Last updated: 2020-06-02

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Rmd 2851240 fmorgante 2020-06-02 Fix bug in dense scenario
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Rmd 3c70b54 fmorgante 2020-06-02 Update dense scenario
html 2ace633 fmorgante 2020-06-02 Build site.
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Rmd ceda278 fmorgante 2020-06-01 Add case study with dense scenario

library(mr.mash.alpha)
library(glmnet)
Loading required package: Matrix
Loading required package: foreach
Loaded glmnet 2.0-16
###Set options
options(stringsAsFactors = FALSE)

###Function to compute accuracy
compute_accuracy <- function(Y, Yhat) {
  bias <- rep(as.numeric(NA), ncol(Y))
  r2 <- rep(as.numeric(NA), ncol(Y))
  mse <- rep(as.numeric(NA), ncol(Y))
  
  for(i in 1:ncol(Y)){
    fit  <- lm(Y[, i] ~ Yhat[, i])
    bias[i] <- coef(fit)[2] 
    r2[i] <- summary(fit)$r.squared
    mse[i] <- mean((Y[, i] - Yhat[, i])^2)
  }
  
  return(list(bias=bias, r2=r2, mse=mse))
}

Dense scenario

Here we investigate why mr.mash does not perform as well in denser scenarios. Let’s simulate data with n=600, p=1,000, p_causal=500, r=5, r_causal=5, PVE=0.5, shared effects, independent predictors, and independent residuals. The models will be fitted to the training data (80% of the full data).

###Set seed
set.seed(123)

###Set parameters
n <- 600
p <- 1000
p_causal <- 500
r <- 5
r_causal <- r
pve <- 0.5
B_cor <- 1
X_cor <- 0
V_cor <- 0

###Simulate V, B, X and Y
out <- mr.mash.alpha:::simulate_mr_mash_data(n, p, p_causal, r, r_causal=r, intercepts = rep(1, r),
                                             pve=pve, B_cor=B_cor, B_scale=0.9, X_cor=X_cor, X_scale=0.8, V_cor=V_cor)

colnames(out$Y) <- paste0("Y", seq(1, r))
rownames(out$Y) <- paste0("N", seq(1, n))
colnames(out$X) <- paste0("X", seq(1, p))
rownames(out$X) <- paste0("N", seq(1, n))

###Split the data in training and test sets
test_set <- sort(sample(x=c(1:n), size=round(n*0.2), replace=FALSE))
Ytrain <- out$Y[-test_set, ]
Xtrain <- out$X[-test_set, ]
Ytest <- out$Y[test_set, ]
Xtest <- out$X[test_set, ]

We build the mixture prior as usual including zero matrix, identity matrix, rank-1 matrices, low, medium and high heterogeneity matrices, shared matrix, each scaled by a grid from 0.1 to 2.1 in steps of 0.2.

grid <- seq(0.1, 2.1, 0.2)
S0 <- mr.mash.alpha:::compute_cov_canonical(ncol(Ytrain), singletons=TRUE, hetgrid=c(0, 0.25, 0.5, 0.75, 1), grid, zeromat=TRUE)

We run glmnet with \(\alpha=1\) to obtain an inital estimate for the regression coefficients to provide to mr.mash, and for comparison.

###Fit group-lasso
cvfit_glmnet <- cv.glmnet(x=Xtrain, y=Ytrain, family="mgaussian", alpha=1)
coeff_glmnet <- coef(cvfit_glmnet, s="lambda.min")
Bhat_glmnet <- matrix(as.numeric(NA), nrow=p, ncol=r)
ahat_glmnet <- vector("numeric", r)
for(i in 1:length(coeff_glmnet)){
  Bhat_glmnet[, i] <- as.vector(coeff_glmnet[[i]])[-1]
  ahat_glmnet[i] <- as.vector(coeff_glmnet[[i]])[1]
}
Yhat_test_glmnet <- drop(predict(cvfit_glmnet, newx=Xtest, s="lambda.min"))

We run mr.mash with EM updates of the mixture weights, updating V, and initializing the regression coefficients with the estimates from glmnet.

###Fit mr.mash
fit_mrmash <- mr.mash(Xtrain, Ytrain, S0, tol=1e-2, convergence_criterion="ELBO", update_w0=TRUE, 
                     update_w0_method="EM", compute_ELBO=TRUE, standardize=TRUE, verbose=FALSE, update_V=TRUE,
                     mu1_init = Bhat_glmnet, w0_threshold=1e-8)
Processing the inputs... Done!
Fitting the optimization algorithm... Done!
Processing the outputs... Done!
mr.mash successfully executed in 2.557808 minutes!
Yhat_test_mrmash <- predict(fit_mrmash, Xtest)
layout(matrix(c(1, 1, 2, 2,
                1, 1, 2, 2,
                0, 3, 3, 0,
                0, 3, 3, 0), 4, 4, byrow = TRUE))

###Plot estimated vs true coeffcients
##mr.mash
plot(out$B, fit_mrmash$mu1, main="mr.mash", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
##glmnet
plot(out$B, Bhat_glmnet, main="glmnet", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)

###Plot mr.mash vs glmnet estimated coeffcients
colorz <- matrix("black", nrow=p, ncol=r)
zeros <- apply(out$B, 2, function(x) x==0)
for(i in 1:ncol(colorz)){
  colorz[zeros[, i], i] <- "red"
}

plot(Bhat_glmnet, fit_mrmash$mu1, main="mr.mash vs glmnet", 
     xlab="glmnet estimated coefficients", ylab="mr.mash estimated coefficients",
     col=colorz, cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
legend("topleft", 
       legend = c("Non-zero", "Zero"), 
       col = c("black", "red"), 
       pch = c(1, 1), 
       horiz = FALSE,
       cex=2) 

