version 1.1.1

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
-Version: 1.1.0
-Date: 2023-11-30 11:27:54 UTC
-SHA: 2d1acfb0ed23f8362bafc4b8951a479173bb2fd1
+Version: 1.1.1
+Date: 2023-12-07 19:44:41 UTC
+SHA: eefe4588d1b8b8614a681efef40f75f57bc08f91

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: collinear
 Title: Seamless Multicollinearity Management
-Version: 1.1.0
+Version: 1.1.1
 Authors@R: 
     person(given = "Blas M.", 
           family = "Benito", , 

NEWS.md

@@ -1,3 +1,7 @@
+# collinear 1.1.1
+
+Hotfix of issue with solve(tol = 0) in systems with no large double support (noLD). This one wasn't fun.
+
 # collinear 1.1.0
 
 Added argument "smoothing" to `target_encoding_mean()` function to implement original target encoding method.

R/vif_df.R

@@ -4,13 +4,14 @@
 #'
 #' Computes the Variance Inflation Factor of all variables in a training data frame.
 #'
+#' Warning: predictors with perfect correlation might cause errors, please use [cor_select()] to remove perfect correlations first.
+#'
 #' The Variance Inflation Factor for a given variable `y` is computed as `1/(1-R2)`, where `R2` is the multiple R-squared of a multiple regression model fitted using `y` as response and all the remaining variables of the input data set as predictors. The equation can be interpreted as "the rate of perfect model's R-squared to the unexplained variance of this model".
 #'
 #' The possible range of VIF values is (1, Inf]. A VIF lower than 10 suggest that removing `y` from the data set would reduce overall multicollinearity.
 #'
-#' This function computes the Variance Inflation Factor (VIF) in three steps:
+#' This function computes the Variance Inflation Factor (VIF) in two steps:
 #' \itemize{
-#'   \item Computes the correlation matrix between all pairs of predictors using `\link[stats]{cor}`.
 #'   \item Applies `\link[base]{solve}` to obtain the precision matrix, which is the inverse of the covariance matrix.
 #'   \item Uses `\link[base]{diag}` to extract the diagonal of the precision matrix, which contains the variance of the prediction of each predictor from all other predictors.
 #' }
@@ -31,6 +32,14 @@
 #' #subset to limit example run time
 #' vi <- vi[1:1000, ]
 #'
+#' #reduce correlation in predictors with cor_select()
+#' vi_predictors <- cor_select(
+#'   df = vi,
+#'   response = "vi_mean",
+#'   predictors = vi_predictors,
+#'   max_cor = 0.75
+#' )
+#'
 #' #without response
 #' #only numeric predictors are returned
 #' df <- vif_df(
@@ -101,19 +110,25 @@ vif_df <- function(
   #and replace them with 0.99 or -0.99
   cor.matrix.range <- range(cor.matrix[upper.tri(cor.matrix)])
   if(1 %in% cor.matrix.range){
-    cor.matrix[cor.matrix == 1] <- 0.99999999
+    cor.matrix[cor.matrix == 1] <- 0.999
     diag(cor.matrix) <- 1
   }
   if(-1 %in% cor.matrix.range){
-    cor.matrix[cor.matrix == -1] <- -0.99999999
+    cor.matrix[cor.matrix == -1] <- -0.999
+  }
+
+  if(capabilities("long.double") == TRUE){
+    tolerance = 0
+  } else {
+    tolerance = .Machine$double.eps
   }
 
   #vif data frame
   tryCatch(
     {
 
       vif.df <- cor.matrix |>
-        solve(tol = 0) |>
+        solve(tol = tolerance) |>
         diag() |>
         data.frame(stringsAsFactors = FALSE) |>
         dplyr::rename(vif = 1) |>
@@ -126,7 +141,7 @@ vif_df <- function(
       rownames(vif.df) <- NULL
 
     }, error = function(e) {
-      stop("the VIF computation failed. Please check for perfect correlations between predictors, or an excessive number of NA values in the 'df' argument.")
+      stop("the VIF computation failed. Please use cor_df() or cor_select() to check and remove perfect correlations from df before the VIF assessment.")
     }
   )
 

