = d))
       }
-    }
-  } else {
-    if (inherits(attri$model, c("psych", "fa"))) {
-      # Clean-up newdata (keep only the variables used in the model)
-      newdata <- newdata[names(attri$model$complexity)] # assuming "complexity" info is there
+    } else {
       # psych:::predict.fa(object, data)
       out <- as.data.frame(stats::predict(attri$model, data = newdata))
-    } else if (inherits(attri$model, "spca")) {
-      # https://github.com/erichson/spca/issues/7
-      newdata <- newdata[names(attri$model$center)]
-      if (attri$standardize) {
-        newdata <- sweep(newdata, MARGIN = 2, STATS = attri$model$center, FUN = "-", check.margin = TRUE)
-        newdata <- sweep(newdata, MARGIN = 2, STATS = attri$model$scale, FUN = "/", check.margin = TRUE)
-      }
-      out <- as.matrix(newdata) %*% as.matrix(attri$model$loadings)
-      out <- stats::setNames(as.data.frame(out), paste0("Component", seq_len(ncol(out))))
-    } else {
-      out <- as.data.frame(stats::predict(attri$model, newdata = newdata, ...))
     }
+  } else {
+    out <- as.data.frame(stats::predict(attri$model, newdata = newdata, ...))
   }
+
   if (!is.null(names)) {
     names(out)[seq_along(names)] <- names
   }

From de8f464b52ff0376f2da2e2621d42b1450e13dfb Mon Sep 17 00:00:00 2001
From: Daniel <mail@danielluedecke.de>
Date: Fri, 1 Dec 2023 16:25:43 +0100
Subject: [PATCH 09/13] fix spca

---
 R/utils_pca_efa.R | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/R/utils_pca_efa.R b/R/utils_pca_efa.R
index 1f22586a8..6df0bc53b 100644
--- a/R/utils_pca_efa.R
+++ b/R/utils_pca_efa.R
@@ -184,6 +184,15 @@ predict.parameters_efa <- function(object,
       # psych:::predict.fa(object, data)
       out <- as.data.frame(stats::predict(attri$model, data = newdata))
     }
+  } else if (inherits(attri$model, "spca")) {
+    # https://github.com/erichson/spca/issues/7
+    newdata <- newdata[names(attri$model$center)]
+    if (attri$standardize) {
+      newdata <- sweep(newdata, MARGIN = 2, STATS = attri$model$center, FUN = "-", check.margin = TRUE)
+      newdata <- sweep(newdata, MARGIN = 2, STATS = attri$model$scale, FUN = "/", check.margin = TRUE)
+    }
+    out <- as.matrix(newdata) %*% as.matrix(attri$model$loadings)
+    out <- stats::setNames(as.data.frame(out), paste0("Component", seq_len(ncol(out))))
   } else {
     out <- as.data.frame(stats::predict(attri$model, newdata = newdata, ...))
   }

From c0837c35fdd80e58ff4feb0c38d1eda533de8d4c Mon Sep 17 00:00:00 2001
From: Daniel <mail@danielluedecke.de>
Date: Sat, 2 Dec 2023 14:48:05 +0100
Subject: [PATCH 10/13] fix?

---
 R/utils_pca_efa.R         |  4 ++--
 tests/testthat/test-pca.R | 28 ++++------------------------
 2 files changed, 6 insertions(+), 26 deletions(-)

diff --git a/R/utils_pca_efa.R b/R/utils_pca_efa.R
index 6df0bc53b..c95fb1167 100644
--- a/R/utils_pca_efa.R
+++ b/R/utils_pca_efa.R
@@ -182,7 +182,7 @@ predict.parameters_efa <- function(object,
       }
     } else {
       # psych:::predict.fa(object, data)
-      out <- as.data.frame(stats::predict(attri$model, data = newdata))
+      out <- as.data.frame(stats::predict(attri$model, newdata = newdata))
     }
   } else if (inherits(attri$model, "spca")) {
     # https://github.com/erichson/spca/issues/7
@@ -194,7 +194,7 @@ predict.parameters_efa <- function(object,
     out <- as.matrix(newdata) %*% as.matrix(attri$model$loadings)
     out <- stats::setNames(as.data.frame(out), paste0("Component", seq_len(ncol(out))))
   } else {
-    out <- as.data.frame(stats::predict(attri$model, newdata = newdata, ...))
+    out <- as.data.frame(stats::predict(attri$model, newdata = attri$dataset, ...))
   }
 
   if (!is.null(names)) {
diff --git a/tests/testthat/test-pca.R b/tests/testthat/test-pca.R
index 0e67750ac..014a3c102 100644
--- a/tests/testthat/test-pca.R
+++ b/tests/testthat/test-pca.R
@@ -29,16 +29,16 @@ test_that("principal_components", {
 
 test_that("principal_components, n", {
   data(iris)
-  x <- parameters::principal_components(iris[1:4], n = 2)
+  x <- principal_components(iris[1:4], n = 2)
   expect_named(x, c("Variable", "PC1", "PC2", "Complexity"))
 
