customizedTraining (version 1.3)

nonzero.customizedGlmnet: Return selected variables from a customizedGlmnet object

Description

Returns a list of vectors of selected variables for each separate glmnet model fit by a customizedGlmnet object

Usage

# S3 method for customizedGlmnet
nonzero(object, lambda = NULL, ...)

Value

a list of vectors, each vectors representing one of the glmnet models fit by the customizedGlmnet model. Each vector gives the indices of the variables selected by the model

Arguments

object

fitted customizedGlmnet model object

lambda

value of regularization parameter to use. Must be specified

...

ignored

Examples

require(glmnet)

# Simulate synthetic data
n = m = 150
p = 50
q = 5
K = 3
sigmaC = 10
sigmaX = sigmaY = 1
set.seed(5914)

beta = matrix(0, nrow = p, ncol = K)
for (k in 1:K) beta[sample(1:p, q), k] = 1
c = matrix(rnorm(K*p, 0, sigmaC), K, p)
eta = rnorm(K)
pi = (exp(eta)+1)/sum(exp(eta)+1)
z = t(rmultinom(m + n, 1, pi))
x = crossprod(t(z), c) + matrix(rnorm((m + n)*p, 0, sigmaX), m + n, p)
y = rowSums(z*(crossprod(t(x), beta))) + rnorm(m + n, 0, sigmaY)

x.train = x[1:n, ]
y.train = y[1:n]
x.test = x[n + 1:m, ]
y.test = y[n + 1:m]

# Example 1: Use clustering to fit the customized training model to training
# and test data with no predefined test-set blocks

fit1 = customizedGlmnet(x.train, y.train, x.test, G = 3,
    family = "gaussian")

# Extract nonzero regression coefficients for each group:
nonzero(fit1, lambda = 10)

# Example 2: If the test set has predefined blocks, use these blocks to define
# the customized training sets, instead of using clustering.
group.id = apply(z == 1, 1, which)[n + 1:m]

fit2 = customizedGlmnet(x.train, y.train, x.test, group.id)

# Extract nonzero regression coefficients for each group:
nonzero(fit2, lambda = 10)