Minor fixes

R/confusion.R

@@ -12,6 +12,9 @@
 #'   frequencies of actual vs. predicted class with attributes `accuracy`
 #'   and `error` giving the overall rates of correct and incorrect prediction.
 #' @seealso [MASS::lda()], [MASS::qda()]
+#' 
+#' @importFrom insight get_response
+#' 
 #' @export
 #'
 #' @examples

R/plot_discrim.R

@@ -12,7 +12,7 @@
 # DONE: ✔️ Added `rev.axes` parameter for reversing discriminant axes
 # DONE: ✔️ Added `xlim`, `ylim` arguments to control axis limits; useful for plots in discrim space.
 #
-# TOTO: ❌ Fix mapping for stat_ellipse() when specifying `geom = "polygon"`
+# TODO: ❌ Fix mapping for stat_ellipse() when specifying `geom = "polygon"`
 # TODO: Create vignette detailing how to use more generally with ggplot
 
 #' Discriminant Analysis Decision Plot using ggplot.
@@ -26,7 +26,7 @@
 #' 
 #' In the case of discriminant analysis, the predicted values are class membership,
 #' so this can be visualized by mapping the categorical predicted class to discrete colors used as the background for the plot, or
-#' plotting the **contours** of predicted class membership as lines (for `[MASS::lda()]`) or qauadratic curves (for `[MASS::qda()]`) in the plot.
+#' plotting the **contours** of predicted class membership as lines (for `[MASS::lda()]`) or quadratic curves (for `[MASS::qda()]`) in the plot.
 #' The predicted class of any observation in the space of the variables displayed can also be rendered as colored **tiles** or **points**
 #' in the background of the plot.
 #' 

