fix examples

R-package/R/callbacks.R

@@ -970,11 +970,10 @@ xgb.cb.cv.predict <- function(save_models = FALSE, outputmargin = FALSE) {
 #' # rate does not break the convergence, but allows us to illustrate the typical pattern of
 #' # "stochastic explosion" behaviour of this lock-free algorithm at early boosting iterations.
 #' bst <- xgb.train(
-#'   param,
+#'   c(param, list(eta = 1.)),
 #'   dtrain,
 #'   evals = list(tr = dtrain),
 #'   nrounds = 200,
-#'   eta = 1.,
 #'   callbacks = list(xgb.cb.gblinear.history())
 #' )
 #'
@@ -985,14 +984,18 @@ xgb.cb.cv.predict <- function(save_models = FALSE, outputmargin = FALSE) {
 #' # With the deterministic coordinate descent updater, it is safer to use higher learning rates.
 #' # Will try the classical componentwise boosting which selects a single best feature per round:
 #' bst <- xgb.train(
-#'   param,
+#'   c(
+#'     param,
+#'     xgb.params(
+#'       eta = 0.8,
+#'       updater = "coord_descent",
+#'       feature_selector = "thrifty",
+#'       top_k = 1
+#'     )
+#'   ),
 #'   dtrain,
 #'   evals = list(tr = dtrain),
 #'   nrounds = 200,
-#'   eta = 0.8,
-#'   updater = "coord_descent",
-#'   feature_selector = "thrifty",
-#'   top_k = 1,
 #'   callbacks = list(xgb.cb.gblinear.history())
 #' )
 #' matplot(xgb.gblinear.history(bst), type = "l")
@@ -1002,11 +1005,10 @@ xgb.cb.cv.predict <- function(save_models = FALSE, outputmargin = FALSE) {
 #'
 #' # For xgb.cv:
 #' bst <- xgb.cv(
-#'   param,
+#'   c(param, list(eta = 0.8)),
 #'   dtrain,
 #'   nfold = 5,
 #'   nrounds = 100,
-#'   eta = 0.8,
 #'   callbacks = list(xgb.cb.gblinear.history())
 #' )
 #' # coefficients in the CV fold #3
@@ -1028,11 +1030,10 @@ xgb.cb.cv.predict <- function(save_models = FALSE, outputmargin = FALSE) {
 #' # For the default linear updater 'shotgun' it sometimes is helpful
 #' # to use smaller eta to reduce instability
 #' bst <- xgb.train(
-#'   param,
+#'   c(param, list(eta = 0.5)),
 #'   dtrain,
 #'   evals = list(tr = dtrain),
 #'   nrounds = 50,
-#'   eta = 0.5,
 #'   callbacks = list(xgb.cb.gblinear.history())
 #' )
 #'
@@ -1043,11 +1044,10 @@ xgb.cb.cv.predict <- function(save_models = FALSE, outputmargin = FALSE) {
 #'
 #' # CV:
 #' bst <- xgb.cv(
-#'   param,
+#'   c(param, list(eta = 0.5)),
 #'   dtrain,
 #'   nfold = 5,
 #'   nrounds = 70,
-#'   eta = 0.5,
 #'   callbacks = list(xgb.cb.gblinear.history(FALSE))
 #' )
 #' # 1st fold of 1st class