Example on Simulated Data

In miniLNM: Miniature Logistic-Normal Multinomial Models

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
knitr::knit_hooks$set(output = miniLNM::ansi_aware_handler)
options(crayon.enabled = TRUE)

library(miniLNM)
library(dplyr)
set.seed(20240904)

This vignette illustrates use of the lnm function using a simulated dataset. First, we create some example data coming from a true LNM model.

example_data <- lnm_data(N = 200, K = 20)
xy <- bind_cols(example_data[c("X", "y")])

Next, we define the regression. We are using an extension of the formula interface that allows for multiple outcomes. This allows us to use it for a wider range of integration problems, like in our multimedia package for mediation analysis.

fit <- lnm(starts_with("y") ~ starts_with("x"), xy, refresh = 0)
fit

Once we have estimated the model, we can use predict to get the fitted compositions on the training data. We can also draw new samples at different read depths and use newdata to sample at new design points.

newx <- lnm_data()$X
new_p_hat <- predict(fit, newx, depth = 300)

Let's verify that the estimates are close to the truth. First we'll get some fitted values.

p_hat <- predict(fit, example_data$X)
y_star <- sample(fit, newdata = example_data$X, depth = 1e4)

The block below compares some posterior predictive samples with the original data. The overlap between red and black points means that the simulated data are a close match to the original samples.

true <- colMeans(example_data$y / rowSums(example_data$y))
fitted <- colMeans(y_star / rowSums(y_star))
plot(true, colMeans(p_hat), asp = 1)
points(true, fitted, col = "red")
abline(a = 0, b = 1)

We can also check the estimated coefficients. Everything is slightly shrunk towards zero, but this is what you would expect given the normal prior.

plot(example_data$B, beta_mean(fit))
abline(a = 0, b = 1)

sessionInfo()

miniLNM documentation built on Sept. 14, 2024, 1:08 a.m.