# Print summary of 'ecpc' object Print summary of the fitted model given in an 'ecpc' object. ```r ## S3 method for class 'ecpc' print(x, ...) ## S3 method for class 'ecpc' summary(object, ...) ``` ## Arguments - `x`: An 'ecpc' object returned by `ecpc`. - `object`: An 'ecpc' object returned by `ecpc`. - ``...``: ... ## See Also See `ecpc` for model fitting. ## Examples ```r ##################### # Simulate toy data # ##################### p<-300 #number of covariates n<-100 #sample size training data set n2<-100 #sample size test data set #simulate all betas i.i.d. from beta_k~N(mean=0,sd=sqrt(0.1)): muBeta<-0 #prior mean varBeta<-0.1 #prior variance indT1<-rep(1,p) #vector with group numbers all 1 (all simulated from same normal distribution) #simulate test and training data sets: Dat<-simDat(n,p,n2,muBeta,varBeta,indT1,sigma=1,model='linear') str(Dat) #Dat contains centered observed data, response data and regression coefficients ################### # Provide co-data # ################### continuousCodata <- abs(Dat$beta) Z1 <- cbind(continuousCodata,sqrt(continuousCodata)) #setting 2: splines for informative continuous Z2 <- createZforSplines(values=continuousCodata) S1.Z2 <- createS(orderPen=2, G=dim(Z2)[2]) #create difference penalty matrix Con2 <- createCon(G=dim(Z2)[2], shape="positive+monotone.i") #create constraints #setting 3: 5 random groups G <- 5 categoricalRandom <- as.factor(sample(1:G,p,TRUE)) #make group set, i.e. list with G groups: groupsetRandom <- createGroupset(categoricalRandom) Z3 <- createZforGroupset(groupsetRandom,p=p) S1.Z3 <- createS(G=G, categorical = TRUE) #create difference penalty matrix Con3 <- createCon(G=dim(Z3)[2], shape="positive") #create constraints #fit ecpc for the three co-data matrices with following penalty matrices and constraints #note: can also be fitted without paraPen and/or paraCon Z.all <- list(Z1=Z1,Z2=Z2,Z3=Z3) paraPen.all <- list(Z2=list(S1=S1.Z2), Z3=list(S1=S1.Z3)) paraCon <- list(Z2=Con2, Z3=Con3) ############ # Fit ecpc # ############ tic<-proc.time()[[3]] fit <- ecpc(Y=Dat$Y,X=Dat$Xctd, Z = Z.all, paraPen = paraPen.all, paraCon = paraCon, model="linear",maxsel=c(5,10,15,20), Y2=Dat$Y2,X2=Dat$X2ctd) toc <- proc.time()[[3]]-tic print(fit) summary(fit) ```

print.ecpc function