Version Author Date
5328c22 fmorgante 2020-06-02
42ec51c fmorgante 2020-06-01
print("Prediction performance with glmnet")
[1] "Prediction performance with glmnet"
compute_accuracy(Ytest, Yhat_test_glmnet)
$bias
[1] 0.7973441 0.7171240 1.1201318 0.9992656 0.9375002

$r2
[1] 0.12560024 0.10044653 0.14357016 0.13204939 0.09516285

$mse
[1] 502.2840 531.2713 573.5326 575.3974 722.7162
print("Prediction performance with mr.mash")
[1] "Prediction performance with mr.mash"
compute_accuracy(Ytest, Yhat_test_mrmash)
$bias
[1] 2.268094 2.196013 3.435006 2.831555 3.026599

$r2
[1] 0.1310161 0.1128144 0.2345789 0.1833654 0.1535817

$mse
[1] 518.2328 530.0913 591.4375 587.3239 730.5908
print("True V (full data)")
[1] "True V (full data)"
out$V
         [,1]     [,2]     [,3]     [,4]     [,5]
[1,] 331.4137   0.0000   0.0000   0.0000   0.0000
[2,]   0.0000 331.4137   0.0000   0.0000   0.0000
[3,]   0.0000   0.0000 331.4137   0.0000   0.0000
[4,]   0.0000   0.0000   0.0000 331.4137   0.0000
[5,]   0.0000   0.0000   0.0000   0.0000 331.4137
print("Estimated V (training data)")
[1] "Estimated V (training data)"
fit_mrmash$V
         Y1       Y2       Y3       Y4       Y5
Y1 600.7891 271.7844 213.0465 197.5704 231.8269
Y2 271.7844 581.4331 244.8055 243.7165 255.7953
Y3 213.0465 244.8055 542.5477 252.0331 239.4896
Y4 197.5704 243.7165 252.0331 532.5570 214.1998
Y5 231.8269 255.7953 239.4896 214.1998 516.2954

It looks mr.mash shrinks the coeffcients too much and the residual covariance is absorbing part of the effects. Let’s now try to fix the residual covariance to the truth (although it is going to be a little different since we are fitting the model to the training set only).

###Fit mr.mash
fit_mrmash_fixV <- mr.mash(Xtrain, Ytrain, S0, tol=1e-2, convergence_criterion="ELBO", update_w0=TRUE, 
                     update_w0_method="EM", compute_ELBO=TRUE, standardize=TRUE, verbose=FALSE, update_V=FALSE,
                     mu1_init = Bhat_glmnet, w0_threshold=1e-8, V=out$V)
Processing the inputs... Done!
Fitting the optimization algorithm... Done!
Processing the outputs... Done!
mr.mash successfully executed in 3.218407 minutes!
Yhat_test_mrmash_fixV <- predict(fit_mrmash_fixV, Xtest)
layout(matrix(c(1, 1, 2, 2,
                1, 1, 2, 2,
                0, 3, 3, 0,
                0, 3, 3, 0), 4, 4, byrow = TRUE))

###Plot estimated vs true coeffcients
##mr.mash
plot(out$B, fit_mrmash_fixV$mu1, main="mr.mash", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
##glmnet
plot(out$B, Bhat_glmnet, main="glmnet", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)

###Plot mr.mash vs glmnet estimated coeffcients
colorz <- matrix("black", nrow=p, ncol=r)
zeros <- apply(out$B, 2, function(x) x==0)
for(i in 1:ncol(colorz)){
  colorz[zeros[, i], i] <- "red"
}

plot(Bhat_glmnet, fit_mrmash_fixV$mu1, main="mr.mash vs glmnet", 
     xlab="glmnet estimated coefficients", ylab="mr.mash estimated coefficients",
     col=colorz, cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
legend("topleft", 
       legend = c("Non-zero", "Zero"), 
       col = c("black", "red"), 
       pch = c(1, 1), 
       horiz = FALSE,
       cex=2) 

Version Author Date
5328c22 fmorgante 2020-06-02
42ec51c fmorgante 2020-06-01
print("Prediction performance with glmnet")
[1] "Prediction performance with glmnet"
compute_accuracy(Ytest, Yhat_test_glmnet)
$bias
[1] 0.7973441 0.7171240 1.1201318 0.9992656 0.9375002

$r2
[1] 0.12560024 0.10044653 0.14357016 0.13204939 0.09516285

$mse
[1] 502.2840 531.2713 573.5326 575.3974 722.7162
print("Prediction performance with mr.mash")
[1] "Prediction performance with mr.mash"
compute_accuracy(Ytest, Yhat_test_mrmash_fixV)
$bias
[1] 0.3739759 0.4820432 0.5484524 0.5637339 0.4956777

$r2
[1] 0.07025924 0.11696855 0.12904463 0.13990240 0.08790078

$mse
[1] 642.2267 599.7516 637.9042 632.1933 801.5090

Prediction performance looks worse in terms of MSE and \(r^2\) while bias, although still off, is closer to 1. However, looking at the plots of the coefficients, we can see that many coefficients are no longer overshrunk. Let’s see what happens if we initialize V to the truth but then we update it.

