gmus: Generalized Matrix Uncertainty Selector

In osorensen/hdme: High-Dimensional Regression with Measurement Error

Generalized Matrix Uncertainty Selector

Description

Generalized Matrix Uncertainty Selector

Usage

gmus(W, y, lambda = NULL, delta = NULL, family = "gaussian", weights = NULL)

Arguments

W	Design matrix, measured with error. Must be a numeric matrix.
y	Vector of responses.
lambda	Regularization parameter.
delta	Additional regularization parameter, bounding the measurement error.
family	"gaussian" for linear regression, "binomial" for logistic regression or "poisson" for Poisson regression. Defaults go "gaussian".
weights	A vector of weights for each row of X.

Value

An object of class "gmus".

References

\insertRef

rosenbaum2010hdme

\insertRef

sorensen2018hdme

Examples

# Example with linear regression
set.seed(1)
n <- 100 # Number of samples
p <- 50 # Number of covariates
# True (latent) variables
X <- matrix(rnorm(n * p), nrow = n)
# Measurement matrix (this is the one we observe)
W <- X + matrix(rnorm(n*p, sd = 1), nrow = n, ncol = p)
# Coefficient vector
beta <- c(seq(from = 0.1, to = 1, length.out = 5), rep(0, p-5))
# Response
y <- X %*% beta + rnorm(n, sd = 1)
# Run the MU Selector
fit1 <- gmus(W, y)
# Draw an elbow plot to select delta
plot(fit1)
coef(fit1)

# Now, according to the "elbow rule", choose
# the final delta where the curve has an "elbow".
# In this case, the elbow is at about delta = 0.08,
# so we use this to compute the final estimate:
fit2 <- gmus(W, y, delta = 0.08)
# Plot the coefficients
plot(fit2)
coef(fit2)
coef(fit2, all = TRUE)

osorensen/hdme documentation built on May 18, 2023, 11:35 p.m.