Prediction accuracy
Fabio Morgante
April 28, 2020
Last updated: 2020-04-28
Checks: 7 0
Knit directory: mr_mash_test/
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options(stringsAsFactors = FALSE)
accuracy <- function(Y, Yhat) {
bias <- rep(NA, ncol(Y))
r2 <- rep(NA, ncol(Y))
mse <- rep(NA, ncol(Y))
for(i in 1:ncol(Y)){
fit <- lm(Y[, i] ~ Yhat[, i])
bias[i] <- coef(fit)[2]
r2[i] <- summary(fit)$r.squared
mse[i] <- mean((Y[, i] - Yhat[, i])^2)
}
return(list(bias=bias, r2=r2, mse=mse))
}Simulation 1 – Shared effects, independent variables
dat1 <- readRDS("output/fit_mr_mash_n600_p1000_p_caus50_r5_pve0.5_sigmaoffdiag1_sigmascale0.8_gammaoffdiag0_gammascale0.8_Voffdiag0.2_Vscale0_updatew0TRUE_updatew0TRUE_updatew0methodmixsqp_updateVTRUE.rds")
n1 <- dat1$params$n
p1 <- dat1$params$p
p_causal1 <- dat1$params$p_causal
r1 <- dat1$params$r
k1 <- length(dat1$fit$w0)
pve1 <- dat1$params$pve
prop_testset1 <- dat1$params$prop_testset
B1 <- dat1$inputs$B
V1 <- dat1$inputs$V
Sigma1 <- dat1$inputs$Sigma
Gamma1 <- dat1$inputs$Gamma
Ytrain1 <- dat1$Ytrain
Ytest1 <- dat1$Ytest
mu11 <- dat1$fit$mu1
fitted1 <- dat1$fit$fitted
Yhat_test1 <- dat1$Yhat_testThe results below are based on simulation with 600 samples, 1000 variables of which 50 were causal, 5 responses with a per-response proportion of variance explained (PVE) of 0.5. Variables, X, were drawn from MVN(0, Gamma), causal effects, B, were drawn from MVN(0, Sigma). The responses, Y, were drawn from MN(XB, I, V).
cat("Gamma (First 5 elements)")Gamma (First 5 elements)Gamma1[1:5, 1:5] [,1] [,2] [,3] [,4] [,5]
[1,] 0.8 0.0 0.0 0.0 0.0
[2,] 0.0 0.8 0.0 0.0 0.0
[3,] 0.0 0.0 0.8 0.0 0.0
[4,] 0.0 0.0 0.0 0.8 0.0
[5,] 0.0 0.0 0.0 0.0 0.8cat("Sigma")SigmaSigma1 [,1] [,2] [,3] [,4] [,5]
[1,] 0.8 0.8 0.8 0.8 0.8
[2,] 0.8 0.8 0.8 0.8 0.8
[3,] 0.8 0.8 0.8 0.8 0.8
[4,] 0.8 0.8 0.8 0.8 0.8
[5,] 0.8 0.8 0.8 0.8 0.8cat("V")VV1 [,1] [,2] [,3] [,4] [,5]
[1,] 25.55836 0.00000 0.00000 0.00000 0.00000
[2,] 0.00000 25.55836 0.00000 0.00000 0.00000
[3,] 0.00000 0.00000 25.55836 0.00000 0.00000
[4,] 0.00000 0.00000 0.00000 25.55836 0.00000
[5,] 0.00000 0.00000 0.00000 0.00000 25.55836mr.mash was fitted to the training data (80% of the data) updating V and updating the prior weights using mixSQP. Then, responses were predicted on the test data (20% of the data). The mixture prior consisted of 101 components.
In the plots below, each color/symbol defines a diffrent response.
Here, we compare the estimated effects with the true effects.
plot(B1[, 1], mu11[, 1], xlab="True effects", ylab="Estimated effects", main="True vs Estimated Effects", pch=1, cex.lab=1.5, cex.axis=1.5)
points(B1[, 2], mu11[, 2], col="blue", pch=2)
points(B1[, 3], mu11[, 3], col="red", pch=3)
points(B1[, 4], mu11[, 4], col="green", pch=4)
points(B1[, 5], mu11[, 5], col="yellow", pch=8)
Then, we compare the predicted responses with the true responses in the training data (left panel) and test data (right panel).
