predict.mvtweedie: Predict proportions for new data

In mvtweedie: Estimate Diet Proportions Using Multivariate Tweedie Model

Predict proportions for new data

Description

Predict proportions and associated standard errors using a standard S3 object interface

Usage

## S3 method for class 'mvtweedie'
predict(object, category_name = "group", newdata, se.fit = FALSE, ...)

Arguments

object	output from gam or glmmTMB, but with class(object)=c("mvtweedie",...) where ... indicates the original values for class(object)
category_name	name of column that indicates grouping variable
newdata	An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
se.fit	Whether to approximate the standard errors for predicted proportions
...	Not used

Details

After fitting Tweedie GLM using a log-link and multiple categories, we transform predicted densities to yield predicted proportions and associated SEs. This estimator for proportions arises naturally when analyzing a double-marked point process for diet samples, with marks for category and size.

predict.mvtweedie does this transformation for a model fitted using:

• A generalized additive model (GAM) using gam

• A generalized linear mixed model (GLMM) using glmmTMB

• A spatio-temporal generalized linear mixed model (GLMM) using tinyVAST

It then also calculates an approximation to the standard error for this proportion. Specifically, we calculate the proportion for each category as the density X for that category, and the sum of densities Y for all other categories:

p_X = \frac{X}{X+Y}

Assuming we have an estimator for the standard error s(X) and s(Y), and assuming that those estimators are independent such that s(X+Y)^2 = s(X)^2 + s(Y)^2 , we then apply the delta method to approximate the standard error for the proportion as:

s(p_X)^2 = \frac{X^2}{(X+Y)^2} \left( \frac{s(X)^2}{X^2} - 2\frac{s(X)^2}{X(X+Y)}+ \frac{s(X)^2 + s(Y)^2}{(X+Y)^2} \right)

Predictions X and Y, and standard errors s(X) and s(Y) are then supplied by the predict function that is native to the software used when fitting the model.

Value

predict.mvtweedie produces a vector of predicted proportions or a list containing predicted proportions and standard errors.

Examples

# Load packages
library(mvtweedie)
library(mgcv)

# load data set
data( Middleton_Island_TUPU, package="mvtweedie" )

# Run Tweedie GLM
gam0 = gam(
  formula = Response ~ 0 + group,
  data = Middleton_Island_TUPU,
  family = tw
)

# Inspect results
class(gam0) = c( "mvtweedie", class(gam0) )
predict(
  gam0,
  se.fit = TRUE
)

mvtweedie documentation built on Jan. 7, 2026, 9:06 a.m.