Merge pull request #39 from ocbe-uio/readme

Improved formatting of README.md

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.
 
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 Author: Theophilus Asenso, Manuela Zucknick
 
 
 ## Usage
 
+```r
 devtools::install_github("ocbe-uio/MADMMplasso")
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 set.seed(1235)
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-N = 100 ; p =50;nz=4; K=nz
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+N <- 100; p <- 50; nz <- 4; K <- nz
 X <- matrix(rnorm(n = N * p), nrow = N, ncol = p)
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-mx=colMeans(X)
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-sx=sqrt(apply(X,2,var))
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-X=scale(X,mx,sx)
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-X=matrix(as.numeric(X),N,p)
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+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)
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-mz=colMeans(Z)
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-sz=sqrt(apply(Z,2,var))
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-Z=scale(Z,mz,sz)
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+mz <- colMeans(Z)
+sz <- sqrt(apply(Z,2,var))
+Z <- scale(Z,mz,sz)
 beta_1 <- rep(x = 0, times = p)
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-beta_2<-rep(x = 0, times = p)
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-beta_3<-rep(x = 0, times = p)
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-beta_4<-rep(x = 0, times = p)
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-beta_5<-rep(x = 0, times = p)
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-beta_6<-rep(x = 0, times = p)
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+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)
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-beta_2[1:5]<-c(2, 2, 2, 2,2)
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-beta_3[6:10]<-c(2, 2, 2, -2,-2)
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+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)
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 beta_5[11:15] <- c(-2,  -2,-2, -2,-2)
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 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)
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+colnames(Beta) <- c(1:6)
 
-theta<-array(0,c(p,K,6))
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-theta[1,1,1]<-2;theta[3,2,1]<-2;theta[4,3,1]<- -2;theta[5,4,1]<- -2;
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-theta[1,1,2]<-2;theta[3,2,2]<-2;theta[4,3,2]<- -2;theta[5,4,2]<- -2;
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-theta[6,1,3]<-2;theta[8,2,3]<-2;theta[9,3,3]<- -2;theta[10,4,3]<- -2;
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-theta[6,1,4]<-2;theta[8,2,4]<-2;theta[9,3,4]<- -2;theta[10,4,4]<- -2;
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-theta[11,1,5]<-2;theta[13,2,5]<-2;theta[14,3,5]<- -2;theta[15,4,5]<- -2;
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-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)
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-pliable = matrix(0,N,6)
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-  for (e in 1:6) {
-  pliable[,e]<-	compute_pliable(X, Z, theta[,,e])
-   }
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-esd<-diag(6)
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-e<-MASS::mvrnorm(N,mu=rep(0,6),Sigma=esd)
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-y_train<-X%*%Beta+pliable+e
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-y=y_train
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-#colnames(y)<-c(1:6)
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-colnames(y)<- c( paste("y",1:(ncol(y)),sep = "") )
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-TT=tree_parms(y)
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+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)
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+```
 
 ![githubb](https://github.com/ocbe-uio/MADMMplasso/assets/85598983/1a843b46-7154-405c-8db6-cec5b7a0982d)
 
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-gg1=matrix(0,2,2)
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-gg1[1,]<-c(0.02,0.02)
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-gg1[2,]<-c(0.2,0.2)
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-nlambda = 50
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-e.abs=1E-4
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-e.rel=1E-2
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-alpha=.5
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-tol=1E-3
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+```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,
@@ -144,72 +84,33 @@ fit <- MADMMplasso(
   pal=TRUE, gg=gg1, tol=tol
 )
 
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 plot(fit)
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+```
 
 ![1](https://github.com/ocbe-uio/MADMMplasso/assets/85598983/b8841ba1-aac6-4539-9924-70c70accddd9)
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 ![2](https://github.com/ocbe-uio/MADMMplasso/assets/85598983/c2e4bfcf-22c8-49a7-bf99-07ddb436437b)
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 ![3](https://github.com/ocbe-uio/MADMMplasso/assets/85598983/b319ad79-71bf-4de2-9d9e-457f50393a1e)
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-![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)
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+![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)
 
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-gg1=fit$gg
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+```r
+gg1 <- fit$gg
 
 cv_admp <- cv_MADMMplasso(
   fit, nfolds=5, X, Z, y, alpha=alpha, lambda=fit$Lambdas, max_it=5000,
   e.abs=e.abs, e.rel=e.rel, nlambda, rho=5, my_print=FALSE, alph=1,
   foldid=NULL, parallel=FALSE, pal=TRUE, gg=gg1, TT=TT, tol=tol
 )
 
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 plot(cv_admp)
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+```
 
 ![cv](https://github.com/ocbe-uio/MADMMplasso/assets/85598983/0118f157-dd7a-4387-88f9-f0e18434d59d)
 
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-s_ad=which(cv_admp$lambda[,1]==cv_admp$lambda.min)
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+```r
+s_ad <- which(cv_admp$lambda[,1]==cv_admp$lambda.min)
 fit$beta[[s_ad]]
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+```
 
 ![Screenshot 2023-09-11 at 16 25 59](https://github.com/ocbe-uio/MADMMplasso/assets/85598983/f762b9e1-9212-43c7-a21c-b83a9a48662f)