Error in if (length(scores.temp) == 1 && scores.temp == 0) { : Missing Value Where TRUE/FALSE Needed

In this blog post, we will delve into the intricacies of missing value handling in R and explore a common issue encountered when using the ampute function from the mice package. We will also discuss the underlying reasons behind the error message and provide practical advice on how to resolve it.

The Error

When working with data that contains missing values, it’s essential to handle them appropriately to maintain data integrity and avoid introducing biases into your analysis. The ampute function is designed to impute missing values in a dataset while preserving the original data structure. However, when using this function, you might encounter an error message indicating that there is a missing value where TRUE/FALSE needs to be evaluated.

Error Message

Error in if (length(scores.temp) == 1 && scores.temp == 0) { : 
    missing value where TRUE/FALSE needed

In this specific case, the error message occurs when the length of scores.temp is equal to 1 and its contents are all zero. The problem lies in the fact that when there’s only one row with missing values, the standard deviation calculation fails due to division by zero.

Understanding the ampute Function

The ampute function from the mice package is designed to impute missing values using various methods. One of its primary functions is to standardize scores for amputing data. The std parameter controls this standardization process. When set to TRUE, it means that the scores will be centered and scaled (i.e., taken as their standard deviation) before imputation.

Embedded Score Summing Function

The ampute function employs an embedded score summing function to calculate standardized scores. This embedded function sums the scores for each missing pattern in the dataset. If the std parameter is set to TRUE, this function attempts to center and scale the scores by taking their standard deviation.

Error Due to Single Row with Missing Values

When there’s only one row with missing values, the score summing process encounters an issue. In this case, trying to calculate the standard deviation of each column in a single-row matrix results in NaN (Not a Number) due to division by zero. This is because the standard deviation calculation requires at least two non-missing values to compute.

Workaround: Setting std Parameter to FALSE

Fortunately, there’s an easy fix for this issue. By setting the std parameter to FALSE, you can avoid the attempt to center and scale scores in single-row matrices with missing values. This approach ensures that the standardization process doesn’t fail due to NaN values.

A_miss <- ampute(A, std = FALSE)

This workaround should not impact the structure of the imputed data since scaling is merely an affine transform applied to the weights used for inducing missingness.

Conclusion

In this blog post, we explored a common issue encountered when using the ampute function from the mice package. By understanding the underlying reasons behind the error message and learning how to avoid it, you can ensure that your data imputation process is accurate and reliable.

Remember to set the std parameter to FALSE when working with datasets containing single rows with missing values to prevent errors due to NaN values in the score summing function.

Last modified on 2023-12-07