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mvMR_glmnet.R
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#
# 11th April 2019
# Calculating the lasso estimate using the multivariable input data
# cv = True: Estimate the regularisation parameter bY cross-validation
# cv = False: Parameter can be specified manually
#Note: Intercept is always true
library(glmnet)
#
# class mv-mr input
#
setClass("mvMRInput",
representation(betaX = "matrix",
betaY = "matrix",
betaXse = "matrix",
betaYse = "matrix",
exposure = "character",
outcome = "character",
snps = "character",
effect_allele = "character",
other_allele = "character",
eaf = "numeric",
correlation = "matrix")
)
#
# output class
#
setClass("MRl1",
representation(Exposure = "character",
Outcome = "character",
Estimate = "numeric",
Lambda = "numeric")
)
setClass("MRenet",
representation(Exposure = "character",
Outcome = "character",
Estimate = "numeric",
Lambda1 = "numeric",
Lambda2 = "numeric")
)
glmnet_l1_mr = function(object, cv=TRUE, lambda=0.1, cv.param="lambda.1se"){
bX = object@betaX
bY = object@betaY
if(cv==TRUE){
cv = cv.glmnet(bX, bY, family = "gaussian", nfold = 10, type.measure = "mse", intercept=FALSE, alpha = 1, standardize = FALSE)
if(cv.param=="lambda.1se"){bestlambda=cv$lambda.1se}
if(cv.param=="lambda.min"){bestlambda=cv$lambda.min}
g.out = glmnet(bX, bY, family = "gaussian", intercept=FALSE, lambda = bestlambda, alpha = 1, standardize = FALSE)
l1.coeff = coef(g.out)[2:(ncol(bX)+1)]
}
else{
bestlambda = lambda
g.out = glmnet(bX, bY, family = "gaussian", intercept=FALSE, lambda = bestlambda, alpha = 1, standardize = FALSE)
l1.coeff = coef(g.out)[2:(ncol(bX)+1)]
}
return(new("MRl1",
Exposure = object@exposure,
Outcome = object@outcome,
Estimate = l1.coeff,
Lambda = bestlambda
))
}
glmnet_enet_mr = function(object, cv=TRUE, lambda=0.1, alpha=0.1, cv.param="lambda.1se"){
bX = object@betaX
bY = object@betaY
if(cv==TRUE){
a = seq(0.1, 0.9, 0.1)
if(cv.param=="lambda.1se"){
search = foreach(i = a, .combine = rbind) %do% {
cv = cv.glmnet(bX, bY, family = "gaussian", nfold = 10, type.measure = "mse", intercept=FALSE, alpha = i, standardize = FALSE)
data.frame(cvm = cv$cvm[cv$lambda == cv$lambda.1se], lambda.1se = cv$lambda.1se, alpha = i)
}
cv = search[search$cvm == min(search$cvm), ]
bestlambda = cv$lambda.1se
bestalpha = cv$alpha
}
if(cv.param=="lambda.min"){
search = foreach(i = a, .combine = rbind) %do% {
cv = cv.glmnet(bX, bY, family = "gaussian", nfold = 10, type.measure = "mse", intercept=FALSE, alpha = i, standardize = FALSE)
data.frame(cvm = cv$cvm[cv$lambda == cv$lambda.min], lambda.min = cv$lambda.min, alpha = i)
}
cv = search[search$cvm == min(search$cvm), ]
bestlambda = cv$lambda.min
bestalpha = cv$alpha
}
enet.out = glmnet(bX, bY, family = "gaussian", intercept=FALSE,lambda = bestlambda, alpha = bestalpha, standardize = FALSE)
enet.coeff = coef(enet.out)[2:(ncol(bX)+1)]
}
else{
bestlambda = lambda
bestalpha = alpha
enet.out = glmnet(bX, bY, family = "gaussian", intercept=FALSE, lambda = bestlambda, alpha = bestalpha, standardize = FALSE)
enet.coeff = coef(enet.out)[2:(ncol(bX)+1)]
}
return(new("MRenet",
Exposure = object@exposure,
Outcome = object@outcome,
Estimate = enet.coeff,
Lambda1 = bestlambda,
Lambda2 = bestalpha
))
}