Nothing
## -----------------------------------------------------------------------------
library(MendelianRandomization) # loading the package
## -----------------------------------------------------------------------------
MRInputObject <- mr_input(bx = ldlc,
bxse = ldlcse,
by = chdlodds,
byse = chdloddsse)
MRInputObject # example with uncorrelated variants
MRInputObject.cor <- mr_input(bx = calcium,
bxse = calciumse,
by = fastgluc,
byse = fastglucse,
corr = calc.rho)
MRInputObject.cor # example with correlated variants
## ----eval=FALSE---------------------------------------------------------------
# MRInputObject <- mr_input(ldlc, ldlcse, chdlodds, chdloddsse)
## -----------------------------------------------------------------------------
IVWObject <- mr_ivw(MRInputObject,
model = "default",
robust = FALSE,
penalized = FALSE,
correl = FALSE,
weights = "simple",
psi = 0,
distribution = "normal",
alpha = 0.05)
IVWObject <- mr_ivw(mr_input(bx = ldlc, bxse = ldlcse,
by = chdlodds, byse = chdloddsse))
IVWObject
IVWObject.correl <- mr_ivw(MRInputObject.cor,
model = "default",
correl = TRUE,
distribution = "normal",
alpha = 0.05)
IVWObject.correl <- mr_ivw(mr_input(bx = calcium, bxse = calciumse,
by = fastgluc, byse = fastglucse, corr = calc.rho))
IVWObject.correl
## -----------------------------------------------------------------------------
WeightedMedianObject <- mr_median(MRInputObject,
weighting = "weighted",
distribution = "normal",
alpha = 0.05,
iterations = 10000,
seed = 314159265)
WeightedMedianObject <- mr_median(mr_input(bx = ldlc, bxse = ldlcse,
by = chdlodds, byse = chdloddsse))
WeightedMedianObject
SimpleMedianObject <- mr_median(mr_input(bx = ldlc, bxse = ldlcse,
by = chdlodds, byse = chdloddsse), weighting = "simple")
SimpleMedianObject
## -----------------------------------------------------------------------------
EggerObject <- mr_egger(MRInputObject,
robust = FALSE,
penalized = FALSE,
correl = FALSE,
distribution = "normal",
alpha = 0.05)
EggerObject <- mr_egger(mr_input(bx = ldlc, bxse = ldlcse,
by = chdlodds, byse = chdloddsse))
EggerObject
EggerObject.corr <- mr_egger(MRInputObject.cor,
correl = TRUE,
distribution = "normal",
alpha = 0.05)
EggerObject.corr <- mr_egger(mr_input(bx = calcium, bxse = calciumse,
by = fastgluc, byse = fastglucse, corr = calc.rho))
EggerObject.corr
## -----------------------------------------------------------------------------
MaxLikObject <- mr_maxlik(MRInputObject,
model = "default",
correl = FALSE,
psi = 0,
distribution = "normal",
alpha = 0.05)
MaxLikObject <- mr_maxlik(mr_input(bx = ldlc, bxse = ldlcse,
by = chdlodds, byse = chdloddsse))
MaxLikObject
MaxLikObject.corr <- mr_maxlik(mr_input(bx = calcium, bxse = calciumse,
by = fastgluc, byse = fastglucse, corr = calc.rho))
MaxLikObject.corr
## -----------------------------------------------------------------------------
MBEObject <- mr_mbe(MRInputObject,
weighting = "weighted",
stderror = "delta",
phi = 1,
seed = 314159265,
iterations = 10000,
distribution = "normal",
alpha = 0.05)
MBEObject <- mr_mbe(mr_input(bx = ldlc, bxse = ldlcse,
by = chdlodds, byse = chdloddsse))
MBEObject
## ----eval = FALSE-------------------------------------------------------------
# HetPenObject <- mr_hetpen(MRInputObject,
# prior = 0.5,
# CIMin = -1,
# CIMax = 1,
# CIStep = 0.001,
# alpha = 0.05)
## -----------------------------------------------------------------------------
HetPenObject <- mr_hetpen(mr_input(bx = ldlc[1:10], bxse = ldlcse[1:10],
