From 063bbe13ce19a3352d172f1c718630c3f7c60a70 Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Mon, 11 Mar 2024 14:39:58 +0100 Subject: [PATCH] Reformatting of README.md --- README.md | 205 ++++++++++++++---------------------------------------- 1 file changed, 53 insertions(+), 152 deletions(-) diff --git a/README.md b/README.md index 8e1d43e..166bfef 100644 --- a/README.md +++ b/README.md @@ -7,135 +7,75 @@ The method can be powperful in situations where one assumes that; 2. There exists some form of grouping within the responses and want to include this information. We assume that the responses form overlapping groups that follows a certain hierarchy. A typical example is when one wants to model drug response for multiple drugs and assumes that some of the drugs share certain properties in common, for example drug target and chemical compounds and aims to include this information to improve prediction and also aim to predict which drug could be suitable for which patient (given a particular disease). The various diseases under study could be the modifying variable. - - - - - - - Author: Theophilus Asenso, Manuela Zucknick ## Usage +```r devtools::install_github("ocbe-uio/MADMMplasso") - - set.seed(1235) - -N = 100 ; p =50;nz=4; K=nz - +N <- 100; p <- 50; nz <- 4; K <- nz X <- matrix(rnorm(n = N * p), nrow = N, ncol = p) - -mx=colMeans(X) - -sx=sqrt(apply(X,2,var)) - -X=scale(X,mx,sx) - -X=matrix(as.numeric(X),N,p) - +mx <- colMeans(X) +sx <- sqrt(apply(X,2,var)) +X <- scale(X,mx,sx) +X <- matrix(as.numeric(X),N,p) Z =matrix(rnorm(N*nz),N,nz) - -mz=colMeans(Z) - -sz=sqrt(apply(Z,2,var)) - -Z=scale(Z,mz,sz) - +mz <- colMeans(Z) +sz <- sqrt(apply(Z,2,var)) +Z <- scale(Z,mz,sz) beta_1 <- rep(x = 0, times = p) - -beta_2<-rep(x = 0, times = p) - -beta_3<-rep(x = 0, times = p) - -beta_4<-rep(x = 0, times = p) - -beta_5<-rep(x = 0, times = p) - -beta_6<-rep(x = 0, times = p) - - - +beta_2 <- rep(x = 0, times = p) +beta_3 <- rep(x = 0, times = p) +beta_4 <- rep(x = 0, times = p) +beta_5 <- rep(x = 0, times = p) +beta_6 <- rep(x = 0, times = p) beta_1[1:5] <- c(2, 2, 2, 2,2) - -beta_2[1:5]<-c(2, 2, 2, 2,2) - -beta_3[6:10]<-c(2, 2, 2, -2,-2) - +beta_2[1:5] <- c(2, 2, 2, 2,2) +beta_3[6:10] <- c(2, 2, 2, -2,-2) beta_4[6:10] <- c(2, 2, 2, -2,-2) - beta_5[11:15] <- c(-2, -2,-2, -2,-2) - beta_6[11:15] <- c(-2, -2, -2, -2,-2) Beta<-cbind(beta_1,beta_2,beta_3,beta_4,beta_5,beta_6) -colnames(Beta)<-c(1:6) - +colnames(Beta) <- c(1:6) -theta<-array(0,c(p,K,6)) - -theta[1,1,1]<-2;theta[3,2,1]<-2;theta[4,3,1]<- -2;theta[5,4,1]<- -2; - -theta[1,1,2]<-2;theta[3,2,2]<-2;theta[4,3,2]<- -2;theta[5,4,2]<- -2; - -theta[6,1,3]<-2;theta[8,2,3]<-2;theta[9,3,3]<- -2;theta[10,4,3]<- -2; - -theta[6,1,4]<-2;theta[8,2,4]<-2;theta[9,3,4]<- -2;theta[10,4,4]<- -2; - -theta[11,1,5]<-2;theta[13,2,5]<-2;theta[14,3,5]<- -2;theta[15,4,5]<- -2; - -theta[11,1,6]<-2;theta[13,2,6]<-2;theta[14,3,6]<- -2;theta[15,4,6]<- -2 +theta <- array(0,c(p,K,6)) +theta[1,1,1] <- 2; theta[3,2,1] <- 2; theta[4,3,1] <- -2; theta[5,4,1] <- -2; +theta[1,1,2] <- 