###Fit mr.mash
fit_mrmash_initV <- mr.mash(Xtrain, Ytrain, S0, tol=1e-2, convergence_criterion="ELBO", update_w0=TRUE, 
                     update_w0_method="EM", compute_ELBO=TRUE, standardize=TRUE, verbose=FALSE, update_V=TRUE,
                     mu1_init = Bhat_glmnet, w0_threshold=1e-8, V=out$V)
Processing the inputs... Done!
Fitting the optimization algorithm... Done!
Processing the outputs... Done!
mr.mash successfully executed in 2.56133 minutes!
Yhat_test_mrmash_initV <- predict(fit_mrmash_initV, Xtest)
layout(matrix(c(1, 1, 2, 2,
                1, 1, 2, 2,
                0, 3, 3, 0,
                0, 3, 3, 0), 4, 4, byrow = TRUE))

###Plot estimated vs true coeffcients
##mr.mash
plot(out$B, fit_mrmash_initV$mu1, main="mr.mash", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
##glmnet
plot(out$B, Bhat_glmnet, main="glmnet", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)

###Plot mr.mash vs glmnet estimated coeffcients
colorz <- matrix("black", nrow=p, ncol=r)
zeros <- apply(out$B, 2, function(x) x==0)
for(i in 1:ncol(colorz)){
  colorz[zeros[, i], i] <- "red"
}

plot(Bhat_glmnet, fit_mrmash_initV$mu1, main="mr.mash vs glmnet", 
     xlab="glmnet estimated coefficients", ylab="mr.mash estimated coefficients",
     col=colorz, cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
legend("topleft", 
       legend = c("Non-zero", "Zero"), 
       col = c("black", "red"), 
       pch = c(1, 1), 
       horiz = FALSE,
       cex=2) 

Version Author Date
5328c22 fmorgante 2020-06-02
2ace633 fmorgante 2020-06-02
print("Prediction performance with glmnet")
[1] "Prediction performance with glmnet"
compute_accuracy(Ytest, Yhat_test_glmnet)
$bias
[1] 0.7973441 0.7171240 1.1201318 0.9992656 0.9375002

$r2
[1] 0.12560024 0.10044653 0.14357016 0.13204939 0.09516285

$mse
[1] 502.2840 531.2713 573.5326 575.3974 722.7162
print("Prediction performance with mr.mash")
[1] "Prediction performance with mr.mash"
compute_accuracy(Ytest, Yhat_test_mrmash_initV)
$bias
[1] 2.249720 2.175435 3.415054 2.814300 3.008327

$r2
[1] 0.1300084 0.1116978 0.2339525 0.1826696 0.1530939

$mse
[1] 518.3294 530.2425 591.2687 587.2565 730.4772
print("True V (full data)")
[1] "True V (full data)"
out$V
         [,1]     [,2]     [,3]     [,4]     [,5]
[1,] 331.4137   0.0000   0.0000   0.0000   0.0000
[2,]   0.0000 331.4137   0.0000   0.0000   0.0000
[3,]   0.0000   0.0000 331.4137   0.0000   0.0000
[4,]   0.0000   0.0000   0.0000 331.4137   0.0000
[5,]   0.0000   0.0000   0.0000   0.0000 331.4137
print("Estimated V (training data)")
[1] "Estimated V (training data)"
fit_mrmash_initV$V
         Y1       Y2       Y3       Y4       Y5
Y1 600.7643 271.7859 213.0299 197.5317 231.7817
Y2 271.7859 581.4730 244.8228 243.7119 255.7840
Y3 213.0299 244.8228 542.5543 252.0104 239.4596
Y4 197.5317 243.7119 252.0104 532.4978 214.1496
Y5 231.7817 255.7840 239.4596 214.1496 516.2267

Here, we can see that that initilizing the residual covariance to the truth leads to essentially the same solutions as using the default initialization method.

After modifying mr.mash, we can now update the residual covariance imposing a diagonal structure. Let’s see how that works.

###Fit mr.mash
fit_mrmash_diagV <- mr.mash(Xtrain, Ytrain, S0, tol=1e-2, convergence_criterion="ELBO", update_w0=TRUE, 
                            update_w0_method="EM", compute_ELBO=TRUE, standardize=TRUE, verbose=FALSE, update_V=TRUE,
                            update_V_method="diagonal", mu1_init=Bhat_glmnet, w0_threshold=1e-8)
Processing the inputs... Done!
Fitting the optimization algorithm... Done!
Processing the outputs... Done!
mr.mash successfully executed in 3.011415 minutes!
Yhat_test_mrmash_diagV <- predict(fit_mrmash_diagV, Xtest)
layout(matrix(c(1, 1, 2, 2,
                1, 1, 2, 2,
                0, 3, 3, 0,
                0, 3, 3, 0), 4, 4, byrow = TRUE))

###Plot estimated vs true coeffcients
##mr.mash
plot(out$B, fit_mrmash_diagV$mu1, main="mr.mash", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
##glmnet
plot(out$B, Bhat_glmnet, main="glmnet", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)

###Plot mr.mash vs glmnet estimated coeffcients
colorz <- matrix("black", nrow=p, ncol=r)
zeros <- apply(out$B, 2, function(x) x==0)
for(i in 1:ncol(colorz)){
  colorz[zeros[, i], i] <- "red"
}

plot(Bhat_glmnet, fit_mrmash_diagV$mu1, main="mr.mash vs glmnet", 
     xlab="glmnet estimated coefficients", ylab="mr.mash estimated coefficients",
     col=colorz, cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
legend("topleft", 
       legend = c("Non-zero", "Zero"), 
       col = c("black", "red"), 
       pch = c(1, 1), 
       horiz = FALSE,
       cex=2) 

