114 lines
5.0 KiB
R
114 lines
5.0 KiB
R
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#C:\Users\Lukas\Desktop\owncloud\Shared\Lukas\CVE
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install.packages("C:/Users/Lukas/Desktop/owncloud/Shared/Lukas/CVE_1.0.tar.gz", repos=NULL, type="source")
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install.packages(file.choose(), repos=NULL, type="source")
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dim<-12
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N<-100
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s<-0.5
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dat<-creat_sample(rep(1,dim)/sqrt(dim),N,fsquare,0.5)
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test<-cve(Y~.,data=as.data.frame(dat),k=1)
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##############
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#initialize model parameterss
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m<-100 #number of replications in simulation
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dim<-12 #dimension of random variable X
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truedim<-2 #dimension of B=b
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qs<-dim-truedim # dimension of orthogonal complement of B
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b1=c(1,1,1,1,1,1,0,0,0,0,0,0)/sqrt(6)
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b2=c(1,-1,1,-1,1,-1,0,0,0,0,0,0)/sqrt(6)
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b<-cbind(b1,b2)
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#b<-b1
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P<-b%*%t(b)
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sigma=0.5 #error standard deviation
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N<-70 #sample size
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K<-30 #number of arbitrary starting values for curvilinear optimization
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MAXIT<-30 #maximal number of iterations in curvilinear search algorithm
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var_vec<-mat.or.vec(m,12)
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M1_weight<-mat.or.vec(m,13)
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#colnames(M1_weight)<-c('CVE1','CVE2','CVE3','CVE1_Rcpp','CVE2_Rcpp','CVE3_Rcpp','meanMAVE','csMAVE','phd','sir','save','CVE4')
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#link function for M1
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fM1<-function(x){return(x[1]/(0.5+(x[2]+1.5)^2))}
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for (i in 1:m){
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#generate dat according to M1
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dat<-creat_sample_nor_nonstand(b,N,fsquare,diag(rep(1,dim)),sigma)
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#est sample covariance matrix
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Sig_est<-est_varmat(dat[,-1])
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#est trace of sample covariance matrix
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tr<-var_tr(Sig_est)
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#calculates Vhat_k for CVE1,CVE2, CVE3 for k=qs
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CVE1<-stiefl_opt(dat,k=qs,k0=K,h=choose_h_2(dim,k=dim-truedim,N=N,nObs=(N)^(0.8),tr=tr),maxit = MAXIT,sclack_para = 0)
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CVE2<-stiefl_opt(dat,k=qs,k0=K,h=choose_h_2(dim,k=dim-truedim,N=N,nObs=(N)^(2/3),tr=tr),maxit = MAXIT,sclack_para = 0)
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CVE3<-stiefl_opt(dat,k=qs,k0=K,h=choose_h_2(dim,k=dim-truedim,N=N,nObs=(N)^(0.5),tr=tr),maxit = MAXIT,sclack_para = 0)
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CVE4<-stiefl_weight_partial_opt(dat,k=qs,k0=K,h=choose_h_2(dim,k=dim-truedim,N=N,nObs=(N)^(0.8),tr=tr),maxit = MAXIT,sclack_para = 0)
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CVE5<-stiefl_weight_partial_opt(dat,k=qs,k0=K,h=choose_h_2(dim,k=dim-truedim,N=N,nObs=(N)^(2/3),tr=tr),maxit = MAXIT,sclack_para = 0)
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CVE6<-stiefl_weight_partial_opt(dat,k=qs,k0=K,h=choose_h_2(dim,k=dim-truedim,N=N,nObs=(N)^(0.5),tr=tr),maxit = MAXIT,sclack_para = 0)
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#
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CVE7<-stiefl_weight_full_opt(dat,k=qs,k0=K,h=choose_h_2(dim,k=dim-truedim,N=N,nObs=(N)^(0.8),tr=tr),maxit = MAXIT,sclack_para = 0)
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CVE8<-stiefl_weight_full_opt(dat,k=qs,k0=K,h=choose_h_2(dim,k=dim-truedim,N=N,nObs=(N)^(2/3),tr=tr),maxit = MAXIT,sclack_para = 0)
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CVE9<-stiefl_weight_full_opt(dat,k=qs,k0=K,h=choose_h_2(dim,k=dim-truedim,N=N,nObs=(N)^(0.5),tr=tr),maxit = MAXIT,sclack_para = 0)
