# print.customizedGlmnet: print the summary of a fitted 'customizedGlmnet' object In customizedTraining: Customized Training for Lasso and Elastic-Net Regularized Generalized Linear Models

## Description

Print the numbers of training observations and test observations in each submodel of the `customizedGlmnet` fit

## Usage

 ```1 2``` ```## S3 method for class 'customizedGlmnet' print(x, ...) ```

## Arguments

 `x` fitted `customizedGlmnet` object `...` ignored

## Author(s)

Scott Powers, Trevor Hastie, Robert Tibshirani

`print`, `customizedGlmnet`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45``` ```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") # Print the customized training model fit: fit1 # 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) # Print the customized training model fit: fit2 ```