by = chdlodds[1:10], byse = chdloddsse[1:10]), CIMin = -1, CIMax = 5, CIStep = 0.01)
HetPenObject
## -----------------------------------------------------------------------------
bcrp =c(0.160, 0.236, 0.149, 0.09, 0.079, 0.072, 0.047, 0.069)
bcrpse =c(0.006, 0.009, 0.006, 0.005, 0.005, 0.005, 0.006, 0.011)
bchd =c(0.0237903, -0.1121942, -0.0711906, -0.030848, 0.0479207, 0.0238895,
0.005528, 0.0214852)
bchdse =c(0.0149064, 0.0303084, 0.0150552, 0.0148339, 0.0143077, 0.0145478,
0.0160765, 0.0255237)
HetPenObject.multimode <- mr_hetpen(mr_input(bx = bcrp, bxse = bcrpse,
by = bchd, byse = bchdse))
HetPenObject.multimode
## -----------------------------------------------------------------------------
ConMixObject <- mr_conmix(MRInputObject,
psi = 0,
CIMin = NA,
CIMax = NA,
CIStep = 0.01,
alpha = 0.05)
ConMixObject <- mr_conmix(mr_input(bx = ldlc, bxse = ldlcse,
by = chdlodds, byse = chdloddsse))
ConMixObject
## -----------------------------------------------------------------------------
LassoObject <- mr_lasso(MRInputObject,
distribution = "normal",
alpha = 0.05,
lambda = numeric(0))
LassoObject <- mr_lasso(mr_input(bx = ldlc, bxse = ldlcse,
by = chdlodds, byse = chdloddsse))
LassoObject
## -----------------------------------------------------------------------------
DIVWObject <- mr_divw(MRInputObject,
over.dispersion = TRUE,
alpha = 0.05,
diagnostics = FALSE)
DIVWObject <- mr_divw(mr_input(bx = ldlc, bxse = ldlcse,
by = chdlodds, byse = chdloddsse))
DIVWObject
## -----------------------------------------------------------------------------
PIVWObject <- mr_pivw(MRInputObject,
over.dispersion = TRUE,
delta = 0,
sel.pval = NULL,
Boot.Fieller = TRUE,
alpha = 0.05)
PIVWObject <- mr_pivw(mr_input(bx = ldlc, bxse = ldlcse,
by = chdlodds, byse = chdloddsse))
PIVWObject
## ----eval=FALSE---------------------------------------------------------------
# cMLObject <- mr_cML(MRInputObject,
# MA = TRUE,
# DP = TRUE,
# K_vec = 0:(length(object@betaX)-2),
# random_start = 0,
# num_pert = 200,
# random_start_pert = 0,
# maxit = 100,
# random_seed = 314,
# n,
# Alpha = 0.05)
## -----------------------------------------------------------------------------
cMLObject <- mr_cML(mr_input(bx = ldlc, bxse = ldlcse,
by = chdlodds, byse = chdloddsse), n = 17723)
cMLObject
## ----eval=FALSE---------------------------------------------------------------
# pcGMMObject <- mr_pcgmm(MRInputObject.cor,
# nx,
# ny,
# r = NULL,
# thres = 0.999,
# robust = TRUE,
# alpha = 0.05)
## -----------------------------------------------------------------------------
pcGMMObject <- mr_pcgmm(mr_input(bx = calcium, bxse = calciumse,
by = fastgluc, byse = fastglucse, corr = calc.rho),
nx=6351, ny=133010)
pcGMMObject
## -----------------------------------------------------------------------------
MVMRInputObject <- mr_mvinput(bx = cbind(ldlc, hdlc, trig),
bxse = cbind(ldlcse, hdlcse, trigse),
by = chdlodds,
byse = chdloddsse)
MVMRInputObject
MVMRInputObject.cor <- mr_mvinput(bx = cbind(ldlc, hdlc, trig),
bxse = cbind(ldlcse, hdlcse, trigse),
by = chdlodds,
byse = chdloddsse,
correlation = diag(length(ldlc)))
## -----------------------------------------------------------------------------
MVIVWObject <- mr_mvivw(MVMRInputObject,
model = "default",
correl = FALSE,
correl.x = NULL,
nx = NA,
distribution = "normal",
alpha = 0.05)
MVIVWObject <- mr_mvivw(MVMRInputObject)
MVIVWObject
