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Improved formatting of README.md #39

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205 changes: 53 additions & 152 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -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,
Expand All @@ -144,72 +84,33 @@ 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,
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
)


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)
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