random forest variable lengths differ - r

Question

I am trying to run RF using a feature as the response variable. I am having trouble passing a string through a variable to be used as the response in RF. First I try running RF on the string passed through a variable as the response and I am getting a "vector lengths differ error". After this, I try just inputing the actual string(feature) as the response and it works fine. Can you shed some light on why the variable lengths are differing? Thanks.
> colnames(Data[1])
[1] "feature1"
> rf.file = randomForest(formula =colnames(Data[1])~ ., data = Data, proximity = T, importance = T, ntree = 500, nodesize = 3)
Error in model.frame.default(formula = colnames(Data[1]) ~ ., :
variable lengths differ (found for 'feature1')
Enter a frame number, or 0 to exit
1: randomForest(formula = colnames(Data[1]) ~ ., data = Data, proximity = T, importance = T, ntree = 500, nodesize = 3)
2: randomForest.formula(formula = colnames(Data[1]) ~ ., data = brainDataTrim, proximity = T, importance = T, ntree = 500, nodesize = 3)
3: eval(m, parent.frame())
4: eval(expr, envir, enclos)
5: model.frame(formula = colnames(Data[1]) ~ ., data = Data, na.action = function (object, ...)
6: model.frame.default(formula = colnames(Data[1]) ~ ., data = Data, na.action = function (object, ...)
Selection: 0
> rf.file = randomForest(formula =feature1~ ., data = Data, proximity = T, importance = T, ntree = 500, nodesize = 3)
> rf.file
Call:
randomForest(formula = feature1 ~ ., data = Data, proximity = T, importance = T, ntree = 500, nodesize = 3)
Type of random forest: regression
Number of trees: 500
No. of variables tried at each split: 3
Mean of squared residuals: 0.1536834
% Var explained: 34.21
>

Answer 1

You are simply misunderstanding how formulas work. Basically, your first attempt isn't supposed to work.
Formulas should consist of names of variables, possibly simple functions of them. e.g.
var1 ~ var2
var1 ~ log(var2)
Note the lack of quotes. If you didn't quote it, it's not a string, its a symbol.
So, avoid raw strings, weird evaluation demands (like Data[1], or any use of $) in your formulas. To construct a formula from strings, paste it together and then call as.formula on the resulting string.
Keep in mind that the whole point of a formula is that you have provided a symbolic representation of the model, and R will then go look for the specific columns you named in the data frame provided.
I think some functions will do the coercion of a string representation of a formula for you (e.g. "var1 ~ var2"), but I wouldn't count on, or expect it.