Я пытаюсь вписать значения, передав «knnImpute» аргументу preProcess метода train () Карета. Исходя из следующего примера, кажется, что значения не вменяются, остаются как NA и затем игнорируются. Что я делаю неправильно?

Буду признателен за любую оказанную помощь.

library("caret")

set.seed(1234)
data(iris)

# mark 8 of the cells as NA, so they can be imputed
row <- sample (1:nrow (iris), 8)
iris [row, 1] <- NA

# split test vs training
train.index <- createDataPartition (y = iris[,5], p = 0.80, list = F)
train <- iris [ train.index, ]
test  <- iris [-train.index, ]

# train the model after imputing the missing data
fit <- train (Species ~ ., 
              train, 
              preProcess = c("knnImpute"), 
              na.action  = na.pass, 
              method     = "rpart" )
test$species.hat <- predict (fit, test)

# there is 1 obs. (of 30) in the test set equal to NA  
# this 1 obs. was not returned from predict
Error in `
library("caret")

set.seed(1234)
data(iris)

# mark 8 of the cells as NA, so they can be imputed
row <- sample (1:nrow (iris), 8)
iris [row, 1] <- NA

# split test vs training
train.index <- createDataPartition (y = iris[,5], p = 0.80, list = F)
train <- iris [ train.index, ]
test  <- iris [-train.index, ]

# train the model after imputing the missing data
fit <- train (Species ~ ., 
              train, 
              preProcess = c("knnImpute"), 
              na.action  = na.pass, 
              method     = "rpart" )
test$species.hat <- predict (fit, test)

# there is 1 obs. (of 30) in the test set equal to NA  
# this 1 obs. was not returned from predict
Error in `$<-.data.frame`(`*tmp*`, "species.hat", value = c(1L, 1L, 1L,  : 
  replacement has 29 rows, data has 30
lt;-.data.frame`(`*tmp*`, "species.hat", value = c(1L, 1L, 1L,  : 
  replacement has 29 rows, data has 30

ОБНОВИТЬЯ был в состоянии использовать функцию preProcess непосредственно для вменения значений. Я до сих пор не понимаю, почему этого не происходит в функции поезда.

# attempt to impute using nearest neighbors
x <- iris [, 1:4]
pp <- preProcess (x, method = c("knnImpute"))
x.imputed <- predict (pp, newdata = x)

# expect all NAs were populated with an imputed value
stopifnot( all (!is.na (x.imputed)))
stopifnot( length (x) == length (x.imputed))