Version Author Date
5328c22 fmorgante 2020-06-02
print("Prediction performance with glmnet")
[1] "Prediction performance with glmnet"
compute_accuracy(Ytest, Yhat_test_glmnet)
$bias
[1] 0.7973441 0.7171240 1.1201318 0.9992656 0.9375002

$r2
[1] 0.12560024 0.10044653 0.14357016 0.13204939 0.09516285

$mse
[1] 502.2840 531.2713 573.5326 575.3974 722.7162
print("Prediction performance with mr.mash")
[1] "Prediction performance with mr.mash"
compute_accuracy(Ytest, Yhat_test_mrmash_diagV)
$bias
[1] 0.4126154 0.3465257 0.5752667 0.5588387 0.5719653

$r2
[1] 0.08870657 0.06277994 0.14750399 0.14278998 0.12151611

$mse
[1] 628.5332 698.4433 623.4901 649.4620 762.7409
print("True V (full data)")
[1] "True V (full data)"
out$V
         [,1]     [,2]     [,3]     [,4]     [,5]
[1,] 331.4137   0.0000   0.0000   0.0000   0.0000
[2,]   0.0000 331.4137   0.0000   0.0000   0.0000
[3,]   0.0000   0.0000 331.4137   0.0000   0.0000
[4,]   0.0000   0.0000   0.0000 331.4137   0.0000
[5,]   0.0000   0.0000   0.0000   0.0000 331.4137
print("Estimated V (training data)")
[1] "Estimated V (training data)"
fit_mrmash_diagV$V
         Y1       Y2       Y3       Y4       Y5
Y1 397.6517   0.0000   0.0000   0.0000   0.0000
Y2   0.0000 340.6878   0.0000   0.0000   0.0000
Y3   0.0000   0.0000 335.3694   0.0000   0.0000
Y4   0.0000   0.0000   0.0000 337.8452   0.0000
Y5   0.0000   0.0000   0.0000   0.0000 319.6164

Although the estimated residual covariance is much closer to the truth and now many coefficients are not overshrunk, prediction performance is still poor. Another possibility is that overshrinking of coefficients happens because the mixture weights are poorly initialized. Let’s now try to initialize the mixture weights to the truth.

###Fit mr.mash
w0 <- rep(0, length(S0))
w0[c(104, 111)] <- c(0.5, 0.5)
fit_mrmash_diagV_initw0 <- mr.mash(Xtrain, Ytrain, S0, w0=w0, tol=1e-2, convergence_criterion="ELBO", update_w0=TRUE, 
                                   update_w0_method="EM", compute_ELBO=TRUE, standardize=TRUE, verbose=FALSE, update_V=TRUE,
                                   update_V_method="diagonal", mu1_init=Bhat_glmnet, w0_threshold=1e-8)
Processing the inputs... Done!
Fitting the optimization algorithm... Done!
Processing the outputs... Done!
mr.mash successfully executed in 0.3481156 minutes!
Yhat_test_mrmash_diagV_initw0 <- predict(fit_mrmash_diagV_initw0, Xtest)
layout(matrix(c(1, 1, 2, 2,
                1, 1, 2, 2,
                0, 3, 3, 0,
                0, 3, 3, 0), 4, 4, byrow = TRUE))

###Plot estimated vs true coeffcients
##mr.mash
plot(out$B, fit_mrmash_diagV_initw0$mu1, main="mr.mash", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
##glmnet
plot(out$B, Bhat_glmnet, main="glmnet", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)

###Plot mr.mash vs glmnet estimated coeffcients
colorz <- matrix("black", nrow=p, ncol=r)
zeros <- apply(out$B, 2, function(x) x==0)
for(i in 1:ncol(colorz)){
  colorz[zeros[, i], i] <- "red"
}

plot(Bhat_glmnet, fit_mrmash_diagV_initw0$mu1, main="mr.mash vs glmnet", 
     xlab="glmnet estimated coefficients", ylab="mr.mash estimated coefficients",
     col=colorz, cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
legend("topleft", 
       legend = c("Non-zero", "Zero"), 
       col = c("black", "red"), 
       pch = c(1, 1), 
       horiz = FALSE,
       cex=2) 

Version Author Date
5328c22 fmorgante 2020-06-02
print("Prediction performance with glmnet")
[1] "Prediction performance with glmnet"
compute_accuracy(Ytest, Yhat_test_glmnet)
$bias
[1] 0.7973441 0.7171240 1.1201318 0.9992656 0.9375002

$r2
[1] 0.12560024 0.10044653 0.14357016 0.13204939 0.09516285

$mse
[1] 502.2840 531.2713 573.5326 575.3974 722.7162
print("Prediction performance with mr.mash")
[1] "Prediction performance with mr.mash"
compute_accuracy(Ytest, Yhat_test_mrmash_diagV_initw0)
$bias
[1] 0.6137635 0.6313922 0.7989912 0.8056523 0.6864326

$r2
[1] 0.1287113 0.1368245 0.1864412 0.1935577 0.1148231

$mse
[1] 525.1827 533.7511 549.6033 545.9291 725.6260
print("True V (full data)")
[1] "True V (full data)"
out$V
         [,1]     [,2]     [,3]     [,4]     [,5]
[1,] 331.4137   0.0000   0.0000   0.0000   0.0000
[2,]   0.0000 331.4137   0.0000   0.0000   0.0000
[3,]   0.0000   0.0000 331.4137   0.0000   0.0000
[4,]   0.0000   0.0000   0.0000 331.4137   0.0000
[5,]   0.0000   0.0000   0.0000   0.0000 331.4137
print("Estimated V (training data)")
[1] "Estimated V (training data)"
fit_mrmash_diagV_initw0$V
         Y1       Y2       Y3       Y4       Y5
Y1 407.9608   0.0000   0.0000   0.0000   0.0000
Y2   0.0000 346.9626   0.0000   0.0000   0.0000
Y3   0.0000   0.0000 349.2363   0.0000   0.0000
Y4   0.0000   0.0000   0.0000 345.4373   0.0000
Y5   0.0000   0.0000   0.0000   0.0000 328.7146

Now, prediction performance is better. Next, let’s try to fix the mixture weights to the truth.

