tests/Regression_testthat/testthat_SSDRegression_Case2_Group4.R
In Qianrao-Fu/SSDbain: Sample Size Determination using AAFBF for Bayesian Hypothesis Testing Implemented in bain
rm(list=ls())
library(testthat)
library(stringr)
library(conflicted)
library(pkgmaker, warn.conflicts = FALSE)
library(foreach)
library(doParallel)
library(doRNG)
library(pkgmaker, warn.conflicts = FALSE)
no_cores <- detectCores() - 1
## Loading required package: iterators
cl <- makeCluster(no_cores)
registerDoParallel(cl)
clusterEvalQ(cl, .libPaths())
#========================================================================================")
# using SSDanova to test the BF01 for the two-sided ANOVA if H0 is true
#========================================================================================")
N_min<-10
N_max<-1000
T_sim<-1000
#R_square<-0.13#effect size
k<-4#number of predictors
l=0.2
rho<-matrix(runif(k*k),nrow=k)
for (i in 1:k){
for (j in 1:k){
if(i==j){
rho[i,j]=1
}else
rho[i,j]=l
}
}
#rho<-matrix(c(1,0.2,0.2,0.2,0.2,1,0.2,0.2,0.2,0.2,1,0.2,0.2,0.2,0.2,1),nrow=4)
Hyp1<-'beta1=beta2=beta3=beta4=0'
Hyp2<-'beta1>0&beta2>0&beta3>0&beta4>0'
ratio<-c(1,1,1,1)
R_square1=0
R_square2=0.13
BFthresh=3
eta=0.8
seed=10
standardize=FALSE
Res<-SSDRegression(Hyp1,Hyp2,k,rho,R_square1,R_square2,T_sim,BFthresh,eta,seed,standardize,ratio)
N_J1<-Res[[1]][1]
eta1_J1<-Res[[2]][[1]][[1]]
eta2_J1<-Res[[2]][[1]][[2]]
N_J2<-Res[[1]][2]
eta1_J2<-Res[[2]][[2]][[1]]
eta2_J2<-Res[[2]][[2]][[2]]
N_J3<-Res[[1]][3]
eta1_J3<-Res[[2]][[3]][[1]]
eta2_J3<-Res[[2]][[3]][[2]]
T<-T_sim
#========================================================================================
# J=1
#========================================================================================
J<-1
N<-N_J1
beta<-cal_beta(k,R_square1,R_square2,Hyp1,Hyp2,ratio,rho)
beta1<-beta[[1]]
beta2<-beta[[2]]
varnames<-c()
b<-character()
for (i in 1:k){
varnames[i]=paste('beta',as.character(i),sep='')
}
num_eq <- sum(unlist(gregexpr("[=]",Hyp1))>0)
num_0 <- sum(unlist(gregexpr("[0]",Hyp1))>0)
num_larger <- sum(unlist(gregexpr("[>]",Hyp1))>0)
#bf0j_H0<-cal_bf(N,k,beta1,R_square1,R_square2,T,rho,J,Hyp1,Hyp2,flag_Hyp = 1,seed,standardize)
beta<-beta1
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
library(pkgmaker, warn.conflicts = FALSE)
library(foreach)
library(doParallel)
library(doRNG)
library(pkgmaker, warn.conflicts = FALSE)
no_cores <- detectCores() - 1
## Loading required package: iterators
cl <- makeCluster(no_cores)
registerDoParallel(cl)
clusterEvalQ(cl, .libPaths())
flag_Hyp = 1
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf<-unlist(BF)
bf0u_H0<-bf
flag_Hyp = 0
beta<-beta1
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf<-unlist(BF)
bfju_H0<-bf
bf0j_H0<-na.omit(bf0u_H0/bfju_H0)
T0<-length(bf0j_H0)
medbf0j_H0<-median(bf0j_H0)
kk<-lapply(1:T0, function (x) bf0j_H0[x]>BFthresh )
eta1_test_J1<-sum(unlist(kk)*1)/T0
flag_Hyp = 1
beta<-beta2
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf0u_Hj<-unlist(BF)
beta<-beta2
flag_Hyp = 0
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bfju_Hj<-unlist(BF)
bfj0_Hj<-bfju_Hj/bf0u_Hj
bfj0_Hj<-na.omit(bfj0_Hj)
T0<-length(bfj0_Hj)
# medianbfj0_Hj<-median(bfj0_Hj)
kk<-lapply(1:T0, function (x) bfj0_Hj[x]>BFthresh )
