error in linear regression while using the train function in caret package

0
votes

I have a data set called value that have four variables (ER is the dependent variable) and 400 observations (after removing N/A). I tried to divide the dataset into training and test sets and train the model using linear regression in the caret package. But I always get the errors: 

In lm.fit(x, y, offset = offset, singular.ok = singular.ok,  ... :
  extra argument ‘trcontrol’ is disregarded.

Below is my code:

ctrl_lm <- trainControl(method = "cv", number = 5, verboseIter = FALSE)
value_rm = na.omit(value)
set.seed(1)
datasplit <- createDataPartition(y = value_rm[[1]], p = 0.8, list = FALSE)
train.value <- value_rm[datasplit,]
test.value <- value_rm[-datasplit,]
lmCVFit <- train(ER~., data = train.value, method = "lm",
                   trcontrol = ctrl_lm, metric = "Rsquared")
predictedVal <- predict(lmCVFit, test.value)
modelvalues <- data.frame(obs = test.value$ER, pred = predictedVal)
lmcv.out = defaultSummary(modelvalues)
1
rcross-validation

1 Answers

2
votes

The right sintax is trControl, not trcontrol. Try this:

library(caret)
set.seed(1)
n <- 100
value <- data.frame(ER=rnorm(n), X=matrix(rnorm(3*n),ncol=3))

ctrl_lm <- trainControl(method = "cv", number = 5, verboseIter = FALSE)
value_rm = na.omit(value)
set.seed(1)
datasplit <- createDataPartition(y = value_rm[[1]], p = 0.8, list = FALSE)
train.value <- value_rm[datasplit,]
test.value <- value_rm[-datasplit,]
lmCVFit <- train(ER~., data = train.value, method = "lm",
                   trControl = ctrl_lm, metric = "Rsquared")
predictedVal <- predict(lmCVFit, test.value)
modelvalues <- data.frame(obs = test.value$ER, pred = predictedVal)
( lmcv.out <- defaultSummary(modelvalues) )

#      RMSE  Rsquared       MAE 
# 1.2351006 0.1190862 1.0371477