> For the complete documentation index, see [llms.txt](https://slmetrics-docs.gitbook.io/v1/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://slmetrics-docs.gitbook.io/v1/reference/regression-metrics/tweedie-deviance/deviance.tweedie.md). # deviance.tweedie.md | | | | ---------------- | --------------: | | deviance.tweedie | R Documentation | ### Tweedie Deviance #### Description A generic S3 function to compute the *tweedie deviance* score for a\ regression model. This function dispatches to S3 methods in`deviance.tweedie()` and performs no input validation. If you supply NA\ values or vectors of unequal length (e.g. `length(x) != length(y)`), the\ underlying `C++` code may trigger undefined behavior and crash your `R`\ session. **Defensive measures** Because `deviance.tweedie()` operates on raw pointers, pointer-level\ faults (e.g. from NA or mismatched length) occur before any `R`-level\ error handling. Wrapping calls in `try()` or `tryCatch()` will *not*\ prevent `R`-session crashes. To guard against this, wrap `deviance.tweedie()` in a "safe" validator\ that checks for NA values and matching length, for example: {% code overflow="wrap" lineNumbers="true" %} ```r safe_deviance.tweedie <- function(x, y, ...) { stopifnot( !anyNA(x), !anyNA(y), length(x) == length(y) ) deviance.tweedie(x, y, ...) } ``` {% endcode %} Apply the same pattern to any custom metric functions to ensure input\ sanity before calling the underlying `C++` code. #### Usage ```r ## Generic S3 method ## for Tweedie Deviance deviance.tweedie(...) ## Generic S3 method ## for weighted Tweedie Deviance weighted.deviance.tweedie(...) ``` #### Arguments | `...` |

Arguments passed on to deviance.tweedie.numeric,weighted.deviance.tweedie.numeric

actual,predicted

A pair of \ vectors of length n.

power

A \ value, default = 2. Tweedie power parameter. Either
power <= 0 or power >= 1.

The higher power, the less weight is given
to extreme deviations between actual and predicted values.

w

A \ vector of sample weights.

| | ----- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | #### Value A \ value #### References James, Gareth, et al. An introduction to statistical learning. Vol. 112.\ No. 1. New York: springer, 2013. Hastie, Trevor. "The elements of statistical learning: data mining,\ inference, and prediction." (2009). Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for\ scientific computing in Python." Nature methods 17.3 (2020): 261-272. #### Examples ```r ## Generate actual ## and predicted values actual_values <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2) predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2) ## Evaluate performance SLmetrics::deviance.tweedie( actual = actual_values, predicted = predicted_values ) ``` ``` ``` --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://slmetrics-docs.gitbook.io/v1/reference/regression-metrics/tweedie-deviance/deviance.tweedie.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `build a script that syncs our docs to a CMS` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.