Understanding the Issue with Model.matrix Row Count in NeuralNet

The question at hand revolves around the issue of inconsistent row counts when working with the neuralnet library in R. Specifically, it’s about how to ensure that the model.matrix function produces matrices with a consistent number of rows, despite differences in missing values between the training and test datasets.

Background on Model.matrix

In R, the model.matrix() function is used to create a design matrix for linear models, including those built using the neuralnet() library. The resulting matrix contains the predictor variables as columns, with 1s or -1s in the intercept column depending on the model formula.

When working with datasets containing missing values (NA), the model.matrix() function may produce matrices with fewer rows than expected. This is because NA values are often treated as unknown or missing data points, rather than distinct observations.

The Problem

In this question, we have two separate issues:

1. When building a matrix from the training dataset (matrix.train), there are significantly more rows (714) compared to the test dataset (331). This discrepancy arises because some NA values in the original training data frame are dropped during model creation.
2. The compute() function’s output matrix, which represents the predictions for the test dataset, also has fewer rows than expected.

To resolve this issue and create a consistent number of rows across both datasets, we need to figure out how to account for missing values in the training data when building the design matrix.

Account For Missing Values When Building Model Matrix

When building the model.matrix from the training dataset, if there are NA values in any row, R will drop those entire rows. This is because R considers a row with at least one NA value to be an incomplete or invalid observation.

However, when we’re working with datasets that contain a mix of complete and missing data points, it’s often useful to keep all the available data points intact. To do this, we can use the na.action argument in model.matrix() to specify how R should handle NA values during model creation.

Solution Overview

Our goal is to ensure that all rows from both datasets are represented consistently across the design matrix. We’ll need to modify the way missing values are handled when building the model matrix for each dataset.

Here’s a step-by-step solution:

1. Use na.action="mask" with model.matrix() to remove any row containing an NA value.
2. If we want to preserve all rows, including those with NA values, use na.action="lastobs" instead.

Modifying Model Matrix Creation for Consistency

To create a consistent number of rows across both datasets, we’ll need to adjust the model matrix creation process for each dataset individually.

Here’s an example code snippet demonstrating how to modify model matrix creation:

# Create the training data frame
set.seed(123)
train_data <- data.frame(x = rnorm(100), y = rnorm(100))

# Model creation without accounting for missing values
model <- lm(y ~ x, data = train_data)

# Create the design matrix with NA handling
design_matrix_train <- model.matrix(model, na.action = "mask")

# Create the test data frame
set.seed(123)
test_data <- data.frame(x = rnorm(50), y = rnorm(50))

# Model creation without accounting for missing values
model_test <- lm(y ~ x, data = test_data)

# Create the design matrix with NA handling
design_matrix_test <- model_test.matrix(na.action = "mask")

In this example, we create two separate models (model and model_test) using linear regression on different datasets. We then use model.matrix() to create design matrices for each dataset, adjusting the NA action to mask any rows containing an NA value.

Alternative Approach: Use na.action="lastobs" for Consistency

If we want to preserve all rows from both datasets, including those with NA values, we can specify na.action = "lastobs" when creating the model matrix.

Here’s the modified code:

# Create the training data frame
set.seed(123)
train_data <- data.frame(x = rnorm(100), y = rnorm(100))

# Model creation without accounting for missing values
model <- lm(y ~ x, data = train_data)

# Create the design matrix with NA handling
design_matrix_train <- model.matrix(model, na.action = "lastobs")

# Create the test data frame
set.seed(123)
test_data <- data.frame(x = rnorm(50), y = rnorm(50))

# Model creation without accounting for missing values
model_test <- lm(y ~ x, data = test_data)

# Create the design matrix with NA handling
design_matrix_test <- model_test.matrix(na.action = "lastobs")

By using na.action = "lastobs", we ensure that all rows from both datasets are included in the design matrices.

Conclusion

In this solution, we discussed how to handle missing values when creating design matrices for linear models built with R’s neuralnet library. By adjusting the NA action when building model matrices using model.matrix(), we can create a consistent number of rows across both datasets, including those with NA values.

Note that the choice between masking or preserving NA values depends on your specific use case and requirements.

Last modified on 2023-05-20