I am applying rpart function to a data frame named train having all the integer values. There are too many features so for that I have created a formula.
columns_features <- (paste(colnames(train)[31:50], collapse = "+"))
formulas <- as.formula(train$left_eye_center_x ~ columns_features)
tree_pred <- rpart(formulas , data = train)
Here , I get the error message
Error in model.frame.default(formula = formulas, data = train, na.action = function (x) : variable lengths differ (found for 'columns_features')
When I check formulas it has
train$left_eye_center_x ~ columns_features
and for column_features it has
[1] "l_1+ l_2+ l_3+ l_4+ l_5+ l_6+ l_7+ l_8+ l_9+ l_10+ l_11+ l_12+ l_13+ l_14+ l_15+ l_16+ l_17+ l_18+ l_19+ l_20"
For checking purpose when I manually enter the column names here, it works
formulas <- as.formula(train$left_eye_center_x ~ l_1+ l_2+ l_3+ l_4+ l_5+ l_6+ l_7+ l_8+ l_9+ l_10+ l_11+ l_12+ l_13+ l_14+ l_15+ l_16+ l_17+ l_18+ l_19+ l_20 )
tree_pred <- rpart(formulas , data = train)
Is double quote creating the error? What could be solution to this? I have many features so I cannot afford to enter each and every feature manually.
From the ?as.formula examples:
## Create a formula for a model with a large number of variables:
xnam <- paste0("x", 1:25)
(fmla <- as.formula(paste("y ~ ", paste(xnam, collapse= "+"))))
Which implies that in your case the following should work:
formulas <- as.formula(paste("train$left_eye_center_x ~", paste(colnames(train)[31:50], collapse = "+")))
A work-around, instead of using your approach would be (NB: I never used rpart, but I am confident that this works):
formulas <- as.formula(train$left_eye_center_x ~ .)
tree_pred <- rpart(formulas , data = train[,31:50])
If rpart does not like getting indexed data you could define a new dataframe:
train4rpart <- train[,31:50]
tree_pred <- rpart(formulas , data = train4rpart)
Actually, reading through ?rpart, you can skip the whole formula thing:
tree_pred <- rpart(train$left_eye_center_x ~ . , data = train[,31:50])
OR
tree_pred <- rpart(train$left_eye_center_x ~ . , data = train4rpart)