R/vif_select.R

@@ -4,6 +4,8 @@
 #'
 #' Automates multicollinearity management by selecting variables based on their Variance Inflation Factor (VIF).
 #'
+#' Warning: predictors with perfect correlation might cause errors, please use [cor_select()] to remove perfect correlations first.
+#'
 #' The [vif_select()] function is designed to automate the reduction of multicollinearity in a set of predictors by using Variance Inflation Factors.
 #'
 #' If the 'response' argument is provided, categorical predictors are converted to numeric via target encoding (see [target_encoding_lab()]). If the 'response' argument is not provided, categorical variables are ignored.
@@ -45,6 +47,14 @@
 #' vi <- vi[1:1000, ]
 #' vi_predictors <- vi_predictors[1:10]
 #'
+#' #reduce correlation in predictors with cor_select()
+#' vi_predictors <- cor_select(
+#'   df = vi,
+#'   response = "vi_mean",
+#'   predictors = vi_predictors,
+#'   max_cor = 0.75
+#' )
+#'
 #' #without response
 #' #without preference_order
 #' #permissive max_vif

README.md

@@ -516,15 +516,15 @@ selected_predictors_response <- cor_select(
   predictors = vi_predictors
 )
 tictoc::toc()
-#> 0.432 sec elapsed
+#> 0.414 sec elapsed
 
 tictoc::tic()
 selected_predictors_no_response <- cor_select(
   df = vi,
   predictors = vi_predictors
 )
 tictoc::toc()
-#> 34.798 sec elapsed
+#> 34.603 sec elapsed
 ```
 
 ``` r

cran-comments.md

@@ -1,27 +1,168 @@
-## Fixed issue
+## Identified issue
 
- Reading CITATION file fails with
-     there is no package called 'collinear'
-   when package is not installed.
-
-This error was caused by this line of code in inst/CITATION:
+This new release responds to the following email by Prof Brian Ripley:
 
-note <- sprintf("R package version %s", packageVersion("collinear")) 
+  Dear maintainer,
+
+  Please see the problems shown on
+  <https://cran.r-project.org/web/checks/check_results_collinear.html>.
+
+  Please correct before 2023-12-21 to safely retain your package on CRAN.
+
+  Do remember to look at the 'Additional issues'.
+
+  The CRAN Team
+
+I can only guess that the received notice refers to the noLD check shown in the "Additional issues" section, which reads as follows:
+
+* using log directory ‘/data/gannet/ripley/R/packages/tests-noLD/collinear.Rcheck’
+* using R Under development (unstable) (2023-12-04 r85659)
+* using platform: x86_64-pc-linux-gnu
+* ...
+* checking tests ...
+  Running ‘spelling.R’
+  Running ‘testthat.R’ [169s/170s]
+ [170s/170s] ERROR
+Running the tests in ‘tests/testthat.R’ failed.
+Complete output:
+  > # This file is part of the standard setup for testthat.
+  > # It is recommended that you do not modify it.
+  > #
+  > # Where should you do additional test configuration?
+  > # Learn more about the roles of various files in:
+  > # * https://r-pkgs.org/tests.html
+  > # * https://testthat.r-lib.org/reference/test_package.html#special-files
+  > 
+  > library(testthat)
+  > library(collinear)
+  > 
+  > test_check("collinear")
+  [ FAIL 1 | WARN 0 | SKIP 0 | PASS 120 ]
+
+  ══ Failed tests ════════════════════════════════════════════════════════════════
+  ── Error ('test-vif_df.R:37:3'): `vif_df()` works ──────────────────────────────
+  Error in `value[[3L]](cond)`: the VIF computation failed. Please check for perfect correlations between predictors, or an excessive number of NA values in the 'df' argument.
+  Backtrace:
+      ▆
+   1. └─collinear::vif_df(df = vi, response = "vi_mean", predictors = vi_predictors) at test-vif_df.R:37:3
+   2.   └─base::tryCatch(...)
+   3.     └─base (local) tryCatchList(expr, classes, parentenv, handlers)
+   4.       └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
+   5.         └─value[[3L]](cond)
+
+  [ FAIL 1 | WARN 0 | SKIP 0 | PASS 120 ]
+  Error: Test failures
+  Execution halted
+* checking PDF version of manual ... OK
+* checking HTML version of manual ... OK
+* checking for non-standard things in the check directory ... OK
+* checking for detritus in the temp directory ... OK
+* DONE
+
+Status: 1 ERROR, 1 NOTE
+See
+  ‘/data/gannet/ripley/R/packages/tests-noLD/collinear.Rcheck/00check.log’
+for details.
+
+Command exited with non-zero status 1
+Time 4:36.35, 249.72 + 10.45
+
+## Solution
+
+The the offensive code was in the line 116 of the function vif_df(), which read as follows:
+
+```r 
+vif.df <- cor.matrix |>
+       solve(tol = 0) |>
+       diag() |>
+       ...
+```
+       
+The new version reads:
+
+```r 
+vif.df <- cor.matrix |>
+       solve() |>
+       diag() |>
+       ...
+```
+
+However, when tol != 0, solve() breaks when variables with perfect correlations are introduced. As such, all failing test and examples now ensure that no perfect correlations reach solve(), as they produce errors. Also, a warning has been added to the documentation of vif_df() and vif_select() to let the user now that perfect correlations may break these functions.     
+
+## Testing changes in noLD
+
+I tested these changes in the noLD platform provided by rhub:
+
+```r
+rhub::local_check_linux(".", image = "rhub/debian-gcc-devel-nold")
+
+...
+
+<R-hub local check results>
+• image: rhub/debian-gcc-devel-nold
+• output:
+  R-hub Linux builder script v0.10.0 (c) R Consortium, 2018-2019
+
+  Package: /tmp/RtmpLeiUIg/file3558a245af2e8/collinear_1.1.1.tar.gz
+  Docker image: rhub/debian-gcc-devel-nold
+  Env vars: 
+  ...
+• container_name: 3c762b6e-3759-4368-adfb-e71cad78f781-2
+• artifacts: 
+  /tmp/RtmpLeiUIg/file3558a12449344
+• check_result:
+── R CMD check results ────────────────────────────────────── collinear 1.1.1 ────
+Duration: 0ms
+
+❯ checking data for non-ASCII characters ... NOTE
+    Note: found 89 marked Latin-1 strings
+    Note: found 1203 marked UTF-8 strings
+
+0 errors ✔ | 0 warnings ✔ | 1 note ✖
+```
+
+The only NOTE refers to Latin-1 and UTF-8 strings.
 
