profile_residuals: Calculate Pearson profile residuals
In Selbosh/scrooge: Statistical modelling of heterogeneous citation networks

Description Usage Arguments Details Value References See Also Examples

Residuals are calculated using the formula

(y_i - E[y_i]) / √{E[y]_i}

which treats each element of the citation counts vector as a Poisson variate. If the expected value is zero, NA is returned. (You can decide for yourself whether to omit these observations, treat them as zero or use some other approach.)

1	profile_residuals(expected, observed)

`expected`	Predicted vector of citations, based on some model
`observed`	Vector of observed citations from the journal of interest

We might want to make a generic S3 function like stats::residuals(). Deviance residuals may be added later. For now we have Pearson residuals only.

A numeric vector of standardised residuals, the same length as expected and observed

Agresti, Alan (2002). Categorical Data Analysis (2nd ed., pp. 366–367). New York, NY: Wiley.

stats::rstandard.glm()

Other functions for residual analysis of communities: community_residuals, fitted_citations

# Compare citations of 'Biometrika' to those of all stats journals
Bka_counts <- citations[, 'Bka']
average_profile <- rowSums(citations) / sum(citations)
expected_counts <- average_profile * sum(Bka_counts)
profile_residuals(expected_counts, Bka_counts)