Adhere to generic function signatures

1 files changed, 19 insertions, 19 deletions

diff --git a/R/predict.gensvm.R b/R/predict.gensvm.R
index 0b5fa91..43a0d52 100644
--- a/R/predict.gensvm.R
+++ b/R/predict.gensvm.R
@@ -3,7 +3,7 @@
 #' @description This function predicts the class labels of new data using a 
 #' fitted GenSVM model.
 #'
-#' @param fit Fitted \code{gensvm} object
+#' @param object Fitted \code{gensvm} object
 #' @param newdata Matrix of new data for which predictions need to be made.
 #' @param add.rownames add the rownames from the training data to the 
 #' predictions
@@ -45,7 +45,7 @@
 #' # compute the accuracy with gensvm.accuracy
 #' gensvm.accuracy(y.test, y.test.pred)
 #'
-predict.gensvm <- function(fit, newdata, add.rownames=FALSE, ...)
+predict.gensvm <- function(object, newdata, add.rownames=FALSE, ...)
 {
     ## Implementation note:
     ## - It might seem that it would be faster to do the prediction directly in 
@@ -55,48 +55,48 @@ predict.gensvm <- function(fit, newdata, add.rownames=FALSE, ...)
     ## the C implementation is *much* faster than doing it in R.
 
     if (missing(newdata)) {
-        newdata <- eval.parent(fit$call$x)
+        newdata <- eval.parent(object$call$x)
     }
     x.test <- as.matrix(newdata)
 
     # Sanity check
-    if (ncol(x.test) != fit$n.features) {
+    if (ncol(x.test) != object$n.features) {
         cat("Error: Number of features of fitted model and testing",
             "data disagree.\n")
         return(invisible(NULL))
     }
 
-    x.train <- eval.parent(fit$call$x)
-    if (fit$kernel == 'linear') {
+    x.train <- eval.parent(object$call$x)
+    if (object$kernel == 'linear') {
         y.pred.c <- .Call("R_gensvm_predict",
                       as.matrix(x.test),
-                      as.matrix(fit$V),
+                      as.matrix(object$V),
                       as.integer(nrow(x.test)),
                       as.integer(ncol(x.test)),
-                      as.integer(fit$n.classes)
+                      as.integer(object$n.classes)
                       )
     } else {
         kernels <- c("linear", "poly", "rbf", "sigmoid")
-        kernel.idx <- which(kernels == fit$kernel) - 1
+        kernel.idx <- which(kernels == object$kernel) - 1
         y.pred.c <- .Call("R_gensvm_predict_kernels",
                           as.matrix(x.test),
                           as.matrix(x.train),
-                          as.matrix(fit$V),
-                          as.integer(nrow(fit$V)),
-                          as.integer(ncol(fit$V)),
+                          as.matrix(object$V),
+                          as.integer(nrow(object$V)),
+                          as.integer(ncol(object$V)),
                           as.integer(nrow(x.train)),
                           as.integer(nrow(x.test)),
-                          as.integer(fit$n.features),
-                          as.integer(fit$n.classes),
+                          as.integer(object$n.features),
+                          as.integer(object$n.classes),
                           as.integer(kernel.idx),
-                          fit$gamma,
-                          fit$coef,
-                          fit$degree,
-                          fit$kernel.eigen.cutoff
+                          object$gamma,
+                          object$coef,
+                          object$degree,
+                          object$kernel.eigen.cutoff
                           )
     }
 
-    yhat <- fit$classes[y.pred.c]
+    yhat <- object$classes[y.pred.c]
 
     if (add.rownames) {
         yhat <- matrix(yhat, length(yhat), 1)