Fix family

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: monaLisa
 Type: Package
 Title: Binned Motif Enrichment Analysis and Visualization
-Version: 1.17.0
+Version: 1.17.1
 Authors@R: c(
     person("Dania", "Machlab", email = "dania.machlab@gmail.com", role = c("aut"), 
            comment = c(ORCID = "0000-0002-2578-6930")),
@@ -61,7 +61,7 @@ Suggests:
     TxDb.Mmusculus.UCSC.mm10.knownGene
 License: GPL (>= 3)
 Encoding: UTF-8
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3
 VignetteBuilder: knitr
 biocViews: 
     MotifAnnotation, 

NAMESPACE

@@ -120,7 +120,6 @@ importFrom(ggplot2,theme)
 importFrom(ggplot2,theme_classic)
 importFrom(ggplot2,ylim)
 importFrom(glmnet,glmnet)
-importFrom(glmnet,predict.glmnet)
 importFrom(grDevices,col2rgb)
 importFrom(grDevices,colorRampPalette)
 importFrom(grid,gpar)
@@ -150,6 +149,7 @@ importFrom(stats,model.matrix)
 importFrom(stats,p.adjust)
 importFrom(stats,pbinom)
 importFrom(stats,ppois)
+importFrom(stats,predict)
 importFrom(stats,quantile)
 importFrom(stats,reorder)
 importFrom(stats,runif)

NEWS.md

@@ -1,3 +1,7 @@
+# monaLisa 1.17.1
+
+* clarifications of supported families for the stability selection
+
 # monaLisa 1.15.3
 
 * minor updates to adjust to breaking changes in `ggplot2` version 4

R/stability_selection.R

@@ -41,8 +41,8 @@
 #'
 #' @seealso \code{\link[stabs]{glmnet.lasso}} and \code{\link[glmnet]{glmnet}}
 #'
-#' @importFrom glmnet glmnet predict.glmnet
-#' @importFrom stats model.matrix runif
+#' @importFrom glmnet glmnet
+#' @importFrom stats model.matrix runif predict
 #' @importFrom cli cli_abort cli_inform
 #'
 #' @keywords internal
@@ -71,7 +71,7 @@
       x, y, dfmax = q - 1,
       penalty.factor = 1/stats::runif(ncol(x), weakness,  1), ...)
 
-  selected <- glmnet::predict.glmnet(fit, type = "nonzero")
+  selected <- predict(fit, type = "nonzero")
   selected <- selected[[length(selected)]]
   ret <- logical(ncol(x))
   ret[selected] <- TRUE
@@ -92,7 +92,9 @@
 #'     package, but implements the randomized lasso version.
 #'
 #' @param x The predictor matrix.
-#' @param y The response vector.
+#' @param y The response vector. This should be a numeric vector (also for 
+#'     the binomial case - in this case it will be converted to a factor 
+#'     internally by \code{glmnet}).
 #' @param weakness Value between 0 and 1 (default = 0.8).
 #'     It affects how strict the method will be in selecting predictors. The
 #'     closer it is to 0, the more stringent the selection. A weakness value
@@ -105,7 +107,8 @@
 #'     \code{cutoff} and \code{PFER}). 
 #'     The available arguments to \code{.glmnetRandomizedLasso} are the same as 
 #'     the ones for \code{\link[stabs]{glmnet.lasso}}. A typical use case would 
-#'     be to define the \code{family} argument to \code{\link[glmnet]{glmnet}}. 
+#'     be to define the \code{family} argument to \code{\link[glmnet]{glmnet}} 
+#'     (currently "gaussian" and "binomial" are supported). 
 #' @param cutoff Value between 0 and 1 (default = 0.8) which is the cutoff
 #'     for the selection probability. Any variable with a selection probability
 #'     that is higher than the set cutoff will be selected.
@@ -238,7 +241,7 @@ randLassoStabSel <- function(x, y, weakness=0.8, cutoff=0.8, PFER=2,
     if (!is(x, "matrix")) {
         cli_abort("{.arg x} must be a {.cls matrix}")
     }
-    .assertVector(y, type = "numeric")
+    .assertVector(x = y, type = "numeric")
     if (nrow(x) != length(y)) {
         cli_abort(paste0(
         "nrow of {.arg x} and length of {.arg y} are not equal. The rows of ",
@@ -268,6 +271,10 @@ randLassoStabSel <- function(x, y, weakness=0.8, cutoff=0.8, PFER=2,
                            paste(names(glmnet.args)[i], collapse = ",")))    
             glmnet.args <- glmnet.args[!i]
         }
+        if ("family" %in% names(glmnet.args) && 
+            !glmnet.args$family %in% c("binomial", "gaussian")) {
+            cli_abort("currently only binomial and gaussian families are supported")
+        }
     }
 
     # run randomized lasso stability selection

tests/testthat/test_stability_selection.R

@@ -5,11 +5,11 @@ test_that("randLassoStabSel() works properly", {
     Y <- rnorm(n = 500, mean = 2, sd = 1)
     X <- matrix(data = NA, nrow = length(Y), ncol = 50)
     for (i in seq_len(ncol(X))) {
-        X[ ,i] <- runif(n = 500, min = 0, max = 3)
+        X[, i] <- runif(n = 500, min = 0, max = 3)
     }
     s_cols <- sample(x = seq_len(ncol(X)), size = 10, replace = FALSE)
     for (i in seq_along(s_cols)) {
-        X[ ,s_cols[i]] <- X[ ,s_cols[i]] + Y
+        X[, s_cols[i]] <- X[, s_cols[i]] + Y
     }
 
     X2 <- X
@@ -61,6 +61,12 @@ test_that("randLassoStabSel() works properly", {
                                                       x = 3, weakness = 0.1))
     }, "Ignoring the following elements")
     expect_identical(ssbnf, ssbnf2)
+
+    expect_error(randLassoStabSel(x = X, y = factor(Y > 2)), 
+                 ".y. must be of class .numeric.")
+    expect_error(randLassoStabSel(x = X, y = as.numeric(Y > 2), 
+                                  glmnet.args = list(family = "multinomial")),
+                 "currently only binomial and gaussian")
 })
 
 test_that("randLassoStabSel() is deterministic", {