오늘 초부터 답변을 수정했습니다. 이제 코드를 실행할 예제 데이터를 생성했습니다. 다른 사람들은 당신이 내가 동의하는 캐럿 패키지를 사용하는 것을 옳게 제안했습니다. 그러나 경우에 따라 자체 코드를 작성해야 할 수도 있습니다. 아래에서는 R의 sample () 함수를 사용하여 교차 검증 배에 관측치를 무작위로 할당하는 방법을 보여 주려고 시도했습니다. 또한 10 개의 학습 세트에서 변수 사전 선택 (관용적 인 p 값 컷오프가 0.1 인 일 변량 선형 회귀 사용) 및 단계적 회귀 사용을 사용하여 변수 사전 선택을 수행하기 위해 루프를 사용합니다. 그런 다음 자체 코드를 작성하여 결과 모델을 유효성 검사 폴더에 적용 할 수 있습니다. 도움이 되었기를 바랍니다!

################################################################################
## 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.
################################################################################

교차 유효성 검사를 수행하기 전에 올바른 사용법에 대해 읽으십시오. 이 두 참고 문헌은 교차 검증에 대한 훌륭한 토론을 제공합니다.

1. Simon RM, Subramanian J, Li MC, Menezes S. 교차 검증을 사용하여 고차원 데이터를 기반으로 생존 위험 분류기의 예측 정확도를 평가합니다. 간단한 생물 정보. 2011 년 5 월; 12 (3) : 203-14. Epub 2011 년 2 월 15 일. http://bib.oxfordjournals.org/content/12/3/203.long
2. Richard Simon, Michael D. Radmacher, Kevin Dobbin 및 Lisa M. McShane. 진단 및 예후 분류를위한 DNA Microarray 데이터 사용의 함정. JNCI J Natl Cancer Inst (2003) 95 (1) : 14-18. http://jnci.oxfordjournals.org/content/95/1/14.long

이 논문은 생물 통계학자를 대상으로하지만 누구에게나 유용 할 것입니다.

또한 단계적 회귀를 사용하는 것은 위험합니다 (교차 유효성 검사를 사용하면 과적 합을 완화하는 데 도움이되지만). 단계별 회귀에 대한 자세한 내용은 http://www.stata.com/support/faqs/stat/stepwise.html에서 확인할 수 있습니다 .

추가 질문이 있으면 알려주세요!

답변

# extract nonzero coefficients
coefs.all <- as.matrix(coef(cv.glmnet.obj, s="lambda.min"))
idx <- as.vector(abs(coefs.all) > 0)
coefs.nonzero <- as.matrix(coefs.all[idx])
rownames(coefs.nonzero) <- rownames(coefs.all)[idx]