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#
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#
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var_vec[i,1]<-CVE1$var
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var_vec[i,2]<-CVE2$var
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var_vec[i,3]<-CVE3$var
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var_vec[i,4]<-CVE4$var
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var_vec[i,5]<-CVE5$var
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var_vec[i,6]<-CVE6$var
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#
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var_vec[i,7]<-CVE7$var
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var_vec[i,8]<-CVE8$var
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var_vec[i,9]<-CVE9$var
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CVE1$est_base<-fill_base(CVE1$est_base)
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CVE2$est_base<-fill_base(CVE2$est_base)
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CVE3$est_base<-fill_base(CVE3$est_base)
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CVE4$est_base<-fill_base(CVE4$est_base)
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CVE5$est_base<-fill_base(CVE5$est_base)
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CVE6$est_base<-fill_base(CVE6$est_base)
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CVE7$est_base<-fill_base(CVE7$est_base)
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CVE8$est_base<-fill_base(CVE8$est_base)
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CVE9$est_base<-fill_base(CVE9$est_base)
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# calculate distance between true B and estimated B
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M1_weight[i,1]<-subspace_dist(CVE1$est_base[,1:truedim],b)
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M1_weight[i,2]<-subspace_dist(CVE2$est_base[,1:truedim],b)
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M1_weight[i,3]<-subspace_dist(CVE3$est_base[,1:truedim],b)
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M1_weight[i,4]<-subspace_dist(CVE4$est_base[,1:truedim],b)
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M1_weight[i,5]<-subspace_dist(CVE5$est_base[,1:truedim],b)
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M1_weight[i,6]<-subspace_dist(CVE6$est_base[,1:truedim],b)
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M1_weight[i,7]<-subspace_dist(CVE7$est_base[,1:truedim],b)
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M1_weight[i,8]<-subspace_dist(CVE8$est_base[,1:truedim],b)
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M1_weight[i,9]<-subspace_dist(CVE9$est_base[,1:truedim],b)
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CVE1_Rcpp<-cve(Y~.,data=as.data.frame(dat),k=truedim,nObs=N^0.8,attempts=K,tol=10^(-3),slack=0)[[2]]
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CVE2_Rcpp<-cve(Y~.,data=as.data.frame(dat),k=truedim,nObs=N^(2/3),attempts=K,tol=10^(-3),slack=0)[[2]]
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CVE3_Rcpp<-cve(Y~.,data=as.data.frame(dat),k=truedim,nObs=N^0.5,attempts=K,tol=10^(-3),slack=0)[[2]]
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# CVE4_Rcpp<-cve(Y~.,data=as.data.frame(dat),k=truedim,h=h_opt,attempts=K,tol=10^(-3))[[2]]
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#M1_Rcpp[i,12]<-subspace_dist(CVE4_Rcpp$B,b)
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var_vec[i,10]<-CVE1_Rcpp$loss
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var_vec[i,11]<-CVE2_Rcpp$loss
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var_vec[i,12]<-CVE3_Rcpp$loss
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#calculate orthogonal complement of Vhat_k
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#i.e. CVE1$est_base[,1:truedim] is estimator for B with dimension (dim times (dim-qs))
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M1_weight[i,10]<-subspace_dist(CVE1_Rcpp$B,b)
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M1_weight[i,11]<-subspace_dist(CVE2_Rcpp$B,b)
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M1_weight[i,12]<-subspace_dist(CVE3_Rcpp$B,b)
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#meanMAVE
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mod_t2<-mave(Y~.,data=as.data.frame(dat),method = 'meanMAVE')
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M1_weight[i,13]<-subspace_dist(mod_t2$dir[[truedim]],b)
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print(paste(i,paste('/',m)))
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}
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boxplot(M1_weight[1:(i-1),]/sqrt(2*truedim),names=colnames(M1_weight),ylab='err',main='M1')
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summary(M1_weight[1:(i-1),])
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