###Fit mr.mash
fit_mrmash_diagV_truew0 <- mr.mash(Xtrain, Ytrain, S0, w0=w0, tol=1e-2, convergence_criterion="ELBO", update_w0=FALSE, 
                                    update_w0_method="EM", compute_ELBO=TRUE, standardize=TRUE, verbose=FALSE, update_V=TRUE,
                                    update_V_method="diagonal", mu1_init=Bhat_glmnet, w0_threshold=1e-8)
Processing the inputs... Done!
Fitting the optimization algorithm... Done!
Processing the outputs... Done!
mr.mash successfully executed in 0.7473614 minutes!
Yhat_test_mrmash_diagV_truew0 <- predict(fit_mrmash_diagV_truew0, Xtest)
layout(matrix(c(1, 1, 2, 2,
                1, 1, 2, 2,
                0, 3, 3, 0,
                0, 3, 3, 0), 4, 4, byrow = TRUE))

###Plot estimated vs true coeffcients
##mr.mash
plot(out$B, fit_mrmash_diagV_truew0$mu1, main="mr.mash", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
##glmnet
plot(out$B, Bhat_glmnet, main="glmnet", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)

###Plot mr.mash vs glmnet estimated coeffcients
colorz <- matrix("black", nrow=p, ncol=r)
zeros <- apply(out$B, 2, function(x) x==0)
for(i in 1:ncol(colorz)){
  colorz[zeros[, i], i] <- "red"
}

plot(Bhat_glmnet, fit_mrmash_diagV_truew0$mu1, main="mr.mash vs glmnet", 
     xlab="glmnet estimated coefficients", ylab="mr.mash estimated coefficients",
     col=colorz, cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
legend("topleft", 
       legend = c("Non-zero", "Zero"), 
       col = c("black", "red"), 
       pch = c(1, 1), 
       horiz = FALSE,
       cex=2) 

Version Author Date
5328c22 fmorgante 2020-06-02
print("Prediction performance with glmnet")
[1] "Prediction performance with glmnet"
compute_accuracy(Ytest, Yhat_test_glmnet)
$bias
[1] 0.7973441 0.7171240 1.1201318 0.9992656 0.9375002

$r2
[1] 0.12560024 0.10044653 0.14357016 0.13204939 0.09516285

$mse
[1] 502.2840 531.2713 573.5326 575.3974 722.7162
print("Prediction performance with mr.mash")
[1] "Prediction performance with mr.mash"
compute_accuracy(Ytest, Yhat_test_mrmash_diagV_truew0)
$bias
[1] 0.7032514 0.7018678 0.9482747 0.9433495 0.8535756

$r2
[1] 0.1447663 0.1448465 0.2249874 0.2273485 0.1521071

$mse
[1] 501.5878 516.7773 516.6486 516.4104 680.2251
print("True V (full data)")
[1] "True V (full data)"
out$V
         [,1]     [,2]     [,3]     [,4]     [,5]
[1,] 331.4137   0.0000   0.0000   0.0000   0.0000
[2,]   0.0000 331.4137   0.0000   0.0000   0.0000
[3,]   0.0000   0.0000 331.4137   0.0000   0.0000
[4,]   0.0000   0.0000   0.0000 331.4137   0.0000
[5,]   0.0000   0.0000   0.0000   0.0000 331.4137
print("Estimated V (training data)")
[1] "Estimated V (training data)"
fit_mrmash_diagV_truew0$V
         Y1       Y2       Y3      Y4       Y5
Y1 419.9455   0.0000   0.0000   0.000   0.0000
Y2   0.0000 349.1219   0.0000   0.000   0.0000
Y3   0.0000   0.0000 355.0746   0.000   0.0000
Y4   0.0000   0.0000   0.0000 355.097   0.0000
Y5   0.0000   0.0000   0.0000   0.000 333.5331

Prediction accuracy is now good and better than group-lasso. Finally, let’s fix both the residual covariance and mixture weights to the truth.

###Fit mr.mash
fit_mrmash_trueV_truew0 <- mr.mash(Xtrain, Ytrain, S0, w0=w0, tol=1e-2, convergence_criterion="ELBO", update_w0=FALSE, 
                                    update_w0_method="EM", compute_ELBO=TRUE, standardize=TRUE, verbose=FALSE, update_V=FALSE,
                                    update_V_method="diagonal", mu1_init=Bhat_glmnet, w0_threshold=1e-8, V=out$V)
Processing the inputs... Done!
Fitting the optimization algorithm... Done!
Processing the outputs... Done!
mr.mash successfully executed in 0.7254679 minutes!
Yhat_test_mrmash_trueV_truew0 <- predict(fit_mrmash_trueV_truew0, Xtest)
layout(matrix(c(1, 1, 2, 2,
                1, 1, 2, 2,
                0, 3, 3, 0,
                0, 3, 3, 0), 4, 4, byrow = TRUE))