eta2_test_J1<-sum(unlist(kk))/T0
#========================================================================================
# J=2
#========================================================================================
J<-2
N<-N_J2
beta<-cal_beta(k,R_square1,R_square2,Hyp1,Hyp2,ratio,rho)
beta1<-beta[[1]]
beta2<-beta[[2]]
varnames<-c()
b<-character()
for (i in 1:k){
varnames[i]=paste('beta',as.character(i),sep='')
}
num_eq <- sum(unlist(gregexpr("[=]",Hyp1))>0)
num_0 <- sum(unlist(gregexpr("[0]",Hyp1))>0)
num_larger <- sum(unlist(gregexpr("[>]",Hyp1))>0)
#bf0j_H0<-cal_bf(N,k,beta1,R_square1,R_square2,T,rho,J,Hyp1,Hyp2,flag_Hyp = 1,seed,standardize)
beta<-beta1
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
library(pkgmaker, warn.conflicts = FALSE)
library(foreach)
library(doParallel)
library(doRNG)
library(pkgmaker, warn.conflicts = FALSE)
no_cores <- detectCores() - 1
## Loading required package: iterators
cl <- makeCluster(no_cores)
registerDoParallel(cl)
clusterEvalQ(cl, .libPaths())
flag_Hyp = 1
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf<-unlist(BF)
bf0u_H0<-bf
flag_Hyp = 0
beta<-beta1
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf<-unlist(BF)
bfju_H0<-bf
bf0j_H0<-na.omit(bf0u_H0/bfju_H0)
T0<-length(bf0j_H0)
medbf0j_H0<-median(bf0j_H0)
kk<-lapply(1:T0, function (x) bf0j_H0[x]>BFthresh )
eta1_test_J2<-sum(unlist(kk)*1)/T0
flag_Hyp = 1
beta<-beta2
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf0u_Hj<-unlist(BF)
beta<-beta2
flag_Hyp = 0
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bfju_Hj<-unlist(BF)
bfj0_Hj<-bfju_Hj/bf0u_Hj
bfj0_Hj<-na.omit(bfj0_Hj)
T0<-length(bfj0_Hj)
# medianbfj0_Hj<-median(bfj0_Hj)
kk<-lapply(1:T0, function (x) bfj0_Hj[x]>BFthresh )
eta2_test_J2<-sum(unlist(kk))/T0
#========================================================================================
# J=3
#========================================================================================
J<-3
N<-N_J3
beta<-cal_beta(k,R_square1,R_square2,Hyp1,Hyp2,ratio,rho)
beta1<-beta[[1]]
beta2<-beta[[2]]
varnames<-c()
b<-character()
for (i in 1:k){
varnames[i]=paste('beta',as.character(i),sep='')
}
num_eq <- sum(unlist(gregexpr("[=]",Hyp1))>0)
num_0 <- sum(unlist(gregexpr("[0]",Hyp1))>0)
num_larger <- sum(unlist(gregexpr("[>]",Hyp1))>0)
#bf0j_H0<-cal_bf(N,k,beta1,R_square1,R_square2,T,rho,J,Hyp1,Hyp2,flag_Hyp = 1,seed,standardize)
beta<-beta1
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
library(pkgmaker, warn.conflicts = FALSE)
library(foreach)
library(doParallel)
library(doRNG)
library(pkgmaker, warn.conflicts = FALSE)
no_cores <- detectCores() - 1
## Loading required package: iterators
cl <- makeCluster(no_cores)
registerDoParallel(cl)
clusterEvalQ(cl, .libPaths())
flag_Hyp = 1
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf<-unlist(BF)
bf0u_H0<-bf
flag_Hyp = 0
beta<-beta1
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf<-unlist(BF)
bfju_H0<-bf
bf0j_H0<-na.omit(bf0u_H0/bfju_H0)
T0<-length(bf0j_H0)
medbf0j_H0<-median(bf0j_H0)
kk<-lapply(1:T0, function (x) bf0j_H0[x]>BFthresh )
eta1_test_J3<-sum(unlist(kk)*1)/T0
flag_Hyp = 1
beta<-beta2
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf0u_Hj<-unlist(BF)
