r erreur glmnet: objet ne peut pas être contraint de taper « double »

Question

Je suis en train d'obtenir la valeur lambda, j'ai trouvé cette question: R glmnet : "(list) object cannot be coerced to type 'double' "

ne peut pas comprendre comment cependant faire appliquer. voici le code:

faba <- read.table("abalone.txt",sep=",") 
faba$y <- ifelse(faba$V9>9,1,0) 
head(faba) 

xtrain <- faba[1:3133,1:8] 
ytrain <- faba[1:3133,10] 
xtest <- faba[-c(1:3133),1:8] 
ytest <- faba[-c(1:3133),10] 


#find mean, std in xtrain 
mean_xtrain <- sapply(xtrain[2:8], mean) 
sd_xtrain <- sapply(xtrain[2:8], sd) 


ytrain_norm <- as.factor(ytrain) 
ytest_norm <- as.factor(ytest) 


#standardise xtrain with overall mean,sd 
xtrain_norm <- faba[1:3133,1:8] 
for(i in c(2,3,4,5,6,7,8)){ 
    xtrain_norm[i] <- (xtrain_norm[i]-mean_xtrain[i-1])/sd_xtrain[i-1] 
} 


#standardise xtest with xtrain mean,sd 
xtest_norm <- faba[-c(1:3133),1:8] 
for(i in c(2,3,4,5,6,7,8)){ 
    xtest_norm[i] <- (xtest_norm[i]-mean_xtrain[i-1])/sd_xtrain[i-1] 
} 


cvstd <- cv.glmnet(xtrain_norm, ytrain, family = "binomial", 
        alpha = 0, nfolds = 10, type.measure = "class") 

je puis obtenir l'erreur:

Error in lognet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : 
    (list) object cannot be coerced to type 'double' 

Et je ne peux pas comprendre comment résoudre ce problème. Que signifie même l'erreur? Tous les conseils seraient grandement appréciés!
C'est ce que les données ressemble à:

> head(faba) 
    V1 V2 V3 V4  V5  V6  V7 V8 V9 y 
1 M 0.455 0.365 0.095 0.5140 0.2245 0.1010 0.150 15 1 
2 M 0.350 0.265 0.090 0.2255 0.0995 0.0485 0.070 7 0 
3 F 0.530 0.420 0.135 0.6770 0.2565 0.1415 0.210 9 0 
4 M 0.440 0.365 0.125 0.5160 0.2155 0.1140 0.155 10 1 
5 I 0.330 0.255 0.080 0.2050 0.0895 0.0395 0.055 7 0 
6 I 0.425 0.300 0.095 0.3515 0.1410 0.0775 0.120 8 0 

Answer 1

faba <- read.table("abalone.txt",sep=",") 
faba$y <- ifelse(faba$V9>9,1,0) 
head(faba) 

xtrain <- faba[1:3133,1:8] 
ytrain <- faba[1:3133,10] 
xtest <- faba[-c(1:3133),1:8] 
ytest <- faba[-c(1:3133),10] 

#find mean, std in xtrain 
(mean_xtrain <- sapply(xtrain[2:8], mean)) 
(sd_xtrain <- sapply(xtrain[2:8], sd)) 

#standardise xtrain with overall mean,sd 
xtrain_norm <- cbind(xtrain[,1],scale(xtrain[,2:8])) 
ytrain_norm <- as.factor(ytrain) 
#standardise xtest with xtrain mean,sd 
xtest_norm <- faba[-c(1:3133),1:8] 
xtest_norm <- cbind(xtest_norm[,1],scale(xtest_norm[,2:8],center=mean_xtrain,scale=sd_xtrain)) 
ytest_norm <- as.factor(ytest) 

Cela permet d'avoir sous forme de matrice qui est nécessaire pour la glmnet