https://github.com/james-thorson-noaa/mvtweedie

Interpret output from a Tweedie GLM as a multivariate logit Tweedie model
https://github.com/james-thorson-noaa/mvtweedie

Last synced: 3 months ago
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Interpret output from a Tweedie GLM as a multivariate logit Tweedie model

• Host: GitHub
• URL: https://github.com/james-thorson-noaa/mvtweedie
• Owner: James-Thorson-NOAA
• License: gpl-3.0
• Created: 2021-02-23T19:56:57.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2023-12-22T15:08:57.000Z (about 1 year ago)
• Last Synced: 2023-12-23T00:01:41.614Z (about 1 year ago)
• Language: R
• Homepage: https://james-thorson-noaa.github.io/mvtweedie/
• Size: 6.49 MB
• Stars: 8
• Watchers: 5
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# mvtweedie

[![DOI](https://zenodo.org/badge/341673481.svg)](https://zenodo.org/badge/latestdoi/341673481)

An R package to interpret a Tweedie generalized linear model (GLM) or generalized additive model (GAM) involving multiple classes as an estimate of proportions for each class, implicitly involving a multivariate-logit transformation for parameters and predictions.  This approach generalizes the Poisson-to-multinomial transformation to allow for non-integer responses, and can analyze either pre-processed (transformed to proportions) or raw (zero-inflated positive real values) data.

This approach is helpful, e.g., when analyzing diet samples that are heavily zero inflated without pre-processing the samples prior to analysis.  In these cases, the Tweedie distribution can be interpreted mechanistically as a thinned and double-marked Poisson point process representing foraging processes.

### Demo

```R

library( ggplot2 )

library( mgcv )

library( mvtweedie )

# Load data

data( "Middleton_Island_TUPU", package="mvtweedie" )

Middleton_Island_TUPU$Year = as.numeric(as.character( Middleton_Island_TUPU$Year_factor ))

# Fit GAM

fit = mgcv::gam( Response ~ group + s(Year, by=group), data=Middleton_Island_TUPU, family="tw" )

class(fit) = c( "mvtweedie", class(fit) )

# Predict values

newdata = expand.grid("group"=levels(Middleton_Island_TUPU$group), 

  "Year"=min(Middleton_Island_TUPU$Year):max(Middleton_Island_TUPU$Year))

pred = predict( fit,

                   se.fit=TRUE,

                   origdata = Middleton_Island_TUPU,

                   newdata = newdata )

newdata = cbind( newdata, fit=pred$fit, se.fit=pred$se.fit )

newdata$lower = newdata$fit - newdata$se.fit

newdata$upper = newdata$fit + newdata$se.fit

# Plot

theme_set(theme_bw())

ggplot(newdata, aes(Year, fit)) +

  geom_pointrange(aes(ymin = lower, ymax = upper)) +

  facet_wrap(vars(group)) +

  scale_color_viridis_c(name = "SST") +

  ylim(0,1) +

  labs(y="Predicted proportion")

```

### Citation

Thorson, J. T., Arimitsu, M. L., Levi, T., & Roffler, G. (2022). [Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie](https://doi.org/10.1002/ecy.3637). Ecology. 103(5): e3637.