How to fix "R neuralNet: non-conformable arguments

When working with neural networks in R, especially using the neuralnet package, encountering the error "non-conformable arguments" can be frustrating. This error typically indicates a mismatch in the dimensions of matrices or vectors being used in the computation. This article will guide you through understanding the causes of this error and how to fix it.

Understanding the Error

The "non-conformable arguments" error occurs when the operations involve matrices or vectors that do not align correctly for matrix multiplication or other element-wise operations. In the context of the neuralnet package, this often happens due to issues with the input data or model specification.

Common Causes of the Error

Now we will discuss some Common Causes of the error.

• Mismatched Dimensions in Data: One of the most common causes of this error is when the dimensions of the input data (X) and the response variable (Y) do not match. For example, if X has 100 rows and Y has 90 rows, matrix operations will fail.
• Incorrect Formula Specification: Another common cause is an incorrect formula specification in the neuralnet function. Ensure that the formula correctly references the columns in the dataset.
• Scaling Issues: Neural networks often require input data to be scaled appropriately. If the data is not scaled, it can lead to convergence issues and errors.
• Data Types and Missing Values: Ensure that the data types are appropriate and that there are no missing values in the dataset. Factors should be converted to numeric or dummy variables, and missing values should be handled.

Certainly! Let's go through an example where we intentionally generate the "non-conformable arguments" error using the neuralnet package in R Programming Language.

Step 1: Generate the Error

We'll start by creating an example that generates the "non-conformable arguments" error.

# Load necessary library
library(neuralnet)

# Sample data with mismatched dimensions
set.seed(123)
X <- data.frame(matrix(rnorm(100 * 3), ncol = 3)) # 100 rows, 3 columns
Y <- data.frame(matrix(rnorm(90), ncol = 1))      # 90 rows, 1 column

# Combine data incorrectly (this will cause the error)
data <- cbind(X, Y)
colnames(data) <- c("Input1", "Input2", "Input3", "Output")

# Specify the formula
formula <- Output ~ Input1 + Input2 + Input3

# Attempt to fit the neural network (this will cause the error)
nn <- neuralnet(formula, data = data, hidden = c(5, 3), linear.output = TRUE)

Output:

Error in eval(predvars, data, env) : 
  non-conformable arguments

Step 2: Fix the Error

To resolve this error, we need to ensure that the dimensions of the input (X) and output (Y) data match. Here’s how to fix it:

1. Ensure Matching Dimensions: Both X and Y should have the same number of rows.
2. Combine Data Correctly: Combine X and Y only after ensuring they have the same number of rows.
3. Verify and Correct Data Preparation: Check for any other potential issues like missing values or data type mismatches.

# Correct data preparation
set.seed(123)
X <- data.frame(matrix(rnorm(100 * 3), ncol = 3)) # 100 rows, 3 columns
Y <- data.frame(matrix(rnorm(100), ncol = 1))    # 100 rows, 1 column (matching X)

# Combine data correctly
data <- cbind(X, Y)
colnames(data) <- c("Input1", "Input2", "Input3", "Output")

# Ensure data dimensions match
if (nrow(X) != nrow(Y)) {
  stop("Input and output data must have the same number of rows.")
}

# Specify the formula
formula <- Output ~ Input1 + Input2 + Input3

# Fit the neural network (correctly)
nn <- neuralnet(formula, data = data, hidden = c(5, 3), linear.output = TRUE)

# Print the model summary
print(nn)

Output:

$call
neuralnet(formula = formula, data = data, hidden = c(5, 3), linear.output = TRUE)

$response
         Output
1   -0.71524219
2   -0.75268897
3   -0.93853870
4   -1.05251328
5   -0.43715953.........................................................................................................................

Intercept.to.Output   -7.960927e+00
2layhid1.to.Output     8.408532e+00
2layhid2.to.Output    -7.181079e+00
2layhid3.to.Output     6.869261e+00

attr(,"class")
[1] "nn"

X and Y now have 100 rows each, ensuring they are conformable for matrix operations. Combine X and Y into a single data frame data with correctly named columns.

• Formula Specification: The formula Output ~ Input1 + Input2 + Input3 correctly references the columns in data.
• Fitting the Neural Network: The neuralnet function now works without error because the dimensions of the input and output data match.

By ensuring the dimensions of the input and output data match, we can avoid the "non-conformable arguments" error and successfully fit the neural network model. This example demonstrates the importance of proper data preparation and dimensional matching in neural network modeling.