Drop missing values manually

Oct 13, 2025 by thesweetdesigns.com 103 views

Introduction

When working with survey data and performing predictions from a svyglm model, it is essential to handle missing values in the newdata correctly. Passing weights from a newdata that contains missing values can lead to incorrect results. In this article, we will explore this issue and provide solutions to obtain accurate predictions.

The Problem

When using the predict_response function from the ggeffects package to compute predictions from a svyglm model, passing weights from a newdata that contains missing values can result in incorrect predictions. This is because the predict_response function does not handle missing values in the newdata correctly.

Reproducer

Here is a reproducer that demonstrates the issue:

library(ggeffects)
library(survey)
data(api)

dstrat <- svydesign(id=~1, weights=~pw, data=apistrat)
m <- svyglm(api00~ell+acs.core+awards, design=dstrat)

# Incorrect
predict_response(m, "awards", margin="empirical", newdata=dstrat$variables, weights="pw")
# Correct
dstrat2 <- subset(dstrat, !is.na(acs.core))
predict_response(m, "awards", margin="empirical", newdata=dstrat2$variables, weights="pw")
predict_response(m, "awards", margin="empirical", newdata=m$model, weights="(weights)")

The Issue with Older Versions of `marginaleffects`

In older versions of the marginaleffects package (e.g., version 0.15.1), passing weights from a newdata that contains missing values would result in an error instead of incorrect predictions. This was safer, as it prevented incorrect results from being produced.

Solutions

To obtain accurate predictions, you can use one of the following solutions:

Drop missing values manually: You can drop missing values from the newdata manually using the subset function, as shown in the reproducer above.
Use the model frame: You can use the model frame, which does not contain missing values, to compute predictions. This can be done by passing the weights argument as "(weights)", as shown in the reproducer above.

Conclusion

In conclusion, passing weights from a newdata that contains missing values can lead to incorrect predictions when computing predictions from a svyglm model. To obtain accurate predictions, you can drop missing values manually or use the model frame. It is essential to handle missing values correctly to ensure the accuracy of your results.

Additional Tips

When working with survey data, it is essential to handle missing values correctly to ensure the accuracy of your results.
The predict_response function from the ggeffects package does not handle missing values in the newdata correctly.
You can use the subset function to drop missing values from the newdata manually.
You can use the model frame to compute predictions, which does not contain missing values.

References

ggeffects package: https://cran.r-project.org/web/packages/ggeffects/index.html
survey package: https://cran.r-project.org/web/packages/survey/index.html
marginaleffects package: https://cran.r-project.org/web/packages/marginaleffects/index.html
Q&A: Incorrect Predictions when Passing Weights from newdata with Missing Values ====================================================================================

Q: What is the issue with passing weights from `newdata` with missing values?

A: When passing weights from newdata with missing values, the predict_response function from the ggeffects package does not handle missing values correctly, leading to incorrect predictions.

Q: What happens when I pass weights from `newdata` with missing values?

A: When you pass weights from newdata with missing values, the predict_response function will produce incorrect predictions. This is because the function does not handle missing values correctly.

Q: How can I drop missing values manually?

A: You can drop missing values manually using the subset function. For example:

dstrat2 <- subset(dstrat, !is.na(acs.core))
predict_response(m, "awards", margin="empirical", newdata=dstrat2$variables, weights="pw")

Q: Can I use the model frame to compute predictions?

A: Yes, you can use the model frame to compute predictions. The model frame does not contain missing values, so you can pass the weights argument as "(weights)". For example:

predict_response(m, "awards", margin="empirical", newdata=m$model, weights="(weights)")

Q: What is the difference between using the `subset` function and using the model frame?

A: Using the subset function will drop missing values from the newdata manually, while using the model frame will use the model frame to compute predictions, which does not contain missing values.

Q: Why did older versions of `marginaleffects` produce an error instead of incorrect predictions?

A: Older versions of marginaleffects produced an error instead of incorrect predictions because the function was designed to handle missing values more robustly. This was safer, as it prevented incorrect results from being produced.

Q: How can I ensure that my predictions are accurate?

A: To ensure that your predictions are accurate, you should handle missing values correctly. You can do this by dropping missing values manually using the subset function or by using the model frame to compute predictions.

Q: What are some additional tips for handling missing values in survey data?

A: Here are some additional tips for handling missing values in survey data:

Always check for missing values in your data before performing any analysis.
Use the subset function to drop missing values manually.
Use the model frame to compute predictions, which does not contain missing values.
Consider using multiple imputation techniques to handle missing values.
Always verify the accuracy of your results by checking for missing values and outliers.

Q: Where can I find more information about handling missing values in survey data?

A: You can find more information about handling missing values in survey data by consulting the following resources:

The ggeffects package documentation: https://cran.r-project.org/web/packages/ggeffects/index.html
The survey package documentation: https://cran.r-project.org/web/packages/survey/index.html
The marginaleffects package documentation: https://cran.r-project.org/web/packages/marginaleffects/index.html
Online tutorials and courses on survey data analysis and missing values.