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.Rhistory (sam-laptop's conflicted copy 2021-07-30)
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.Rhistory (sam-laptop's conflicted copy 2021-07-30)
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library(BCD)
?ricf
mvtnorm
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
epsilon <- mvtnorm::rmvnorm(200, mean = rep(0, 4), sigma = Omega.weights)
Omega.weights
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
epsilon <- mvtnorm::rmvnorm(200, mean = rep(0, 4), sigma = Omega.weights)
epsilon
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
epsilon <- t(mvtnorm::rmvnorm(200, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
Y
y <- Y - rowMeans(Y)
Y <- Y - rowMeans(Y)
rowMeans(Y)
ricf(B, Omega, Y)
Omega
Omega.weights != 0
Omega <- Omega.weights != 0
Omega
Omega <- (Omega.weights != 0) + 0
Omega
B <- (B.weights != 0) + 0
B
Omega
ricf(B, Omega, Y)
B
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 500
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 500
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 500
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 500
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 500
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 500
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 500
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 500
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 500
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 500
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 500
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 500
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 500
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 200
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 200
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 200
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 200
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 200
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 200
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 200
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 200
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
# Select True Parameters
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# Generate data
n <- 200
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
# Center Data Y
Y <- Y - rowMeans(Y)
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
B.weights
cov(t(Y))
library(BCD)
library(BCD)
library(BCD)
install.packages("roxygen2")
library(BCD)
?density
plot(ecdf(tavr_dat$Delay_days))
tavr_dat <- read.csv("tavr_3_28_18.csv")
max.days <- 75
tavr_inc <- subset(tavr_dat, tavr_dat$Delay_days <= max.days)
breaks <- c(0, 7, 10, 14, 30, max.days)
priorEvent <- (tavr_inc$Prior_CABG == "Yes" |
tavr_inc$Prior_MI == "Yes"|
tavr_inc$Prior_PCI == "Yes") + 0
bloodThinner <- (tavr_inc$DVT_PE == "Yes" |
tavr_inc$Prior_GIB == "Yes"|
tavr_inc$Other_Indication_for_AC == "Yes") + 0
AV_cut <- as.numeric(tavr_inc$AV_MG < 40 | is.na(tavr_inc$AV_MG))
MVD <- as.numeric(tavr_inc$Severity_of_Mitral_Regurgitation %in% c("Moderate", "Severe") )
site <- as.numeric(tavr_inc$Valve_Sheath_Access_Site == "Transfemoral")
after11 <- as.numeric(gsub(".*/","",tavr_inc$Procedure_Date) > 11)
medReason <- rep(1, dim(tavr_inc)[1])
medReason[which(tavr_dat$Include == T)] <- 0
setwd("~/Dropbox/oldProjects/Cyclic Graphs/BCD")
tavr_dat <- read.csv("tavr_3_28_18.csv")
max.days <- 75
tavr_inc <- subset(tavr_dat, tavr_dat$Delay_days <= max.days)
breaks <- c(0, 7, 10, 14, 30, max.days)
priorEvent <- (tavr_inc$Prior_CABG == "Yes" |
tavr_inc$Prior_MI == "Yes"|
tavr_inc$Prior_PCI == "Yes") + 0
bloodThinner <- (tavr_inc$DVT_PE == "Yes" |
tavr_inc$Prior_GIB == "Yes"|
tavr_inc$Other_Indication_for_AC == "Yes") + 0
AV_cut <- as.numeric(tavr_inc$AV_MG < 40 | is.na(tavr_inc$AV_MG))
MVD <- as.numeric(tavr_inc$Severity_of_Mitral_Regurgitation %in% c("Moderate", "Severe") )
site <- as.numeric(tavr_inc$Valve_Sheath_Access_Site == "Transfemoral")
after11 <- as.numeric(gsub(".*/","",tavr_inc$Procedure_Date) > 11)
medReason <- rep(1, dim(tavr_inc)[1])
medReason[which(tavr_dat$Include == T)] <- 0
setwd("~/Dropbox/oldProjects/Cyclic Graphs/BCD")
setwd("~/Dropbox/tavr")
library(BCD)
Omega.weights <- diag(rep(1, 4))
Omega.weights[1, 2] <- Omega.weights[2, 1] <- .5
B.weights <- matrix(0, nrow = 4, ncol = 4)
B.weights[3, 1] <- .5; B.weights[4, 2] <- .7
# ## Generate data
n <- 200
epsilon <- t(mvtnorm::rmvnorm(n, mean = rep(0, 4), sigma = Omega.weights))
Y <- solve(diag(rep(1, 4)) - B.weights, epsilon)
#
# ## Form ricf arguments
B <- (B.weights != 0) + 0
Omega <- (Omega.weights != 0) + 0
ricf(B, Omega, Y)
#
# ## Form ricf arguments
B <- as.numeric(B.weights != 0)
B
#
# ## Form ricf arguments
B <- (B.weights != 0)
B
out <- ricf(B, Omega, Y)
names(out$SigmaHat)
out$SigmaHat
Omega.weights
out$OmegaHat
out$BHat
B.weights
dim(Y)
names(out)
library(BCD)
install.packages("roxygen2")
library(BCD)
library("devtools")
install_github("ysamwang/BCD")
names(out)
se <- var.ricf(Y, B, Omega, out$BHat, out$OmegaHat, type = "expected")
out
se <- var.ricf(Y, B, Omega, out$BHat, out$OmegaHat, type = "expected")
names(se)
se
cbind(c(out$BHat[which(B !=0)], out$OmegaHat[which(Omega !=0 & lower.tri(Omega))],
diag(se))
cbind(c(out$BHat[which(B !=0)], out$OmegaHat[which(Omega !=0 & lower.tri(Omega))]),
diag(se))
cbind(c(out$BHat[which(B !=0)], out$OmegaHat[which(Omega !=0 & lower.tri(Omega))]),
diag(se))
c(out$BHat[which(B !=0)], out$OmegaHat[which(Omega !=0 & lower.tri(Omega))])
which(Omega !=0 & lower.tri(Omega))
lower.tri(Omega)
Omega !=0
cbind(c(out$BHat[which(B !=0)], out$OmegaHat[which(Omega !=0 & lower.tri(Omega, diag = T))]),
diag(se))
output <- cbind(c(out$BHat[which(B !=0)], out$OmegaHat[which(Omega !=0 & lower.tri(Omega, diag = T))]),
sqrt(diag(se)))
names(output) <- c("estimates", "SE")
which(B != 0, arr.ind = T)
apply(which(B != 0, arr.ind = T), MAR = 1, function(x){paste("B [", paste(x, collapse = ","), "]", sep = "")})
c(apply(which(B != 0, arr.ind = T), MAR = 1, function(x){paste("B [", paste(x, collapse = ","), "]", sep = "")}),
apply(which(Omega != 0 & lower.tri(Omega, diag = T)), arr.ind = T), MAR = 1, function(x){paste("B [", paste(x, collapse = ","), "]", sep = "")}))
apply(which(Omega != 0 & lower.tri(Omega, diag = T)), arr.ind = T), MAR = 1, function(x){paste("B [", paste(x, collapse = ","), "]", sep = "")})
apply(which(Omega != 0 & lower.tri(Omega, diag = T), arr.ind = T), MAR = 1, function(x){paste("B [", paste(x, collapse = ","), "]", sep = "")})
c(apply(which(B != 0, arr.ind = T), MAR = 1, function(x){paste("B [", paste(x, collapse = ","), "]", sep = "")}),
apply(which(Omega != 0 & lower.tri(Omega, diag = T), arr.ind = T), MAR = 1, function(x){paste("O [", paste(x, collapse = ","), "]", sep = "")}))
rownames(output) <- c(apply(which(B != 0, arr.ind = T), MAR = 1, function(x){paste("B [", paste(x, collapse = ","), "]", sep = "")}),
apply(which(Omega != 0 & lower.tri(Omega, diag = T), arr.ind = T), MAR = 1, function(x){paste("O [", paste(x, collapse = ","), "]", sep = "")}))
output
colnames(output) <- c("estimates", "SE")
rownames(output) <- c(apply(which(B != 0, arr.ind = T), MAR = 1, function(x){paste("B [", paste(x, collapse = ","), "]", sep = "")}),
apply(which(Omega != 0 & lower.tri(Omega, diag = T), arr.ind = T), MAR = 1, function(x){paste("O [", paste(x, collapse = ","), "]", sep = "")}))
output <- cbind(c(out$BHat[which(B !=0)], out$OmegaHat[which(Omega !=0 & lower.tri(Omega, diag = T))]),
sqrt(diag(se)))
colnames(output) <- c("estimates", "SE")
rownames(output) <- c(apply(which(B != 0, arr.ind = T), MAR = 1, function(x){paste("B [", paste(x, collapse = ","), "]", sep = "")}),
apply(which(Omega != 0 & lower.tri(Omega, diag = T), arr.ind = T), MAR = 1, function(x){paste("O [", paste(x, collapse = ","), "]", sep = "")}))
output
library(BCD)
se <- var.ricf(Y, B, Omega, out$BHat, out$OmegaHat, type = "expected")
s
se
se <- var.ricf(Y, B, Omega, out$BHat, out$OmegaHat, type = "expected")
# get parameter estimates and marginal standard errors
output <- cbind(c(out$BHat[which(B !=0)], out$OmegaHat[which(Omega !=0 & lower.tri(Omega, diag = T))]),
sqrt(diag(se)))
colnames(output) <- c("estimates", "SE")
rownames(output) <- rownames(se)
output
library(BCD)
output <- cbind(c(out$BHat[which(B !=0)], out$OmegaHat[which(Omega !=0 & lower.tri(Omega, diag = T))]),
sqrt(diag(se)))
colnames(output) <- c("estimates", "SE")
rownames(output) <- rownames(se)
output
se <- var.ricf(Y, B, Omega, out$BHat, out$OmegaHat, type = "expected")
se
output <- cbind(c(out$BHat[which(B !=0)], out$OmegaHat[which(Omega !=0 & lower.tri(Omega, diag = T))]),
sqrt(diag(se)))
colnames(output) <- c("estimates", "SE")
rownames(output) <- rownames(se)
output