predict.customizedGlmnet {customizedTraining}

R Documentation

make predictions from a customizedGlmnet object

Description

Returns predictions for the test set provided at the time of fitting the customizedGlmnet object.

Usage

## S3 method for class 'customizedGlmnet'
predict(object, lambda,
  type = c('response', 'class'), ...)

Arguments

object	a fitted customizedGlmnet object
lambda	regularization parameter
type	Type of prediction, currently only "response" and "class" are supported. Type "response" returns fitted values for "gaussian" family and fitted probabilities for "binomial" and "multinomial" families. Type "class" applies only to "binomial" and "multinomial" families and returns the class with the highest fitted probability.
...	ignored

Value

a vector of predictions corresponding to the test data input to the model at the time of fitting

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, 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]


# 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")

# Compute test error using the predict function:
mean((y.test - predict(fit1, lambda = 10))^2)


# 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)

# Compute test error using the predict function:
mean((y.test - predict(fit2, lambda = 10))^2)

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