问题描述
我想用 LASSO 来训练下面的数据集，使用 10-fold CV 对每种方法重复 5 次。
这是我当前的代码，但我收到一条错误消息，我认为我做错了。请告诉我应该如何更改它！
错误信息：
Error in if (nulldev == 0) stop("y is constant; gaussian glmnet fails at standardization step") : missing value where TRUE/FALSE needed
代码：
set.seed(150847)
model.mat <- model.matrix( Class ~ .,train.dat)
x.train <- model.mat [,-1]
model.mat <- model.matrix( Class ~ .,test.dat)
x.test <- model.mat [,-1] #
dv = "Class"
y.train <- as.matrix(train.dat[,dv])
y.test <- as.matrix(test.dat[,dv])
#-----------------------------------------
train.means <- colMeans(x.train)
train.sd <- apply(x.train,2,sd,na.rm = TRUE)
x.train <- as.matrix( scale(x.train,center=TRUE,scale=TRUE))
x.test <- scale(x.test,center = train.means,scale = train.sd)
tr.Control <- trainControl(
method = "repeatedcv",number = 10,repeats = 5)
folds.k = 10
cv.lasso = cv.glmnet (x.train,y.train,alpha=1,nfolds=folds.k,standardize = FALSE,intercept=TRUE)
训练数据“train.dat”
structure(list(x1 = c(-2.48941991263215,-0.338448010439568,-1.07796826066294,1.47833943928667,-0.19013864138727),x2 = c(-1.05660014431803,-1.75938416652951,-1.94445363537753,2.65603302304451,-0.818464313993987
),x3 = c(-0.928819609794076,-0.24431689960579,-0.26055539595143,-0.500006066823682,0.19947842697796),x4 = c(0.167674885884102,-0.714651010370962,0.501841366660604,-0.261356553409404,-0.121081806911108
),x5 = c(0.826293680351228,-0.0522530856542289,0.456970179919153,-0.483860304113477,0.827117071952671),x6 = c(0.229410925647244,0.367363323224708,0.0097867208532989,0.6599692159798,0.454895325470716
),x7 = c(0.277445634594187,0.00411403737962246,0.912381467409432,0.0911673668306321,0.0729619956109673),x8 = c(0.403632419444111,-1.76177968998027,0.818339220424296,0.77257524859948,-1.45634200383022
),x9 = c(0.666298305218494,1.28068782733132,0.243489971387096,0.00907678612957343,0.0688231437305274),x10 = c(-0.674113519037765,-0.221583500325269,0.555570222138564,0.572105515491289,2.32224808146226
),x11 = c(-0.503906052691753,-0.170463238913734,1.81239693119702,-0.310259330876175,0.373355276436323),x12 = c(0.569346066655445,0.665270271264321,-1.04590277174209,-1.08749423169221,-0.717326819631265
),Class = c("No","Yes","No","Yes")),row.names = c(NA,5L),class = "data.frame")
测试数据“test.dat”
structure(list(x1 = c(-1.64667008195797,-1.12098964581992,0.473422701448559,-1.60461690923768,-0.00749172927415004),x2 = c(-1.16286992117132,-0.141705544905757,1.51853911670816,-0.424087214057948,0.377124786278201
),x3 = c(1.19241045039945,1.48443779149667,-1.88482327525843,0.534626743634202,0.310818572560298),x4 = c(-0.930735300584522,-1.7670393982441,-1.14191107118164,0.61126176594059,0.155931701957036
),x5 = c(-0.820323897991329,-0.926557129248977,0.965568253770471,-0.599971735384315,-0.512967912014574),x6 = c(0.29261250467971,0.269901459803805,0.9167238867376,0.000339579302817583,0.398331164848059
),x7 = c(0.262839384144172,0.246979274321347,0.105181680992246,0.170856263954192,0.310768554685637),x8 = c(-0.547301867028384,0.888728318998235,0.289208399599502,-1.37035914659536,-1.25498394079555
),x9 = c(1.91475599789737,-0.663301448358402,0.73492523418078,0.252558835925375,-1.13201069045815),x10 = c(-1.56005878668401,0.47042681670553,-0.294760033296374,-3.26819220678081,-0.921147419029862
),x11 = c(0.287604446919617,-0.161270837465456,-2.23402479016399,-2.25198777628389,-0.61977925827879),x12 = c(1.21464666058049,-0.752948562276805,-1.03149583160279,0.618987295189923,0.274782272114187
),class = "data.frame")
解决方法
暂无找到可以解决该程序问题的有效方法，小编努力寻找整理中！
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