-This line has been removed from the file.
 
 ## R CMD check results
 
+── R CMD check results ────────────── collinear 1.1.1 ────
+Duration: 1m 15.1s
+
 0 errors ✔ | 0 warnings ✔ | 0 notes ✔
 
+R CMD check succeeded
+
 ### Tested platforms
 
   + macos-latest (release)
   + ubuntu-latest (devel)
   + ubuntu-latest (oldrel-1)
   + ubuntu-latest (release)
   + windows-latest (release)
+
+## Test results
+
+==> devtools::test()
+
+ℹ Testing collinear
+✔ | F W  S  OK | Context
+✔ |          5 | auc_score                                  
+✔ |          2 | case_weights                               
+✔ |         16 | collinear [1.4s]                           
+✔ |          6 | cor_df [9.8s]                              
+✔ |          8 | cor_matrix [9.5s]                          
+✔ |          4 | cor_select [18.7s]                         
+✔ |          3 | cramer_v                                   
+✔ |          6 | identify [1.2s]                            
+✔ |         33 | preference_order [13.6s]                   
+✔ |          9 | target_encoding_lab                        
+✔ |         11 | target_encoding_methods                    
+✔ |          7 | validate [1.2s]                            
+✔ |          8 | vif_df                                     
+✔ |          6 | vif_select                                 
 
-## Comments
+══ Results ═════════════════════════════════════════════════
+Duration: 59.1 s
 
-This is a new version (1.1.0) of the package with several new minor features and bug fixes described in the NEWS.md file.
+[ FAIL 0 | WARN 0 | SKIP 0 | PASS 124 ]

dev_scripts/noLD_check.R

@@ -0,0 +1,8 @@
+#relevant info: https://blog.r-hub.io/2019/05/21/nold/
+install.packages("rhub")
+
+#check online
+rhub::check(".", platform = "debian-gcc-devel-nold")
+
+#check local
+rhub::local_check_linux(".", image = "rhub/debian-gcc-devel-nold")

tests/testthat/test-vif_df.R

@@ -4,8 +4,13 @@ testthat::test_that("`vif_df()` works", {
   vi <- vi[1:1000, ]
 
   #create a few perfect correlations in vi
-  vi$a <- vi$b <- vi$c <- vi$d <- vi$soil_sand
-  vi_predictors <- c(vi_predictors, "a", "b", "c")
+  #reduce correlation in predictors with cor_select()
+  vi_predictors <- cor_select(
+    df = vi,
+    response = "vi_mean",
+    predictors = vi_predictors,
+    max_cor = 0.75
+  )
 
   # Test with only numeric predictors
   df <- vif_df(