###Plot estimated vs true coeffcients
##mr.mash
plot(out$B, fit_mrmash_trueV_truew0$mu1, main="mr.mash", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
##glmnet
plot(out$B, Bhat_glmnet, main="glmnet", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)

###Plot mr.mash vs glmnet estimated coeffcients
colorz <- matrix("black", nrow=p, ncol=r)
zeros <- apply(out$B, 2, function(x) x==0)
for(i in 1:ncol(colorz)){
  colorz[zeros[, i], i] <- "red"
}

plot(Bhat_glmnet, fit_mrmash_trueV_truew0$mu1, main="mr.mash vs glmnet", 
     xlab="glmnet estimated coefficients", ylab="mr.mash estimated coefficients",
     col=colorz, cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
legend("topleft", 
       legend = c("Non-zero", "Zero"), 
       col = c("black", "red"), 
       pch = c(1, 1), 
       horiz = FALSE,
       cex=2) 

Version Author Date
5328c22 fmorgante 2020-06-02
print("Prediction performance with glmnet")
[1] "Prediction performance with glmnet"
compute_accuracy(Ytest, Yhat_test_glmnet)
$bias
[1] 0.7973441 0.7171240 1.1201318 0.9992656 0.9375002

$r2
[1] 0.12560024 0.10044653 0.14357016 0.13204939 0.09516285

$mse
[1] 502.2840 531.2713 573.5326 575.3974 722.7162
print("Prediction performance with mr.mash")
[1] "Prediction performance with mr.mash"
compute_accuracy(Ytest, Yhat_test_mrmash_trueV_truew0)
$bias
[1] 0.7029293 0.6891626 0.9544747 0.9387136 0.8622740

$r2
[1] 0.1516346 0.1464096 0.2389721 0.2360162 0.1627364

$mse
[1] 498.3975 517.7781 507.2839 510.7310 671.4638

Not surprisingly we now achieve the best accuracy of all models.

However, looking at ELBO for all the models fitted so far reveals something interesting. The models that provide the best prediction performance in the test set are those with smaller ELBO, and viceversa. My intuition is that the largest ELBO is due to those models having more free parameters (since the include updating the weights and V with no structure imposed).

print(paste("Update w0, update V:", fit_mrmash$ELBO))
[1] "Update w0, update V: -10789.3132448488"
print(paste("Update w0, fix V to the truth:", fit_mrmash_fixV$ELBO))
[1] "Update w0, fix V to the truth: -10858.0496418998"
print(paste("Update w0, update V initializing it to the truth:", fit_mrmash_initV$ELBO))
[1] "Update w0, update V initializing it to the truth: -10789.303405305"
print(paste("Update w0, update V imposing a diagonal structure:", fit_mrmash_diagV$ELBO))
[1] "Update w0, update V imposing a diagonal structure: -10855.1638789792"
print(paste("Update w0 initializing them to the truth, update V imposing a diagonal structure:", fit_mrmash_diagV_initw0$ELBO))
[1] "Update w0 initializing them to the truth, update V imposing a diagonal structure: -10869.1875885718"
print(paste("Fix w0 to the truth, update V imposing a diagonal structure:", fit_mrmash_diagV_truew0$ELBO))
[1] "Fix w0 to the truth, update V imposing a diagonal structure: -10936.6653594846"
print(paste("Fix w0 and V to the truth:", fit_mrmash_trueV_truew0$ELBO))
[1] "Fix w0 and V to the truth: -10942.8402970471"

Sparser scenario

For comparison let’s try to repeat the analysis above (updating V) on a sparser scenario, i.e., p_causal=50.

###Set parameters
n <- 600
p <- 1000
p_causal <- 50
r <- 5
r_causal <- r
pve <- 0.5
B_cor <- 1
X_cor <- 0
V_cor <- 0

###Simulate V, B, X and Y
out50 <- mr.mash.alpha:::simulate_mr_mash_data(n, p, p_causal, r, r_causal=r, intercepts = rep(1, r),
                                             pve=pve, B_cor=B_cor, B_scale=0.8, X_cor=X_cor, X_scale=0.8, V_cor=V_cor)

colnames(out50$Y) <- paste0("Y", seq(1, r))
rownames(out50$Y) <- paste0("N", seq(1, n))
colnames(out50$X) <- paste0("X", seq(1, p))
rownames(out50$X) <- paste0("N", seq(1, n))

###Split the data in training and test sets
test_set50 <- sort(sample(x=c(1:n), size=round(n*0.2), replace=FALSE))
Ytrain50 <- out50$Y[-test_set50, ]
Xtrain50 <- out50$X[-test_set50, ]
Ytest50 <- out50$Y[test_set50, ]
Xtest50 <- out50$X[test_set50, ]

###Fit group-lasso
cvfit_glmnet50 <- cv.glmnet(x=Xtrain50, y=Ytrain50, family="mgaussian", alpha=1)
coeff_glmnet50 <- coef(cvfit_glmnet50, s="lambda.min")
Bhat_glmnet50 <- matrix(as.numeric(NA), nrow=p, ncol=r)
ahat_glmnet50 <- vector("numeric", r)
for(i in 1:length(coeff_glmnet50)){
  Bhat_glmnet50[, i] <- as.vector(coeff_glmnet50[[i]])[-1]
  ahat_glmnet50[i] <- as.vector(coeff_glmnet50[[i]])[1]
}
Yhat_test_glmnet50 <- drop(predict(cvfit_glmnet50, newx=Xtest50, s="lambda.min"))

