In bingx1990/LoveER: Latent-model based overlapping clustering and essential regression

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

LoveER

LoveER performs prediction of $Y$, estimation and inference of $\beta$ with statistical guarantees under the structured latent factor regression model $X = A\ Z+E$ and $Y = Z^\top \beta + \varepsilon.$

Installation

You can install the development version from GitHub with:

install.packages("devtools")
devtools::install_github("bingx1990/LoveER")

It requires to pre-install the LOVE package with

devtools::install_github("bingx1990/LOVE")

Example

This is a basic example which shows you how to use the ER package. We start by generating a synthetic data set.

p <- 6
n <- 50
K <- 2
A <- rbind(c(1, 0), c(-1, 0), c(0, 1), c(0, 1), c(1/3, 2/3), c(1/2, -1/2))
Z <- matrix(rnorm(n * K, sd = 2), n, K)
E <- matrix(rnorm(n * p), n, p)
X <- Z %*% t(A) + E

eps <- rnorm(n)
beta <- c(1, -0.5)
Y <- Z %*% beta + eps

The following code calls the LOVE function to perform overlapping clustering of the columns of the $\mathbf{X}$ matrix. It should be noted that pure_homo = TRUE is required for downstream prediction of $Y$ and inference of $\beta$.

library(LOVE)
library(LoveER)
# overlapping clustering
res_LOVE <- LOVE(X, pure_homo = TRUE, delta = seq(0.1, 1.1 ,0.1))

To predict $Y$, estimate the coefficient $\beta$ and provide confidence intervals of $\beta$, the following code provides an example.

# use the output of LOVE to perform prediction and inference.
res_ER <- ER(Y, X, res_LOVE, CI = TRUE)
res_ER <- ER(Y, X, res_LOVE, beta_est = "Dantzig", CI = FALSE)

bingx1990/LoveER documentation built on Jan. 17, 2022, 12:04 p.m.