beta<-beta2
flag_Hyp = 0
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bfju_Hj<-unlist(BF)
bfj0_Hj<-bfju_Hj/bf0u_Hj
bfj0_Hj<-na.omit(bfj0_Hj)
T0<-length(bfj0_Hj)
# medianbfj0_Hj<-median(bfj0_Hj)
kk<-lapply(1:T0, function (x) bfj0_Hj[x]>BFthresh )
eta2_test_J3<-sum(unlist(kk))/T0
# #test Tables in paper
#test Tables in paper
test_that("SSDbain", {expect_equal(eta1_test_J1,eta1_J1 ,tolerance = .01)})
test_that("SSDbain", {expect_equal(eta2_test_J1,eta2_J1,tolerance = .01)})
test_that("SSDbain", {expect_equal(eta1_test_J2,eta1_J2,tolerance = .01)})
test_that("SSDbain", {expect_equal(eta2_test_J2,eta2_J2,tolerance = .01)})
test_that("SSDbain", {expect_equal(eta1_test_J3,eta1_J3 ,tolerance = .01)})
test_that("SSDbain", {expect_equal(eta2_test_J3,eta2_J3,tolerance = .01)})
Qianrao-Fu/SSDbain documentation built on Oct. 23, 2023, 10:30 p.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
rm(list=ls())
library(testthat)
library(stringr)
library(conflicted)
library(pkgmaker, warn.conflicts = FALSE)
library(foreach)
library(doParallel)
library(doRNG)
library(pkgmaker, warn.conflicts = FALSE)
no_cores <- detectCores() - 1
## Loading required package: iterators
cl <- makeCluster(no_cores)
registerDoParallel(cl)
clusterEvalQ(cl, .libPaths())
#========================================================================================")
# using SSDanova to test the BF01 for the two-sided ANOVA if H0 is true
#========================================================================================")
N_min<-10
N_max<-1000
T_sim<-1000
#R_square<-0.13#effect size
k<-4#number of predictors
l=0.2
rho<-matrix(runif(k*k),nrow=k)
for (i in 1:k){
for (j in 1:k){
if(i==j){
rho[i,j]=1
}else
rho[i,j]=l
}
}
#rho<-matrix(c(1,0.2,0.2,0.2,0.2,1,0.2,0.2,0.2,0.2,1,0.2,0.2,0.2,0.2,1),nrow=4)
Hyp1<-'beta1=beta2=beta3=beta4=0'
Hyp2<-'beta1>0&beta2>0&beta3>0&beta4>0'
ratio<-c(1,1,1,1)
R_square1=0
R_square2=0.13
BFthresh=3
eta=0.8
seed=10
standardize=FALSE
Res<-SSDRegression(Hyp1,Hyp2,k,rho,R_square1,R_square2,T_sim,BFthresh,eta,seed,standardize,ratio)
N_J1<-Res[[1]][1]
eta1_J1<-Res[[2]][[1]][[1]]
eta2_J1<-Res[[2]][[1]][[2]]
N_J2<-Res[[1]][2]
eta1_J2<-Res[[2]][[2]][[1]]
eta2_J2<-Res[[2]][[2]][[2]]
N_J3<-Res[[1]][3]
eta1_J3<-Res[[2]][[3]][[1]]
eta2_J3<-Res[[2]][[3]][[2]]
T<-T_sim
#========================================================================================
# J=1
#========================================================================================
J<-1
N<-N_J1
beta<-cal_beta(k,R_square1,R_square2,Hyp1,Hyp2,ratio,rho)
beta1<-beta[[1]]
beta2<-beta[[2]]
varnames<-c()
b<-character()
for (i in 1:k){
varnames[i]=paste('beta',as.character(i),sep='')
}
num_eq <- sum(unlist(gregexpr("[=]",Hyp1))>0)
num_0 <- sum(unlist(gregexpr("[0]",Hyp1))>0)
num_larger <- sum(unlist(gregexpr("[>]",Hyp1))>0)
#bf0j_H0<-cal_bf(N,k,beta1,R_square1,R_square2,T,rho,J,Hyp1,Hyp2,flag_Hyp = 1,seed,standardize)
beta<-beta1
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
library(pkgmaker, warn.conflicts = FALSE)
library(foreach)
library(doParallel)
library(doRNG)
library(pkgmaker, warn.conflicts = FALSE)
no_cores <- detectCores() - 1
## Loading required package: iterators
cl <- makeCluster(no_cores)
