Save an object in R using a function parameter as name - r

Question

I looked all over the website and could not get the correct answer for this dilemma:
I have an UDF for evaluating some classification models, with different datasets, and i wanted to have a single function for evaluating them. I want to have something like the following, that given the name of the model and the data, it computes some metrics (confusion matrix for example) and saves them to an object outside the function.
The problem here is that I want to create this object using the name of the model I am evaluating.
I ended up with something like this:
foo <- function(x) {return(as.character(substitute(x)))}
model1 <- lm(Sepal.Width ~ Sepal.Length, iris)
Validation.func <- function(model_name, dataset){
Pred_Train = predict(model_name, dataset)
assign(paste("Pred_Train_",foo(model_name), sep=''), Pred_Train, envir=globalenv())
Pred_Train_prob = predict(model_name, dataset, type = "prob")
MC_Train = confusionMatrix(Pred_Train, dataset$target_salto)
}
Running it for Validation.func(model1,iris) We would want to get the variable stored as "Pred_Train_model1".
As model_name is not a string we had to try to convert it using the foo function (which is the answer i found in here) foo = function(x)deparse(substitute(x)) I do not get what I want, since it saves the object as: "Pred_Train_model_name" instead of "Pred_Train_model1".
Does anyone know how to solve it?

Answer 1

model_name in your function must be a model object, hence cannot be used in paste function, which expects characters.
I think you want your function to know that the model object is actually called "model1" in the environment where it comes from. I think this is quite tricky attempt since your model object may be called by various names.
The easiest implementation would be to give both model object and the name separately, and the use the former for prediction and the latter for naming the outcome.
func1 <- function(model, model_str, dataset)
{
p <- predict(model, dataset)
assign(paste("predict_", model_str, sep=""), p, envir=globalenv())
}
model1 <- lm(mpg ~ cyl, data=mtcars)
func1(model1, "model1", mtcars)
predict_model1
Another implementation, tricky but works if used with care, would be to give only the character name of the model and obtain the model object by get function from the parent environment.
func2 <- function(model_str, dataset)
{
p <- predict(get(model_str, envir=parent.env(environment())), dataset)
assign(paste("predict_", model_str, sep=""), p, envir=globalenv())
}
model2 <- lm(mpg ~ cyl, data=mtcars)
func2("model2", mtcars)
predict_model2
Finally, in order to give the model object to the function and let the function to find the variable name, then you can use match.call function to recover how the function has been called.
func3 <- function(model, dataset)
{
s <- match.call()
model_str <- as.character(s)[2]
p <- predict(model, dataset)
assign(paste("predict_", model_str, sep=""), p, envir=globalenv())
}
model3 <- lm(mpg ~ cyl, data=mtcars)
func3(model3, mtcars)
predict_model3

Answer 2

So here's a suggestion, that does not exactly solve the problem, but does make the function work.
Validation.func <- function(model_name, dataset){
model_name_obj<- eval(parse(text = model_name))
Pred_Train = predict(model_name_obj, dataset)
assign(paste("Pred_Train_",model_name, sep=''), Pred_Train, envir=globalenv())
Pred_Train_prob = predict(model_name_obj, dataset, type = "prob")
MC_Train = confusionMatrix(Pred_Train, dataset$target_salto)
}
Validation.func("model1", data)
What I did is pretty much the opposite of what you were trying. I passed model_name as a string, and then evaluate it using parse(text = model_name). Note that the evaluated object is now called model_name_obj and it is passed in the predict function.
I got some errors later on in the function, but they are irrelevant to the issue at hand. They had to do with the type argument in predict and about not recognizing the confusionMatrix, because I assume I didn't load the corresponding package.