predict.cv.customizedGlmnet {customizedTraining}

make predictions from a cv.customizedGlmnet object

Description

Returns predictions for test set provided at time of fitting, using regulariztion parameter which minimizes CV error

Usage

## S3 method for class 'cv.customizedGlmnet'
predict(object, ...)

Arguments

object

a fitted cv.customizedGlmnet object

...

additional arguments to be passed to predict.customizedGlmnet

Value

a vector of predictions corresponding to the test set provided when the model was fit. The results are for the regularization parameter chosen by cross-validation

Author(s)

Scott Powers, Trevor Hastie, Robert Tibshirani

References

Scott Powers, Trevor Hastie and Robert Tibshirani (2015) "Customized training with an application to mass specrometric imaging of gastric cancer data." Annals of Applied Statistics 9, 4:1709-1725.

See Also

predict, cv.customizedGlmnet

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]
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)