par(mfrow=c(1,2))
plot(Ytrain1[, 1], fitted1[, 1], xlab="True responses", ylab="Fitted values", main="True vs Fitted values \nTraining data", pch=1, cex.lab=1.5, cex.axis=1.5)
points(Ytrain1[, 2], fitted1[, 2], col="blue", pch=2)
points(Ytrain1[, 3], fitted1[, 3], col="red", pch=3)
points(Ytrain1[, 4], fitted1[, 4], col="green", pch=4)
points(Ytrain1[, 5], fitted1[, 5], col="yellow", pch=8)
abline(0, 1)
plot(Ytrain1[, 1], fitted1[, 1], xlab="True responses", ylab="Predicted responses", main="True vs Predicted Responses \nTest data", pch=1, cex.lab=1.5, cex.axis=1.5)
points(Ytest1[, 2], Yhat_test1[, 2], col="blue", pch=2)
points(Ytest1[, 3], Yhat_test1[, 3], col="red", pch=3)
points(Ytest1[, 4], Yhat_test1[, 4], col="green", pch=4)
points(Ytest1[, 5], Yhat_test1[, 5], col="yellow", pch=8)
abline(0, 1)
par(mfrow=c(1,1))
r2_train1 <- round(accuracy(Ytrain1, fitted1)$r2, 4)
r2_test1 <- round(accuracy(Ytest1, Yhat_test1)$r2, 4)
bias_train1 <- round(accuracy(Ytrain1, fitted1)$bias, 4)
bias_test1 <- round(accuracy(Ytest1, Yhat_test1)$bias, 4)
mse_train1 <- round(accuracy(Ytrain1, fitted1)$mse, 4)
mse_test1 <- round(accuracy(Ytest1, Yhat_test1)$mse, 4)
acc1 <- rbind(r2_train1, r2_test1, bias_train1, bias_test1, mse_train1, mse_test1)
colnames(acc1) <- paste0("Y", seq(1, r1))
part_metric1 <- c("Training data r2", "Test data r2", "Training data bias", "Test data bias" , "Training data MSE" , "Test data MSE")
res1 <- data.frame(part_metric1, acc1)
colnames(res1)[1] <- c("Partition_metric")
rownames(res1) <- NULL
print(res1) Partition_metric Y1 Y2 Y3 Y4 Y5
1 Training data r2 0.5338 0.5418 0.4904 0.5239 0.5620
2 Test data r2 0.4591 0.4476 0.4630 0.4523 0.4237
3 Training data bias 1.0606 1.0586 0.9935 1.0013 1.0971
4 Test data bias 0.9892 1.0660 1.0666 1.0905 0.9659
5 Training data MSE 24.6985 23.8540 25.3913 22.4603 23.9106
6 Test data MSE 24.5054 29.4890 27.1642 29.6734 26.7496Simulation 2 – Independent effects, independent variables
dat2 <- readRDS("output/fit_mr_mash_n600_p1000_p_caus50_r5_pve0.5_sigmaoffdiag0_sigmascale0.8_gammaoffdiag0_gammascale0.8_Voffdiag0.2_Vscale0_updatew0TRUE_updatew0TRUE_updatew0methodmixsqp_updateVTRUE.rds")
n2 <- dat2$params$n
p2 <- dat2$params$p
p_causal2 <- dat2$params$p_causal
r2 <- dat2$params$r
k2 <- length(dat2$fit$w0)
pve2 <- dat2$params$pve
prop_testset2 <- dat2$params$prop_testset
B2 <- dat2$inputs$B
V2 <- dat2$inputs$V
Sigma2 <- dat2$inputs$Sigma
Gamma2 <- dat2$inputs$Gamma
Ytrain2 <- dat2$Ytrain
Ytest2 <- dat2$Ytest
mu12 <- dat2$fit$mu1
fitted2 <- dat2$fit$fitted
Yhat_test2 <- dat2$Yhat_testThe results below are based on simulation with 600 samples, 1000 variables of which 50 were causal, 5 responses with a per-response proportion of variance explained (PVE) of 0.5. Variables, X, were drawn from MVN(0, Gamma), causal effects, B, were drawn from MVN(0, Sigma). The responses, Y, were drawn from MN(XB, I, V).