R/redundancy.R

@@ -1,110 +1,110 @@
-
-#' Canonical Redundancy Analysis
-#' 
-#' @description
-#' Calculates indices of redundancy (Stewart & Love, 1968) from a canonical
-#' correlation analysis. These give the proportion of variances of the
-#' variables in each set (X and Y) which are accounted for by the variables in
-#' the other set through the canonical variates.
-#' 
-#' @details
-#' 
-#' The term "redundancy analysis" has a different interpretation and implementation in the
-#' environmental ecology literature, such as the \pkg{vegan}.
-#' In that context, each \eqn{Y_i} variable is regressed separately on the predictors in \eqn{X},
-#' to give fitted values \eqn{\widehat{Y} = [\widehat{Y}_1, \widehat{Y}_2, \dots}.
-#' Then a PCA of \eqn{\widehat{Y}} is carried out to determine a reduced-rank structure of 
-#' the predictions.
-#' 
-#' 
-#' @aliases redundancy print.cancor.redundancy
-#' @param object A `"cancor"` object
-#' @param x A `"cancor.redundancy"` for the `print` method.
-#' @param digits Number of digits to print
-#' @param \dots Other arguments
-#' @return An object of class `"cancor.redundancy"`, a list with the 
-#' following 5 components:
-#'    \item{Xcan.redun}{Canonical redundancies for the X variables, i.e., the
-#'       total fraction of X variance accounted for by the Y variables through each
-#'       canonical variate.} 
-#' \item{Ycan.redun}{Canonical redundancies for the Y variables} 
-#' \item{X.redun}{Total canonical redundancy for the X variables,
-#'       i.e., the sum of `Xcan.redun`.} 
-#' \item{Y.redun}{Total canonical redundancy for the Y variables} 
-#' \item{set.names}{names for the X and Y sets of variables}
-#' @author Michael Friendly
-#' @seealso \ [cancor()]
-#' @references 
-#' Muller K. E. (1981).
-#'Relationships between redundancy analysis, canonical correlation, and multivariate regression. 
-#' *Psychometrika*, **46**(2), 139-42.
-#' 
-#' Stewart, D. and Love, W. (1968). A general canonical correlation
-#' index.  *Psychological Bulletin*, 70, 160-163.
-#'
-#' Brainder, "Redundancy in canonical correlation analysis", 
-#' <https://brainder.org/2019/12/27/redundancy-in-canonical-correlation-analysis/>
-#' 
-#' @keywords multivariate
-#' @examples
-#' 
-#' 	data(Rohwer, package="heplots")
-#' X <- as.matrix(Rohwer[,6:10])  # the PA tests
-#' Y <- as.matrix(Rohwer[,3:5])   # the aptitude/ability variables
-#' 
-#' cc <- cancor(X, Y, set.names=c("PA", "Ability"))
-#' 
-#' redundancy(cc)
-#' ## 
-#' ## Redundancies for the PA variables & total X canonical redundancy
-#' ## 
-#' ##     Xcan1     Xcan2     Xcan3 total X|Y 
-#' ##   0.17342   0.04211   0.00797   0.22350 
-#' ## 
-#' ## Redundancies for the Ability variables & total Y canonical redundancy
-#' ## 
-#' ##     Ycan1     Ycan2     Ycan3 total Y|X 
-#' ##    0.2249    0.0369    0.0156    0.2774 
-#' 
-#' 
-#' @export redundancy
-redundancy <- function(object, ...) {
-    if (!inherits(object, "cancor")) 
-        stop("Not a cancor object")
-    cancor <- object$cancor
-    Xstruc <- object$structure$X.xscores
-    Ystruc <- object$structure$Y.yscores
-    
-    # for each canonical variate, fraction of total X, Y variance associated
-    Xcan.vad <- apply(Xstruc^2, 2, mean, na.rm = TRUE)
-    Ycan.vad <- apply(Ystruc^2, 2, mean, na.rm = TRUE)
-    
-    # canonical redundancies for X, Y variables (total fraction of X variance accounted for by Y variables through canonical
-    # variables, and vice-versa)
-    Xcan.redun <- Xcan.vad * cancor^2
-    Ycan.redun <- Ycan.vad * cancor^2
-    
-    result <- list(Xcan.redun=Xcan.redun, 
-	               Ycan.redun=Ycan.redun, 
-	               X.redun=sum(Xcan.redun), 
-	               Y.redun=sum(Ycan.redun),
-	               set.names=object$names$set.names)
-    class(result) <- "cancor.redundancy"
-    # invisible(result)
-    result
-} 
-
-#' @describeIn redundancy `print()` method for `"cancor.redundancy"` objects.
-#' @export
-print.cancor.redundancy <- function(x, digits=max(getOption("digits") - 3, 3), ...) {
-	Xname <- x$set.names[1]
-	Yname <- x$set.names[2]
-	cat(paste("\nRedundancies for the", Xname, "variables & total X canonical redundancy\n\n"))
-	Xredun <- c(x$Xcan.redun, "total X|Y"=x$X.redun)
-	print(Xredun, digits=digits)
-	
-	cat(paste("\nRedundancies for the", Yname, "variables & total Y canonical redundancy\n\n"))
-	Yredun <- c(x$Ycan.redun, "total Y|X"=x$Y.redun)
-	print(Yredun, digits=digits)
-	
-}
+
+#' Canonical Redundancy Analysis
+#' 
+#' @description
+#' Calculates indices of redundancy (Stewart & Love, 1968) from a canonical
+#' correlation analysis. These give the proportion of variances of the
+#' variables in each set (X and Y) which are accounted for by the variables in
