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Gibbs sampler algorithm +#' +#' @param y the response variable +#' @param x the predictors +#' @param H the number of predictors in small groups, default is 30 +#' @param kapp the number of selected predictors in first step, default is 20 +#' @param tau the threshold to select the important predictors in second step, default is 0.9 +#' @param len the half number of generated samples, default is 200 +#' @param k the tuning parameter, default is 1 +#' @param gamma the parameter for extended BIC, default is 0.5 +#' @param info the selected model selection criterion from AIC, AICc, BIC and exBIC +#' @param family the type of model from linear, logistic, poisson +#' @param n.iter the number of iterations +#' @param n.models the number of top selected models +#' +#' @return a list contains a summary of final result +#' @export + +BlockGibbsSampler <- function(y, x, n.iter = 3, n.models =10, H = 30, kapp = 20, + tau = 0.9, len = 200, k = 1, gamma = 0.5, + info = c("AIC", "BIC", "AICc", "exBIC"), + family = c("gaussian","poisson", "binomial")){ + p <- dim(x)[2] + x.predictors <- colnames(x) + colnames(x) <- 1:p + + x1 <- x + x2 <- vector() + + if(n.iter < 2){ + result <- BlockGibbsSampler.step3(y, x1, x2, n.models, x.predictors, H, + kapp, tau, len, k, gamma, p, info, family) + } + else{ + j <- 1 + while(j < n.iter){ + v.select <- BlockGibbsSampler.step2(y, x1, x2, H, kapp, tau, len, k, + gamma, p, info, family) + x1 <- x[, -v.select] + x2 <- x[, v.select] + + j <- j+1 + } + result <- BlockGibbsSampler.step3(y, x1, x2, n.models, x.predictors, H, + kapp, tau, len, k, gamma, p, info, family) + } + result$x.predictors <- x.predictors + return(result) +} diff --git a/.Rproj.user/3992D3D4/sources/s-9F8C73CA/462FD7FF b/.Rproj.user/3992D3D4/sources/s-9F8C73CA/462FD7FF new file mode 100644 index 0000000..283001f --- /dev/null +++ b/.Rproj.user/3992D3D4/sources/s-9F8C73CA/462FD7FF @@ -0,0 +1,26 @@ +{ + "id": "462FD7FF", + "path": "H:/UbuntuRv2/TCGS/IBGS.Rmd", + "project_path": null, + "type": "r_markdown", + "hash": "2363041877", + "contents": "", + "dirty": false, + "created": 1637269087029.0, + "source_on_save": false, + "relative_order": 5, + "properties": { + "tempName": "Untitled1", + "cursorPosition": "75,17", + "scrollLine": "48", + "last_setup_crc32": "9F8C73CAbb338d19" + }, + "folds": "", + "lastKnownWriteTime": 1637272918, + "encoding": "UTF-8", + "collab_server": "", + "source_window": "", + "last_content_update": 1637272918280, + "read_only": false, + "read_only_alternatives": [] +} \ No newline at end of file diff --git a/.Rproj.user/3992D3D4/sources/s-9F8C73CA/462FD7FF-contents b/.Rproj.user/3992D3D4/sources/s-9F8C73CA/462FD7FF-contents new file mode 100644 index 0000000..1ba7f15 --- /dev/null +++ b/.Rproj.user/3992D3D4/sources/s-9F8C73CA/462FD7FF-contents @@ -0,0 +1,78 @@ +--- +title: "An introduction to IBGS" +author: "Lizhong Chen" +date: "11/19/2021" +output: + pdf_document: default + html_document: + df_print: paged +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +## Introduction + +Here is a brief introduction to the R package "IBGS". The iterated block Gibbs sampler is a powerful variable selection method in high dimensions. It is important to identify the potential predictors which has significant influence on the response variable. + +The "IBGS" package works for the generalized linear models including linear, logistic and poisson models and uses AIC, AICc, BIC, and exBIC as the model selection criterion. + +There are two main goals for "IBGS" package: + +* Identify the important predictors with the marginal probability + +* Make a prediction based on the best selected model or model averaging using top selected models + +## Install + +To install and load the "IBGS" package, use this command +```{r, echo=FALSE} +cat("install.packages(\"IBGS_0.1.0.tar.gz\", repos = NULL)\nlibrary(IBGS)") +``` + +The "IBGS" package requires "doParallel" package. Please ensure that "doParallel" package is installed and loaded, then set up the register-cores. +```{r, echo=FALSE} +cat("library(doParallel)\nregisterDoParallel(ncores)\n") +``` +where ncores is the number of CPU cores used in the calculation. + +## Main function + +The main function is +```{r, echo=FALSE} +cat("BlockGibbsSampler <- function(y, x, n.iter = 3, n.models =10, H = 30, kapp = 20, + tau = 0.9, len = 200, k = 1, gamma = 0.5, + info = c(\"AIC\", \"BIC\", \"AICc\", \"exBIC\"), + family = c(\"gaussian\",\"poisson\", \"binomial\"))") +``` +For details, please use help document. +```{r, echo=FALSE} +cat("?BlockGibbsSampler") +``` + +## Example + +Here is an example to show how "IBGS" works. We use the tropical cyclone genesis data. +```{r, echo=FALSE} +load("tcg_ar.RData") +``` + +It has 1856 samples and 149 predictors. +```{r, collapse=TRUE} +dim(x) +``` +We divide the data into train and test set and use the train set ($\approx 80\%$ of data) to run "IBGS" algorithm. Then we use the test set ($\approx 20\%$ of data) to verify the results. + +The simple following command is used +```{r} +cat("model <- BlockGibbsSampler(y_train, x_train, info = \"exBIC\", family = \"binomial\")") +``` +We only need to specify the response, predictors, the model selection criterion and the model type. +### Variable ranking + +We can plot the marginal probabilities for those important predictors using plots.Gibbs command. +```{r, collapse=TRUE} +plots.Gibbs(model) +``` + diff --git a/.Rproj.user/3992D3D4/sources/s-9F8C73CA/9A53FC16-contents b/.Rproj.user/3992D3D4/sources/s-9F8C73CA/9A53FC16-contents new file mode 100644 index 0000000..aa1d1fd --- /dev/null +++ b/.Rproj.user/3992D3D4/sources/s-9F8C73CA/9A53FC16-contents @@ -0,0 +1,189 @@ +% Options for packages loaded elsewhere +\PassOptionsToPackage{unicode}{hyperref} +\PassOptionsToPackage{hyphens}{url} +% +\documentclass[ +]{article} +\usepackage{lmodern} +\usepackage{amssymb,amsmath} +\usepackage{ifxetex,ifluatex} 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+\def\maxheight{\ifdim\Gin@nat@height>\textheight\textheight\else\Gin@nat@height\fi} +\makeatother +% Scale images if necessary, so that they will not overflow the page +% margins by default, and it is still possible to overwrite the defaults +% using explicit options in \includegraphics[width, height, ...]{} +\setkeys{Gin}{width=\maxwidth,height=\maxheight,keepaspectratio} +% Set default figure placement to htbp +\makeatletter +\def\fps@figure{htbp} +\makeatother +\setlength{\emergencystretch}{3em} % prevent overfull lines +\providecommand{\tightlist}{% + \setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}} +\setcounter{secnumdepth}{-\maxdimen} % remove section numbering + +\title{An introduction to IBGS} +\author{Lizhong Chen} +\date{11/19/2021} + +\begin{document} +\maketitle + +\hypertarget{introduction}{% +\subsection{Introduction}\label{introduction}} + +Here is a brief introduction to the R package ``IBGS''. The iterated +block Gibbs sampler is a powerful variable selection method in high +dimensions. It is important to identify the potential predictors which +has significant influence on the response variable. + +The ``IBGS'' package works for the generalized linear models including +linear, logistic and poisson models and uses AIC, AICc, BIC, and exBIC +as the model selection criterion. + +There are two main goals for ``IBGS'' package: + +\begin{itemize} +\item + Identify the important predictors with the marginal probability +\item + Make a prediction based on the best selected model or model averaging + using top selected models +\end{itemize} + +\hypertarget{install}{% +\subsection{Install}\label{install}} + +To install and load the ``IBGS'' package, use this command + +\begin{verbatim} +## install.packages("IBGS_0.1.0.tar.gz", repos = NULL) +## library(IBGS) +\end{verbatim} + +The ``IBGS'' package requires ``doParallel'' package. Please ensure that +``doParallel'' package is installed and loaded, then set up the +register-cores. + +\begin{verbatim} +## library(doParallel) +## registerDoParallel(ncores) +\end{verbatim} + +where ncores is the number of CPU cores used in the calculation. + +\hypertarget{main-function}{% +\subsection{Main function}\label{main-function}} + +The main function is + +\begin{verbatim} +## BlockGibbsSampler <- function(y, x, n.iter = 3, n.models =10, H = 30, kapp = 20, +## tau = 0.9, len = 200, k = 1, gamma = 0.5, +## info = c("AIC", "BIC", "AICc", "exBIC"), +## family = c("gaussian","poisson", "binomial")) +\end{verbatim} + +For details, please use help document. + +\begin{verbatim} +## ?BlockGibbsSampler +\end{verbatim} + +\hypertarget{example}{% +\subsection{Example}\label{example}} + +Here is an example to show how ``IBGS'' works. We use the tropical +cyclone genesis data. + +It has 1856 samples and 149 predictors. + +\begin{Shaded} +\begin{Highlighting}[] +\KeywordTok{dim}\NormalTok{(x)} +\CommentTok{## [1] 1856 149} +\end{Highlighting} +\end{Shaded} + +We divide the data into train and test set and use the train set +(\$\approx\$80\% of data) + +\end{document} diff --git a/.Rproj.user/3992D3D4/sources/s-9F8C73CA/A15EC043 b/.Rproj.user/3992D3D4/sources/s-9F8C73CA/A15EC043 new file mode 100644 index 0000000..07bef63 --- /dev/null +++ b/.Rproj.user/3992D3D4/sources/s-9F8C73CA/A15EC043 @@ -0,0 +1,25 @@ +{ + "id": "A15EC043", + "path": "H:/UbuntuRv2/TCGS/tcg_ar.R", + "project_path": null, + "type": "r_source", + "hash": "0", + "contents": "", + "dirty": false, + "created": 1637268014092.0, + "source_on_save": false, + "relative_order": 4, + "properties": { + "tempName": "Untitled1", + "cursorPosition": "3,7", + "scrollLine": "0" + }, + "folds": "", + "lastKnownWriteTime": 1637268337, + "encoding": "UTF-8", + "collab_server": "", + "source_window": "", + "last_content_update": 1637268337695, + "read_only": false, + "read_only_alternatives": [] +} \ No newline at end of file diff --git a/.Rproj.user/3992D3D4/sources/s-9F8C73CA/A15EC043-contents b/.Rproj.user/3992D3D4/sources/s-9F8C73CA/A15EC043-contents new file mode 100644 index 0000000..2457986 --- /dev/null +++ b/.Rproj.user/3992D3D4/sources/s-9F8C73CA/A15EC043-contents @@ -0,0 +1,29 @@ +library(IBGS) +library(doParallel) + +setwd("/mnt/h/UbuntuRv2/TCGS") + +#parallel setting +registerDoParallel(12) +set.seed(101) + +tcg <- read.csv("TC_data_12h_AR_Matched.csv") +z <- tcg[,8:157] + +y <- z$y +x <- as.matrix(z[,-1]) + +n <- dim(x)[1] +p <- dim(x)[2] + +index_train <- sample(x = 2, size = n, replace = TRUE, prob = c(0.8,0.2)) + +x_train <- x[index_train == 1, ] +y_train <- y[index_train == 1 ] + +x_test <- x[index_train == 2, ] +y_test <- y[index_train == 2 ] + +model <- BlockGibbsSampler(y_train, x_train, info = "exBIC", family = "binomial") + +save.image("tcg_ar.RData") diff --git a/.Rproj.user/3992D3D4/sources/s-9F8C73CA/EB3BE702 b/.Rproj.user/3992D3D4/sources/s-9F8C73CA/EB3BE702 index f1f102e..78675c6 100644 --- a/.Rproj.user/3992D3D4/sources/s-9F8C73CA/EB3BE702 +++ b/.Rproj.user/3992D3D4/sources/s-9F8C73CA/EB3BE702 @@ -3,7 +3,7 @@ "path": "H:/UbuntuRv2/IBGS/test.R", "project_path": null, "type": "r_source", - "hash": "524880278", + "hash": "0", "contents": "", "dirty": false, "created": 1637209496925.0, @@ -11,8 +11,8 @@ "relative_order": 10, "properties": { "tempName": "Untitled1", - "cursorPosition": "23,27", - "scrollLine": "1" + "cursorPosition": "23,8", + "scrollLine": "0" }, "folds": "", "lastKnownWriteTime": 1637237719, diff --git a/.Rproj.user/shared/notebooks/8107308D-IBGS/1/3992D3D49F8C73CA/chunks.json b/.Rproj.user/shared/notebooks/8107308D-IBGS/1/3992D3D49F8C73CA/chunks.json new file mode 100644 index 0000000..56836b2 --- /dev/null +++ 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