我已经修改了今天早些时候的答案。现在，我生成了一些示例数据，可在这些数据上运行代码。其他人正确地建议您研究使用插入符号包，我同意。但是，在某些情况下，您可能会发现有必要编写自己的代码。下面，我试图说明如何在R中使用sample（）函数将观察结果随机分配给交叉验证折叠。我还使用for循环对10个训练集执行变量预选择（使用单变量线性回归，其最大p值截止值为0.1）和模型构建（使用逐步回归）。然后，您可以编写自己的代码，以将结果模型应用于验证折叠。希望这可以帮助！
################################################################################
## Load the MASS library, which contains the "stepAIC" function for performing
## stepwise regression, to be used later in this script
library(MASS)
################################################################################
################################################################################
## Generate example data, with 100 observations (rows), 70 variables (columns 1
## to 70), and a continuous dependent variable (column 71)
Data <- NULL
Data <- as.data.frame(Data)
for (i in 1:71) {
for (j in 1:100) {
Data[j,i] <- rnorm(1) }}
names(Data)[71] <- "Dependent"
################################################################################
################################################################################
## Create ten folds for cross-validation. Each observation in your data will
## randomly be assigned to one of ten folds.
Data$Fold <- sample(c(rep(1:10,10)))
## Each fold will have the same number of observations assigned to it. You can
## double check this by typing the following:
table(Data$Fold)
## Note: If you were to have 105 observations instead of 100, you could instead
## write: Data$Fold <- sample(c(rep(1:10,10),rep(1:5,1)))
################################################################################
################################################################################
## I like to use a "for loop" for cross-validation. Here, prior to beginning my
## "for loop", I will define the variables I plan to use in it. You have to do
## this first or R will give you an error code.
fit <- NULL
stepw <- NULL
training <- NULL
testing <- NULL
Preselection <- NULL
Selected <- NULL
variables <- NULL
################################################################################
################################################################################
## Now we can begin the ten-fold cross validation. First, we open the "for loop"
for (CV in 1:10) {
## Now we define your training and testing folds. I like to store these data in
## a list, so at the end of the script, if I want to, I can go back and look at
## the observations in each individual fold
training[[CV]] <- Data[which(Data$Fold != CV),]
testing[[CV]] <- Data[which(Data$Fold == CV),]
## We can preselect variables by analyzing each variable separately using
## univariate linear regression and then ranking them by p value. First we will
## define the container object to which we plan to output these data.
Preselection[[CV]] <- as.data.frame(Preselection[CV])
## Now we will run a separate linear regression for each of our 70 variables.
## We will store the variable name and the coefficient p value in our object
## called "Preselection".
for (i in 1:70) {
Preselection[[CV]][i,1] <- i
Preselection[[CV]][i,2] <- summary(lm(Dependent ~ training[[CV]][,i] , data = training[[CV]]))$coefficients[2,4]
}
## Now we will remove "i" and also we will name the columns of our new object.
rm(i)
names(Preselection[[CV]]) <- c("Variable", "pValue")
## Now we will make note of those variables whose p values were less than 0.1.
Selected[[CV]] <- Preselection[[CV]][which(Preselection[[CV]]$pValue <= 0.1),] ; row.names(Selected[[CV]]) <- NULL
## Fit a model using the pre-selected variables to the training fold
## First we must save the variable names as a character string
temp <- NULL
for (k in 1:(as.numeric(length(Selected[[CV]]$Variable)))) {
temp[k] <- paste("training[[CV]]$V",Selected[[CV]]$Variable[k]," + ",sep="")}
variables[[CV]] <- paste(temp, collapse = "")
variables[[CV]] <- substr(variables[[CV]],1,(nchar(variables[[CV]])-3))
## Now we can use this string as the independent variables list in our model
y <- training[[CV]][,"Dependent"]
form <- as.formula(paste("y ~", variables[[CV]]))
## We can build a model using all of the pre-selected variables
fit[[CV]] <- lm(form, training[[CV]])
## Then we can build new models using stepwise removal of these variables using
## the MASS package
stepw[[CV]] <- stepAIC(fit[[CV]], direction="both")
## End for loop
}
## Now you have your ten training and validation sets saved as training[[CV]]
## and testing[[CV]]. You also have results from your univariate pre-selection
## analyses saved as Preselection[[CV]]. Those variables that had p values less
## than 0.1 are saved in Selected[[CV]]. Models built using these variables are
## saved in fit[[CV]]. Reduced versions of these models (by stepwise selection)
## are saved in stepw[[CV]].
## Now you might consider using the predict.lm function from the stats package
## to apply your ten models to their corresponding validation folds. You then
## could look at the performance of the ten models and average their performance
## statistics together to get an overall idea of how well your data predict the
## outcome.
################################################################################
在执行交叉验证之前，重要的是您了解其正确用法。这两个参考文献提供了关于交叉验证的出色讨论：
- Simon RM，Subramanian J，Li MC，MenezesS。使用交叉验证评估基于高维数据的生存风险分类器的预测准确性。简短的生物信息。2011年5月； 12（3）：203-14。电子版2011年2月15 http://bib.oxfordjournals.org/content/12/3/203.long
- 理查德·西蒙（Richard Simon），迈克尔·D·拉德马赫（Michael D.Radmacher），凯文·多宾（Kevin Dobbin）和丽莎·麦克沙恩（Lisa M. 使用DNA微阵列数据进行诊断和预后分类的陷阱。JNCI J Natl癌症研究所（2003）95（1）：14-18。http://jnci.oxfordjournals.org/content/95/1/14.long
这些论文是针对生物统计学家的，但是对任何人都有用。
另外，请始终记住使用逐步回归是危险的（尽管使用交叉验证应有助于减轻过度拟合）。有关逐步回归的详细讨论，请参见：http : //www.stata.com/support/faqs/stat/stepwise.html。
如果您还有其他问题，请告诉我！