Question

我将数据集分为70％的训练和30％的验证集。 NaN变量也很多，也许正因为如此，我无法训练我的数据。虽然，我能够将数据集区分为训练和测试数据集。但是当我想训练时，我得到了这个错误（“ na.fail.default（list（ndvi = c（0.426755102040816，0.409，0.501735849056604，：缺少对象中的值”）。

我想用NDVI估算生物量，然后查看与观察到的生物量的关系。

set.seed(123)
inTrain = createDataPartition(newdata$ndvi, p = 0.7, list = FALSE)
training = newdata[ inTrain,]
testing = newdata[-inTrain,]
cols <- c("ndvi", "first", "second", "third","DMY_kg_ha")
newdata[cols] <- lapply(newdata[cols], factor)  ## as.factor() could also be used
set.seed(32343)
modelFit<-train(DMY_kg_ha~first+second+third+treatment, data=training, method='glm',na.rm = na.omit)
modelFit

创建了模型拟合后，我想在R中使用'vif'来找出哪些变量很重要。

Answer 1

尝试一下

# load library
library(caret)

# set seed value
set.seed(123)

# remove NA's in data
newdata = na.omit(newdata)

# split data set
inTrain = createDataPartition(newdata$ndvi, p = 0.7, list = FALSE)
training = newdata[ inTrain,]
testing = newdata[-inTrain,]

# convert columns to factors
cols <- c("ndvi", "first", "second", "third","DMY_kg_ha")
newdata[cols] <- lapply(newdata[cols], factor)  ## as.factor() could also be used

# reset seed value
set.seed(32343)

# train model
modelFit<-train(DMY_kg_ha~first+second+third+treatment, data=training, method='glm',na.rm = na.omit)

# view model
modelFit

在R Studio中训练数据集

1 个答案: