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sim-summary-case5-est.R
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sim-summary-case5-est.R
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##### Case-5 in summary-level simulations (estimates) #####
# Figures S16 and S17 in Supplementary Document
rm(list = ls())
library(BWMR)
library(TwoSampleMR)
source("gsmr_md.R")
library(ggplot2)
library(distr)
D <- DExp(rate = 1)
sim_without_outlier_laplace <- function(beta_true, sqtau_true, sqsigma_true, N, sigmaX_true, sigmaY_true){
gamma_true <- rnorm(N, 0, sqrt(sqsigma_true))
gammahat <- rnorm(N, gamma_true, sigmaX_true)
Gammahat <- rnorm(N, beta_true*gamma_true, sigmaY_true) + sqrt(sqtau_true)*r(D)(N)
df_data <- data.frame(
gammahat = gammahat,
Gammahat = Gammahat,
sigmaX = sigmaX_true,
sigmaY = sigmaY_true
)
}
Rp <- 50
beta_choice <- c(0.0, 0.2, 0.5)
tau_choice <- c(0.1, 0.2, 0.3)
sigma_choice <- c(0.6, 0.8, 1.0)
L <- length(beta_choice)
L1 <- length(tau_choice)
L2 <- length(sigma_choice)
sigmaX_min <- 0.3
sigmaX_max <- 0.5
sigmaY_min <- 0.3
sigmaY_max <- 0.5
N <- 50
### change tau
sqsigma_true <- 0.8^2
# declare sets
b.bwmr.tau <- matrix(nrow = L, ncol = Rp*L1)
b.raps.tau <- matrix(nrow = L, ncol = Rp*L1)
b.gsmr.tau <- matrix(nrow = L, ncol = Rp*L1)
b.egger.tau <- matrix(nrow = L, ncol = Rp*L1)
for (k in 1:L) {
beta_true <- beta_choice[k]
i <- 0
for (j in 1:L1) {
sqtau_true <- tau_choice[j]^2
for (iter in 1:Rp) {
i <- i+1
set.seed(iter)
sigmaX_true <- runif(N, sigmaX_min, sigmaX_max)
sigmaY_true <- runif(N, sigmaY_min, sigmaY_max)
dat <- sim_without_outlier_laplace(beta_true, sqtau_true, sqsigma_true, N, sigmaX_true, sigmaY_true)
colnames(dat) <- c("b.exposure", "b.outcome", "se.exposure", "se.outcome")
# BWMR
res_bwmr <- try(BWMR(gammahat = dat$b.exposure, Gammahat = dat$b.outcome,
sigmaX = dat$se.exposure, sigmaY = dat$se.outcome))
b.bwmr.tau[k, i] <- res_bwmr$beta
# GSMR
res_gsmr = try(gsmr(bzx = dat$b.exposure, bzx_se = dat$se.exposure,
bzy = dat$b.outcome, bzy_se = dat$se.outcome,
ldrho = diag(N),
heidi_outlier_flag = T,
nsnps_thresh = 1))
b.gsmr.tau[k, i] <- drop(res_gsmr$bxy)
## Two sample MR
dat$id.exposure <- rep("uVXQCX", nrow(dat))
dat$id.outcome <- rep("uVXQCX", nrow(dat))
dat$mr_keep <- rep(TRUE, nrow(dat))
dat$exposure <- rep('exposure', nrow(dat))
dat$outcome <- rep('outcome', nrow(dat))
colnames(dat) = c("beta.exposure", "beta.outcome", "se.exposure", "se.outcome", "id.exposure", "id.outcome", "mr_keep", "exposure", "outcome")
result <- try(mr(dat, method_list=c("mr_raps", "mr_egger_regression")))
b.raps.tau[k, i] <- result$b[1]
b.egger.tau[k, i] <- result$b[2]
}
}
}
### change sigma
sqtau_true <- 0.3^2
# declare sets
b.bwmr.sigma <- matrix(nrow = L, ncol = Rp*L2)
b.raps.sigma <- matrix(nrow = L, ncol = Rp*L2)
b.gsmr.sigma <- matrix(nrow = L, ncol = Rp*L2)
b.egger.sigma <- matrix(nrow = L, ncol = Rp*L2)
for (k in 1:L) {
beta_true <- beta_choice[k]
i <- 0
for (j in 1:L2) {
sqsigma_true <- sigma_choice[j]^2
for (iter in 1:Rp) {
i <- i+1
set.seed(iter)
sigmaX_true <- runif(N, sigmaX_min, sigmaX_max)
sigmaY_true <- runif(N, sigmaY_min, sigmaY_max)
dat <- sim_without_outlier_laplace(beta_true, sqtau_true, sqsigma_true, N, sigmaX_true, sigmaY_true)
colnames(dat) <- c("b.exposure", "b.outcome", "se.exposure", "se.outcome")
# BWMR
res_bwmr <- try(BWMR(gammahat = dat$b.exposure, Gammahat = dat$b.outcome,
sigmaX = dat$se.exposure, sigmaY = dat$se.outcome))
b.bwmr.sigma[k, i] <- res_bwmr$beta
# GSMR
res_gsmr = try(gsmr(bzx = dat$b.exposure, bzx_se = dat$se.exposure,
bzy = dat$b.outcome, bzy_se = dat$se.outcome,
ldrho = diag(N),
heidi_outlier_flag = T,
nsnps_thresh = 1))
b.gsmr.sigma[k, i] <- drop(res_gsmr$bxy)
## Two sample MR
dat$id.exposure <- rep("uVXQCX", nrow(dat))
dat$id.outcome <- rep("uVXQCX", nrow(dat))
dat$mr_keep <- rep(TRUE, nrow(dat))
dat$exposure <- rep('exposure', nrow(dat))
dat$outcome <- rep('outcome', nrow(dat))
colnames(dat) = c("beta.exposure", "beta.outcome", "se.exposure", "se.outcome", "id.exposure", "id.outcome", "mr_keep", "exposure", "outcome")
result <- try(mr(dat, method_list=c("mr_raps", "mr_egger_regression")))
b.raps.sigma[k, i] <- result$b[1]
b.egger.sigma[k, i] <- result$b[2]
}
}
}
a <- ls()
rm(list=a[which(a!='b.bwmr.tau' & a !='b.egger.tau' & a !='b.gsmr.tau' & a !='b.raps.tau' & a!='b.bwmr.sigma' & a !='b.egger.sigma' & a !='b.gsmr.sigma' & a !='b.raps.sigma')])
save.image("case-5-estimate.RData")
#################################################################################################################
### Plot
rm(list = ls())
library(ggplot2)
library(reshape2)
method_0 <- c("BWMR", "RAPS", "GSMR", "Egger")
method_level <- c("BWMR", "Egger", "GSMR", "RAPS")
beta_0 <- c("beta=0.0", "beta=0.2", "beta=0.5")
beta_num <- c(0.0, 0.2, 0.5)
tau_num <- c(0.1, 0.2, 0.3)
sig_num <- c(0.6, 0.8, 1.0)
# load data
load("case-5-estimate.RData")
## Boxplot (chang tau)
# df
df_boxplot_est <- data.frame(
beta.num = rep(beta_num, rep(150, 3)),
beta.title = rep(beta_0, rep(150, 3)),
tau.num = factor(rep(rep(tau_num, rep(50, 3)), 3)),
BWMR = c(b.bwmr.tau[1, ], b.bwmr.tau[2, ], b.bwmr.tau[3, ]),
RAPS = c(b.raps.tau[1, ], b.raps.tau[2, ], b.raps.tau[3, ]),
GSMR = c(b.gsmr.tau[1, ], b.gsmr.tau[2, ], b.gsmr.tau[3, ]),
Egger = c(b.egger.tau[1, ], b.egger.tau[2, ], b.egger.tau[3, ])
)
df_boxplot_est <- melt(df_boxplot_est, id.vars = c("beta.num", "beta.title", "tau.num"),
variable.name = 'Method', value.name = 'beta.est')
df_boxplot_est$Method <- factor(df_boxplot_est$Method, levels = method_level)
# plt
plt_boxplot_est <- ggplot(df_boxplot_est, aes(x = tau.num, y = beta.est)) +
geom_boxplot(aes(fill = Method)) +
geom_hline(aes(yintercept = beta.num), colour = "blue", linetype = "dashed", size = 1) +
labs(x = "True value of parameter tau", title = "Estimation") +
theme(axis.title.y = element_blank()) +
theme(legend.position = "top") +
theme(axis.title.x = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size=25),
axis.text = element_text(size = 12),
legend.title = element_text(size = 15, face = "bold"),
legend.text = element_text(size = 15))
plt_boxplot_est <- plt_boxplot_est + facet_grid(~ beta.title) +
theme(strip.text.x = element_text(size = 15),
strip.text.y = element_text(size = 15))
plt_boxplot_est
## Boxplot (chang sigma)
df_boxplot_est <- data.frame(
beta.num = rep(beta_num, rep(150, 3)),
beta.title = rep(beta_0, rep(150, 3)),
sig.num = factor(rep(rep(sig_num, rep(50, 3)), 3)),
BWMR = c(b.bwmr.sigma[1, ], b.bwmr.sigma[2, ], b.bwmr.sigma[3, ]),
RAPS = c(b.raps.sigma[1, ], b.raps.sigma[2, ], b.raps.sigma[3, ]),
GSMR = c(b.gsmr.sigma[1, ], b.gsmr.sigma[2, ], b.gsmr.sigma[3, ]),
Egger = c(b.egger.sigma[1, ], b.egger.sigma[2, ], b.egger.sigma[3, ])
)
df_boxplot_est <- melt(df_boxplot_est, id.vars = c("beta.num", "beta.title", "sig.num"),
variable.name = 'Method', value.name = 'beta.est')
df_boxplot_est$Method <- factor(df_boxplot_est$Method, levels = method_level)
plt_boxplot_est <- ggplot(df_boxplot_est, aes(x = sig.num, y = beta.est)) +
geom_boxplot(aes(fill = Method)) +
geom_hline(aes(yintercept = beta.num), colour = "blue", linetype = "dashed", size = 1) +
labs(x = "True value of parameter sigma", y = "Estimation", title = "Estimation") +
theme(axis.title.y = element_blank()) +
theme(legend.position = "top") +
theme(axis.title.x = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size=25),
axis.text = element_text(size = 12),
legend.title = element_text(size = 15, face = "bold"),
legend.text = element_text(size = 15))
plt_boxplot_est <- plt_boxplot_est + facet_grid(~ beta.title) +
theme(strip.text.x = element_text(size = 15),
strip.text.y = element_text(size = 15))
plt_boxplot_est