cat("Gamma (First 5 elements)")Gamma (First 5 elements)Gamma2[1:5, 1:5] [,1] [,2] [,3] [,4] [,5]
[1,] 0.8 0.0 0.0 0.0 0.0
[2,] 0.0 0.8 0.0 0.0 0.0
[3,] 0.0 0.0 0.8 0.0 0.0
[4,] 0.0 0.0 0.0 0.8 0.0
[5,] 0.0 0.0 0.0 0.0 0.8cat("Sigma")SigmaSigma2 [,1] [,2] [,3] [,4] [,5]
[1,] 0.8 0.0 0.0 0.0 0.0
[2,] 0.0 0.8 0.0 0.0 0.0
[3,] 0.0 0.0 0.8 0.0 0.0
[4,] 0.0 0.0 0.0 0.8 0.0
[5,] 0.0 0.0 0.0 0.0 0.8cat("V")VV2 [,1] [,2] [,3] [,4] [,5]
[1,] 39.87305 0.00000 0.00000 0.00000 0.00000
[2,] 0.00000 24.41042 0.00000 0.00000 0.00000
[3,] 0.00000 0.00000 27.69452 0.00000 0.00000
[4,] 0.00000 0.00000 0.00000 25.53166 0.00000
[5,] 0.00000 0.00000 0.00000 0.00000 29.00472mr.mash was fitted to the training data (80% of the data) updating V and updating the prior weights using mixSQP. Then, responses were predicted on the test data (20% of the data). The mixture prior consisted of 101 components.
In the plots below, each color/symbol defines a diffrent response.
Here, we compare the estimated effects with the true effects.
plot(B2[, 1], mu12[, 1], xlab="True effects", ylab="Estimated effects", main="True vs Estimated Effects", pch=1, cex.lab=1.5, cex.axis=1.5)
points(B2[, 2], mu12[, 2], col="blue", pch=2)
points(B2[, 3], mu12[, 3], col="red", pch=3)
points(B2[, 4], mu12[, 4], col="green", pch=4)
points(B2[, 5], mu12[, 5], col="yellow", pch=8)
Then, we compare the predicted responses with the true responses in the training data (left panel) and test data (right panel).
par(mfrow=c(1,2))
plot(Ytrain2[, 1], fitted2[, 1], xlab="True responses", ylab="Fitted values", main="True vs Fitted values \nTraining data", pch=1, cex.lab=1.5, cex.axis=1.5)
points(Ytrain2[, 2], fitted2[, 2], col="blue", pch=2)
points(Ytrain2[, 3], fitted2[, 3], col="red", pch=3)
points(Ytrain2[, 4], fitted2[, 4], col="green", pch=4)
points(Ytrain2[, 5], fitted2[, 5], col="yellow", pch=8)
abline(0, 1)
plot(Ytrain2[, 1], fitted2[, 1], xlab="True responses", ylab="Predicted responses", main="True vs Predicted Responses \nTest data", pch=1, cex.lab=1.5, cex.axis=1.5)
points(Ytest2[, 2], Yhat_test2[, 2], col="blue", pch=2)
points(Ytest2[, 3], Yhat_test2[, 3], col="red", pch=3)
points(Ytest2[, 4], Yhat_test2[, 4], col="green", pch=4)
points(Ytest2[, 5], Yhat_test2[, 5], col="yellow", pch=8)
abline(0, 1)
par(mfrow=c(1,1))
r2_train2 <- round(accuracy(Ytrain2, fitted2)$r2, 4)
r2_test2 <- round(accuracy(Ytest2, Yhat_test2)$r2, 4)
bias_train2 <- round(accuracy(Ytrain2, fitted2)$bias, 4)
bias_test2 <- round(accuracy(Ytest2, Yhat_test2)$bias, 4)
mse_train2 <- round(accuracy(Ytrain2, fitted2)$mse, 4)
mse_test2 <- round(accuracy(Ytest2, Yhat_test2)$mse, 4)
acc2 <- rbind(r2_train2, r2_test2, bias_train2, bias_test2, mse_train2, mse_test2)
colnames(acc2) <- paste0("Y", seq(1, r2))
part_metric2 <- c("Training data r2", "Test data r2", "Training data bias", "Test data bias" , "Training data MSE" , "Test data MSE")
