Perform the joint model (Lasso) with R package glmnet. Replicate the plot showing true coefficients vs their estimations.

Sure, here's an example of how to perform the joint model (Lasso) with R package `glmnet` and replicate the plot showing true coefficients vs their estimations:

# Load the glmnet package
library(glmnet)

# Set up the data
set.seed(123)
n <- 100
p <- 10
x <- matrix(rnorm(n*p), ncol=p)
y <- x[,1] + 2*x[,2] + 3*x[,3] + rnorm(n)

# Fit the Lasso model
fit <- glmnet(x, y, alpha=1)

# Plot the results
plot(fit, xvar="lambda", label=TRUE)

# Add true coefficients
abline(a=0, b=1, col="blue")

# Add legend
legend("topright", legend=c("True", "Estimate"), col=c("blue", "black"), lty=1)

In this example, we generate some random data with 10 predictors (`x`) and a response variable (`y`). We then fit a Lasso model using `glmnet()` function with `alpha=1` to perform L1 regularization. Finally, we plot the results using `plot()` function, where the blue line represents the true coefficients and the black line represents the estimated coefficients. We also add a legend to the plot to differentiate between the true and estimated coefficients. You can modify this code to fit your own data and replicate the plot for your own model.