registerDoParallel(cl)
clusterEvalQ(cl, .libPaths())
flag_Hyp = 1
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf<-unlist(BF)
bf0u_H0<-bf
flag_Hyp = 0
beta<-beta1
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf<-unlist(BF)
bfju_H0<-bf
bf0j_H0<-na.omit(bf0u_H0/bfju_H0)
T0<-length(bf0j_H0)
medbf0j_H0<-median(bf0j_H0)
kk<-lapply(1:T0, function (x) bf0j_H0[x]>BFthresh )
eta1_test_J1<-sum(unlist(kk)*1)/T0
flag_Hyp = 1
beta<-beta2
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf0u_Hj<-unlist(BF)
beta<-beta2
flag_Hyp = 0
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bfju_Hj<-unlist(BF)
bfj0_Hj<-bfju_Hj/bf0u_Hj
bfj0_Hj<-na.omit(bfj0_Hj)
T0<-length(bfj0_Hj)
# medianbfj0_Hj<-median(bfj0_Hj)
kk<-lapply(1:T0, function (x) bfj0_Hj[x]>BFthresh )
eta2_test_J1<-sum(unlist(kk))/T0
#========================================================================================
# J=2
#========================================================================================
J<-2
N<-N_J2
beta<-cal_beta(k,R_square1,R_square2,Hyp1,Hyp2,ratio,rho)
beta1<-beta[[1]]
beta2<-beta[[2]]
varnames<-c()
b<-character()
for (i in 1:k){
varnames[i]=paste('beta',as.character(i),sep='')
}
num_eq <- sum(unlist(gregexpr("[=]",Hyp1))>0)
num_0 <- sum(unlist(gregexpr("[0]",Hyp1))>0)
num_larger <- sum(unlist(gregexpr("[>]",Hyp1))>0)
#bf0j_H0<-cal_bf(N,k,beta1,R_square1,R_square2,T,rho,J,Hyp1,Hyp2,flag_Hyp = 1,seed,standardize)
beta<-beta1
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
library(pkgmaker, warn.conflicts = FALSE)
library(foreach)
library(doParallel)
library(doRNG)
library(pkgmaker, warn.conflicts = FALSE)
no_cores <- detectCores() - 1
## Loading required package: iterators
cl <- makeCluster(no_cores)
registerDoParallel(cl)
clusterEvalQ(cl, .libPaths())
flag_Hyp = 1
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf<-unlist(BF)
bf0u_H0<-bf
flag_Hyp = 0
beta<-beta1
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf<-unlist(BF)
bfju_H0<-bf
bf0j_H0<-na.omit(bf0u_H0/bfju_H0)
T0<-length(bf0j_H0)
medbf0j_H0<-median(bf0j_H0)
kk<-lapply(1:T0, function (x) bf0j_H0[x]>BFthresh )
eta1_test_J2<-sum(unlist(kk)*1)/T0
flag_Hyp = 1
beta<-beta2
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf0u_Hj<-unlist(BF)
beta<-beta2
flag_Hyp = 0
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bfju_Hj<-unlist(BF)
bfj0_Hj<-bfju_Hj/bf0u_Hj
bfj0_Hj<-na.omit(bfj0_Hj)
T0<-length(bfj0_Hj)
# medianbfj0_Hj<-median(bfj0_Hj)
kk<-lapply(1:T0, function (x) bfj0_Hj[x]>BFthresh )
eta2_test_J2<-sum(unlist(kk))/T0
#========================================================================================
# J=3
#========================================================================================
J<-3
N<-N_J3
beta<-cal_beta(k,R_square1,R_square2,Hyp1,Hyp2,ratio,rho)
beta1<-beta[[1]]
beta2<-beta[[2]]
varnames<-c()
b<-character()
for (i in 1:k){
varnames[i]=paste('beta',as.character(i),sep='')
}
num_eq <- sum(unlist(gregexpr("[=]",Hyp1))>0)
num_0 <- sum(unlist(gregexpr("[0]",Hyp1))>0)
num_larger <- sum(unlist(gregexpr("[>]",Hyp1))>0)
#bf0j_H0<-cal_bf(N,k,beta1,R_square1,R_square2,T,rho,J,Hyp1,Hyp2,flag_Hyp = 1,seed,standardize)
beta<-beta1
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