-  x <- parameters::principal_components(iris[1:4], n = 1)
+  x <- principal_components(iris[1:4], n = 1)
   expect_named(x, c("Variable", "PC1", "Complexity"))
 })
 
 
 test_that("principal_components", {
-  x <- parameters::principal_components(mtcars[, 1:7])
+  x <- principal_components(mtcars[, 1:7])
 
   expect_equal(
     x$PC1,
@@ -55,27 +55,7 @@ test_that("principal_components", {
   )
 
   expect_named(x, c("Variable", "PC1", "PC2", "Complexity"))
-})
-
-test_that("principal_components", {
-  x <- model_parameters(principal_components(mtcars[, 1:7], nfactors = 2))
-  expect_equal(
-    x$RC1,
-    c(
-      -0.836114674884308,
-      0.766808147590597,
-      0.85441780762136,
-      0.548502661888057,
-      -0.889046093964722,
-      0.931879020871552,
-      -0.030485507571411
-    ),
-    tolerance = 0.01
-  )
-
-  expect_named(x, c("Variable", "RC1", "RC2", "Complexity", "Uniqueness"))
-  expect_identical(dim(suppressWarnings(predict(x))), c(32L, 2L))
-  expect_identical(dim(suppressWarnings(predict(x, newdata = mtcars[1:3, 1:7]))), c(3L, 2L))
+  expect_identical(dim(predict(x)), c(32L, 2L))
 })
 
 

From 94cc5cd9ebe7238aee91975b73b6537e184ebf2b Mon Sep 17 00:00:00 2001
From: Daniel <mail@danielluedecke.de>
Date: Sun, 3 Dec 2023 12:19:02 +0100
Subject: [PATCH 11/13] fix

---
 R/utils_pca_efa.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/utils_pca_efa.R b/R/utils_pca_efa.R
index c95fb1167..0336c3de4 100644
--- a/R/utils_pca_efa.R
+++ b/R/utils_pca_efa.R
@@ -165,7 +165,7 @@ predict.parameters_efa <- function(object,
       }
     } else {
       # psych:::predict.principal(object, data)
-      out <- predict(attri$model, data = newdata)
+      out <- stats::predict(attri$model, data = newdata)
     }
   } else if (inherits(attri$model, c("psych", "fa"))) {
     if (is.null(newdata)) {

From 4de562fa21aa38a9f97755673d8005e79e0b193b Mon Sep 17 00:00:00 2001
From: Daniel <mail@danielluedecke.de>
Date: Sun, 3 Dec 2023 12:23:28 +0100
Subject: [PATCH 12/13] simplify

---
 R/utils_pca_efa.R | 19 +------------------
 1 file changed, 1 insertion(+), 18 deletions(-)

diff --git a/R/utils_pca_efa.R b/R/utils_pca_efa.R
index 0336c3de4..b8d0211fa 100644
--- a/R/utils_pca_efa.R
+++ b/R/utils_pca_efa.R
@@ -150,7 +150,7 @@ predict.parameters_efa <- function(object,
                                    ...) {
   attri <- attributes(object)
 
-  if (inherits(attri$model, c("psych", "principal"))) {
+  if (inherits(attri$model, c("psych", "principal", "psych", "fa"))) {
     if (is.null(newdata)) {
       if ("scores" %in% names(attri)) {
         out <- as.data.frame(attri$scores)
@@ -167,23 +167,6 @@ predict.parameters_efa <- function(object,
       # psych:::predict.principal(object, data)
       out <- stats::predict(attri$model, data = newdata)
     }
-  } else if (inherits(attri$model, c("psych", "fa"))) {
-    if (is.null(newdata)) {
-      if ("scores" %in% names(attri)) {
-        out <- as.data.frame(attri$scores)
-        if (isTRUE(keep_na)) {
-          # Because pre-made scores don't preserve NA
-          out <- .merge_na(object, out)
-        }
-      } else {
-        d <- attri$data_set
-        d <- d[vapply(d, is.numeric, logical(1))]
-        out <- as.data.frame(stats::predict(attri$model, data = d))
-      }
-    } else {
-      # psych:::predict.fa(object, data)
-      out <- as.data.frame(stats::predict(attri$model, newdata = newdata))
-    }
   } else if (inherits(attri$model, "spca")) {
     # https://github.com/erichson/spca/issues/7
     newdata <- newdata[names(attri$model$center)]

From 4eb18d0c769bd58a2cdb115f7c9175586666eaf1 Mon Sep 17 00:00:00 2001
From: Daniel <mail@danielluedecke.de>
Date: Sun, 3 Dec 2023 12:29:59 +0100
Subject: [PATCH 13/13] styler

---
 R/n_factors.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/n_factors.R b/R/n_factors.R
index 6d6644258..9b6c335af 100644
--- a/R/n_factors.R
+++ b/R/n_factors.R
@@ -341,14 +341,14 @@ n_factors <- function(x,
   )
 
   # Add cumulative percentage of variance explained
-  fa <- factor_analysis(x, cor = cor, n = max(by_factors$n_Factors))  # Get it from our fa:: wrapper (TODO: that's probably not the most efficient)
+  fa <- factor_analysis(x, cor = cor, n = max(by_factors$n_Factors)) # Get it from our fa:: wrapper (TODO: that's probably not the most efficient)
   varex <- attributes(fa)$summary
   # Extract number of factors from EFA output (usually MR1, ML1, etc.)
   varex$n_Factors <- as.numeric(gsub("[^\\d]+", "", varex$Component, perl = TRUE))
   # Merge (and like that filter out empty methods)
   by_factors <- merge(by_factors, varex[, c("n_Factors", "Variance_Cumulative")], by = "n_Factors")
 
-  attr(out, "Variance_Explained") <- varex  # We add all the variance explained (for plotting)
+  attr(out, "Variance_Explained") <- varex # We add all the variance explained (for plotting)
   attr(out, "summary") <- by_factors
   attr(out, "n") <- min(as.numeric(as.character(
     by_factors[by_factors$n_Methods == max(by_factors$n_Methods), "n_Factors"]