fusedL2DescentGLMNet {fuser}

Optimise the fused L2 model with glmnet (using transformed input data)

Description

Optimise the fused L2 model with glmnet (using transformed input data)

Usage

fusedL2DescentGLMNet(transformed.x, transformed.x.f, transformed.y, groups,
  lambda, gamma = 1, ...)

Arguments

transformed.x

Transformed covariates (output of generateBlockDiagonalMatrices)

transformed.x.f

Transformed fusion constraints (output of generateBlockDiagonalMatrices)

transformed.y

Transformed response (output of generateBlockDiagonalMatrices)

groups

Grouping factors for samples (a vector of size n, with K factor levels)

lambda

Sparsity penalty hyperparameter

gamma

Fusion penalty hyperparameter

...

Further options passed to glmnet.

Value

Matrix of fitted beta values.

A matrix with the linear coefficients for each group (p by k).

Examples

#' set.seed(123)

# Generate simple heterogeneous dataset
k = 4 # number of groups
p = 100 # number of covariates
n.group = 15 # number of samples per group
sigma = 0.05 # observation noise sd
groups = rep(1:k, each=n.group) # group indicators
# sparse linear coefficients
beta = matrix(0, p, k)
nonzero.ind = rbinom(p*k, 1, 0.025/k) # Independent coefficients
nonzero.shared = rbinom(p, 1, 0.025) # shared coefficients
beta[which(nonzero.ind==1)] = rnorm(sum(nonzero.ind), 1, 0.25)
beta[which(nonzero.shared==1),] = rnorm(sum(nonzero.shared), -1, 0.25)

X = lapply(1:k,
           function(k.i) matrix(rnorm(n.group*p),
                                n.group, p)) # covariates
y = sapply(1:k,
           function(k.i) X[[k.i]] %*% beta[,k.i] +
                           rnorm(n.group, 0, sigma)) # response
X = do.call('rbind', X)

# Pairwise Fusion strength hyperparameters (tau(k,k'))
# Same for all pairs in this example
G = matrix(1, k, k)

# Generate block diagonal matrices
transformed.data = generateBlockDiagonalMatrices(X, y, groups, G)

# Use L2 fusion to estimate betas (with near-optimal information
# sharing among groups)
beta.estimate = fusedL2DescentGLMNet(transformed.data$X,
                                     transformed.data$X.fused,
                                     transformed.data$Y, groups,
                                     lambda=c(0,0.001,0.1,1),
                                     gamma=0.001)