###Fit mr.mash
fit_mrmash50 <- mr.mash(Xtrain50, Ytrain50, S0, tol=1e-2, convergence_criterion="ELBO", update_w0=TRUE, 
                     update_w0_method="EM", compute_ELBO=TRUE, standardize=TRUE, verbose=FALSE, update_V=TRUE,
                     mu1_init = Bhat_glmnet50, w0_threshold=1e-8)
Processing the inputs... Done!
Fitting the optimization algorithm... Done!
Processing the outputs... Done!
mr.mash successfully executed in 0.9007726 minutes!
Yhat_test_mrmash50 <- predict(fit_mrmash50, Xtest50)
layout(matrix(c(1, 1, 2, 2,
                1, 1, 2, 2,
                0, 3, 3, 0,
                0, 3, 3, 0), 4, 4, byrow = TRUE))

###Plot estimated vs true coeffcients
##mr.mash
plot(out50$B, fit_mrmash50$mu1, main="mr.mash", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
##glmnet
plot(out50$B, Bhat_glmnet50, main="glmnet", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)

###Plot mr.mash vs glmnet estimated coeffcients
colorz <- matrix("black", nrow=p, ncol=r)
zeros <- apply(out50$B, 2, function(x) x==0)
for(i in 1:ncol(colorz)){
  colorz[zeros[, i], i] <- "red"
}

plot(Bhat_glmnet50, fit_mrmash50$mu1, main="mr.mash vs glmnet", 
     xlab="glmnet estimated coefficients", ylab="mr.mash estimated coefficients",
     col=colorz, cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
legend("topleft", 
       legend = c("Non-zero", "Zero"), 
       col = c("black", "red"), 
       pch = c(1, 1), 
       horiz = FALSE,
       cex=2) 

Version Author Date
42ec51c fmorgante 2020-06-01
print("Prediction performance with glmnet")
[1] "Prediction performance with glmnet"
compute_accuracy(Ytest50, Yhat_test_glmnet50)
$bias
[1] 1.1055781 1.2342390 0.8929952 1.2093933 1.1997154

$r2
[1] 0.3720343 0.4451268 0.2709590 0.3909355 0.4424055

$mse
[1] 50.11301 49.98023 54.17894 53.96508 46.36995
print("Prediction performance with mr.mash")
[1] "Prediction performance with mr.mash"
compute_accuracy(Ytest50, Yhat_test_mrmash50)
$bias
[1] 0.9295387 1.1205934 0.8382439 0.9741666 1.0034935

$r2
[1] 0.4227190 0.5544520 0.3641726 0.4142314 0.4747928

$mse
[1] 46.04314 39.62819 48.00426 51.00798 42.98684
print("True V (full data)")
[1] "True V (full data)"
out50$V
       [,1]   [,2]   [,3]   [,4]   [,5]
[1,] 43.262  0.000  0.000  0.000  0.000
[2,]  0.000 43.262  0.000  0.000  0.000
[3,]  0.000  0.000 43.262  0.000  0.000
[4,]  0.000  0.000  0.000 43.262  0.000
[5,]  0.000  0.000  0.000  0.000 43.262
print("Estimated V (training data)")
[1] "Estimated V (training data)"
fit_mrmash50$V
           Y1        Y2         Y3        Y4         Y5
Y1 37.0783688  2.693322  2.7229456  3.426152 -0.6935028
Y2  2.6933223 42.243137  2.0293575  1.966294  2.6486737
Y3  2.7229456  2.029357 37.9331012 -2.456907 -0.2269764
Y4  3.4261520  1.966294 -2.4569072 42.752140 -3.0717702
Y5 -0.6935028  2.648674 -0.2269764 -3.071770 43.5576891

The results of this simulation look very good in terms of prediction performance, estimation of the coefficients, and estimation of the residual covariance.

Dense scenario (fewer variables)

Just as an additional test let’s now try to maintain the same signal-to-noise ratio as the first simulation but with fewer total variables, i.e., p=500 and p_causal=250.

###Set parameters
n <- 600
p <- 500
p_causal <- 250
r <- 5
r_causal <- r
pve <- 0.5
B_cor <- 1
X_cor <- 0
V_cor <- 0

###Simulate V, B, X and Y
out500 <- mr.mash.alpha:::simulate_mr_mash_data(n, p, p_causal, r, r_causal=r, intercepts = rep(1, r),
                                             pve=pve, B_cor=B_cor, B_scale=0.8, X_cor=X_cor, X_scale=0.8, V_cor=V_cor)

colnames(out500$Y) <- paste0("Y", seq(1, r))
rownames(out500$Y) <- paste0("N", seq(1, n))
colnames(out500$X) <- paste0("X", seq(1, p))
rownames(out500$X) <- paste0("N", seq(1, n))

###Split the data in training and test sets
test_set500 <- sort(sample(x=c(1:n), size=round(n*0.2), replace=FALSE))
Ytrain500 <- out500$Y[-test_set500, ]
Xtrain500 <- out500$X[-test_set500, ]
Ytest500 <- out500$Y[test_set500, ]
Xtest500 <- out500$X[test_set500, ]

###Fit group-lasso
cvfit_glmnet500 <- cv.glmnet(x=Xtrain500, y=Ytrain500, family="mgaussian", alpha=1)
coeff_glmnet500 <- coef(cvfit_glmnet500, s="lambda.min")
Bhat_glmnet500 <- matrix(as.numeric(NA), nrow=p, ncol=r)
ahat_glmnet500 <- vector("numeric", r)
for(i in 1:length(coeff_glmnet500)){
  Bhat_glmnet500[, i] <- as.vector(coeff_glmnet500[[i]])[-1]
  ahat_glmnet500[i] <- as.vector(coeff_glmnet500[[i]])[1]
}
Yhat_test_glmnet500 <- drop(predict(cvfit_glmnet500, newx=Xtest500, s="lambda.min"))