+#' the other set through the canonical variates.
+#' 
+#' @details
+#' 
+#' The term "redundancy analysis" has a different interpretation and implementation in the
+#' environmental ecology literature, such as the \pkg{vegan}.
+#' In that context, each \eqn{Y_i} variable is regressed separately on the predictors in \eqn{X},
+#' to give fitted values \eqn{\widehat{Y} = [\widehat{Y}_1, \widehat{Y}_2, \dots}.
+#' Then a PCA of \eqn{\widehat{Y}} is carried out to determine a reduced-rank structure of 
+#' the predictions.
+#' 
+#' 
+#' @aliases redundancy print.cancor.redundancy
+#' @param object A `"cancor"` object
+#' @param x A `"cancor.redundancy"` for the `print` method.
+#' @param digits Number of digits to print
+#' @param \dots Other arguments
+#' @return An object of class `"cancor.redundancy"`, a list with the 
+#' following 5 components:
+#'    \item{Xcan.redun}{Canonical redundancies for the X variables, i.e., the
+#'       total fraction of X variance accounted for by the Y variables through each
+#'       canonical variate.} 
+#' \item{Ycan.redun}{Canonical redundancies for the Y variables} 
+#' \item{X.redun}{Total canonical redundancy for the X variables,
+#'       i.e., the sum of `Xcan.redun`.} 
+#' \item{Y.redun}{Total canonical redundancy for the Y variables} 
+#' \item{set.names}{names for the X and Y sets of variables}
+#' @author Michael Friendly
+#' @seealso [cancor()]
+#' @references 
+#' Muller K. E. (1981).
+#'Relationships between redundancy analysis, canonical correlation, and multivariate regression. 
+#' *Psychometrika*, **46**(2), 139-42.
+#' 
+#' Stewart, D. and Love, W. (1968). A general canonical correlation
+#' index.  *Psychological Bulletin*, 70, 160-163.
+#'
+#' Brainder, "Redundancy in canonical correlation analysis", 
+#' <https://brainder.org/2019/12/27/redundancy-in-canonical-correlation-analysis/>
+#' 
+#' @keywords multivariate
+#' @examples
+#' 
+#' 	data(Rohwer, package="heplots")
+#' X <- as.matrix(Rohwer[,6:10])  # the PA tests
+#' Y <- as.matrix(Rohwer[,3:5])   # the aptitude/ability variables
+#' 
+#' cc <- cancor(X, Y, set.names=c("PA", "Ability"))
+#' 
+#' redundancy(cc)
+#' ## 
+#' ## Redundancies for the PA variables & total X canonical redundancy
+#' ## 
+#' ##     Xcan1     Xcan2     Xcan3 total X|Y 
+#' ##   0.17342   0.04211   0.00797   0.22350 
+#' ## 
+#' ## Redundancies for the Ability variables & total Y canonical redundancy
+#' ## 
+#' ##     Ycan1     Ycan2     Ycan3 total Y|X 
+#' ##    0.2249    0.0369    0.0156    0.2774 
+#' 
+#' 
+#' @export redundancy
+redundancy <- function(object, ...) {
+    if (!inherits(object, "cancor")) 
+        stop("Not a cancor object")
+    cancor <- object$cancor
+    Xstruc <- object$structure$X.xscores
+    Ystruc <- object$structure$Y.yscores
+
+    # for each canonical variate, fraction of total X, Y variance associated
+    Xcan.vad <- apply(Xstruc^2, 2, mean, na.rm = TRUE)
+    Ycan.vad <- apply(Ystruc^2, 2, mean, na.rm = TRUE)
+
+    # canonical redundancies for X, Y variables (total fraction of X variance accounted for by Y variables through canonical
+    # variables, and vice-versa)
+    Xcan.redun <- Xcan.vad * cancor^2
+    Ycan.redun <- Ycan.vad * cancor^2
+
+    result <- list(Xcan.redun=Xcan.redun, 
+	               Ycan.redun=Ycan.redun, 
+	               X.redun=sum(Xcan.redun), 
+	               Y.redun=sum(Ycan.redun),
+	               set.names=object$names$set.names)
+    class(result) <- "cancor.redundancy"
+    # invisible(result)
+    result
+} 
+
+#' @describeIn redundancy `print()` method for `"cancor.redundancy"` objects.
+#' @export
+print.cancor.redundancy <- function(x, digits=max(getOption("digits") - 3, 3), ...) {
+	Xname <- x$set.names[1]
+	Yname <- x$set.names[2]
+	cat(paste("\nRedundancies for the", Xname, "variables & total X canonical redundancy\n\n"))
+	Xredun <- c(x$Xcan.redun, "total X|Y"=x$X.redun)
+	print(Xredun, digits=digits)
+
+	cat(paste("\nRedundancies for the", Yname, "variables & total Y canonical redundancy\n\n"))
+	Yredun <- c(x$Ycan.redun, "total Y|X"=x$Y.redun)
+	print(Yredun, digits=digits)
+
+}

R/reflect.R

@@ -47,7 +47,7 @@
 #' X <- as.matrix(Rohwer[,6:10])  # the PA tests
 #' Y <- as.matrix(Rohwer[,3:5])   # the aptitude/ability variables
 #' Rohwer.can <- cancor(X, Y, set.names=c("PA", "Ability"))
-#' coef(Rohwer)
+#' coef(Rohwer.can)
 #' Rohwer.can |> reflect() |> coef()
 #'
 #' 

inst/CITATION

@@ -15,7 +15,7 @@ c(
                      family = "Fox", role="aut")),
     year     = year,
     note     = note,
-    url      = "https://CRAN.R-project.org/package=heplots"
+    url      = "https://CRAN.R-project.org/package=candisc"
   ),
 
   bibentry(