miniLNM (version 0.1.0)

lnm: Fit a logistic normal multinomial model using R's formula interface.

Description

This function fits a logistic normal multinomial (LNM) model to the data using R's formula interface. The LNM model is a generalization of the multinomial logistic regression model, allowing for correlated responses within each category of the response variable. It can be used to learn the relationship between experimental/environmental factors and community composition. It is a statistical model that estimates the probabilities of different outcomes in a multinomial distribution, given a set of covariates. The LNM model assumes that a log-ratio of the outcome probabilities follow a multivariate normal distribution. By fitting the LNM model to observed data, we can infer the effects of the covariates on the outcome compositions.

Usage

lnm(formula, data, sigma_b = 2, l1 = 10, l2 = 10, ...)

Value

An object of class "lnm" representing the fitted LNM model.

Arguments

formula

A formula specifying the model structure.

data

A data frame containing the variables specified in the formula.

sigma_b

The prior standard deviation of the beta coefficients in the LNM model. See the 'Stan' code definition in inst/stan/lnm.stan for the full model specification.

l1

The first inverse gamma hyperprior parameter for sigmas_mu.

l2

The first inverse gamma hyperprior parameter for sigmas_mu.

...

Additional arguments to be passed to the underlying vb() call from 'rstan'.

Examples

example_data <- lnm_data(N = 50, K = 10)
xy <- dplyr::bind_cols(example_data[c("X", "y")])
fit <- lnm(
    starts_with("y") ~ starts_with("x"), xy, 
    iter = 25, output_samples = 25
)