###Fit mr.mash
fit_mrmash500 <- mr.mash(Xtrain500, Ytrain500, S0, tol=1e-2, convergence_criterion="ELBO", update_w0=TRUE, 
                     update_w0_method="EM", compute_ELBO=TRUE, standardize=TRUE, verbose=FALSE, update_V=TRUE,
                     mu1_init = Bhat_glmnet500, w0_threshold=1e-8)
Processing the inputs... Done!
Fitting the optimization algorithm... Done!
Processing the outputs... Done!
mr.mash successfully executed in 1.153135 minutes!
Yhat_test_mrmash500 <- predict(fit_mrmash500, Xtest500)
layout(matrix(c(1, 1, 2, 2,
                1, 1, 2, 2,
                0, 3, 3, 0,
                0, 3, 3, 0), 4, 4, byrow = TRUE))

###Plot estimated vs true coeffcients
##mr.mash
plot(out500$B, fit_mrmash500$mu1, main="mr.mash", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
##glmnet
plot(out500$B, Bhat_glmnet500, main="glmnet", xlab="True coefficients", ylab="Estimated coefficients",
     cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)

###Plot mr.mash vs glmnet estimated coeffcients
colorz <- matrix("black", nrow=p, ncol=r)
zeros <- apply(out500$B, 2, function(x) x==0)
for(i in 1:ncol(colorz)){
  colorz[zeros[, i], i] <- "red"
}

plot(Bhat_glmnet500, fit_mrmash500$mu1, main="mr.mash vs glmnet", 
     xlab="glmnet estimated coefficients", ylab="mr.mash estimated coefficients",
     col=colorz, cex=2, cex.lab=1.8, cex.main=2, cex.axis=1.8)
legend("topleft", 
       legend = c("Non-zero", "Zero"), 
       col = c("black", "red"), 
       pch = c(1, 1), 
       horiz = FALSE,
       cex=2) 

Version Author Date
42ec51c fmorgante 2020-06-01
print("Prediction performance with glmnet")
[1] "Prediction performance with glmnet"
compute_accuracy(Ytest500, Yhat_test_glmnet500)
$bias
[1] 0.7460558 0.8379627 0.8827708 1.0150154 0.8988217

$r2
[1] 0.1566151 0.1754140 0.2246133 0.2126951 0.2354353

$mse
[1] 262.5938 307.5913 280.3553 259.2769 265.2623
print("Prediction performance with mr.mash")
[1] "Prediction performance with mr.mash"
compute_accuracy(Ytest500, Yhat_test_mrmash500)
$bias
[1] 0.8007963 1.0543346 0.9437460 0.9812104 1.0269983

$r2
[1] 0.1917870 0.2740736 0.2402338 0.2626540 0.2816563

$mse
[1] 249.0113 269.0810 268.4814 242.8198 247.6815
print("True V (full data)")
[1] "True V (full data)"
out500$V
         [,1]     [,2]     [,3]     [,4]     [,5]
[1,] 168.3364   0.0000   0.0000   0.0000   0.0000
[2,]   0.0000 168.3364   0.0000   0.0000   0.0000
[3,]   0.0000   0.0000 168.3364   0.0000   0.0000
[4,]   0.0000   0.0000   0.0000 168.3364   0.0000
[5,]   0.0000   0.0000   0.0000   0.0000 168.3364
print("Estimated V (training data)")
[1] "Estimated V (training data)"
fit_mrmash500$V
           Y1         Y2        Y3        Y4         Y5
Y1 190.385218   4.826421  31.00999  22.18042   5.743413
Y2   4.826421 181.999035  16.65374   8.40475   9.590063
Y3  31.009989  16.653736 198.77957  39.23005  35.494926
Y4  22.180421   8.404750  39.23005 209.04480  25.167324
Y5   5.743413   9.590063  35.49493  25.16732 194.837000

In this last simulation, we can see that mr.mash performs pretty well, even in a denser scenario with fewer variables.


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] glmnet_2.0-16        foreach_1.4.4        Matrix_1.2-15       
[4] mr.mash.alpha_0.1-76

loaded via a namespace (and not attached):
 [1] MBSP_1.0           Rcpp_1.0.4.6       compiler_3.5.1    
 [4] later_0.7.5        git2r_0.26.1       workflowr_1.6.1   
 [7] iterators_1.0.10   tools_3.5.1        digest_0.6.25     
[10] evaluate_0.12      lattice_0.20-38    GIGrvg_0.5        
[13] yaml_2.2.1         SparseM_1.77       mvtnorm_1.0-12    
[16] coda_0.19-3        stringr_1.4.0      knitr_1.20        
[19] fs_1.3.1           MatrixModels_0.4-1 rprojroot_1.3-2   
[22] grid_3.5.1         glue_1.4.0         R6_2.4.1          
[25] rmarkdown_1.10     mixsqp_0.3-43      irlba_2.3.3       
[28] magrittr_1.5       whisker_0.3-2      codetools_0.2-15  
[31] backports_1.1.5    promises_1.0.1     htmltools_0.3.6   
[34] matrixStats_0.55.0 mcmc_0.9-6         MASS_7.3-51.1     
[37] httpuv_1.4.5       quantreg_5.36      stringi_1.4.3     
[40] MCMCpack_1.4-4