Hi @T0m07
The negative (d^2) value indicates that the full model's deviance is unexpectedly higher than the null model's. Kindly refer to the quick checks and fixes mentioned below:
Verify Calculations: Ensure “calculateRates” and “calculateDeviance’ are correctly implemented. Use a small constant (eps) to avoid division by zero.
function rates = calculateRates(x, y, int)
rates = exp((x * y) + int);
end
function dev = calculateDeviance(observed, predicted)
% Avoid division by zero or log of zero by adding a small constant
observed = observed + eps;
predicted = predicted + eps;
% Calculate scaled log ratio and raw difference
scaledLogRatio = log(observed ./ predicted) .* observed;
rawDifference = observed - predicted;
% Deviance calculation
diffOfTerms = scaledLogRatio - rawDifference;
dev = nansum(diffOfTerms) * 2;
end
Model Overfitting: Check if the model is overfitting. Adjust lambda and alpha values in “lassoglm” (https://www.mathworks.com/help/stats/lassoglm.html).
Data Issues: Inspect the data for anomalies or outliers that might affect predictions.
Poisson Assumptions: Ensure the Poisson model assumptions hold (mean ≈ variance). If not, consider alternatives like the negative binomial model.
Cross-validation: Use cross-validation to validate model performance and prevent overfitting.
By addressing these areas, you should improve model performance and resolve the deviance issue.
I hope this will be helpful.
