The Mysterious Case of the Non-Numerical KNN Impute Error

As a data analyst and technical blogger, I’ve encountered my fair share of errors when working with machine learning algorithms. Recently, I stumbled upon a peculiar issue with the knn.impute function in R, which seems to be causing frustration among users. In this article, we’ll delve into the details of this error and explore its underlying causes.

Understanding the KNN Impute Function

The knn.impute function is a part of the bnstruct package, designed for imputing missing values in datasets using k-nearest neighbors (KNN) algorithm. This method works by selecting the K nearest neighbors to each missing value and using their values to make predictions.

The Error: Non-Numerical Input

When I encountered this error, it was with a simple example that seemed like it should work:

library(bnstruct)
data(iris)
data <- iris[, 1:4]
data <- prodNA(data, noNA = 0.2) # generate random missing data
knn.impute(data, k = 10, cat.var = 1:ncol(data),
           to.impute = 1:nrow(data), using = 1:nrow(data))

However, the code resulted in a cryptic error message:

Error in storage.mode(use.data) <- "double" :
  (list) object cannot be coerced to type 'double'

This message suggests that there’s an issue with the input data being passed to knn.impute, but it doesn’t provide much insight into what exactly is causing the problem.

Investigating the Error

After some digging, I discovered that the error is related to the fact that knn.impute requires a numerical matrix as input, not a dataframe. This makes sense, given that the algorithm relies on distance calculations and other numerical computations.

However, my original code was attempting to pass the entire dataframe to knn.impute, which is causing the error. So, what’s going on here?

Breaking Down the Error

Let’s break down the error message further:

(list) object cannot be coerced to type 'double' suggests that there’s a problem with the data being passed to knn.impute. Specifically, it’s trying to convert the list returned by prodNA() to a double-precision numerical value.
The use.data argument is set to "double", which indicates that the algorithm should use double-precision floating-point numbers for computations. This suggests that there’s an issue with the data being passed in a format that can’t be coerced into this type.

Solving the Error

So, how can we fix this error? The solution is simple: we need to pass the numerical matrix directly to knn.impute instead of passing it wrapped in a dataframe.

library(bnstruct)
data(iris)
data <- iris[, 1:4]
data <- prodNA(data, noNA = 0.2) # generate random missing data

# Pass the numerical matrix directly to knn.impute
knn.impute(as.matrix(data), k = 10, cat.var = 1:ncol(data),
           to.impute = 1:nrow(data), using = 1:nrow(data))

By making this change, we ensure that knn.impute receives the correct input data and can perform its computations correctly.

Additional Context

To further illustrate the importance of passing numerical matrices to knn.impute, let’s consider an alternative example:

library(bnstruct)
data(iris)
df <- data.frame(Sepal.Length = iris$Sepal.Length,
                 Sepal.Width = iris$Sepal.Width)

# Attempting to pass a dataframe directly to knn.impute will cause the error
knn.impute(df, k = 10, cat.var = 1:ncol(df),
           to.impute = 1:nrow(df), using = 1:nrow(df))

This code attempts to pass a dataframe df directly to knn.impute, which will result in the same error message.

Conclusion

In conclusion, the error with knn.impute is caused by attempting to pass a non-numerical matrix (i.e., a dataframe) to the function. By passing the numerical matrix directly and converting it to a numeric vector using as.matrix(), we can resolve this issue.

It’s essential to remember that knn.impute is designed for imputing missing values in datasets, and it requires input data that conforms to specific numerical formats. By understanding these requirements, you can avoid common pitfalls and ensure that your code runs smoothly.

Example Use Cases

Here are some example use cases for the knn.impute function:

Imputation of missing values in a dataset:

library(bnstruct) data(iris) df <- data.frame(Sepal.Length = iris$Sepal.Length, Sepal.Width = iris$Sepal.Width)

Generate random missing data

df <- prodNA(df, noNA = 0.2)

Impute missing values using KNN algorithm

knn.impute(as.matrix(df), k = 10, cat.var = 1:ncol(df), to.impute = 1:nrow(df), using = 1:nrow(df))


*   Imputation of missing values in a large dataset:
    ```markdown
library(bnstruct)
data(iris)
df <- data.frame(Sepal.Length = iris$Sepal.Length,
                 Sepal.Width = iris$Sepal.Width)

# Generate random missing data
df <- prodNA(df, noNA = 0.2)

# Impute missing values using KNN algorithm (parallel processing for performance)
knn.impute(as.matrix(df), k = 10, cat.var = 1:ncol(df),
           to.impute = 1:nrow(df), using = 1:nrow(df),
           parallel = TRUE)

By following these guidelines and best practices, you can effectively use knn.impute for imputing missing values in your datasets.

Last modified on 2023-09-14