predict.customizedGlmnet {customizedTraining} | R Documentation |
customizedGlmnet
object
Returns predictions for the test set provided at the time of fitting the
customizedGlmnet
object.
## S3 method for class 'customizedGlmnet'
predict(object, lambda,
type = c('response', 'class'), ...)
object |
a fitted |
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 |
a vector of predictions corresponding to the test data input to the model at the time of fitting
Scott Powers, Trevor Hastie, Robert Tibshirani
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.
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)