The package implements the hierarchical Dirichlet process Generalized Linear Models proposed in the paper Modeling Context-Dependent Latent Effect Heterogeneity. The model can be used to estimate latent heterogeneity in the marginal effect of GLM linear coefficients, cluster data points based on that latent heterogeneity, and investigate if Simpson’s Paradox occurs due to latent or omitted features. It also can be used with hierarchical data to estimate the effect of upper-level covariates on the latent heterogeneity in the effect of lower-level features.
For details of the model and the MCMC algorithm, see Ferrari, D. (2020) Modeling Context-Dependent Latent Effect Heterogeneity, Political Analysis, Volume 28, Issue 1, January, pp. 20-46. <DOI: 10.1017/pan.2019.13>.
devtools::install_github("DiogoFerrari/hdpGLM")
# If you don't want to update the dependencies, use: (you may need to install some dependencies manually)
devtools::install_github("DiogoFerrari/hdpGLM", dependencies=F)
NOTE: it may be necessary to create a token to install the package
from the git repository in case it is private (see note at the bottom of
help page in R by running help(install_github)).
Here is a simple example of how to use the package:
library(hdpGLM)
## Example for the dpGLM (no hierarchical structure)
## -------------- ------
set.seed(10)
K = 3 # number of latent clusters
nCov = 3 # number of observed covariates
simdata = hdpGLM_simulateData(400, nCov=nCov, K=K, family='gaussian')
data = simdata$data
mcmc = list(burn.in=1, n.iter=400)
samples = hdpGLM(y~., data=data, mcmc=mcmc, n.display=200)
summary(samples)
coef(samples)
classify(data, samples)
plot(samples)
plot(samples, terms="X1")
plot(samples, separate=T)
plot(samples, true.beta=summary(simdata)$beta)
plot(samples, true.beta=summary(simdata)$beta, separate=T)
## Example for the hdpGLM (with hierarchical structure)
## -------------- -------
set.seed(6667)
simdata = hdpGLM_simulateData(40, 3, 2, nCovj=2, J=4, family='gaussian')
data = simdata$data
mcmc = list(burn.in=1, n.iter=100)
samples = hdpGLM(y~X1+X2, y~W1+W2, data=data, mcmc=mcmc, n.display=10)
summary(samples)
classify(data, samples)
coef(samples)
plot(samples)
plot(samples, terms = 'X1', j.idx=2:4)
plot_hdpglm(samples, ncol.tau=2)
plot_tau(samples)
plot_tau(samples, X='X1')