## -----------------------------------------------------------------------------
MVIVWObject.condF <- mr_mvivw(MVMRInputObject, nx = 17723)
MVIVWObject.condF
## -----------------------------------------------------------------------------
MVEggerObject <- mr_mvegger(MVMRInputObject)
MVEggerObject
MVMedianObject <- mr_mvmedian(MVMRInputObject)
MVMedianObject
MVLassoObject <- mr_mvlasso(MVMRInputObject)
MVLassoObject
MVcMLObject <- mr_mvcML(MVMRInputObject, n = 17723)
MVcMLObject
MVGMMObject <- mr_mvgmm(MVMRInputObject, nx=rep(17723,3), ny=17723)
MVGMMObject
MVpcGMMObject <- mr_mvpcgmm(MVMRInputObject.cor, nx=rep(17723,3), ny=17723)
MVpcGMMObject
## -----------------------------------------------------------------------------
MRInputObject <- mr_input(bx = ldlc,
bxse = ldlcse,
by = chdlodds,
byse = chdloddsse)
MRAllObject_all <- mr_allmethods(MRInputObject, method = "all")
MRAllObject_all
MRAllObject_egger <- mr_allmethods(MRInputObject, method = "egger")
MRAllObject_egger
MRAllObject_main <- mr_allmethods(MRInputObject, method = "main")
MRAllObject_main
## ----eval = FALSE-------------------------------------------------------------
# mr_plot(mr_input(bx = ldlc, bxse = ldlcse, by = chdlodds, byse = chdloddsse),
# error = TRUE, orientate = FALSE, line = "ivw")
## -----------------------------------------------------------------------------
mr_plot(mr_input(bx = ldlc, bxse = ldlcse, by = chdlodds, byse = chdloddsse),
error = TRUE, orientate = FALSE, line = "ivw", interactive = FALSE)
## -----------------------------------------------------------------------------
mr_plot(mr_input(bx = ldlc, bxse = ldlcse, by = chdlodds, byse = chdloddsse),
error = TRUE, orientate = FALSE, line = "ivw", interactive = FALSE, labels = TRUE)
## -----------------------------------------------------------------------------
mr_plot(MVMRInputObject, interactive = FALSE)
## -----------------------------------------------------------------------------
mr_plot(MRAllObject_all)
## -----------------------------------------------------------------------------
mr_plot(MRAllObject_egger)
## -----------------------------------------------------------------------------
mr_plot(mr_allmethods(mr_input(bx = hdlc, bxse = hdlcse,
by = chdlodds, byse = chdloddsse)))
## -----------------------------------------------------------------------------
mr_forest(MRInputObject, ordered=TRUE)
## -----------------------------------------------------------------------------
mr_forest(MRInputObject,
methods = c("ivw", "median", "wmedian", "egger", "maxlik", "mbe", "conmix"),
snp_estimates = FALSE)
## -----------------------------------------------------------------------------
mr_funnel(mr_input(bx = ldlc[1:8], bxse = ldlcse[1:8],
by = chdlodds[1:8], byse = chdloddsse[1:8]))
## -----------------------------------------------------------------------------
mr_loo(MRInputObject)
## ----eval=FALSE---------------------------------------------------------------
# library(ggplot2)
# forest = mr_forest(mr_input(ldlc, ldlcse, chdlodds, chdloddsse))
# forest2 = forest + coord_cartesian(xlim=c(-5,5))
# forest2
## ----eval = FALSE-------------------------------------------------------------
# mr_ivw(pheno_input(snps=c("rs12916", "rs2479409",
# "rs217434", "rs1367117",
# "rs4299376", "rs629301",
# "rs4420638", "rs6511720"),
# exposure = "Low density lipoprotein",
# pmidE = "24097068",
# ancestryE = "European",
# outcome = "Coronary artery disease",
# pmidO = "26343387",
# ancestryO = "Mixed"))
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