Resolving the 'input.data' Not Found Error When Using foreach() Inside gamlss in R

Understanding the Issue with R Package gamlss Inside foreach()

The gamlss package is used for generalized additive models and its foreach() function can be used to perform parallel computations. However, when using foreach() inside a function to get leave-one-out predicted values from a model fitted with gamlss, users often encounter the issue of failing to find an object named “input.data”.

Background on gamlss and foreach()

The gamlss package is used for generalized additive models. It provides an interface to various families of distributions, including Beta-binomial, which is suitable for modeling proportions.

The foreach() function in R is a powerful tool for parallel computing. It allows users to perform computations in parallel, improving the efficiency and speed of their code.

How foreach() Works Inside gamlss

Inside the foreach() function, each iteration creates a new environment (also known as a “scope”) where the current model object (model.obj) is bound to the current iteration. This binding allows the code inside the loop to access the model parameters and data.

When using foreach() to get leave-one-out predicted values from a model fitted with gamlss, we need to create an updated version of the model for each iteration, excluding one observation at a time. However, in this specific case, users are facing an issue where the object “input.data” is not found.

The Problem with input.data

The problem lies in how the code inside the foreach() loop tries to access and manipulate the data. By default, variables within a scope do not persist between iterations, which leads to issues when trying to exclude observations from the model.

In this case, the updated model (updated.model.obj) includes the entire dataset initially, but inside the loop, it is used without specifying the data source explicitly. The variable input.data is supposed to hold the current iteration’s row (i.e., an individual observation), which should be excluded when creating the new model.

However, in this case, because of how R handles variables and loops within a scope, the code tries to access input.data without specifying its source or context. This results in R not finding the “object ‘input.data’” as expected.

Solving the Problem: Providing Data Along with Newdata

The solution to this problem is to explicitly provide both newdata and data arguments when calling the predict() function inside the loop.

By providing both, we ensure that the model receives the correct data for prediction. In this case, we pass in the updated model (updated.model.obj) along with the data excluding the current row (input.data[-i,]). We also include the new observation’s row (input.data[i,]) as a separate argument to handle prediction correctly.

# Get the leave-one-out values
loo_predict.mu &lt;- function(model.obj, input.data) {
  yhat &lt;- foreach(i = 1 : nrow(input.data), .packages="gamlss", .combine = rbind) %dopar% {
    updated.model.obj &lt;- update(model.obj, data = input.data[-i, ])
    predict(updated.model.obj, what = "mu", data = input.data[-i, ],
            newdata = input.data[i,], type = "response")
  }
  return(data.frame(result = yhat[, 1], row.names = NULL))
}

Conclusion

In this article, we have explored the issue of gamlss failing to find an object named “input.data” when used inside a parallel computation using foreach(). The problem stems from R’s handling of variables and scope within loops.

We discussed how the code fails due to not explicitly specifying data or context for certain variables, leading to R not finding the expected objects. We also introduced the solution by providing both newdata and data arguments when calling predict() inside the loop.

By understanding this issue and using the provided solution, you can solve similar problems when working with parallel computations in R, ensuring that your code handles data correctly and efficiently.

Additional Advice

When working with foreach() or other parallel computation tools in R, it is crucial to pay attention to scope and variable handling. This will help prevent unexpected behavior and errors due to variables not being available as expected within the loop.

Additionally, when using gamlss models inside a parallel context, make sure to provide data and newdata arguments correctly to ensure accurate predictions and model evaluations.