res2 <- data.frame(part_metric2, acc2)
colnames(res2)[1] <- c("Partition_metric")
rownames(res2) <- NULL
print(res2) Partition_metric Y1 Y2 Y3 Y4 Y5
1 Training data r2 0.5373 0.5341 0.5146 0.5716 0.5570
2 Test data r2 0.4600 0.3854 0.4395 0.4473 0.4948
3 Training data bias 1.1737 1.1069 1.1264 1.1019 1.1270
4 Test data bias 0.9847 0.9370 1.1124 1.1859 0.9449
5 Training data MSE 38.1690 22.6568 25.8972 21.0610 26.7705
6 Test data MSE 41.8959 28.1329 34.3800 34.3338 30.6942Simulation 3 – Shared effects, correlated variables
dat3 <- readRDS("output/fit_mr_mash_n600_p1000_p_caus50_r5_pve0.5_sigmaoffdiag1_sigmascale0.8_gammaoffdiag0.5_gammascale0.8_Voffdiag0.2_Vscale0_updatew0TRUE_updatew0TRUE_updatew0methodmixsqp_updateVTRUE.rds")
n3 <- dat3$params$n
p3 <- dat3$params$p
p_causal3 <- dat3$params$p_causal
r3 <- dat3$params$r
k3 <- length(dat3$fit$w0)
pve3 <- dat3$params$pve
prop_testset3 <- dat3$params$prop_testset
B3 <- dat3$inputs$B
V3 <- dat3$inputs$V
Sigma3 <- dat3$inputs$Sigma
Gamma3 <- dat3$inputs$Gamma
Ytrain3 <- dat3$Ytrain
Ytest3 <- dat3$Ytest
mu13 <- dat3$fit$mu1
fitted3 <- dat3$fit$fitted
Yhat_test3 <- dat3$Yhat_testThe results below are based on simulation with 600 samples, 1000 variables of which 50 were causal, 5 responses with a per-response proportion of variance explained (PVE) of 0.5. Variables, X, were drawn from MVN(0, Gamma), causal effects, B, were drawn from MVN(0, Sigma). The responses, Y, were drawn from MN(XB, I, V).
cat("Gamma (First 5 elements)")Gamma (First 5 elements)Gamma3[1:5, 1:5] [,1] [,2] [,3] [,4] [,5]
[1,] 0.8 0.4 0.4 0.4 0.4
[2,] 0.4 0.8 0.4 0.4 0.4
[3,] 0.4 0.4 0.8 0.4 0.4
[4,] 0.4 0.4 0.4 0.8 0.4
[5,] 0.4 0.4 0.4 0.4 0.8cat("Sigma")SigmaSigma3 [,1] [,2] [,3] [,4] [,5]
[1,] 0.8 0.8 0.8 0.8 0.8
[2,] 0.8 0.8 0.8 0.8 0.8
[3,] 0.8 0.8 0.8 0.8 0.8
[4,] 0.8 0.8 0.8 0.8 0.8
[5,] 0.8 0.8 0.8 0.8 0.8cat("V")VV3 [,1] [,2] [,3] [,4] [,5]
[1,] 13.98626 0.00000 0.00000 0.00000 0.00000
[2,] 0.00000 13.98625 0.00000 0.00000 0.00000
[3,] 0.00000 0.00000 13.98625 0.00000 0.00000
[4,] 0.00000 0.00000 0.00000 13.98625 0.00000
[5,] 0.00000 0.00000 0.00000 0.00000 13.98625mr.mash was fitted to the training data (80% of the data) updating V and updating the prior weights using mixSQP. Then, responses were predicted on the test data (20% of the data). The mixture prior consisted of 101 components.
In the plots below, each color/symbol defines a diffrent response.
Here, we compare the estimated effects with the true effects.
plot(B3[, 1], mu13[, 1], xlab="True effects", ylab="Estimated effects", main="True vs Estimated Effects", pch=1, cex.lab=1.5, cex.axis=1.5)
points(B3[, 2], mu13[, 2], col="blue", pch=2)
points(B3[, 3], mu13[, 3], col="red", pch=3)
points(B3[, 4], mu13[, 4], col="green", pch=4)
points(B3[, 5], mu13[, 5], col="yellow", pch=8)
Then, we compare the predicted responses with the true responses in the training data (left panel) and test data (right panel).