2; theta[3,2,2] <- 2; theta[4,3,2] <- -2; theta[5,4,2] <- -2; +theta[6,1,3] <- 2; theta[8,2,3] <- 2; theta[9,3,3] <- -2; theta[10,4,3] <- -2; +theta[6,1,4] <- 2; theta[8,2,4] <- 2; theta[9,3,4] <- -2; theta[10,4,4] <- -2; +theta[11,1,5] <- 2; theta[13,2,5] <- 2; theta[14,3,5] <- -2; theta[15,4,5] <- -2; +theta[11,1,6] <- 2; theta[13,2,6] <- 2; theta[14,3,6] <- -2; theta[15,4,6] <- -2 library(MASS) - -pliable = matrix(0,N,6) - - for (e in 1:6) { - pliable[,e]<- compute_pliable(X, Z, theta[,,e]) - } - -esd<-diag(6) - -e<-MASS::mvrnorm(N,mu=rep(0,6),Sigma=esd) - -y_train<-X%*%Beta+pliable+e - -y=y_train - -#colnames(y)<-c(1:6) - -colnames(y)<- c( paste("y",1:(ncol(y)),sep = "") ) - -TT=tree_parms(y) - +pliable <- matrix(0,N,6) +for (e in 1:6) { + pliable[,e] <- compute_pliable(X, Z, theta[,,e]) +} +esd <- diag(6) +e <- MASS::mvrnorm(N,mu=rep(0,6),Sigma=esd) +y_train <- X %*% Beta + pliable + e +y <- y_train +colnames(y) <- c( paste("y",1:(ncol(y)),sep = "") ) +TT <- tree_parms(y) plot(TT$h_clust) - - +``` ![githubb](https://github.com/ocbe-uio/MADMMplasso/assets/85598983/1a843b46-7154-405c-8db6-cec5b7a0982d) - -gg1=matrix(0,2,2) - - -gg1[1,]<-c(0.02,0.02) - -gg1[2,]<-c(0.2,0.2) - -nlambda = 50 - -e.abs=1E-4 - -e.rel=1E-2 - -alpha=.5 - -tol=1E-3 - +```r +gg1 <- matrix(0,2,2) +gg1[1,] <- c(0.02,0.02) +gg1[2,] <- c(0.2,0.2) +nlambda <- 50 +e.abs <- 1E-4 +e.rel <- 1E-2 +alpha <- .5 +tol <- 1E-3 fit <- MADMMplasso( X, Z, y, alpha=alpha, my_lambda=NULL, @@ -144,34 +84,18 @@ fit <- MADMMplasso( pal=TRUE, gg=gg1, tol=tol ) - plot(fit) - +``` ![1](https://github.com/ocbe-uio/MADMMplasso/assets/85598983/b8841ba1-aac6-4539-9924-70c70accddd9) - - ![2](https://github.com/ocbe-uio/MADMMplasso/assets/85598983/c2e4bfcf-22c8-49a7-bf99-07ddb436437b) - - - - ![3](https://github.com/ocbe-uio/MADMMplasso/assets/85598983/b319ad79-71bf-4de2-9d9e-457f50393a1e) - - -![4](https://github.com/ocbe-uio/MADMMplasso/assets/85598983/34d8d6e1-c912-4654-a497-4bade67d5ee1) ![5](https://github.com/ocbe-uio/MADMMplasso/assets/85598983/fe375fff-51e2-4b49-9520-f7cbcaec6bbb) - - - - - +![4](https://github.com/ocbe-uio/MADMMplasso/assets/85598983/34d8d6e1-c912-4654-a497-4bade67d5ee1) +![5](https://github.com/ocbe-uio/MADMMplasso/assets/85598983/fe375fff-51e2-4b49-9520-f7cbcaec6bbb) ![6](https://github.com/ocbe-uio/MADMMplasso/assets/85598983/c4c46d9b-3cd3-4c55-95d1-abbb59405422) - - - -gg1=fit$gg - +```r +gg1 <- fit$gg cv_admp <- cv_MADMMplasso( fit, nfolds=5, X, Z, y, alpha=alpha, lambda=fit$Lambdas, max_it=5000, @@ -179,37 +103,14 @@ cv_admp <- cv_MADMMplasso( foldid=NULL, parallel=FALSE, pal=TRUE, gg=gg1, TT=TT, tol=tol ) - plot(cv_admp) - - - - - - - - - - +``` ![cv](https://github.com/ocbe-uio/MADMMplasso/assets/85598983/0118f157-dd7a-4387-88f9-f0e18434d59d) - - - - - -s_ad=which(cv_admp$lambda[,1]==cv_admp$lambda.min) - - - - +```r +s_ad <- which(cv_admp$lambda[,1]==cv_admp$lambda.min) fit$beta[[s_ad]] - - - - - - +``` ![Screenshot 2023-09-11 at 16 25 59](https://github.com/ocbe-uio/MADMMplasso/assets/85598983/f762b9e1-9212-43c7-a21c-b83a9a48662f)