README.md

In zhzhao07/UMA: Universal Model Averaging

UMA

This package provides adaptive model averaging (MA) with both linear and nonparamatric methods. It also allows the use of other averaging methods such as smoothed information criteria and Mallow's MA.

Authors:

Li Wen <[e-mail]>, Zhihao Zhao <[e-mail]>, Yuhong Yang <[e-mail]>.

Installation

To install this package in R, run the following commands: library(devtools) devtools::install_github("zhzhao07/UMA")

Example usage:

Below is an example of using the function gma, uarm, and uma.predict: ```#generate simulation data library(UMA) n <- 50 p <- 8 beta <- c(3,1.5,0,0,2,0,0,0) b0 <- 1 x <- matrix(rnorm(np,0,1),nrow=n,ncol=p) e <- rnorm(n,0,3) y <- x%%beta+b0+e

the weighted estimation using L1-ARM, MMA and SFIC with all subsets candidate models

l1armbetahat <- gma(x, y, factorID = NULL, method= 'L1-ARM', candi_models = 2)$wbetahat mmabetahat <- gma(x, y, factorID = NULL, method= 'MMA' , candi_models = 2)$wbetahat sficbetahat <- gma(x, y, factorID = NULL, method= 'SFIC' , candi_models = 2)$wbetahat

compute weight and weight_se for model using L1-UARM with all subsets candidate models

l1uarm <- uarm(x, y, factorID = NULL, candi_models = 2, n_train = ceiling(n/2), no_rep=50, psi=0.1, method = 'L1-UARM', prior = TRUE, p0 = 0.5) l1uarmw <- LC$weight l1uarmse <- LC$weight_se

compute weight and weight_se for candidate models using UARM with all subsets candidate models

uarm <- uarm(x, y, factorID = NULL, candi_models = 2, n_train = ceiling(n/2), no_rep=50, psi=0.1, method = 'UARM' , prior = TRUE, p0 = 0.5) uarmw <- LC2$weight uarmse <- LC2$weight_se

compute weight and weight_se for model using MMA and SFIC with all subsets candidate models

mmaw <- gma(x, y, factorID = NULL, method = 'MMA' , candi_models = 2)$weight sficw <- gma(x, y, factorID = NULL, method = 'SFIC', candi_models = 2)$weight

compute the prediction by methods L1-UARM, MMA, SFIC and BMA

l1uarm.predict <- uma.predict(x, y, factorID = NULL, newdata = x, candi_models = 2, weight = l1uarmw, method = 'L1-UARM', dim = 'L')$pre_out mma.predict <- uma.predict(x, y, factorID = NULL, newdata = x, candi_models = 2, weight = mmaw , method = 'MMA' , dim = 'L')$pre_out sfic.predict <- uma.predict(x, y, factorID = NULL, newdata = x, candi_models = 2, weight = sficw , method = 'SFIC' , dim = 'L')$pre_out

the BMA prediction does not depend on candidate models

bma.predict <- uma.predict(x, y, factorID = NULL, newdata = x, candi_models = 2, method = 'BMA',dim = 'L')$pre_out

zhzhao07/UMA documentation built on Sept. 1, 2022, 2:49 p.m.