par(mfrow=c(1,2))
plot(Ytrain3[, 1], fitted3[, 1], xlab="True responses", ylab="Fitted values", main="True vs Fitted values \nTraining data", pch=1, cex.lab=1.5, cex.axis=1.5)
points(Ytrain3[, 2], fitted3[, 2], col="blue", pch=2)
points(Ytrain3[, 3], fitted3[, 3], col="red", pch=3)
points(Ytrain3[, 4], fitted3[, 4], col="green", pch=4)
points(Ytrain3[, 5], fitted3[, 5], col="yellow", pch=8)
abline(0, 1)
plot(Ytrain3[, 1], fitted3[, 1], xlab="True responses", ylab="Predicted responses", main="True vs Predicted Responses \nTest data", pch=1, cex.lab=1.5, cex.axis=1.5)
points(Ytest3[, 2], Yhat_test3[, 2], col="blue", pch=2)
points(Ytest3[, 3], Yhat_test3[, 3], col="red", pch=3)
points(Ytest3[, 4], Yhat_test3[, 4], col="green", pch=4)
points(Ytest3[, 5], Yhat_test3[, 5], col="yellow", pch=8)
abline(0, 1)
par(mfrow=c(1,1))
r2_train3 <- round(accuracy(Ytrain3, fitted3)$r2, 4)
r2_test3 <- round(accuracy(Ytest3, Yhat_test3)$r2, 4)
bias_train3 <- round(accuracy(Ytrain3, fitted3)$bias, 4)
bias_test3 <- round(accuracy(Ytest3, Yhat_test3)$bias, 4)
mse_train3 <- round(accuracy(Ytrain3, fitted3)$mse, 4)
mse_test3 <- round(accuracy(Ytest3, Yhat_test3)$mse, 4)
acc3 <- rbind(r2_train3, r2_test3, bias_train3, bias_test3, mse_train3, mse_test3)
colnames(acc3) <- paste0("Y", seq(1, r3))
part_metric3 <- c("Training data r2", "Test data r2", "Training data bias", "Test data bias" , "Training data MSE" , "Test data MSE")
res3 <- data.frame(part_metric3, acc3)
colnames(res3)[1] <- c("Partition_metric")
rownames(res3) <- NULL
print(res3) Partition_metric Y1 Y2 Y3 Y4 Y5
1 Training data r2 0.4892 0.5037 0.4710 0.4907 0.5390
2 Test data r2 0.4148 0.4341 0.4220 0.4639 0.4251
3 Training data bias 1.0358 1.0376 0.9876 0.9817 1.0887
4 Test data bias 1.0015 1.0979 1.0552 1.1736 1.0079
5 Training data MSE 14.4128 14.0238 14.5096 12.8917 13.6560
6 Test data MSE 14.0750 15.9244 15.3882 15.9627 14.0499Simulation 4 – Independent effects, correlated variables
dat4 <- readRDS("output/fit_mr_mash_n600_p1000_p_caus50_r5_pve0.5_sigmaoffdiag0_sigmascale0.8_gammaoffdiag0.5_gammascale0.8_Voffdiag0.2_Vscale0_updatew0TRUE_updatew0TRUE_updatew0methodmixsqp_updateVTRUE.rds")
n4 <- dat4$params$n
p4 <- dat4$params$p
p_causal4 <- dat4$params$p_causal
r4 <- dat4$params$r
k4 <- length(dat4$fit$w0)
pve4 <- dat4$params$pve
prop_testset4 <- dat4$params$prop_testset
B4 <- dat4$inputs$B
V4 <- dat4$inputs$V
Sigma4 <- dat4$inputs$Sigma
Gamma4 <- dat4$inputs$Gamma
Ytrain4 <- dat4$Ytrain
Ytest4 <- dat4$Ytest
mu14 <- dat4$fit$mu1
fitted4 <- dat4$fit$fitted
Yhat_test4 <- dat4$Yhat_testThe results below are based on simulation with 600 samples, 1000 variables of which 50 were causal, 5 responses with a per-response proportion of variance explained (PVE) of 0.5. Variables, X, were drawn from MVN(0, Gamma), causal effects, B, were drawn from MVN(0, Sigma). The responses, Y, were drawn from MN(XB, I, V).
cat("Gamma (First 5 elements)")Gamma (First 5 elements)Gamma4[1:5, 1:5] [,1] [,2] [,3] [,4] [,5]
[1,] 0.8 0.4 0.4 0.4 0.4
[2,] 0.4 0.8 0.4 0.4 0.4
[3,] 0.4 0.4 0.8 0.4 0.4
[4,] 0.4 0.4 0.4 0.8 0.4
[5,] 0.4 0.4 0.4 0.4 0.8cat("Sigma")SigmaSigma4 [,1] [,2] [,3] [,4] [,5]
[1,] 0.8 0.0 0.0 0.0 0.0
[2,] 0.0 0.8 0.0 0.0 0.0
[3,] 0.0 0.0 0.8 0.0 0.0
[4,] 0.0 0.0 0.0 0.8 0.0
[5,] 0.0 0.0 0.0 0.0 0.8cat("V")VV4 [,1] [,2] [,3] [,4] [,5]
[1,] 31.75545 0.00000 0.00000 0.00000 0.00000
[2,] 0.00000 31.47091 0.00000 0.00000 0.00000
[3,] 0.00000 0.00000 14.55202 0.00000 0.00000
[4,] 0.00000 0.00000 0.00000 42.12604 0.00000
[5,] 0.00000 0.00000 0.00000 0.00000 15.37456mr.mash was fitted to the training data (80% of the data) updating V and updating the prior weights using mixSQP. Then, responses were predicted on the test data (20% of the data). The mixture prior consisted of 101 components.