library(pkgmaker, warn.conflicts = FALSE)
library(foreach)
library(doParallel)
library(doRNG)
library(pkgmaker, warn.conflicts = FALSE)
no_cores <- detectCores() - 1
## Loading required package: iterators
cl <- makeCluster(no_cores)
registerDoParallel(cl)
clusterEvalQ(cl, .libPaths())
flag_Hyp = 1
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf<-unlist(BF)
bf0u_H0<-bf
flag_Hyp = 0
beta<-beta1
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf<-unlist(BF)
bfju_H0<-bf
bf0j_H0<-na.omit(bf0u_H0/bfju_H0)
T0<-length(bf0j_H0)
medbf0j_H0<-median(bf0j_H0)
kk<-lapply(1:T0, function (x) bf0j_H0[x]>BFthresh )
eta1_test_J3<-sum(unlist(kk)*1)/T0
flag_Hyp = 1
beta<-beta2
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bf0u_Hj<-unlist(BF)
beta<-beta2
flag_Hyp = 0
set.seed(seed,kind="Mersenne-Twister",normal.kind="Inversion")
#beta<-c(d,d,d)
bf<-numeric(T)
samph<-rep(N,k)
sampm<-samph/J
BF<- foreach(ii = 1:T, .packages = c("bain")) %dorng% {
# for (ii in 1:T){
res<- list()
library(MASS)
X<-mvrnorm(n=N,mu=numeric(k),Sigma=rho)
if(flag_Hyp){
sd<-sqrt(1-R_square1)
}else{
sd<-sqrt(1-R_square2)
}
epsilon<-rnorm(n=N,mean=0,sd=sd)
#beta1<-beta2<-beta3<-d
Y<- epsilon
for (i in 1:k)
{
Y<-Y+beta[i]*X[,i]
}
library(fungible)
library(stats)
if(standardize==TRUE){
# Obtain standardized regression coefficients
capture.output(int <-fungible:: seBeta(X = X, y = Y, Nobs = N,
estimator = "Normal"), file='NUL')
# Store the parameter estimates
estimate <- int$CIs[, 2]
for (i in 1:k)
{
names(estimate)[i]<-paste('beta',i,sep='')
}
#names(estimate)<-c('beta1','beta2','beta3')
# Store the variance covariance matrix for the parameters
# of interest
cov <- list(int$cov.mat)
cov<-matrix(unlist(cov),nrow = k)
}else{
M=cbind(X,Y)
data<-as.list(as.data.frame(M))
for (i in 1:k)
{
names(data)[i]<-paste('beta',i,sep='')
}
Equ<-'Y ~ beta1'
for (i in 2:k){
Equ<-paste(Equ,' + beta',i,sep='')
}
Equ<-paste('lm(',Equ,'-1',',data)',sep='')
# execute a multiple regression using lm()
regr<-eval(parse(text=Equ))
# take a look at the estimated regression coefficients and their names
coef(regr)
estimate <- coef(regr)
cov<-vcov(regr)
}
if(flag_Hyp){
capture.output(res<-bain::bain(estimate,Hyp1,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}else{
capture.output(res<-bain::bain(estimate,Hyp2,fraction = J,n=N,Sigma=cov,group_parameters=0,joint_parameters=k), file='NUL')
}
if(Hyp2=='Hc'){
bf<-res$fit$BF[[1]]
} else{
bf<-res$fit$Fit[[1]]/res$fit$Com[[1]]
}
return(bf)
}
bfju_Hj<-unlist(BF)
bfj0_Hj<-bfju_Hj/bf0u_Hj
bfj0_Hj<-na.omit(bfj0_Hj)
T0<-length(bfj0_Hj)
# medianbfj0_Hj<-median(bfj0_Hj)
kk<-lapply(1:T0, function (x) bfj0_Hj[x]>BFthresh )
eta2_test_J3<-sum(unlist(kk))/T0
# #test Tables in paper
#test Tables in paper
test_that("SSDbain", {expect_equal(eta1_test_J1,eta1_J1 ,tolerance = .01)})
test_that("SSDbain", {expect_equal(eta2_test_J1,eta2_J1,tolerance = .01)})
test_that("SSDbain", {expect_equal(eta1_test_J2,eta1_J2,tolerance = .01)})
test_that("SSDbain", {expect_equal(eta2_test_J2,eta2_J2,tolerance = .01)})
test_that("SSDbain", {expect_equal(eta1_test_J3,eta1_J3 ,tolerance = .01)})
test_that("SSDbain", {expect_equal(eta2_test_J3,eta2_J3,tolerance = .01)})
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