customizedTraining (version 1.2)

print.cv.customizedGlmnet: print a ``cv.customizedGlmnet'' object

Description

Print the number of customized training subsets chosen by cross-validation and the number of variables selected in each training subset.

Usage

# S3 method for cv.customizedGlmnet
print(x, ...)

Arguments

x

a fitted cv.customizedGlmnet object

…

ignored

See Also

print, cv.customizedGlmnet

Examples

# NOT RUN {
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]
foldid = sample(rep(1:10, length = nrow(x.train)))


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

fit1 = cv.customizedGlmnet(x.train, y.train, x.test, Gs = c(1, 2, 3, 5),
    family = "gaussian", foldid = foldid)

# Print the optimal number of groups and value of lambda:
fit1$G.min
fit1$lambda.min

# Print the customized training model fit:
fit1

# Compute test error using the predict function:
mean((y[n + 1:m] - predict(fit1))^2)

# Plot nonzero coefficients by group:
plot(fit1)


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

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

# Print the optimal value of lambda:
fit2$lambda.min

# Print the customized training model fit:
fit2

# Compute test error using the predict function:
mean((y[n + 1:m] - predict(fit2))^2)

# Plot nonzero coefficients by group:
plot(fit2)


# Example 3: If there is no test set, but the training set is organized into
# blocks, you can do cross validation with these blocks as the basis for the
# customized training sets.

fit3 = cv.customizedGlmnet(x.train, y.train, foldid = foldid)

# Print the optimal value of lambda:
fit3$lambda.min

# Print the customized training model fit:
fit3

# Compute test error using the predict function:
mean((y[n + 1:m] - predict(fit3))^2)

# Plot nonzero coefficients by group:
plot(fit3)
# }