In the plots below, each color/symbol defines a diffrent response.
Here, we compare the estimated effects with the true effects.
plot(B4[, 1], mu14[, 1], xlab="True effects", ylab="Estimated effects", main="True vs Estimated Effects", pch=1, cex.lab=1.5, cex.axis=1.5)
points(B4[, 2], mu14[, 2], col="blue", pch=2)
points(B4[, 3], mu14[, 3], col="red", pch=3)
points(B4[, 4], mu14[, 4], col="green", pch=4)
points(B4[, 5], mu14[, 5], col="yellow", pch=8)
Then, we compare the predicted responses with the true responses in the training data (left panel) and test data (right panel).
par(mfrow=c(1,2))
plot(Ytrain4[, 1], fitted4[, 1], xlab="True responses", ylab="Fitted values", main="True vs Fitted values \nTraining data", pch=1, cex.lab=1.5, cex.axis=1.5)
points(Ytrain4[, 2], fitted4[, 2], col="blue", pch=2)
points(Ytrain4[, 3], fitted4[, 3], col="red", pch=3)
points(Ytrain4[, 4], fitted4[, 4], col="green", pch=4)
points(Ytrain4[, 5], fitted4[, 5], col="yellow", pch=8)
abline(0, 1)
plot(Ytrain4[, 1], fitted4[, 1], xlab="True responses", ylab="Predicted responses", main="True vs Predicted Responses \nTest data", pch=1, cex.lab=1.5, cex.axis=1.5)
points(Ytest4[, 2], Yhat_test4[, 2], col="blue", pch=2)
points(Ytest4[, 3], Yhat_test4[, 3], col="red", pch=3)
points(Ytest4[, 4], Yhat_test4[, 4], col="green", pch=4)
points(Ytest4[, 5], Yhat_test4[, 5], col="yellow", pch=8)
abline(0, 1)
par(mfrow=c(1,1))
r2_train4 <- round(accuracy(Ytrain4, fitted4)$r2, 4)
r2_test4 <- round(accuracy(Ytest4, Yhat_test4)$r2, 4)
bias_train4 <- round(accuracy(Ytrain4, fitted4)$bias, 4)
bias_test4 <- round(accuracy(Ytest4, Yhat_test4)$bias, 4)
mse_train4 <- round(accuracy(Ytrain4, fitted4)$mse, 4)
mse_test4 <- round(accuracy(Ytest4, Yhat_test4)$mse, 4)
acc4 <- rbind(r2_train4, r2_test4, bias_train4, bias_test4, mse_train4, mse_test4)
colnames(acc4) <- paste0("Y", seq(1, r4))
part_metric4 <- c("Training data r2", "Test data r2", "Training data bias", "Test data bias" , "Training data MSE" , "Test data MSE")
res4 <- data.frame(part_metric4, acc4)
colnames(res4)[1] <- c("Partition_metric")
rownames(res4) <- NULL
print(res4) Partition_metric Y1 Y2 Y3 Y4 Y5
1 Training data r2 0.4107 0.4811 0.3574 0.5330 0.4272
2 Test data r2 0.3990 0.2959 0.3766 0.3298 0.3542
3 Training data bias 1.1022 1.0512 1.0676 1.0356 1.0803
4 Test data bias 1.0478 0.7843 1.1576 0.9455 0.9463
5 Training data MSE 36.2095 32.5375 17.8351 39.5427 18.2162
6 Test data MSE 36.5641 38.1858 20.4419 62.0936 19.4614
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
loaded via a namespace (and not attached):
[1] workflowr_1.6.1 Rcpp_1.0.4.6 digest_0.6.25 later_0.7.5
[5] rprojroot_1.3-2 R6_2.4.1 backports_1.1.5 git2r_0.26.1
[9] magrittr_1.5 evaluate_0.12 stringi_1.4.3 fs_1.3.1
[13] promises_1.0.1 whisker_0.3-2 rmarkdown_1.10 tools_3.5.1
[17] stringr_1.4.0 glue_1.4.0 httpuv_1.4.5 yaml_2.2.1
[21] compiler_3.5.1 htmltools_0.3.6 knitr_1.20