Debugging Confidence Intervals in KPPM Models

======================================================

Problem Overview

The kppm function in the spatstat package returns NA values for the confidence intervals of model parameters. This occurs when the variance estimates are calculated and contain NA values.

Steps to Reproduce the Error

1. Install the latest version of R with the following packages: rprojroot, spatstat, and stats.
2. Load the required libraries in your R script:

library(spatstat)

3. Define a sample dataset (e.g., a matrix) that contains random values for the covariates used in the `kppm` model.

## Code Snippet
```r
# Sample data
set.seed(123)
x <- matrix(rnorm(1000 * 10), nrow = 100)
y <- rnorm(1000)

# Model specification
model <- kppm(y ~ log(x1) + log(x2) + x3, data = data.frame(X = cbind(x1, x2, x3)), verbose = FALSE)

# Confidence interval estimation
ci <- confint(model)

# Print the results
print(ci)

Error Explanation

The kppm function may return NA values for confidence intervals when it encounters NA values in its variance estimates. This can happen due to various reasons, such as:

• Power-law models: Power-law relationships are represented by the formula ~ log(Z), where Z is the original covariate. If a power-law relationship exists among the model parameters, the variance estimates will be NA.
• NA values in the data: If there are NA values present in the data used to fit the model, this can lead to NA values in the standard errors and confidence intervals.

Troubleshooting Steps

To debug the issue:

1. Check for any NA values in the covariates or other variables used in the model.
2. Verify that there are no power-law relationships among the model parameters.
3. If using a dataset, consider checking its data quality and handling missing values appropriately.

Solution

As of version spatstat.model 3.2-8.004, a bug has been fixed in vcov.kppm that handles NA values in the covariates more accurately. This should resolve NA values in confidence intervals.

To update R packages, use the following command:

install.packages("spatstat.model")

Additional Resources

For more information on power-law models and their impact on variance estimates, refer to section 9.3.8 of Baddeley Rubak and Turner (2015). The documentation for the kppm function can be accessed using the following command:

?kppm

By understanding the reasons behind NA values in confidence intervals, you can effectively debug and resolve issues with your R models.

Last modified on 2025-03-29