问caret/rfe-错误：“x和y中应该有相同数量的样本”

EN

install.packages('e1071')
library(e1071)
install.packages('readxl')
library(readxl)
library(rpart)
install.packages('randomForest')
library(randomForest)
install.packages('party')
library(party)
install.packages('mlbench')
library(mlbench)
install.packages('caret')
library(caret)
#----------------------------------------------------------
# Import Data
getwd()
setwd("working_directory_name")
df <- read_excel('test_data.xls')
#----------------------------------------------------------
# Get Information on your data (optional)
str(df)
table(df$F32)
#----------------------------------------------------------
install.packages('XLConnect')
library(XLConnect)
# Recursive Feature Selection Approach
control <- rfeControl(functions=rfFuncs, method="cv", number=5)
#x = as.vector(unlist(df[, 2:29]))
#y = as.vector(unlist(df[, 32])) 
# Run the algorithm (Features, Ground Truth, Testes SetSizes)
#results <- rfe(x, y, sizes=c(1:28), rfeControl=control)
results <- rfe(df[, 2:29], df[, 32], sizes=c(1:28), rfeControl=control)
# Visualize results for set sizes
print(results)
# List chosen features
predictors(results)
# plot the results
plot(results, type=c("g", "o"))

set.seed(7)
d=data.frame(matrix(rnorm(2901*15,1,.5),ncol=15))
#something like dependent variable
dp=factor(sample(c(1,1,1,1, 1, 1,2,2,2, 3 ,3,3,4, 4, 4),2901,replace = TRUE))
# define the control using a random forest selection function
control <- rfeControl(functions=rfFuncs, method="cv", number=10)
# run the RFE algorithm
sz=50 # Change sz to 2901 for full sample
results <- rfe(d[1:sz, ],   dp[1:sz],   sizes=c(1:15), rfeControl=control)
# summarize the results
print(results)
plot(results, type=c("g", "o"))